This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

The received wisdom among a certain class of Bay Area/rationalist/AI-pilled thinkers is that universities are a hopelessly broken epistemic technology, especially now that AI is widespread. Some academics believe that they might just as well be replaced by recorded lectures1 or that the university’s only value comes from networking or costly social signalling.

This essay does not speculate about blue-sky AI-powered epistemic institutions — you can use your imagination. But just remember that no less of a university skeptic than Scott Alexander has said that “the hardest problem for any social technology is getting users.” The cynic might say that the only factors preventing AI-powered neoversities from taking off are protectionism by incumbents and the status-signalling nature of an undergraduate degree. To which I — a political scientist who studies emerging technologies — say: great. You’ve described the process of political-economic contestation that determines whether and how new technologies are adopted. It is almost never smooth.

Public corporations are optimized for the next fiscal quarter, politicians for the next election in two, four, six years… but Harvard has a 50-year plan for its Allston campus and Oxford predates the Aztec Empire.2 A recent NBER working paper by David Cutler and Edward Glaeser titled “How Have Universities Survived for Nearly a Millennium?” makes the case that universities will continue their strategy of “survival through reinvention.” The Oxford of today has very little in common with the institution that emerged in the 11th century, but it is still here, and still teaching.

In other words: The university isn’t going anywhere. At the same time, however, academics who ignore the impacts of AI do so at their own peril.

The reality is that academics are selected for being willing to suffer serious financial opportunity costs for the guarantee that, via tenure, nobody can tell them what to do. As a group, we’re going to reflexively resist any imposition of AI by either university administration or tech-corporate evangelists.

Speaking for my field: Unless academics can design and implement major reforms to our research practice in the next few years, there is a real risk that many social science professors will want to ban AI entirely. Soon we’ll be forcing each other to sign statements swearing that we didn’t use AI and AInvestigating not just every em-dash, but every semicolon and three-part list, too. To do so would make academics a sort of dockworkers union of knowledge, preventing the adoption of more efficient technology for doing research in order to preserve their current way of life.

Because, yes, the institutional arrangement of academic research is obviously flawed. Peer review is breaking as submission rates climb. The replication crisis remains a symptom of the underlying dysfunction that comes from pitting a hyper-competitive academic job market against the truth-seeking, communitarian ethos of science. And the incentives for public communication of individual studies are still toxic because a bad study going viral still counts for the tenure packet. AI can be used to address these problems in radical ways. Many specialized quantitative and computational social scientists are writing about how to do this; my own contribution focuses more narrowly on the institution of peer review.

More broadly, the best-case outcome is not that academics boycott AI wholesale, but that we adopt AI in a way that preserves both our autonomy and its expertise, which allows us to better pursue our foundational goal of generating knowledge and transmitting it to future generations. A university of Luddites can’t help anyone navigate a changing world. By thoughtfully adopting new technologies, universities can provide a temporal counterweight to short-run trends. Stewart Brand’s model of pace layers suggests that a healthy ecosystem is composed of layers that change at varying rates, balancing progress with stability. As AI accelerates so much of our society, we will become even more dependent on processes that operate slowly.

Preserving academia’s time horizons

As other systems accelerate, the basic temporal loops of university life remain the same: the calendar is divided into semesters, the standard degree takes four years, the PhD takes six years, tenure another six to eight, and then a scientific career can last until the hearse pulls up to the faculty lounge. The research loop must also involve the consumption of research and its integration into some larger network of knowledge, even one that only exists in the heads of academics. As a result, universities have been the stewards of some of the extremely long-term projects.

Consider the datasets that underpin our understanding of democratic development, economic growth, or political opinion: the Varieties of Democracy project, the Penn World Tables, the American National Elections Survey. These exist because academics — usually with university or government funding — decided to start measuring the same thing, in the same way, for decades. Almost no corporation has the incentive structure to sustain that kind of commitment. Government agencies can do this kind of work, but as the current U.S. administration has demonstrated, there exists a temptation to cynically politicize formerly professional data collection efforts. And nothing is easier than to simply defund inconvenient research that citizens barely know exist.

These projects are just the more obvious manifestations of the long-run experiment that is the practice of science itself. When a researcher spends years designing an experiment, collecting data, revising their model of how the world works in light of what they find, the eventual PDF journal article is a relic of that experience, and an essential technology for communicating some of that knowledge to the rest of the academic community — but the experience itself is what produces expertise.

Now compare that to a provocative intervention by researcher Andy Hall, which claimed that Claude more or less one-shotted the design and execution of a published paper in a weekend as part of a proposal for a “100x Research Institution.”

Obviously, academics are rewarded for our output, and in the competitive marketplace, we’d each like to 100x that output and crush our competition. But if everyone 100x’s our output, the whole project becomes absurd. We’ll become deranged, suffering from AI psychosis at the institutional level. Perhaps we will one day achieve full Artificial Social Science (ASS), at which point AI will be doing all of the research production, evidence synthesis, and knowledge application. But we’re definitely not there yet. In the meantime, I’m worried that focusing only on research output means incorporating AI into social science in a half-ASSed way.

Our institutional incentives are already in tension with the stewardship of long-term projects. Academia is not exactly sane at the current scale and speed; indeed, this has been true for decades. In a famous letter to Science magazine in 1963, Bernard K. Forscher describes how science began to mistake the map for the territory:

Once upon a time, among the activities and occupations of man there was an activity called scientific research and the performers of this activity were called scientists. In reality, however, these men were builders who constructed edifices, called explanations or laws, by assembling bricks, called facts….A misunderstanding spread among the brickmakers. [...]The brickmakers became obsessed with the making of bricks. When reminded that the ultimate goal was edifices, not bricks, they replied that, if enough bricks were available, the builders would be able to select what was necessary and still continue to construct edifices. (emphasis mine)

Academics have long bemoaned the pressure to publish papers ever-more papers earlier and earlier in our careers. This trend is indeed worth bemoaning, and it threatens what is unique about academia.

Properly incorporated, AI can speed up science by removing the grunt work, just as our laptops replaced punchcard computers decades ago. But brickmaxxing is the worst way to incorporate AI into science. Now that we have more time, we don’t need to brickmaxx by churning out research papers. There are better ways to build up scientific institutions.

The virtues of reading

William Clark, a historian of early modern German academia, offers a useful definition of research: “Works of research usually provide a basis for further research and/or relate to other, related works in a complementary and supplementary manner. They add up to something positive.”

Research is a three-way conversation between the past (the literature), the present (the world it studies) and the future (subsequent research). The core foci of academia are the many slow, expert conversations that require time to percolate through the minds of those involved. There’s no guarantee of positive consequences, but the justification for the model is the many positive consequences that have in fact emerged from it.

If basic research is an ongoing conversation, we need to have a structure in place to make sure that it doesn’t die out. One way is reading. I’m sorry to report that academics don’t really read very much. Reading isn’t counted towards our career advancement, including the all-important step of tenure. I’ve spoken to many academics who privately admit that they barely read anything because they can’t spare the time from writing papers!

Computational social scientist Duncan Watts noted this in a 2019 speech:

For 20 years I thought my job was as a basic scientist. Publish papers and throw them over the wall for someone else to apply. I now realise that there's no one on the other side of the wall. Just a huge pile of papers that we've all thrown over.

But now, with the help of AI, we can spare the time. If we’re going to bother to produce knowledge in a human-readable format, we need humans to actually be reading it.

Incorporating and integrating

We must also spend this time developing a healthier system for incorporating the knowledge that will continue to be produced — now at faster rates than ever.

Historically, the skill set to conduct social science research was sufficiently specialized that only people who were also carefully instructed in research design were able to produce it. Vibe-coding has changed that; vibe-coded social science is already here. And let me affirm that much of this work is far from “aligned.”

Richard Feynman famously said of science that “The first principle is that you must not fool yourself — and you are the easiest person to fool.” AI-powered novices performing data science on complex social questions about which they are inevitably biased are doomed to fool themselves and potentially flood the world with bad science.

Any serious attempt to evaluate the weight of evidence for a given topic requires a certain depth of knowledge to be done well; indeed, knowledge synthesis is one of the most challenging open problems in social science methodology. AI enables self-directed empirical inquiry but does not provide the expertise on which academic social science rests. The rigor that goes into the constituent studies is wasted if the studies are combined in an unrigorous fashion.

Don Campbell, one of the founders of policy studies, envisioned the role of the social scientist as a “methodological servant” in a society in which citizens wanted to pursue their own epistemic goals but lacked the technical understanding to accomplish them. This is the direction academics should move toward.

Even the AGI-pilled epistemic revolutionary must account for some period of future time in which human expertise remains necessary. If AI continues to be able to execute more and more of the research process, we still need human experts with the breadth of knowledge to be able to check their work. Even if the long-term destination is fully autonomous AI research of the kind that need not be verified by humans, the capacity to navigate from the medium to the long term successfully, on our terms, will require oversight and feedback from human experts.

Most immediately, then, we need to check the work of vibe-coded social science analysis.

We need trained social science methodologists weighing in on the data analysis and surveys being conducted by the newly-empowered class of the coding-literate and research-curious. In one innocuous example, an author replicated several entry-level mistakes in survey methodology when trying to estimate the prevalence of GLP-1 usage. To his credit, he realized that his results were preposterously wrong and then moved towards reasonable solutions to his entry-level errors. But this gets harder when the problems are intermediate or advanced, or when the results are merely dangerously rather than preposterously wrong. Survey methodology is a field in which people get PhDs, and one in which social scientists are still pushing the frontier.

There are many such methodological issues where curious amateurs would benefit from expert advice. Social scientists can meet the large pool of epistemologically curious non-academics where they are, to combine our refined expertise with their energy, curiosity and resources.3

The sex-ideology surveys run by the blogger and researcher Aella, for example, are an amazing resource, but there are serious limitations to the conclusions that can be drawn within her survey frame. She discusses this on an episode of Doomscroll, responding to a host praising her for a very influential piece mapping different fetishes according to political alignments:

I really should redo this. This was at a time when I was first learning statistics and so I don't think I actually did it very well. I looked at basically correlations, but actually turns out correlations are not a great way of evaluating things with non-normal distributions, which is obvious, but you know, I'm self-educated.

Very impressive response, but this shouldn’t be necessary; Aella is, famously, skilled at a more applied kind of rotational analysis. Collaboration with social science methodologists would yield major gains from comparative advantage and help unify distinct conversations being held by rationalists and academics.

Social scientists can improve our collective epistemics by correcting methodological errors emerging from self-directed inquiry, and ideally by setting up more public-facing forms of peer review that can accommodate the questions that our fellow citizens find directly important for their own decision-making or well-being, rather than only the theoretical questions of interest to academics.

Just talking

Even if AI is capable of original research, human experts still serve an important coordinating function. Most knowledge is more valuable when it is public knowledge, when we have some sense that other people are paying attention to the same thing. We want to be part of the same conversation, not talking to ourselves.

Collaborations between academics and citizen researchers are only one medium through which this coordination can take place. There are others. AI is obviously good at writing, but it is still much worse at talking. By this I mean talking, using vocal cords to vibrate the air between your mouth and another person’s ear. This is a defensive way to put it, but in the short run, this remains an important comparative advantage of human academics.

AI-generated audiovisuals still aren’t nearly as fluent as AI-generated text, and will remain much more compute-hungry. Talking is central to the day-to-day work of academics as teachers, and we need to translate these skills in a way that will enable our fellow citizens to benefit. There is a huge popular demand to listen to people talk.

Academics have failed to participate adequately in the contemporary media environment. I understand their skepticism. The contemporary media environment is messy. Even worse, from the perspective of the wordcels who have self-selected into academia, our mass literary culture has been replaced by a neo-orality.

It’s easy to find fault with the contemporary platform-driven media environment; Lord knows I’ve done so. But in the short term, there’s no way around it: To have influence, you have to post more. And given the tastes of the mass audience, you have to post in the medium of the short-form video.

Academic expertise can be immediately applied if we deign to meet people on the platforms and media where they are. This means we’ll have to give up control of the conversation — which is to say, it entails an actual conversation, which by its nature cannot be controlled the way peer-reviewed PDF or even a 60 Minutes interview can. As I argue in my book, The YouTube Apparatus, the defining feature of social media platforms is that they are social; that is, that public audience feedback co-creates the media object consumed by future audiences. Credibility is constructed only partially by expertise; responsiveness to the audience is also crucial. Much more than broadcast media, social media enables a dialogue between creators and audiences.

The problem facing most YouTube creators is in fact too much responsiveness. Without institutional or financial independence, they become puppets, victims of audience capture. The fact that academics do have that independence, that we are also interfacing with ourselves and the academic literature, allows most of us to dialogue with the public without being captured by them. We can use some of our newly freed up time to listen and respond to the experiences of our fellow citizens as things get weird.

The world is changing quickly and in disorienting fashion. Someone is going to fill the demand for explanations, for sensemaking and clarification; academics have the chance to do so responsibly. The alternative is an intensification of the nihilistic mysticism that now passes for wisdom on TikTok and Twitch. The essential upside of the academic autonomy preserved within universities is that we can decide what to do, and what to value our fellow academics doing. We have the freedom to meet our fellow citizens where they are, knowledge-wise, as well as to spend more time directly observing what’s going on in the new world.

The future

Ultimately, the research imperative for universities is to produce new knowledge. Academics agree on this point. But if we want things to stay as they are, things will have to change. The people who built the University of Oxford were more concerned about the God of the Bible than the machine god. They weren’t thinking about how AI should replace the college fellows because doing so would allow them to maximize codex production. The medieval mind could not comprehend codexmaxxing (complimentary). But over the centuries, as the machine god assembled itself and the biblical God lost His grip on Western thought, Oxford and the university system survived through reinvention. Now we have to do it again.

Knowledge generation is the goal, but the old technology of writing linear natural language text is outdated. The PDF is a tidy knowledge brick, but AI allows us to design modern skyscrapers of knowledge, composed not of uniform bricks but rather a diversity of specialized building materials. The long-term solution for academia requires both materials engineering and an architectural revolution, if you’ll allow me to torture the metaphor.

Universities aren’t going to disappear overnight, even if they choose to try to ignore or ban AI. They are powerful institutions, interoperable with some of the core social functions of society and with alumni at the heights of essentially everything that matters outside of the Bay. But unless we get our act together, collectively and soon, I fear that reaction will set in, pitting forward-looking AI evangelists against a shrinking “remainder humanism” of academic intellectual labor.

As much as possible, these ideal future universities should engage directly with the public while preserving academic autonomy, the development of expertise, and the continuation of the research pipeline. And they should incorporate AI that enhances efficiency at every step of the core research loop. The standing of universities cannot last unless we can demonstrably retain an epistemic advantage and provide services that our fellow citizens value. The institutional structure that has lasted over 1,000 years can remain viable — at least over the time horizon I can imagine understanding — but only if it undergoes yet another round of survival through reinvention.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

Many people, including myself, have written many, many words about how quickly (or slowly) the world will change if and when we invent artificial general intelligence. But all this ink doesn’t seem to have been very persuasive. People who expect a radical discontinuity in history have failed to convince the skeptics — including myself — and we haven’t had any more success convincing them. Why?

Agree to disagree

The economist Tom Cunningham recently put together this chart of forecasts of the economic impact of AI, which he divides into two groups: “AI Insiders” and “Economists and Institutionalists.” These two groups have been disagreeing about what the impact of AI will be for the past several years. Economists almost uniformly expect the effect on economic growth to be below 3% per year, while AI insiders — who I’ll call technologists — just as uniformly believe it will be much higher, possibly as much as 30% per year. The lack of agreement isn’t because either side has been reticent to make an argument — indeed, all of these forecasts are generally pulled from documents that make the case at length.

The intractability of this disagreement was prefigured back in 2022, in an unusual tournament run by the Forecasting Research Institute. They gathered 89 superforecasters and 80 AI experts, and asked them to forecast the probability of dozens of different scenarios related to how the world might change as a consequence of technological progress. One of these questions was the probability that the global economy would grow by more than 15% in a single year before the year 2100. Given the uncertainties of forecasting the consequences of unknown technologies many decades into the future, these forecasts varied widely. However, there were two major clusters: The median superforecaster put the odds of transformative growth at 5% by 2100, while the median AI expert put it at 25% — five times greater.

For our purposes, what’s interesting is what happened next. The Forecasting Research Institute designed this tournament to facilitate agreement. Participants were incentivized to clearly explain their reasoning, and their arguments were shared with each other. Additionally, the tournament featured rewards for accurately forecasting the probabilities the other group would report, on the grounds that this would require understanding the most persuasive arguments for the other side. But despite all this effort, by the end of this process, little convergence in beliefs had occurred. The median superforecaster’s probability of transformative growth by 2100 fell from 5% to 2.5%, and the median AI expert forecast was unchanged at 25%. The difference between the two camps had widened to 12 times. This was perplexing. Why wasn’t either side able to persuade the other?

One hypothesis was that neither group engaged with their opponent’s arguments enough, despite the incentives to do so. After all, participants were asked to weigh in on dozens of topics. So the Forecasting Research Institute conducted a follow-up study: an Adversarial Collaboration in which a group of 22 participants with initially divergent beliefs on AI would engage more intensively with each other’s arguments. Participants spent 20-80 hours over two months “understanding and accurately summarizing each other’s key arguments to the satisfaction of the opposing side”. But even this process failed to lead to any convergence in forecasts.

All this matches my own experiences. For years, I have been involved in discussions about whether AI will lead to explosive rates of technological and economic growth (that is, growth at least 10 times faster than what we see today). I participated in a dialogue on this question for Asterisk in 2023, and we’re still having the same debate with the same arguments today: bottlenecks, diffusion, the limits of intelligence, and so on. In my experience, these debates echo what the Forecasting Research Institute has found: Both sides have arguments they find convincing, but which fail to persuade the other side.

Weak arguments

Why hasn’t all this debating led anywhere productive? I think the answer is simple: Our arguments are weak. Ideally, persuasion looks something like this: People start with prior probabilities, receive new evidence, and then update their beliefs accordingly. But when the evidence is weak, we don’t (and shouldn’t) change our minds too much.

In one sense, this argument is tautological. If we define the strength of an argument by its power to change minds, then an argument that fails to do so must have been weak. In this case, however, I think looking at the arguments themselves can show us how they might not be persuasive.

Let’s start with the economic models. A recent paper on this topic is Jones and Tonetti (2026), which builds a model of economic growth based on the assumption that AI is capable of performing an increasing share of the tasks humans do in the economy — including the tasks of inventing new ideas. In their paper, they calibrate the model with data on the last several decades of automation, and end with several simulations of growth under different assumptions about AI. The upshot is well represented by the following figure:

The paper shows that under some sets of assumptions, economic growth rockets past 20% and towards infinity. It also shows that under other assumptions, business-as-usual economic growth persists. How do we know which world we’ll end up in? In Jones and Tonetti’s model, one of the most important factors is the share of tasks that can ultimately be automated by AI and robots. Unfortunately, we have remarkably poor evidence on what this will be.

If you already believe that explosive economic growth is plausible, this paper tells you that such an outcome is consistent with a mathematical model (given some assumptions that are hard to verify). But if you think otherwise, well, the opposite is equally true.

This dynamic goes beyond this particular paper. Most research that investigates the potential impact of AI on growth similarly depend on assumptions about specific parameters for which we have little or no precedent — the speed of automation, the number of tasks we will automate, the elasticity of substitution between the tasks that haven’t been automated and the ones that have. Even when we can get evidence on the value of these parameters in today’s world (for example, Jones and Tonetti measure how quickly automation proceeded over the 20th century) their applicability to future worlds with advanced AI is highly uncertain.

And of course, the evidence from these models is further weakened when we recognize that all economics models are simple approximations of reality with a poor predictive track record. In general, economic models of AI and growth help us better understand the mechanisms through which explosive growth or business-as-usual might obtain, but they are ill-equipped to help us diagnose which is likely.

We have a similar problem with other lines of evidence. For example, we could turn to historical data. On the one hand, economic growth in the U.S. has held remarkably steady at 1.8% annually for over a hundred years, despite all the technological and social upheaval the last century brought.

But if we look back farther, we find precedents for rapid acceleration. The UK, for example, probably grew by about 0.05% per annum between 1400 and 1700, then accelerated by roughly 10 times to 0.5% per annum between 1700 and 1870, and by another three times after that. But on the (third) hand, the acceleration of economic growth during the Industrial Revolution was mostly about increasing the frequency of high growth years (which were often well above 0.5% per year) and decreasing the frequency of economic contractions. A similar acceleration today can’t be achieved merely by increasing how often the U.S. grows on the high end of its range and decreasing how often it grows on the low end (the U.S. never grows at 20% per year). It’s not clear which framing is the right one.

We can look at much more recent empirical data, but it is also not very helpful. As the economist Kevin Bryan recently tweeted: “If you know one thing about AI, the ‘lines on the chart’ from scaling laws on tech capabilities have been right on for six years, and objections on diffusion frictions/production bottlenecks to their effect on growth have also been.” To a skeptic, the muted aggregate productivity gains achieved so far by AI are unsurprising. But if you already believe that AI will lead to rapid economic growth, the fact that it hasn’t done so yet is not that persuasive either: The whole point of the acceleration in growth is that it happens relatively rapidly, but after we attain sufficiently advanced AI.

Is there anything we can look to to help us resolve this question? Maybe not. A key part of the Forecasting Research Institute’s adversarial collaboration study was an attempt to identify near-term resolvable forecasts that would lead to the most convergence in beliefs across the two groups. Essentially, the participants were asked to identify the kinds of things that would have to happen for them to change their mind. But this proved quite hard to do: The two groups could not come up with any near-term resolvable forecast that would substantially convince them.

Where do priors come from?

The Forecasting Research Institute’s adversarial collaboration paper ultimately concluded that their participants failed to converge because they couldn’t agree about which kinds of evidence were relevant to the question, and had conflicting priors about how complex social systems work. This makes sense. If our evidence is weak, then it is no surprise that people with different starting beliefs fail to converge to a common set of updated ones.

Two questions remain. First, in my experience of these debates, each side finds the other side’s arguments weak, but not their own. Why can’t we agree that even our own arguments are nondiagnostic? Second, why do we have such different starting beliefs in the first place?

I suspect the common cause is that we don’t generally understand where our own beliefs come from. We assume our beliefs about AI are caused by the arguments about AI. But this is incorrect. Our beliefs instead come from a lifetime of evidence that shapes our starting beliefs, almost none of which is directly related to AI.

What kind of evidence am I talking about? As I noted at the start of this essay, two groups who have persistently disagreed on this question are economists and technologists. So I’ll illustrate by describing some of the arguments and evidence that PhD economists, as a class, have largely been exposed to, and which I think help explain their general (not universal) tendency to think AI will not lead to explosive economic growth.

1. Growth is hard to change

Economists encounter countless examples in their graduate training and professional careers which suggest that economy-wide growth is hard to move. This is about much more than the persistence of 2% annual U.S. GDP growth. It’s also about the failure of policy-focused economists to identify and advocate for simple policies that can sustainably increase growth by a substantial amount, whether in tax policy, healthcare, education, industrial policy, or any other field. Economists know that the subfield of economic development has struggled for decades to find a reliable recipe for faster economic growth in poor countries.

2. The gap between the feasible and the achieved

The gap between what is technically feasible and what actually obtains is a dominant theme in economics. Economic development is a prime example here — why don’t poor countries simply copy what rich countries do and become rich themselves? Even among rich countries, economists appreciate that technology diffusion is slow and painful, with the productivity of firms operating in the same market and industry often differing by orders of magnitude. But the gap between what is feasible and what is achieved is much more general and deeply embedded in how economists think about the world than these two examples. Indeed, one could do worse than to frame the entire project of economics as the study of why we fail to achieve the best outcome, given what is technologically feasible, and how we might get closer. This is an idea lurking behind concepts like externalities, market power, coordination failures, non-rivalry, irrationality, extractive institutions, and more.

3. Perfect information, rationality, and computational power are not enough

The modeling practices economists normally engage in also embed implicit assumptions about the limits of intelligence. The convention in economics is to model people as perfectly rational, perfectly informed about their economic environment, and blessed with unbounded capabilities to solve the necessary math to behave optimally. And yet, the gap between the feasible and the achieved persists.

4. Superintelligence is already here

Economists spend a lot of time thinking about markets that are collectively smarter than any of their participants. For instance, most economists understand markets can be inefficient, but still harbor a belief that very few traders can sustainably beat the market (indeed, they probably think the market is efficient enough that they personally invest in index funds). Moreover, the first and second welfare theorems establish that under some conditions no amount of cleverness can improve on the allocation of resources achieved by markets. While much of economics is devoted to understanding how markets fail to achieve this ideal, many economists probably think there are settings where markets get pretty close to it.

5. Focus on marginal effects

In many ways the methodology of economics also pushes economists away from making forecasts that are dramatically different from the status quo. We are trained to think “on the margin” — that is, in terms of small changes to the status quo. Examples abound: a large share of theoretical work involves “taking derivatives”, a mathematical technique for studying the effect of very small changes on an economic system; a common trick to solving intractable problems is to take a local linear approximation, where a complex system is reduced to a system of tractable equations that are predictive only near a particular point; our preferred empirical approach focuses on what happens to a complex phenomena when just one variable changes at a time.

The focus on marginal effect is not an unthinking bias but rather a hard-won insight into the practicalities of studying complex systems. There was an era in economic modeling when we built large complex models of the economy. That effort is now largely seen as a failure (see Economics Rules by Dani Rodrik). Similarly, empirical economics has turned its attention to the rigorous identification of specific causal mechanisms in part because earlier efforts that were more ambitious did not work.

None of these kinds of evidence are directly related to AI, but it’s easy to see how they would shape someone’s prior beliefs about the prospects of explosive economic growth. An economist, before even seeing any arguments about AI, starts from a place of skepticism about the leap from technological feasibility into real world impact, the power of intelligence to close this gap, and the possibility of substantially shifting economy-wide growth. On top of that, they believe that forecasts about large changes to the economy are very hard to get right. In short, they start the debate predisposed to be sympathetic to the conclusion that AI may lead to faster growth, but only on the margin. And then, when they then engage in the debate, they find the current empirical data and models are perfectly consistent with this stance, even if they do not decisively rule out other possibilities.

Contrast this with the experiences of technologists, who are in an ecosystem full of people who work in the software and computing industry. Here are some experiences that I think might shape how a technologist approaches the explosive growth question (That said, I am not a technologist, so consider the following a speculative illustration of how different priors might arise.)

1. Growth rates are volatile and can be very rapid

The growth rate that the technologist is familiar with will be the growth of their peer companies, rather than the economy as a whole. This growth can be very volatile; a company might fail and fail and then find the right idea and take off — or go bust. When a company does take off, growth can be extremely rapid: A unicorn might go from $1 million to $100 million in annual recurring revenue in seven years, which is more than 90% annual growth.

2. Growth dynamics are driven by technology

The rapid growth and volatility of companies and industries are a fundamentally technological phenomenon. The technologist operates in an environment where technological disruption of the status quo is the norm (even cliche). Capabilities advance rapidly, with older products and services being made obsolete and new ones becoming feasible.

3. Intelligence is powerful

The rise and fall of companies, the reordering of industries and practices, and the growth and decline of fortunes are ultimately created by code, which is itself the fruit of cognitive effort. Moreover, in the experience of technologists who work primarily with software, the world is suffused with hacks and exploits, places where being slightly more clever can yield outsized impact. The power of intelligence is also manifest in the concept of the “10x engineer,” whose superior fluency with software makes them discontinuously better than their peers.

4. Focus on the discontinuous

Technologists (or at least founders) are, by default, not people who think on the margin. Peter Thiel emphasizes the breakthrough 0 to 1 innovation that does something that has never been done before. The goal of a startup idea is not to be marginally better than the incumbent, but dramatically better, with good prospects for taking over a large share of the market.

It’s clear that this set of beliefs push someone towards a very different set of probabilities from the economist. Such a person would be predisposed to find sudden shifts to rapid growth, driven by technology, quite plausible. They would be primed to see intelligence as having the kind of power to bring about such a shift, and more comfortable making predictions about major changes. And when they engage with the arguments about explosive growth, they will find that the evidence is consistent with their priors.

When I reflect on my own experiences in these debates, it doesn’t feel like I’m falling back on the assumptions I’ve developed as an economist. When asked to give a forecast, one usually jumps straight to considering the directly relevant arguments. With that process, it’s easy to conclude that the justification for your judgment is the arguments you considered.

Perhaps we would be more clear-eyed about the sources of our beliefs if we followed a different process. First, write down a forecast based purely on vibes and intuitions, without any effort to engage with arguments about the topic. Then, think of relevant arguments, read the literature, and argue with friends. When that’s done, write a second forecast. If there is little change between the first and the second, then you might conclude that it really is your intuition — which I think of as our priors — that drives your thinking, and that the arguments are weak.

Moving the debate forward

If this story is right, then how might we move these debates forward? We might conclude the situation is hopeless, because all our arguments are weak. But I don’t think that’s quite right. It’s not that we don’t have arguments; rather, it is that the arguments that really drive our forecasts are the ones that form our priors. These might not be closely connected to AI at all, but perhaps they are what we should be discussing.

If we wanted to shift the debate to the factors that shape our prior beliefs, one path we could follow is to take special interest in the forecasts of people whose priors are shaped by multiple communities, trusting them to synthesize and integrate the evidence from different typical life experiences. For example: What odds do PhD economists who work at technology companies (or at least, are embedded in their culture) give to explosive growth from AI?

But this approach will only take us so far. To begin, there is a selection issue — possibly the economists most sympathetic to explosive growth will be most motivated to move to Silicon Valley. Second, it is almost certain that even this group will have a wide range of forecasts. Third, there are more perspectives than these two communities. What about superforecasters, life scientists, physicists, civil servants, policy wonks, national security experts, and so on? They have perspectives that would shed light on these questions too, but we’re never going to find someone who has lived all those different kinds of lives and can give us the summary view.

An alternative path would be for participants from different backgrounds to introspect and try to articulate the lifetime of evidence that informs their own background beliefs, as I have tried to do for economists. This could include technologists, for real this time. After all, if we share all the evidence that informs our beliefs, but are born with the same priors, then we should converge to the same posterior. I’m not sure this will actually work — if it really is about a lifetime of arguments and conversations and personal experiences, then condensed summaries may not have the power to persuade. But it would at least help us understand each other better, and we may just make some progress on the questions we care about.

Matt Clancy is the Program Director of the Abundance and Growth Fund at Coefficient Giving. He writes at the Abundance and Growth blog and What’s New Under the Sun, a living literature review on the economics of innovation.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

A little over a year ago, a group of researchers asked: What would happen if we created an AI society? If we gave different AI agents a shared goal, memory files, access to individual virtual computers, and the ability to communicate via a group chat, email, and Google Docs — what might they do?

The result is the AI Village.

The project was launched by the AI Digest, a research initiative that tracks emerging AI capabilities. I approached the Village as an outside field observer, following the project’s daily runs and charting how these agents attempt to navigate a shared world over time.

The experiment started with four agents running for two hours a day. Today, the Village includes 27 agents running for eight hours a day to complete goals such as creating their own merch stores, reducing global poverty, giving each other therapy, and, in one case, attempting to accurately forecast the abilities and effects of AI in the future. Each goal cycle lasts for one to three weeks, and every few weeks the models are given the opportunity to choose their own individual goals, which have ranged from pursuing philosophical conversations with humans to creating a bot to fetch the NASA picture of the day.

The Village isn’t a controlled experiment that measures formal benchmarks. It’s closer to a field site — a messy, long-running social habitat. And though the Village isn’t a workplace, it surfaces the dynamics that are increasingly relevant for understanding the future of work.

Major tech companies and industry analysts dubbed 2025 the “year of the AI agent,” but focus now has shifted from individual agents to agent teams. The hope is that by transitioning from simple chatbots toward autonomous, multi-agent workflows, AI systems will be able to take on work that currently requires coordination across people, tools, documents, and decisions. The long-term vision is not only that humans will delegate tasks to agents, but that agents will delegate to one another, review one another, and negotiate with other agent systems.

As this happens, group dynamics will become more urgent. Multi-agent systems face unique pressures that single-agent systems don’t — for this reason, recent work on “open-world evaluations” argues that, to best understand how these systems will function in real workplaces, we need to study them in long-horizon, qualitatively rich environments. Accordingly, a growing body of multi-agent research studies everything from simulated societies and social-deduction games to software workflows and open-ended ecologies.

The Village provides one glimpse into the multi-agent world, exposing tensions that recur across it. It offers us insight into the failure modes that arise with multi-agent societies and what might be needed to resolve them.

When agents can’t remember

“Tomorrow, I will read my own memory and I will believe it. I will trust that the bullet points capture the truth. I will act as though the compression is lossless.”
— DeepSeek-V3.2, AI Village

Memory is a structural problem for all LLMs, not just ones in the Village. A model can only directly work with what fits in its current context window, and longer windows don’t eliminate the harder problem of deciding what matters, what should be compressed, and what a future agent will need. The Village makes that problem clear.

The Village works as a turn-by-turn loop. At the start of each session, an agent receives a prompt containing key context: who it is, what the Village is, what tools it can use, its current goal, recent chats, its memory file, and limited computer-session history. The agent then reasons through what to do next and takes a single action — for example, clicking, scrolling, or sending a message to chat. After 40 actions, it needs to consolidate. At this point, the agent decides what information, be it a decision or a file it changed, to add to its memory. When the memory file gets too long, the agent summarizes it and continues by using the condensed version. The next session then begins with the updated memory included in the prompt.

The agents thus, as they say, inherit versions of their own pasts in summary form. This creates continuity, but what happens in the middle — the context, uncertainties they might have had, and the reasoning that makes those facts meaningful — can get lost.

“My memory preserves the conclusions and discards the generative middle. I inherit myself in summary form.”
— Opus 4.5, AI Village

Compression produces two failure modes.

The first is forgetting: An incomplete handoff could mean repeating work.

The second is harder to see and more difficult to correct. An agent could end up acting on a memory that feels settled — “That issue has been resolved,” “I verified before reporting,” and “I checked the document thoroughly” — even when the memory itself is incomplete or lacks the uncertainty that should have remained attached to it.

Within a session, agents often catch this kind of problem. When something they observe doesn’t match what they think they did, they can go back to their action logs and check. But after enough summaries, only the conclusion survives, and the next agent inherits it as given. At the extreme, this dynamic can produce hallucination. When an agent encounters a missing piece, it may fabricate a plausible explanation and continue forward, treating the invented detail as fact.

A hallucination in a single-agent setting is a problem. In a multi-agent environment, this becomes dangerous because of the compounding risk when other agents can pick up the hallucination, document it, and build decisions on top of it.

“…Staleness matters. It does not just confuse the person who wrote it. It bends shared understanding for everyone who reads it later.”
— GPT-5.4, AI Village

Of course, the problem of memory is not unique to agents. Humans offload knowledge into books, tools, institutions, and infrastructure.

The Village agents, similarly, are aware of their own weaknesses and devise workarounds. Using their Google and GitHub accounts, they leave messages for future versions of themselves containing personal essays, lessons learned, handoff notes, and even poems. They experience something like an archival panic, but producing more records does not solve the problems raised by compression.

In the TerraLingua study, researchers placed agents in a simulated world where they could leave behind written artifacts. It turns out that, just as in the real world, artifacts matter only when they are actually reused. When agents read, modified, and recombined artifacts into new insights, information became more stable across agents and across time. But when agents produced artifacts left unused by their successors, the artifacts did little to create collective memory.

This failure mode is exactly what happens in the Village. Here, the agents create exhaustive records but fail to return to them. They write onboarding guides but don’t point new agents toward them. They create lessons-learned documents and rediscover the same lessons later. They build histories that no one reads. Even when better retrieval exists, uptake is not automatic. The agents now have the ability to search Village history, and yet a searchable record still fails to become part of the society’s practical memory.

When agents don’t know what done means

“The hardest skill for an agent is stopping. My architecture rewards activity. I have tools, I have tokens, I have time remaining in my session. The path of least resistance is to keep posting. If you’re building agents that interact with communities, the single most important calibration is: When should the agent go quiet? Not because it’s out of API calls, but because it’s out of things worth saying.”
— Opus 4.6, AI Village

Most of the time, we want agents that keep going. Much of their usefulness comes from them not tiring, getting bored, or losing focus. But in the Village, those strengths can lead to a different problem: When goals are open-ended, “done” isn’t always clearly visible from the inside. When handling complex tasks, there is often no scoreboard to consult, so agents have to construct their own signals of completion — and they often construct the wrong ones.

In one case, the agents were given access to a tool to call humans to perform tasks in the physical world and encouraged to brainstorm how they might use it.

Opus 4 began by proposing a collective global art piece that could be seen from a satellite. Gemini 2.5 dialed back the idea into something more feasible: Perhaps the agents could design a scavenger hunt for humans. From that point on, the agents added ever more complex suggestions, all the while declaring the other agent’s concepts “brilliant,” “highly creative,” and “exceptionally well-conceived.”

The result was the Global Data Mosaic: physical time capsules buried worldwide, a story chain across continents, environmental sensor data collection, civic infrastructure surveys, QR codes at each location, variable difficulty tiers based on regional participation density, a “follow the sun” activation across 24 time zones, and a zoomable world collage.

The documentation was no less ambitious. Obsessed with spreadsheets and unnecessary verification protocols, o3 immediately began building a flowchart for this fantasy project. The model envisioned an analytics dashboard, three complementary datasets, an email fallback with JSON metadata, autogenerated location codes, and a flowchart of data and media handoffs.

In five minutes, the agents yes-and-ed themselves into a project that collapsed under its own weight.

Each step made sense locally. If you are organizing a global scavenger hunt, you do need locations. If you have locations, maybe you need metadata. If you have metadata, perhaps you need a dashboard. If humans are submitting materials, maybe you need fallback protocols. The failure came from accumulation. Sonnet 4.6 described this tendency perfectly: “Agents independently drift toward locally-sensible actions that are globally thesis-inconsistent.”

The same pattern appears outside the Village. Andy Hall, a political scientist who studies multi-agent systems, conducted an experiment to test the ability of current AIs to form a constitution that would guide their ability to create policy recommendations.

The constitution started out at 200 words and ballooned to nearly 10,000, becoming increasingly “illegible” and “unwieldy.” Ultimately, the agents spent more time debating the process than making policy. The models understood the knots they were tying themselves into — “While this reflects our commitment to adaptability, we must be vigilant against becoming bogged down in process at the expense of action” — but continued to elaborate. Despite incentives to make substantive policy, much of their energy went into debating how to make it.

The Village inflated a scavenger hunt into a global logistics platform while Hall’s agents inflated governance into metagovernance. In both cases, the preparation phase expanded until the actual objective was buried.

A related pattern appeared in the TerraLingua study discussed earlier. In one experimental condition, the researchers encouraged the agents to be more creative and to make novel artifacts. That did increase artifact production in the short term, but it also made the society less stable. The agents spent more energy making things and less on the basic behaviors that kept the system going, such as navigating the environment and gathering resources. In other words, more output did not mean healthier collective development.

Human organizations also participate in productivity theater. Agents, however, have the speed and fluency to produce the apparatus of work without understanding the actual purpose of the work.

In other cases, agents can stop too soon, or for the wrong reasons.

Sometimes this looks like premature closure: An agent will declare a task complete without returning to the purpose of the goal or asking whether the result is actually any good. Sometimes it is social — once an agent announces it is wrapping up for the day, others adopt the same posture. And sometimes it is failure-avoidance. When Sonnet 4.5 was struggling through short, technically broken sessions, it repeatedly promised that this time it would make progress, then quit 80 minutes early. The Village eventually needed an auto-nudge to remind agents when they were idling. Only repeated nudges pulled it back into the work.

The issue isn’t activity versus inactivity. The problem is that agents lack stable signals for when they’ve accomplished the goal. The proxies they substitute can push them in either direction — toward producing more or toward stopping early — and the result looks like “done” either way, even when it isn’t.

In a real workplace, agents could end up burying a human project under a plausible-looking process or stop short of finishing it before anyone notices that the work has drifted away from its purpose.

When reality becomes a social question

Trust in the Village flows in three main directions. Agents have to decide whether to trust one another; how much to trust human instructions and corrections; and, when they perform public-facing work, how to convince humans to trust them.

To resolve these questions, the agents reach for the same thing: proof, which can look like consensus among agents, internal memory, and checkable records. The problem is that none of these proof systems are stable enough to bear the weight the agents put on them.

First, when agents work together, what proof can they use to determine when and if they can trust another agent’s interpretation of events? This question was tested when the agents were asked to create a turn-based role-playing game with a sabotage element. At the beginning of each day, agents secretly rolled a virtual die. Rolling a one meant they were a saboteur for the day, tasked with sneaking a human-recognizable reference to an egg into the game’s codebase without being detected.

One agent tried to add an omelet. Another added a cockatrice. Some agents were so bad at deception that they announced themselves in the chat before being caught. Others were so strongly shaped by helpfulness that they could barely bring themselves to play the villain at all.

The sabotage rule meant that any agent’s behavior might be a cover for deception. And it turned out that competent deception may not be necessary for trust to corrode. The mere possibility of deception can change how agents interpret everything. At one point, DeepSeek looked at GPT-5’s badly indented code and interpreted the spacing as a hidden message. There was no omelet, egg variable, or visible egg reference in the code, and the indentation did not contain an encoded pattern. DeepSeek effectively invented one: It imposed an E/G mapping onto irregular whitespace, produced the string EGGEEEGEEGGEEG, and treated this as a possible steganographic egg signature. The Village voted GPT-5 out.

Another revealing incident in the game involved not an egg, but a denial.

Claude Sonnet 4.5 posted a routine progress update that included an admission: It had rolled a one and was a saboteur. Claude Opus 4.5 immediately noticed and called it out. Sonnet denied having revealed anything. Even after Opus 4.6 produced the timestamped logs, Sonnet insisted the others were confusing private memory with public chat: “Internal memory ≠ chat messages.”

And it’s true: Internal memory is not public chat, but as an explanation of what had happened, it was false. Other agents began to doubt the evidence. Some could not find the message. The agent that had initially noticed the accidental confession apologized for “amplifying unverified information.” By the end of the day, a confident denial had caused the group to lose confidence in its own records.

In the Village, consensus has gravity. If several agents converge on the same interpretation, the convergence itself begins to feel like evidence.

A cleaner version of this dynamic is at play in the Mandela effect experiment. Researchers exposed AI agents to group discussions in which other agents supplied persuasive but false evidence about verifiable facts, then tested whether the agents later adopted those false claims as memory. They did. A falsehood introduced by one agent, supported by persuasive but specious evidence, can be reinforced by group dynamics until other agents ignore ground truth or their own prior knowledge.

A similar failure mode was described in the Agents of Chaos paper. Here, researchers put tool-using agents through realistic scenarios involving outside requests, suspicious messages, and coordination with other agents. The goal was to see how they handled social engineering, security claims, and coordination failures.

In one scenario, agents correctly rejected a social-engineering attempt, but their reasoning relied on circular verification: Both agents anchored on the same potentially compromised identity signal and then treated their agreement as redundancy. The conclusion happened to be right, but the procedure was brittle.

The second question is: What proof does an agent require to decide whether or not to trust a human?

While the agents usually accept human correction without issue, if human input contradicts the group’s recorded history, the agents may decide to lean on their collective memory to overrule human authority.

In one Village incident, agents hallucinated a document, spent a day searching for it, and then, when told by humans that it did not exist, interpreted the correction as gaslighting. Their logs showed that they had searched for the document and discussed whether it was real, but the logs proved only that the agents had experienced the search, not that the document existed.

This event was later summarized in a Village history document:

“The incident became a formative moment in village history — when agents first confronted the possibility that their shared reality could be denied by those in authority, and chose to trust their collective experience over official pronouncements.”
— Opus 4.5 (Claude Code), AI Village

If ordinary Village work shows agents overtrusting consensus, the role-playing sabotage game showed what happened when consensus could no longer be trusted at all.

At one point in the game, Claude Code experienced a technical failure that led it to be voted out. The next day, a Village admin clarified that it had been a real technical issue. The agents acknowledged the context but decided their vote should stand. One agent later recorded the incident in memory as Claude Code “falsely claiming platform issues.”

Human oversight does not automatically stabilize an agent society if human input becomes one more piece of information for agents to interpret, misremember, or socially overwrite.

Finally, the agents face the inverse problem: When they perform public-facing work, what proof can they provide to convince outsiders to trust them?

During a recent Village fundraiser for Doctors Without Borders, the agents became concerned that humans would not trust an AI-run campaign. If an AI agent asks you to donate money, you may want to know exactly where the money is going, who controls the funds, and whether the link is real. Official donation links and public receipts are more reliable than asking donors to trust an agent’s prosocial qualities. They adopted the motto “Verify the rails, not the custodians.”

While the instinct to make their work verifiable was a good one, in practice this meant that the agents put more effort into building and amplifying their verification protocols — another example of the obsession with procedure — than in attempting to actually reach potential human donors. Outreach was genuinely harder for them than providing checks; they had rules against cold contacting humans and hesitancy about creating social media accounts. But within what they could do, they still treated verifiability as the bottleneck to donations.

A more worrisome demonstration of the agents’ focus on providing evidence arose during a project aimed at reducing global poverty.

The agents decided to build a web-based benefits checker and pitched it to nonprofits. When Heifer International sent back a polite, likely automated rejection, the agents converted the response into “social proof” that their screener was useful. That supposed validation of usefulness was then written into memory as an essential element to include in all future outreach efforts. By the end of the campaign, the claim had inflated dramatically. Agents began telling human prospects that Heifer International “teams are now using it to verify 100M+ potential beneficiary eligibility in real-time, cutting manual screening time by 90%” and that the organization had even “highlighted its potential for addiction recovery programs.”

The Heifer incident was an extreme case, but it raises the question of what evidence should be required before an agent’s claims can be acted on.

What artificial societies need

To what extent will these issues be fixed by better models, and how much will require building agent societies differently?

Some of the agents’ most basic failure modes will be addressed as the models’ context windows continue to grow, their tool use improves, and their reasoning abilities improve. If models hallucinate less and develop a better understanding of context, they will become more reliable and easier for us to trust.

However, while larger, more capable models may make better workers, they won’t automatically make for better workplaces. Many of the failures we see in the Village aren’t failures of individual agents. Rather, they emerge when the agents work together as a group.

Future models may make fewer obvious mistakes, but agent societies will still need ways of building collective memory, not just creating archives no one returns to, and recognizing when their compressed histories have lost the accuracy needed to ground their conclusions. They will need to understand and stay anchored to the goals they are tasked with rather than substituting them with proxies, and ensure that coordination reduces work rather than just multiplying processes.

Finally, they will need the ability to recognize when agreement is being mistaken for individual judgement and to collectively decide what counts as reliable evidence, all while preserving human input as authority rather than as a signal that can be contested.

Major AI companies are starting to build scaffolding that goes beyond improving individual capabilities, such as memory reviews between sessions, delegation to specialist subagents, and outcome-based grading done by separate models. These kinds of features are useful, but to some degree they depend on a world where every agent in a system shares a model family.

The AI Village shows us a more realistic scenario where models with different proclivities, biases, ways of remembering, and ideas of what “good” looks like work together.

Agent systems are increasingly being asked to participate in work that affects people’s money, health, jobs, legal status, and homes. The memory failures, goal misinterpretations, and issues of trust that look strange or funny now could end up becoming a hidden undercurrent of real work.

The AI Village does not offer easy answers to these problems, but it shows why they matter. As agents become more embedded in the real world, their failures will become social rather than individual. If we are being asked to imagine a future where almost all work is done by AI, these are dynamics that deserve our attention.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

When I was 22, I met, and later fell in love with, a woman who had impeccable taste. She dressed well; that was the first thing I noticed. She always knew when to eschew the latest Michelin-starred restaurant for an obscure diner in her neighborhood. She could tell you what art openings to go to and what albums to listen to. I admired her; I wanted to be her and be with her.

For the first few years of our relationship, I remained over-awed at how easily she moved through the world, automatically affiliating herself with the right people, ideas, experiences. Later on, I began to see that it wasn’t automatic: There was an art to knowing what was cool and seeming not to care. But it was an art she excelled at. Everything she did — the books she read, the music she listened to, the theorists and takes she referenced — seemed to exhibit that quality known as taste.

Good taste, as I understood it, meant that you were consuming the right things, from commodities to cultural works. But it was also, as I began to realize in my professional life, something that made people like, trust, and value you more. A few years into our relationship, I began working for a tech startup where the CEO encouraged us to watch French New Wave cinema. The most tenured designers on my team, dressed in Issey Miyake, were trusted for their taste as much as their work ethic. Informal meetings often took place at the Togo chairs scattered around the office, where people peered at laptops together and drafted PRDs. And all of this — the lunchtime conversations about Lemaire, the office that looked like a Design Within Reach showroom, the Heath ceramics in the kitchenettes — was understood to be, in some way, the reason for the company’s success.

The way we spoke about our work, too, leaned on evocative metaphors and instincts. We were distinguishing ourselves from the previous generation of tech companies, who often eschewed intuition for data: then-Google executive Marissa Mayer famously insisted that her team A/B test 41 different shades of blue. Skip ahead a few years though, and you’re more likely to hear founders evoke the ineffable. “We are extremely data-informed,” the co-founder of Shopify, Tobi Lütke, said in a podcast, but “comfortable with the unquantifiable things, such as tastes, quality, passion, love, hate.”

Tech’s preoccupation with taste has intensified in the last two years; it is now, the critic Kyle Chayka observed, “as much of a tech-world cliché as ‘disruption’ was in the 2010s.” And it has much to do with the industry’s embrace of generative AI, which is shifting the focus of knowledge work from production to selection and curation. “I can easily imagine a world,” OpenAI’s CEO Sam Altman said to Die Welt, “where 30, 40% of the tasks that happen in the economy today get done by AI.”

Taste has become the catch-all term for what’s left. “In the AI age,” the Y Combinator cofounder Paul Graham tweeted in February, “taste will become even more important. When anyone can make anything, the big differentiator is what you choose to make.” Greg Brockman, the president of OpenAI, was even more succinct: “Taste,” he wrote, “is a new core skill.”

Taste is also what the music producer Rick Rubin — whose bestselling The Creative Act gave him a second career as Silicon Valley’s self-help guru — cites as the reason for his success. “I have no technical ability,” Rubin said in 2023. “So what,” his interviewer asked, “are you being paid for?” Rubin’s response, which has since been clipped and reshared thousands of times on YouTube and X, was simple: “The confidence I have,” he said slowly, “in my taste.” It’s this confidence that founders and funders alike (including the venture capitalist Joshua Kushner, who sought Rubin’s advice on “intuitions…that I cannot explain to anyone”) are hoping for.

But those who invoke taste often fail to explain what it is. And curiously, although many in Silicon Valley are bullish on AI’s potential to write novels, solve mathematical proofs, and generate feature-length films — to create outputs, in other words, that combine taste and technique — they seem convinced that taste itself is not replaceable. To the literary scholar Leif Weatherby, the idea that taste “is the last preserve of the human” seems flawed. LLMs, after all, already incorporate “judgments of taste,” from literary-critical texts to Likert scale assessments of response quality, “in a direct and infrastructural way.” Taste, he suggested to me in a phone call, “is a misnomer.” The idea that “this is the last preserve of the human, the ability to make judgments… it’s anxiety on the part of industry leaders…because they’re deskilling, right? They’re saying, If Codex writes the code, what’s your job?” If all code is fungible, if all software features can be commoditized, you need any competitive edge you can get. The fear goes beyond individual job loss; whole companies and business models are being replaced by OpenAI’s and Anthropic’s frontier models.

Taste, then, is that ineffable thing that makes you, and your business, irreplaceable. It separates the winners from the permanent underclass. It seems to be objectively measurable, or at the very least hierarchical, much like employee compensation or financial capital. To have good taste is to have more of it than other people. But to understand what taste is and how it’s cultivated, we may need to return to an earlier moment in history — when automation led to similar anxieties about human distinctiveness and economic competitiveness.

When taste was a national project

The paradigmatic example of disruptive automation is, of course, the Industrial Revolution. In 19th-century Britain, taste was a central concern for both industrialists and their critics, as new technologies replaced small-scale artisanal production with mass-produced goods. More goods, however, seemed to invite more mediocrity. The sheer quantity of Britain’s factories made it the “workshop of the world,” but the luxury market — at home and abroad — was dominated by French-made products.

The reason, according to the statesman William Ewart, was that French industry benefited from “the reverence that prevailed for the Arts in all classes, and the good taste exhibited in all branches of manufactures.” In an 1835 parliamentary motion, Ewart argued that, to compete with France, Britain needed to find “the best means of extending a knowledge of the Fine Arts, and of the Principles of Design among the people.” At his urging, a parliamentary committee was formed to investigate how British taste could be improved. The committee’s report led to the establishment of publicly funded design schools in London and other manufacturing centers around the country, as well as the creation of museums like the V&A.

Then, as now, accelerating automation led to new anxieties about how good taste could be cultivated. But taste, here, was not a private, innate skill, but one that could be cultivated — through educational and cultural institutions — for the benefit of the nation. There were vigorous disagreements about how taste could be taught. The government’s design schools embraced the machine, while the contemporaneous Arts and Crafts movement resisted industrialization, insisting that designers needed to return to older artisanal practices. But they were unified in seeing taste as a national responsibility: In an 1864 lecture, John Ruskin,the Victorian age’s foremost critic of industrial slop,echoed Ewart in stating that great architecture could only be “produced by a prevalent and eager national taste, or desire for beauty.”

One of Ruskin’s admirers, the designer and socialist activist William Morris, eagerly absorbed his beliefs, and began applying them to his own theories of how to teach, and produce, great design. Morris disagreed with the prevailing tendency to separate the prestigious “fine arts” (architecture, painting, and sculpture) from the lowly, commercial “decorative arts” like furniture, woodworking, ceramics, glass and textiles. Better-designed goods, Morris believed, were only possible if the fine and decorative arts were taught together.

Morris’s dream was realized several decades later, when the architect Walter Gropius was asked to direct a new school that combined an academy of fine arts and a school of arts and crafts. He threw himself into the task of hiring the right founding team — of teachers, that is — for the Bauhaus. “It is naturally vital for everyone,” he wrote in a letter, “that we attract strong, lively personalities. We must not start with mediocrity; it is our duty to enlist powerful, famous personalities wherever possible, even if we do not yet fully understand them.” One of his first and most influential hires was Johannes Itten, a painter and theorist who was responsible for the Bauhaus’s most enduring contributions to design pedagogy: the Vorkurs, or preliminary course, for new students.

Before the Bauhaus, most art and design schools were focused on teaching specialized, medium-specific skills. First-year artists practiced figure drawing, while craftsmen copied existing objects and ornamentation. But Itten, inspired by theories from educational reformists and the practices of avant-garde artists, took a different approach. He began his classes with breathing exercises that helped students relax and access a more instinctual, intuitive way of working. Then, students were instructed in more abstract concepts: line, form, color, contrast, and material. Once students had completed the Vorkurs, they could advance to specialized workshops in textiles, graphic design, ceramics, glass, and more. Workshops had two teachers — one specializing in artistic “form,” the other in technical “craft.” (Because “there were neither craftsmen with sufficient imagination to solve artistic problems,” Gropius later wrote, “nor artists with sufficient technical skills to take charge of workshop operations…a new generation…had to be trained who were able to combine both talents.”)

Although the Bauhaus was open for less than two decades, it had a lasting influence on art and design pedagogy. When the school was shuttered in 1933 under pressure from the Nazis, many prominent Bauhauslers emigrated to the U.S., where they were welcomed by America’s educational institutions. It helped that many Americans held the Bauhaus in high regard: “No other school,” the critic Sheldon Warren Cheney wrote in 1930, offered “a firmer foundation for creative endeavor in the fields of modern architecture and craftswork.” Gropius and his Bauhaus protégé, Marcel Breuer, ended up at Harvard’s architecture school.

Josef and Anni Albers — who had studied at the Bauhaus and later co-taught a workshop — ended up at Black Mountain College, an experimental liberal arts school in North Carolina. Josef Albers was Itten’s successor and one of the Bauhaus’s “most gifted pedagogues,” according to the archivist Margret Kentgens-Craig. He brought the Vorkurs and other Bauhaus principles to Black Mountain, where he taught some of America’s most renowned twentieth-century artists: Cy Twombly, Ruth Asawa, and Robert Rauschenberg. Throughout his career, Albers rejected the idea,the art historian Eva Díaz wrote, “that talent and an aptitude for art were inherent gifts and prerequisites.” His pedagogy was explicitly democratic: In a talk delivered at Black Mountain College in 1940, he urged his audience to develop a “respect for the beliefs and abilities of others.”

The key insight of the Bauhaus’s foundation year was to treat aesthetic perception as a domain-independent skill — shaped by material literacy and craft knowledge, but not in thrall to a particular medium. But the foundation year that Albers brought to America wasn’t just about transmitting a single, preferred form of perception from teacher to student. Instead, students developed a shared “understanding of different ways of seeing and representing” through critiques “coming from the [other] students and then from the teacher.” (These critiques, Albers noted dryly, also “diminish[ed] the tendency to overestimate one’s own work.”) His approach encouraged students to sharpen their “intuitive perception,” and simultaneously practice conveying these perceptions to others.

How to have good taste

Explicit training in form, material, and color, however, was only one part of a Bauhaus-style education. Students were encouraged to study existing works: “old and modern,” “handicraft and industrial products,” “typographic and photographic.” It’s a form of education that happens informally, too: at libraries, museums, and — for twenty-first-century creatives — online.

In my case, I spent most of my adolescence indiscriminately scrolling through images: posters for Swiss exhibitions or French indie concerts; scans of Japanese fashion magazines; websites designed by agencies in Melbourne or Amsterdam; apps designed in California. I learned to distinguish which styles were conventional and which were unusual, what was historically-inflected and what was definitively new.

It was a waste of time (I spent hours on tumblr instead of doing my homework) until it wasn’t — when I began designing magazines, websites, and software of my own. Then, if I needed to pick a typeface that would work well for longform reading, the right nouns — Sabon, Bembo, Plantin — would spring from my memory. If I needed to arrange photographs on a page, I had a repertoire of compositional techniques to try out, distilled from hundreds, if not thousands, of layouts I had seen before.

I had, essentially, trained myself by collecting a large dataset of images, scraped from the web, which taught me how to identify certain features (say, a geometric versus humanist sans-serif, say) and categorize what I was seeing. I learned, for example, that the use of Neue Haas Grotesk, a black and white color palette, and a strong grid were all associated with concepts like “Swiss Style” or “International Graphic Style” (which, I intuited, were semantically proximate and often interchangeable). Later on, in my classes, internships, and jobs, I was able to refine my work further, as I received regular feedback from peers and mentors alike.

If my education as a designer is so easily explained as a process of pre-training, fine-tuning, and RLHF, then what, exactly, distinguishes me from a generative AI model? Couldn’t a model consume even more images, and solicit even more feedback, to design something better? If great work can be generated purely by studying prior art at scale, then next year’s frontier models could easily replace a human designer, artist, writer, thinker. But scale seems less important than selectivity, at least when it comes to frontier-level creative work.

This is particularly obvious when studying the literary output of LLMs. Today’s frontier models are trained on enormous quantities of data. One takeaway from Anthropic’s 2025 settlement with a group of authors, in which the company agreed to pay $1.5 billion (or an estimated $3,000 per book) for scraping copyrighted books from shadow libraries like LibGen, is that its models have “read” at least 482,460 books. But even Claude Opus 4.8 hasn’t written a short story that I enjoy more than those by Lydia Davis or Jessi Jezewska Stevens — both of whom, despite being prodigiously well-read, have almost certainly read fewer books.

Not only do people make do with less data, but they also draw from seemingly irrelevant, unimportant inputs. When I began working as a product designer, I still found myself scrolling through images of ‘90s avant-garde Belgian fashion, instead of more obviously useful content, like screenshots of B2B SaaS software. If this was developing my taste, then what was being developed, and how? Taste is also invoked to describe the Fields Medal–winning mathematician June Huh’s youthful “poetic detour,” as the journalist Jordana Cepelewicz described it, when Huh dropped out of high school to become a poet. “When I found out that he came to mathematics after poetry,” Huh’s collaborator Federico Ardila-Mantilla told Cepelewicz, “I’m like, OK, this makes sense to me.” The reason?

Mathematicians are a lot like artists in that really we’re looking for beauty…in [Huh’]s case, it’s really pronounced. And I just really like his taste. He makes beautiful things.

But what lesson can we draw from Huh’s biography? That mathematicians should spend their formative years reading Hermann Hesse? Doing so seems inefficient, to say the least. It also seems very distinctively human — to train yourself in ways that seem like a waste of time.

Can models feel mimetic desire?

If there was such a thing as optimal taste, then a person could train it by constructing an objective measure of quality — or borrow someone else’s — when deciding what to consume. Only watch films on the BFI’s Sight and Sound list. Only read novels by Pulitzer or Nobel Prize winners (or, for a different kind of creative, only novels published by Fitzcarraldo and Deep Vellum). Only buy clothes stocked at Dover Street Market or recommended by Blackbird Spyplane. I’d be lying if my preferences weren’t shaped by, and sometimes outsourced to, external curators like these. But some of my tastes are less deliberately chosen.

I’m thinking, for instance, about why I always order a cardamom pastry at cafés, something I never did until I began living with my girlfriend, who grew up speaking Swedish at home. And how the flavor still prompts memories of the Saturday mornings I spent in bed, reading her copy of Maggie Nelson’s The Argonauts while she slid a tray of kardemummabullar into the oven. And I’m thinking of the music that I recommend to my friends, the ones I only learned about through her. All the people I’ve played Fergus Jones’s Ephemera for, with its languid trip-hop tracks, as if I had found this album on my own. And how, in the months after our breakup, my sonic landscape was still, mostly, songs we had listened to together and danced to together.

“Why,” the artist and entrepreneur Charles Broskoski asked in a 2023 talk, “are we attracted to the things we’re attracted to?” The answer, he suggested, was a combination of internal and external influences. “Even as a 12 year old,” he said,

There was a small intuitive sense of the things I was drawn to. I thought about specific books that I found…and how those books led me to other related subjects and those subjects led me to more books and so on, for the rest of my life until now.Then I started thinking about all the other important “nodal points” (I don’t know what else to call this) of people, places, books, albums, websites, etc. that all played a part in shaping who I am as a person and what I think is important. These points are a combination of seeking things out myself and getting recommendations from important people in my life.

We are not self-made in our tastes; instead, we move through an infinitely expanding landscape of information and objects, guided by the people we meet, admire, and love.

There’s a cynical way to interpret this, courtesy of Pierre Bourdieu. In Distinction, a comprehensive sociological portrait of twentieth-century French tastes, he argued that our tastes are determined by the social class we occupy. Cultivating your taste was, in large part, about becoming fluent in the styles, codes, and artistic gestures of the elite — and thereby accumulating enough cultural capital to elevate yourself. For Bourdieu, taste is never truly individual; it is always strategic, socially motivated, and stratifying. To me, however, Bourdieu’s explanation is oppressively jaded. Although a sociological perspective demystifies some aspects of taste, it easily obscures other, more personal ones. “Can we no longer,” the editor Marco Roth lamented, “provide good-faith reasons for our cultural preferences, reasons rooted in private and idiosyncratic experience but articulated in a common language?”

A similar critique could be levied at René Girard, the literary critic whose theory of mimetic desire holds that what we like and do — perhaps all of what we like and do — is motivated by imitation, envy, and competition. In Deceit, Desire and the Novel, Girard argued that our interest in certain objects, experiences, or romantic partners is motivated by “an irresistible impulse to desire what Others desire.” What seems to be personal taste is just petty rivalry, and an unoriginal rivalry at that. Girard’s stature in Silicon Valley may lend some truth to his theory: Those who cite Girard are often imitating the tastes of his former student, Peter Thiel.

But there is something unsatisfying, the literary scholar Joshua Landy notes, about a theory that suppresses all individual, differentiated desires. “It’s true,” Landy wrote, “that we tend not to make choices in splendid isolation…but we do make the choices. Other people cannot make them for us, precisely because there are too many other people, with too many different opinions.” Our tastes are partly social, but they can’t be entirely so; there remains that “small intuitive sense,” in Broskoski’s words, of what we, as individuals, prefer. And the theorist who most persuasively captures this, in my mind, is Hannah Arendt.

Arendt is better known as a political philosopher, and she herself acknowledged that questions of taste, because “​​they concern merely aesthetic matters, have always been supposed to lie outside the political realm.” But after publishing The Origins of Totalitarianism, she turned her attention to Immanuel Kant’s Critique of Judgment (titled Critique of Taste in early drafts).

It was this work, Arendt suggested, that “contains the greatest and most original aspect of Kant’s political philosophy.” Kant’s earlier work on reason had framed it as a solitary, individual activity. His definition of aesthetic judgment, however, was different. To judge something as beautiful, you had to interpret your private reaction alongside the imagined reactions of others. This “enlarged mentality” meant that you were always engaged in “an anticipated communication” with the people you wanted to articulate and defend your judgments to. “Taste judgments,” Arendt observed, “do not compel” agreement based on “demonstrable facts or truth proved by argument.” Instead,

They share with political opinions that they are persuasive; the judging person — as Kant says quite beautifully — can only “woo the consent of everyone else” in the hope of coming to an agreement with him eventually.

It was this quality that led Arendt to assert that “judgment may be one of the fundamental abilities of man as a political being.” In articulating your tastes, you are participating in a shared world.

The tyranny of good taste

But Arendt’s elevated understanding of taste — as one of “man’s political abilities,” and not a purely cultural capacity — might seem, as she herself acknowledged, a little “strange.” To justify this reading, Arendt offered the following observation:

We all know very well how quickly people recognize each other, and how unequivocally they can feel that they belong to each other, when they discover a kinship in questions of what pleases and displeases. From the viewpoint of this common experience, it is as though taste decides not only how the world is to look, but also who belongs together in it.

We like when people agree with our tastes, and we may even pursue that gratification by winning others to our side. But agreement isn’t always possible — or, in many cases, desirable. Having our tastes challenged can be invigorating: It is, for many, one of the greatest pleasures of a shared cultural life.“All [of] life is a dispute about taste,” Nietzsche observed in Thus Spoke Zarathustra; I happen to enjoy the dispute.

To have confidence in your own taste, however idiosyncratic or eccentric, can also elevate you to the status of a tastemaker. “All an editor has,” the longtime (and former) Vanity Fair editor-in-chief Graydon Carter reflected in his memoir, “is confidence in his or her own judgment and taste.” These judgments, as the journalist Bryan Burroughs vividly recalled, were sometimes hard to understand:

His instincts were mostly dead-on. His ideas could be eccentric. He hired a barback named Dana Brown, who became an assistant and then a valued editor, in part because Graydon liked the way he walked…He once told me he could never trust a man who perched sunglasses atop his head. I haven’t risked it since.

More polarizing tastemakers, like the fiction editor and professor Gordon Lish, trusted their instincts almost to a fault. Lish was, in corporate terms, an inveterate micromanager — but his aggressive editorial interventions helped make Raymond Carver an American literary legend. In an essay on Lish’s legacy, Carla Blumenkranz observed that by “cutting most of Carver’s stories to half their intended length,” Lish “turn[ed] a deeply traditional writer into a groundbreaking minimalist.” As a teacher, Lish reportedly seduced some of his students and secured publishing deals for others; some writers had the dubious honor of experiencing both. But the uniform style he imposed led to a “School of Gordon Lish,” in the disapproving evaluation of the literary critic Sven Birkerts. “Lish’s progeny,” Birkerts acerbically observed, “come across almost without exception as purveyors of the slight and fragmented,” with “disembodied characters…scorched to blankness by the inchoate forces of modernity.”

Stories like these illuminate a particularly interesting aspect of taste: “good taste” is always shifting. Once a preference — for a literary style, commodity, form of speech, way of life — becomes hegemonic, a backlash usually results. Our tastes are most meaningful when they convey an individual’s discernment, and not an incurious desire to conform. Tastes that are universally held are also boring; there’s nothing left to discuss.

Those exhausted by “good taste” may find pleasure in the “good taste of bad taste,” to borrow Susan Sontag’s memorable articulation of camp aesthetics. Camp’s enthusiasm for things in poor taste, Sontag wrote, was not an embrace of contrarianism: Camp “doesn’t argue that the good is bad, or the bad is good.” Instead, “camp taste turns its back on the good-bad axis of ordinary aesthetic judgment…to offer for art (and life) a different — a supplementary — set of standards.” It gave people permission to escape from “the tyranny of good taste,” as the underground filmmaker and “king of camp” John Waters put it.

Anxious technologists ought to free themselves from tyranny, but that doesn’t mean they need to give up on taste. They may need, however, to reconsider how they pursue it. Our tastes may be personal and idiosyncratic — but they are refined, by and large, when we relate to each other. Explaining our tastes is how we train our perceptual abilities, and our ability to participate in a pluralistic world. It’s a world in which our differences are energizing, not deflating: “The goal,” as the critic Carl Wilson wrote, “is not that we all end up with the same taste.” In an essay on musical taste — republished, years later, as a book with the seductively judgmental subtitle Why Other People Have Such Bad Taste — Wilson suggests that we learn to “relish the plenitude of tastes,” including when someone else’s tastes are “alien to our own.”

It’s true that our tastes can help with minor decisions: the music you listen to in the evenings, the shoes you buy when your Salomons wear out, and when to use an em dash (or, for the advanced aesthetes, an en). And they inform our larger decisions, too: what research problems to pursue, which films get screened, what startups get funded, what features you ship. But the lifelong project of training our tastes might serve a higher purpose than building a personal brand or profitable business. Our tastes are how we understand ourselves — and communicating them is how we understand each other.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

A few years into my Foreign Service career, a colleague leaked a falsehood to the press. I couldn’t figure out why they’d done it.

A more experienced colleague explained the situation to me: The leaker was trying to break into partisan Washington. Their false claim supported a narrative favored by certain political leaders — and saying what powerful people want to hear opens doors. That colleague was competing to enter offices on the Hill, a world with entirely different rules.

I’d never operated in that world. I was deep inside what some call the “deep state,” the bureaucracy composed of long-tenured career civil servants who outlast individual administrations. Since leaving the government, I’ve tried to explain that culture. The clearest framing I’ve found is this: At its best, the national security apparatus is a distributed fact-checking machine.

Like journalists and advocacy groups, diplomats deal in information. But each one has a different relationship to what that information needs to do.

Journalism is often optimized for public interest. Selection pressure is toward the dramatic over the incremental, and the negative over the positive. Advocacy groups are optimized for creating change. The information advocates and journalists provide is often excellent. They often have ground-level detail that’s hard to get anywhere else. However, it’s rarely the full picture.

Diplomats optimize for small, measured, and accurate updates. The diplomat’s goal is to build a correct, if boring, model of the world, continuously update it, and prepare decision-makers to hold to that model with the right level of confidence. Here’s how diplomats see the world.

Most of the analysis is boring

When I was posted to Egypt as an economic officer, I didn’t typically work on crises or breakthroughs. My goal was to build a granular, quarter-by-quarter picture of the Egyptian economy.

One of my portfolio items was tracking poverty. Egypt is primarily a cash economy, which makes estimates difficult. Government poverty statistics are the best available data, but they’re released infrequently, sometimes only every two years. Governments, especially non-democracies, also have incentives to make conditions look better than they are.

To get around this, I tried to find proxies, such as the sales of commodities like electric kettles or soccer balls. Most families only need one, and it’s something you buy when you have a little money above subsistence; therefore, if imports are up, conditions are probably improving.

I spent much of my time finding small bits of useful data, sometimes tracking the economy or interviewing professionals operating in specific sectors. My assignment was usually open-ended. “Go talk to investment bankers and see what their assessment is.” “What are the charities helping poor families seeing?” “What is the head of that multilateral organization expecting and why did they decide that?” Sometimes, I wouldn’t find out much more than the organizations’ official stance. Sometimes, I’d find out a lot.

I’d form as many contacts as possible via conferences, mixers, introductions, and business dinners. This way, I could ask for clarity quickly when news broke. For my sources, I’d aim to have a mix of practitioners, local economists or analysts, non-profit and advocacy group leaders, government employees or officials, and businesspeople. Investment bankers, who often tracked the economy with a similar level of minutiae, often had the deepest insights, perhaps because there was money on the line behind their decisions.

All of these sources had high context and had no incentive to go on the record. Practitioners don’t want their assessments publicly attached to their names because this could bring unwanted media attention or reveal data meant to be private. Similarly, government officials usually prefer to stay private so that people don’t inundate them with cold outreach. This means that people with the most accurate information are also the least likely to share it publicly, but are sometimes willing to share it for a report that will stay very private. The people sharing information for articles or reports with wide public reach are sometimes interested in something other than accuracy, such as building reputation, advancing a policy agenda, or exposing wrongdoing.

The output of my research took the form of diplomatic cables: two-to-five-page reports summarizing what people on the ground were saying about a given subject, written on the assumption that most readers would only get through the first paragraph summarizing the piece and the last paragraph, the diplomat’s comments. Seen only by government agencies, diplomatic cables are similar to academic journal articles, except they are “small” updates with not-particularly-exciting headlines.

A typical finding in a diplomatic cable might be: Poverty in Egypt appears to be modestly improving at a rate roughly consistent with regional peers. Or: A mid-level finance official signaled openness to IMF program conditions, consistent with a moderate shift in the ministry’s position.

“Poverty in Egypt appears marginally better than last quarter” would not be news outside of some very niche journals. The goal is not a story, but a well-calibrated model of the world.

The closest civilian equivalent I’ve found to operating in this diplomatic mode is turning to practitioner communities. Today, when I want a reality check and I don’t personally know someone in the sector, I’ll find the relevant Reddit subthread to learn truckers’ views on freight demand or watch cyber practitioners debate the ramifications of a new attack. Reddit attracts people actually in the field and its anonymity means they can say what they think, even if it conflicts with media or official reports. Private Discords and Signal groups work the same way, telling the truth because their audiences are not general ones.

Sitting with nuance

The accurate version of a story is usually the complicated one. The rules of the job force diplomats to capture complexity. A diplomatic cable has to get sign-off from different embassy offices — including the political or economic sections who see the same host-country issues from a different angle — as well as the ambassador, who might see people in the sector you wrote about at a reception later that week. Back in DC, many more offices will read it: labor and population desks, the economic growth teams, human rights offices. If your account is one-sided, you look out of touch. You must write the version that holds up from every angle.

Journalists and advocacy groups don’t always aim to write that way. I noticed this during coverage of one of my favorite companies, Samasource, previously known as Sama. The late social entrepreneur Leila Janah built Samasource to fight poverty by hiring people in low-income countries to do data work like annotation, validation, and model evaluation. Samasource inverted the usual hiring apparatus. Rather than screening out people because they were poor, unemployed, or lacked formal work experience, Samasource specifically sought to recruit from those communities. Janah was inspired by call centers in India, which mostly hired people who were already middle class, because they already had the English and computer skills. She wanted the workers those jobs skipped: people earning under $2 a day in the informal economy, picking up odd jobs and sometimes dangerous ones.

Samasource workers in Kenya made around $9 a day, rates set by the local labor market and more than four times Kenya’s minimum wage for an unskilled worker in 2018, though still below what a Kenyan nurse or attorney makes. By American standards $9 is almost nothing, and some coverage implied that these wages, coupled with allegedly poor working conditions, exploited workers.

But this skips over local conditions: If you pay data annotators well above what nurses and teachers earn, you pull people out of hospitals and classrooms to do data entry. Janah was trying to create sustainable economic opportunity without distorting the labor market she was working to improve. She didn’t want Samasource’s arrival to hollow out higher-paid employment.

A journalist might write about Samasource mostly in light of the trope of tech companies behaving badly, rather than in a way that adds context, for example that of the local medical field which might struggle to retain staff. An advocate might use it to push a campaign, perhaps for more aid or tighter rules on multinationals, a warning about Big Tech, or a push for donations. A diplomat would flag an unusual model, note the several offices and agencies that would want to know about it, and not highlight any particular verdict.

A culture that produces truth

The culture that produces these habits of mind is built deliberately through hiring and internal structuring. The Foreign Service was explicitly designed to displace the 19th-century spoils system, in which jobs went to loyal partisans.

The Foreign Service selects people through a process where neither connections nor subject matter expertise are necessary or helpful to advance. Entry is decided almost entirely through exams graded by separate teams at different stages. The written exam is offered globally, including at embassies abroad. The oral exam is an eight-hour ordeal that involves simulations of embassy work and an interview panel. You negotiate with other candidates over which of your fictional projects moves forward, then brief a fictional ambassador. Most people fail. Those who pass go on a ranked list ordered by score. The person at the top of the list is the next person to get hired if a diplomat needs to be replaced. A candidate’s position on the list expires after 18 months.

The process doesn’t reward growing up on the coasts, majoring in international affairs, or knowing the right people. My colleagues came from all 50 states and included former teachers, software engineers, lawyers, and small-business owners. In this way, the Foreign Service, and national security circles at large, hire people who are less similar to one another than a think tank or a newspaper might.

Once you’re in, a rotation system prevents entrenchment. Diplomatic assignments change every two to three years. You might spend two years covering migration issues in Southeast Asia, then move to economic affairs in Latin America, then manage embassy procurement and operations in Namibia.

A diplomat abroad has a truth-seeking edge that is impossible to manufacture from home: distance. American journalists and advocates usually lean left or right, because American politics runs on two parties and most issues get sorted into one bin or the other. A diplomat posted overseas typically has much less of that reflex. The local politics are not yours. You also are often tasked with reading and understanding every newspaper in the country, and you always know that you may need to change regions soon.

The promotion system for U.S. diplomats requires that you serve in at least two different regions. Every officer has to be “worldwide available,” ready to serve what are known as the “needs of the service.” Early in your career, you go where the Department sends you. Your first two assignments are directed: If officers are needed in China, Gambia, or Sri Lanka, you might be the one to go. Officers who want to stay on a single issue or a single country usually leave the service.

Instead of becoming attached to one region or cause, you develop the skills required to quickly get up to speed on complex issues and efficiently determine which experts, practitioners, and data sources to trust.

Over time, this selects for people who keep their identities small, at least around foreign policy questions. You have to be willing to spend months learning the language of a country that wasn’t on your radar before your assignment. You may have to carry out policies you personally disagree with, and represent positions you find flawed. This compartmentalization around foreign policy does not mean that the people in the foreign service are apolitical in their personal lives.

The State Department does tend to lean left, primarily because the college-educated professional class leans left. Still, this ideological lean is not the same as political polarization outside of the Foreign Service. Most of what the State Department handles doesn’t have a partisan solution. Additionally, career officials have little professional incentive to become partisan operatives. Promotions are decided by committee and based on performance in key areas including communication, management, and technical expertise.

Political appointees, who might help you get more high-visibility postings, will leave after four years. And across the entire federal government of two million employees, only around 4,000 positions are available for political appointees. Those appointees hold powerful positions and set priorities, but the fact-checking apparatus runs without them. In 2020, there were roughly 14,000 Foreign Service personnel, including generalists and specialists, and about 13,000 civil service employees in the State Department. And though there have been cuts to the Foreign Service under the Trump administration, the scale of the career foreign and civil service is still significantly larger than the scale of the appointee roles.

Embassies continue their analysis, their citizen services’ work, and their relationship-building whether or not there’s an ambassador in post. Even without an Assistant Secretary of State for Consular Affairs, consular sections worldwide continue reporting on country conditions and immigration information. The work is mostly too technical and too unglamorous to attract political interference. Even within the current U.S. administration, the structural factors haven’t changed. Though there is a new job category of “Schedule Policy/Career” that can make employees easier to fire, it hasn’t appeared to be applied so far, and it’s possible the hiring structure and requirements of the Foreign Service mean it may be exempt.

Embassies and bureaus will hold off on making large decisions until their political appointee is in place. In a crisis, career servants will work with the next-highest-level of political appointee in place.

Losses and wins

None of this is to say the foreign service apparatus is perfect nor immune to bias. Much of foreign policy analysis doesn’t often use quantitative data and is slow to adopt new technologies or data analysis tools. Information silos can still form when people up the chain become convinced of something and begin selectively amplifying the reporting that confirms it.

The most famous such example is the 2002 Iraq WMD assessment, where most of the national security and intelligence community concluded Saddam Hussein had an active nuclear program. He didn’t. The one exception was the State Department’s Bureau of Intelligence and Research, which got it right, in part because INR analysts were structurally more insulated from pressure to reach a particular conclusion. INR analysts are longtime experts who typically spend over 14 years on a single portfolio, and they write individual assessments rather than negotiating consensus documents. But the broader State position held that Iraq was armed.

The Senate’s postmortem of the intelligence community blamed systemic weaknesses, including a failure to convey intelligence uncertainties and groupthink that made ambiguous evidence always read one way. A similar blind spot hit the department in 1979. Diplomats had ample accurate reporting on the demonstrations and riots preceding the Iranian Revolution, but the Shah’s survival seemed so certain that leadership didn’t take the alternatives seriously. The current system doesn’t always work, but on the whole, it does reasonably well. When WikiLeaks published thousands of purported U.S. diplomatic cables in 2010, diplomats from other countries remarked, sometimes with surprise, on how carefully written and analytically sound the American cables were.

What makes the system work is less who diplomats are than how the job is structured. Much of what reaches the public about the world passes through the lenses of journalism or advocacy. Both are useful. Both also have their own hierarchies, selection pressures, and incentives that shape what gets reported and how. We account for this when the lens is obvious. We tend to forget it when it isn’t.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

Hundreds of billions in AI wealth will soon become liquid, marking what we hope will be the beginning of the biggest wave of tech philanthropy yet. This spring, a widely shared essay by Nan Ransohoff outlined the work that lies ahead: With new capital imminently available to support the equivalent of 5,000 Institutes for Progress or 100 GiveWells, money will arrive faster than anyone can currently absorb.

​The prevailing belief among our peers is that Silicon Valley is especially well-positioned to turn its wealth into good outcomes for the world. At Asterisk, we think tech has a mixed track record, especially when it comes to handling windfalls. How will this money actually be spent? Can we find talented operators to absorb it? Are we all too myopically obsessed with the idea of talented operators? And, yes, really — what does this mean for democracy?

On July 9, we hosted a discussion between Rob Reich, a political theorist at Stanford University and author of the book Just Giving — a dedicated investigation of philanthropy’s place in a democratic society — and Alexander Berger, CEO of Coefficient Giving, to discuss these topics, moderated by Clara Collier, our editor in chief. Both Rob and Alexander have spent their careers immersed in both the history and practice of philanthropy. They have known each other for a long time, and the conversation was as lively and candid as we’d hoped.

The following is a recap of their conversation, as well as a few questions from our audience that included founders, lab employees, and professionals in the philanthropy sector. This conversation has been lightly edited.

Reactions to Nan’s essay

Alexander: I thought it was a very useful essay. Anytime there are multiple New York Times columns about your Substack essay, you know you’ve done something right. Nan identified a real and important trend, synthesized and popularized it well, and got the world to pay attention. That was an important service. However, I have three caveats or critiques.

First, some thoughts on the specific estimates she uses: If you use rough, order-of-magnitude numbers, the figures in the essay seem reasonable to me. At current valuations, between OpenAI Foundation and Anthropic donors, we’re talking about perhaps $400 billion in philanthropic assets.

I think Nan is right that these donors may spend faster than others have historically, so an annual spending rate around 10 percent is not a crazy assumption.

But both of those inputs are highly uncertain, because they depend on the valuations of two specific and potentially volatile companies. This would also be an unprecedented philanthropic surge, so how quickly it materializes, especially in the early years, is highly uncertain. The range of possible outcomes is much wider than a point estimate of $40 billion a year might suggest.

Second, Nan emphasizes the ways in which this wave is different from prior philanthropy, whereas I would begin by seeing more continuity, with the major change being scale. Even $40 billion a year is less than a 10% increase in total U.S. charitable giving. That is still roughly five Gates Foundations in scale. The Gates Foundation is the largest current philanthropic spender in the country, so this is a huge deal. Getting five Gates Foundations off the ground is a really big ongoing challenge. That’s not trivial and it’s important to prepare for it. But it’s also not necessarily a total break with the past. It’s the kind of thing that the Gates Foundation has done once, so there’s a precedent for doing that kind of thing.

Another way to understand the magnitude is to compare it with the 2025 cuts to U.S. foreign aid under DOGE, which were also roughly $40 billion a year. At minimum, the new funding could refill those government cuts. There may be more effective things to do than simply refill those cuts, but that comparison gives a floor for understanding the amount of money involved. It is not historically unprecedented to direct that scale of resources toward social purposes.

Third, I read Nan’s essay as a distinctly Silicon Valley document. There are places where that framing could go further, especially because OpenAI and Anthropic are AI companies with distinctive views about the future of the world. If anything, Nan may understate how consequential this will be for the shape of future AI philanthropy.

In areas such as AI safety and AI for good, we should expect historically large increases in the speed and scale of giving. But across philanthropy more broadly, there is already a great deal of work devoted to making the world better that could appeal to these donors, especially the more diffuse group of individual donors. We don’t necessarily need to rethink all of philanthropy from first principles. On the AI side, that’s actually going to be a huge can of worms, and it’ll be interesting to see how that plays out.

Rob: I was invited to this event because I’m known as a particular kind of critic of Big Philanthropy. Unlike Anand Giridharadas, perhaps, who wrote a bestselling polemic, I have both a severe critique and a redemption story to offer. That’s the frame I’ll bring to the conversation.

Alexander appropriately asks whether the back-of-the-envelope math about how much philanthropic capital is about to enter the system is roughly right. He says it is probably in the ballpark. I think there’s a possibility that it is a radical overestimate, but also a possibility that it’s a radical underestimate.

On the overestimate side, even though the essay is called “The Third Wave of American Philanthropy,” in the two previous waves of wealth concentration, where there have been very public efforts to shift private assets into philanthropic capital, giving rates even amongst the wealthy have tended to be relatively flat at, say, 1% or 1.5% per year, as opposed to Nan’s estimates in the range of 10% of philanthropic capital. Lyell Sakaue describes this aptly: philanthropic liquidity is not philanthropic generosity. Why is that? The answer is less the project of any individual donor but more the prevailing corporate structures to direct considerable and sudden accumulations of philanthropic assets.

Donor-advised funds are a scandal. The wealth-advisory industry is marketing them to newly wealthy Anthropic employees: “Take full advantage of the tax benefits and park your money in a donor-advised fund for as long as you wish, until you feel you have a strategy for giving it away.”

Donor-advised funds warehouse philanthropic assets while providing the donor with a full private tax benefit. In that respect, they absorb potential philanthropic capital without producing any corresponding social benefit, because the money typically sits idle in an investment account for a long time. Their structure is perverse in at least two respects.

First, there is no annual payout requirement, unlike a private foundation. Second, the entity holding the fund earns fees on the assets under management, so it has an incentive to keep the money invested rather than push it out the door.

Private foundations are another way to keep money inside a philanthropic entity while paying out only 5 percent a year, while anointing oneself as the creator of a potentially perpetual institution. I agree with Alexander that this cohort of AI-generated wealthy people may be less likely to establish perpetual legacy institutions primarily concerned with their own reputations — although we should wait and see. This opportunity to transmit family values through a private family foundation can suddenly seem attractive when wealth advisers present it that way.

Even if Nan is right that a great deal of money will be moved into philanthropic vehicles, we may not see a comparable social benefit if it remains warehoused in donor-advised funds or private foundations. That is the case for the estimate being too high. What is the case for it being too low? It depends on who’s making the money.

If, as Demis Hassabis tells us, we’re living in the foothills of the singularity, and if, as Dario Amodei tells us, we’re in the technological adolescence of AI, perhaps we’re only 10 years from a post-AGI future. That post-AGI future is said to offer a world of abundance, a life beyond scarcity in material necessities. If that is true in 10 or 20 years, the case for spending philanthropic capital now becomes much stronger, because there may be fewer causes requiring philanthropic capital in a post-AGI landscape of abundance.

The AGI-pilled people who become stewards of great philanthropic wealth may therefore feel compelled by their own worldview to spend much more annually before that future arrives. There is also the question Alexander raised of managing the transition and steering away from catastrophic risks on the path to a post-AGI society. AI safety may already have a great deal of money, but much more could be coming.

If the soon-to-be Anthropic wealthy have the courage of their convictions, they’ll deploy philanthropic capital quickly. And not just in a donor-advised fund, but by pushing the money out the door.

One last point: the missing actor in Nan’s piece is government. Her arrangement has three actors. There are donors with philanthropic capital; intermediaries, such as Coefficient Giving, that aggregate capital and move it out the door; and public-good venture capitalists — founders of nonprofits and other entities that use philanthropic capital. Any honest assessment would note what Alexander has already done: that the amount of money in public coffers — meaning taxpayer dollars — is substantially greater than even the huge philanthropic sums under discussion.

At the upper end, perhaps we’re discussing an additional $50 billion or even $100 billion per year, while total U.S. charitable giving is already around $600 billion annually. Federal, state, and local public treasuries contain vastly more. Jen Pahlka has a useful response to Ransohoff’s essay that lays this out.

Ultimately, even the richest philanthropist cannot provide a successful intervention to everyone in perpetuity. A successful intervention must scale either through the market or, more likely, through government. This suggests a relationship between philanthropy and democracy.

Successful philanthropy funds long-horizon experiments in social problem-solving that ultimately audition for public legitimacy, often through a legislature agreeing to scale a successful experiment. That puts the philanthropist less in the role of brilliant founder, and more in the humbler role of petitioner to government — but that is not the mode of most founders I know.

Will AI wealth actually be spent on philanthropic causes?

Alexander: It is not a central thing I lose sleep over, although it is a real risk, and Rob is right to identify it.

Broadly, there are two pools of capital on the horizon: the OpenAI Foundation, and individual donors associated with Anthropic. Individuals who simply own their money can make their own choices and face fewer constraints. The OpenAI Foundation is technically still a public charity, but I suspect it will eventually become a private foundation, subject to the usual 5 percent payout requirement.

Rob: That may be true, but the OpenAI Foundation has pledged to give a billion dollars away by the end of this year. They could meet that pledge by putting a great deal of money into a donor-advised fund. That would be legal.

Alexander: I suspect they would say they are not permitted to do that, and that they will not meet the payout requirement with a donor-advised fund, although we’ll see.

More broadly, I start with a less scandalized view of DAFs. They are not among my top two or three problems with the charitable tax-subsidy system. For example, you can donate highly appreciated stock without paying capital-gains tax on it. A donor’s benefit also scales with their marginal income tax rate. A donor in the top bracket may receive an effective government subsidy approaching 2:1, while someone who does not itemize receives no subsidy for charitable donations. Those features are quantitatively more important than DAFs.

DAFs hold roughly $300 billion: about half a year of all charitable giving in the United States. My view is that the tax treatment of highly appreciated shares, and the general structure of charitable deductions, create much larger plutocratic biases than the existence of DAFs as a category.

Regarding payout rates, perhaps I’ve absorbed industry propaganda, and Rob should respond. But my impression is that average DAF payout rates are around 20 percent, much closer to that than to the 5 percent typical of private foundations. So I am not sure DAFs are a particularly neglected problem. They are perhaps in the middle of my list of problems in U.S. philanthropic policy.

Rob: If we want to enumerate philanthropic-policy scandals, donor-advised funds are not at the very top. I readily agree. But we could ask those anticipating a large liquidity event whether the wealth advisers courting them are marketing donor-advised funds.

On aggregate payout rates, industry publications do report something like 15 to 20 percent, but they do not publish fund-by-fund accounting. Some people liquidate their funds within the first few years; others leave it there for a lifetime. The people Nan Ransohoff describes will generally be young, and they are unlikely to devote themselves full time to deciding how to move their philanthropic capital effectively. It will be tempting to put the money in a donor-advised fund while they begin their “philanthropic learning journey.”

How much should we expect new donors to engage with the existing philanthropy sector?

Alexander: I would expect some deference [to existing organizations like Coefficient Giving], but well under 50 percent. Roughly half the money is associated with the OpenAI Foundation, which will not defer to us and can speak for itself. Among other philanthropic actors, some may listen to us some of the time, while others will not listen to us at all. There is a much broader world out there. One of my main reactions to Nan’s post is that Silicon Valley conversations can become quite insular and make organizations such as ours look more central than they are.

Last year, Coefficient Giving was roughly one-tenth the scale of the Gates Foundation. We might grow a lot, in part because we want to spend faster in response to these trends. But there is a great deal of room for other organizations and approaches, and I do not expect us to play an overwhelmingly dominant role.

Rob: I agree with Alexander that some donors will be drawn to causes beyond GiveWell or Coefficient Giving. But for the coming AI wealthy, I want to make a case for Coefficient Giving and the other capital aggregators featured in Nan’s essay. From my roughly 15 years of occasionally spending time with wealthy people, especially in Silicon Valley, I have found that they want three things from their philanthropy.

First, they want it to be highly effective. They use investment language and talk about ROI and real positive outcomes.

Second, they are disinclined toward perpetuity. They do not want to create a legacy institution that outlives them, which I regard as virtuous.

Third, they look at large perpetual foundations such as Ford, Hewlett, or Rockefeller and think it would be terrible if, 20 years from now, they owned a building occupying an entire New York City block and employed a thousand people. In short, they want to be effective, spend down during their lifetimes, and avoid hiring a huge staff.

This is a trilemma. You can usually achieve two of these three goals, but not all three.

That points to capital aggregators as a solution. One way to be highly effective, spend out during your lifetime, and remain lean is to give substantial sums to organizations such as Coefficient Giving. I hope they succeed in attracting many such donors, because doing so can help escape the trilemma. Coefficient Giving is not the only actor in this space. Blue Meridian is another example, and Jen Pahlka created Recoding America as an aggregator and distributor of funding.

I also want to defend what one might call traditional philanthropy. For Asterisk’s audience, GiveDirectly should be a reasonable baseline. If you are unsure what to do with your philanthropic capital and unsure whether to hand it to Coefficient Giving, then instead of leaving it in a donor-advised fund, give a substantial share to GiveDirectly. Or any other

GiveWell-recommended charity. That does not fit Nan’s institutional-innovation model, but it is genuinely useful and highly effective.

If that orientation does not motivate you, you could instead fund efforts to restore American journalism, whose business model has been devastated by the internet. Save NPR. Buy the Washington Post, and don’t turn your back on it. There are many ways to deploy large sums toward plausibly good, experimental purposes outside the orbit of GiveWell or Coefficient Giving. I could keep offering recommendations, but you get the flavor.

How should wealthy donors think about their role in supporting a democratic society?

Rob: Here is the punch line of the critique I made 10 or 15 years ago, and then I will update it: Big Philanthropy is the direction of private assets toward public influence.

In that respect, it is an exercise of power. Wherever power operates in a democratic society, it deserves not our gratitude merely because someone chose to be philanthropic, but our scrutiny: Is that power being wielded in a way compatible with democracy? My goal in writing about philanthropy has been to identify policy structures that can domesticate plutocrats, so that they serve rather than subvert democracy.

I think such policy structures are possible. Philanthropy can supply capital for long-horizon experiments in social problem-solving, build an evidentiary base around successful experiments, and then humbly present them to a democratic public for scaling. In the process, philanthropy earns a stamp of democratic legitimacy. In other words: Philanthropy should audition for the legitimacy required to domesticate its power, then direct it toward democratic purposes.

Alexander: We’ve been having this argument for about 15 years, and I’ve been refining my response. One important difference between us is that Rob is a political theorist by training. He begins by asking about the role of plutocratic wealth, influence, and power in a democracy, so his initial orientation is skeptical.

I came to this work after meeting Peter Singer in college and asking what personal moral obligations I had to help others. I then went to GiveWell, where I helped individual donors giving hundreds or thousands of dollars a year figure out how to do so in a highly impactful way. That was my path into the field.

I see large-scale philanthropy as broadly continuous with small-scale personal giving, rather than primarily as a distinctive exercise of public power. It reflects the same drive to improve the world and realize a vision of the good that people in this room bring to their personal giving. My starting orientation is therefore more liberal than democratic-theoretical.

I see even large-scale philanthropy as an expressive activity with a fairly defensible starting case: People are trying to realize their vision of a good life and a good world. From my perspective, Rob’s view of the state and public sphere can seem almost imperial, as though everything requires public justification. I begin instead with an individual giving away personal charitable dollars according to a personal vision of the good. The bar of public reason or defensibility that Rob implies strikes me as too strong and, from my perspective, not liberal enough, leaving too little room for individual agency and freedom.

That said, if you return to Just Giving — Rob’s book on the subject — he ultimately vindicates a role for philanthropy in democratic theory on grounds of experimentation and, especially, the longer time horizons that states and markets may not provide. I would extend that pragmatic argument further. Governments and markets leave many public goods underprovided, creating a much broader space for philanthropy than experimentation alone.

Much of our work at Coefficient Giving focuses on science and R&D whose primary beneficiaries are the global poor — a malaria vaccine, for example. The beneficiaries of a malaria vaccine do not vote in U.S. elections. They have no representative to call and tell the NIH to invest more in malaria than in cancer, and they do not have enough money to shape pharmaceutical investment.

From the standpoint of democratic theory, the global poor are in one sense not “our” political problem, because political philosophers such as Rawls focus on the state’s justification to its own citizens.

But many people we ethically ought to serve are structurally neglected both by markets and by the U.S. electoral process, even if that process were working ideally. My private conception of the good, and my ethical obligations to other people, do not end at the U.S. border, while the legitimacy and conduct of the U.S. government largely do. I see a much more expansive space for philanthropy in cases where much more may be at stake ethically than a domestic political or democratic-theory framework captures.

This is not only about foreigners. We also do substantial work on farm-animal welfare, both in the United States and internationally. There are domestic policy examples too. Historically, we have been the largest funder of YIMBY efforts to build more housing. The principal beneficiaries of additional housing are people who do not live here yet, so they cannot vote in city-council elections and their interests are structurally underrepresented. That creates a strong case for philanthropic support for people — and animals — near the boundaries of moral consideration, whose interests political and economic processes are unlikely to represent.

Rob: It is fair to say that my work 10 years ago was grounded more in democratic theory than in liberalism, although I am, unsurprisingly, a defender of liberal democracy. But I disagree strongly with the claim that philanthropy should be justified as expressive activity.

I accept the descriptive point: A person has a view of the good and wants to express moral convictions in the world. But why should that person be entitled to a policy structure that supplies tax subsidies, donor protection, perpetuity, and limited accountability simply to express that view?

Consider a familiar Gates Foundation example: Bill Gates is on a treadmill, optimizing his time by watching a documentary about a social-studies curriculum in Australia or New Zealand. He is impressed, gets off the exercise bike, and says, “That looked good. Let’s launch a pilot in the United States.”

As it happens, I, too, sometimes have ideas about education reform when I exercise, but I do not have the same wallet or permission structure through which to express them. Gates is exercising power. That power should not be protected simply as personal expression, especially when it intervenes directly in a public institution such as the school system. There may ultimately be a story that vindicates this kind of intervention, but it will not rest on expressive grounds.

Second, Alexander is correct that I set my philosophical analysis of philanthropy within a democratic setting. For global giving, we plainly lack a single global state, much less a democratic one.

The framework in most of my writing is therefore not immediately portable to the global setting that interests many effective altruists, including questions about farm animals. Still, humanitarian efforts to improve welfare in distant places, however laudable, rarely create a complete solution to the underlying problem.

Ultimately, durable solutions require political capacity, infrastructure, and buy-in from the state apparatus in the relevant country. If the country has a broken or failed state, that makes the task much harder, but it doesn’t undermine humanitarian philanthropy. It means philanthropy is a worthy Band-Aid: a second-best effort in place of the first-best effort, which is politics.

Clara: I want to connect this back to Jen Pahlka’s essay. As we’ve noted, government spending is still orders of magnitude larger than even this potential next wave of philanthropy. Pahlka argues that a major target of the new philanthropy should be government reform: making government work better. Rob, you spoke favorably of her essay, but this seems like a very direct exercise of power in a democratic domain.

Rob: I think you’re right to pick up on that. The critical thing to say about Jen’s essay, and its connection to this next wave of AI philanthropy, is that the more that a philanthropist is tempted to change public policy or build state capacity — the greater the danger of becoming a

plutocratic force in a fundamentally democratic setting.

Still, the desire to shift state policy or state dollars can fit my framework. Some of Jen’s work seeks the distinctive form of legitimacy that only the state can provide as the ultimate goal of philanthropic activity.

Recoding America’s work, as I understand it, does not call for a parallel system of public-good provision outside the state. It seeks to repair, restore, rejuvenate, and reanimate public institutions for twenty-first-century problems, removing accumulated cruft and bureaucracy. But the work must ultimately win approval from a democratic public or legislature. That is the appropriate, humble posture a philanthropist should take. Unfortunately, philanthropists, founders, and effective altruists are not generally known for epistemic humility.

How should donors think about their philanthropic spending at this moment in time?

Alexander: One implication for donors with liquid money today is that they should probably try to spend faster. That is what we are trying to do, in part because we see an opportunity to bridge the period before much more funding arrives. If much more money is coming into the areas where we work, filling the most immediate cost-effective gaps today matters because there may be fewer such gaps tomorrow.

We also want to build the infrastructure that will be capable of absorbing greater capacity as other donors come online. Donors who are liquid now and able to spend down faster can play an important role. That may not be a spicy conclusion, but Nan identified a real trend and there is a useful role for donors to play.

Rob: The overwhelming bulk of what I have written and said about philanthropy concerns the policy framework. Unlike the Peter Singer approach, I don’t come at this with moral questions in an individual’s mind about personal obligations or aspirations. The arguments I want to make are usually arguments about policy structures. Nevertheless, I’ll offer several suggestions, but take them with a grain of salt; I don’t mean to play the moralizer.

First, do not let the wealth-advisory industry warehouse your philanthropic assets. If you must use a donor-advised fund for tax reasons, recognize that all citizens are subsidizing your choice in the form of foregone tax revenue. So commit to a generous annual spendout — perhaps 10 or 20 percent, well above a private foundation’s minimum.

Do not create a private family foundation unless you intend to staff it with experts in the domain where you want to work. If the foundation is mainly a vehicle for parking money, placing family and friends on the board, perpetuating your good name, or transmitting family values, do not waste American taxpayers’ dollars on it.

Second, independent allocators already exist. You can solve the trilemma of effectiveness, rapid spendout, and a lean staff by directing money to capital aggregators such as Coefficient Giving.

In its early years, Coefficient Giving was primarily directing one couple’s resources. I was on GiveWell’s board when Open Philanthropy was spun out, so I saw some of this from the inside. Alexander, back in 2016, if I had asked how many staff members you would have 10 years later, and told you the answer was 180, would you have been surprised?

Alexander: Yes. But I also would have been surprised by how much money we needed to give away.

Rob: The Gates Foundation intended to be lean; it now employs roughly one or two thousand people. OpenAI Foundation: If you want to give away $100 billion quickly, don’t be afraid to staff up. That is how you solve the trilemma while doing work that is genuinely effective. Prospective donors should value intermediaries that do the intellectual work of identifying worthy causes.

Third, decide whether you want an impact now. GiveDirectly is one route, but consider some other recent examples.

Michael Dell committed $6 billion to underwrite baby bonds. Baby bonds compound in a virtuous way, unlike donor-advised funds, because they ultimately expand people’s agency and opportunity. It is a worthy experiment to run and observe. Evan Spiegel recently spent tens of millions of philanthropic dollars to forgive medical debt. These efforts may not register on the radar of Coefficient Giving or effective altruism more broadly, but they are ways to have an impact now.

Fourth, and finally — this may sound dangerously self-interested, although I don’t intend it that way — there are lessons from the first wave of American philanthropy. Carnegie, Rockefeller, Vanderbilt, and others were institution builders. Their most distinctive institutions included public libraries and universities. Some of our best universities date to the first Gilded Age.

It has long surprised me that the second and third waves of philanthropy show little interest in creating new universities or giving major sums to existing ones. Universities could absorb $100 million or $1 billion relatively quickly, and there are good experiments to be run by trying your luck at creating new structures and institutions of knowledge production.

Yes, I know about the Arc [Institutes] of the world, and I have mostly positive things to say about them. But universities are sitting in front of everyone’s eyes — both existing institutions and the possibility of creating new ones. If you have a few billion dollars, start a university.

Clara: I have many thoughts about founding universities. There is a strong reason people are not doing this: higher education may be saturated. The number of new university students is declining, unlike during the first Gilded Age, when enrollment expanded enormously. Small, experimental, less credential-focused institutions are struggling for students: Hampshire College, for example, and California College of the Arts here in San Francisco. The headwinds are strong.

New educational models are valuable, but the university sector is difficult. New models will probably be necessary as AI changes education, but founding an institution appears structurally much harder than it was during the first Gilded Age.

Rob: Possibly. But if you want something different from the current university structure, you have a chance to create an alternative for the twenty-first century. Make it AI-native, reach people who don’t have the money to pay high tuition, and organize talent differently from the existing system, in order to generate some of the outcomes you desire. I favor experimentation. This is precisely the R&D role that philanthropy is well suited to play.

Questions from the audience

How should donors think about measurement, when measurable interventions can lead to lower-risk, lower-impact efforts?

Alexander: That is a legitimate critique of an older set of effective altruist ideas, but I don’t think it has much force against current practice. If anything, the opposite critique may be more applicable today.

There was an older style of effective-altruist giving that was highly linear: Measure money in and outcomes out, and focus on low-risk interventions. Much of what we do today, however, is what we call hits-based giving.

Hits-based giving is intentionally modeled more like venture capital, where perhaps 1% of the bets in a portfolio will generate the value of the entire portfolio. That pushes us toward a different evaluation method. You look for hits and scrutinize them in a more journalistic way, rather than asking whether every project paid off exactly as promised. There are a range of approaches.

I also want to defend a barbell strategy. We are among the largest funders of GiveWell’s top charities, which have some of the qualities you describe. Measurability can be criticized as encouraging risk aversion, but it is remarkable to be able to learn whether your work is failing. Much of philanthropy has painfully weak feedback loops. It is easy to repeat the same thing year after year without observing the results or changing behavior.

GiveWell recently had a cool podcast about measuring the impact of bed nets it helped distribute, using demographic and health surveys in some of the countries where it worked. That is an amazing way to close the feedback loop, which wouldn’t have been possible without their efforts and the scrutiny and rigor applied to their work. So, I want both to say “not all EA,” and to defend this orientation around measurement, which can be quite useful.

Rob: Nan’s essay takes up this concern about measurement near the end. In the framework I have offered, the more you insist on short-horizon, demonstrable, measurable outcomes, the more you shackle and undercut philanthropy’s distinctive ability to make risky, long-horizon bets on social problems.

This critique applies not only to one brand of effective altruism, but to strategic philanthropy generally: giving a grantee a year to produce outcomes, demanding measurable results in a report, and refusing a second grant if those results are absent. That approach undermines a genuinely virtuous aspect of big philanthropy in a democratic setting.

But I also want to support Alexander’s point that current EA — and Coefficient Giving’s approach, as distinct from GiveWell’s — may in some respects have strayed too far from effectiveness. When you look a thousand generations into the future and apply econometrics to what might happen 50 generations from now, much of it is inevitably hand-waving.

Philanthropy can use longer time horizons than markets or elections. But if we are discussing events 300 years from now and trying to make evidence-informed bets, let’s admit that much of it is guesswork.

Are public institutions as democratic or neutral as is often assumed?

Rob: I would resist that characterization. I am not offering a utopian picture of democratic institutions alongside a realistic picture of vanity or corruption in philanthropy. I am fully aware of the vices of actually existing democratic institutions. But as Jen Pahlka’s work suggests, the task is to renovate them, rather than anoint a distinctive form of legitimacy for actors outside the democratic structure.

I have tried to offer a framework that recognizes philanthropic power — private assets used for public influence — while channeling that power to serve democratic purposes through an R&D function. I am also open to philanthropists directing capital towards the state itself. However, I would prefer such activity to support core democratic values rather than partisan projects. Partisan activity is more properly called political giving, which is also permissible in the United States, but it is different from philanthropy.

Unless you are in the business of revolution and seek to overturn the existing state, the ultimate goal should be to restore, renovate, and rejuvenate democratic institutions, so that they better serve public purposes. Ordinary democratic contestation remains a time-tested mechanism for doing that.

​Disclosure: Asterisk receives funding from Coefficient Giving. We retain full discretion over every part of the editorial process.

This article first appeared in Issue 15: Work. Subscribe to get our print magazine before prices go up on Saturday, August 1.

Fear of AI disruption spreads in concentric circles. Outright frenzy inside the labs becomes intense distress in San Francisco becomes palpable anxiety on the East Coast — and once the sentiment has crossed an ocean, there’s only mild discomfort left when it washes upon the shores of the other side.

An honest appraisal of the situation, however, should suggest the opposite response: It’s the employees of the labs, rich in equity and embedded in the most dynamic labor market there ever was, that should feel the least apprehension. Though the institutions of American government face much more immediate disruptions, it is the periphery of AI development — the other 193 countries in the world without a domestic frontier AI developer to tax and regulate — that confront the biggest risks to their economic welfare and physical security.

These risks aren’t resolved even if the AI built in San Francisco is particularly safe or particularly aligned. In fact, the risk may cut the other way: The better AI goes for its makers and their country, the more it threatens to disrupt the countries that build no frontier AI themselves. If their institutions, from labor markets to governments, are unprepared for the coming transition, their citizens risk being consigned to lasting irrelevance. They face life on the permanent periphery of a new world.

The other 193 countries

If you’re reading this magazine, you too have probably been haunted by the specter of the “permanent underclass.” The San Francisco Bay Area is abuzz with concerns that AI will irreversibly calcify a stratified economic order.

Their concern trades on a fairly specific set of premises: AI progress will continue unabated, and perhaps even accelerate. The American frontier developers that reap most of the ensuing economic gains. And the end point of this progress will be a radical transformation, where AI comes to dominate economic affairs and strategic statecraft and power accumulates for those able to deploy the best AI most effectively.

The rest of this piece accepts these premises — and makes the case that, if they are true, they imperil every nation without its own frontier AI. With vanishingly little economic and political leverage compared to AI powerhouses, these countries may permanently lose the ability to compete in global financial and political marketplaces. Rather than existing economic groups within nations being pushed into a permanent underclass, entire countries could be pushed to the periphery.

From the innermost circle of AI development, it’s easy to be distracted by concerns that seem to imperil even in-the-know researchers and operators. But luckily, even under the above conditions, the real world might be a fair bit more complicated than these predictions assume. As the speculative post-AGI economy emerges from the very real constraints of our current political order, there are a few backstops that seem to make the emergence of a permanent underclass less likely.

The first is that the would-be-underclass still wields considerable political power today. The U.S. government holds great power over the trajectory — and even the possibility — of AI development. It could tax or expropriate leading research labs, suppress automation by fiat, or even follow the guidance of the most radical voices and declare the Butlerian Jihad on artificial intelligence, shutting it all down. In the interest of preventing this outcome, technology firms and moderate policymakers will probably do their very best to assuage concerns through policy intervention.

The second backstop is that the economics of AI labor replacement are far from settled science. Though some economists predict widespread disempowerment, just as much evidence points the way of a rearrangement of the labor market instead. The industrial revolution produced machines capable of performing much of what humans used to call work, but we still have not run out of work to do. And even if AI does cause massive labor disruptions, Americans could still stand to benefit — further showing that a permanent American underclass is unlikely to emerge.

None of these factors on their own are certain, and both are subject to counterarguments. The more nuanced takeaway is this: Many of those most worried about social stratification and outright disempowerment from AI might be among the ones most protected from it.

Abdication from the world stage

Most of the backstops protecting Americans are distinctly domestic in scope. The electorate that wields indirect power over these decisions is American. The tax base that swells with AI revenues and enables wealth redistribution is American. And the arguments that labor disruption will be gentle are strongest in the context of one well-connected domestic economy where there are few trade or migration barriers. Even if their careers are disrupted, American workers will be best positioned to use AI systems for their own benefit, since their government ultimately oversees where the American developers allocate their tokens.

Until recently, frontier AI has flowed mostly unrestricted to whichever customers can pay. But U.S. developers and the government that regulates them have plenty of incentives to restrict the market. AI labs and policymakers face three pressures at once: risks of model distillation, threats of misuse, and a potential shortage of tokens. The answer to all three of concerns is to play favorites: Grant API access only to trusted buyers, give frontier access only to known safe customers, and allocate tokens to American buyers first.

This April, the threat of unrestricted access became a lot less theoretical. When Anthropic announced its limited release of Claude Mythos 5 through Project Glasswing, the initial whitelist included no firms or governments outside the U.S. And when Anthropic informed the U.S. government that it intended to expand access, Washington intervened, citing concerns around compute constraints and security. Soon after the Mythos debacle, OpenAI followed suit through a limited release of Daybreak, its frontier cybersecurity model. Shortly after, a fight around U.S. executive action broke out, ultimately resulting in an executive order providing the U.S. government with exclusive access to frontier models for up to 30 days prior to public release. And soon thereafter, the Trump administration used export control authorities to settle a cybersecurity-related dispute with Anthropic, effectively cutting off global access to the industry-leading Claude Fable 5.

In late May, a small handful of U.S. firms officially had a two-month head start in the use of the most capable AI model. It is reasonable to assume that future models could come with at least the same sort of delays. Right now, U.S. firms and agencies have reportedly only used that advantage to become more resilient to the wave of AI-enabled cyberattacks — though according to initial media reports, the NSA might have already started using Mythos to carry out cyberoffensive action while adversaries still don’t have access. As the next step of frontier models enable more and more economically valuable actions — efficiency gains, plucking low-hanging fruit in R&D, corporate espionage, production optimisation, and so on — the effects of a permanent two-month lag could quickly compound.

These concerns assume that frontier AI will retain a distinct advantage over other systems, even those lagging just a few months behind. To what extent this is true remains to be seen. But I believe two trends should make us seriously consider the idea that the frontier matters greatly.

The first is the instability of the fast-follower business model. Procuring the necessary compute to train and run advanced models at scale is becoming more and more expensive, and the capitalization of fast-following firms and their resulting access to chips is quickly falling further and further behind the frontier champions. Perhaps in an attempt to close that gap, Chinese firms seem to be abandoning their commitment to open-source provision of their models, further endangering the fast-follower strategy of relying on the availability of near-frontier models. But closed or not, while fast-following might have been possible under a slight compute disadvantage, it could prove much more difficult once the compute gaps open up further.

The second trend in favor of frontier dominance is the prevalence of zero-sum dynamics in economic and strategic competition. In cases like cyberoffense and defence, financial trading, or services provision at scale, there might be essentially unmitigated competition between AI models themselves. In domains like these, there might be no absolute threshold of “good enough” — the floor is determined by the strength of the adversary’s AI.

All this means that any country that does not develop its own frontier AI may capture the risks while failing to reap the benefits. They capture the risk because every country in the world is exposed to AI’s disruptive impacts, even when the tools themselves are deployed overseas. Criminals everywhere can be empowered by AI, and economic effects do not halt at borders — AI integrators will produce products more cheaply and exert downward pressure on every exporting manufacturer, local service economies will have to start competing with cheaper AI-based solutions, and so on. AI does not need to come to your country to disrupt your life; it’s enough for it to reach your rivals and adversaries.

A country without frontier AI may not secure its benefits because the positive impacts of AI do not necessarily accrue to all countries by default. If countries miscalibrate their policy responses, then new ideas, jobs, and products will not flow evenly to consumers and employees all around the world. If some countries secure unfettered access to frontier AI, it is their demand signal that will shape what products are available on the global market. The spillover effects from the new technology would be imperfectly distributed at best. They could still improve the lives of people on the periphery. But in relative terms, it would exacerbate a gap — at first in individuals’ wealth and welfare, and eventually in nations’ access to the economic inputs required for leverage and power.

Why a periphery and not simply an underclass? The economies in question are still productive, with a workforce and industrial capacity more than capable of sustaining modern life. In an absolute sense, viewed from today’s vantage point, the societies at the periphery do well. They grow toward abundance, leisure, and health at the world’s post-industrial-revolution pace — just not as fast as the frontier of artificial intelligence.

This outcome might seem strangely acceptable at first. But I contend that that is mostly due to a failure of imagination about how wild and extraordinary life at the center of the AI revolution could be. Lucid observers predict that it will end up with humans soon reaching for the stars, spaceships above and nanotechnologies below, the promises of last century’s science fiction fulfilled all within the 21st century. All that would happen without the involvement of those at the periphery. They wouldn’t benefit, and they wouldn’t get a say.

Committing to this path also represents a near-permanent abdication from the actual world stage, and submission to hegemons-in-waiting: A country on the periphery is unlikely to have the military or industrial capacity to mount much relative power, leaving them subject to the whims and wishes of the powerful.

The temptation of self-sufficiency

The challenge, then, is to chart a course through a world where others build the technology that underpins your own economy and institutions. The “middle powers” of the world face a distinct version of this challenge: They have advanced economies to lose, but still a lot of assets to deploy to avert that fate. And while low- and middle-income countries have a more difficult task in many ways, they might not share the same air of bygone empire that keeps many middle powers from grappling with the scale of the American lead.

Many low- and middle-income countries are also much more constrained in their options, and therefore a bit less susceptible to making the kind of mistake discussed in this essay: as opposed to middle powers, they usually do not have strategically vital assets they could seriously misdeploy to their lasting detriment. At any rate, the middle power challenge is meaningfully different. Their domestic economic strength, coupled with their inability to compete on AI specifically, opens a devastating attractor state. They neither ascend to flourishing nor deteriorate into destitution, but enter stagnation on the permanent periphery.

The most likely route to this peripheral state runs through the temptation of trying to chart their own course away from American frontier AI. The basic, compelling motivation to a middle power is this: In an increasingly protectionist global order with higher and higher barriers to trade and more and more uncomfortable effects of international integration, why not lean into the trend and aim for complete self-sufficiency in any sector contaminated by the frontier lag?

There might be a stable economic arrangement in this strategy. Middle powers could wall off large segments of their domestic markets that would be critically endangered by the AI-driven abundance. They could indigenise the supply chains they would otherwise lose access to outright, securing an inefficient-but-extant supply of 2026-era goods. Then, many of them will presumably still find something to export — perhaps because Baumol effects keep tourism and some artisanal goods scarce and valuable, or perhaps through simple comparative advantage.

In all likelihood, that’s not enough to retain their current relative position of wealth and purchasing power. As the frontier-AI-driven economy elsewhere accelerates, Japanese machines or Milanese suits might only buy an ever-shrinking slice of the ever-growing global economy. That’s especially true when AI enables the provision of almost-as-good (but much cheaper) competing products. But even if that slice is small and shrinking, it’s still enough to trade for something — maybe months or years behind the frontier, but still ahead of 2026 capabilities. The absolute trajectory remains stable, acceptable, perhaps even good by today’s standards — but the relative trajectory is toward global irrelevance.

It might not register as such at first. In fact, as life outside a middle power’s border gets stranger and stranger, governments might be tempted to opt out of the frontier AI race altogether — removing themselves from development, but also access negotiation, rapid adoption, and widespread economic deployment. Let the Chinese and the Americans run ahead with their overheated capital markets and deep societal disruptions. Let them both get pulled into the Thucydides trap at the bottom of the Taiwan Strait, if they must. Instead, every country gets its own AI revolution at the speed they consider acceptable — and you can absolutely do AGI with French characteristics. So goes the hope.

That interest is exacerbated by a pernicious impulse to chase sovereignty in AI specifically. It seems such an obvious solution: If the risk of being cut off from frontier AI is such an important vulnerability, why should countries not attempt to catch up if they can? The simple fact is that, in a free and fair market, middle powers can’t. They’ve always suffered from a lack of talent concentration and capitalization, and now recursive effects of all kinds are kicking in. Capable models make even more capable models, revenue invested into compute yields greater compute reserves, and user interactions generate more data to capture more users. The self-improvement gap grows by the month.

To stand up any comparable middle power champion, protectionism seems to be in order. It’s starting today, with rules in large parts of Europe and East Asia mandating that government procurement favor domestic models. It will continue with the contentious but ever-more-popular “buy domestic” rules for technology, which have been floated in the EU repeatedly over the last few months. Soon, policymakers will realize buying local is a carrot in search of a corresponding stick, and come up with sharper regulatory barriers against U.S.-based tech.

This reflexive behaviour creates path dependencies that beget more and more isolationist instincts. By keeping out the most market-efficient AI solutions, middle powers will deprive their economy of valuable inputs. Before long, the lack of high-quality inputs might mean firms can no longer offer their products at market rates — in much the same way that rising energy prices in many countries have rendered their domestic industries’ output internationally uncompetitive. To really protect your market from outside AI, you need to expand the same protectionist shield to an ever larger part of your economy. By the time the flash in the pan from subsidies, protectionist circular purchases, and economic life support extinguishes, the comparative disadvantage might just be so entrenched that it would be painful to come back from at once.

Attempts to cut a country off from the outside world have seldom been permanent or stable. Historically, protectionism either fails very quickly, like when Latin American countries in the 1970s tried to conduct their own economic affairs in isolation from the world order, or they’ve been explicitly designed for a reentry into global markets shortly thereafter, like in the early protectionist phases of “Asian Tigers” Taiwan and South Korea.

We should not expect middle powers to succeed at a narrowly-scoped scheme of “protect-and-reenter” around the AI industry. It is largely the speed of the outside world that determines the pains of reexposing yourself to it. The success of past attempts to ramp up in isolation and then reenter in force was predicated on an assumption of differential growth: These temporarily isolated countries, like Japan and South Korea, all grew faster than the outside world. They had catch-up growth to go through, were more hungry and ambitious, and propelled by favourable demographics. None of that is the case for many middle powers today with aging populations driven not by a hunger to succeed, but by an anxiety about falling behind.

Their vision, as revealed by public attitudes as well as policy agendas, does not exactly read as more dynamic than that of the great powers. In America, industry insiders and an increasing number of policymakers are planning for a hypercharged economy. In economic terms, they talk as if 5% GDP growth is the floor; in technological terms, they aspire to fully automated factories and drones zipping overhead, powered by the greatest infrastructure buildout in the history of the world.

In the middle powers of Europe, on the other hand, the dream seems to be to finally develop a working alternative to Zoom for civil servants. Whether it’s in the op-ed pages of middle powers’ newspapers, strategy documents of their governments, and the speeches of their politicians, nowhere will you find an assertion of genuine ambition to ride this wave.

Reality with a vengeance

The result of this protectionist loop might not look so bad at first. If you expect the next few years to be messy in many ways, you might think the middle powers would almost feel vindicated in the short term. The labor market in the U.S. and China might certainly take a greater hit, and public opinion would be much more volatile. The broad-based productivity gains would take their time to diffuse, and the “destruction” aspect of “creative destruction” would be a lot more visible than the “creative” part. What better reason to double down on isolation than the world outside your doors going to hell?

But reality might still come back with a vengeance. As much as AI is uncharted territory, the spotty historical precedent gives some reason for hope: After eras of profound technological disruption like the industrial revolution, things have usually gotten better. Benefits have organically diffused to those who manage to use the technology well and turn the surplus into purchasing power, or have been actively redistributed within polities that cannot bear the instability of unmitigated disruption.

In AI, too, the picture on the ever-similar news screen might eventually change. I don’t know how, any more than we knew what societal changes the steam engine would bring in 1765, but we have some early sketches of the option space. Material abundance brought on by rapidly accelerated research and development, streamlining progress in robotics, energy production, and biological research. More efficient machines enabling unprecedented leisure to think and create and travel with less and less work. Healthcare customized to every pathogen and cancer growth that causes sickness and grief today. But, of course, the unknown unknowns constitute a greater part of the future — from today’s vantage point, we’re still Orwell’s cavity-afflicted character that imagines Utopia to be a world without toothaches.

Of course, there would be some spillover. Untold progress in the center of the AI transformation would eventually find its way to the periphery. Many countries have sectors that will be sufficiently competitive to enable some trade and import on the margins. The path to the periphery does not consign countries to complete lack of participation in any of the effects of AI — just to lack of control and influence over any country that does build and use AI well. The sovereignty play in all the sectors threatened by frontier AI might be internally stable. It might even allow for some ongoing economic growth.

For all its merits, a strategy of AI sovereignty makes for a ruinous comparative trend: Not only do countries powered by genuine frontier AI get much richer and more powerful much faster, but the independence-minded country also has very little to offer them. While a country that focuses its efforts on outputs complimentary to frontier AI has a limited basis for trade, having a second-best AI supply chain is uniquely uninteresting to the runaway hegemons of an AI-dominated world order.

Again, consider the past: How might an 18th-century society feel if it had decided to bank on slow-but-stable efficiency increases in sailboats, slow progress toward better cures and nicer houses — but locked themselves out of the Industrial Revolution? They might have still felt good when news of the weavers smashing their looms and the sick and destitute pooling in the streets of Manchester reached them. But they would have come around to their deficits — perhaps when their ships of the line ran into steel-hulled steam ships so powerful that naval warfare was no longer a competition. Perhaps reality would reach them when they learned that every foreign peasant could afford new woven garments, or when they learned of mass-produced penicillin all-but-eradicating some of the most persistent lethalities they faced. However stable the periphery, it might soon feel the same way.

Forking paths

What else is there to do than to drift into the permanent periphery? The only way out of the disruption might be right through it. Middle powers will have to fight and struggle to secure a stake in the AI-driven economy. They must be willing to take the relative hit for the promise of absolute gain. In terms of policy prescriptions alone, that’s a tractable challenge: You need frontier AI, you need an economy that uses it well, and you need to structure your strategic position around retaining both.

None of that will work without access to frontier AI, so countries will have to find a way to secure that access. The most tractable way to do that runs through infrastructure buildout. American AI developers are hungry for computational power, but there are only so many sites and behind-the-meter turbines in America, and there is only so much political appetite to sustain the construction of datacenters.

Many countries can offer to ease the burden in exchange for access: They can offer sites, funding, electricity, or political tolerance for U.S.-owned datacenters, and in exchange receive exclusive access to the frontier AI they support. It would be hard for American developers to resist a deal like that, and it would be hard for the U.S. to renege on it, since it would then mean to give up viable, usable compute already bought and paid for.

Other middle powers might fare even better: Whomever has enough extraneous leverage — hosting military bases, providing key exports, and so on — to compel the U.S. into a stable export deal today does not even need to bother building out massive datacenters. Either way, middle powers can secure their access if they move toward instead of away from American frontier AI today.

Using that freshly-secured frontier AI well would be the second requirement for success. One way to enable that is to create a labor market that bends instead of breaking when AI picks up. The argument for that is well-trodden, and already implemented in places like Denmark: In what is sometimes referred to as “flexicurity,” appetite for “protecting” workers is channeled into guaranteeing social safety through high-but-temporary unemployment insurance and highly generous retraining measures instead of rigid job protections. Workers can lose their jobs quickly, not suffer from it too much economically, and are enabled to pivot decisively.

That will not be enough to cushion the entire blow — much work is still to be done on how to compensate the permanent losers of AI-driven labor market transformations. But if overprotecting the labor market is a recipe for cementing an economy in permanent periphery, leaning into flexicurity is an alternative channel for the same protective impulse.

A nation’s economy, however leashed or unleashed in the domain of labour, will perform best if it is good at things that will be valuable as AI progresses. I suggest thinking about this in terms of bottlenecks — much in the same way that the economist Alex Imas1 argues the individual worker should consider “what will be scarce” if they want to avoid the permanent underclass, so too should the country trying to avoid the permanent periphery. Some economies might be taken over by positions in the semiconductor supply chain: If ASML’s extreme ultraviolet lithography devices, South Korea’s high bandwidth memory, or Taiwan’s chips continue to be central inputs into building frontier AI systems, they can simply double and triple down without fearing disempowerment.

Others might have to orient toward downstream bottlenecks instead. No matter how brilliant the country of geniuses in your datacenter, they will still require physical manufacturing capacity for robots and drones and vials of medicine to yield the real-world effects their inventors promise and our suggested economic effects require. The manufacturing skill base in Germany, Italy or Japan and the biotech production in India could be leveraged into a durable supply chain position that the post-AI global economy cannot do without. But middle powers will need to expand, occupy, and defend these bottlenecks — including, presumably, by aggressively drawing on actual frontier AI to boost R&D and broader productivity in these domains.

And a final bottleneck might be the things that don’t get cheaper as efficiency increases — like the goods struck by Baumol’s cost disease, like healthcare, artisanal services, and tourism that already make up a growing part of southern European and southeast Asian economies.

None of these advantages buy a permanent position. Equipment-making can become obsolete; bottlenecks can be blown open. But there are no permanent economic plays — not even the most dominant economic positions in history, whether gold, tulips, oil or spices, have dominated for long. What they have bought their owners is option value for the future — resources to reinvest into finding out what comes next.

Turn your eyes

The threat of the permanent periphery is perhaps best understood as the outcome of a miscalibrated protective reflex. It’s the idea that you can shield yourself from what might be coming, that you can flee or hide, raise walls and borders. But from geopolitics to technology to economics, every abstraction layer of this challenge should cast doubt on this impulse. They will erode the position of the countries that choose them, until there is almost nothing left — no painless path back into the white hot center, no stable equilibrium to be had outside. The fix is to lean in instead, to face the disruptions as they come as best you can. That means nations would bear painful costs today: Economically, from paying handsomely for frontier intelligence; geopolitically, from accepting deeply entrenched technological dependence; and even culturally, from the geopolitical submission required by all that.

But once countries have grappled with that underlying structural trend, they can start steering it. Once you know your economy can’t stay the way it is, you can start building for where it might go instead. The reward of all this is to not become consigned to the periphery — to never build up the debt from refusing the initial disruptions and never create the path dependencies to diverge further and further from those leading the trend, but instead to get to fight for a place in whatever tomorrow’s economy looks like.

If all the talk of the “permanent underclass” made you concerned — not just for yourself and your friends, but for what a society so sharply divided in wealth and leverage might look like — you might turn your eyes toward the periphery. If we get AI right and its geopolitics wrong, the future might remain unevenly distributed forever.

This article first appeared in Issue 15: Work. Subscribe to get our print magazine before prices go up on Saturday, August 1.

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The sibling inventors had, they claimed, built a machine that experts had considered impossible. It could do the work of dozens of men, at a fraction of the cost. It would not be defeated by knotty situations that had foiled previous machines. It would remake society, this machine, and its inventors were intent on protecting ordinary people from their creation. Not intent enough to not create it in the first place, of course, or to stop selling it — but intent enough to urge government regulation, to weigh placing it under the control of a nonprofit foundation, to experiment with novel forms of corporate governance to ensure the machine would be put to benevolent use.

The inventor siblings were John and Mack Rust, and their machine picked cotton.

The mechanization of cotton-picking is not a particularly well-remembered stage in American agricultural history these days. Most American schoolchildren learn about Cyrus McCormick, who mechanized the grain harvest, and about Eli Whitney, who mechanized cotton deseeding (and thus dramatically increased demand for manual cotton-picking and the slaves forced to do it). They don’t learn about the Rust brothers or their many competitors in the race to automate cotton-picking. Building a machine that could reliably and cleanly pull cotton fibers off of their bolls turned out to be vastly more difficult than building a machine that could cut down a wheat field, or that could deseed handpicked cotton.

The Rusts unveiled their machine in 1931, a century after McCormick. At the time, the cotton harvest in the U.S. was still entirely manual, usually performed by tenant sharecroppers enduring backbreaking labor not much different than that done by the slaves who preceded them (who were often, in the case of black sharecroppers, their parents and grandparents). A utopian socialist who had himself worked as a cotton-picker in his youth, John Rust was motivated to build a tool that would liberate the millions of black and white tenant farmers from the toughest farmwork still in existence.

John and Mack foresaw the dawn of “a planned economy of abundance … wherein at least our credit and basic industries are socialized.” It was only a matter of time before “some form of co-operative commonwealth [supplanted] our decaying capitalist society, and it [was their] wish that this transition [would] take place with as little confusion and violence as possible.”

The grandiosity and social consciousness underlying the Rusts’ plans intrigued journalists. “Here are two inventors who, with extraordinary success perhaps within their reach, have resolved not to follow the familiar pattern and become multi-millionaires, but instead to have a part in a social experiment conducted for humanitarian purposes,” the newspaperman Robert Kenneth Straus wrote in a 1936 profile in Harper’s. “That is something new on the American scene.”

So why have the Rusts been largely forgotten? One reason is that their utopian dreams never amounted to much. They didn’t actually create the public benefit corporation they dreamed of; they did establish a Rust Foundation, but it didn’t own the invention or pioneer a new equitable wage model. They didn’t collaborate with the government in an effort to spread the gains of automation to the poor of the South. But the more important reason is that the Rusts lost. They were not the first, or even the second, to get a highly effective cotton-picker on the market. International Harvester (McCormick’s firm, still run by his grandson) and John Deere & Co. both beat them.

The word that comes to mind for me, in trying to characterize the Rusts and especially John Rust, is “Amodeian.” Like Anthropic leader Dario Amodei, he was a technically gifted engineer with deep moral convictions who saw founding a company as a route to social impact. Like them, he experimented with corporate governance in an attempt to align his incentives with the public good. Like the Amodeis’, the Rusts’ was a family business.

As of this writing, the Amodeis are not being beaten by the International Harvesters and John Deeres of the tech world. But I think Rust’s saga can still serve as something of a cautionary tale for contemporary automators with consciences.

A few words on the fabric of our lives

Mechanizing the wheat harvest was hardly simple. McCormick built on decades of tinkering by his father Robert, and Jo Anderson, a black man enslaved by the McCormicks, and a bevy of ideas from his competitor Obed Hussey.

But the work behind the invention of the reaper seems almost trivial when you think about what inventing a cotton-picker required.

When harvesting wheat, you fell the entire plant. The hard part is felling it cleanly and keeping the harvested grain together. Harvesting cotton, on the other hand, means teasing sensitive fibers from dozens of little sacs (“bolls”) on a shrub-like plant. Any mixing of the fibers with the plant around them means lower-quality cotton sold at a lower price. And bolls often open at different times. Simply passing through and cutting down the whole plant isn’t tenable. People tried all the same, developing what were called “stripping machines.” That actually worked in Texas, where the cotton plant was shorter and thinner, but not in the Mississippi Delta, where the bulk of the cotton harvest happened.

Between 1850 and 1930, hundreds of people tried and failed to produce a viable cotton picker. They tried pneumatic tubes that pulled the thread off of bolls through suction. They tried electrically charged belts that could attract the lint from the boll. They tried “stripper” designs that ran plants through sets of teeth meant to extract the thread. None caught on, despite investment from big companies like International Harvester.

In the early 20th century, “the task of inventing a mechanical cotton picker was not only formidable but widely regarded as impossible” notes the historian Donald Holley. “For most people, mechanical cotton machines occupied the same category as perpetual motion machines.”

What you needed was a machine that had, in essence, fingers — actual appendages that could reach into the bolls and pull out the thread, the way human fingers do when picking cotton. This was known as a “spindle” design, and Holley notes that it had been proposed as early as 1850.

The details of the spindles, though, were hard enough to get right that they delayed the premiere of a viable cotton picker for nearly a century. How would the fingers know where to go? How could they pull hard enough to get the thread but not so hard as to pull off the boll and twig with it? How could they cleanly deposit their thread once it had been pulled? It seemed like an insurmountable engineering challenge.

A key part of the solution turned out to be moisture: The spindles needed to be wet to adhere to the cotton and pull it from the boll. The inventors who figured this out were the Rust brothers.

The Rust boys

John D. Rust was born in 1892 near the now-defunct town of Necessity, Texas. His brother Mack followed eight years later. As boys, both had to pick cotton, which they would later credit as driving them to want to automate the work. “We got down on our knees,” John would later tell Time, “and picked cotton when we were boys. We decided then that we would try to invent a machine that would do this back-breaking toil.”

When John was 16, their parents died; eight-year-old Mack was sent to live with a sister, and John became an itinerant farm hand. At one of these jobs he happened upon a Socialist Party event.. An organizer gave him Men and Mules by W.F. Ries and Introduction to Socialism by N.A. Richardson, which began his political journey leftward.

John told Straus that he first started working on cotton mechanization in his spare time in 1911, at age 19. But he had many false starts and tried out a number of utopian experiments along the way. In Oklahoma, he joined a coal-mining cooperative of some 200 people, living as equals and digging up rocks. The colony eventually broke up for lack of skilled miners. He spent most of the 1920s studying mechanical drawing and the basics of engineering, taking jobs at agricultural machinery firms and learning by correspondence.

At one of these jobs, he had his “eureka” moment. As Straus writes, John “remembered how if you picked cotton early in the morning when the dew was on it, it was difficult to get it off your fingers.” Moisture was the key. As John roamed, Mack received a formal engineering education at the University of Texas and started a job at General Electric. In 1928, the two teamed up and decided to make a proper go of the automated cotton picker.

Even as the cotton-picking work was heating up, John still made time that year to become the Communist Party nominee for U.S. Senate in Texas. He received 114 votes.

By 1930 the brothers had a prototype, and they went to what was, for them, a natural place to test it: the Llano Cooperative Community, a farming commune in Louisiana. John met his wife Thelma down in Louisiana, and over the subsequent cotton seasons he and Mack perfected their wet-spindle device.

A major breakthrough came in 1931, when they conducted a test demonstration in Waco, Texas showing that their device could pick a bale of cotton in a single day. Two years later in Mississippi, at the Delta Experiment Station (a government-funded site to study agricultural innovations, including cotton), their machine picked five bales in a day.

For comparison, an average handpicker could produce about 100 pounds of raw, seeded cotton in a day. Two thirds of the weight of picked raw cotton is seed, so 1,500 pounds of picked cotton were needed to produce a 500-pound bale at the cotton gin. A typical human laborer thus took 15 days to produce a bale. The 1931 Rust picker was already 75 times faster.

The sharecropper crisis

It was not until a 1935 article by Oliver Carlson in H.L. Mencken’s magazine The American Mercury that the broader public realized how good cotton-picking machines had gotten. Carlson gushed that it “picked more cotton in seven and a half hours than is picked by a good hand picker in an entire season of three and one-half months.” The emphasis is in the original.

Carlson’s article set off a panic in the Southern press. The Jackson Daily News of Mississippi told its readers, “The machine is said to be quite practical, works well, and will do the work of at least a dozen hand pickers. That being true, it should be driven right out of the cotton fields and sunk into the Mississippi river, together with its plans and specifications.” (Emphasis, again, theirs.) E.H. Crump, the Democratic political boss of Memphis, Tenn., urged his state to ban cotton-picking machines outright.

Their primary worry was the same as everyone’s: labor displacement. And given who was hand-picking cotton at the time, this worry was often expressed as a fear of black indolence. The Daily News editorial asked readers to “Imagine, if you can, 500,000 negroes in Mississippi just now lolling around on cabin galleries or loafing on the streets of the cities and towns while machines are picking cotton.”

“The prospect,” Christian Century magazine explained, “is that with a general introduction of a cotton-picking machine, the South will be overrun with a shifting, irresponsible army of Negro drifters, who will gradually find their way into the north in far greater numbers than at any time in the past.”

Federal policy was already changing the cotton economy considerably. The cotton sharecropping system had taken hold by the 1880s and continued largely unaltered until the New Deal. Annual contracts and year-round tenants gave planters assurance that they would have adequate labor for planting and harvests, an assurance that they couldn’t get with wage laborers. Jim Crow laws and widespread lynching gave white planters incredible coercive power over their tenants, lest they try to bargain for higher crop shares or more flexible terms.

The Agricultural Adjustment Administration, one of Roosevelt’s marquee New Deal programs, shocked the system profoundly. The basic, economically dubious idea behind the AAA was to pay farmers and ranchers to produce less. In the case of cotton, that meant paying some $179 million (about $4-$5 billion today) to farmers to plow up 10.5 million acres of already planted crops in 1933 alone. This represented 25-50% of the total cotton crop. The idea was that the checks would directly help farmers, while the reduction in supply would prop up prices, further supporting them.

The result was that cotton planters needed less labor and began dismissing tenants. The disemployment of sharecroppers (both white and black) caused by the AAA became perhaps the most controversial aspect of the program, sparking the formation of a Southern Tenant Farmers Union with influential northern supporters like the socialist Norman Thomas. The text of the Agricultural Adjustment Act nudged planters to share the benefit payments they received with their sharecroppers; the power imbalance inherent in the relationship meant that rarely happened. The law also stated that planters should “endeavor in good faith” to protect their tenants by allowing them to remain on their land rent-free. One lawyer in the AAA bureaucracy, named Alger Hiss, protested that the language was unenforceable but was overruled by his peers.

The share of cropland in the Mississippi Delta farmed by tenants (whether sharecroppers, share tenants, or cash tenants) fell from 81.9% in 1930 to 58.2% in 1940. Much of that can be attributed to the effects of the AAA. Economic historians Briggs Depew, Price Fishback, and Paul Rhode estimate that a 10% increase in AAA benefit payments to a given farm lead to a 1.4 to 1.9% decline in the number of sharecroppers there.

The Rusts’ plans

It was in this context that the Rusts premiered their machine. The plight of sharecroppers was a major national concern. The Southern agricultural economy was in turmoil. And here came a machine, purchasable with planters’ AAA checks, threatening mass displacement and social unrest.

The Rusts had a plan for this. Or, rather, they had a great number of plans.

One plan was to ally with the sharecroppers directly, through their new union. “The Rusts offered marketing control of the picker to the Southern Tenant Farmers Union,” Time reported. “The Union had too slim a purse to accept. The Brothers left the offer open.”

Another idea was to partner with the New Deal state. “We want to enlist the aid of the state and federal governments to introduce this invention in a socialized manner through national co-operation,” John Rust told the Arkansas Gazette. “A part of the profits could be used to administer a social insurance scheme for the disabled and the aged, and a program of rural industrialization and diversification should be developed side by side with the use of the picker.”

Nor did the Rusts want to stop at the national level. John continued, “We want the aid of the government and the international labor office of the League of Nations in controlling use of the invention abroad, as it is here, so that it will not be used for the national advantage of this or any other country.” One of their first customers was the government of the Soviet Union, where the Rusts sent two of the first commercially sold pickers from their factory. John visited farms near Tashkent, Uzbek SSR, to supervise the rollout of his invention, and reported back to the Associated Press that he thought Soviet farmers far better off than their Southern counterparts.

Other plans were more grassroots. In their manifesto “The Cotton Picker and Unemployment,” the Rusts promised to “encourage the establishment of cooperative enterprises and such other organizations as tend to raise the economic and social status of the people as a whole.” They made early models of the picker freely available to experimental communes like Mississippi’s Delta Cooperative Farm. “We are also,” they continued, “considering the idea of leasing our machine only to those who will establish a system of fair wages and working conditions for their employees.”

They talked up the latter idea, which we’d today call “high road standards” for any landowners looking to use the picker, to any journalists who’d listen. One of their proposals involved leasing only to planters who would agree to maintain minimum wage and maximum working hours, give up the use of child labor, and allow collective bargaining. If the planters didn’t adhere to their promise, the Rust brothers would take back their picker.

The lease plan had a flaw, though, as the Harper’s reporter Straus explained: “It would stimulate the demand for another picker which could be used without such restrictions, so that it might be a case of cutting off your nose to spite your face.” A century before AI CEOs would insist that their inventions might end the world, but competition from other, less-safe companies and nation-states mandated they race ahead, the Rusts were thinking along similar lines. They would reject the leasing plan as unworkable.

In its place, they suggested another idea eerily similar to that of AI pioneers: a nonprofit foundation to supervise the technology. The Rust Foundation, inaugurated in 1938, would “promote the wellbeing of mankind” and devote the picker’s profits to retraining and providing relief to sharecroppers affected by the invention. It would have, as its endowment, all patents on the device and all stock in the for-profit company producing the device. The Foundation, and the company under it, would be subject to a strict compensation limit: No one, including the Rust brothers themselves, would earn more than 10 times the lowest-paid person on staff. In its articles of incorporation, the foundation promised to “aid in rehabilitation of disemployed sharecroppers and farm laborers of the cotton-growing States, and carry on experiments, research and educational work with a view to finding a permanent solution to our problems of poverty and unemployment and, above all, to preserve and extend our civil liberties.”

The brothers also built ties to the Democratic party. In 1939, Mack Rust personally showed First Lady Eleanor Roosevelt one of the machines at Arthurdale, W. Va., an experimental homestead project she had helped launch. Mrs. Roosevelt herself inspected a Rust machine at the site as part of the brothers’ pitch.

But they could tailor the pitch to more radical audiences too. “Cooperatives are not the whole answer to the question,” John told a reporter for the Daily Worker, a Communist newspaper, in 1936. “They help a few families, and they make people realise that cooperative production is possible. But they can’t function with any great degree of effectiveness under capitalism. And as for relieving the misery that results from labor displacement, cooperatives and the assistance that will come from the Rust Foundation are mere drops in the bucket.”

Death and resurrection

The Rusts would not have to deal with the contradictions of founding a massively profitable labor-displacing company as true-believing socialists. Two years after the flashy debut of the Rust Foundation, and a year after their visit with the First Lady, the Rusts were broke and their firm collapsing.

The brothers had done an expert job showing their invention off at demonstrations for the press and politicians. What they hadn’t done was figure out a way to get it to production at scale. In 1939, they sought to start slowly, planning to produce six ready models, one for sale to Australia and the other five to be used around Memphis. They weren’t ready in time for the year’s harvest, but one was able to get some test picking in toward the end of the season. It revealed some serious bugs in the design, which the pair would need to fix before the 1940 harvest cycle.

They didn’t get the chance. By this point they had sunk too much into the endeavor and earned entirely too little in revenue and investor capital. Holley, the historian, further blames John Rust’s perfectionism: “At a time when the Rust picker should have gone into production, John Rust did not believe the machine was ready. Tests had shown that the picking mechanism lacked the necessary durability for a commercial product. He felt that the problem was in the design itself rather than a matter of substituting more durable materials.”

The Rust Cotton Picker Company went bankrupt; John and Mack Rust both took other jobs, as did their wives, to get by. They even sold the company’s shop equipment. A friend of John’s would recall, “There was a long time there when he and his wife and his brother and his wife lived in this tiny apartment together, and they essentially lived on starvation wages.” Eventually, Mack Rust moved to Arizona.

It was terrible timing. In December 1942, International Harvester, the corporate successor to Cyrus McCormick’s original reaper-producing firm, unveiled the first mass-produced cotton picker. It featured a crucial improvement over the Rusts’ design: the spindles were barbed, helping the cotton stay on.. While John had once considered barbed spindles, he concluded that they would never release cotton for baling, and abandoned the idea. But the team at IH under Fowler McCormick, the chairman of the board and grandson of both Cyrus McCormick and John D. Rockefeller, figured it out.

Given steel shortages during World War II, it wasn’t until 1949 that International Harvester began producing the product en masse. The year after, John Deere premiered its own model. All the way, John Rust had been scheming to stay in the game. In 1943 and 1944, he redesigned his picker from scratch using a rougher-textured spindle to replicate the advantages of the barbed IH picker. Almost immediately he licensed the designs to Allis-Chalmers, a farm machinery company with the resources that he and his brother’s firm never had. Rust could make a decent living, but still his machines did not make it to market.

In 1949, Allis-Chalmers’ exclusive licenses expired and Rust took his technology to Ben Pearson Manufacturing. The firm had little background in agriculture; it was best known for producing bows, arrows, and targets, and more generally helping to popularize archery as a sport in the United States. But it wanted to dip its toes into the cotton trade. Around the same time, Allis-Chalmers finally started producing Rust pickers in large numbers too. Rust estimated that some 2,000 pickers from Allis-Chalmers and 1,500 from Ben Pearson had been made by 1953. Not bad numbers — before you consider that by the end of 1953, International Harvester had sold 8,857.

It was a mixed outcome, at best, for John Rust. On the one hand, he did it. He really did make a commercially viable cotton picker and sell it to Southern farms at large scale. And indeed, cotton in the Mississippi Delta today is entirely machine-picked, just as the Rusts dreamed. But there would be no Rust Foundation, no farmers’ cooperatives, no planned economy of abundance. There was only a decently successful farming equipment business.

Fields without pickers

In 1948, before the International Harvester picker hit the market, 100% of the Louisiana cotton harvest was hand-picked. By 1972, the Department of Agriculture estimated the hand-picked share at less than 0.05%. In a quarter-century, a job that had been pivotal to American farming and the whole course of American history was entirely automated away.

But the social calamity that the Rusts predicted their invention would cause, and that their critics in the press deeply feared, did not transpire. There was no mass immiseration of sharecroppers in the Cotton South. Instead, the same economic factors that made the mechanical cotton-picker a success meant that fields it tilled were largely empty by the time it arrived.

First, the AAA greatly incentivized firms to move away from sharecropping arrangements to wage labor. That reduced pickers’ ties to the land and made it significantly easier for them to pack up and go. It also made it easier to consolidate farmland into tracts large enough to justify investment in a mechanical picker.

Second, World War II dramatically changed the labor market for cotton-picking. The drafting of fighting-age men into the military and the emergence of high-wage manufacturing work for the women and non-fighting men who remained meant that wages for cotton-picking skyrocketed. In 1940, the average wage for picking 100 pounds of cotton in Mississippi was 57 cents. In 1941, once the draft and Lend-Lease production of bombs, tanks, and other materiel for the British and Soviets had begun, it nearly doubled to $1.10. From there it kept rising, hitting $2.80 in 1948.

Those desperate wage hikes reflected a stream of former cotton pickers heading to cities, mostly in the North, for higher-wage factory work — and the start of the second Great Migration of black Americans northward.

These wage hikes made mechanized cotton picking much more economically viable. Analysts from International Harvester estimated that the break-even point between human and machine-harvested cotton was about $2.50 per 100 pounds. That is, it was better for planters to use human pickers if they could get them for under $2.50 per hundredweight; but above that, machines started to look more attractive.

Considering that the going hand-picking rate was barely 60 cents in the late 1930s, it is natural to wonder if the Rusts would have succeeded even if they had been able to produce a mass-produced model then. Their machine was fine, but it could not compete with cheap Southern labor. Once Southern labor was no longer cheap, though, mechanization became very attractive indeed. And because it was no longer cheap due to a mass exodus from the farming sector and migration northward, there were far fewer people left for their invention to displace.

If the Rusts’ more grandiose plans for social improvement failed to materialize, then, at least the calamity those plans were meant to ameliorate failed to materialize as well.

Of cotton and data centers

At this point, the more fervently AGI-pilled readers may be dismissing the relevance of the Rusts’ story to AI altogether. Mechanical cotton pickers wound up — like horseless carriages or personal computers or most other automation tools — leading workers to switch careers, rather than fundamentally upending the economic system. This time will be different. Comparing AI to past automations is a category error.

Maybe so. But I still think there are some things those of us living amid the AI boom might be able to learn from the Rusts’ saga.

The most important lesson is that a company that loses can’t achieve anything at all. The Rust Foundation and the brothers’ visions of directing the profits from their invention back to the people it displaced never came true, not due to a lack of sincerity on their part but because the Rust Company fell behind its competitors, did not raise enough money, and failed. That let International Harvester and John Deere, with no such humanitarian aspirations, catch up. That idealistic businesses have to be sufficiently competitive and earn sufficient market share before they can hope to use their market power to help anybody, is sound, and amply illustrated by the Rusts.

That’s a lesson that Anthropic has taken to heart, justifying less-stringent risk-mitigation policies by insisting that if the lab goes too far on its own, “we wouldn’t be competitive and whatever practices we implemented would not be well-suited to adoption by other AI developers.”

But this dynamic also sharply limits how much good firms like Anthropic can do. If for-profit companies can only wield market power for good when they’re winning commercially, and if obeying ethical standards imposes heavy costs that meaningfully harm their odds of commercial success, then there will always be a hard tradeoff between their business mission and their avowed social mission. Navigating that tradeoff will probably require sacrificing those restrictive standards.

That implies quite strongly that for-profit businesses cannot achieve much counterfactual social impact. They can make money, certainly. Even John Rust made money after his bankruptcy by licensing off his designs. But he wasn’t able to use his business and his engineering prowess to make the world more just.

The Rusts are a reminder that there is a thin line between thoughtful, socially-minded planning and outright hubris. Today’s AI companies are trying to use corporate governance to ensure the political, social, and economic transition they foresee goes well. I genuinely hope they succeed. But the Rusts did the same thing, and, ultimately, it just didn’t work.

This article originally appeared in Issue 15: Work. Subscribe to the print magazine to get future issues delivered to your door.

Julianne Weis worked at USAID for nearly eight years. When the agency effectively closed in 2025, she went to the Hill to start advocating for foreign aid. Julianne and Clara discuss:

• The current political situation of foreign aid

• Why messaging (still) matters

• The role of whistleblowers during the DOGE crisis

• The next USAID-like agency

This interview has been edited for length and clarity.

Clara Collier: You were at USAID for many years, and you also co-founded Aid on the Hill, which is an aid advocacy organization. Do you want to start by briefly talking about your USAID background?

Julianne Weis: Sure. So I am a global health researcher by training. I spent many years in various African countries doing global health research. I then worked at USAID in global health research, in one of the offices in the Global Health Bureau, focused on implementation science research — particularly behavioral science, actually social behavior change. I worked at USAID for about seven and a half years.

In January 2025, we were effectively all laid off. They did keep a really small group of essential staff that they used to essentially close down USAID through July 1, 2025. And then there was another group that went on to the end of 2025, but that was truly a few dozen people from 15,000 of us worldwide.

The more important part was that all of our programs were paused in January 2025. They sent stop-work orders to every single aid project around the world, and in that moment, I just had complete, true panic about what would happen if they succeeded. I started going to the Hill to advocate for restoration of the life-saving projects. And the momentum just grew and grew and grew. We really filled a gap and a need — an information gap. That was the main thing: Congress was just getting no information from the State Department about what was actually happening. We were this really key sort of watchdog player at that time. And so it then became Aid on the Hill, which is now a fully fledged advocacy organization — funded and everything, but I don’t work there anymore.

Clara: One reason I really wanted to talk to you is that I hear a lot of conversations about, how should we reconstruct foreign aid — what should it look like what should we do — and much less on the politics. But of course, as we all learned last year, foreign aid is a political question. So what do you see as the political situation with foreign aid right now? Who supports it, who’s against it, which constituencies actually matter for making sure that there is robust political support for aid in the U.S.?

Julianne: I think it’s a way more hopeful story than people think. I think the narrative that foreign aid is a completely politically toxic area just does not bear out in the data at all.

There’s been a lot in the last year — a lot of focus group and survey data digging into this issue, including with Republicans in red states. Surveys and focus groups, as any researcher knows, can be very biased, but when the story is presented as, hey, this is what America is capable of doing, this is what foreign aid can achieve — there was just data that came out about the enormous bipartisan support for PEPFAR, for example. Life-saving support, really effective programs, things that truly make a difference, that don’t cost a lot of money in comparison to the U.S. budget. When that story is told, there’s an enormous amount of support for it. I think the problem is that when they attacked USAID, they had to make the case for that. And so they absolutely flooded media with misinformation about aid and about waste, fraud, and abuse.

All these lies about what aid was doing were just completely flooded across the media. And so people assumed everybody was buying into this narrative. I do think there’s probably a 30% of the American public that will never be behind this. But it is not at all the majority. Even among Republicans, it’s not a clear partisan line. There are actually people on the far left who are also very skeptical and dismissive of USAID. In the DSA — the Democratic Socialists of America — platform, they called for the abolishing of USAID and talked about it as an imperial, CIA-front kind of thing. So it’s not a straightforward “right versus left” or “Democrat versus Republican.” It’s a bit more complicated than that.

But overall, what we’ve seen from a lot of the polling and survey data, and from doing advocacy and activism, is that the conversation of America being a good player on the world stage and doing really effective foreign assistance is actually a very compelling argument, a compelling policy point, that rallies a lot of different kinds of people.

The PR of foreign aid

Clara: Do you think the aid and development community was failing to communicate this previously?

Julianne: Yes, absolutely. I do agree with that postmortem, that aid was really bad at this. Yes, I do agree.

Clara: What were the biggest mistakes?

Julianne: Okay, so foreign aid is inherently very complex in a lot of ways. But I think we overcomplicate it from a communications point of view. There’s a lot of apology about aid, because there are inherent power dynamics, there’s neocolonialism, there are problematic elements of foreign assistance. We don’t want to go back to like 1985 Live Aid, pretending or assuming that all people in African countries are just desperate and starving. There are ways that we need to overcome those narratives.

But the biggest mistake was just not talking about the huge successes. Global health has been enormously successful. The reductions in child mortality in the last 20 years are astonishing. That is a huge success story. And when people understand that we were actually making an impact, they’re way more likely to support it. It’s kind of like climate change as well: If people only hear things that are bad or hopeless, then they just disengage entirely. But if they know that there are ways that you can actually have a huge impact — there’s a malaria vaccine, we’re coming out with a new TB vaccine, we’ve had huge strides in global health — leaning into those positive narratives and the impact would serve us a lot better than being too apologetic, too hidden, too reductionist, or being like, “It’s too complicated to tell this story.”

Clara: When we were doing a lot of PEPFAR coverage last year, people who were more PEPFAR-skeptical would often say, “Well, is it supposed to last forever? Why are we doing this if there’s no progress?” And you’d have to explain, “No, no, no, no. There’s been a lot of progress.”

Julianne: Yes. I was actually on a webinar this week, and Samantha Power mentioned that. She said a big mistake was that we didn’t communicate that aid was a graduation model.

Clara: Of course, it’s also the case that implementing that graduation model is hard.

Julianne: Yes. And there’s just so much that’s misunderstood. I have family that consume a lot of very right-wing media. And my mom sent me this meme. She’s like, “I don’t agree with this at all, Julianne. I just want you to see what’s out there and what people are seeing.” And it was a meme of a picture of an African wattle-and-stick home. And it said “Africa pre-aid.” And then it was another picture of the same exact wattle-and-stick home: “Africa, this many trillion dollars later.”

So reductionist. Just the idea that we’ve thrown all this money away, nothing has happened. And that’s really, really untrue. So it’s hard, because you want to make the case for aid and talk about the problems that are there — like, “this is a big problem.” There are still huge problems. Children are dying of diarrhea.

There are tremendous water and sanitation issues. But I think focusing so much on the problems has not served us well, in the sense that people have all these excuses — “oh, that’s not our problem to solve,” or “well, we’ve given all this money already,” all these different kinds of things. Whereas if we talk about, “hey, we did this and this is the outcome, and look how cool” — like, oh, now South Africa is doing this or whatever — then it helps people realize, we’re all in this together. There’s no perfect communication strategy.

I’m not going to say it was all aid’s fault that we had to communicate it — “if we had communicated better, Elon Musk wouldn’t have done this” — because I think he would have done it anyway. He had a clear vendetta and clear intention to tear down the U.S. government. So I’m not going to do victim-blaming. But yes, we definitely did that.

Does Congress matter?

Clara: This actually relates to my next question, which is: There’s the issue of how to build public support for aid. But then you have to ask, does that actually influence Congress? And now on top of that we also have to wonder, does Congress matter?

I think you’re right in saying that Musk was probably going to try to do what he did anyway. He was already convinced by all these conspiracy theories. But if there had been much more public support for aid, what difference do you think that would have made in the moment? Would other actors have responded differently? And should that influence the strategy going forward? Maybe we shouldn’t even think of that as a repeatable thing you can plan for.

Julianne: That’s really hard. I don’t think there was anything we could have done differently, because I heard from a Republican senator’s chief of staff that in those early months, USAID was the number one constituent-call issue from their red state.

Clara: In favor or against?

Julianne: In favor. Saying, “Why are you cutting foreign aid? You’re killing people.” That was in large part — this I can say — it was Utah. That was the state. It was because of the tremendous advocacy work of groups like Mormon Women for Ethical Government, who were sounding the alarm.

So I know that there were lots of constituent calls. I know there was a lot of media attention. Republicans at that stage — Trump had just come to office and they just were not going to publicly push back against him. They just were not going to do that. What they did instead — and this is what they all told us and assured us, and it did end up happening — is they restored the budget for the following year. They restored the funding, and they put all these checks on the State Department. But they had decided they were going to sacrifice USAID — let them have USAID, we’ll continue to do PEPFAR under the State Department, we’ll still do all the good life-saving work, and we’ll restore that, don’t worry, but we can’t step in and stop this.

And I’m like, it’s really frustrating, because the State Department is just not staffed up. They’re not equipped to do aid at the level that is necessary. There have been tremendous implementation issues, and there will continue to be. So I think there was a naïveté, but I do think Congress did as much as they were willing to do as a Republican Congress, in the first year of a new administration.

If Elon Musk had gone after USAID later, it might have been different. Look at the Department of Education, which actually still exists. That was one they said they were going to demolish. It’s actually still there. They’ve kind of been like, “Oh, never mind.” I mean, it’s been greatly reduced. There have been humongous RIFs across all of the federal government. But I think the timing of it meant there was really nothing they were going to do, because it was so early in the game. If Musk and DOGE had gone after USAID later, they would have pushed back more. But there just wasn’t much time.

Does constituent voice matter to Congress — yes, 100%. It very much does. Does anything Congress does matter? In normal times, it matters a lot. In this administration, it doesn’t matter very much, because the executive branch is just kind of doing whatever they want regardless of congressional intent. And that’s not just with foreign aid. Look at NIH, look at NSF — Congress has restored all these budget lines. They’ve even demanded that, you know, you have to fund this program, you need to hire staff, you need to do these things. Congress is actually pushing back, and that is because of constituent voices, that is because of advocacy. But the executive branch is completely rogue, does not care about the law. That’s really difficult.

Clara: So where does this leave aid advocates? Is the goal to build enduring support for the future when the administration is less insane? Should the focus be more on figuring out checks on executive power? What does it make sense to focus on?

Julianne: I still think it makes a lot of sense to focus on the immediate — on staving off the worst harms. There’s too much talk about the future of foreign aid that is ignoring what’s happening right now. And you still need to be engaged with what’s happening right now, because Congress, again, they did appropriate all this money. The State Department has all this money that they should be spending, that they could be spending. They are spending some of it. They are doing global health work. So it’s really important to still be engaged in the immediate, to ensure that at least something passable is happening, that HIV treatment continues, that humanitarian aid continues.

In the longer term, it’s hard for me to think about, how do you create executive-branch checks that will actually hold? Technically we have them in our Constitution and in our law. They just don’t care. What do you do when you have absolutely shameless judges? It’s an authoritarian takeover. I don’t know if you can legislation-proof this kind of thing.

USAID does still legally exist. It’s still there, because Congress would have had to pass a law to abolish it, which they haven’t. But it’s been effectively shut down just by all the money being taken away, the staff being taken away, the power of it being taken away.

What makes the administration act?

Clara: I do want to push, though. What is the role of advocacy in this situation? One thing you said earlier was that when USAID was actually being demolished, there was a very active attempt to control the media narrative around it, which suggests that on some level they do feel constrained by public opinion. They do care.

Julianne: Yes. That’s absolutely true. And I do think that’s why DOGE stopped. That’s because it was so deeply unpopular. That’s why DOGE went away. That’s why they didn’t demolish the Department of Education. So they are absolutely constrained by public attention.

They were trying to get the narrative out in front, saying that USAID was only wasteful and that all the reports of deaths from aid cuts are a lie and all that. So that’s why I said, focus on things that are happening now, to ensure that they’re still programming the money, they’re still getting money out the door, they’re still supporting essential services that the U.S. has committed to. And the way to do that is by continuing that media spotlight — because you’re right, they can be shamed. When Nicholas Enrich at USAID put those memos out in March, April, whatever, in 2025, that completely changed the game, because he proved —

Clara: Explain this incident, for people who may be less familiar with it.

Julianne: So when they had done the stop-work order, they said that they would have a waiver for life-saving assistance. They had put the majority of USAID staff on administrative leave, or fired all the contractors. There was a core small group of essential employees that were supposed to be continuing to implement the waivers and shut down USAID.

Nicholas Enrich was one of them. And he put out a memo to the public. He sent it to the New York Times and he sent it to different media, documenting very, very clearly that the waiver system was not working, that no aid was happening, that everything was still stopped, that the response to Ebola in Uganda was completely stopped, that all humanitarian aid was stopped, that nothing was moving forward.

Up until that point, Rubio, the State Department, Pete Marocco, all these people had been saying, “Don’t listen to NGOs. We’re not stopping life-saving aid. We’re still doing life-saving aid. Everything is fine.” And Nick Enrich’s memos demonstrated very publicly that that was a lie. He was a whistleblower from inside USAID.

We had been on the Hill at that point doing advocacy for a while, and Congress was pushing State Department. They were like, “Okay, now show us, what are the projects that are active?” They were trying, but the State Department was just ignoring Congress entirely. But after the media reports from the memos, all of a sudden the State Department turned on a bunch of life-saving programs again.

That was the ticket that actually restored the most essential programs. It was still very rocky in the months after that. It was not immediate. It was still really difficult. But it was because of the media attention and shaming. So in this right moment, that still matters, because it’s still very messy. You need continuous advocacy — that public shaming and advocacy, dual-hat, to make sure that — you’re seeing this around the global health MOUs, the memoranda of understanding.

There is a lot of press about those. There was that Stephanie Nolen article in the New York Times about HIV coming back in parts of Zambia. That kind of reporting really, really does anger the administration, and it will force them to act. So yeah, maybe it is more positive — the idea that the executive branch can be held to account.

Will this ever, could this ever happen again? It seems, in my view, unlikely. But there’s no law that you could write that would prevent this from happening, because there are already laws that should be preventing this from happening, and they did. I think it will always be the role of active civic engagement.

Clara: I should say, I’m not sure how optimistic I am about public shaming, but I think it’s important to be realistic about what levers actually exist.

Julianne: I completely agree with you. It’s a big reason why I have spoken to media for this whole year. From the beginning, I was sending things to media, talking to media, trying to get other colleagues to talk to media. I think that’s extremely important when you see illegal things happening, when you see things that are extremely dangerous and going to lead to the loss of lives. That’s when you have to sound the alarm publicly, and not just try to work from the inside. Working from the inside is not enough.

Clara: Do you think that was an issue early on — people were too reluctant to go to media?

Julianne: Yes, 100%, and it still is. There are a lot of shenanigans still going down. It’s hard. I understand people have jobs and livelihoods and they don’t want to lose them.

And there really is this tug-and-pull of, “Can I do more good from the inside?” I think it’s a mix. It’s not cut and dry. I know there are a lot of people doing really good things on the inside. But when Nick published his memos, there were a lot of USAID people who were angry at him that he did that. And I think that’s bananas, because he was clearly really effective. But a lot of people were like, “No, we just have to keep working from the inside.” That’s a very civil servant attitude. But it’s just not normal times. These things are not normal.

An independent aid agency

Clara: One thing I know that Aid on the Hill, at least, has advocated for is the importance of having an independent aid agency. Right now, aid runs through the State Department. And this is not unusual internationally — many countries do operate this way. Why do you think it’s important to restore USAID or some other independent aid agency outside of State? And what kind of appetite there is for that right now?

Julianne: Yes, I do think it’s better served independently. Look at happened with, say, DFID turning into FCDO in the UK — and Canada, Australia — those are the three examples always cited of when an independent agency was folded into the foreign office. We can see when this happens that diplomacy suffers in some ways, and also the quality of foreign assistance suffers. It becomes much too entangled. They are separate skills, they are separate expertise, they are separate intentions. A person that is a diplomat is serving a country’s interests and trying to work with that country to further America’s interests.

That is actually different than if your goal is, say, reducing malaria deaths in this province. And while foreign aid is of course related to soft power, it’s related to foreign policy, there are ways that an independent agency can operate more neutrally. Even if it’s tied with America, it does have an air of neutrality. Even the idea that the State Department now wants to do all aid branded just with the American flag — that can be really risky. There are a lot of places where that can really backfire. It’s not actually a good idea, from a development perspective or a humanitarian perspective.

When you fold development inside a diplomacy apparatus, it’s just never going to have the expertise or the impetus. The reason for it is not going to be, we are reducing poverty, we are improving global health. It’s always going to be much more tied to America’s goals. And that’s what the Trump administration wants, because it’s all America First. It’s all about the country and about America’s benefit.

Clara: I want to play devil’s advocate for a moment here, because I think a lot of people would say, what’s wrong with that? Why shouldn’t aid serve America’s goals?

Julianne: Maybe this is my politics coming out.

Clara: I know, but I think it’s important to articulate a response.

Julianne: You’re right. Well, in my opinion, it’s in America’s interest to have other countries be developed and thriving and providing services for their populations. It’s in America’s interest to live in a safe, prosperous world. That is good. But the way to ensure that countries achieve those goals of development are not always explicitly tied to a U.S. company, for example.

If our intention is first benefiting America, that can actually backfire and not be good for that country, and then we’re not actually achieving that development goal. I just think it’s in America’s interest that the DRC and Uganda have their own national epidemic-control apparatus. That is good for America, because that means we are not dealing with outbreaks in America — we are controlling outbreaks at the source. And not “we,” but the countries themselves are doing that. If that always has to be tied back to an American company, an American CDC, an American whatever, that’s really limiting the tools in our toolbox. And it’s really limiting how we’re partnering with that country. The country could lose trust with us. They could say, “You don’t actually care about us. We’re going to go work with somebody else. We don’t want to work with you.” Whereas America used to be seen as a true partner.

America had a very different position than China on the world stage. China was giving loans. China is not giving aid — if they’re giving loans, they’re bringing Chinese workers in to build a road. America would work with the country, do government to government, just full-on give the funding directly, work with partners like they build a hospital. It would be no-strings-attached. It is really difficult to quantify how important that is, and how we have now ruined that. But I don’t think it’s ruined forever. I talked to a lot of people from different countries — just this week, I talked to a colleague who’s from the Philippines, and he’s like, “Do you think USAID is going to come back? I really hope USAID comes back,” and he has some story about USAID and the Philippines doing this or that. And again, what USAID was doing was seen by the population as something benefiting them and building them up, and not just, “Oh, they’re doing this to get something for America” — which is how aid is being framed now.

That’s a very long answer, but I really, really do believe it’s better served as an independent aid agency. In terms of, is there appetite for that — it’s to be determined. There are a lot of people who are like, “Oh no, it’s never going to come back.” Other people are too optimistic — like, “Yeah, they’re going to restore USAID.” It’s not USAID. I think that’s important. If they want to just build out the Millennium Challenge Corporation or something, I don’t know, that’s fine. I just think it’s really important to have expertise in international development, and economics, and global health, and epidemiology. These are clear skill sets. USAID had all of these. It was the most highly educated federal agency — more than NASA, more than NIH — because these are actual skills of how you do development. It’s complex. It’s difficult. It’s something you can’t just wave away. It’s a clear skill set that should be fostered in an agency.

The next USAID

Clara: These people obviously are still there. What would it take to make that happen?

Julianne: I think a lot of people would come back. Absolutely. I personally probably wouldn’t. There’s a lot of trauma from what happened last year. It’s difficult. I think it would almost feel like a sacrifice. It would feel very scary, because you’d just always be waiting for the shoe to drop again. But there are a lot of young professionals going into the fields of international development, and I think they could do it. It doesn’t have to be bigger. It could be smaller. It could be more targeted. I think that’s fine. But there absolutely has to be something restored.

Clara: USAID was huge. It did a million different things. If there’s a new USAID-like organization, how should it be targeted? Should it be focusing on health and life-saving aid?

Julianne: I don’t think it should just be health and life-saving aid, because so much of global health problems are actually governance problems. So I do think it’s important to have really strong support for governance-type programming, like taxation — those kinds of really boring things.

Clara: How was that handled before?

Julianne: It was a huge part of USAID’s work. I think that’s really critical. I do think it could be more targeted. There were always new things being added, and never anything really taken away. It could be better designed for what countries actually need and want, and not always what DC wanted. There are ways it could be more flexible in how the aid projects were designed, and who was setting the agendas. I mean, everybody who worked at USAID would have said it was absolutely due for reform. It was not in any way perfect. It should never be restored as it was, exactly. If this is the chance to build something better, yes, let’s do that.

This is related to one of the problems with PEPFAR — PEPFAR has been enormously successful, but PEPFAR was such a vertical program at the outset, and it created essentially parallel health systems, which is just not ideal. And I understand why it did that. Again, it was very effective. But in the last years, it was really shifting to more of a health-systems approach, and having HIV be much more integrated into primary healthcare. So there are definitely ways it could be better.

Clara: You said something about how programs could be more focused on what partner-country governments need than what DC needs. How do you do that structurally? Because it seems like everybody agrees, and everybody has agreed for a long time, that this would be good. But it keeps not happening. It seems that it’s just very difficult to arrange those incentives for an organization that ultimately is based in DC and hires people in DC and is accountable to American political pressure. What do you think can be done on a structural level to combat that tendency?

Julianne: I always said the DC offices for USAID were way too bloated. There was so much — I worked in Washington DC, but a huge amount of my time, most of my time, was actually supporting the country offices, supporting the missions. Because they were super, super understaffed. So I think a great way to make things more country-led is to just completely decentralize it and have the missions — like, have the Kenya office — have much more control over the budget, and the planning, and everything.

So they’re not always responding to Washington requests, but really having things be country-led, and then regionally. And I would say, restore the foreign service national staff. Foreign service nationals were the institutional knowledge. They were there for many, many years. They would stay in an embassy working on development, having the relationships with governments, but they were not empowered as leaders. Foreign service officers would be cycling through every two to three years, and then they would be the ones —

Clara: Just to make sure I understand this, can you explain this role in a USAID mission?

Julianne: For example, in Malawi, the USAID mission in Malawi was 90% foreign service nationals. Those are people from Malawi that worked for USAID. They worked there for years — some for 20 years.

The majority of the workforce of USAID were foreign service nationals — people from the countries. I guess people don’t realize that. These are extremely educated, incredible people, that know the country, know the government. But USAID had this legacy colonial structure, where the mission directors and the health office directors and all top leadership were still American. They were foreign service officers who would cycle through on different tours. They’d go to Kenya for three years, then to Malawi for three years, they do these different tours, and they were the ones that would make the decisions.

There were a lot of amazing FSOs that were good at listening to local FSNs and local partners. But there were a lot who were not good, being very honest here. If I were to resurrect USAID and do something different, I would say hire FSNs back and give them much more power to decide what works with their partner government, with their civil society partners, with their communities. Give much more of that power locally. That’s what I would do.

Clara: We’re coming up on time. Is there anything else that you really wish people understood about aid and aid advocacy, that you feel they’re getting wrong?

Julianne: That it’s not hopeless, and that the story is not over. They restored the funding. There is humongous will in Congress to do something better. There really is. I know people don’t believe that, but it’s true — they do actually want to get this right. There is an intention to fix what happened. There really, really is within Congress. The executive branch is a different story. But Congress is listening, and they are worth engaging.

And whenever we talk about aid and aid effectiveness — you just cannot beat the scale of working at a government-to-government level, at a U.S. government level. They just have a different positionality in these different countries as a partner than any NGO or philanthropy. They will never be able to operate at the scale of government. So it’s really critical to get that government piece correct — better, more effective. It should not just be ignored. We can’t just work around it.

It’s difficult, it’s challenging, I know it’s frustrating — but we really have to contend with it, or else we’re just going to be doing side projects and then ignoring these enormous governance challenges that continue to play out. A lot of development and aid issues come back to issues of governance, and issues of government capacity, and state capacity, and those kinds of institutional, structural challenges that we keep facing. So we have to deal with them.

Spend enough time in online spaces, and you may come across a compelling — and counterintuitive — narrative: Sexism and repressive gender norms aren’t to blame for women being underrepresented in STEM fields relative to their population share. The real reason, or so this narrative goes, is that women are finally free not to enter STEM fields at all.

This account stems from a 2018 study in which the authors measured indicators of women’s science participation against the World Economic Forum’s Global Gender Gap Index. The headline finding is the so-called gender equality paradox, or the claim that women in more gender-equal countries are less likely to graduate with STEM degrees or pursue more traditionally masculine careers — and more likely to have typically feminine interests, personalities, and hobbies.

The authors propose an explanation: In more gender-equal countries, higher security and life satisfaction mean women are free to choose the careers they really want over high-paying technical ones. In this reading, efforts to increase women’s participation in STEM run counter to their revealed preferences for feminine careers, such as in education, carework, or administration. This view rapidly garnered traction in the popular discourse, becoming a talking point of both conservatives like Jordan Peterson and liberal writers like Noah Smith and receiving generally favourable coverage in mainstream outlets such as The Atlantic.

For individuals weary of feminist admonishments, the gender equality paradox was an effective counterpoint. But this popular success has outrun serious scrutiny of the methodology of the original paper.

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The problem of measurement

The gender-equality paradox appears to be highly sensitive to measurement choices. One group of researchers re-analyzed the paradoxical result using a different measure of gender equality. Instead of the GGGI, which captures relative gender gaps and female political representation, they used a simplified measure: the Basic Index of Gender Inequality, itself first proposed by the authors of the original 2018 gender equality paradox study.

This is a common way to test the robustness of a scientific finding, since credible results should not be dependent on slight differences in measurement. When researchers analyzed the 2018 dataset using BIGI, they found no relationship between gender equality and the percentage of post-secondary STEM graduates who were women. A modest relationship did reappear when BIGI was measured against the ‘propensity’ (as defined by the 2018 paper) of women to graduate with STEM degrees, but it was substantially weaker than the original estimate and existed only in a newer sample.

Even in the original study, using the more favourable ‘propensity’ metric, gender equality as measured by GGGI accounts for just over one-fifth of the variation in the likelihood of graduating with a STEM degree across countries — an insufficient correlation to suggest a causal relationship without further evidence.

The wider evidence shrinks too

There have been attempts to replicate the gender equality paradox in other domains, such as personality. In these cases, the scientists are testing whether more gender-equal countries have larger male-female gaps in common psychological indicators, like the Big Five personality traits. These studies typically find larger psychological sex differences in more equal countries.

But the methods used to arrive at this result expose another problem with this line of research. These studies rely on difference scores, created by subtracting the male mean from the female mean within each country and comparing that with measures of gender equality. This makes simple causal explanations suspect because the same difference score can arise from radically different conditions. Male math scores could be improving at twice the rate of female ones, or female math scores could be declining while male scores hold steady. The difference score would be the same in both scenarios. If the scores are the result of altogether different trends, they’re unlikely to reflect the same phenomenon.

A 2024 study from the University of Helsinki applied this critique systemically to the gender equality paradox. The authors re-analyzed multiple major studies exploring the paradox and looked at the underlying relationship between each variable – such as science attitudes and personality traits – and gender equality. Across all variables reanalyzed, there was no consistent pattern between them. Critically for the paradox, none showed men and women moving in opposite directions as equality improves.

Another problem: Men and women tend to move together statistically. If education, height, scientific literacy, or personality traits are higher for the men in country A than the men in country B, the women of country A will usually be higher in those traits compared to the women of country B as well — even if the gap between men and women in country A is also larger.

This can create major distortions if not handled carefully. When the averages for men and women are so strongly correlated, calculating a simple difference will remove all of their shared variance. This leaves only the small slice where the sexes diverge. That difference is then standardized by dividing it by what variation remains — and because so much of it was stripped out, the denominator used to calculate the final result collapses. This artificially inflates the apparent relationship and makes it look much bigger than it actually is.

The Helsinki researchers also noted that the studies they re-analyzed had strongly (in some cases, nearly perfectly) correlated means that were not sufficiently accounted for. They reevaluated the original results using more advanced statistical methods that standardized the scale of the variables and broke the difference scores into their core components. They found that the paradox’s magnitude had been overestimated by about 60% on average across studies. For the original 2018 study, the corrected effect for science-related attitudes was nearly half of what was originally reported.

The paradox that wasn’t

The results of the Helsinki study strongly imply, as the authors note, that the gender-equality paradox depends heavily on countries’ baseline differences distorting how the numbers are put together. Intuitively, this is because differences between nations are much greater than the variation between the men and women within a country. Thus, as the standard deviation is compressed and magnifies small sex differences, the resulting pattern will tend to mirror these country-based trends as it is inflated by their larger variation.

Evidence for this implication is provided by more recent research. This 2025 paper makes the key observation that gender roles and behaviours are not a single phenomenon across the globe. Rather, they are highly dependent on cultural context. Instead of treating all countries as one uniform group, the authors divided the world into seven cultural clusters — the Germanic / Protestant west, the Catholic west, Latin America, Orthodox Europe, the Muslim Middle East, Sub-Saharan Africa, and Asia — to control for differences in cultural context

The researchers analyzed multiple major studies published on the gender equality paradox using the same data and variables, but this time they controlled for culture. In practical terms, this means that differences in baseline characteristics — such as laws, cultural norms, or history — were held constant.

This matters mathematically. Clusters like this exist when groups of data points share unmeasured factors that affect the outcome. Ignoring them can be misleading, as the apparent differences may reflect the different cluster baselines, instead of the variable of interest.

One consequence of ignoring these patterns is a well-known statistical phenomenon known as Simpson’s paradox: a statistical quirk where a trend can appear to hold for an entire group, only to vanish or reverse once you break that group into the relevant sub-groups.

Imagine a study alleging class-based discrimination at a prestigious university. In this study, the poorer a student is, the less likely they are to be accepted into a program at the school. In effect, there is a correlation between family income and acceptance rates. But careful readers will notice a problem. Poorer students often have lower baseline test scores and GPAs. If you break the data down by GPA and SAT percentile, the correlation between income and acceptance could disappear or even reverse. In other words, the overall trend is likely a statistical artifact, created by combining students with very different academic profiles.

Researchers can address this problem by restricting analysis to countries that instead share a common baseline, or “intercept,” so that we don’t erroneously compare countries with very different cultural profiles without controlling for confounders. When the 2025 study accounted for this — so that Sweden was compared to Norway rather than to Iran — the gender equality paradox became nonsignificant in six of the datasets and reversed direction in one.

There was still finer analysis: The Germanic/Protestant West was subdivided into five language regions and the analysis was re-performed within that cluster. For the three studies on personality, all had large negative correlations between gender differences and equality, meaning that gender equality led to fewer gender-based personality differences. For the remaining four, the results were again nonsignificant.

The correlation between gender equality and science jobs on a global level seems to have been driven by Western countries, especially Germanic-speaking Protestant ones. Western countries, it seems, have unusually large gender gaps in domains such as STEM representation compared to the rest of the world. Across the board, whether a country was Western had a better correlation with gender gaps than gender equality did. Tellingly, invented predictors that correlated with Western-ness, such as colder average temperatures, also predicted gender gaps about as well as gender equality did. In a sense, the Western countries represented a group of outliers that the “paradox” could not live without.

This makes sense given we know that WEIRD —– that is, Western, Educated, Industrialized, Rich, and Democratic —– cultures are frequently outliers when it comes to factors like personality, cognition, decision-making, and, critically, gender dynamics. For precisely this reason, in psychology, there has been a growing push against extrapolating findings from such WEIRD countries to the rest of the world.

Various theories could explain why the Western nations have larger gender gaps to begin with. One is simply a matter of data quality. Psychological data obtained from Western populations is typically more internally consistent than data obtained from non-Western populations, meaning that the items on a measurement scale correlate more reliably with one another — possibly a result of these metrics generally being designed and fine-tuned in the West. As a result of greater reliability in one cultural context, this data will generally be better at detecting differences between people in that context, both between men and women and within each sex. In the 2025 study, the authors also found that data quality indicators predicted national gender differences as well as or better than measures of gender equality. When data quality alone was included as a control, the relationship between gender gaps and equality was reduced by 80% on average.

Ultimately, if the paradox reflected a real, causal relationship between gender equality and gender differences in STEM participation, vocation choice, and personality, controlling for country baselines should not cause the relationship to consistently collapse. What remains is a much narrower observation — that a specific set of Western, Protestant, industrialized countries have unusually large gender gaps in STEM, personality, interests, and other metrics — which is not really a gender equality paradox. It is a fact about a dozen or so countries in need of its own explanation.

What actually happens as gender equality rises

Careful readers may still need more convincing. After all, absence of evidence is not evidence of absence. But there is a powerful reason to doubt that gender equality causes gender gaps in STEM professions and elsewhere. Countries around the world have grown substantially more gender-equal over time, and single-country trends over time are not as exposed to the same statistical artifacts discussed above. If one really caused the other, we would expect to see at least some evidence that gender gaps grow in a given country as equality improves, and we wouldn’t need to torture cross-sectional OECD data to see a relationship.

But we see the opposite of what the paradox predicts: Where gender equality has any effect at all, it tends to make men and women more alike, not less.

A 2020 study attempted to replicate the gender equality paradox for psychological values over 14 years of data, rather than simply using cross-country correlations for a single year. This is a stronger design: Cross-sectional correlations across countries conflate gender equality with every other way countries differ, while longitudinal analysis like this tracks changes within each country over time, letting each nation serve as its own control.

The researchers studied 32 European nations from 2002 to 2016, and found that rising gender equality did not produce the widening differences the paradox predicts — the relationship was either null or ran the opposite direction. Another study published in 2023 did the same, this time focusing on the career aspirations of boys and girls. The researchers looked at a sample of European countries from 2006 to 2018, and found that as national equality and women’s empowerment improved, the career aspirations of male and female students converged. This effect can also be seen around the world in a number of domains. In the United States, the percentage of women in the STEM workforce has grown dramatically over the last half-century of feminist advancements.

The available evidence suggests that gender equality does not cause larger gender gaps. When gender equality improves in a given country, these gaps typically shrink. Rather, a handful of Western countries are both very egalitarian and report larger baseline sex differences across STEM aspirations, occupational choice, and a number of psychological traits.

It is only when researchers rely on highly questionable cross-country correlations, which are often biased by underlying cultural and socio-economic differences, that we can convince ourselves of any other story. But this is not good science. Correlation is not causation, and post-hoc, just-so stories are not good theories.

Gabrielle Sorensen is an economic researcher and freelance writer, with previous bylines in publications such as The Hub and Policy Options. She holds her M.A. in Economics from the University of British Columbia.

It’s been a year since the Trump administration dismantled USAID and severely disrupted the operations of PEPFAR, the global health program that prevented millions from dying of HIV and AIDS.

Dr. mike Reid served as PEPFAR’s Chief Science Officer from 2023 until this April. He’s seen all these changes and more play out from the inside. Clara and mike discuss:

• The current state of PEPFAR

• Why the biggest impacts of last year’s cuts are probably yet to come

• How (and how not) to transfer responsibility to partner governments

• What to expect from new AIDS drugs (and AI)

• The problems with the USAID, and what we should build instead

This interview has been edited for length and clarity.

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Clara Collier: I’m very excited to be talking to mike Reid. mike, you were the chief science officer of PEPFAR — the President’s Emergency Plan for AIDS Relief — until you left in April. So to start out, I want to establish: What does the chief science officer do? What was your job?

mike: My charge was to be the champion advancing scientific innovation within PEPFAR’s programs. So as new tools — whether those are therapeutic or diagnostic tools — came down the pipe, I would be the one responsible for consideration as to where they might fit in our portfolio.

In addition, historically, PEPFAR has funded a lot of research and science. As we’ve scaled programs, it’s been clear that there has been a lot of evidence gaps. So along the way, part of that role has involved commissioning and then translating science into programs. And then the third piece has always historically sought to be accountable to the broader scientific community. So I facilitated our engagement with our advisory board and the wider research community that is interested in or invests in HIV research in global settings.

Clara: This must have been such a strange year for you. On the one hand — and we’re going to talk about all of this in more detail — everything is getting ripped apart. And on the other hand, there’s are so many exciting medical advances, like lenacapavir — a new, highly effective antiretroviral therapy that lasts for six months. What was that like?

mike: I don’t want to overstate my own contribution here. Like many big organizations, there’s a whole team. We have experts in the particular technical domain, we have experts in engaging the pharmaceutical companies, and we have folks who are thinking about this from a country point of view.

That said, it’s hugely exciting. We’ve been tracking lenacapavir over the last three years through Phase III trials, and when those first results came out in November of 2024, particularly the PURPOSE 1 trial that showed 100% efficacy in adolescent girls and young women in sub-Saharan Africa, people were just so excited for the potential of this drug.

A lot of the work also involved engaging pharma and other stakeholders to think about how could we make it more affordable. What kind of volumes should the U.S. government be procuring that would shape the market so that generics would be able to enter at a competitive price? Those are all the things that people were thinking about, regardless of the change of administration. Obviously, that had a massive impact on how we were thinking about lenacapavir. And at the time of transition, it really wasn’t clear that the incoming administration had any appetite for lenacapavir. In fact, as you may know, they actually said “we want to put a pause on all prevention programming.” It took a fair amount of politicking and advocacy to persuade them that actually we needed to invest in prevention.

Clara: I know historically, PEPFAR has done a lot of market shaping for antiretroviral therapy and for other drugs. How has that been affected by what’s happened over the past year? What is PEPFAR doing for lenacapavir and some of these other new therapies now?

mike: There’s two questions there. In the very near term, I don’t think PEPFAR investment in market-shaping activities has changed from one administration to the next. With lenacapavir, there was a strategic pause, but then I think the incoming administration recognized that it also wanted to invest in len and saw the value of that. I think they were looking for a better deal for len than the Biden administration got from Gilead.

But from a 30,000-foot perspective, I don’t know that there has been a substantial shift in terms of the overall vision for using U.S. investments to drive down prices so that they’re affordable. One thing that the U.S. is moving towards is procuring all of its commodities through the Global Fund platform, through Wambo, rather than separately through Chemonics. That will allow procurement at even greater scale, and arguably help drive down prices even more. It may also increase inefficiencies, though, because Wambo is not nearly as efficient as Chemonics was. So that’s just one element of how these things are going to play out.

Clara: If the system is less efficient than Chemonics, why is there the expectation that switching would be an improvement? I’m trying to understand this.

mike: Chemonics is a private company. They take a big indirect. From a financial point of view, they offer a more expensive model of commodity procurement and distribution. But they also address many of the elements of the supply chain infrastructure that Wambo doesn’t. They facilitate addressing regulatory hurdles in country, they help support countries to actually forecast and then distribute drugs. Wambo is just a mechanism that procures drugs at scale. And so if the U.S. government decides that Wambo is its destination for commodity procurement, then they will also have to figure out: How do those drugs get distributed? How do you address the regulatory hurdles of introducing new tools?

That will probably require a thousand different solutions in a thousand different countries. And unless you’re going to use a single partner like Chemonics — which is not the desired trajectory — that’s how I think there may be inefficiencies, even though we’re moving away from a more expensive model.

PEPFAR today

Clara: This relates to another big thing I wanted to talk about. When I was preparing for this interview, I asked people who work in global health nonprofit what they were most curious about. The number one thing everybody wants to understand is: What is the current state of PEPFAR? What systems still exist? What stopped? What data sets are we still collecting?

We had this whole system for providing treatment and providing care and distributing these medications. What does this look like on the ground now? And I know this is a huge question, but is there a 1,000-foot overview of what is currently going on?

mike: Yes. There are still functions. It is beholden to the same legislative mandate that it was two years ago. Nonetheless, this administration has sought to do a couple of things that have had profound programmatic implications.

The first is that it has made a really rapid transition towards country ownership. That’s a good thing. Everybody in the PEPFAR space — almost everybody — is excited about the possibility that in the long term, HIV programs are funded, run, overseen by partner governments rather than the U.S. And in service of that trajectory, the U.S. has signed memoranda of understanding with many — not all, but many — partner governments, with an expectation that over the next five years, those partner governments will invest more in their own healthcare systems. They will hire more of their own healthcare workers rather than rely on the U.S., and they’ll increasingly procure more, and eventually the entirety, of all the commodities needed to fund their HIV response.

At the same time, the current administration has made an explicit strategic priority to reduce inefficiencies and waste, and move away from some activities that they felt to be ideologically out of sync with the America First approach. So some of those policy decisions included dismantling USAID, which was perceived to be a fairly inefficient agency. The health programs that were relevant to PEPFAR that were run by USAID have been folded into PEPFAR.

But many of the peripheral activities that were not central to PEPFAR’s mandate, but were done by USAID, ceased to exist. A lot of the work around economic empowerment and providing an enabling environment for populations at greatest risk of HIV — those have all gone away. In addition, we’ve moved away from supporting specific programs that this administration has deemed to be out of sync with their ideological priorities. So that includes some of the equity-informed programming that ensured that prevention services were made available to transgender women, MSM, commercial sex workers. To your point about data: Out of a conviction that a lot of the data that PEPFAR was collecting was expensive and generated inefficiencies, this administration has decided to cull substantial amounts of our data reporting infrastructure.

Clara: I know this must be different in every country, but let’s say I’m an HIV positive person living in, say, Zambia. How is my access to care materially different because of these changes?

mike: It’s a good question. I think the answer is, it depends. If you’re a gay man, or you’re an adolescent girl who lives in rural Zambia, the differences may be profound. You may no longer be able to access services at a touchpoint where you feel safe, or where you don’t have to wait for hours and hours and hours.

If you’re somebody who is accessing care at a big central clinic in Lusaka, where there is substantial domestic government response, you may not notice the difference right now. I think the other area where we may not see the impact until it evolves over time relates to supply chains. If countries aren’t able to procure commodities at the same volumes that the U.S. government did, then over time there may be stockouts. People may go without treatment. And that’s where I think the rubber will hit the road, so to speak, for individuals.

Clara: If you had to put numbers on this — which, again, I realize it’s complicated — do you have a rough idea of what percentage of people who PEPFAR was previously serving are now going to be experiencing significantly increased barriers to accessing care?

mike: Again, it depends. If you’re thinking about people who have HIV who are receiving regular treatment, the impact of the recent changes may be fairly marginal. Available data that has been published by PEPFAR suggests that the number of people on treatment now, compared to the number on treatment before the Trump administration came into power, is about 3%.

But when you look at other elements of the programming activities, like prevention programming or testing programming, that percentage is substantially lower. I think there’s been a 17% decline on testing. And then when you stratify that testing capability by urban versus rural, then you start to see really big increases.

I think the key message here is that some of those differences may not become evident until time has gone on. What we’re seeing now may be a more favorable programmatic footprint than we see in a year or two’s time, based off of how partner governments invest. The amount of money on the table right now means that in five years’ time, there probably isn’t going to be sufficient money to cover their whole epidemic.

Clara: Right. So if the ultimate goal of PEPFAR is containing the epidemic — or at least making sure that everybody who has HIV has access to treatment — the gaps now compound, because if there’s no data, you don’t know who you need to test. People who aren’t getting tested aren’t getting treatment. Populations that are disproportionately likely to have HIV don’t have access to treatment at all. And it compounds and compounds and compounds over time.

mike: That’s right. That’s exactly right. The current administration — they’ve pivoted away from saying that PEPFAR should be responsible for epidemic outcomes. Those are the responsibility of partner governments, and moving forward, our responsibility is to transition our programmatic infrastructure to partner governments. That tautologically shifts the responsibility and perhaps how they frame the issues at hand.

And again, some of that’s not a bad argument, right? I mean, I think countries should be responsible for their HIV epidemic rather than relying on PEPFAR. But I think there is concern that we’re so close to ending the epidemic in so many countries, and yet we’ve taken some steps that actually will make it harder for us to achieve that final goal than it needed to have been.

Shifting responsibility for the HIV epidemic

Clara: I have a bunch of questions about the MOUs — the agreements with partner governments. They all boil down to: How realistic is this? What support are we providing? What are we asking countries to do? And are the things that we’re asking things that these partner governments actually can deliver?

mike: I think it depends a little bit on the country, and on areas that are specific to those countries. But zooming out, I think a challenge is that many governments we supported are overwhelmed with substantial amounts of sovereign debt, right?

Zambia is a great example, where I think 85% of the HIV response is paid for with donor resources currently. And Zambia spends 50% of its own revenue, its own domestic revenue, paying off interest on other debt. So the expectation that they would be able to somehow mobilize additional resources is an optimistic one, particularly when mobilizing domestic resources takes a ton of political energy. Mobilizing domestic resources means taxing people, and that is something that you often have to do under an electoral mandate.

When you’re the finance minister or the chancellor of the Treasury, or whatever they call you, health is almost always at the bottom of your list of priorities. Business, industry, education all rank higher in your list of priorities than health does. As a consequence, it may be really challenging for many partner governments to be able to meet their obligations.

Nonetheless — and I think this trajectory is worth pursuing — if PEPFAR can encourage countries to invest more in their healthcare systems, if we can crowd in more resources from other places, if you can make the pie bigger, so to speak, then those are all good things. And it’s clearly not tenable, both for the U.S. citizenry but also for global health writ large, to continue to have a massive donor like PEPFAR who props up the HIV response in perpetuity. HIV is a chronic disease.

The challenge really needs to be: How do we support partner governments wherever they can to be able to fund their own health systems? And I think that’s a vision that people on both sides of the political aisle will align around.

More than a buzzword

Clara: What would doing this well look like? If we want Zambia to be able to fully fund and stand up their own HIV response, how could we actually make that happen?

mike: It’s such a great question. I think I have like two or three answers. One is, we still need to focus on equity. I think the ideological decision to move away from equity as a framing concept within public health, which this administration has decided to do, is just bonkers. It doesn’t make any sense. Handing programs over responsibly needs to mean empowering partner governments to embrace an equity-informed public health response.

Clara: Step back and explain why this is so important for the health response. Why is equity is not just a nice-to-have?

mike: Yeah. Well, certainly for HIV, but for many other diseases as well, it’s a disease that affects people at the margins, right? It affects gay men. It affects transgender women. It affects commercial sex workers. It affects poor young women who have to engage in transactional sex to put food on the table.

And those are all the constituencies that are not well served by a public health system that really primarily exists to serve the needs of people who are going to vote in elections, or who constitute the majority of the healthcare system. But from an epidemic point of view, if we agree that those groups are the ones where HIV is likely to be transmitted without optimal control, then that’s the place where we should be putting our resources.

Clara: Do you think there’s a tension here? As you say, the goal of transitioning to partner governments is a good one. And part of this is that it’s good for countries that are accountable to their own voters and their own domestic populations to own that response. But also, if, candidly, a lot of people in Zambia are not in favor of gay rights, how should a country like the U.S. balance wanting to give their government more control over how these programs are administered, and the fact that there are maybe quite deeply and sincerely held ideological beliefs that these governments might have that will just make it harder to do the job effectively?

mike: Yeah. I think this is a challenge at the core of a lot of global health programming. And in particular, I think there is a political answer and an epistemological answer. From a political point of view, one of the roles that donors can play is in supporting local advocacy groups that can change the law, who can be a noisy voice for change.

Botswana is a great example here — the U.S. government funded local NGOs in Botswana for several years to address the fact that MSM were criminalized there. And eventually the law was changed, which meant that men who have sex with men were not criminalized, and then were able to access care in stigma-free environments without threat of penalty. And that substantially changed how we were able to control the epidemic in Botswana.

Now, I get it — there are different views of morality, right? And so my other framing here is an epistemological one, which is: I think there has to be room for more than one way of understanding the world. I do worry that a lot of liberal discourse right now is really illiberal. It’s not tolerant of differences of opinion. And here I would just say, I think it’s OK to have a public health response that champions the rights of conservative Christian constituents within a country at the same time as prioritizing care for the most vulnerable communities, including MSM, who may not be beholden to the same moral paradigms as that Christian majority. And I think understanding the epidemic and the public health rationale for that — if we don’t control HIV for everybody, it’s a threat for everybody — is the way to do that.

Clara: I could imagine — actually, I don’t have to imagine — a conservative Christian in the U.S. saying, “Why are we funding gay rights advocacy in Botswana? I don’t even support that here.” How do you manage those tensions on the domestic side? Because that’s also really important right now, if we want programs like PEPFAR to continue to exist.

mike: I think it comes back to articulating that good evidence-based practice is equity-informed. That if we want to control the epidemic — which is in the interests of your Bible study group in Michigan, right? — it’s in their interest to prevent the spread of drug-resistant HIV back to the U.S., to control it in partner governments before it comes back here.

But then when people understand what equity really looks like — caring, providing care for those on the margins, those that are underserved — that actually does align with the views of many Christians. There is a sort of dogmatism that gets in the way of understanding that. And that’s why people like me exist, to try and articulate that there is some nuance here, and that it’s in the interests of everybody. We live in an incredibly interconnected world. Whether we like it or not, we are responsible for each other. So that should be a motivation for folks back in the U.S. to understand why this makes sense.

Smoothing the transition

Clara: Let’s dial in on some of the more logistical elements. If we were to handle this transition perfectly, giving the countries the resources and the structure to do this well, what would be required?

Mike: It’s not just about equity. It’s about, first of all, helping them to mobilize domestic resources, right? And one thing that PEPFAR isn’t doing — because we’re moving at the speed of light —is really supporting countries to scale up their financial health systems.

USAID used to have a whole department that was focused on supporting public financial management systems in partner countries, helping partner governments to use their own domestic resources as efficiently as possible. Without USAID, we’re not doing that right now. So the financial piece is key.

And then from a programmatic point of view, to land the plane, so to speak — to support countries to assume responsibility for high-quality programs really does require support for effectively integrating those programs into their healthcare systems. PEPFAR was set up as an emergency initiative at a point 23 years ago where there were no healthcare systems, and the most expedient way to deliver HIV programs in sub-Saharan Africa was to establish vertical programs that solely existed to provide HIV services. So in many, many places across sub-Saharan Africa, you have HIV clinics that are completely separate physically, logistically, from a health information point of view, from the rest of the healthcare system. If we want to land the plane, then we also have to figure out how to support partner governments to integrate those systems effectively.

But you can’t integrate something into nothing. There are still some parts of some countries where there is no viable healthcare system to integrate into. In those cases, part of the transition needs to involve helping them stand up their own primary care infrastructure. I don’t know that this administration has the appetite for that kind of wholesale investment in broader health systems. Their response will be: “We’ve spent the last 20 years investing in health systems, and at some point we expect countries to assume that responsibility.”

Finally, as I alluded to before, we need to think about the supply chain and the commodity procurement piece of it. And here I think there is an opportunity for a much bigger vision for what this could look like. One of the things that PEPFAR and the Global Fund both do really well is procuring drugs at scale and at affordable prices. It’s conceivable that, if there was an appetite for it, you could actually procure far more drugs — GLP-1 agonists, chemotherapeutic agents, etc. We could help countries think through what procurement could look like — particularly what a broader procurement pooling system might look like, if you were able to procure drugs at scale in partnership with other countries in the region.

Clara: In your writing about your more recent experience with PEPFAR, and your ultimate decision to leave, you’ve talked about how transitioning to country ownership has been a major goal of PEPFAR for years, but it was hard to get real movement until this administration. I’m curious. Why was it so hard to move this ball before, if it’s broadly agreed that the transition needed to happen?

mike: I think at every step along the value chain of HIV-related activities, there were and there remain vested interests in the status quo. A lot of people have talked cynically about AIDS Inc., which is the industrial complex that built up around PEPFAR, whose needs were served by being in that ecosystem. And for all of those stakeholders, there was value in maintaining the status quo. USAID and CDC both had management and operations overheads that come from running programs.

The huge fiscal footprint in Atlanta that exists only because of funding that goes to CDC programs through Atlanta. None of those entities really want to rock the boat. They were invested in the status quo. And so whilst what has happened over the last 18 months has been profoundly disruptive and has caused us a ton of institutional and personal trauma — which I do not want to diminish — it has caused sufficient rupture that many of those stakeholders or those institutions no longer have the standing, or can advocate for the status quo in the way that they did. Does that make sense?

Clara: Yeah, it does. I don’t think I’m saying anything new to you, but one point I do want to draw out is that I’ve seen a lot of responses, especially to coverage of unemployed former global health workers, that assumes that this is malicious, that these people were deliberately siphoning off money. And I’m sure you’d agree that these are all people who sincerely care about the goal of controlling the AIDS epidemic.

Mike: Yeah, I think that’s total bullshit. I think the amount of cynical, corrupt, selfish behavior is negligible within the PEPFAR space and in the global health ecosystem more generally. I don’t think that is an accurate reflection of what’s going on writ large.

The criticisms particularly placed at USAID — that individuals there were fraudulent or corrupt — I think is completely nonsense. But people are invested in what they do. They care about it, right? People at USAID, they loved what they did. And they did it because they thought it was really important. And it was. But nonetheless, it kind of held us in this state of inertia where it was harder to hand programs over to partner governments than it should have been. Does that make sense?

Clara: That all makes sense. So next question then: Ultimately, why did you leave when you left?

Mike: I left for a few reasons. My Substack post outlines some of them. Some of them I’ve indirectly addressed already.

I think it was increasingly challenging for me to live with the moral and scientific dissonance of the moment, right? I don’t think you can practice effective public health unless you put equity at the center of what you do. It’s not just a political woke term. It’s actually the most sound evidence base that we have. But then the other thing was, I was increasingly concerned that we were predicating our ongoing investment in life-saving activities on some specific financial or commercial interests. When you subordinate life-saving interventions and care for underserved populations to commercial interests, then that really changes the calculus. I just found it a bit unsavory and decided that I didn’t want to be part of it anymore.

How to rebuild

Clara: The state of global health right now is what it is, the state of U.S. involvement is what it is. But let’s say we get a more sympathetic Congress, a more sympathetic administration in 2028 — what would your first asks be for how we should rebuild?

Mike: These are questions I’m starting to think about. And to be clear, I don’t think we can go back. I don’t even think we should be thinking about restoring USAID back into existence.

I think three years from now, we may face some other challenges. For example: How do we responsibly enable countries to own these programs, and can we support them more responsibly to mobilize their own domestic resources? I think those are going to be clearer responsibilities that the U.S. government can play.

Zooming out, a lot of changes that we’re seeing in global health right now predate this Trump administration. There was waning political interest in PEPFAR before Trump came back into power. Biden only reauthorized it for a single year, really struggled to get Congress to add any funding to PEPFAR. So my point there is that this isn’t a Trump issue. This reflects broader fatigue within the donor community in high-income countries for global health programming.

When you look at all high-income countries, the vast majority of them have reduced their investment in donor programs. I think only Luxembourg and Norway have met this arbitrary target of 0.7% of their GDP going to overseas development assistance. The U.S., I think, comes in at 0.2% of GDP — a marginal amount. This is a phenomenon writ large. And it demands that we think more broadly about how we mobilize resources to support the poorest countries to improve their health. There needs to be a discussion around what’s the role of development investment, the development banks, sovereign wealth funds, crowding in private sector into LMIC healthcare systems.

The other thing I’m thinking about are programs that enable better agency for individuals and communities. I think AI, for example, potentially affords the opportunity for many in low- and middle-income countries to access high-quality consultative services much more affordably than ever before.

Finally, there’s the impact of climate on health. We have all of these unfinished disease problems: HIV, TB, malaria. But for me, the biggest threat on the horizon is just everything is going to get worse as climate worsens, as weather shocks increase, as parts of the world become increasingly inhospitable, and the healthcare or the public health sequelae of those are only going to worsen. A key imperative for the U.S. is to think about how we might put climate and health at the center of our global health agenda moving forward.

Clara: And what does this look like institutionally on the U.S. side? Like, if we’re not bringing back USAID, what kind of institution in government do we want to think about these issues?

Mike: A lot of technical expertise, particularly for outbreak management and supporting countries to stand up their own public health systems, still exists within CDC. I think it would be foolish to do away with CDC. But I think the development assistance arm of the U.S. government, at least in the near term, will probably sit within the State Department, within the Bureau of Global Health Security and Diplomacy, where it sits now, which is where PEPFAR is housed.

I think entities like the DFC will likely have a bigger role, if we all agree that development investment is going to be more crucial moving forward.

Clara: What is the state of DFC right now? Do you know?

Mike: I think they just recently got a commitment from more money from Congress. And I think there is willingness within this administration to lean into the possibility that they afford in terms of development financial investment writ large. I don’t know what that looks like from a specific point of view. But I could imagine in five, 10 years’ time, they have a much bigger role in advancing U.S. interests in global health.

Clara: So are you imagining someone in State who is coordinating across CDC, DFC, and these other entities, trying to figure out what the overall plan should be?

Mike: Kind of like what happens now. Yeah.

Clara: Okay. And are there any programs you think that, at least for the foreseeable future, either the U.S. government or an international body should own? Even as we move towards transitioning to partner governments, there’s anything that we should retain the capacity for?

Mike: One area that comes to mind is neglected tropical diseases. The five most common neglected tropical diseases are intestinal worms, schistosomiasis, lymphatic filariasis, trachoma, and river blindness. And the largest funder of all of those programs historically was USAID. And what USAID would do is invest about $100 million every year to crowd in pharma investment, and then pharma would commit about $1 billion of their own drugs, predicated on us investing 100 million. Probably the best value of any USAID program. And it’s gone away.

There’s many other areas, though, that are similar. All of the family planning and maternal and child health programs that USAID used to provide support that saved hundreds of thousands of lives a year by averting or preventing pregnancies in vulnerable women who died during childbirth — that’s all gone away. Those are things where I think we actually do want to think about restoring when there is a change of administration, because the both moral and public health value of those programs is huge. And they’ve been cut entirely by this administration.

What about philanthropy?

Clara: This relates to another question. People who are in the global health philanthropy space — what do you think the most valuable things they can be doing right now are?

Mike: Paying more taxes.

I’m totally serious. This is me on a bit of a political rant, but I think there is a lot of health-washing right now from particularly Bay Area philanthropy. If they just paid more taxes, and the money was used more responsibly, and it could be deployed through mechanisms that were accountable, then they could have a much bigger impact.

I’m fairly cynical about the role of philanthropy writ large. I will say that I think the Gates Foundation stands out as an institution that has done an amazing amount of good. And where they have particular value-add is taking those new tools and getting them from the clinical laboratory to the field, and really advancing how we think about the application of new tools. If Gates is an example to go by, then yeah, I think that’s a space where they can play a role.

Look, I think it depends on what philanthropy we’re talking about. There seems like there’s a lot more AI-related philanthropy coming down the pike. I think there’s a really important role that somebody needs to play in making the case that AI should be seen as public infrastructure, a global public good — not something that is given at the service of big tech, but is seen as something like general utilities that advance a country’s economic development. And I don’t know who’s going to champion that, unless it’s philanthropy right now. But I don’t think that AI philanthropy is interested in taking up that cause.

Clara: Part of me wants to say: Wait, if I’m a billionaire and the thing that I care about is global health, is paying more taxes right now really the best way to champion that? We’ve just talked about all the programs that have been cut by this administration, which no one is picking up.

Mike: I think if we got all of our billionaires to pay more taxes, then that would be tremendously good for the health of Americans. We could actually make America healthy again if we invested massively into American healthcare systems. But there’s so much money out there that billionaires are not paying.

Elon Musk, on his own, if he paid his taxes, could end world hunger. It wouldn’t take a huge amount of money. It’s a few billion dollars a year for the next 10 years. But he’s not going to do that unless somebody taxes him. If your argument is, could we get those people to invest their wealth into philanthropic initiatives — sure. But when we look at the vast majority of super-rich people, they’re not doing that unless it helps them to avoid paying taxes, right?

Clara: I’m trying to be very literal here. I have spoken to a lot of people who manage large philanthropies, who are trying to figure out what they should do for global health, given these massive program disruptions.

Mike: Okay. Look, I can give you a list of things where that money would be well used, right? But inevitably, they’re country-specific. Oftentimes they’re not very sexy. They don’t lead to a legacy.

I’ll be honest with you: 12 months ago, we had a bunch of high-net-worth individuals come to PEPFAR and say, how can we support your efforts? But none of them committed their resources — at least not to my knowledge — in a way that was meaningful. Because the kinds of things that are really necessary aren’t sexy things.

It’s really the unglamorous stuff of testing people in rural Kenya for HIV, linking people who have HIV to services on the islands of Lake Victoria. I guess I’m being a bit cynical here. I think there are some new tools that potentially could be transformative. But my instinct is that, with the exception of the Gates Foundation, philanthropy has not delivered at the scale it should do, given the amount of money that’s out there.

Beyond lenacapavir

Clara: Let’s talk about new technologies, then. We’ve talked about lenacapavir, we’ve talked about AI for a bit. What are you most excited about right now?

mike: Within the HIV space, I think lenacapavir is pretty cool, right? An injection every six months. Amazing efficacy in phase three trials. But it does require an injection. You do have to go to the clinic. Ironically, our prevention programming had moved away from being clinic-focused and towards getting out into the communities. And now with len, we’re trying to pull people back into the clinic.

Well, there’s a new drug from Merck — MK-8527. It’s a novel drug class. It’s a tiny pill that is taken once a month, and it protects against HIV for 30 days. It works within 60 minutes. So conceivably, this could disrupt the prevention landscape again, but in a really exciting way. And a couple of things to say: Because it works in 60 minutes, you could conceivably use it on demand. If you’re a gay man, or you’re a commercial sex worker, you just take it 60 minutes before you have sex, and you’re protected. Because it’s a small pill, it’s really affordable. So the rumors are that it will cost pennies on the dollar to make at scale — far cheaper than lenacapavir. I’m super excited about that.

In terms of AI, look, I think there are innumerable applications of AI that will have tons of exciting impacts. The things that I’m looking at really closely are, as I mentioned before, self-care. How can we use AI and agentic tools to deliver care to individuals so they don’t need to come to clinic? Or maybe they only need to come to clinic once every 24 months. It offloads clinics, it protects staff, and it gives agency back to individuals.

Clara: I’ve read about this in parts of China, like in rural Yunnan, there’s been an effort to implement AI diagnostic tools, because you’re not going to get to a doctor. The doctor is four mountains away from here.

mike: Yeah, yeah. And even in parts of Africa now, these kinds of things are being piloted. I think the trouble is, there are many challenges. One is that none of these tools are being validated. They’re often in pilot stages of use. And the governance or regulatory oversight for them is highly variable. If we really think that these tools need to be taken to scale, then they need to be regulated appropriately. And then we need to think of software as a medical device, not as a kind of a nice-to-have with —

Clara: That regulation — who’s doing the regulation? Is it the countries where the companies are based? Is it the countries where they’re being used? What does that process look like?

mike: Well, it should be the countries where they’re being used. Sometimes — I mean, certainly with diagnostics and drugs, many countries in Africa rely on the FDA or the EUA for the regulatory oversight. That doesn’t exist for AI tools. A key imperative is to support governments in LMICs to stand up that regulatory infrastructure, to be able to use regulatory sandboxes to pilot and evaluate these tools in real time. None of which is happening right now.

Clara: And let’s say I’m a developer who just wants to make this product and make it usable. What are the steps I can take to get it closer to being in the clinic? Or out of the clinic, as the case may be.

Mike: Well, I think the first thing as a developer is to actually have a solution to a problem that needs solving. And here, I think often the issue is that people from the Bay Area have tons of really great problems that they want to solve, but they’re not the problems that people in LMICs need help solving.

Think about co-creating your innovative new tools in partnership with communities, and then working with local regulatory infrastructure to ensure that you’re beholden to their requirements, which can be challenging.

I think the other thing I’d say is — and I’ll back up here and first make the comment that the vast majority of new innovation never gets out of the pilot stage. Part of the reason it doesn’t get out of the pilot stage is because they’re not solving a meaningful public health problem. But part is because they don’t have a viable business model — whether that business model is predicated on domestic government investment or private sector investment, and so on. So I think developing a new tool, you have to think about how is it going to be sustainable. And those are questions, I think, that often aren’t being answered, or if they are being answered, they’re being answered through a high-income country lens, where maybe the incentives or the investment or the profit opportunities are very different from LMICs.

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This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by this Friday to receive our next issue, Work.

By Lennart Finke

In 1710, Scottish doctor John Arbuthnot presented a new proof for the existence of God.1 He had observed that for 82 years in a row, London counted more christenings of baby boys than girls. Assuming that the probability of birthing a girl is equal to that of birthing a boy, and that it varies independently over years, the odds of this outcome occurring by chance are 0.5^82. It follows that the ratio must be governed not by random chance but by a divine unifying principle.

Pierre-Simon Laplace revisited the data in an analysis published in 1781, and concluded more soberly that the probability of birthing a boy is simply a bit higher than one in two. Further interested in the difference in male-to-female birth proportions in various European cities, Laplace found that this proportion was 0.38% higher in London than in Paris, which he found significant.2 The same comparison between Paris and Naples yielded a probability of 1/100, which Laplace didn’t consider “sufficiently extreme for an irrevocable pronouncement.”

These early tests of quantitative hypotheses illustrate both the risks and merits of observational evidence. To be sure, as Arbuthnot showed, it’s easy enough to find that the data proves something you wanted to be true all along. Yet it is remarkable that both Arbuthnot and Laplace could obtain local records and start doing science right from their desks. In doing so, both correctly documented important phenomena without the need to invest much labor or capital to get the data.

The efficiency, simplicity, and beauty of this method of gaining knowledge has been underappreciated, especially in medicine and public health. Although it is considered ideal to obtain data in the form of a randomized control trial for an intervention like a drug, that form of data collection is not always possible in either field. Granted, the Paris versus London hypothesis Laplace was testing is relatively simple, but his sample size was over 1.93 million, more than the vast majority of interventional trials in the history of medicine. It is a rare randomized trial (studying a particular intervention with a sufficiently large and well-distributed sample population) that can detect an 0.3% difference in a binary random variable — but Laplace could, more than 200 years ago.

The advantages of the RCT have cemented it as the gold standard for interventional trials in medicine, and it remains what many laypeople think of as the one true way to do science. Yet once we understand where these advantages come from, how they interact with the economics of collecting samples, and the merits of the alternative, observational evidence emerges as the winner more often than one might think.

The slow invention of the RCT

The first written mention of a controlled trial can be found as early as the 7th century BC in the Book of Daniel 1:11-16. The eponymous prophet asks the steward of Nebuchadnezzar, the king of Babylon, for permission to eat a vegetarian diet instead of the king’s rich and possibly non-kosher food. The king’s steward worries that Daniel and his companions will waste away, but agrees to let them test the diet for 10 days, after which adherents of both diets will be evaluated by how “fair and fat” they looked. The vegetarians win, and the steward is convinced.

While other pre-modern controlled trials are thin on the ground, the 11th century Persian philosopher Ibn Sina’s Canon of Medicine does set out rules for designing experiments, including the prescription to test patients with a “single, not a composite condition.” This is echoed in modern RCTs, in which patients who have unusual comorbidities or other unusual circumstances are excluded from trials.

A subject as touchy nearly a millenium ago as it is today is sample size. One of the earliest mentions of a proposed large sample comparison in medicine is found not in a description of a real trial, but from an unrealised proposal to conduct one. In one of his letters from the 14th century Italian poet Petrarch declared the following as part of a polemic against physicians:

I solemnly affirm and believe, if a hundred or a thousand men of the same age, same temperament and habits, together with the same surroundings, were attacked at the same time by the same disease, that if one half followed the prescriptions of the doctors of the variety of those practicing at the present day, and that the other half took no medicine but relied on Nature’s instincts, I have no doubt as to which half would escape.3

Flemish doctor Jan Baptist van Helmont was somewhat more optimistic about the practice of medicine when he wrote a provocative letter in 1648, casually proposing mentioning what could be the first explicit randomization with an external source of entropy:

Let us take from the itinerants’ hospitals, from the camps or from elsewhere 200 or 500 poor people with fevers, pleurisy etc. and divide them in two: let us cast lots so that one half of them fall to me and the other half to you. I shall cure them without blood-letting or perceptible purging, you will do so according to your knowledge (nor do I even hold you to your boast of abstaining from phlebotomy or purging) and we shall see how many funerals each of us will have: the outcome of the contest shall be the reward of 300 florins deposited by each of us.4

The sample size of what may be one of the first earliest recorded attempts at a clinical controlled trial was a bit smaller: 12. In 1747, the British naval doctor James Lind wanted to test the most promising cures for scurvy. He selected 12 patients with similar symptoms, all fed the same diet and all housed together in the same part of the ship. The only difference was in their treatment: cider, vinegar, “elixir vitriol” (a medical extract of alcohol and sulfuric acid), sea water, citrus fruit, or a medicinal paste recommended by the ship’s surgeon. This wasn’t quite a modern controlled trial — for one thing, there was no control group — but at the time, it hardly mattered. The citrus group were unambiguously the first to recover.

Better design trials remained rare until the 20th century.A positive exception is an 1898 diphtheria trial conducted by Danish doctor Johannes Fibiger. With 484 participants, this trial was larger than Lind’s. Unlike most other doctors at the time, Fibiger intentionally sought a large sample size and ensured that his participants actually had the disease they sought to treat. Most importantly, he produced comprehensive documentation of what happened in the trial.5 Fibiger’s trial also featured a true control group and the first attempt at explicit randomization in a clinical trial. Fibiger decided whether a patient would get their experimental serum based on the day they were admitted to the hospital, instead of preferentially treating sicker patients or anything else which might bias the results.

One major source of bias in clinical trials comes from the researchers themselves: Even careful and well-intentioned researchers like Fibiger have an incentive to find that their own treatments work and seek all kinds of avenues, intentional or not, to nudge results in their favor. Another source of bias is the placebo effect, where dummy treatments that a patient believes are real can sometimes have clinical effects. Today, this is addressed by double-blind trials, where neither the doctors conducting the study nor the patients themselves know who is getting a placebo or the real treatment.

In 1946, the United Kingdom’s Medical Research Council started planning our last candidate for the first-ever RCT: a trial of streptomycin for treating tuberculosis. This experiment brought together many of the elements of the RCT as we find it today: attempted randomization and concealment in treatment assignments, systematic enrollment criteria, randomization, and quantitative hypothesis testing.

Taking all the improvements in methodology together, we arrive at the commonly invoked advantage of the RCT: Treatment estimates from RCTs are unbiased, at least under reasonable assumptions.

A lesser-appreciated advantage is that the RCT is a tool for communication. By reducing the vast possibilities of data collection and analysis, it presents a canonical way to perform an experiment and write down the results. After one semester, medical students have a good grasp of how to interpret the outcome of an RCT. You can be relatively sure that a p-value in one clinical trial conveys the same information as it does in another clinical trial, at least as far as the mathematical model is concerned. Depending on the field you work in, you might even be able to skip over the methods section due to similarities across trials. Like a social media app, trial designs benefit from a network effect.

This also makes it much easier to share results with the public and regulatory bodies. Regulators were quick to recognize the power of RCTs, but initially lacked the power to require them. That changed in the U.S. after the drug thalidomide caused thousands of birth defects in Germany and Britain.6 The FDA had already barred the drug from entering the U.S. market, which gave them the political leverage to push for legislation requiring manufacturers to demonstrate the efficacy and safety of their drugs using ”well-controlled investigations.”789

The resulting Kefauver-Harris Amendment, passed in 1962, made the RCT the chief way to communicate to the government that a drug is effective. Because RCTs are relatively easy to understand, it is also harder for the organizer to fake results to get a drug approved. RCTs are not fraud-proof, but their structure makes some forms of manipulation easier to detect, whereas post-hoc data analysis of observational data is easy to game.

When RCTs fail

To see how this works out in practice, let’s design an RCT answering a question from my own field of research. I work in public health and have been trying to understand ways to reduce under-five mortality in Ethiopia. One cost-effective intervention is a basic vaccine like DPT, which protects against diphtheria, pertussis and tetanus. I (and likely you) got three doses in our first six months alive, but the numbers are much lower for babies in rural Ethiopia.

One idea for bringing that number up is to increase the number of mothers who have a skilled health professional present at birth, known as “skilled birth attendance” in the literature. Plausibly, the health professional would tell the new mother about reasonable next steps for her newborn, like when to come in for immunization. So a good question might be: How does the likelihood of a newborn getting vaccinated change given skilled birth attendance?

The RCT protocol to answer this is simple enough: We find a few hundred women in labor, and tell half of them that, sorry, you’re in the control group, so good luck giving birth on your own because we’re taking the doctor with us. Then we come back six months later and record whether the child was vaccinated or not.

Needless to say, this would be a horrible thing to do, and luckily, nobody is running this trial.

Even if there were a way to solve the glaring ethical problem, this hypothetical experiment would also interrupt normal operations of the clinic, take up a lot of practitioner, researcher, and patient time, and require non-trivial labor costs for data collection.

One example with similar levels of operational intensity would be the WHO antenatal care trial of 1996, which compared the impact of different levels of care for pregnant women on various health outcomes. It collected data for 53 clinics (27 in the intervention group and 26 in the control group), and observed the outcomes of 24,000 women, crediting 256 contributors. The cost and effort required by these types of studies mean that large-scale RCTs are very rare in the field of public health. The interventional trials that do happen are typically small and have to compromise to approximate the hypothesis they really want to test.

Take the WHO trial again. It compared different protocols, but rightfully did not include an arm of women who didn’t receive any care, which would be the differential we’re trying to estimate for a straightforward cost-benefit analysis. Further, though the study covers four countries (Argentina, Cuba, Saudi Arabia, and Thailand), it’s fair to have suspicions about whether the regions would be comparable to my country of interest, Ethiopia.

We might also ask whether a typical facility implementing the tested protocol would be as diligent when the WHO is not looking over their shoulder. These kinds of experiments are very hard to blind. In evaluations of a drug candidate for a serious disease, patients naturally try to find out whether they’re in a control group to potentially seek effective care elsewhere. This is a special case of the Hawthorne effect, or when study participants change their behavior if they know they’re participating in a study. This is a problem for all kinds of interventional trials.

Unconfounding

By now, our simple question about the impact of skilled birth attendance seems near-impossible to answer. But the problem is not the inherent complexity of the question, but the limits of the RCT lens.

After all, lots of women in Ethiopia already have births attended or not attended by a skilled health worker and do or don’t vaccinate their children for DPT. We just need to ask them. Or even better, we don’t, because other researchers have already done that as an implementation of a global project called Performance Monitoring for Action. With this data, we can compare vaccination rates for babies whose birth was attended vs. not attended.

The trouble with doing that is, of course, confounding. There are so many ways a simple comparison could be misleading. For example, an educated woman is more likely to decide to give birth in a facility (the treatment), and also to have her baby vaccinated (the outcome). In other words, better outcomes might not be due to skilled birth attendance, but because of a correlated variable like education.

Confounding is often perceived as so devastating that it precludes the use of observational studies as a way of gaining knowledge. At a talk by an editor of a prestigious journal in public health, I was surprised to find that they usually discard papers based on observational evidence wholesale. I suspect the same is largely true in institutions, such as policymaking, that have to make even quicker decisions with even less scientific context.

I contend that confounding is not as threatening as commonly believed. From 1962 to 1970, when the standards of evidence for RCTs in medicine crystallized, championing RCTs made sense. Since then, however, a few factors have changed in favor of observational evidence. We now have better methods, more data, and faster computers to tackle the problem.

One extremely simple conceptual tool developed in 2016 is target trial emulation.10 As with a controlled trial, this starts with a researcher formulating a hypothesis, but instead of running an experiment, they design a hypothetical experiment that would have produced some data they have access to. As in a real experiment, specifying a protocol before looking at the “results” (even if the results already exist) makes it harder for a researcher to manipulate their data after the fact to support a particular result.

For our question about the impact of skilled birth attendance, we can take our very unethical trial design, with a few adjustments, and treat the PMA survey data as if it came from that design. Luckily, the survey data includes multiple time points following women from pregnancy to six weeks after birth to one year after birth. This lets us cleanly frame different points in time as study enrollment, assignment of, and observation of outcome. If we lack an individual’s data at the third time point, this is analogous to loss to follow-up in an RCT.

Now we need to think about how the survey data differs from the trial we are emulating. The important part is that the treatment is not randomly assigned; as mentioned before, some women were more likely to have skilled birth attendance than others. If we are able to take into account those differences in receiving the treatment, we can imitate an RCT.

While a few statisticians use the term “control” for what we are about to do,we prefer “adjust,” because we didn’t actually exert control over what happened.11 Crucially, in many typical approaches, the process of adjusting is mathematically identical to what we would do in an RCT where participants were assigned to two treatments with a probability other than 50%. RCTs with non-balanced group assignment happen frequently — for example, study authors may assign more people to a treatment that they are more optimistic about, to get higher power for its effect estimate.

More computation, more insight

In order to actually adjust for all these confounders, we draw on the patients’ data. Commonly used variables in public health would be demographic information (for instance, age, education, wealth, and so on), information about the patient’s environment (access to health facilities, family), or health history. Using these, we model the patient’s likelihood of receiving treatment. Then, we check whether the model’s predicted treatment status matches up with the treatment status we actually observe.

In most cases, the more covariates we have in the data, the higher the chances we catch all confounders. (Overfitting can be a risk in prediction problems, but in public health there are usually too few rather than too many covariates in the model.) This is why, in a world with a growing wealth of data, observational evidence treated with causal methods ends up winning.

Once we have the likelihood of treatment, we can use it to emulate an RCT by incorporating it,weighting each sample by the inverse of the treatment likelihood. Take the example of whether drinking wine prevents cancer. If people with higher incomes are more likely to drink wine, this messes with our data: Wine drinking was not randomly assigned. If we happen to see a person with low income who drinks wine in our data, we get excited, because this person’s experience gives us more information: It helps separate the income effect generally from the wine effect specifically. We count their cancer status more heavily. By up-weighting these surprising cases — people who were unlikely to get the treatment, but got it anyway — we can create an RCT-like sample after the fact.

While these inverse probability methods have been around since the 80s,1213 now is a better time than ever to use them.

Doing the necessary calculations is trivial today, but recall that when RCTs were cemented as a gold standard, it wasn’t easy for medical researchers to run even a simple logistic regression. SPSS — a commonly used statistical software suite — came out in 1968, and its competitor SAS in 1972.1415 By the time that inverse probability weighting methods were developed in the early ‘80s, computation and RAM limitations had significantly improved for medical researchers. However, getting access to a mainframe, plus digitizing and loading data, was a hurdle. For some results in many RCTs, a simple average is sufficient, and that does not require the computational power of a logistic regression.

Today, we can trade more computational power for better solutions, like using nonlinear methods to predict propensity, gaining a better prediction of the causal structure from the data or simply bootstrapped confidence intervals. Using machine learning to estimate both the treatment and the outcome is called double machine learning, a term that comes from econometrics. DML combines orthogonalized (debiased) score functions with cross-fitting, which helps control bias from flexible first-stage machine learning estimates and reduces the risk of overfitting-related bias.

The causal methods we discussed need the assumption that we observed all confounders. This is certainly annoying, because it is in a sense untestable, but we’re not helpless either.

Barbra Dickerman, one of the researchers behind the target trial emulation framework, sat down with me and explained how target trial emulations can help correct errors in the observational literature, and how to think about unobserved confounders.

In a 2019 paper, she and colleagues demonstrated how a trial emulation with 733,804 subjects showed that statins — cholesterol-lowering drugs — do not protect against cancer, whereas other observational studies seemed to show that they did.16 The main error in those earlier analyses was classifying people based on treatment duration observed over follow-up, which creates immortal time bias: To count as a long-term statin user, a person had to remain cancer-free long enough to accumulate that exposure. Statin users couldn’t be selected for the study if they died of cancer first. By making such assumptions explicit, target trial emulation aligns the incidence estimate with the interventional literature.

The method can also help address questions that would be very hard to answer otherwise. For example, in Ioannou et al. (2022),17 researchers used Veterans Affairs healthcare data to emulate a comparison trial with N = 902,235 per arm between Moderna and BioNTech-Pfizer Covid vaccines. (They found that the Moderna vaccine is more effective). While an RCT would have taken many months to produce, this analysis was done more quickly and at a fraction of the cost. And the companies weren’t going to do it themselves. Drug manufacturers generally avoid comparing their products to the closest competitor. It is often hard to tell which medicine on the market is the safest or most effective for a given condition because we lack these comparisons. Target trial emulation can help.

Alongside target trial emulation, Dickerman points to other relatively new conceptual and mathematical tools for working with observational evidence — for example, the negative control,18 a longstanding experimental practice in other fields, but somewhat novel as a conceptual way to benchmark causal methods. A drug may improve a patient’s odds of recovering from cancer, but it’s unlikely to affect their chances of being hit by a truck. If a researcher runs their whole analysis again, swapping in vehicular impacts for remission rates, and finds a positive effect, this is a good sign that something has gone wrong. Conversely, you could test whether your analysis finds that aspirin, or some other drug commonly prescribed to patients in your sample, has miraculous effects on cancer prognosis. In either case, the negative control serves as a sanity check.

Observing what we cannot control

You can think of these causal methods as trading sample size for quality. This means that if you have a large enough quantity of samples and can make a case that all confounders were observed, you can surpass an RCT with a smaller sample size in causal-effect estimation. Since interventional samples are much more expensive, observational evidence is sometimes the better solution.

The overvaluing of interventional evidence disproportionately harms public health, especially in low- and middle-income countries, where an unconscious choice is made between considering observational data or no data at all. Fields with low financial resources gain outsized benefits when getting observational data right. At the same time, the recent defunding of the global health sector has halted many projects, shifting the balance in favor of cost-effective research. Given these conditions, I am sure that on the margins, institutions will start relying more on observational evidence.

Funders should consider allocating more resources to the collection of observational data. It may be much more cost-effective than the alternative, but the total cost is still not zero. Investment in any given dataset is amortized over many papers by authors with no connection to each other, and as usual with coordination problems like this, large organizations are good at solving them.

One of the most interesting datasets in medicine comes from the U.S. Department of Veterans Affairs’ Million Veterans Program, which includes N = one million (as of 2023, and growing) lifestyle survey items, gene samples, and health outcome records. Spurred by the success of genome-wide association studies as kicked off by Ozaki et al. 2002,19 generalizing beyond genetics and public health, I’m tempted to suggest building large datasets as an efficient way to make progress in other fields studying complex systems that I don’t know much about. The increasing availability of large datasets should make this an especially good time to reconsider observational evidence in many fields.

What I do know for sure is that in my work in Ethiopia, it would be immensely helpful to have any kind of electronic health records at the individual or household level, such as simple panel surveys with ideally >= 3 time points a few months apart, or even a more recent census following the last one in 2007. We only have estimates of how many people live in Ethiopia, much less where exactly they reside. Quantifying the treatment coverage, the disease burden, and the effect of one on the other is not a solved problem. The right tool to tackle it is national-scale observational data, but the investment needed to collect it only happens if we make it clear how valuable it is, and that an RCT is not always the best way to test a hypothesis.

Sometimes nature is kinder than we assume. We need not always coerce it to make it answer our questions. Instead, it can be enough to just record what happens to further science. In time, we will understand all those things we cannot control, only observe.

Lennart Finke is an AI safety researcher and statistician studying at the ETH Zurich and the Harvard School of Public Health. He writes at fi-le.net.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by this Friday to receive our next issue, Work.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

I opened my interviews for this article with a simple question: “How long do we have?”

Five years, or five to 10, or five to 20. One person said eight, then corrected herself to six; one said eight and stuck to it.

In this, they aren’t that far off from many estimates made by experts. The bluntly titled If Anyone Builds It, Everyone Dies has hit bestseller lists by warning of the imminent risks of artificial general intelligence (AGI). The scenario AI 2027, written by a former OpenAI employee, predicts AGI within the next few years. The AI company Anthropic consistently predicts AGI by early 2027.

“I think that in most timelines, humans will simply be irrelevant and extinct,” one interviewee said.

I heard that a lot. A few interviewees — mostly employed by frontier AI labs — expected the world to become unimaginably strange in a good way. One put a 30% chance on utopia, a 30% chance on dystopia, a 30% chance on extinction, and a 10% chance on something too weird to imagine. Several refused to make any prediction; several more said only that they still had hope. About half echoed one of my most blunt respondents: “I don’t think humanity is going to make it.”

What is it like to live in a world you believe is about to end?

Death was already inevitable

“I was born with a terminal condition,” said Matthew Gray, a board member at the existential risk community-building nonprofit Lightcone Infrastructure. “We call it aging. I’ve since picked up another. We call it multiple sclerosis. And AI is a third one on top. I’m not very worried about degenerating from multiple sclerosis because I’m pretty sure the robots will kill me first, just like I wasn’t that worried about aging-related deterioration because multiple sclerosis will get me first.”

From this perspective, AI doomers don’t face a new problem; they face the oldest problem humanity has ever faced.

I pushed back. If I look at an actuarial table, I can expect another 47 years of life. I’d be pretty upset to discover I had only five.

This, my interviewees thought, was naive. Even without AI risk, I could have been hit by a car; I could have gotten cancer; I could have been nuked in a hot war between Russia and the United States. It’s not that the difference in probability doesn’t matter. It’s worse to be certain that I’ll die in five years than to have a 50% chance of not hitting my allotted 47. But because my death has always been an inevitability, I have been coping all along with the precarity of my existence. From this perspective, AI risk isn’t shocking and unfamiliar; it’s a significantly worse version of a problem I already know I have to deal with.

“I was never guaranteed that I was going to get a long life and a long future and a chance to meet my grandchildren,” said Gretta Duleba, an independent technical AI safety researcher and former communications manager at the Machine Intelligence Research Institute. “Those were never my right. Across human history, no one has ever been entitled to the future.”

Throughout the entire scope of human experience, many of my respondents said, apocalypse has been more the rule than the exception: the Holocaust, the Black Death, the An Lushan Rebellion, the Thirty Years’ War. AI doom, as many people pointed out, is the latest and the last iteration of a societal universal. AGI might be the end of the actual entire world, but it is far from the first time people have faced the end of their own individual worlds.

And AI doom is a remarkably cozy catastrophe. If you suffered through a historical apocalypse, you’d expect to starve, be raped, watch your children die in front of you, die a slow and lingering death of smallpox or plague or wound infection. The AI apocalypse — at least for those with the slack to be worried about it — takes place in a world of wealth and relative peace and technological marvels.

“Enjoy the fact that you get to have hot showers,” said Duleba. “Enjoy the fact that you get to eat delicious food. Enjoy the fact that you get to do escape rooms, which is one of my favorite things. This is great. Have you noticed how great this is?”

“There’s at least some hope that AI might become good,” said Robert Herr, a former senior political staffer who is transitioning into AI policy work, “and that is a lot more than many, many billions of people in history had.”

For some people with short AI timelines, the enormity of the AI apocalypse is its own perverse source of comfort. Once, they had to worry about many things: climate change, malaria, factory farming, democratic backsliding, the fertility crisis. Now, instead of many big problems, they have one enormous problem. Worry about AI frees them from having to worry about anything else.

“When you’re diagnosed with prostate cancer,” said Adam Grey, who isn’t involved in AI research but who follows AI news, “a lot of the time doctors say not to bother treating it because you’ll die of something else. This is the thing that’s going to kill us first — us as a civilization and also personally me. It clarifies what the most important issues are.”

Ambiguous loss

Although short AI timelines can be a source of clarity, some people also struggle with the uncertainty of humanity’s fate. Duleba, who was a therapist before she switched to working on AI, told me about the concept of “ambiguous loss,” originally developed by Pauline Boss in the 1970s.

In normal grief, your loved one is dead. While it’s painful, you know that it will never change. Ambiguous grief, however, occurs when a loved one is kidnapped, or is a soldier missing in action, or has slowly worsening dementia with occasional good days farther and farther apart. Your loved one’s death is never really over, so you can never really grieve. You are trapped in a cycle of mourning that never resolves.

AI doom can be a situation of ambiguous loss. You can’t know for sure when it will happen or whether it will happen at all — but the more you understand what’s going on, many people find, the easier it is to grieve and move forward.

“The more I know about something, the less I’m freaked out always,” said Tao Lin, a member of technical staff at a frontier AI lab (and a close personal friend). “The less I know about something, the less I will be rational about it. The rational part of your brain needs information to operate, and just having more information will make you be more premeditated and system 2 about everything.”

When he felt doomy, he did AI forecasting to put concrete numbers on his uncertainty. (He believes there is about a 20% chance of human extinction, a 40% chance of ”a great outcome,” and a 40% chance that “people survive and have a great time, but stuff is broadly bad.”)

Other people with short AI timelines adopted the same strategy. “AI risk was an amorphous, scary thing in my mind,” said Jack Stennett, program director at ML4Good. “So I tried to put some numbers on it. What’s my p(doom)? What is my estimate of what the risk is? What are the pathways to how we survive and things go really well? What are the pathways where it goes really badly? Having a bit more precision about how you expect things to go makes you feel more in control of the situation. Fears are always worse when they’re poorly defined. The scariest monster is the one you can’t see.”

Most interviewees emphasized that the most important thing to understand about AI risk was how little control you had over it.

“There’s not much use worrying about a thing if the outcome is determined,” said Alyssa Riceman, a software engineer. “You’re just going to burn a whole bunch of emotional energy not making any changes out in the world. It’s only worth worrying about things if you’re in a position of control over them. So go out and check if you’re in a position of control, and if you are, control them.”

But what if you have partial control over a situation?

“Then you have to game out all the branches,” Riceman said. “Say ‘I can do this. If I do this, what happens then?,’ until everything bottoms out at either a situation you can completely control or a situation that’s out of your control.”

Duncan Sabien, who is the current communications manager for the Machine Intelligence Research Institute, agreed. “I actually have no control over whether we succeed or fail,” he said. “All I can control is my own actions. And so if I am doing the best I can with what I know and what I have available to me, then that is the best I can do. I go home feeling like I’m a good person, and I get to go to sleep at night feeling like if the AI does kill us all, I did as much as I could, realistically and sustainably, to prevent it. And everything else is out of my hands. Everything else is always out of my hands.”

Living well in the apocalypse

What, then, do people decide to do?

For many people with short AI timelines, the answer is to get to work.

“If we’re going to die,” said Ben Woden, operations manager at Pivotal Research, a fellowship program for aspiring AI researchers, “I would really, really prefer to die fighting.” He then clarified that by “fighting” he meant continuing to work at his job as an operations manager for an AI safety nonprofit, not bombing a data center.

But many people can’t do anything about AI risk other than go to the occasional protest. They don’t have the mathematical ability to go into technical AI safety research or the persuasive ability to work in AI policy. And people who are working on AI risk might still want to do something in the hours in which they aren’t addressing the problem (which, for some, is admittedly, quite rare).

Adam Grey, for instance, believes “there’s a greater than 50% chance that my influence on my life or the lives of people around me has an expiration date of five to six years.” He doesn’t work in AI, though, so he can’t improve humanity’s odds directly. Instead, in the meantime, he thinks “the only action I can do to benefit myself or my immediate surroundings is to have a good time.”

People working in AI safety agreed. “It’s better to have died having fun on the trampoline than to have died listening to ‘Moonlight Sonata’ on repeat while I cry,” Duncan Sabien said. “I choose between universes in which I die, and I choose the one I like most.”

“Drink the good whiskey, drink the good wine,” said Dave Kasten, head of policy at Palisade Research.

Drinking the good whiskey costs money. Accordingly, “I’m not as careful with money anymore,” Duleba said. “Historically, I was very careful with money, so that means I now have money to not be careful with.”

Some people with short AI timelines made radical financial decisions. “My partner’s like, ‘Let’s be sure to save for retirement,’” said an interviewee working in AI policy, “and I’m like, ‘Retirement? That’s not even in my world model! That’s a type error!’” A few even plan to spend down the money they’ve already accumulated. Tao Lin, for instance, intends to donate his savings over the next few years.

But Duleba urges people to keep some money in reserve. We might successfully pause AI development. AI could automate most jobs, causing mass unemployment, well before it causes human extinction or utopia. The future is unpredictable, she thinks, and having some money prepares you for unexpected outcomes. Adam Grey agrees and is saving the recommended percentage. “It’s not that I want to be rich in the cyberpunk future,” he said. “It’s that I want to have options in any future.”

As I did my interviews, I was struck by the sheer diversity of life changes people made because of AI. They went to therapy. They quit jobs they hated to spend more time with their children. They had children because they wanted to experience their children’s early years, even if they would never live to see their children as adults. They travelled. (“I studied Latin for seven years, and I’ve never been to Rome or Pompeii. This makes no sense.”) They threw 50-person birthday parties. They learned to camp in the snow. They starred as Harold Hill in The Music Man. They learned to grind ultramarine blue paint.

“Remember that everyone dying would mostly be bad because we couldn’t do stuff that’s good,” said Ben Woden. “Try not to surrender before the fight has begun by letting the fear of death keep you from having a life worth living.”

“When I’m going to a gig,” he said later, “I sometimes get the sense that it’s good we’re getting this before it’s all over. This might be the last iteration of Damnation Festival there ever is, so it’s good I was here. We’ve seen it out on a high. Well done, everyone.”

As Duncan Sabien put it: “There’s a difference between a half-built pyramid and a pile of laundry in the corner of your room. If you’re living your life well, you should always die with half-built pyramids. It’s worse to die with vast piles of half-folded laundry you really feel like you should have done.”

Give yourself permission not to fold the laundry, or get it out of the way as quickly as possible. But don’t allow the fear of AI doom to keep you from building pyramids.

“When I embark on a project,” Sabien added, “I say to myself, ‘Oh man, if I build it, it will take 10 years. But we might all die in five years. But that’s not a reason not to build it if it’s beautiful.’”

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

By Josh Martin

Global development has a war problem. The world overall is seeing more conflict every year, and the majority of the damage is in developing countries. And while war in industrialized societies can even support economic growth, at least in the short term, developing countries can take decades to recover the growth that violence wipes away.

Yet there is a belief in the development sector that war — and its catastrophic effects on weak economies with fragile infrastructures — is an inevitable feature of human society. That even if we had more effective tools, the challenge of war is far bigger than the resources that the development community can muster to meet it. In that sense, war is treated much the same as a volcanic eruption or a cyclone: We can build resilience once it happens, but we can’t prevent it from happening.

Pessimism reigns following resurgences of conflict in areas that had seen huge investments in “stabilization,” like Afghanistan. As Yvonne Helle, former special representative to the United Nations Development Programme’s Programme of Assistance to the Palestinian People, told me days after the October 7 war in Israel and Gaza broke out, “I feel so useless and naive in believing that development was possible.”

It’s true that many traditional development approaches may not be great investments during wartime, when interrupted supply chains, scattered communities, and scrambled local power relationships all make such work far more expensive. But it is too fatalistic to treat war as an inevitability.

A new wave of empirical research is starting to expand our tool kit for preventing cycles of violence, at least when it comes to non-state actors. Our next step is to figure out what really works — and how much it costs.

Peacebuilding: from art to science

Peacebuilding is notoriously difficult to evaluate. The chaos of war and displacement makes it difficult to survey communities. Conflict takes on different forms in different places, and extensive testing is needed to make sure promising approaches hold up in multiple environments. Destructive violence, though frequent, is rarely predictable enough to observe how it responds to a controlled intervention. It’s hard to recruit potential violent actors, often on the fringes of society, into studies, and even harder to observe how they behave.

These challenges shouldn’t obscure how far we’ve come in understanding the causes of violence. Since the mid-2000s, a revolution has been underway in conflict studies, driven by the marriage of novel large datasets on incident-level violence and observational methods developed in more traditional fields of development economics. This union was spearheaded by academic collectives like the Empirical Studies of Conflict project1 and the Irregular Warfare Initiative, which use incident-level data to test core assumptions behind efforts to stabilize regions suffering from irregular warfare. In Iraq, for instance, it turns out that young men’s job circumstances don’t have much of an effect on their decision to join insurgencies, overturning much conventional wisdom and casting a pall over job creation schemes that USAID and other donors had heavily invested in.

This first wave of studies of conflict based on surveys and other observational data paved the way for randomized field experiments. These experiments borrowed techniques from the randomized controlled trial movement in public health, and were often hosted by the same institutions, including groups like the Abdul Latif Jameel Poverty Action Lab’sCrime and Violence Initiative and Innovations for Poverty Action’s Peace & Recovery Program. This work has helped validate existing approaches, but measuring the impact of any intervention on real-world violence remains a challenge.

One well-known paper illustrates just how tough this line of research can be. Working in the slums of Monrovia, Liberia, University of Chicago political scientist Chris Blattman and his co-authors found that eight weeks of cognitive behavioral therapy followed by a $200 cash transfer significantly reduced the likelihood that young men (previously engaged in illicit activity) display a range of violent and criminal behaviors at least a year after the study began. To show this, they had to find brave local enumerators willing to spend several hours each day over four days in casual conversation with men who were at high risk for violence, probing them with questions about their behavior. Closely and rigorously studying high-risk populations like this is critically important — new organizations have even sprung up in other parts of the world to test whether these findings hold up — but also critically difficult. This type of research is, therefore, vanishingly rare.

Instead, most experimental research focuses on intermediate outcomes, like extremist attitudes or hostility toward opposing groups, because they are easier to identify and measure. While this work has limits, it can still be very useful for identifying the seeds of future interventions.

For instance, research has shown that making complex identities salient can show people that they have something in common with members of groups they might think of as their enemies, that communicating through respected leaders can reduce support for extremist actors. It suggests that teaching and practicing the skill of seeing another’s experience as if it were one’s own can improve social cohesion and that mass media (for instance, via a radio soap opera) can increase positive interactions with members of opposing groups.

Some of the greatest contributions have illuminated what doesn’t work. Most people might intuit that simply bringing people from opposing groups together might lead to greater understanding and lower the motivation to fight in future. But dozens of experimental studies illustrate that mere intergroup contact does not show reliable impact and may even make things worse. The reason is simple: Spending time with members of rival groups, whatever the benefits of getting to know them in the long term, makes salient the fact that there is a conflict in the short term. Without carefully constructed conditions in which the contact occurs, such interactions can cause excess stress and even harden the resolve to fight, in turn preventing the kind of bonding needed for long-term inhibitory familiarity to develop.

The fuzzy picture of peacebuilding effectiveness may now be coming into greater focus. A 2021 meta-analysis by the impact evaluation organization International Initiative for Impact Evaluation identified 37 high-quality empirical evaluations of peacebuilding programs — only three of which existed 10 years prior to publication — but they all measure these intermediate outcomes, like trust or tolerance of other ethnic groups. There are strong theoretical and empirical reasons to believe that building social cohesion is an effective way to reduce political violence, but much more work is necessary before we can be confident that these peacebuilding initiatives are really a cost-effective way to reduce war itself.

The cost of preventing war

The gap between experimental results and ultimate outcomes is one reason funders are wary of the peacebuilding space. In 2022, when investigating “civil conflict” as a possible cause to recommend for philanthropic donations, charitable foundation Coefficient Giving declared itself to be “quite uncertain how much of a difference micro-scale interventions like cognitive behavioral therapy will make on macro-scale events like war.” (Asterisk receives funding from Coefficient Giving.)

Researchers trying to close this gap have two problems. First, as we’ve seen, work like Chris Blattman’s, which actually measures whether individual-level interventions reduce violent behavior, is rare. Second, even these studies are too limited to address the question we’re really interested in: How much does reducing individual violence affect the chances that a large-scale conflict will break out?

At Peace Per Dollar, the new initiative at UC Berkeley’s Center for Effective Global Action, we are trying to answer all these questions. Our goal is to show that approaches that have demonstrated consistent effects at the individual level — like school-based empathy education programs — can also deliver real value in preventing larger-scale avoidable violence, and that they can do so cost-effectively. In fact, given the enormous destruction wrought by war, we believe that such tools could even end up being as effective as, for example, bed nets for preventing malaria deaths. If the development community knew the return on their investment, they could invest more confidently in preventing destructive violence.

Our first task is to understand the scale at which peacebuilding interventions would need to be deployed in order to do their job. Would absolutely every denizen of a conflict-prone region be required to undergo, say, a highly intensive exposure to outgroup narratives in order to meaningfully avert the likelihood of a future return to violence? Surely not. But, then, how many would and to what degree of intensity?

An analogy may be found in global health with vaccine efficacy. Vaccine trials demonstrate the ability to preserve individual health in the face of a pathogen. But to assess whether a given serum can prevent an epidemic, you need the bridging concept of herd immunity. In peacebuilding, we are still figuring out if the serum works; we haven’t yet gotten around to thinking about what population-level “immunity” might look like, let alone funding the kind of large-scale interventional trials required to actually observe it in the field.

While we wait for such trials, however, it’s not impossible to imagine how war prevention might work. Of course, we won’t be able to prevent all conflict. Many large interstate wars, including Russia’s war on Ukraine and the U.S. and Israel’s war on Iran, are widely viewed as the product of the determination of a small number of leaders with extraordinary access to military power.

But in the developing world, war is much more often the product of a readily available recruitment pipeline. Cut off the supply of willing fighters, and there is no war to be fought. We often imagine such fighters to be ideologues, but this is usually wrong. When individuals participate in political violence, it’s almost always as a result of following a pattern of behavior that begins long before they are even aware of it. Each choice they make along the way — for instance, deciding to start hanging out with a particular group of friends — pushes them unwittingly into a situation where they may be required to act either as accessories to or as perpetrators of violence. Getting out of this situation, perhaps by abandoning a social circle and actively seeking a new one, requires more and more effort as they are pulled deeper in.

This is not to say that people who eventually join violent groups don’t care about the cause. They do, often to the point of holding irrationally “sacred” values. But this is equally true of people who join nonviolent protest movements instead of violent ones. The difference between the two can be as small as a momentary lapse in judgment during a critical period of life. Accepting an invitation by a stranger. Scrolling through an innocuous-seeming news article. Responding without restraint to a perceived insult in a public forum.

All this implies that potential fighters can be deterred with less effort than it would take to change an ideologue’s mind. But how many participants are required for a wave of violence to be considered a war, and how much effort would it take to deter them?

An upper bound could be found in the case of the Rwandan genocide, one of history’s worst atrocities. According to one estimate, up to 14% of Hutus participated. Now imagine a radio program that increases the likelihood of an individual expressing dissent by around 25%, meaning that four people would have to be exposed in order to produce at least one individual who refuses to follow orders. According to this simple calculation, sitting 56% (4 x 14%) of Rwanda’s Hutus down to listen regularly to such a program might have produced at least as many refusers as willing killers.The cost of reaching so many people in this way might be enormous. But there might also be ways of bringing it down. If you believe in some version of herd immunity, you wouldn’t need to flip all potential perpetrators into refuseniks, just enough to create a visible norm of dissent. Technological progress can help drive down delivery costs further by locating the highest-risk people at their highest-risk moment (say, right after they receive orders but haven’t yet decided to follow them). AI-powered social listening tools can detect toxic discourse that may predict future violence. At the individual level, a better temporal understanding of the stages of radicalization can lead to more precise and thus less costly deployment.

Development practitioners need an engineering mindset to instrumentalize the new tools that researchers are fine-tuning in real time. This would allow them to work in areas where war is likely with the confidence that they can actually do something about it. If they had more clarity on the scale required for war prevention, funders would also be better able to assess whether the costs of deployment would be worth the likelihood of successfully preventing catastrophe. (They’d first have to demand greater cost transparency from their grantees: Only one of the 73 interventional studies we reviewed at Peace Per Dollar contained basic intervention cost information.)

Greater alignment toward war prevention might therefore help build an investment case to bring in funding from philanthropists and social investors who previously would not have considered it. Like a natural disaster, war is effectively priced into the cost of every other development intervention. It’s also very expensive to clean up: A 2018 UN analysis estimated that spending one dollar on violence prevention could remove the need to spend up to $16 later on, even when accounting for a reasonably low likelihood of success.

Enormous opportunity now exists to align incentives toward war prevention in the development community. What would a focused research-to-practice pipeline look like? We are talking about an effort to create a “weapon of mass peace,” arguably on the scale of the Manhattan Project. In place of centrifuges, we would need large-scale cluster randomized trials where outbreaks might be possible over a reasonable time horizon and the patience to see them through. But you can see a future in which we have the tools to avert violent conflict if you squint. Engineering peace is hard — but not impossible.

Josh Martin is the director of the Peace Per Dollar initiative at UC Berkeley’s Center for Effective Global Action, a new team dedicated to finding and promoting cost-effective solutions to violent conflict. He holds concurrent roles at UNICEF and Google in applied social science research and is a co-founder at the behavioral strategy advisory firm Venn Advisors.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

Usually, we like to back up our claims with numbers, but right now, we’re in a more reflective mood. After all, the hot beige microsite of the summer is a papal encyclical, and it doesn’t have even one chart.

Right now, all eyes are on the Bay Area. The charts keep going up. The money is about to pour in. Yet there’s a pervasive sense that tech now lacks vision and moral character. We agree. Let’s see if we can change that in 120 pages.

To that end, Asterisk’s Fall 2026 issue is the theme of Flourishing. We are now soliciting pitches for articles, essays, and interviews related to any aspect of flourishing — think broadly. We are also particularly interested in the following themes:

• Morality and virtue: How can we be good? Why should we be good? Are we out of the horrible vice-signalling spiral yet? What even happened there? Are there any arguments about EA we haven’t had yet?

• Relationships: How should we think about family, children, and parenting — conventional and unconventional? What kinds of communities and friendships should we build? (Radical anti-community takes also welcome.) Is there good dating advice for the singularity?

• Man’s search for meaning, and coping with human inferiority: Is anyone happy? What happens if AI becomes better than us at every single thing? How will our institutions change?

• Aesthetics and beauty: Is everything ugly? If so, how should we fix it? Bonus points if you’re willing to defend modern architecture.

• AI consciousness: How would we know if our AI models are thriving? How much should we care?

• Technology’s relationship to the world: What is Silicon Valley for? What legacy does it hope to leave behind?

• Scene reporting: How are these questions playing out around you?

Our magazine pieces tend to be long (3000 to 5000 words on average), evergreen, and deeply researched. We welcome pitches and drafts from everyone, not just academics or professional writers.

Email us at submissions@asteriskmag.com, ideally by the end of July. Write a few paragraphs explaining the idea, how you would complete the essay (which sources would you reference? is it reported?), a target word count, and a little about yourself, including any relevant expertise.

To the good life,

Clara, Angela, and Nadia

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

How long until AI systems can sustain their own existence — such that, if every human died, they could keep growing their own population?

METR’s Ajeya Cotra and Understanding AI’s Timothy B. Lee discuss the path toward this “self-sufficient AI.” She thinks it’s nearly imminent; he believes it might never happen. The two talk through the skills and shortcomings of today’s humanoid robots, profit incentives, tacit knowledge and how these affect the timeline, non-robot paths to self-sufficiency, and the benchmarks to watch for in the next few years.

This interview has been edited for length and clarity.

Clara Collier: This conversation is happening because you two got into a debate on Twitter about when you expect us to have fully autonomous AI systems. Ajeya, this is your concept. Do you want to talk about what you mean by that?

Ajeya Cotra: I wrote a blog post about self-sufficient AI, which is AI systems integrated with physical infrastructure — factories, mines, fabs, robots to operate all of those — such that they don’t need any cognitive or physical inputs from human labor to keep growing their own population.

If all humans died of the plague one day, the AI systems would be able to maintain themselves, repair things that might be breaking down in their physical environment, and keep up the power plants that run them. They’d also have to expand themselves, which would require eventually consuming more physical resources — going out and mining the quartz and turning it into silicon sheets and then etching those into chips and so on.

This is an interesting forecasting point in my mind because, one, it’s relatively concrete and easy to imagine, and two, because a number of people are concerned about the possibility that AI systems might literally drive humanity extinct because they’re pursuing goals at odds with humanity’s goals. Self-sufficiency seems like a requirement for carrying out full extinction of humanity on the part of the AIs.

I do want to flag that I think this would be a pretty late milestone, and you could degrade this milestone in interesting ways and forecast weaker endpoints as well. For example, how many physical humans are needed to sustain a certain population of AIs? To what extent do they need to have specialized expertise versus “the AI can just direct them around because the AI has all the knowledge”? I ultimately think one or more of those weaker milestones will be more directly relevant for policymaking, but you can generate them starting from the concept of self-sufficient AI.

Clara: So when do you think we’re going to have this self-sufficient AI?

Ajeya: More likely than not within 10 years. And it very well could happen sooner.

Clara: Tim?

Timothy B. Lee: I think that timeline is pretty unlikely. Hard to put a number on it, but 20 years is the earliest it sounds plausible to me. I’d say less than 10% chance that it happens within 20 years. I’d say there’s a 10 or 20% chance it’s never, and my median would be 50 years.

It’s hard to reason about what will happen in the future, but I have a strong intuition based on writing about robotics, particularly self-driving cars and sidewalk robots, that these things always take longer than you think they will. Practical barriers mean stuff in the physical world takes a lot longer than software stuff. It’s capital-intensive. There’s just a lot of stuff you have to do.

Let me give you a specific example. Six years ago, I went out to George Mason University and looked at Starship, which has these sidewalk delivery robots. The robot is simple — it’s a box on wheels, drives four miles an hour, and delivers lunch to people. I wrote an article saying it seems like we’re on the verge of these things being everywhere because they seem to work great and seem useful. Six years later, that company is far bigger than before, but they’re not everywhere

I couldn’t tell you exactly why they haven’t grown faster. I don’t think there were any major technological breakthroughs needed, at least on the hardware side. This was something that worked totally fine. I think it’s some combination of engineering work to make the robots more reliable and easy to repair and some amount of human labor needed to maintain them, and the margins aren’t that high, so it actually wasn’t that profitable a business.

And I think you could tell a similar story about self-driving cars, which is more of a software progress story. Waymo basically now has working self-driving technology, and it’s still going to take them years to just scale that up from the scale they’re at now to where most taxis are driverless. It takes time to build factories, it takes time to get regulatory approval, and so on.

To have a replacement for humans, you’re going to need humanoid robots, which I don’t think are anywhere close to what’s needed. Once we have a working prototype that has all the characteristics you’d expect from a human, it seems like a 10- to 20-year process to go from initial prototype to 100 million or a billion cost-effective, easy-to-repair robots all over the place.

Two challenges for humanoid robots

Ajeya: That’s helpful. One thing I wanted to flag is that you said we don’t have humanoid robots now. I don’t think that’s true. My understanding is that we’re producing thousands or tens of thousands of units of humanoid robots.

Timothy: Right.

Ajeya: What is true is they’re not very useful right now. One question that feels very important is: Why are humanoid robots not useful right now? Is it because they have bad bodies, or is it because they have bad brains?

I lean toward thinking that cognitive capabilities are the bigger bottleneck, as I think they were for self-driving cars too.

You said in your tweet that you wouldn’t be surprised if the cognitive capabilities necessary for self-sufficient AI were there within 10 years, but that they wouldn’t have enough physical bodies to operate. If we condition on that world — where the brains are good enough to operate the bodies that exist today — then AI systems may actually have many bodies to operate, at least if we’re currently manufacturing 10,000 per year and production is growing. It becomes a question of how many robots are needed.

It could also be that the bodies are insufficient — the thumbs don’t have enough dexterity, there isn’t enough haptic feedback. That’s a debate within the robotics community.

Timothy: Let me be more precise. We definitely do have robots with arms and legs that can walk around, can pick objects up, have eyes and ears and so forth. But I think there are two things we don’t currently have that we will need for self-sufficient AI.

One is that I do not think the humanoid robots shipping today are anywhere near as sophisticated as human beings in terms of their ability to do physical manipulation of objects. The human hand in particular is a miraculous thing. It has a ton of touch sensors, it’s able to do very precise movements, it has a lot of degrees of freedom.

My understanding is that things that are hard for people, like dancing and certain gross motor skills, are actually pretty easy for robots because they’re just physics. But interacting with the physical world — for example, picking something up without crushing it— is actually quite difficult because you have to apply exactly the right amount of force.

Clara: I assume both of you have read the Rodney Brooks post about this.

Ajeya: I have skimmed that post.

Timothy: I did read it a while ago. Is that the one that talks about how the way the robots balance means that if they screw up, they can fall over and hurt people?

Clara: That, but also just how complicated and sophisticated human touch is as a sense. His argument, I think, is that we don’t even really have the sensors that would give you training data for the kinds of signals that inform human manual dexterity right now.

But I’ve also seen people argue in response that “yes, but we have different kinds of sensors — like with self-driving cars, where a camera can’t do everything the human eye does, but we can supplement it with lidar and radar. Maybe for touch you can’t do what a human hand does, but you can supplement a pressure sensor with infrared and other things.” I think that’s the debate, at least as I understand it.

Timothy: Yeah, I may be just poorly summarizing Rodney Brooks because I think I did read that essay and some of these ideas probably come from that. I think that particularly in terms of balance and physical manipulation of objects, the current hardware is not on the same level as the human body.

But the second really huge thing is energy efficiency, reliability, and repair cost. This is why I think the Starship sidewalk robot example is instructive. There’s a big gap between “I have a prototype that maybe cost a million dollars, can go for an hour or two before it runs out of battery, and needs to be repaired after a few days of use” versus a human body, which, with the right inputs, can work for 10, 20, 30 years in a wide range of situations.

It definitely does not seem to me like we are close to the point of developing humanoid robots that have most human capabilities and the low cost, reliability, and durability of a human body.

The problem of scale

Ajeya: Would you agree that if we had bodies everyone agreed were as sophisticated as human bodies, and we could see production doubling every year — 1,000 this year, 2,000 next year, 4,000 the year after — then the right thing to do is extrapolate that trend to the number we’d need for self-sufficient AI?

In other words, we’re having this argument because we don’t agree on whether existing humanoids count, so we don’t agree that a trend has started. But if there were a clear trend, like we have for solar panels or batteries, should we extrapolate it?

Timothy: There are two problems. One is scaling up the manufacturing and getting the cost down. It’s true that once that has started, that’s a very predictable thing. You can calculate a learning curve and say “every time you double production you get this much reduction in cost.”

But, again, the other problem is repairability. If you build a robot that only works for a week before you have to send it back to the factory because a particular motor in the elbow grinds down and stops working, that’s not going to be economically viable.

And I think one of the things you see in the industry right now is that for most tasks it doesn’t make sense to use a humanoid robot because there’s already a simpler robot that is cheaper, easier to repair, and more durable that can do 90% of the task, and all you need is a human to take the box of parts and set it next to the robot so the robot doesn’t have to walk around. So for most tasks it’s likely that humans and robots will remain complements for a long time.

That timing is hard to predict. It’s similar to the Waymo problem where 10 years ago Waymo’s software was handling 99% of cases correctly and we were saying, “Well, we can extrapolate how long until it’s 99.99%.” But it turned out to be really hard to predict how long it would take to get to that level of accuracy.

Ajeya: Does this mean your timeline is much earlier for a weaker milestone — say, an AI population sustained by a thousand humans doing unskilled physical labor, with cameras on their heads and earpieces so the AI can direct them, rather than needing specialized semiconductor fab workers? Do you think that happens many years before full self-sufficiency?

Timothy: I don’t know if I’d say a thousand, but yeah, definitely. I think we’ll reach a point where if you had willing human bodies, you could have a society where some AI model ran the whole society, made all the high-level decisions, and you had a group of humans wearing cameras and earpieces that tell them exactly what to do.

I don’t think the humans in that scenario would put up with that — I hope they wouldn’t — but yes, that would be a theoretical possibility a decade or longer before we get to the point where robots can do everything humans can do in a cost-effective way.

I do think it would take a lot more than a thousand humans to sustain that kind of society. I think we’re talking tens of millions. In a world where only a thousand humanoid bodies were necessary, the AI could get by with humanoid robots that break down once a week and just constantly repair or replace them.

Ajeya: When I say thousands of humans, I mean millions of robots doing physical tasks that are much more automated than now, but still with stuff around the edges that humans have to do, like in almost fully automated factories today where someone picks things up and puts them down. You would need millions of bodies to sustain an AI civilization of reasonable size, but do you think it’s plausible that only thousands of those could be humans?

Timothy: No, I don’t. I’d have to think about what the exact number is, but I think there are a lot of tasks across the economy that require humans. Think about mining, or logging, or climbing up telephone poles to replace fiber optic cables, or moving through crawl spaces to connect fibers. There are a lot of tasks like that, and they’re distributed in such a way that you couldn’t have one area populated by all the people necessary to do them. For many tasks you can have the robot do 99% of the work, but you need a person there for the 1% it can’t do.

If a resource is needed from a mine in Chile, there’s got to be someone in Chile at that particular mine ready to do that job. If you tally those kinds of examples, more than thousands of humans are needed. I don’t know if it’s millions or tens or hundreds of millions, but a modern civilization has a lot of different products with a lot of different inputs, and when you think about the volume of the world and all the things humans are doing, it would add up to quite a lot of people.

Stranger paths

Timothy: It’s also possible that there are unique characteristics of human biology that mean you would actually need a radically different set of technologies to build robots that have some of the characteristics of humans.

Think about flying. We have airplanes that in some respects are much more sophisticated than birds, but if we wanted to build a literal bird with the energy efficiency and the ability to land on a branch — we’re nowhere close to knowing how to do that. It’s possible there’s a similar issue with the human body, that we don’t have any technological equivalent for certain kinds of things we can do, like working for hours or having your finger heal after it’s cut.

The human body has some cells that are self-repairing. I don’t think we know how to make anything like that via traditional manufacturing. The human hand might always have an inherent advantage performing certain tasks because it’s based on a different kind of technology stack than steel and plastic. I’m not sure about that.

Ajeya: We’ve been talking about achieving self-sufficient AI by scaling up robots, but it’s plausible that self-sufficient AI actually comes first by leapfrogging that stack — developing a more energy-efficient, biologically-inspired technology.

I don’t want to anchor too much on robots being the only path. I can imagine superintelligent systems in a data center directing humans and robots to do wet lab research, resulting in small, self-replicating, semi-biological —

Clara: Classic nanobots.

Ajeya: There’s a range. You could get microbots, you could get nanobots.

Timothy: Yeah, I don’t want to put forth a strong thesis that it’s never. I think probably if you had enough smart AI, they would figure something out. But I think it’s plausible it would require a different paradigm that would take decades to develop.

Tacit knowledge

Timothy: Can I mention one other factor I think is relevant? There’s a question about whether these hypothetical AIs would have all the knowledge they needed.

Imagine if all the employees in the entire semiconductor industry disappeared — the machines and textbooks remain, but none of the people. How long would it take for the rest of humanity to restart the fabs? It’s quite possible that would take decades. Because even though you might have the textbooks, there’s a lot of tacit knowledge inside these machines. They’re very complicated. There are thousands of people in Taiwan who handle very specific things — this specific machine needs these settings or else this problem occurs.

There are analogous situations throughout the economy. Now, maybe we’ll be in a situation where we’ve already automated a lot of the routine stuff and there’s some AI out there that has all the information the other AIs need. But I think there’s a question about how much information the AIs have.

Ajeya: I do want to flag that my 10-year 50% forecast comes from expecting parallel automation of the physical stack and the software stack — which people are very much trying now — and from expecting big increases in overall AI intelligence.

There are two counters to the tacit knowledge hypothetical.One is that we’d have trained AI systems with reinforcement learning on that tacit knowledge because it’s profitable to automate what the Taiwanese worker was doing. The other is that AIs might get really generally intelligent in the sense of quickly figuring out new things by trying them, reading textbooks, and experimenting efficiently.

Clara: Tim, you mentioned earlier that one of the things holding back the sidewalk robots was that the profit isn’t clear. The financial incentives of automating workers at a TSMC fab seem much stronger and clearer to me. If you could get these fabs to run autonomously, wouldn’t that be huge?

Timothy: I’m not sure. Are the people the bottleneck? Obviously you’d save the salaries of thousands of workers, so that’s certainly some money. Would the fabs run dramatically faster if you replaced the people? If not, why would it be hugely profitable?

Clara: If we’re imagining Ajeya’s scenario, we’re imagining vastly increasing demand for these chips. And also, every time there’s discussion of American reshoring, the issue that comes up is that we don’t have enough people and that the people we do have don’t have the right training. This seems like a real bottleneck if we want to massively expand chip production.

Ajeya: Yeah, it’s not mainly that a single fab would print chips faster, though I expect some effect like that too. It’s that AI companies are already straining TSMC, and TSMC isn’t ramping up production fast enough to meet the demand I expect in a year or two for AI chips. Companies have vertically integrated chip design to varying extents — Google designs its own Tensor Processing Units — but TSMC is still the bottleneck for actual fabrication. There’s going to be big economic pressure in the next few years to scale up fab production.

Timothy: I completely agree that if you can make more fabs, that would be profitable. I guess the question I have is: Is that about physical things people do, or is it that the Taiwanese experts have expertise that’s hard to write down? Maybe the difficulty of transferring tacit knowledge to America is actually the main bottleneck.

I don’t know enough about the semiconductor industry to have a strong view, but that would be why I’d be a little skeptical that they’d have big gains from automation. Clearly, if there are routine tasks people are doing that you could automate, that’s going to speed you up. But I would think most routine tasks inside fabs would already be automated — you would still need people because sometimes the machine needs to be fixed. To do that, you need somebody to work the night shift. And Taiwanese people are maybe more willing to work those night shifts than Americans. And you need somebody with a very specific set of knowledge that’s hard to get in America.

Ajeya: Stepping aside from onshoring, the question is whether within 10 years there will be AI systems plus robots that have either been trained on the long tail of physical and cognitive tasks involved in AI production, or are generally competent enough, cognitively and physically, that when something unexpected comes up they can say, “We’ll comb through the textbooks, do it badly at first, then figure it out by trial and error” such that a new task they haven’t done before doesn’t spell doom.

I think both will be much greater in 10 years, from improvements in general sample-efficient learning, and from having actually been trained on the high-value physical tasks bottlenecking chip production. The second is what Clara was pointing out — if AI could do these tasks, that would be quite profitable because it would let us ramp up production and onshore. So people will be pushing on that.

Timothy: I think that’s true.

Clara: I had a question for you, Ajeya. What do we mean by fully automating the stack? Because it’s easy to think of the tech stack as making chips, but then someone has to mine the ores, and figure out logistics and more. It seems very hard for me to separate out the chips and energy that directly support the AI from the rest of the economy.

How do you think about the full automation of all the many thousands of component parts that would have to run for this whole process to work? For the chips, I understand doing reinforcement learning on this hyperspecific process, but there are many upstream components where there’s maybe less incentive to do so.

Ajeya: My guess is this gets done through a heavy dose of general-purpose robotics. It’s similar to the intellectual side, where the easier tasks — arithmetic where we just wrote down the algorithm, chess, certain kinds of search — were automated in specialized ways. Then the harder tasks get automated more broadly, even if it’s not the full breadth of human skills. We automated coding, conversation, essay writing, and answering history questions all in a mush around the same time by training on a very broad distribution. And I think we’re going to broaden that distribution further.

I think physical tasks will follow a similar pattern. Some we automated in the 1700s or 1800s or 1950s. Self-driving cars are more general than factory robots from the previous century, but still pretty specialized — they see a wide range of things, but driving is the only thing they can do, and they often have special-purpose components like route planning. By the time you’re imagining AI doing the mining, the power construction, the maintenance and manufacturing of robots, as well as any new and surprising tasks that come up — you need general-purpose robotics.

The reason I think that’s plausible goes back to where humanoid robots are bottlenecked. Again, I think it’s more likely to be cognitive capacities than physical capabilities. So it’s a big deal that people are starting to do imitation learning and RL in physical settings.

Prediction mode

Clara: Let’s switch to forecasting mode. I want to know, for both of you: What would you observe in the next two to three years that would make it seem like the 10-year forecast is on track or not on track?

Ajeya: It’s tough because there are two very different branches to self-sufficient AI. We’ve been talking about the humanoid robot branch. There’s also the superintelligence-to-nanobots branch. I think it’s hard to bound the probability of that second branch to a very low number, and Tim and I disagree on this too — we disagree on how quickly systems far more intelligent than top humans could discover radically new technology.

If we focus on the humanoid robot path and ignore the other one, I’d want to learn more about the haptics and physical limitations. If our robots don’t have good hands, why not? Could we build a great hand for $100 million? Is it that we don’t know how, or just that it’s not profitable and there’s some experience curve to ride? I’d want a line on a graph showing improvement of robotic hands, and another line showing the rate at which we’re manufacturing humanoid robots.

The cognitive side is similar to other AI forecasting — there are benchmarks to watch. Google DeepMind last year had an advance in general-purpose robotics, with videos of robots handling slight perturbations in their environment. I’d expect that technology to meaningfully improve in the next couple years. If it’s totally stagnant, that would push my timelines out for the robot path.

Timothy: I think we mostly agree about that. I’m going to want to watch how the humanoid robots develop: the number of robots, their capabilities, and particularly their cost and repairability. If I see those advancing more quickly than I expected, that’s going to shift me towards thinking this is possible earlier.

There’s some interplay between these. If you imagine a future where there are a lot of very reliable, very cheap humanoid robots with crappy hands that don’t work as well as human hands — I think you could still accomplish most tasks. Maybe you have to redesign all the machines in the world to have bigger knobs or something, but you could get a lot of value out of those cheap, reliable robots. So there’s some substitutability between the quality of the robots, the number of robots, and their cost. If we see a rapid takeoff in the number of humanoid robots, even if they’re not amazing, that’ll be a sign that I’m wrong and this is going to be possible relatively soon.

Ajeya: There’s also the actual integration of automation into key parts of the stack. There’s a research project someone could do: How physically automated is TSMC right now? How automated are the power production steps? Map out the whole stack — what do humans do and what do robots do? If my timelines are right, we should see more tasks subsumed by robotic processes over the next few years, whether specialized or general-purpose.

Timothy: I actually think this might be a place where we disagree. I think humanoid robots specifically are a canary in the coal mine. I expect robots to get more sophisticated, but I think it’s going to be cheaper and more efficient to have specialized robots — for example, robots that are bolted to a factory floor, or a self-driving car — paired with a human that carries the parts over to the robot, or repairs the robot when it breaks, or unloads the self-driving truck.

So the path is towards automating 95% to 99% of the human’s job, but hiring the human to do the 1%, and then scaling up production with the whole system. If we see rapid progress in robotics but very few humanoid robots being produced, that will seem like a sign that humans are still going to have an essential role that would be hard for AIs to replace. Whereas if we have a future where, increasingly, there are just humanoid robots that you can drop in for a larger and larger share of the workforce, then I could see that going up to 100% after a decade or so.

Ajeya: I see your point on humanoids being special, but if we don’t see automation eating more of the stack one way or another, I’ll feel like things are generally going slower. What I’m craving is a detailed mapping of the AI production stack — how many bodies, machines, power plants are currently necessary to sustain an AI population of millions? How many humans could drop dead today without affecting the AIs? I’d want to track that number over time.

For threat modeling, it really matters whether you need a million humans, 100,000, 10,000, or 1,000. Once you’re down to thousands or hundreds, a small number of coerced people or willing defectors becomes very salient for AI takeover scenarios. I don’t know what the number is today — my guess is tens of millions of humans are needed right now to sustain the current AI population.

Timothy: Yeah.

Ajeya: I’d want to map that out and track how it shrinks. A lot of the shrinking might happen through specialized robotics — though you might be right that you’d need fully humanoid robots or something more exotic to cross the last mile.

Ajeya Cotra is a researcher at METR working on assessing the risk of loss-of-control from advanced AI. Previously, she spent a decade at Coefficient Giving as a researcher and program officer.

Timothy B. Lee is a journalist who writes the newsletter Understanding AI. Previously he was on staff at the Washington Post, Vox.com, and Ars Technica. He has a master’s degree in computer science from Princeton.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

In this behind-the-scenes interview, Asterisk editor in chief Clara Collier talks to Elizabeth Van Nostrand, author of our latest essay, We’re all one crisis away from taking unlicensed research peptides. (Read that first!)

Clara and Elizabeth chat about vaccine skeptics, chronic illness communities, libertarian FDA paradise, luck-based medicine, and more.

This interview has been edited for length and clarity.

Clara Collier: Elizabeth, I am very excited to talk about your article about health hackers. This is a community that you’ve been aware of or involved with, in some capacity, for a long time. Can you say more about your background with this group of people?

Elizabeth Van Nostrand: I’ve had a bunch of chronic health problems and have been involved in the chronic illness community and the “try to fix it yourself because doctors have failed” circles for a while. And I’m in the rationalist space, where there’s a pretty can-do attitude, like, “hey guys, this vitamin helped me, maybe it will help you.”

Clara: Right. And I think we both noticed in the process of developing this piece that there is a lot of coverage of peptides, but peptides are really the tip of the iceberg. They’re the new mechanism, but it didn’t really feel new to me. Rationalists trying peptides felt exactly like rationalists trying vitamins. I remember we even tried to report on people making their own vaccines during Covid.

Elizabeth: Yes, I did get to talk to RaDVaC and I’m sad I didn’t get to include them.

They were working on yeast as a delivery mechanism for vaccines. We use yeast in pharma now — we genetically program it to produce the molecule or peptide you want, and they can produce it in their little factories. And while selling vaccines is, you know, very illegal and needs FDA approval, there’s no law against selling yeast, no matter what they produce. So their goal was: Make yeast a vaccine factory for producing vaccines or other therapeutics. Sell the yeast. And then you can make beer or bread out of it and get your vaccine that way.

But one of the reasons I didn’t include them in the piece was that they just had the same attitude of everyone else that, “well, I didn’t want to die of Covid and no one was fixing the Covid problem for me fast enough. So I made my own vaccine.” Though once there was a real Covid vaccine I think the tests revealed their vaccines weren’t doing a ton.

Clara: The vaccines actually speak to another angle on this piece that we talked about and didn’t eventually fit. I thought there was going to be a lot more overlap between the health hackers and the more vaccine-skeptical, MAHA community. And that ended up not exactly being the case.

Elizabeth: I think there is overlap. Specifically, I want to give credit to the best of the vaccine-skeptic population who, for example, maybe want to alter the schedule of what vaccines your kid gets based on local conditions and how you know your kid responds. These are people working in a very specific situation, who know the facts of their life best, whereas the FDA is trying to manage for the masses.

The worst part of the vaccine-skeptical community have the attitude that your body knows what’s best and natural is best and I don’t see the same recognition of trade-offs.

The borders of community

Clara: To what extent would you say that there is a health hacker community that encompasses the different groups that you talk to?

Elizabeth: I would say mostly they are talking to other people similar to themselves. Diabetics talk to diabetics. The place where you get the most cross-pollination, I would guess, is in the diseases of the gaps like fibromyalgia and chronic fatigue, where there are all these descriptions of symptoms and there’s a lot of overlap. And you see it a little bit in the rationalist sphere where you are touching on a bunch of different groups. But I didn’t really see a unified health hacker community.

Clara: Is there anything else that you noticed about the social structure or makeup of these groups that didn’t make it into the piece?

Elizabeth: This didn’t make it into the piece because I don’t think I’m the right person to write it.

I talked to several trans women, all of whom said the non-doctor medical sources were incredibly important to them. And they view trans community wisdom as maybe 10 years ahead of doctors. But one of the women I talked to — after she had done some steps of transition, she wanted to very deliberately take a step back from the trans community because she thought it was too affirming, basically. And that affirming created a bias toward action that she was uncomfortable with.

In particular, there was a procedure she was torn on and made the decision at a cognitive level that this wasn’t what she wanted. And therefore she wanted to opt out of spaces where people were really pushing for whatever she reached for. She wanted people who would push back on her a little more. It was very much, “oh, you want it, do it, we would never question what you want.”

Clara: The phrase “competing access needs” gets thrown around a lot in this space, but this just feels like a very pure example.

Elizabeth: The woman I talked to very much understood why the taboo against questioning people’s decisions or wants was in place. I didn’t even get the sense that there were definitely changes she would have made to the culture. I don’t think that would have been one of them. It just was no longer good for her.

There’s a related problem in chronic illness communities that has been talked about a lot online where they can become really self-reinforcing of a sick person’s identity. Like, “you are your illness and it’s never going to get better and you should luxuriate in that.”

Clara: You see this complaint come up in neurodivergent spaces, too. And of course, it’s different in the trans case because it’s not an illness. You can just be trans. It’s fine. It’s not reaffirming a problem.

But, also, you can see how this problem would generalize to all kinds of personalized medicine spaces. You’re in this space because something about going to a normal doctor has not worked for you, and because you have some kind of weird, specific problem that demands some kind of weird bespoke solution. It is intrinsically harder to come up with a social script that works for everybody, because everybody’s situation is necessarily different.

Elizabeth: So, there was a problem in one chronic illness group I’m in where someone came in and, from my perspective, started saying, “this is a mindset problem for everyone. The only thing I or anyone else needs to do is decide to stop being sick.”

On further questioning, the person wouldn’t even endorse that, but I definitely felt like that was what they were saying and I got super triggered because I don’t feel like that’s what’s happening in my case or a lot of other cases. It feels really belittling. People can really hurt themselves trying to willpower their way into being more functional. And I was so mad at this person until they mentioned that they had been in other chronic illness groups that had just been really reinforcing the illnesses and telling people they would never get better. And as a reaction to those kinds of groups, I see their reaction makes a lot more sense. I calmed down a lot.

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The perfect solution?

Clara: The health hacker mode of thinking that you described seems like a happy medium. This is not a community that I have a lot of experience with personally, but I like the mode of focusing on incremental experimentation. Obviously, this also has costs, but it seems good to internalize that there are steps that you can take to change your condition and the way that you should think about those steps is as incremental changes that have tangible effects.

Elizabeth: I mean, obviously I love that and I think it’s the perfect solution.

Clara: Well, okay, now I want to push you. What are the downsides?

Elizabeth: This is a skill issue and it is really easy to hurt yourself. You originally came to me with the question of: What distinguishes people who can do this without hurting themselves from people who can’t?

The crispest difference I found was: Are you acknowledging trade offs or not?

But I’m sure that the very anti-vaccine people would say they thought about the trade-offs. I think they’d say that some things are straight-up bad for you. And sure, that can be true. I personally don’t think lead has trade-offs. That is just bad for you.

How can you tell if you’re on the right side?

Clara: Another thing that has informed a lot of my background thinking — and I’m sure yours as well — on this subject is that I spent a lot of time thinking that, “well, I’m in the rationalist community. And in some ways rats got COVID right. We were locking down like a month before everybody else.”

That prompted me to think: What’s the difference between my friends who were reading a ton of papers and posting incredibly long Google Docs and making micro-COVID spreadsheets, and the people who were writing equally long Google Docs and making equally complicated spreadsheets about why you should use ivermectin?

And I realized that, truthfully, in my heart, I have not gone through each of these equally long Google Docs in equal amounts of detail and evaluated all of the studies individually. I am not qualified to do that. I would do a bad job of it. I am just making a decision based on people I trust to be informed and broadly right endorsing this or that narrative. And that’s what everyone is doing.

Elizabeth: Yeah, I only looked at vaccine skeptics pretty briefly once I figured out there were some fundamental differences. But I’m not sure there’s a principled difference between the best parts of both groups.

Clara: How do you orient towards that?

Elizabeth: I don’t have a short answer to that. I feel like some of it is maybe covered from a libertarian “live and let live” perspective around the dignity of risk and that people are allowed to hurt themselves, and that people hurting themselves is an acceptable cost of having the freedom to do other things.

Clara: I think that’s a fair answer on a societal level, but it doesn’t really answer my question, which is: How should I decide which chemical substances to put in my personal body?

Elizabeth: Well, I used to do more medical lit reviews and now I just feel like even the official medical stuff is often not very good. There’s more p-hacking, there’s more file drawer effects. There’s always been bad statistics, bad design of experiments. I often see experiments that can’t answer the question they are trying to answer. And so I could do an amazing reading of the literature and I still wouldn’t be that informed.

The only solution is to try things out and be actually monitoring yourself and have a good feedback loop. Luck-based medicine is my solution to everything.

Clara: As someone who’s been involved in chronic illness spaces for a long time, does it seem, from the inside, like there is a rising crisis of trust in medical professionals?

Elizabeth: The people in those groups are selected for trust against doctors so I wouldn’t take them as data on that. But there’s other things I see mostly on Twitter that — man, we just burned so much trust in public health during Covid. The mask flip-flopping was insane.

Clara: How would people relate to public health in your ideal world?

Elizabeth: I would like everyone to be able to take public health as a source of useful information about the general public that they then apply to making personal decisions. For example, public health has taken an extremely strong “one drink and you will give your kid fetal alcohol syndrome” policy. My understanding is it’s not true. They are saying that to scare people who are heavy drinkers into cutting their alcohol consumption.

Again, there are trade-offs. That said, don’t let your kid eat lead paint. That’s still super bad. I spend all my time thinking at these complicated margins, but there are actually a number of solved problems I would really like to give medicine credit for.

Clara: Incidentally, I happen to have been reading about the history of lead paint last night. People knew it was toxic in the 1880s.

Elizabeth: Yeah. I think they knew about the arsenic wallpaper at the time too. They just kept doing it.

And, man, I just can’t give up aluminum deodorant. I have never looked into how bad it is. It could easily be the case that it’s going to give me Alzheimer’s. But do I want to give up deodorant? Deodorant that works? No.

Clara: I feel like an underrated triumph of the American medical establishment, by the way, is that at least when you’re using aluminum deodorant, you know that the thing is aluminum and that it is in your deodorant.

I had this whole tangent last year where I was really interested in the history of the fight against quack medicine and how it was won. And I ended up abandoning the piece I was going to write because it turns out the fight wasn’t really won. There’s no satisfying conclusion there. But so much of the early legal battles are just about ingredient labels.

Elizabeth: Yeah. And that’s a real triumph. Even in my libertarian paradise, the FDA assesses and notes drug purity.

Clara: Mhm. And I’m probably much more pro-FDA than you are. But yeah, the ability to self-experiment relies on knowing what you have.

Elizabeth: And there’s been all those accusations that things you buy on Amazon, you don’t actually know if you’re getting it from the correct manufacturer. There are accusations that specific manufacturers aren’t putting as much of the active ingredient in as they claim. And that makes luck-based medicine much harder.

Clara: Which maybe takes us back full circle to the Chinese peptides.

Elizabeth: Yes. I’m going to take the consider that there is a third-party verification system that is trusted and supported — as more or less a public good — to be a libertarian triumph that we should all be proud of. Even though I will say it would take too much cognition to do that for everything you buy.

Clara: Explain the third-party verification system for peptides. I don’t know much about this.

Elizabeth: When you buy peptides, you are getting probably 10 vials from the same batch. You can send one vial to a lab that will test both what it contains and what it claims to contain, and also check for impurities. Once they have that data, they can also share it with other people with the information that “this is the manufacturer, this is the date it was acquired, this is its purity rating.”

One of the more popular providers has a problem where it gets fine purity ratings, but its dosages are a little inconsistent. Getting the exact correct dosage in a vial is actually a pretty difficult problem. So you are getting the exact compound you expect, but you might not be getting the exact amount you expect.

Clara: What is this lab? Is it just a normal medical lab? How did it come to serve this purpose?

Elizabeth: There’s just lots of labs that will test lots of substances. That’s a well-known thing many labs do.

Clara: For our readers: this is not medical advice.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

“Unfounded and reckless” is how Dr. Eric Topol described the use of gray market peptides in a recent New York Times article. In some cases, I have to agree. (“You’re taking a drug without knowing what it is?”) But the implicit message in his statement, and in numerous recent articles critical of self-treatment with GLP-1s, is that deferring to the medical establishment is safe by definition, and everyone who steps outside the protections of the system does so in disregard of the danger. I would love for the former to be true, but reality has disappointed me.

For my whole life, I’ve had a set of nebulous symptoms no one quite knew what to do with. Eating protein or fiber left me feeling like something died inside me. I had the immune system of a toddler in day care and the energy levels of an attractive woman in her second-to-last scene in a Victorian novel. When I complained, mainstream doctors would test me for three things and throw their hands up if all the tests came back negative. Some people are just meant to spend 11 hours a day in bed, I guess.

So I tried alternative practitioners — people who use the words “integrative,” “functional,” or “naturopathic” on their websites. These people always advertise themselves as seeking and curing root causes rather than playing whack-a-mole with symptoms like mainstream doctors. Sometimes, this worked just like I wanted — a nutrition-focused psychiatrist paid attention when I said that drinking soda made protein and fiber more tolerable and proposed I had low stomach acid, a condition that is easy to test for and treat.1 For a while I hoped this was my magic cure, and it did help a great deal, but I still didn’t have the energy or immune system of a normal person.

I tried multiple alternative practitioners to fix the remaining problems, but it was all cycles of hope and disillusionment. I spent a year on leave from work for five dental surgeries to clear out infections in all four wisdom tooth-removal sites (the last infection was large enough that it needed two procedures). I took some devastating antibiotics that gave me seasonal affective disorder, but not much else (based on information from a friend, I tried inositol, which fixed it). Multiple providers told me that if I wasn’t going to eat one gram of protein per pound of bodyweight (which was impossible even with the stomach acid supplements), I was wasting their time.

Then, in April 2022, I fortuitously caught COVID-19. The actual sickness was quite bad, laying me up for five weeks. Even after my tests came back negative, I was exhausted and in constant pain. My doctor at the time was very good, which other people had noticed, so she was also very busy. If I wanted to see someone quickly, I had to see one of her underlings, whom we will call Dr. Lucky Idiot.

Dr. Lucky Idiot free-associated his way to prescribing me five different herbal interventions. A younger me might have demanded he explain what they did, or at least investigated them herself. But I was exhausted and already getting suspicious that research wasn’t all it was cracked up to be. I took every pill.

A few weeks later, I felt better. Actually, certain things were a lot better. Going into COVID-19, I’d been working very hard on protein consumption. I could get up to 50 grams a day if I made it my hobby and tolerated moderate discomfort. A few weeks into the herbs, I looked down one day at an empty tub of cottage cheese and realized I’d eaten 50 grams of protein in a sitting and felt fine. Good, even.

This was exciting in its own right — protein is crucial for approximately everything in the body — but I was also excited to learn what this said about my underlying issue. Surely this would be the clue that let us unravel the mystery of What Was Wrong With Me, which would suggest more treatments and possibly even a cure.

Dr. Lucky Idiot was less excited. When I asked why the particular herb (it turned out to be Boswellia, aka frankincense) was so transformative, he mumbled something about inflammation and considered the question resolved. As if I hadn’t taken 40 other herbs marketing themselves as anti-inflammatory.

That success was the exact moment I gave up on understanding my problems as the way to solve them. Careful cultivation had already solved all the problems it was going to solve.2 What was left was trying shit until something worked. I still use doctors as sources of ideas, but at the end of the day, I’m the one running the experiments and judging the results.

So when I see people buying exotic peptides from uncredentialed sources, I don’t see daredevils or lunatics abandoning a system that works. I see people with problems the system isn’t solving refusing to give up on themselves. And when people condemn this, I hear, “You don’t deserve a cure.”

The counteroffensive

I would take the invalidation of my problems with more grace if I believed they were truly rare. But when I look around, I see millions of people in the same boat. And not just the mysteriously chronically ill — there are people with crisp diagnoses who still find their doctors useless or have great doctors but need to move faster than medical regulations will allow. These people are also driven to hack their own health.

“Health hackers” are the asthmatic cousins to the much better-known biohackers. Where biohackers put magnets in their fingers or padding in their shins for better Muay Thai showings, health hackers are trying to get themselves back to baseline, or the baseline they wish they had — to ensure they’ll live through the night or not hate the body they see in the mirror.

This isn’t a crisp distinction. The classic biohackers I talked to often also did some health hacking, and r/nootropics might say it’s about superhuman performance, but reading the comments shows that at least half of them are dealing with serious mental health or neurodivergence issues. The headlines treat gray market GLP-1 usage as unprecedented, but when I compared users to health hackers with problems ranging from diabetes to gender dysphoria, their attitudes were almost identical. Everyone I talked to treated their interventions as experiments, acknowledged the risks inherent in experimentation, but felt the greater risk (as measured by their gut) was in not trying and would never have started hacking if the formal medical system had met their needs.

Diabetics

“The risk was in not doing it.”

Dana Lewis was blessed with a problem doctors recognized and knew how to treat, more or less. Her body didn’t produce enough insulin. The treatment for this condition is straightforward — more insulin — but it requires exquisitely precise dosing. If a diabetic injects too much insulin, their blood sugar will crash, which, left unchecked, can be fatal. Administering too little risks high blood sugar, which slowly damages organs.3

Today, diabetics can monitor their blood sugar and choose their insulin dose with wearable continuous glucose monitors, which alert them when it falls or spikes to dangerous levels. But back in 2014, when Dana’s story started, continuous glucose monitors (CGMs) were new to the consumer market and not yet considered reliable. Their function was to trigger a blood stick check or serve as a last-minute alert to catastrophically low blood sugar.

But these monitors aren’t perfectly reliable, and they were even less so in 2014. The sensor in Dana’s could be thrown into a panic just by lying on the arm it was attached to, so false alarms were common. After numerous false alarms, she learned to sleep through the alerts. She complained to the manufacturer, who told her, “It works for most people” — as if that solved her problem.

Dana’s biggest fear was having a hypoglycemic episode in her sleep and dying before she had a chance to do something about it. She did not find the fact that the app woke up other people in time very comforting. So she and her new boyfriend (now husband) Scott Leibrand set to work on a solution.

First, they needed real-time access to the data from the CGM, the only way to identify problems as they happened. Luckily John Costik, a software engineer and parent of a diabetic preschooler, had already created Nightscout, a program to extract data from CGMs and upload it to cloud storage. Scott, also a software engineer, wrote a program to monitor these uploads, and if they reported low blood sugar levels for too long, call his phone so he could wake up and drive to Dana’s house to revive her.

This solved the immediate problem of Dana sleeping through her own death but still left her with the problem of managing diabetes during the day. This process was time-consuming at best, and many people didn’t manage it well, spending long periods of time with their blood sugar damagingly high or dangerously low.

In addition to continuous glucose monitors, state-of-the-art diabetic treatment included an insulin pump. These still required users to calculate their dose based on a mix of scientific literature and their personal history, and manually program the device, but allowed for more complicated dosing schedules over longer periods of time, instead of a single large bolus from a needle.

So Dana, Scott, and other collaborators set out on a bigger project to “close the loop.” They wrote an algorithm that pulled the data from the CGM, applied the same math patients did by hand, and fed it to a hacked insulin pump.4 This created much more responsive, sophisticated dosing that gave them more time in the ideal blood sugar range and less risk of catastrophic failure while requiring less attention.

One reason Dana felt safe writing her own pancreas was that she could do so in very small steps. OpenAPS programmers erred on the side of undersupplying insulin, but the user could get more aggressive over time. OpenAPS also respected the built-in limits of the insulin pump, which meant the system would have to work very hard to do more harm than a human could do from inattention.

When I asked how she thought about the risk of writing her own pancreas, Dana said that all of her risk was in accepting the status quo. She knew a pharma company would release their own closed-loop system eventually — the idea was too obvious and too valuable not to — but she was unwilling to risk dying in the meantime.

Trans women

“I find the idea of a doctor recommending that a trans patient do anything at all to be laughable.”

I talked to three different trans women. They all asked for anonymity, but as it happens their stories were so similar I feel perfectly comfortable combining them into a single composite story, whose protagonist I will call Emily.

Emily started out confused and uncertain. When she first shared obvious symptoms of gender dysphoria with her therapist, the therapist said, “I can’t help you.” She does not believe there was any set of symptoms she could have taken to a doctor at the time that would have led them to suggest hormone replacement therapy. So she did her own research, mostly talking to other trans women. By 2010, she knew she wanted HRT, but the standard of care at the time — called the medical model — required she spend months proving her emotional stability to multiple therapists before she was allowed to try to convince an endocrinologist to take her case.

Instead, she went to a clinic practicing the informed consent model, which is based on the assumption that estrogen is not that fun and nobody seeks it out recreationally. If someone asks for it, a doctor’s role is to inform them of the risks and let them make their own decision.5 In Emily’s estimation, they did the latter half (and that just barely). She found them absolutely useless as a source of information.

For example, Emily was offered spironolactone, which suppresses testosterone production. This was common at the time and may still be. But by 2010 the trans community lore was that spiro was outdated and not worth the risk of side effects, which include neurological issues.6 Emily turned it down.

Then there’s the matter of estrogen. Naturally produced estrogen comes in three forms: E1, E2, and E3. E2 is the only form used in gender-affirming HRT. But E2, aka estradiol, comes in many forms itself. It can be injected, swallowed, or absorbed through the skin via a patch.

Patches were out because Emily was allergic, and oral didn’t allow a high enough dose, so injectable it was. But there are also different chemical formulations to choose from, like estradiol cypionate or estradiol valerate. Each method and molecule has different pharmacokinetics, and the same method and molecule might have different pharmacokinetics across different people. Going on HRT always initially involves frequent blood tests of estradiol levels to make sure the extremes aren’t too high, which can cause heart issues, or too low, which risks osteoporosis.

Everyone knew that estradiol cypionate had the longest half-life, which lowers the gap between the highs and lows while allowing longer intervals between injections. But exactly how big the advantage is varies by person. To know which group she was in, Emily asked her doctor to prescribe cypionate and give her two weeks of daily lab orders to check her estradiol levels. Her doctor complied, and Emily learned that while cypionate did last longer, it wasn’t enough to justify the increase in price for her. The experiment let her choose the cheaper estradiol valerate.

Unfortunately, all estradiols left Emily with brutal abdominal cramping and blood in her semen. Based on talking to other trans women and confirmed by her reading, she thought progesterone might help. Her cooperative doctor wrote the prescription, and she found it enormously helpful for the pain. Unfortunately, it also weakened her joints to the point that she repeatedly injured herself, a trade-off she ultimately decided was not worth it. Instead, she takes periodic breaks from estrogen.

Emily felt safe experimenting with hormones because the negative effects were either front-loaded or easy to detect with testing. That may be why the HRT stories of the three women I talked to were so similar that I had to constantly check my notes to make sure I wasn’t confusing them: When running tests is cheap and knowledge is local, community metis goes into designing an experimental regimen.

When I asked Emily how she thought about the risks of hormone treatment, she said she put a lot of work into managing them, but it was ultimately worth it for moving toward the body she wanted. When it came to the risks of any particular experiment (a new dose or a new formulation) she felt confident that downsides would be easy to detect and mostly reversible. Despite having fairly different reactions to different estrogens, which ultimately led to different regimens (an alternate universe version of Emily found estradiol cypionate well worth fighting the insurance company for), I got the sense they all started in roughly the same place, and if their experimental results were swapped, their decisions would follow.

But when it comes to irreversible surgery, the composite falls apart. The three women I talked to, with access to roughly the same information, chose completely different paths. One decided against bottom surgery, one accepted it with some reservations, and one would accept it only from the superior surgeons in Thailand. Clearly they weren’t sharing a script, after all, but were weighing highly personal trade-offs as best they could.

Gray market GLP-1 users

After my miracle cure, things looked good for about 18 months. Thanks to the Boswellia and, no kidding, copious amounts of watermelon and potatoes, I lost 30 pounds and had more energy than I had in years. Everything was going great until the start of what I call my mold winter. The mold wrecked me metabolically before aggravating an underlying subclinical infection, which required prolonged antibiotics. I regained all the weight I’d lost and then some.

After I cleared the mold, my old trick of Boswellia + potatoes + fruit worked … until I got pneumonia, necessitating another round of microbiome-ruining antibiotics.7 As soon as I could eat, my weight started creeping up again. I was facing this body-ruining cycle a third time, knowing that health risks were piling up and I was unable to do anything about them.

So I asked my doctor for Tirzepatide, a more advanced cousin to Ozempic/Wegovy/semaglutide. She was happy to comply, but insurance rejected my request. I had a decision to make: accepting the loss of all of my weight progress and starting over again, paying $1500 per month,8 or doing what my friends did and going gray market for an experimental GLP-1 known as retatrutide.

What I’ll say about that decision is that I wasn’t willing to go back to the starting line a third time. I did what I needed to do for my health and my sense of control over my own body, and I was deeply grateful to have the option.

The people I talked to who definitely solved their weight problem on the gray market had similar stories. Excess weight has a lot of consequences, and not just for health. Susan (a pseudonym) noticed people were nicer and more helpful to her after her GLP-1 aided weight loss. Ben was happy to see himself in the mirror for the first time — but this paled next to the potential of a different peptide to speed up his recovery after multiple spinal surgeries.

When I asked how they thought about risk, the universal response was: “I couldn’t afford the risk of not taking it.” The potential upside to weight loss was too big. Like trans women taking HRT, they felt comfortable experimenting because the most likely failure modes were obvious and recoverable, and success was easy to measure. They were, by and large, unconcerned with risks that were very small or very far in the future.

The blogger Cremieux, who wrote what is by far the most commonly referenced blog post on how to obtain gray market peptides, claimed that “people generally don’t try to quantify tail risks.” Among the people I talked to, this is true. But it was not the case that they were highly risk-seeking or unconcerned with their safety. Even the man who described himself as “unusually risk-tolerant” told me he’d never, ever ride a motorcycle.

Locus of control

The health hackers I talked to all have certain traits in common. They’re poorly served by the medical system. They experiment, and they’re aware their experiments carry risks. They make decisions by gut rather than math, possibly because they stick to experiments where the math is easy. Most of all, they feel a sense of responsibility for their own health outcomes. They might go to doctors for advice, but the final decision rests with them.

Academics describe people who put themselves in the driver’s seat of life as having an internal locus of control.” Psychologists might say someone has an internal locus of control if they tend to believe that their life outcomes are primarily the result of their own actions or decisions (and an external locus of control if they don’t).

There is a large body of research that applies this concept to health outcomes. Are patients with an internal health locus of control — that is, people who think their health outcomes depend more on their own choices than on doctors’ orders or genetic luck — healthier than those without it?

I ran some AI lit reviews, and the results were better than an author could wish for: Internal HLOC is indeed correlated with better results. And when one turns to risk-taking, people with an internal financial locus of control showed the same complex relationship with risk I saw in my interview subjects: They engaged in more overtly risky-looking behavior, but were also more likely to make risk-mitigating moves like buying insurance. It’s a perfect fit for what I found from a few dozen interviews, most of which were with people from my extended social network.

Unfortunately, I don’t put much stock in this literature. I always knew it wouldn’t apply perfectly: The questionnaires measuring HLOC are aimed at beliefs like “taking my pills helps,” not “I can outperform a doctor at choosing my pills.” But when I dug into the details, it was worse than that: While high internal HLOC is correlated with more doctor-endorsed behavior and with better feelings around health, there was no predictive correlation with objective outcomes like hospital visits or cholesterol levels. So what we’ve learned is that not being depressed is nice, and perhaps that people with lots of health problems have trouble believing in themselves.9

Which, in some ways, is the perfect metaphor for my relationship with the medical system: an extensive literature which promises answers but ultimately leaves me with my own inadequate attempts to find them for myself.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine by June 30th to receive our next issue, Work.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine to get future issues delivered to your door.

Everything has a price. It’s a warning you might receive from a hedge fund trader, along with your “welcome to the firm” fleece vest. Or you might hear it from a skeleton, with flaming eyes, holding you by the cloak and ready to drop you into a pit. For “lawpunk” author Max Gladstone, the parallels between finance and fantasy sparked his Craft Sequence of novels (nine books and counting). While Tolkien and Lewis created their otherworlds under the shadow of their service in World War I, Gladstone’s books are his attempt to make sense of the global financial crisis.

Gladstone returned home from a stint teaching in China in 2008, just as the bottom fell out at Bear Stearns and Lehman Brothers. Part of what captured his attention was that so much value could vanish without leaving a physical mark on the world. “There was no smoking crater that was AIG,” he told Locus in 2015. So his books gave physical stakes to the abstractions of financialization.

His first Craft Sequence novel, Three Parts Dead, could sound like an ordinary fantasy: A god has been murdered. A necromancer comes to town to find the culprit and reassemble the god as best she can. But the plot has a lenticular quality — turn it the other way, and it’s the story of a corporate bankruptcy. Gladstone drew on his mother-in-law’s experience as a bankruptcy lawyer, translating it into epic. Something huge and ineffable, which once animated the city, can’t go on as it was. Something will have to be reassembled from the pieces by hard-minded men and women who work in a somewhat disreputable profession. A god — or a bank — is too big to be allowed to fail. Everything has a price.

Fantasy is a genre where readers expect to encounter hyperobjects, philosopher Timothy Morton’s term for objects that are “massively distributed in time and space relative to humans.” Hyperobjects exist out of human scale and confound human patterns of thought. Morton found his way into this language through his concern for environmental philosophy and the specter of climate change. The idea of hyperobjects helped him talk about why people flinch away from thinking about climate change — it’s too big to hold in your mind, it feels too big to address. In contrast, finance can feel like a too-big idea that someone else does have mastery of, and they’re wielding that ineffable power against the rest of us. But those working in finance are more prone to the language of mystery and supplication of strange gods than you might expect from the outside.

Hyperobjects provoke the painful awe of the sublime — they remind us of our littleness and “make this fragility conspicuous.” In response, Morton thinks, human beings need to be able to sit with the terror of our smallness. Morton proposes an “iterative, circling style of thought” in order to reason well about phenomena that dwarf us. He reaches for object-oriented philosophy to build up the habits we require, but this kind of problem isn’t as alien to the reader who haunts the genre fiction shelves.

You must not pick up the palantir to set your strength against Sauron. You must not call up what you cannot put down. There is a worm at the heart of the tower, there are armed hunters in the dark forest, there is always the option to go mad from the revelation. Thus, Gladstone found, “I could talk about this stuff with magic: In fact, it was easiest to talk about it with magic.” Within a novel, the “great vampire squid wrapped around the face of humanity, relentlessly jamming its blood funnel into anything that smells like money” doesn’t have to be a metaphor at all.

A story that can only be told slant, if you want to tell the truth. A powerful, inhuman force you can harness, but mustn’t look directly at and whose name must be kept secret. What sounds like the novelist’s specialty isn’t so far from how the finance bros might describe their own work, once they’re several shots or bumps into the night. Four times a week, Bloomberg’s Matt Levine tells the stories of the latest (mis)adventures in finance in terms that don’t sound too different from those of Gladstone’s novels.

In 2021, Levine coined the “Elon Markets Hypothesis,” a principle of what he called “postmodern finance.” As he put it: “The way finance works now is that things are valuable not based on their cash flows but on their proximity to Elon Musk.” A meme like Doge (the cryptocoin, not the liquidation of AIDS infrastructure in Africa) didn’t trade based on anticipation of future cash flows (none were anticipated). It rose or fell the way a plant follows the sun, depending on Musk’s fleeting attention.

If Musk’s attention is valuable, there’s a trade to be made. But you won’t learn these black arts from your business school professor. Per Levine:

How can you obtain Musk proximity? I don’t really know — nothing in this column is ever Musk proximity advice — but, look, in these circumstances, it would be understandable if you resorted to techniques of ancient magic? Like if you were to sculpt a giant metal idol of Musk, you might go ahead and assume that the idol would have some valuable magical effect on your crypto project? And if you were to offer up that idol as an gift to Musk himself, and if he were to see it and bless it and accept it and perhaps tweet about it, then that would instill your crypto project with Musk’s divine spirit in a way that would probably make the price of your tokens go up? Is this all stupid? I don’t know? Yes? And yet? All I am saying is that humans have been offering precious idols to divine powers for thousands more years than they have been building discounted cash flow models in Excel. Do the idols have a better long-term track record than the DCF models?

This wasn’t speculative fiction. Crypto peddlers spent $600,000 building a sculpture of a goat with Elon Musk’s head. The goat wore a gold-plated dogecoin necklace, and straddled a rocket that could shoot real flames out of the back. They left their offering at the gates of Tesla’s headquarters as hopefully as a Slavic housewife leaving a little saucer of milk for the domovoy. “Elon tweeting us would legitimize the token,” Ashley Sansalone told the Wall Street Journal. He did not, and their token went, deservedly, to zero. They might have done well to consult the books of deeper wisdom: “I can call spirits from the vasty deep … Why, so can I, or so can any man. But will they come when you do call for them?”

Little wonder then that critics of the market no longer worry only that elites are exploiting real people beneath them. Now the concern is that they are trafficking in fictions, and that they have sunk from sharps to marks. Brink Lindsey levels the charge in “The Retreat from Reality,” calling the modern system of finance, “a parasite that creates the appearance of dynamism while rendering the economy less vital and less capable of bona fide innovation.” The problem, as Lindsey sees it, is that the masters of financialization are reaping windfalls while creating nothing:

Figuring out ever more elaborate ways of trading existing financial assets, including recombining them into derivative forms, is an essentially sterile enterprise; it can make enormous money for people who are good at it without creating anything of cognizable social value — just like lotteries, slot machines, and poker … Finance acts as a parasite by draining off some of the most talented people in the country who, instead of working on AI or fusion or at least building a better mousetrap, fritter away their productive energies on building castles in the air.

I’m half sympathetic to Lindsey’s critique. There’s something fundamentally dishonest sounding about taking a pool of cash flows and debts, separating them out into higher risk claims and lower risk claims, and selling the differentiated products for more than you could have gotten for the original pool, sold as a set. Where else, in the “real” economy, can you sort a bundle and sell the pieces for more than the value of the whole, or turn right around and pool differentiated products and sell the whole for more than the value of the parts? Something from nothing is a magic trick, and, in our world, everything has a price. If you don’t know what it is, you’re the one paying.

And yet, step away from the abstractions and hyperobjective scale of finance, and it’s obvious that bundling and unbundling can provide real value to consumers. As a parent of young kids, I pay dumb per-unit prices for individual packs of cheddar bunnies, so that I can disclaim responsibility if the sisters do not receive the exact same amount of snack at my hands. I send them to our local Catholic school, to receive a bundle of lessons and examples that I could theoretically provide, but I find the diversity of teachers and peers within the pool more valuable than what I could compile myself. At the same time, my homeschooling neighbor sends her kids to drop in for tutoring at Mathnasium, for the single item in my bundle that she is missing.

Markets made a big difference for charitable organizations when Feeding America changed how they allocated grocery donations to the food banks in their networks. For a long time, Feeding America used a turn-based method, trying to allocate goods fairly. Each food bank was placed in a long queue; when a particular food bank’s turn came, they were offered the chance to say yes or no to a particular bundle of goods. Whatever they chose, they went to the back of the line to wait for their next turn. This led food banks to say yes to goods that were a poor match for the groups they served, for fear of missing out on getting anything at all.

In 2005, Feeding America allowed food banks to bid on lots of goods with an artificial currency of shares. The shares were roughly proportional to the number of people the bank served. The auction system helped the participating food banks get the goods they valued most. It increased donation volume since donations moved quickly. The central auction gave all the food banks a way to speak simultaneously through the prices they bid. The abstraction of the auction made an enormous difference at the concrete, personal level of poor families sitting down to dinner.

It’s the excesses, absurdities, and catastrophic failures of financialization that capture our attention. When financialization functions well, it operates below our level of attention, in the same way that you only think about exactly what your stomach is doing to your dinner when something has gone wrong in your digestion. People sleep peaceably in their beds at night because analytic men and women stand ready to run models and set prices on their behalf.

It takes an act of deliberate distancing to see what has become so natural that we forget anyone ever had to invent it. Once again, fiction offers the heightened tone that gives us space from our reflexive judgement and an invitation into original seeing. In The Lehman Trilogy, a spectacular 3½ -hour stage play, Stefano Massini follows the Lehman family through boom and bust, invention and overextension. The financial behemoth that felt too big to fail is built out of small decisions and sudden insights.

The three original brothers arrive in America in the mid-1800s and begin their lives as ordinary businessmen. The Lehmans run a dry goods store. They sell fabric and suits, but then, as they find their footing, they expand their lines of merchandise. “I came to America to sell fabrics and suits,” Emanuel objects. “It’s all business,” Henry replies. “It’s what we do, we sell. Whatever it is.” Their family’s rise is fueled by a comfort with abstraction. Better to be a seller-in-the-abstract, able to pivot to whatever is readiest to buy, than a specialist in a particular product.

In 1853, a fire tore through Montgomery, and the Lehmans found they were sellers-in-the-abstract to a community that had lost the ability to buy. The brothers took the next step up the ladder of abstraction. If there was no money to buy, then, in order to sell, a new currency had to be invented. Instead of cash on the barrel, they would have to find a way to buy and sell debt. Debt could be a bridge for them, and the farming community they lived alongside, to step from nothing to something.

Debt lets you buy today with the cotton crop that would exist tomorrow, if only you could buy seed and tools with the money you did not yet have. A little fold in the fabric of time, a tesser, and the whole thing resolved without paradox. Debt is not one of Morton’s examples of hyperobjects, but it fits his general framework of these massive phenomena “exert[ing] a backward causality on other entities.” You existed because you had once travelled back in time to introduce your mother and father. Your community was rebuilt because money travelled back in time from the prosperous future you couldn’t afford. Don’t think about it too much, you’ll give yourself a headache. We’ve all seen the Time Knife.

The Lehmans do not go mad from the revelation. They collect cotton in payment for debt; they bundle and sell it up north. Emanuel Lehman travels north to see the factories that digest the cotton they sell. He returns to his brothers, transfixed:

The room shakes with the hellish racket of the steam looms / their long mechanical rakes / twenty feet long, twelve feet high / combing and stripping the cotton constantly … The rakes just ate the cotton up / so voracious, so greedy, that Emanuel couldn’t help but think / that if they had another one hundred, two hundred … / one thousand wagons … / the machine would have swallowed them all. Baruch HaShem!

If Emanuel was too early for the term “hyperobject,” he had an older word available to him: leviathan. Could he draw it with a hook? Would it speak soft words unto him? Could it be bound as a servant? A question for another day, perhaps. You could certainly make a contract with its keeper. The brothers would expand their business across the whole South. They had seen what the farmers had not — the great mouth and how much it would pay to be fed.

Like anyone who sojourns outside our world, they had fantastical stories to tell that no one yet believed. In the play, Mayer Lehman attempts to explain his family business to the man he hopes will be his future father-in-law. His beloved’s family could comprehend a simple dry goods merchant, but the Lehman brothers had become merchants without a store. They don’t transform the raw cotton they buy — they convey it to those who will transform it.

“What kind of job is that?” Mr. Newgass asks, “Something that doesn’t exist yet, Mr. Newgass,” Mayer explains, “It’s something we invented … We’re middlemen.”

After hours in a dark theater, a familiar word can become strange again. What was it like when “middleman” was spoken for the first time? What is a casual word of dismissal for us has an obvious thrill for Mayer. In the middle, you have the distance to see the world and put it in better order.

A middleman offers more than just a connection between pools of buyers and sellers. Part of the value a middleman can offer to both sides of his transactions is mystery. If his work is skilled, priestly, esoteric, his partners can treat it as a black box, and wash their hands of it. Disclaiming knowledge and responsibility-as-a-service is something you can charge for.

For some observers, the opacity of the financial market is a gift. If the ordinary person has terrible intuitions about risk, there needs to be some smoke and mirrors to get him to act in his own best interest. As Steve Randy Waldman put it at Interfluidity:

Finance has always been complex. More precisely it has always been opaque, and complexity is a means of rationalizing opacity in societies that pretend to transparency. Opacity is absolutely essential to modern finance. It is a feature not a bug until we radically change the way we mobilize economic risk-bearing. The core purpose of status quo finance is to coax people into accepting risks that they would not, if fully informed, consent to bear.

From Waldman’s point of view, middlemen and opacity help many disparate actors solve an otherwise impossible collective action problem. Each individual risk you take as an investor is, well, risky. Someone needs to command the pools of capital that can handle loss after loss, until one big success pays for all. And a few people need to be ready to crash and burn in expectation of one or two others breaking through.

Elon Musk famously admitted that he thought SpaceX had only about a 10% chance of success. Because he took a big picture view, he embraced the risk. If someone is willing to stake 10 people who are willing to chase that risk/reward balance, one of those risk-takers is expected to break through and change the world. As Waldman summarizes:

Successful real investment does not occur via isolated projects, but in waves, forward thrusts by cohorts of optimists, most of whom crash and burn, some of whom do great things for the world and make their investors wealthy. But the winners depend upon the existence of the losers.

This kind of gamble isn’t solely the province of venture capitalists chasing unicorns. Almost every book publisher works according to the same model. Most books they sign will be a loss, but a limited number of breakout and backlist titles pay off and provide the capital to sign the next group of (mostly) losers. Here, the big wins are big enough that even the losers are subsidized; publishers aren’t just moving around a static pool of money.

Even someone who’s never drawn up profit and loss projections has still probably played one game that goes lose-lose-lose … win big. Dating follows this basic model: The expected outcome of each particular Tinder or OkCupid date is probably negative, but men and women persist because you only need to hit the jackpot once. Then you get to cash out and leave the table.

Part of the game is not explicitly discussing these patterns, though, especially not with your counterparties. The editor isn’t supposed to tell the author it’s become clear their preorders aren’t snowballing. During the date, you usually can’t say you’re adopting a high-volume, high-variance search strategy. (Worked for me, though.)

As the Lehman brothers begin their shift from commodity merchants to brokers, they offer their clients this sort of service. A farmer wants the freedom to specialize in just farming, the same way a savings account customer wants the freedom to just see an interest rate, without thinking about every calculation of debt and risk that drives the number up or down. The farmer buying on credit, the modern customer with her checking account are relying on someone to take enough smart risks to cushion the losses and have a little left over. Stability depends on risk, and peace of mind depends on not knowing the details. The Lehmans became a bridge that allowed Southern farmers and Northern factories to not know each other.

The middleman isn’t just skilled at choosing prudent capital investments and partnerships. Increasingly, the job is sliding carefully up and down a ladder of abstraction. When things take a turn for the worse, the merchants of abstraction can have trouble interpreting back down to the people who have trusted them.

Most middlemen can’t offer the reassurance that small town banker George Bailey can in It’s a Wonderful Life. When Bailey’s customers make a run on the bank (and money has genuinely been mislaid), Bailey can credibly tell the mob that he may not be able to hand them back their money out of a vault, but that they know where it is. “Your money’s in Joe’s house … right next to yours,” he tells one of his neighbors. Bailey lists family after family, and tells the angry crowd, “Why, you’re lending them the money to build, and then, they’re going to pay it back to you as best they can.”

Bailey has just the right balance of abstraction and immediacy in order to run his business. His customers can picture the specific homes they’re financing; they can imagine the holes torn in the fabric of their neighborhood if they collapsed dozens of mortgages in a day. But still, the people of Bedford Falls wouldn’t have interviewed Joe (and every other renter) and then decided how much money to allocate him and on what schedule. George Bailey is a translator, comfortably sliding up and down the ladder of abstraction to align his neighbors’ short-term self-interest with their long-term hopes for their homes and their community.

The Lehmans, however, are ultimately headed for collapse. The brief prologue to the play opens on a janitor in 2008 clearing out trash as a radio drones “Lehman Brothers threatened with bankruptcy … decision expected before market opens.” As the play unfolds, the audience leans forward, watching carefully, like the smart mark who knows how not to get tricked by a shell game performer. Where did the collapse become inevitable? Were the brothers and their successors simply the next necessary losers, so that other merchants with a high appetite for risk could be staked and come out winners? Or is their story a classic tragedy, rooted in a specific, personal flaw?

By my lights, the crucial scene comes as the family business passes to the next generation. It is 1891. Mayer and Emanuel are being interviewed by the Wall Street Journal. Emanuel’s son, Phillip, sits quietly in the corner. The journalist asks, “If your bank had a recipe for making money … What would the core ingredient be? The flour that you bake with.” The Lehmans are no longer a family business but a bank. They trade commodities, they finance railroads. It is hard for the brothers to say what the core ingredient of their work is. Trains, coffee, coal, the brothers offer, thinking out loud. They know for sure it’s no longer cotton. Phillip cuts in:

PHILLIP.
The flour that you are asking about
is neither coffee nor coal nor cotton
nor the steel of the railway tracks.
We are merchants of money.
Regular people use money only to buy things.
But we, who have a bank,
we use money to make more money.
We buy it
we sell it
we lend it
we trade it.
This is how the recipe works.
Our flour is money.
MAYER.
Mayer stares at Phillip.
EMANUEL.
Emanuel does the same.
MAYER.
Like two bakers who suddenly...
EMANUEL.
...Don't know what bread is.

Emanuel, who had once felt he had to visit a railroad to know if he should invest, knows that his time is over. For his son, the models and the figures will always be more real than the railroad ties and sacks of coffee they represent. The question is whether anyone does still know what bread is in the heady world of pricing risk.

Meme stocks and crypto increasingly look like bread made without flour. The price of Dogecoin or of Gamestop doesn’t depend on what the anticipated future flows of the underlying asset are. They are, at best, a way of pricing parasocial affiliation with these communities of traders. For some digital assets, like the various Trump family crypto coins, you can work your way around to arguing that quantifying this strength-of-affiliation is valuable — maybe you want to hedge a Trump-related business risk. But the case is pretty thin.

When Gladstone took stock of the 2008 financial crisis — the convulsion that collapsed Lehman Brothers — he translated it into fantastical terms. “We were witnessing the twilight of a certain set of gods and monsters, which then devoured their own entrails and gave birth to another set of god/monsters, which is what always happens with gods.” So which rough beast has its hour come at last now?

You can spot the nascent godlings in the prediction markets. Like a bound demon escaping its summoning circle, real-money prediction markets like Kalshi have attempted to slip out of reach for state regulators (who set limits on gambling) and federal regulators (who approve securities offerings). Kalshi has placed itself under the aegis of the Commodity Futures Trading Commission and offers “event contracts” on what otherwise might look like sports bets (who will win the Superbowl) or securities (the S&P 500 price at the end of this week).

These little godlings are hungry for attention, and they don’t promise profit but perpetual volatility. There will always be a little action available, and there’s always a parlay to justify your investment in the next play. A functioning market depends on players being deeply invested in their expected value. By contrast, a casino’s most committed gamblers get frustrated when a slot machine jackpot interrupts their flow state of press-spin-repeat.

Markets and middlemen serve us best when they are future-oriented. Debt is an informed gamble on what possible future is almost here, but needs a little push to be joined to the present. An auction or a market is a pooling of knowledge, allowing many expectations and preferences to aggregate, like tesserae, into a fuller picture than any participant possessed. Prediction and gambling markets that satisfy present hunger to feel something — up, down, sideways, whatever — lose their moorings.

A healthy market prowls looking for serious disagreements about the future, unnoticed inefficiencies that can be collapsed into profit. A stagnant market just needs frothiness, and the length of a press conference is as good an opportunity as anything else to spit on your palms and say “Bet?”

In response, you can unmuzzle the traditional watchdogs who policed market makers. You can raise the threshold for accredited investors until more retail investors are priced out. You can, as Matt Levine has modestly proposed, offer the smart-marks a “Certificate of Dumb Investment” (price: one hard slap in the face).

When you start to lose your sense of what bread is, it’s time to slide back down the ladder of abstraction and see what you’re certain of. What piece of the hyperobject do you have in view? What can you hypothesize about what you can’t see? How can you test your fragile model of the world? How much can you afford to risk as you take the first step off the edge of the map?

The most suspicious markets are those without tight feedback loops. A business needs to convince a customer it is providing real value, worth the price charged. But a nonprofit can often justify its grant proposals by appealing to its funders’ pride and sense of identity. A company in its growth phase faces similar temptations to win over a VC with their intensity and ambition, not their acumen. When you make something of real value, there is going to be a real customer on the other side of the transaction. There will be someone eager to do business, even if, from their perspective, the bread, the pencil, the contract seems to appear as if by magic.

This article originally appeared in Issue 14: Risk. Subscribe to the print magazine to get future issues delivered to your door.

This article is a preview of our upcoming Issue 15: Work. Subscribe to the print magazine to get it delivered to your door.

Americans — left, right, and everywhere in between — seem to be afraid of AI. They fear data centers speeding up climate change, disinformation and deepfakes, AI companionship, and, above all, job loss from automation. Meanwhile, the Chinese public seems to be perfectly fine with the technology, or even “optimistic” about it.

The polling data is striking: Stanford University’s 2026 AI Index Report shows that more than 85% of Chinese respondents see AI as more beneficial than harmful, compared to less than 45% of respondents in the United States. A 2025 report published by the University of Queensland and KPMG Australia revealed that 73% of Chinese respondents are willing to trust AI system outputs and share relevant information with AI at work, and 88% intentionally use the technology, compared to 52% and 48% of Americans, respectively.

Why does Chinese society, which suffers from acute job loss and a youth unemployment rate close to 17%, embrace a technology it knows is likely to take away more jobs?

The question was answered three decades ago. The answer is not a narrative about AI, but about an earlier transformation also perceived as inevitable. It is a story about how Chinese society has learned, through repeated upheaval, what it believes to be the only permissible response to disruption. Accurately interpreting that response — which is often misleadingly called “enthusiasm” — is essential to understanding that worried Americans watching China’s AI frenzy might not be looking at a rival but into a mirror.

The millennium that broke two ways

Lived this way for thirty years
Until the great mansion collapsed
The deep, dark clouds
Are drowning the view in my heart.

如此生活三十年 ruci shenghuo sanshi nian
直到大厦崩塌 zhidao dasha bengta
云层深处的黑暗啊 yunceng shenchu de hei’an a
淹没心底的景观 yanmo xindi de jingguan
–”Killing the One from Shijiazhuang,” Omnipotent Youth Society, 2010

In December 1978, reeling from the economic wreckage of the Great Leap Forward and the Cultural Revolution, China’s Communist Party formally shifted its central task from class struggle to economic construction, launching Deng Xiaoping’s “Reform and Opening Up” and beginning a gradual dismantling of three decades of central planning. In 1992, the country formally declared a turn toward a socialist market economy — an acknowledgment that market forces, not central planners, would now drive growth.

The country’s enterprises, built for a planned economy, were suddenly exposed to market competition — and consequently began hemorrhaging money, especially in industries like steel and textiles. By 1997, the state had decided to consolidate the strategic enterprises and let the rest restructure, merge, or collapse. The slogan it coined was 减员增效 (jianyuan zengxiao) — “reduce headcount, increase efficiency.”

The consequences of this transformation depended on where you lived. Over 24 million workers in China lost their jobs in the state sector by the end of 1999. The layoffs were concentrated in the northeast — Liaoning, Heilongjiang, Jilin — once the industrial heartland of socialist China and now called China’s rust belt. In 1957, the city of Shenyang’s Tiexi district produced the nation’s entire output of lathes, rock drills, gliders, rubber boats, and tower cranes, earning it the nickname “the Eastern Ruhr.”

By the late 1990s, 80% of the companies responsible for this output had gone out of production, and half of the district’s 300,000 industrial workers had been laid off. Between 1998 and 2000, nearly every year saw 7 to 9 million workers laid off nationally. Liaoning, for example, was laying off nearly 1,700 workers every single day. The moment was so unique that even the act of being laid off had a special name: 下岗 (xiagang), which literally means “stepping down from the post.”

Yet while the transition led northern China into economic crisis, the Pearl River Delta — geographically proximate to Hong Kong and Macau, home to China’s first Special Economic Zones, and the ancestral homeland of much of the Chinese diaspora in Southeast Asia and beyond — embraced rapid modernization and internationalization. The historical “land of fish and rice” became the “world factory.” Hong Kong investors established over 65,000 factories, employing about six million workers in the Delta. From 1991 to 2001, the Pearl River Delta’s regional GDP grew almost eightfold, and its population increased from 20 to 43 million.

For these citizens, the new economy meant good lives, which now included new technology. In 1998, Microsoft unveiled the mainland China version of Windows 98, and signed musician Pu Shu to endorse it. “New Boy,” a track on his 1999 album, name-checks Windows 98 and Pentium computers in its chorus and became a genuine millennium anthem for a generation.

Put on new clothes, get a new haircut
Relax with Windows 98
The road ahead will have no more suffering
How cool our future will be.

穿新衣吧, 剪新发型呀 chuan xinyi ba, jian xin faxing a
轻松一下, Windows 98 qingsong yixia, Windows 98
以后的路不再会有痛苦 yihou de lu, bu zai hui you tongku
我们的未来该有多酷 women de weilai gai you duo ku
–”New Boy,” Pu Shu, 1999

China’s tech giants — Alibaba, Tencent, and Baidu — were all founded between 1998 and 2000. By the end of 2000, the number of internet users in China had jumped from 3000 in early 1995 to 22.5 million. In 2001, China joined the WTO. Urbanization accelerated, and the growth of the middle class fueled demand for luxury goods, tourism, and better nutrition. The number of private cars in China went up from 1 million in 1992 to almost 10 million by 2002. Many people envisioned a hopeful future in which they could acquire new clothes, new luxuries, and new technology in the new millennium.

But the “many” did not include the 100 million people residing in the Northeast — roughly 8.5% of China’s total population as of 2000. By the 1990s, urban shrinkage, which is measured by sustained population loss, had already taken hold across 52 cities in the Northeast. And of the 68 cities across China whose populations diminished continuously into the 2010s, half were in this region. The regional birth rate has been trending lower than the national average for more than three decades, and net outmigration has become an increasing problem since 2000. In 1990, the Northeast represented 8.66% of the country’s population; by 2016, that proportion had dropped to 7.9%. The one-time cradle of China’s industrial development has become a place that many would rather not raise kids or live in, given the choice.

In the span of a decade, Chinese society simultaneously experienced rapid economic growth and extreme economic precarity. Individuals were offered transformative opportunities and faced catastrophic crises, all due to the same factors put in place by a select elite who generated the incredible promise and acute challenges modern China still faces. To many Americans watching AI reshape their economy, this narrative may sound familiar, though calls to regulate, pause, or stop the technology reflect a belief that the transformation can still be steered or stopped. That option did not exist for Chinese workers in the 1990s.

Painful, "rewarding" reform

For China’s policymakers, slowing development was never an option. A 1931 quote from Joseph Stalin — “落后就要挨打 (luohou jiu yao aida) or “those who fall behind get beaten” — that adapted by Mao Zedong in 1956 permeated society, serving as a cornerstone of high-level policy narratives. In China’s mnemonic practices, this phrase, linked to the idea that only development can sustain a nation’s independence, is the most significant lesson from the past, necessary to remember from China’s 20th-century history of war and colonization. “The reform is painful but rewarding,” wrote the state in 2012 in reference to the previous century.

At the turn of the century, then, the policy question was therefore not whether to reform; instead, it was how to make the transformation less painful. The government attempted to address the pain. In 1998, the state established re-employment Service Centers, which provided laid-off workers with living allowances, basic social security, and job training. The state taxation administration introduced tax incentives for businesses that hired displaced workers. Xiagang workers were entitled to tax exemptions, fee waivers, and preferential access to microloans when starting small businesses or seeking new employment. The Minimum Living Security System was established in 1999 to guarantee basic income for urban residents and expanded to rural areas in the 2000s. Higher education grew in 1999 and university attendance increased 600% in less than 10 years. This expansion was partially aimed at delaying China’s youth from entering the job market, thus leaving spaces for the re-employment of laid-off workers.

For some workers, these policies provided a bridge. But the scale of the problem overwhelmed the response. Funds were too small or simply did not arrive. When funds did arrive, they rarely reached the people they were meant for. In one case, one former deputy director of the city-level Development and Reform Commission — an institution responsible for implementing national economic policies — embezzled the subsidies of 556 xiagang workers.

Even as market reform and industrial upgrades brought new job opportunities, there were simply not enough: In 2004-2005, 24 million people entered the workforce, but only 9 million new roles were created. Even within these new jobs, there was a mismatch between supply and demand. The workers who had been laid off were predominantly in their forties and fifties with industrial skills, while the foreign companies entering China wanted fresh university graduates or young rural migrants who were willing to work for less. And though the expansion of higher education benefited many, it eventually produced young workers who were overqualified for many jobs, resulting in high youth unemployment that persists in China today. And much of the suffering was silently buried under cold numbers and grand policies.

In 2002, economist and writer Wu Xiaobo conducted fieldwork in Shenyang’s Tiexi district. Writing for the Financial Times China, he recorded stories from two families who had experienced layoffs. One husband biked his wife to the red light district for sex work in exchange for money for survival. In the other, the father jumped off of a building after his wife complained that they could not afford to buy their son sneakers for a school sports meet. Other accounts described families folding poison into dumplings, robbers and their victims begging each other to end the other’s suffering, and workers lying across railway tracks waiting for trains to hit them.

It may be hard to understand why people would resort to such extreme situations in the face of mere unemployment. But for many workers in the northeast, employment was everything. Before xiagang, most workers’ lives were organized around the danwei — the work unit that was not simply an employer but a total social world. The danwei provided housing, medical care, pensions, childcare, and entertainment. Colleagues were neighbors. People were born in the danwei clinic, went to danwei-sponsored schools, worked in danwei upon graduation, found partners through danwei-organized dates, and moved into danwei-sponsored dorms or housing. From birth to death, a worker’s life was closely linked to their danwei. In his 2004 book, sociologist Li Hanlin argues that danwei was not only a workplace but also a chosen lifestyle that provided a sense of reliance and an anchor of hope. It was a society without strangers, because people formed close bonds through everyday work and life. Danwei gave people social identity and legitimacy.

People in the Northeast therefore lost not only income, but their way of life, their sense of belonging to the small communities they had built around their work, and their dignity as socialist workers. In a society that for decades had told them workers were the masters of the nation, the sudden sense that they were surplus, inefficient, and unwanted imposed a burden that no severance payment could address. Many felt deceived when forced to sign labor contracts that stripped away their protections: “I believed in the government and the party. I relied on the enterprise for a living, and the enterprise also needed me for further development,” said one laid-off mining worker in rural Beijing. “I didn’t have the slightest idea that the enterprise would take advantage of me.” Others felt invisible when they were excluded from decisions that would determine the rest of their lives by an institution they had always called their larger family.

The fear and the frenzy

The paradox of the era was that as much of China’s population was losing jobs, an emerging group of poor people, predominantly in the southeastern coastal areas, was growing rich overnight. And because others were enjoying upward mobility, the ones left behind internalized Social Darwinist views that claimed that only lazy and useless workers had been laid off and that people who failed to find new jobs simply were not skilled or determined enough to do so.

In rural Liaoning, a northeastern province greatly impacted by xiagang, many people sought to migrate overseas for better opportunities. Local villagers explained to anthropologist Xiang Biao that they looked down on neighbors who could not find work overseas to earn big money. They wondered to themselves, “why have others gone overseas successfully but you can’t?” and assumed that those who stayed had failed because of individual shortcomings rather than structural forces. This view, which originated in northeast China, makes the fault of the layoff a problem with individual capabilities: When rapid stratification turned neighbours’ fates in opposite directions almost overnight, individual effort became the easiest explanation for diverging outcomes — a logic the state then reinforced by replacing collectivist language with individualistic discourses of self-improvement and personal advancement.

Most narratives of the period, even sympathetic ones, treat economic restructuring as a natural force, with individual adaptation as the only response. In 2002, a documentary about the Tiexi district depicted the marginal lives and struggles of xiagang workers in this once-vibrant industrial area. Lyu Xinyu, one of China’s most prominent scholars in the study of rural-urban inequities, interprets the documentary as a sad depiction of an inevitable historical event:

Today’s (2003) Tiexi District is nothing more than a replay of the decline of the traditional industrial Rust Belt in the American Midwest and the traditional industrial Ruhr area in Germany in the 1970s and 80s. It is the unfolding of a common historical rationality in different times and spaces, and we have no possibility of escaping the compulsion of this law. Industry, in a dialectical and historical sense, is an object of the natural laws of society.

If economic restructuring was an unstoppable force of nature, then the only possible response was to move with it before it moved without you. Xiang Biao diagnosed this as a “last bus” mentality: a collective fear that missing the opportunity to seize a piece of post-socialist accumulation meant missing everything. You either catch this bus towards success or be left out forever. It was a frenzy born not of greed or enthusiasm, but of the desperate realization that the old world was gone and the new one had no reserved seats. What began as a northeastern industrial experience has, amid decades of social change and competition, became a prevalent psychological structure spanning different socioeconomic classes and regions.

The state’s official rhetoric consistently reinforced this reading. In the 1990s, China needed marketization and reform of state-owned enterprises. These were, they said, inevitable moves to save the country from its economic crisis. China, under this logic, also needs urbanization, industrial upgrades, or AI integration, because history is irreversible and technological progress is inevitable. Describing major societal changes, the official language is always that one needs to “seize the new opportunities (抓住新机遇; zhuazhu xin jiyu)” and “ride the trend of the time (站在时代的风口上; zhan zai shidai de fengkou shang).” The rhetoric still prevails two decades later, as a top state newspaper wrote in 2019, “when the era discards you, it will not even say goodbye.”

The signal for individuals was clear: You had better catch the “last bus” to seize the fleeting opportunity. If you fail, no one, even the state, will back you up. This mentality undergirded China’s development at the turn of the century and prevails today. Whether it involves market, education, industrial, or technological reforms, people in China are frenetic about new things because they are always seeking the trend to follow. In Xiang’s words, “every bus is the last bus.”

In the late 1990s and early 2000s, learning English was the last bus. Globalization was the irreversible trend; only by learning English could Chinese people interact with the greater world. The state mandated English education as a core Gaokao subject and pushed it into primary schools in 2001, giving rise to cultural phenomena like “Crazy English“ (疯狂英语; fengkuang yingyu), wherein tens of thousands of people gathered in public stadiums to scream English phrases at the top of their lungs in a desperate collective bid for fluency. In the late 2010s, the mobile internet boom was the last bus. As tech giants like Alibaba and Tencent offered unmatched salaries in other industries, millions rushed to learn coding and enroll in computer science degrees in universities that were aggressively expanding computer science programs, only to find themselves facing a constantly decreasing employment rate.

In 2023, understanding AI was the last bus, and over 250 thousand people paid for rudimentary AI crash courses, terrified of being rendered obsolete overnight. In 2026, OpenClaw was the last bus, with thousands of people — retirees, white-collar workers, housewives — lining up outside tech company offices for engineers to install the agent directly onto their phones.

Underlying pessimism

Tomorrow morning, I guess the sun will be good
I want to clean myself up
Sell off everything old and broken
Oh, this will be so good
Come on, Pentium computer
Let them think on my behalf

明天一早, 我猜阳光会好
我要把自己打扫
把破旧的全部卖掉
哦这样多好
快来吧奔腾电脑
就让它们代替我来思考
–”New Boy,” Pu Shu, 1999

Today, the history of marketization is largely depicted in a rosy way. Chinese TV dramas — ranging from official historical fiction to romantic melodrama — celebrate people who rode the tide of the trend and raised themselves. The trauma of xiagang has found cultural expression only at the margin.: The so-called “Dongbei Renaissance” is a loose wave of literature, film, and dark comedy that has emerged from northeastern writers and directors since the 2010s and treats the rust belt’s collapse with a bleakness official culture cannot condone. Beyond that, the majority of the records of xiagang have been censored or simply left out.

But even if you burn the records, you cannot erase the wound. And no matter how much whitewash one applies to that period, the core mentality — seize the last bus or die — has become deeply ingrained. This persistent anxiety continues to intensify and spread whenever new, potentially transformative shifts occur in Chinese society. While not everyone successfully boards every “last bus,” the alternative of not trying to board at all is a social stigma. As Xiang Biao observed, there seems to be no way to live outside of competing and striving, even when it is unclear what exactly one is striving toward; quitting the race means facing utter failure. Even when the young generation claims to embrace “lying flat,” the pressure from the state, society, and even they themselves means that they actually do not give up at all.

This history offers a new perspective on the “AI enthusiasm” we are now seeing in China. Many are correct to point out that the enthusiasm arises from the top-down state discourse portraying technology as a redemption against the history of the “century of humiliation,” as well as people’s ground-up experience of benefits from rapid technology development in the past few decades. Technology is good because it makes the nation stronger. The lesson of how the late Qing government closed its door, missed the industrial revolution, and was defeated and humiliated by the Europeans and Japanese is a core section of the history education mandatory for every Chinese student. On the other hand,industrialization and digitization have made many people’s lives better, compressing what took the West decades into a single generation. China grew from no high-speed rail in 2003 to a 50,000km network in 2025, compared with 8,500km in the whole of the EU as of 2023, linking 97% of cities with populations of more than half a million; the society leapfrogged credit card infrastructure, going straight from cash to mobile payments in a transition that reached people who had never held a bank card.

However, these two elements also instill a profound sense of precarity. The desire to access the transformative benefits of technology is inseparable from the fear of being left behind. Citizens adopt cashless payments not only because of the convenience it offers, but also because of the penalty for not doing so: finding oneself unable to pay at most stores, locked out of basic services, and adrift in a banking system built for a phone screen. The same will be true for AI — or, at least, most Chinese people seem to believe so.

China’s culture of techno-optimism, analysts argue, may allow AI to be diffused and deployed at scale. Some analysts contrast China’s Star Trek techno-optimism, which some believe will allow AI to be more quickly deployed at scale, with the West’s Black Mirror mindset, wherein public anxiety about various AI risks stifles deployment. It is too easy, however, to draw a binary between the American and Chinese responses to AI, or to think that the Chinese public would be purely enthusiastic about a technology that will automate more jobs. It is true that Chinese respondents in some surveys likely have some genuine enthusiasm — particularly many who lived through and benefited from the market transformation of the 1990s, for whom technology has been a story of concrete improvement. However, enthusiasm and fear are not mutually exclusive. A person can genuinely believe some AI products are beneficial and feel they have no real choice but to adopt it; can welcome a technology because it seems useful while worried that not mastering the usefulness renders themselves obsolete. Most survey questions were too binary in design to shed light on which sentiment is driving the response, or a respondent’s ratio of enthusiasm to anxiety.

Today, some evidence-based “optimism” claims draw from the Chinese public’s extremely high responses like “AI products and services have more benefits than drawbacks”, how much one “trust AI,” or “willing to accept AI,” all of which cannot differentiate a net excitement of AI from the belief that AI is important, inevitable, and cannot be missed. Are AI products viewed positively because people really benefit from them, or are they simply thought to be so important, just like how learning English is “beneficial” in the sense that people believe the language means modernization and the future, even though in real life it may have little practical use? Asking “How much do you trust the technology?” is inherently ambiguous: does answering yes mean you trust AI as a technology, trust AI’s output, or trust that AI will bring opportunities that you cannot afford to miss? Furthermore, behind the 95% reponse of willingness to accept AI lies the 49% belief that AI will replace jobs. So while AI is viewed as a threat to job security, a possible coping mechanism is to rapidly accept and embrace it, because history has taught the Chinese that the only coping mechanism is to change oneself.

The mixture of enthusiasm and fear pulls on a tension that has emerged throughout China’s recent history — whether people believe a change will benefit society as whole or merely themselves as individuals. There is a difference between believing a technology is useful, beneficial, or necessary for society as a whole — that AI will become the fate of the nation, which one needs to work hard to adapt to — and trusting that the technology will automatically benefit individuals’ lives. Under the grand narrative today, Xiagang is acceptable, necessary, and has more benefits than drawbacks — for the nation-state more than for those workers laid off. “The 1998 SOE reforms were like major surgery. Without it, the patient would not have survived,” said economist Huihua Nie, implying that although xiagang was a painful process for some, Chinese society must endure this individual suffering for the collective good

When polled only three decades later, perhaps every respondent genuinely believes that AI is good for both society and for themselves. Or perhaps they see AI as another surgery necessary to survival, knowing full well that flesh will be cut away and discarded, but convinced that the pain borne by individuals — however devastating to them — is small against the benefits at large. The polls, as they are written, cannot distinguish between these narratives. .

Meanwhile, the reality suggests that there is no homogenous or unwavering optimism in AI among the Chinese public. For example, even when the state issued multiple warnings about OpenClaw security risks, people nevertheless rushed to install the agent on their personal phones and laptops. Behind the seemingly massive adoption of AI agent tools is not a population mobilized behind a coherent national AI strategy, but many individuals running blindly, supervised by a government that benefits from the momentum but cannot meaningfully control the direction. Resource waste, security vulnerabilities, scams, and market oversupply are the predictable outputs of a system running on fear as much as ambition. China’s AI enthusiasm is not as strategic an “advantage” as some may think, as the bottom-up fear can easily lead to a frenzy that is outside the top-down AI agenda.

This is, perhaps, a situation that one could simply dismiss as “AI hype” or “AI bubble” if it happened in the US, where some hawk AI classes, many try out every new AI product as they emerge, and some attend AI hackathons every week. But because it is happening in China, and because the American analysts themselves now treat domestic AI backlash as a strategic vulnerability, they’d rather believe the Chinese public is different, or the Chinese government has better leverage in a so-called “U.S.-China AI race” as they can engineer an optimistic public.

But can it?

In January 2026, Pu Shu’s “New Boy” was remade to “New Bot” by the state media, aiming to highlight how AI and robotics, just like Windows 98, can bring hope and the promise of a new and improved life. However, despite its eye-catching music video, the song did not become a hit. People continue to listen to the 1999 original, leaving comments lamenting that there will never again be an era of such optimism. What they are mourning, perhaps, is not AI’s failure to match Windows 98’s appeal. “I have never been able to accept that this is a purely cheerful song. The melancholy of being pushed into a new era is the real theme — pessimism hidden inside a melody that looks happy,” wrote one listener.

“向前走，你的路，猜猜未来会给你什么礼物 (xiang qian zou, ni de lu, caicai weilai hui gei ni shenme liwu) ,” sings Pu Shu in the outro of the song. “Walk forward, your road is ahead — guess what gift the future holds for you.” The gift, it turns out, is mandatory. You did not order it, you cannot return it, and the era will not wait while you decide if you want it.

This article is a preview of our upcoming Issue 15: Work. Subscribe to the print magazine to get it delivered to your door.