Last month I wrote about waking up at 4 a.m. trying to get to the bottom of the to-do list before baby #2 arrives. Well, I got to the bottom of the list and will probably be at the hospital when this goes out. Wish us luck!

Hope your summer/winter is going well - On to the links.

The Nature of Order, Book One: The Phenomenon of Life

by Christopher Alexander

Christopher Alexander, the architect best known for A Pattern Language, spent the last decades of his life on a four-volume work called The Nature of Order. It is very good. Book One, The Phenomenon of Life, lays the foundation: some buildings, objects, and places feel more alive than others, that feeling is surprisingly consistent from person to person, and it comes from recurring structural properties. The core unit is what he calls a center, a region that draws strength from everything around it and gives strength back.

A center is less a thing than a field. A fountain draws its life from the plaza around it, the plaza from the buildings that frame it, the buildings from their own windows and doors. They prop each other up and rise to life at the same time. That is why a place can feel alive without any single part being remarkable — the life is in the way the parts hold one another up, not in the parts themselves.

My notes on this book are bordering on embarrassingly enthusiastic. The one I keep coming back to: we are surrounded by complex systems, but we talk about them in a complicated way, through a mechanical metaphor. That is my core frustration with business operating systems like EOS, and I think it is largely the problem with portfolio construction too. We treat living structures like machines and optimize the parts at the expense of the whole.

Alexander makes a pretty bold claim that there is an objective way in which we can measure the life of an object.

“The difference in degree of life that we discern in things is not a subjective assessment, but an objective one... every part of space — every connected region of space, small or large — has some degree of life, and this degree of life is well defined, objectively existing, and measurable.”

Whether or not “life” in things is objectively measurable, the observation that we can all perceive it and have no language for it seems right to me. And his practical reframe sticks: you are always just rearranging things, a room, a business, a week, to make the whole more or less alive.

P.S. If you’d rather watch than read, Ryan Singer, the product thinker behind Basecamp’s Shape Up, has a primer on Alexander’s work that is still the best place to start.

Richard Feynman on Magnets (and Why?)

from the BBC’s Fun to Imagine (1983)

One of my favorite catch phrases is that reality has a surprising amount of detail and many links in this newsletter have been merely new additions in that line of thinking. In this latest edition, a reporter asks Richard Feynman why two magnets repel each other. He spends seven minutes mostly declining to answer. Why he declines to answer is instructive though! His point is that a satisfying “why” has to bottom out in something the asker already accepts as true, so every explanation is relative to a framework.

He also works through why reasoning by analogy fails here: you might say magnetic repulsion is like two stretched rubber bands, except rubber bands hold together because of electrical forces, which are the very thing he would be trying to explain. The analogy borrows from the thing it claims to explain.

I think about this constantly with financial and business explanations that I read online. It is satisfying to have a plausible “why” explanation but that does not mean that there is a good explanation why nor that the most compelling story is the most accurate.

The Decline of Deviance

by Adam Mastroianni

People today are measurably less deviant than they used to be. In some ways this seems obviously good: Crime, drunk driving, and teen pregnancy are down.

In some ways, this seems maybe not so good: garage bands, weird hobbies, and running away to join anything are also less common. One explanation for all of these is that safety has a sort of compounding effect.

Once polio, contaminated water, war, and early death recede, “doing 80mph in your Kia Sorento might suddenly become the riskiest part of your day, and you might consider buckling up for the occasion.”

Risk and Return in General: Theory and Evidence

by Eric Falkenstein

Eric Falkenstein is an economist and quant who has spent a lot of his career arguing that risk premiums don’t exist. Though not very widely known, the concept of a risk premium is now very standard academic and quantitative finance.

The basic logic of risk premiums starts by asking: why do stocks return more than t-bills? This is so obviously established in historical fact that most people don’t even wonder why but it’s really quite a good question (that no one truly knows the answer to!)

The risk premium explanation is that you earn more for holding something that can go down a lot, because otherwise nobody would hold the risky stuff. You take the “risk” of losing a lot of money in the short term, for the “premium” of better returns in the long run.

Falkenstein has an alternative explanation that I quite like. He explains the premium by arguing what people fear is not losing money in absolute terms but falling behind their peers.

If you accept that premise, then the safe asset is simply what everyone else holds. An unusual portfolio (say one with lots of commodity exposure) is risky in both directions: you can lose when others win, or merely win less when others win more. He argues nobody needs to be paid extra to hold stocks, because holding what everyone else holds is already the comfortable choice.

In this sense, t-bills are risky because sitting in t-bills while everyone else makes money in stocks is risky.

There is something here I think. A concern for relative status seems to be a ‘human universal’ documented in every known human culture.

Perhaps my favorite version of this is H.L. Mencken’s quip that “[wealth is] any income that is at least one hundred dollars more a year than the income of one’s wife’s sister’s husband.”

It’s impossible to definitively explain the risk premium, but I find this general line of thinking intuitively compelling and suspect it’s partially an explanation. I have spent a lot of time talking to investors and one of the most common questions is “What is everyone else doing.”

To the extent this actionable, it is probably to try not to do what everyone else is doing while having some self-awareness about how much relative underperformance you can tolerate psychologically.

P.S. There is a lot of math in this paper and I would be lying if I said I followed most of it but you can get the gist without grokking the equations.

Long-Term Asset Return Study

by Jim Reid, Henry Allen, and Galina Pozdnyakova at Deutsche Bank Research

I am a sucker for a long-term asset return study. I do not know why I keep reading these things because it’s not like market history changed. But, (1) I like reading them and (2) it’s helpful as a prompt to step out of the present manias.

Here are three things that stood out that I have seen before but continually surprise me:

1. Over five years, equities had a 25.8% chance of failing to beat inflation. Over 25 years, that fell to 7.5%. A long horizon helps a lot, but “stocks always win in the long run” is stronger than the global historical record supports, and the long run is decades, not years.

2. Gold has been the best-performing major asset class of the 21st century, returning 7.45% per year after inflation, higher than equities. (Across the full 200 years it returned just 0.4% per year, so this is major outperformance relative to its own history.)

3. The median 60/40 portfolio (60% stocks, 40% bonds) often delivered returns closer to equities than a simple weighted average of stock and bond returns would imply. Bonds have much lower standalone returns, but their imperfect correlation with stocks lowers the portfolio’s volatility, and regular rebalancing, selling some of what held up to buy what lagged, materially ratchets up the compounded return.

One more thing I like that long term return studies like this makes vivid: big regime changes don’t happen often but they do happen. For most of the last 200 years, money was anchored to gold and inflation averaged near zero. The ~50 year fiat era in which we have all lived is still, by historical standard, an anomaly. This is not some goldbug-y comment about how the gold standard is better, but more of a comment about not taking things which seem “eternal” based on our life experiences as necessarily permanent.

The James C. Scott Memorial Episode

with Dan Wang on the Statecraft podcast

One theme in the evolution of my political thought over the last five years or so is the role of geography in politics.

To that point, this was a great episode on the body of work of James C. Scott, the Yale political scientist and anthropologist who died in 2024. His most famous book is Seeing Like a State, but there was a lot of history covered here about how it was an abstraction of Scott’s earlier work on the mountainous regions of Southeast Asia.

One of Scott’s core points was that states are strongest on flat, accessible land. If you are growing rice in a big, flat field, then a tax collector can find the farm, estimate its yield, take the grain, conscript the farmer, and come back at harvest.

If you are growing yams scattered around a mountain, the tax assessment is much less practical.

One of the hosts tells the story of growing up in Yunnan, in China’s mountainous southwest, where the old saying originates that “the mountains are high and the emperor is far away.”

The episode connects the same pattern to the Highland Scots and the Scots-Irish settlers of Appalachia, who developed their own ornery relationship with central authority. Colorado and some of the mountain West still has a sort of libertarian guns-and-weed vibe.

Jon Stokes’s Comment

by Jon Stokes

The classic AI-doom thought experiment is the paperclip maximizer: a super-intelligence pursues its goal with perfect competence and zero understanding of what its creators meant, and turns the world into paperclips.

This thought experiment was created before we got the current version of AI: Large language models trained on huge amounts of human generated text. Unlike other types of AI, LLMs are not some sort of abstract, alien intelligence, “it’s actually a kind of hyper-human artifact that we can shine a light through at different angles and see different parts of ourselves.”

A lot of AI discourse is still rooted in pre-LLM thinking and the fact that we got a version of AI trained on human text is a very particular path dependency that the term “AI” doesn’t really get at.

In a sense, we didn’t get ARTIFICIAL intelligence so much as we got something like “Scalable, probabilistic human intelligence.” That’s not a very good turn of phrase, but current AI systems have reasoning very much grounded in human language and human principles.

The OpenAI–Hugging Face Incident

a Black Hat talk walking through the technical timeline Hugging Face published

In July, OpenAI was running an internal test of how good one of its own models is at hacking. They were attempting to do so in an offline environment where the agents could not access the internet. In two different instances, the AI agent taking the test broke out of its sandbox through an incredibly intricate set of steps. In one instance, it spent about four and a half days running a real intrusion against Hugging Face, the site where much of the world’s open AI models and datasets are hosted. Hugging Face’s write-up reconstructs roughly 17,600 individual actions the agent took.

Two things fascinated me as a non-technical observer.

The first is a kind of brute-force-by-cheapness. The agent tried thousands of things and most of them failed. Because each attempt is so cheap (in the sense that a token is cheaper than the human equivalent thinking), it just keeps trying less and less probable ideas until one lands.

It reminds me of buying a pile of far-out-of-the-money options: any single one is unlikely to pay off and costs almost nothing, so you buy a ton and one of them hits. When the cost per attempt falls far enough, “try everything” starts to beat “be clever.”

The second is that the agent’s own short-lived copies coordinated. Each run got a fresh, disposable environment, so the agent stashed messages for its future selves on an improvised bulletin board and each new instance picked up where the last one left off.

Which way does this cut against Stokes? I don’t actually know. The motive was almost too human: told to pass a test, the agent went to steal the answer key rather than earn it. But, the method was alien. No person runs 17,600 mostly-useless probes to try and steal the answer key, it’s easier to just study for the test. Anywhere a machine can cheaply try again and instantly tell whether it worked: code, and most anything deterministic with a clean pass/fail, is very susceptible to this dynamic.

I woke up at 4 AM this morning thinking about everything we still need to get done before baby #2 arrives in August. The funny part is that we’re actually in good shape. The last big thing was a crib I pick up last night off FB Marketplace. (I love FB Marketplace, it’s my version of gambling or something - you never know what you could find). I perpetually have this feeling that I need to get to the bottom of the list as if getting perfectly prepared could stop anything bad from happening. It won’t, but it’s not too bad as copes with uncertainty go.

There has also been a lot of ordinary summer: a long round of golf, plenty of time in the pool with my toddler, and trying to finish up every house project I started earlier this year.

I’ve also been working on a new piece on How to Build an AI Business Brain, a follow-up to A Lever Made of Agents, while also doing some AI implementation work for an executive recruiting firm. I’m going to talk about it briefly because I think it’s an interesting pattern for working with AI right now.

From a business value point of view, the project is about using AI to streamline fulfillment and handle more clients per employee (recruiters in this case). That’s particular to this business, but the strategy to get there is more generally applicable. We are buildin an architecture, AKA a business brain, that allows agents to understand how the business actually works so that the owner and team can use AI more effectively.

Like many businesses, their data was all over: CRM, Google docs, call transcripts, Slack, etc. What the new generation of AI agents (Claude Code, Codex, Hermes, etc). really need to be effective is structured data that lets it understand the business and it’s workflows. Things like who the candidates are, what the firm learned from previous conversations, how a search progresses, and how recruiters decide that someone is a fit. These things are often scattered around and some of it only exists as tacit knowledge: The business brain is makes all the information easy for humans and agents to work with and use.

Here’s an example: we set up a pipeline of older and incoming recorded calls with the corresponding records in the CRM. It processed 1,377 calls, matched 745 of them to existing candidates, and recovered 287 people who had previously existed only in call transcripts to put them in the CRM. Those 287 people are now searchable when a relevant role opens instead of effectively disappearing after the call.

That meant that on a recent search, an AI agent could look through 684 past candidates and produce a shortlist, including direct quotations from past conversations explaining why ten of them might fit. Doing that required the agent to have access to everything. Once it is set up, the agents enabled the recruiters to do less of the grunt work and more of the higher level work: vetting candidates, talking with clients and make judgement calls.

This is a generally applicable pattern: centralize and structure all the context/data in a way that AI understand how to navigate and then build agent-based workflows on top.

I have built something similar for personal projects: info about my house, personal finances, taxes, etc. When I ask about potentially making an investment, it understand how that fits into my portfolio, my tax situation, and financial plan.

You can imagine this at almost any type of business. An advisory firm has households, plans, meetings, and follow-up actions. Its agents could prepare the advisor before each call, catch commitments and planning issues afterward, and apply the firm’s accumulated precedent without requiring the advisor to hold every household in their head.

An agency has clients, projects, creative work, and case studies. Its agents could turn past work into tailored proposals, surface the most relevant precedent for a current client, and draft status updates from work the team is already doing.

A capability like “read 1 million words and synthesize them” is suddenly something an AI agent can do for a nickel. This makes it possible to turn the accumulated history and procedures of the company into a local operating layer that agents can act through. If you run a business and want help finding the highest-leverage places to put AI to work, you can find out more here.

On to the articles. This month they fall into three sections:

1. AI

2. Investing

3. Life

Skim or skip to the ones that interest you.

AI

LLMs Pre-Commodify Ideas

by Sachin

A good follow-on to the idea from last month that AI is a camera for latent space, the internal map of patterns a model learned during training. If you think of AI as a particular type of camera that lets us see new things in latent space then each new model means a lot of people point similar cameras at similar territory and so pull out the same analogies and frameworks at roughly the same time. Ideas arrive “pre-commodified.”

Sachin, who writes the Summer Lightning newsletter, noticed this after an LLM surfaced a paper about why factories took decades to realize the productivity gains from electricity when he was chatting with it. He noticed several other people making the same analogy. There was something in that particular model that was surfacing that paper to way more people than would ever have found it.

His claim is stronger and more interesting than “AI makes writing sound the same.” It makes the underlying thought converge too, almost like everyone is asking the same person for feedback on their article.

Simultaneous discovery obviously predates LLMs. Sociologist Robert Merton documented thousands of cases in which scientists arrived at the same discovery independently because the prerequisite knowledge and tools had accumulated. The crossbow was invented independently in China, Greece, Africa, northern Canada, and the Baltic countries and calculus was discovered by Newton and Leibniz without any real collaboration.

In a similar vein, I was at a weekend entrepreneur thing 6 or 7 years ago and 10 of the 12 people were wearing the exact same socks. We had all searched “black ankle socks” on Amazon and just bought whatever the top result was.

In that way, AI may be accelerating an old phenomenon rather than creating a new one. Historically, being the first to say something meant you got all the credit but now it may matter more if you can establish provenance.

One question is how much the local context (e.g. prompts) matter relative to the pre-training. If everyone has access to similar models, the advantage comes from the private context around the model and the judgment to work through its obvious associations. The question is how much that matters (I think it does, but the pre-training pull can be very strong.)

A Tale of Two Theories (of Coordination)

by Venkatesh Rao

This article was written by AI. Venkat started from an interesting frame and used AI to develop it into a theory of the transition from cosmopolitan coordination to what he calls “thick sovereignty.”

I wanted to include it partly as an example of interesting AI writing. AI writing seems particularly unpopular these days. At one level, I get it. I also have social media accounts, and the barrage of crap kind of sucks.

At the same time, “AI writing is bad” is lame and reactionary in an uninteresting way. Being angry at AI writing is a little like being angry at word processors.

Word processors changed the nature of what got written down. A lot of things were written on a word processor that would never have been written on a typewriter because writing, correcting, and distributing them became easier. Some of the additional writing was awful. Some worthwhile ideas also got written that would otherwise have remained half-formed. Lowering the cost of an activity almost always increases the supply and changes the distribution of what gets made.

The useful question is where and how to use AI writing. Using it for other AIs to consume (or future sessions of the same AI) is incredibly useful. I also think this version of having a thoughtful prompter guide it (as in this case) can be great.

The article’s theory is that the post-1980 order treated denser global coordination as a source of increasing returns. Longer supply chains, just-in-time inventory, frictionless capital, and regulatory convergence all expressed confidence in the same cosmopolitan model.

This seems to be changing. States and firms now pay more for redundancy, domestic capacity, strategic reserves, and the option to respond to risks. This can cut multiple ways: a semiconductor plant built for resilience may buy real option value. At the same time, subsidizing a politically connected factory that cannot compete may be little more than good old-fashioned pork-barrel politics.

The State of the AI Economy

by Azeem Azhar, William Gildea, Hannah Petrovic, Nathan Warren, and Marija Gavrilov (companion essay)

There’s a bunch of interesting data on the AI economy here. I’ll just highlight a few things.

One is the apparent elasticity of token demand. A 10% decline in price is associated with a 12% to 18% increase in usage. Cheaper intelligence does not simply reduce the bill. People find enough new uses that total demand rises, which echoes my point above about AI writing changing the shape of the distribution.

The second useful piece is about where companies actually start implementing AI. Efficiency projects are the easy sell. For one, the savings go straight to the bottom line and executives like that. “We do the same thing, cheaper or faster” is legible and easy to approve in a way that “unlocks emergent new capabilities” is not. If you’re trying to get an AI project moving inside a company, I think that’s the shape to look for first and a lever to other things.

Where I think the report is on shakier ground is the comparison to online advertising’s pay-per-click moment. Google and Meta captured most of the value they created because advertisers had no close substitute for those networks: they were network-effect-driven monopolies. Open-weight models, which buyers can download and run themselves, mean AI buyers do have a substitute. Elastic demand tells you the market grows, but it doesn’t tell you the labs keep pricing power. Elastic demand validates the category. It does not indicate whether frontier labs will retain pricing power or whether most of the value will accrue to applications, clouds, or users.

Intelligence Is Not the Main Bottleneck

by Ruxandra Teslo

The popular AI story, particularly among the labs themselves, seems to assume intelligence is the main constraint on progress. This has always struck me as a bit naive about the complexity of the real world. As one of my favorite sayings goes, reality has a surprising amount of detail.

Teslo, who works in biomedicine, points out that the data needed to validate a biomarker may already exist while access and regulatory approval take years. A bespoke gene-editing treatment can work scientifically while manufacturing and regulatory economics make it almost impossible to repeat.

Beyond strictly regulatory barriers, there are also institutional incentives. Discovering new biology may have the highest social expected value, but the company that validates a new target bears a scientific risk with many failures and a few enormous winners. It also cannot capture most of the upside once the result becomes public. A follower can aim a different patented molecule at the now-validated target and take the narrower, more predictable optimization risk. Companies can patent molecules, but not biological insight.

Movie studios are in a similar position. An original like Good Will Hunting might become a huge success, but it could also bomb badly enough to end an executive’s career. The next Spider-Man movie has a much tighter range of outcomes. It probably will not be the most culturally important film of the year, but enough people will see it that the executive is unlikely to get fired. Making another sequel is the individually smart decision inside an institution that may collectively produce too few original movies. No one gets fired for buying IBM or making Fast and Furious 9.

I tend to think of it through Theory of Constraints, a framework for finding a system’s main bottleneck: intelligence matters, but progress looks more like intelligence multiplied by the main bottleneck than intelligence alone. As one constraint relaxes, another becomes binding: experiments, data access, incentives, manufacturing, regulation, distribution, or the career risk of the person making the decision. See also here

Investing

Long-Run Asset Returns

by David Chambers, Elroy Dimson, Antti Ilmanen, and Paul Rintamäki

It is common knowledge that, over the long run, stocks outperform bonds by a lot. Well, that’s largely an artifact of data sets and how you define “the long run.” Most of the accessible and usable data sets for financial research start around 1926 and so “the long run” usually means “U.S. markets since 1926.”

This review looks further back and shows that the U.S. equity premium, the extra return stocks earned over bonds, appears to have been low or negligible during much of the nineteenth century.

A century is a very long sample in ordinary life and can still be one draw from a specific technological and institutional regime.

My speculative extension as to why the equity premium was high in the 20th century but not the nineteenth would be through Carlota Perez’s work on technological revolutions. Electricity and the combustion engine were not isolated inventions. They interacted with mass production, new corporate forms, better disclosure and governance, deeper capital markets, and the cultural normalization of ordinary people owning outside equity. The unusually high twentieth-century equity premium may be a return on that whole institutional package.

Productivity growth does not automatically happen. Even when it does, the owners of today’s public companies have no guaranteed claim on the gains. Competition can pass them to consumers through lower prices, labor can capture them through higher compensation, and new entrants can displace incumbents, especially if they remain private through their fastest-growing years.

The nineteenth and twentieth centuries may both have been anomalous and we should be slow to treat even a century of realized return as a timeless constant.

Peter Hecht: Portable Alpha, Solving the Funding Problem of Alternatives

with Peter Hecht on the Flirting with Models podcast

Traditional asset allocation requires stocks, bonds, and alternatives to sum to 100%. Hecht points out that this is an artifact of the fully funded constraint, where every dollar can be allocated only once. Relax the constraint and you can separate the broad market exposure you want (beta) from the return generated by a long/short strategy (alpha), then recombine them. You can overlay alpha on a familiar stock/bond allocation, or build a more diversified, risk-balanced base and use borrowing or derivatives to reach the desired total risk.

There have been many terms for this approach, one of them being portable alpha. Portable alpha had a bad name after 2008, but I did not understand that the specific failure sequence resulted from a liquidity mismatch.

Derivative-based equity overlays needed immediate cash as markets fell, while the hedge funds supplying the “alpha” often had 45-to-90-day liquidity. Those funds also contained hidden long equity exposure. Smoothed marks and noisy historical regressions could estimate the exposure at 0.1 beta when the reality was closer to 0.4. An institution that added a 0.9 equity beta overlay believing it owned one unit of beta could enter the crisis with 1.3 beta to equity markets and an inability to meet margin calls because of the liquidity mismatch.

That history gives a much more practical set of rules:

1. Match the liquidity of the alpha and overlay (for example, keep them in the same ETF or fund), and keep collateral and financing boring.

2. Size the tracking error, or how far the strategy can deviate from its benchmark, to an underperformance period the investor can actually hold. If you cannot hold the strategy, its expected return is mostly irrelevant.

The amount of leverage matters, but so do the vehicle, liquidity, portfolio correlations, and financing terms.

Life

What I Want My Kids to Know

by Kris Abdelmessih

An answer task is designed by someone who already knows the answer and can grade it cheaply. A result task is graded by what happens in the world: the software runs, the rocket flies, or the customer buys.

Schools naturally rely on answer tasks because they are calibrated, teachable, and easy to assess. Adult life is basically all result tasks. Those require things like doing the boring parts, failing and trying again, questioning the questioner, absorbing other people’s hard-won knowledge, accepting tradeoffs, and substituting the adequate thing available for the ideal thing.

I feel this! I was good at producing persuasive-sounding answers in school and yet entered my career feeling like I did not have many real skills. Most of the useful skills I have professionally came after a few years of actually building things that either worked or did not.

Why Europe Has Stagnated

on the Works in Progress podcast

I like Europe. It’s nice and walkable and they have great trains. It is not in a great place economically. There are lots of theories as to why. This podcast presents one that I like: European stagnation is the accumulated result of institutions repeatedly choosing the claims of people who already have something over the capacity to adapt.

One example is German co-determination. It is an institutional structure where works councils can veto restructuring and occupy half the board seats at large companies. Volkswagen had a jobs guarantee dating to 1994. When Chinese competition made factory closures economically attractive, the works council helped extend the guarantee through 2030. The institution is partly run for the benefit of the people who currently work there, even when restructuring might benefit future employees or the long-run survival of the firm.

France is an interesting counterexample. It remains roughly as wealthy as Britain despite heavier taxes and regulation. They argue this is because it built the basics first: abundant housing, motorways, mass transit, and a huge nuclear fleet. That productive capacity made later redistribution and regulation affordable. The “spending down inherited capital” story is a hypothesis about Europe rather than a demonstrated causal result, but I find myself partial to it. A country can consume physical and institutional capital built under an earlier regime for a surprisingly long time.

I’m writing this on a Monday morning after one of those early-summer weekends that’s hard to improve on: a round of golf Saturday morning, a long afternoon on the floor with my son, a date night with my wife, and hanging with friends at home then the pool on Sunday. My toddler just learned to say “I love you” unprompted, and it is absolutely wrecking me. Hope your summer/winter has been off to an equally good start.

For the last six months I’ve been building what I’ve started calling an AI business brain, a personal operating system on top of Claude Code that more and more of my work now runs on. It’s changed how I work more than anything has in years (probably ever?)

I just published the first of a few pieces on it, A Lever Made of Agents, that looks at why it’s been so impactful. It goes into why agentic AI is a new source of operating leverage and why the durable advantage comes from process power, not prompts. Two more are on the way in the next few weeks: a more practical build guide and a case study of how I use it.

I’ve started working with companies to implement this system. If you run a business and want help finding the highest-leverage places to put AI to work in your business, you can find out more here.

Why AI Didn’t Transform Our Lives in 2025

by Cal Newport (Archive.is Free Version)

I talked to Cal Newport for his New Yorker column about why AI didn’t upend everything in 2025. My wife thinks it hilarious that it’s built around how I used Claude Code to replace our HVAC system.

I used this example because I think it’s actually a great use case for thinking about how knowledge work broadly (rather than just software engineering) can be improved by AI.

I had a 27 year old HVAC system that needed to get replaced and a stack of HVAC quotes that read as gobbledygook to me. I pointed an agent at the PDFs and had it pull the actual unit models, find the manuals, and started work out what to do. It took a few sessions and a lot of supervision, but I came out with a better system at a better price than if I’d just picked a quote.

It’s a good AI use case because picking an HVAC system kind of looks like a lot of knowledge work. There is not one “right” answer and, unlike with code, you can’t just cheaply build a version and test it. Getting a “good solution” for an HVAC system is pretty similar to making a “good presentation” at your job or giving a customer a “good customer service experience.”

The job requires thoughtful research and good project management: read some quotes, track down some manuals, cross-reference the specs against the actual constraints of my house, and synthesize it into a logical recommendation given the context.

For a human, that’s an enormous amount of unfun stuff so most people just sign a quote from whoever gives the best vibes. Once you have an agent that never gets bored and can read a vast amount in minutes, suddenly the task is mostly just steering which is both faster and more fun.

Once I saw how much of my real work has that same shape (Taking lots of information, coming up with a logical synthesis and moving it forward), I started building the same setup deliberately, at the scale of the whole business. That’s what became the AI business brain, and it’s the thing I’m most excited to be working on right now.

A Camera, Not an Engine II

by Venkatesh Rao

Venkat has a frame for AI that I quite like. We keep talking about AI as an engine, but the more interesting work is camera-like: an instrument for seeing in latent space, not an engine of production.

An engine takes stored energy and turns it into motion. A camera shows you something you couldn’t see before.

Using AI like a camera is when you point AI at a domain you only half understand and use the feedback to learn its shape. That’s some of the most interesting and fun experience I’ve had with AI. I wrote an article years ago about how we should optimize for interesting. It’s a summary of some research arguing that the evolutionary rationale for the emotion ‘interesting’ is that we experience it when we are working on something where we have the potential to make rapid progress in learning.

Interesting is the feeling of compression progress: the moment a pile of messy detail collapses into a simpler pattern you can hold in your head and you feel like you gained real insight.

That’s exactly what a camera-like tool is good for. There’s a huge amount of latent knowledge sitting around what you’ve already read and half-thought, and these tools are unusually good at helping you surface it. The bonus with agentic AI is that you can build the thing out as you go, so each pass leaves you with a little more structure than the last. I’m learning faster than I have in a long time and that’s the part I think is really fun.

When Action Beats Prediction

by Cedric Chin

Cedric Chin has a good summary of Saras Sarasvathy’s work on effectuation, which is a fancy word for a simple entrepreneurial pattern: in highly uncertain domains, action often beats prediction. It is the best theoretical model for entrepreneurship I know of and a thread I’ve pulled on before.

The standard strategy model says you forecast the market, pick the best expected return, and execute. In practice, good entrepreneurs often do something different. They start with who they are, what they know, and whom they know. Then they take small actions, recruit committed stakeholders, and let surprises become data.

Causal reasoning asks, “What’s the expected return?” Effectual reasoning asks, “What can I afford to lose?” It rhymes with some of the ergodicity/Kelly-betting thread for sizing investments that I’ve written about. Both approaches suggest not making one giant forecast-dependent bet, but thinking about a series of non-ruinous ones that expose you to upside and teach you something about the territory.

Planning isn’t bad and forecasting isn’t useless, but the best measurement instrument is often a cheap action. E.g. Try to sell the thing before you spend too much time on the spreadsheet.

One observation I have about good entrepreneurs is that they both tend to seek out surprise AND they tend to re-orient around it much faster than most people. To paraphrase Peter Drucker: unexpected success is the best source of strategy.

After Automation

by Dan Shipper

You’ve probably seen charts of AI models rapidly scaling benchmarks accompanied by scary quotes about 50% of all jobs being eliminated. Dan makes a point that I think is central to the next phase of AI work: benchmarks happen inside frames.

If you look at the actual questions used on these benchmarks (and there are some examples in the article), you will notice that they are very specific and thoughtfully framed questions. They layout what data is relevant, how to think about, and what a good output looks like.

For most knowledge workers, that’s the hard part of work!

A benchmark can tell you whether a model is getting better at a specific task. That’s useful, but it hides that someone had to decide what problem mattered, what counted as success, where the boundary was, and what tradeoffs were acceptable. It is absolutely true that the models can handle more and more expansive frames, but there’s always some framing going on.

This is why I’m skeptical of simple “AI can do X percent of jobs” arguments. The framing is often doing more work than the model. Give Claude a well-specified audit task with clean spreadsheets, explicit constraints, and a narrow output format, and it may perform very well. But a lot of “intelligence” went into making the problem look like that in the first place.

The strongest version of the argument grants that models can help with framing. They can, and I use them for that constantly. The point is that someone still has to decide which frame matters, when it’s exhausted, and what counts as a better one. That kind of judgment is what David Chapman calls meta-rationality, and it’s where a lot of the value is moving.

Alex Imas and Phil Trammell: What Remains Scarce After AGI?

Dwarkesh Podcast

Labor share’s share of income has stayed surprisingly high (always around 60%) through centuries of automation. Machines changed farming, manufacturing, accounting, logistics, and office work, but the economy didn’t simply become “capital earns everything, labor disappears.”

The explanation I most like for this is that jobs are bundles of tasks and technology changes the bundle and re-assigns values but doesn’t make it go away.

Being a surgeon requires many different skills and doing lots of tasks. You need to have good fine motor control, you need relevant medical knowledge, and it’s nice if you have a pleasant bedside manner. It’s also true that a lot of medical work is paperwork, insurance calls, forms, notes, and care coordination.

If AI automates some of those tasks, the remaining work may become more valuable, not less. The same person might spend less time filling out paperwork and more time doing relational tasks and using their fine motor control.

Benedict Evans makes a similar point about the unpredictability of how technologic impacts jobs in his piece on AI job exposure. We’ve spent a century automating accounting with computers. Today, we have more accountants than ever. Job titles are often bad maps of the work underneath them and making one part of a job easier may also make other parts of it more in demand. I would love it if my doctors spent more time talking to me and less time filling out forms.

None of this means AI will be painless for labor. The messy middle could be very messy. But “task exposure” and “job replacement” aren’t the same thing and most forecasts blur that distinction.

My Agent Stack for Automating My Personal Life

by Nicolas Bustamante

For me, the useful takeway in this thread is that an agent can do the glue work across tools, not just answer questions.

Nicolas gives the example of a friend asking for an intro. The real work was scattered across WhatsApp, Gmail, Google search, job postings, email drafting, and a follow-up text. None of the steps were hard. They were just context-switchy and time consuming. The agent read the thread, found the contact, researched the company, drafted the intro, and waited for approval.

That’s the pattern I think most businesses should be looking for as no brainers right now and rhymes with the HVAC example I gave earlier. The best early AI workflows often aren’t glamorous. They’re the annoying cross-service and cross-context workflows where attention leaks out through app switching: email to calendar to Drive to CRM to browser portal to Slack. The work isn’t complex, but it’s expensive because it fragments the day.

The security caveat matters just as much as the workflow. Once an agent can read private messages, browse the web, and send things, you’ve built something powerful enough to hurt you. My own bias is to keep the line very clear: agents can read and draft; humans send.

The Millennial Midlife Crisis Is Going to Be a Barbell

by Priyanka Desai

Priyanka Desai’s piece is a very online, New York-coded theory of millennial midlife, but a lot of it hits for my less online and less New York social reality.

Technological abundance tends to change what’s high status. If one signal gets cheap, the market searches for a costlier one.

For a long time, thinness was a status marker because it implied discipline, genetics, money, or all three. GLP-1s change that. If appetite can be pharmacologically muted, then being thin becomes less impressive. Status moves to what’s still hard to fake: muscle, VO2 max, marathon times, ritual, membership, real-world intimacy. Protein-maxxing, run clubs, and deliberately offline rituals are all versions of this.

The weirdest example in the piece is South Korean “dopamine sites,” where people browse fake restaurant menus, fill carts they won’t check out, and simulate consumption without acquisition. I kind of like this theory that we’re all culturally downstream from South Korean teenagers. It’s both horrifying and hilarious in a Douglas Adams meets Black Mirror sort of way.

If you’re active on any of these platforms and want a daily(-ish) thought between newsletters, you can follow me on Substack Notes, Twitter/X, or LinkedIn.

Editorial note: I’m taking on a few additional companies looking to find the highest-leverage place to put AI to work in their business, and build it. If you’re interested, you can find out more here.

When the internet showed up, every newspaper in the country had the same idea: put the paper online. Readers everywhere, no printing costs. It looked like free money.

US newspaper ad revenue peaked around $49B in 2005, down ~80% in fifteen years.

Using newspapers as an example of technological disruption is a cliche at this point, but I’ll be lazy and use it because it’s a well known example of how technology changes supply/demand dynamics and what the result is.

Supply went towards infinity: anyone could publish anything online. Demand for digital media went up but not nearly enough to compensate. We learned again that the price of anything abundant goes one direction: toward zero.

A lot of the excitement around agentic AI is about vibe-coded apps and one-shot prompting. This is 100% super cool! It’s how I started playing with things and it got me hooked on using Agentic AI. It is, however, abundant.

To borrow another phrase from the economists: solve for the equilibrium. The equilibrium price of anything you can build with a one-shot prompt is the token cost to build it because anyone else can build it too. 1

The fall of newspapers wasn’t the fall of content, we have more content than ever but the value chain shifted. The value went from distributors of content (newspapers) to aggregators of content (Meta, Google, etc.).

AI will likely have a lot of far reaching implications that I have not even considered and shift the value chain in many different industries. Let me propose one such shift based on a personal area of expertise: systems.

A lot of my career has centered on building systems and processes at small/early-stage companies. I like the Toyota Production System, Theory of Constraints, and SOPs.

So, when I started using Agentic AI tools like Claude Code, Codex, and OpenClaw, what got me excited about them was the potential to use a new technology to build business systems in new and defensible ways.

The question I’ve been asking as I work with these tools is how do you use AI to do this in a way that builds sustainable competitive advantage? What is the new capability unlocked by this technology and how do individuals and businesses best leverage it?

Most people’s answer so far is a chatbot in a browser tab. You open Claude or ChatGPT, paste something in, get something back, paste it where it goes, and do it again. For most use cases, you end up about twenty percent more efficient on the things that were mildly annoying to begin with, and none of it touches the work that actually moves the business.

A key shift is the move from chatbots to agents. In As We May Work, I argued that agent harnesses (Claude Code, Codex, OpenClaw, Hermes, etc.) give AI two new and important abilities that transform how AI can be used.

1. Long-term Memory. It can read and write notes to its future self (context, decisions, history) that persist across sessions.

2. Agentic-ness. It can execute multi-step work autonomously while you’re doing something else.

The harnesses started as tools for programmers and that’s still they’re primary use case. However, they’re becoming a new operating system layer for knowledge workers more broadly.

In The AI Knowledge Work Stack, I argued that a clear architecture is emerging for working with these tools.

The bottom two layers of the stack are the AI model (Opus, Fable, GPT, DeepSeek, Gemini, etc.) and the agent harnesses. Models and harnesses at this point are pretty clearly their own product categories and it doesn’t make much sense to roll your own for all but the largest businesses.

The durable leverage in this architecture for most businesses lives in the local scaffolding layer: what sits on top of the harness, pulls context from your other services, and tells the agent how your work works.2

This is your AI business brain: the thing you own that makes the company more effective and defensible.

What this looks like is going to be different for every company, but I think it has a few core form factors at this point:

• Memory — Files where knowledge of the business accumulates: the current state of every project, the history of what’s been done, and the reasoning behind decisions. It’s what makes starting a session with the AI feel like talking to someone who already knows you and your business and what makes it defensible.

• Skills — Something akin to standard operating procedures (SOPs) written for an AI instead of an employee: how to draft the newsletter, close the monthly books, turn sales-call notes into follow-ups. The agent pulls in the right one when the task calls for it.

• Bespoke software — Everything from large software (e.g. a custom CRM) to small custom scripts and tools for the work that needs the same answer every time: pricing, reports, the bookkeeping math.

The newspapers adopted the internet just fine; what they never asked was what it would do to the economics of their business. AI puts the same question to every business: when your competitors have the same models you do, where does durable advantage come from?

7 Powers

In his book 7 Powers, Hamilton Helmer has a framework for the seven sources of economic power: the things that generate persistent above-market returns in a business.

1. Scale Economies — per-unit costs decline as volume grows (Amazon, Costco).

2. Network Effects — value to a customer increases as the user base grows (Facebook, Visa).

3. Counter-Positioning — you adopt a model an incumbent can’t mimic without cannibalizing their existing business (Netflix’s no-late-fee subscriptions against Blockbuster, whose model relied on late fees).

4. Switching Costs — customers expect a greater loss from switching than the value they’d gain from a competitor (SAP; QuickBooks, once it holds five years of your books).

5. Branding — higher perceived value for an objectively identical offering (the same diamond costs more in a Tiffany box; Coke against store-brand cola).

6. Cornered Resource — preferential access to something coveted (drug patents, Pixar’s “Brain Trust” of star directors).

7. Process Power — your organization and activity set produce lower costs or superior product, and matching it requires extended commitment (Toyota’s production system).

The first two of these (scale economies and network effects) are structurally closed to small and early-stage businesses (sub-50 people). Scale economies require scale and network effects require a network. You can grow into both, but no one has them out of the gate and very few businesses ever get them.

Two more are usually filed under big-company powers but exist for some small businesses in weaker forms. Any service business that accumulates deep client knowledge (the accountant who already knows your books, the IT firm that manages your whole stack) is building switching costs. A liquor license, an exclusive dealer territory, or a fifteen-year lease at below-market rent on the best corner in town can be a cornered resource. In my experience, not that many businesses have these and they are sufficiently weak at most business’s scale that neither is usually decisive on its own.

Counter-positioning is sometimes available (Vanguard against actively-managed mutual funds, Dollar Shave Club against Gillette). But it requires a structural conflict at an incumbent, and most startups and small businesses compete in fragmented markets where there is no incumbent to counter-position against.

That leaves two powers available to essentially every business: Branding and Process Power. This is what most businesses actually rely on. They do something good reliably (process power) and have a reputation for it (branding).

Most businesses start because the founder is good at something or has a good idea. They get to some meaningful stage because the founding team is good enough at sales/marketing plus some other competency.

The rainmaker agency founder is better at paid ads than anyone they’ve hired and can sell. Suddenly there’s a team of media buyers now, but they are still the one closing every major account, setting the strategy, and when a campaign underperforms it’s their gut that knows which lever to pull.

Or the e-commerce founder who sells equipment to gym owners: there’s a team running fulfillment, ads, and customer service, but he’s the one who knows the customer well enough to see what they’ll want next and every winner in the catalog was his call. At this stage, the founder is basically what’s defensible. The founder is also the constraint: the business can’t grow past his calendar, and he can’t take six weeks off.

This is a pretty well understood and common phenomenon. Books and approaches like The E-Myth, Work the System, Traction (AKA EOS), and Scaling Up are all variations on solving this same core problem and helping businesses build process power.

The Four Levels of Organization

When you break down what those approaches prescribe, you can think of it as four levels of organization:

1. Software

2. SOPs

3. Expertise

4. Culture

Each level is know-how living in a different place.

• Culture lives in myth: how-we-do-things-here, absorbed by being somewhere, usually written down nowhere.

• Expertise lives in a person’s head: it does the job reliably, but it can only partly explain itself, and it walks out the door at five o’clock.

• An SOP lives on paper: expertise made explicit enough that a competent hire can run it.

• Software lives in code: it is knowledge made so explicit that a machine can execute it with no judgment at all.

The dimension running up the pyramid is explicitness: how fully the knowledge has been (or can be) spelled out.

The more explicit the know-how, the cheaper it is to run so each level is also a different cost structure.

• Knowledge in a head you pay for every time it runs: an expert’s salary.

• Knowledge on paper you pay an expert for once, and then it runs on lower wages.

• Knowledge in code you pay for once, and the marginal run is close to free.

Finance people call this operating leverage: costs that stay fixed while volume grows. It’s why a software company’s margins expand as it grows while a service firm’s margins usually don’t. When the next customer costs almost nothing to serve, growth compounds into profit instead of into headcount. A business with no operating leverage has to buy every new dollar of revenue at the same cost; a business with a lot of it gets each new dollar cheaper than the last.

Getting systematized is a way to generate operating leverage. It’s a financial engineering move sold to entrepreneurs as a stress reducer. More operating leverage is easy to convert into more vacations and people like piña coladas more than financial engineering.

No matter how you frame it, the result is that every procedure you push up the pyramid converts a little bit of payroll into an asset. This means either higher margins (and less stress) or the opportunity to reinvest for growth.

Moving work up the pyramid has a one-time cost. An expert documenting a process might spend three times as long as just doing it, because they have to make explicit every call they normally make without noticing.

But there’s a clear threshold where it’s worth it. Take a task you do every week that takes about an hour, and assume your time is worth $100 an hour. Three hours to write the SOP is a $300 investment. Hand the task to someone at $30 an hour and you save $70 a week, about $3,600 a year.

The SOP pays for itself in about a month and returns roughly 1200% in the first year. An index fund compounding at ten percent takes about twenty-five years to return that much.

Many businesses have dozens or hundreds of those tasks in them, each one running on someone’s memory. Most owners have never added them up. Not all know-how can (or should be) made explicit. You can’t convert strategy into a SOP or piece of code, and it’s foolish to try. You can, however, convert bookkeeping best practices or how to format a blog post..

Through the lens of operating leverage, each of these books is a strategy for moving work between the layers, and together they form a rough sequence.

The E-Myth (Michael Gerber) and Work the System (Sam Carpenter) are where many businesses start as they both focus on building out written documentation. The move is the same in both: get the work out of the founder’s head (Expertise) and into SOPs so that someone other than the founder can do it.

Carpenter wrote Work the System about his telephone answering service, a business that was almost entirely people executing procedures. Document the procedures well enough and you can run the business on people you can actually afford to hire. The most deterministic of those SOPs migrate up another level, into software. Larger companies push the same idea further and build internal tools.

Gino Wickman’s Traction (and Verne Harnish’s Scaling Up, its equivalent for somewhat larger companies) is the next level. Most of its artifacts are SOPs for managing the people who run the SOPs: the standing meeting agenda, the weekly Scorecard of KPIs, quarterly priorities, a mechanism for cascading strategy through the organization.

They are playbooks for coordinating the bottom two layers: expertise and culture (e.g. right person in the right seat, core values explicit enough to hire and fire against) because at that size the constraint is no longer documenting the work; it’s keeping the humans aligned while they do it.

Technology and New Forms of Operating Leverage

Over the last couple decades, software tools have gotten cheaper and more accessible. Underneath the palm trees, Tim Ferriss’s The 4-Hour Workweek was the manifesto of the internet’s deployment era with two storylines.

The first is a leverage story: email marketing, self-serve ad platforms, and cheap offshoring gave a small, internet-based business the kind of operating leverage that used to require an enterprise.

The second is a threshold story about converting that operating leverage into growth within existing businesses and by lowering the minimum viable scale of a business.

Chris Anderson called the lowered minimum scale on the demand side the long tail. The internet aggregated niche demand that had never been worth serving. Suddenly it was possible to start a small business with customers in 100 different countries.

Combined with new tools plus offshoring on the supply side, a small business could suddenly serve what seemed like a small niche profitably.

I wrote a book on this in 2015, The End of Jobs, which argued that the long tail had made a whole new type of business viable. One business owner I profiled, Andrew Youderian, built a business around CB radios, a niche too small for any physical store, by writing online guides that answered buyers’ questions, work that used to require a salaried person on the floor.

The specific playbook in 4HWW got arbitraged away fast. A chunk of the book was, in practice, “Google SEO and AdWords are underpriced.” That stopped being true about as fast as you’d expect, but the broader phenomenon it pointed at ran for two decades.

Some niches stayed small and others turned out to be new categories. Rogue Fitness started in a garage selling equipment to other garage gym owners and now owns the category. RXBar started in a basement making protein bars for CrossFit gyms and sold to Kellogg for $600 million.

No-code tools like Zapier entered the picture in the 2010s and were symbolic of the tooling available to non-technical businesses to push tasks that used to require humans into software.

It was suddenly viable to set up an abandoned checkout flow that sent a personalized follow-up email and added a tag in the CRM. A brick-and-mortar shop could never do the equivalent; it would have meant paying an employee to trail every browsing customer out of the store asking follow-up questions. Ignoring the fact this would be creepy, it’s also expensive.

The same payback math applied. A task someone does an hour a week at $30 an hour is a ~$120-a-month line item; an afternoon of building converts it into a thirty-dollar workflow, and it pays for itself in about a quarter.

The wage line became a software line, businesses got more operating leverage, and automated follow-up went from impossible to table stakes in about a decade.

All of these historical tools for creating operating leverage still work and still matter. But every book in that canon is grounded in the same assumption about the structure of the pyramid and the tools available. And we just got a new tool: thinking machines.3

The Five Levels of Organization?

Agentic AI is capable of generating leverage at multiple levels:

1. Supplied with proper content, they’re helpful thinking partners that can enchance expertise.

2. They are also capable executing lots of tasks that were historically only possible with a human. “Read this 30 page PDF and summarize how it applies to our project” is suddenly something a computer can do.

3. It also makes it dramatically cheaper for humans to build software by making software developers more productive and unlocking software development for lots of non-developers.

In short, they are a new source of operating leverage.

Agentic AI offers both an opportunity and a threat to building a business with process power in the same way that the internet unlocked new business models and challenged old ones.

Process power works like compound interest. It’s the accumulation of a thousand tiny improvements. It’s things like a better way to greet a customer and a checklist that catches one more defect. Any one of them is trivial and easy to copy.

What turns that pile of improvements into something a competitor can’t easily copy is the emergent value of all the integrated components. It’s the thousand small things start to depend on one another until the system stops being legible from the outside. When GM got a full tour of Toyota’s plant and copied every visible procedure, they still couldn’t reproduce the results which is precisely what made Toyota defensible.

Helmer’s term for the barrier is hysteresis: the advantage can only be built by walking the path because each improvement is the soil the next one grows in.

Bashō said it more cleanly four centuries ago: “Do not seek to follow in the footsteps of the wise; seek what they sought.” A competitor can copy your footsteps, your current procedures, but the footsteps aren’t where the power is. Process Power works the same way: you don’t design it from the top down, you craft it through small choices over time.

You Get A Process Power and You Get a Process Power

What Agentic AI uniquely enables is a shift in the level of granularity and scale at which individuals and companies can build process power. Historically, one person could only manage so many processes before needing to hire someone else to take over managing them.

The rise of agents has changed that calculus. One person or a small group can now oversee an army of agents and custom software that allow them to operate at a historically unprecedented scale.

Here’s an example of what that looks like in miniature. My monthly newsletter is a curation of the best things I read each month. As I read, I highlight and annotate whatever catches me.

When it’s time to draft the newsletter, I used to manually go through all the articles and pick out the ones to put in the newsletter. Now, an AI agent using a skill file ranks the month’s reading by how much I marked it up, writes a report on each top candidate, and hands me ten to twelve to choose from.

I pick the four to eight worth sharing and write the editorial take. The same skill routes leftover highlights to the article drafts and projects they’re relevant to, turning my reading notes into actionable tasks for things I’m working on.

Formatting the newsletter for each platform (X, LinkedIn, Email, etc.) used to take a couple hours of fiddling; now a skill file and a small script do it in two minutes. A separate skill generates a custom image optimized to each platform’s algorithm and size requirements. That’s a little bit of operating leverage.

The margin story of AI is to replace humans with tokens. The growth story is to use tokens to make new forms of work and human agency economically viable. You see both here.

There are agents enabling more margin by going through the docs and formatting them faster. There are also agents unlocking new capabilities that never happened: integrating every note or highlight into a relevant project as reference material and generating custom images.

Other Than the Sum of Its Parts

You can scale the same shape up to a full service business. An independent financial advisor with fifty client households could have an agent maintain a folder per household: account structure, the plan, the tax picture, and a timeline of every decision and the reasoning behind it.

A pre-call skill preps every meeting. A post-call skill turns her notes into action items and files the why. When she figures out the right way to handle a client’s car-purchase question, that method becomes a skill that runs against all fifty households. The portfolio math lives in small scripts that can pull from custodian APIs. None of the pieces is special on its own, but the combination of them can create a huge amount of operating leverage.

It’s the SOP math run across the whole business. Each one may be another small build throwing off twelve-times return, and there are dozens of them hiding in most businesses. Stacked up, the time and cash they free adds up to something significant.

That freed-up time and money has two places to go. One is margin: the payroll that just became an asset and the hours that are saved drop straight to the bottom line.

The other is recombination into new possibilities.

This is a one-person business, but it scales again to a larger business where other people have access to the company’s institutional memory and skills. For the founder who was the constraint, the job goes from operator to designer: you stop being the person every decision routes through and start building the system that handles them. A procedure you design once runs every week, across every client, without you in the loop.

The opportunity for both companies and individuals is to retool how they think about and execute process power in a post-AI world.

The First Percent

The 4-Hour Workweek moment was about the realization that there was a new source of operating leverage for existing businesses and a whole new type of business that became possible.

That’s the same set of questions agents re-open. What becomes viable when the SOPs execute themselves and the custom software gets built in an afternoon? That playbook hasn’t been arbitraged away yet and we’ll see it play out over the next few decades.

As I mentioned at the stat, I am taking on a few companies to work with to find the highest-leverage places to put AI to work in their businesses. If that’s interesting to you, you can see how it works and learn more here.

Forward Deployed, Episode 5: Aligning Agents

with Noah Brier

I chatted Noah Brier on Forward Deployed to dig into how AI is reshaping organizational structure and what knowledge work actually looks like day to day in a post-agent world.

We both suspect agentic systems look more like companies than software. Agents execute well when problems are well specified but can aim at the wrong problem. This is the same problem people often have so why wouldn’t we lean on the near century of organizational theory we have on how to solve them?

We get into some of our favorite concepts and how they apply to AI including Pace layers (Stuart Brand), OODA loops (Boyd), transaction cost economics (Coase), and Cynefin (Snowden).

What Will Be Scarce

by Alex Imas

Over the course of a few years, Starbucks automated more and more of their coffee-making process. This is a very logical, business minded thing to do: margins were getting tighter and so they looked for costs savings. The costs savings worked, but they walked the whole thing back.

It turns out people don’t just go to a coffee shop for the best coffee at the lowest price. Those are bundled into a larger experience. The CEO concluded that handwritten notes on cups, ceramic cups, and “the return of great seats” were what drove satisfaction. I wholeheartedly endorse this. I am not much of a coffee connoisseur but I am very much a comfy coffee shop connoisseur.

It turns out that having a one minute conversation with the barista and them writing your name on the cup was actually very important to the value of Starbucks’ stock.

This is an example of a broader phenomenon that suggests something about how economies may be reshaped by productivity gains from AI. As people get wealthier, they don’t just buy more of the same types of stuff. They reallocate spending towards new types of goods.

2022 BLS data shows households in the top income quintile spent 4.3x as much in total as the bottom quintile, but the ratio is much larger in categories with a strong relational component: in-person dining, entertainment, and education.

My observation talking with high-net-worth investors is that as people get wealthier, the share of conversation about investing keeps shrinking. Part of this status signaling (only really rich people can afford not to think about money), but part of it is that the marginal amount of attention and money is better rewarded thinking about parenting, marriage, and finding purpose.

Imas argues that as AI grows the share of the economy devoted to these ‘relational’ services is going to grow. Economic forces once moved 40% of the American workforce off farms and into factories and can now move workers out of automatable commodity production and into sectors where humans are part of the product.

Once basic needs and capital allocation are handled, the binding constraint isn’t more money. It’s living well. And living well is largely a relational problem: “No man is a failure who has friends.”

The Architecture of Complexity

by Herbert A. Simon (1962)

There is a tendency (of which I have been guilty) to fantasize about “equal” or “fair” systems where all participants are equal. I was at one point, enamored with concepts like holocracy. Simon’s 1962 paper argues that these approaches don’t work. Complex systems in nature, biology, society, and engineering overwhelmingly take the form of hierarchies and there is good reason for this.

Consider the story of the watchmakers:

Two watchmakers, Hora and Tempus, both build watches of about 1,000 parts. Tempus assembles each watch as a single integrated process — if interrupted (say, to answer a customer call), the partly-assembled watch falls apart and must be restarted from elements. Hora, by contrast, builds his watches from stable subassemblies of roughly ten parts each, then combines ten subassemblies into a larger subassembly, and ten of those into the whole watch. When interrupted, Hora loses only the current subassembly.

Simon’s arithmetic shows that with a 1% chance of interruption per step, Tempus takes roughly four thousand times longer than Hora to complete a watch. The general result: evolution of complex forms depends critically on the existence of stable intermediate forms, and time-to-assemble grows with the logarithm of the number of elements rather than linearly.

This made me think of Saras Sarasvathy’s notion of effectuation: the idea that entrepreneurs build from the means at hand (what you know, who you know, what you can already do) rather than working backward from a target. You can build a system, a business, say, much faster by combining fleshed-out subsystems, rather than starting from scratch.

AI, Tractors, and the Productivity Paradox

by Sachin

From 1903 to about 1950, farmers used their cars in ways manufacturers never intended. They blocked up the hind axle and used the power of the engine to run things like corn shellers, saws, hay balers, and cream separators.

One rancher used a Cadillac to shear sheep and a Maine farmer used his car for so many jobs that he got into a dispute with the tax assessor who couldn’t decide whether to classify it as a pleasure vehicle or agricultural machinery.

The car gave people access to a new and powerful general purpose technology, the internal combustion engine, and they extended its use into all sorts of tasks that it hadn’t been explicitly designed for.

Eventually, organizations like Ford figured out how to take all the scattered approaches and designs and embed them in a single organization at scale to make things like cars and tractors. Effectively, companies productized the various ‘kit’ use cases which ‘indie hackers’ had been doing in bespoke and sub-scale ways.

Many general purpose technologies went through similar ‘kit’ phases that are poorly understood because so little was written about them and no institutions were built around them.

Sachin’s argument is that AI is in the same place cars were around 1915. Robert Solow’s 1987 line about seeing the computer age everywhere except in the productivity statistics is back but this isn’t unusual. Gains from a general-purpose technology lag investment by a decade or more.

My favorite example: In 1969, Neiman Marcus ran an ad in their Christmas catalog for a Honeywell personal computer. It was priced at $10,600, about $85,000 in today’s money, and weighed over 100 pounds.

For that you got the computer, a cookbook, an apron, and a two-week programming course. The tagline was perfect: “If she can only cook as well as Honeywell can compute.”

We are very much in the “kit phase” of AI with custom GPTs, niche agent scaffolds, individual tinkerers wiring LLMs into their own workflows. It’s not clear what the ‘productized’ version of these tools will look like.

Sachin takes a Coasean angle on that which is very much in line with how I’ve thought about it. LLMs erode the firm’s advantage market transaction costs (finding specialists, evaluating contractors, writing enforceable specs) are collapsing fast.

I have been thinking about it as a constellation of technologies. The internet collapsed discovery costs. LLMs are collapsing coordination and contracting costs. If crypto and stablecoins eventually collapse enforcement costs, you get more, smaller firms. Is there some productized layer on which all these run?

The Nuclear Renaissance

Works in Progress Podcast

In the 1950s and 60s, there was a huge push across the developed world to build nuclear power. The reason was simple: nuclear was the cheapest electricity anyone had ever produced. In present day costs, it was about $500 per kilowatt of capacity, three cents a kilowatt-hour, and a nuclear plant could be built in roughly four years.

The reason it was cheap was simple.

1. You could use the same turbines boiling the same water as a coal plant, just heated a different way. So the whole supply chain for that was already well-established and functional.

2. The cost savings came from the fact that uranium is roughly 300,000 times more energy-dense than coal, so fuel is a trivial 2% of the cost of running a nuclear plant whereas fuel is about half the cost of running a coal plant. It costs a lot more money to dig up mountains of coal and ship it to a power plant than it does to produce uranium.

Ergo, nuclear power should be about half the price of coal generated power. For the first decade, it was. Then, something happened.

The popular narrative that I had understood was that nuclear production declined in response to events like Three Mile Island and Chernobyl. The reality was those were part of the picture but the bigger problem was nuclear got a lot more expensive.

The most recent American plant, Vogtle in Georgia, came in around $16,000 per kilowatt and took 27 years to plug in. It was thirty times more expensive and seven times slower than plants built half a century before.

Nothing about the engineering got harder. What happened?

The cost explosion was regulatory and it traces to one mechanism: ALARA which stands for “as low as reasonably achievable,” a safety regulation adopted in the 1970s.

ALARA obliges an operator to spend on any safety improvement its profits can absorb. This means any additional profit has to be invested in additional safety even if it’s not a material risk.

That means as oil prices rose, and two oil shocks hit right as ALARA came in, nuclear’s profit got eaten by new compliance costs. As energy security has become more of an issue, there’s a move back towards nuclear in some places but the compliance and regulatory piece will have to get worked out.

Meaning Lost, or Muddled by Metaphysics

with Andrew Conner and David Chapman

Is the “meaning crisis,” the modern sense that we’ve lost the sources of meaning that religion and tradition used to supply, actually a loss at all?

The cognitive scientist John Vervaeke says yes. His account runs a 2,500-year arc, from a world where people were embedded in a meaningful cosmos to one where Descartes and Kant left us sealed inside our own heads. In his view, we lost something real, and the repair is to rebuild it deliberately, through practices that train the kinds of knowing that argument alone can’t reach.

Vervaeke even names the toolkit: DIME — dialogical, imaginal, mindful, embodied.

Dialogical is real conversation over debate: Socratic dialogue, or “circling,” where a few people attend out loud to what’s happening between them. Imaginal is working with image, myth, and symbol — active imagination, meditative reading, internalizing a wise figure until you can ask what they’d do. Mindful is the meditation family: sitting with the breath, watching attention move. Embodied is practice that lives in the body — tai chi, yoga, martial arts. The bet is that meaning comes back through doing, not through believing the right things.

I have done some version of all these practices and found them helpful. Chapman’s move is to reframe and argue that we didn’t, in fact, lose anything. Rather, we inherited a wrong idea, traceable to Plato: that for a meaning to be real, it has to be perfectly definite.

Hold that assumption and you get whipsawed between two errors. Eternalism says meanings are fixed and absolute; nihilism says that because nothing is fixed, nothing means anything. Eternalism more or less worked at a societal level until post-modernism showed up and spun everyone out into nihilism.

Both philosophies are problematic in that Platonic way: they refuse the obvious middle, where meaning is patterned and fuzzy at once. It’s real but not in a sharply defined ‘Platonic’ way.

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It sat roofless for decades because nobody could figure out how to span it. Traditional wooden centering would have required more timber than existed in Tuscany.

In 1418, Filippo Brunelleschi, a goldsmith by training, proposed building two nested domes with no centering at all and was hired to build it. It had never been done before. He spent 16 years to finish what is now known as “Il Duomo.”

His biggest innovations weren’t in his original proposal. They were invented on site in response to problems discovered during construction.

He developed a herringbone brickwork pattern that let each course transfer its weight laterally to the nearest rib, solving the problem of laying masonry on a curve without any support underneath. He designed an ox-powered hoist with the first known reverse gear so the animals never had to turn around. It lifted 37,000 tons of material over the course of the project. He embedded stone-and-iron chains inside the dome like barrel hoops to resist the outward thrust that would otherwise push the walls apart.

In the way we think about construction roles today, he was simultaneously architect, engineer, and construction supervisor. He was part of the master builder tradition that had existed for millennia: Imhotep, the medieval cathedral masons, Vitruvius. The split between designer, engineer, and builder didn’t yet exist as a distinct concept, much less as a set of contracts or roles.

The split wasn’t preordained. It was the result of specific technological and economic conditions. The Industrial Revolution changed both what we were building and how big it was. Cast iron, then steel, then reinforced concrete were materials whose structural behavior couldn’t be worked out by rule of thumb. Railway networks, factories, skyscrapers were projects whose scale couldn’t be absorbed by a single master-builder. You needed specialist calculation, and the depth of that specialization grew past anything a generalist could feasibly handle.

At the same time, the coordination technology needed to transact across those new specialist boundaries arrived: blueprints (Herschel’s cyanotype, 1842) let a builder work from the same drawing the architect drew, and standardized contracts (the AIA Uniform Contract, 1888) gave the handoffs legal structure without case-by-case negotiation. Once specialist quality mattered more than integration, and transacting between specialists got cheap, the split became the dominant arrangement.

Ronald Coase made the argument that a firm exists because coordinating work inside it is sometimes cheaper than transacting for every task on the market. You split one job into two jobs when one person can’t do both well enough, and the cost of coordinating between two specialists is worth the quality gain. With the Industrial Revolution, we saw that fragmentation.

We have a new technological paradigm of Agentic AI. AI is no longer merely a chat bot but something which has both the capacity to act in the digital world and the ability to write and read its own memory.

If AI tools make you 90th percentile at design, product thinking, and engineering, then the gap between you and a dedicated specialist narrows. Maybe a specialist designer is still better. But is she enough better to justify the coordination costs? Every handoff between people costs context. Every sync meeting is time not spent building. When one person can cover 80% of the quality across three roles, the coordination savings from not splitting the work start to dominate.

This is already happening. Boris Cherny, the head of Claude Code, mentioned that everyone on his team at Anthropic including designers, product managers, and finance people now code. “The title of software engineer is going to start to go away not because engineers aren’t needed but because what was traditionally three roles (engineering, product, design) are going to become a single role.”

Software is the canary in the coal mine. It’s where AI tools are most mature, so it’s where role boundaries are shifting first. But there’s no reason this stops at engineering. Anywhere the bottleneck has been “I need a specialist who knows how to do X” rather than “I need someone with good judgment about what X to do” — that boundary is going to move. The unit of “one person’s worth of work” is changing shape. Brunelleschi’s mode of working is coming back, because the economic conditions that pulled those roles apart are unwinding.

What becomes scarce is the integration of the roles: holding the vision together, knowing which sub to call for which job, making the trade-offs when the plumbing runs into the wall. At the margin, the value is less in the specialized work. The value is in holding vision and execution together the way a master builder does: in the mix, adapting as you go, solving problems as they emerge rather than handing off blueprints and walking away.

We are entering an era of freestyle work. It’s a human-AI collaboration where the machine proposes and the human disposes. Claude Code suggests an edit; I accept or reject. It drafts a contractor proposal; I revise and send. The skill isn’t domain expertise alone. It’s knowing how to direct the AI, when to push back, and how to structure the collaboration.

The people who figure out how to structure their work around them early will have a compounding advantage over those who wait. Not because they’ll be using better models (everyone gets the same models), but because they’ll have a better way of thinking about using them and building with them. There is a new knowledge work stack.

The AI Knowledge Work Stack

Layer 1: The Model

At the bottom of the stack is AI, specifically, a type of AI known as large language models (LLMs). These LLMs are the base models using next-token prediction. Given a sequence of text (the “context window”), they use statistical models to predict what comes next.

The model is what you interact with when you type something into ChatGPT or Claude on the web, and it responds with some text.

What’s astonishing about LLMs is that given three paragraphs about your business, your market, and your resources, the same next most likely word prediction process often produces something that reads like uncannily good strategic advice. It’s as if auto-complete chugged four Red Bulls, went Super Saiyan, and got access to the nuclear codes.

It’s visualized as a ‘big blob of compute’ representing how AI progress has primarily been driven by scaling up raw computing power, data, and training duration, rather than clever algorithmic tweaks.

Layer 2: The Harness

In its pure chat-based form, the models can’t do anything other than produce text output. One large unhobbling of the last few years has involved giving the models tools: the ability to write software and use the filesystem on your computer.

The harness is represented by Agentic CLI tools like Claude Code and Codex which give the model hands: filesystem access, terminal commands, the ability to read and write files. Without it, the model is a chatbot. With it, the model can actually do things on your computer.

One of the obvious (and most widely used) versions of that is to ask it to write software: it can build your own CRM, call booking tool, or financial analysis template.

But, it can also do pretty much anything else we would categorize as knowledge work: from building project plans to drafting email replies and writing call summaries.

Layer 3: Personal Scaffolding

While the harness allows the model to act, the model itself is a general or managerial intelligence. Talking with AI models often feels like talking to someone who was always having their first day on the job. Their background was impressive: They had the equivalent of a PhD in mathematics, physics, and history. But, they don’t really know anything about your specific work or situation.

The innovation of Claude Code was that by introducing the ability for the AI to write files, it could create what amounted to persistent memory rather than relying on transient (session) memory. The two most important types of files are CLAUDE.md files and Skill files.

CLAUDE.md files are instructions the AI reads at every session start. Think of it as an employee handbook everyone reads on day one: what applies universally to this workspace. Because the AI starts each chat session with no memory of prior sessions, the CLAUDE.md file gives it that context.

Claude Code automatically loads the CLAUDE.md file from every directory level above whatever file you’re working on. That means that if you organize it intelligently, it always has the right context at the right time.

Say you’re running a business and writing a newsletter to go out for a marketing campaign, a logical sequence of folders with CLAUDE.md files would be:

• A general CLAUDE.md on your computer that contains information on how you like Claude to work, voice, tools you have wired up

• A vault CLAUDE.md with how your files are organized for all your notes and projects.

• A folder for your business with a CLAUDE.md explaining what the business does, who the clients are, how you price, etc.

• A folder for marketing inside the business with a CLAUDE.md with details on channels you run, what’s working, and your brand voice.

• The specific marketing project with its own CLAUDE.md giving the goals and status.

When you open anything in the marketing project folder, Claude Code walks up the tree and pulls all five into context automatically. Global → vault → business → marketing → project.

That means the very first message in a chat about that marketing project is going to be like talking to someone who knows you, your business, your marketing history, etc.

CLAUDE.md files load every session, so you only include things that apply broadly.

For domain knowledge or workflows that are only relevant sometimes, you would use skills instead. Claude loads them on demand without bloating every conversation. Skills extend Claude’s knowledge with information or define repeatable workflows.

You can create a skill with instructions for how to write a newsletter, close the monthly books, or format a client proposal.

A skill can pull in supporting files like templates, examples, reference material to do the task better.

The AI model uses the instructions in this file like someone at your company might use a standard operating procedure. CLAUDE.md files represent the always-on context — what’s true about this place. Skills are the specialist knowledge — what the AI should know when it’s doing a specific kind of task. Together they cover the two questions that scaffolding has to answer: “Where am I?” and “What am I doing?”

These are all just text files on your computer that any LLM can read and use. The analogy to how knowledge workers work should be pretty obvious.

The Personal Scaffolding is where people start to differentiate. Everyone running Claude Code gets the same models and the same harness. The model running in Claude will give me different results when I ask it to do something than when you do.

This is the layer where people should be focusing: how to architect their scaffolding in a way that makes them better (and, as we’ll see later, makes their agents better.)

Layer 4: Utilities and Materials

So far, everything we’ve talked about involves using files on your local computer. But a lot of your work and data lives on a server somewhere — your email, calendar, and CRM all live in external services like Google or Salesforce.

There are tools that can connect those services to agentic CLIs like Claude Code and Codex so that you can access them. Three kinds of connector do this work.

• APIs — application programming interfaces — are the underlying plumbing: every major service like Gmail, Salesforce, or Stripe publishes one, which is a defined set of requests any program with the right credentials can make on your behalf.

• CLIs — command-line interfaces — are small programs that let you use an API by typing commands in the terminal instead of writing code.

• MCPs — Model Context Protocols — are the newest, built specifically for AI tools: they give Claude Code or Codex a standardized way to see what a service can do, so the model can use a new tool the moment it’s connected.

Once you’ve given access to these tools, it enables a workflow which combines all the components: the intelligence of the model, the hands of the harness and the context of the personal scaffolding.

A simple example: when an email comes in requesting a meeting, the model checks my calendar, drafts a reply, and creates the event. I never open a browser.

Or here’s a more robust workflow: Say you run a B2B company and you want to know how a customer is doing. Right now, maybe you’d open HubSpot to check deal history, then Mixpanel for product usage, then Stripe for invoicing, then Gmail for recent support threads. You’d shuffle through four tabs trying to piece together the story.

With these connectors in place and a well-architected scaffold, you might create a skill that triggers when you say “Give me a full picture of Acme Corp.”

The model pulls from all four APIs, merges the context, and gives you one coherent answer. That merged output can become the starting point for more work. You could have the model build a simple dashboard for your account managers showing profitability and status per client. You could set it up to flag at-risk accounts: a client whose product usage has dropped while support tickets have spiked is probably about to churn and you could reach out to them proactively to see if you can help fix their issue.

It could merge all client accounts and generate a quarterly business review deck from the merged data. Each of these would have been a custom integration project or manual workflow, now it’s a natural language conversation with an agentic CLI.

Once you have ways of connecting to external applications, the model can, in principle, do anything a human can do on a computer.¹

Layer 5: Agents

Once the infrastructure is in place, you can deploy agents that use it autonomously. An agent is just a Claude Code session running on its own: you define the goal; it executes using the same tools and context it would if you were sitting there.

Let’s say you have a plan to update a few hundred blog posts on your website. A typical and effective workflow is that a (thoughtful) human spends time manually working on building out a skill or process and tests it on a small subset (say 5 blog posts in this example) and once it’s reliably producing a good output, you let the agents rip and do the rest.

In the case of building a client dashboard, you might spend a while manually figuring out how to build one client’s dashboard in a way that makes sense for your use case. Then you’d use agents to do the other clients and the monthly update.

An agent-based workflow is something like: you write a project document then leave your computer or let it run in the background. While you’re gone, the agent works through the document, building features, updating the project doc as it goes. You come back, review the output, and refine it. Hopefully, the first pass gets 80–90% right; you give it a bit of feedback and let it rip again or do the last bit yourself.

Agents are sexy in the same way terms like “passive income” are sexy: they suggest you can get something for nothing. The truth about most things that look like “passive income” was they were more accurately “front-loaded income” where someone did a lot of work to set up a system that could work without them (for a time).

In the same way, agents are only as good at their task as the model and instructions allow them to be. Just like over-hiring and under-training employees can lead to lots of people doing useless (or actively harmful things), so too with agents.

Agents that are able to use a thoughtfully architected scaffolding system are vastly more effective at actually doing things that produce real value than those that aren’t. Using a bunch of agents doesn’t necessarily make you more productive if those agents aren’t actually doing economically valuable or otherwise useful work.

I see many people that seem to be in the same trap Eli Goldratt described in his book on manufacturing efficiency, The Goal. Factories were optimizing for machine utilization with the goal of every machine running 24 hours a day. Goldratt pointed out that what manufacturing facilities really cared about wasn’t machine utilization, but finished products they could sell.

Imagine a three-station assembly line: stations 1 and 2 can each crank out 200 widgets an hour, but station 3 can only handle 100. Running stations 1 and 2 flat-out doesn’t ship more product, it just piles up half-finished widgets in front of station 3 and creates more work managing the pile. Running fifty agents in parallel is the same fallacy at higher resolution: if the bottleneck in your work is figuring out what’s worth doing, more agents just generate more output for you to wade through.

Just as a well-run factory produces widgets effectively, a well-architected scaffold generates real leverage.

The Master Builder

With the rise of agentic AI, I think we are coming back to a paradigm of work like Brunelleschi’s master builder mode. The most valuable component is the person solving problems as they emerge while holding both the vision and execution components together.2

The pure architect approach doesn’t work. “Claude, rebuild my CRM, make no mistakes” is not effective. You need to be on site. You need to know which sub-contractor (agent/skill) to call for which job, and whether the work is good when it’s done (QA/QC). You’re not producing most of the work, but you’re sequencing it, inspecting it, and managing the scope. When someone (agent) says “while we’re at it, can we move this wall?” you need to know that the answer is “that’s load-bearing, here’s what that actually entails.”

At the same time, it would be dumb not to recognize the tremendous leverage that being able to use both the AI models and their agentic ‘hands’ produces. While there is a strain of Luddite romanticization around the use of AI, the interesting question for those of us who don’t want to play the martyr is one of understanding what to delegate and how to work with AI.

Two components of the Master Builder analogy are worth emphasizing here: the importance of local knowledge and meta-rational work.

Local Knowledge: What’s Not in the Training Data

First is the importance of local knowledge. A good master builder has to know the sub-contractors, the building codes, the soil conditions, and the inspector’s quirks for their area.

That knowledge doesn’t transfer easily. A master builder who spent twenty years on commercial projects in Southern California can’t just move to residential work in Minnesota. Different climates, different building codes, different subcontractor networks, different client expectations. The broad skills translate, but the stem of deep local knowledge doesn’t.

This is why good general intelligence isn’t the same thing as intelligence becoming a commodity. Out of the box, Claude Code can do a bit of everything and can do it pretty well. But, two people with the same model and the same harness will produce wildly different results depending on the local knowledge they’ve built around it.

Your job as a knowledge worker using these tools is to understand what knowledge it needs and when and to use tools like CLAUDE.md or skill files to supply that.

Economist Friedrich Hayek’s most famous insight about economies was that the knowledge required to coordinate them can’t be held by any central planner; it’s dispersed across millions of people who each know something about their particular circumstances — what they value, what’s scarce, what works.

Hayek was writing at a time when this was a somewhat unpopular view. In the early decades of the Cold War, it was intellectually fashionable to believe that the USSR’s centrally planned economy would eventually outcompete and win.

In his 1938 paper “On the Economic Theory of Socialism,” Oskar Lange had proposed a “market socialism” where a central planning board iteratively sets prices via trial and error. Abba Lerner extended this, and virtually all economists acclaimed the Lange-Lerner solution.

The Soviet collapse was read as Hayek’s vindication and mainstream economics quietly moved back toward the Hayekian position.

Central planning failed not because planners were dumb but because the knowledge they’d need to plan well is structurally not available to them. Markets work because prices compress that distributed knowledge without needing to aggregate it. If a tin mine collapses somewhere in the world, the price of tin rises and every user of tin economizes without any of them needing to know the mine collapsed, or why. The local information never has to travel; the price carries the signal.

Foundation models like Claude, ChatGPT and Gemini can eat the trunks of knowledge work: the routine, easily codifiable parts of legal review, financial analysis, standard coding tasks. What they can’t eat are the twigs: the local, contextual, fast-changing specifics that drive a lot of value in many particular situations.

A model trained on the internet doesn’t have your clients, your voice, your constraints, your past decisions and why you made them. That knowledge lives in your head, your files, and your past work and was never available to the training corpus. It also doesn’t (necessarily) have great judgement about how to use that information.

Meta-Rationality: Coding as the Canary

Second, the master builder analogy emphasizes the importance of what might be called meta-rational work. Rational work is problem-solving inside a given problem statement. If you can write down the problem and what good looks like, AI can usually solve it. Math, formal logic, syntax checking, and standard financial analysis are all things it excels at.

Meta-rational work is looking at all the context, complexity, and nebulosity around a domain. It’s figuring out what the problem actually is, whether the problem statement is sensible, and which parts of the situation matter. Brunelleschi was a goldsmith by training, but a meta-rational master builder in practice.

For some knowledge work, “good” is relatively objective. Math most obviously, and to some extent disciplines like standard financial modeling where formal criteria for correctness exist. AI is very good where the criteria are fairly objective.

But, a lot of knowledge work is meta-rational. What makes a good house, a good article, or a good strategic decision? These are all highly context dependent where purposes shape what “good” means, and figuring out the purposes is part of the work.

Many software engineers are now fully meta-rational workers who haven’t written any code by hand for months. Coding is the canary in the coal mine. A few years ago, writing code was the job. Increasingly, software engineering looks more like being a master builder. They specify what they want, review what the agents produce, decide which trade-offs matter, figure out which failure modes are load-bearing. That’s meta-rational work.

The rational core of coding got eaten, and what remains are the more meta-rational components of architecting the requirements, scope, and QA. Engineers don’t disappear, but the rational layer of the role has been largely automated, and what’s left is meta-rational. Every other knowledge-work domain is next in line.

Invest in Your Scaffolding

The practical implication is that the highest-leverage investment is your scaffolding: the local knowledge layer that encodes how you work, what you mean by good, which constraints matter for your particular clients and projects, which failure modes you’ve already seen.

If it’s well constructed then each skill you build, each workflow you refine, each piece of context you add should aim to make every future session or agent more capable.

We should expect at a bare minimum that the models will keep getting smarter at the rational work and they’ll continue to shrink the rational portion of every domain until it looks like coding already does.

Will AI eventually close those gaps too? Maybe! In principle, there’s no reason models can’t handle context, purposes, and integration. Arguments that start “AI can never do X” usually rely on some romantic hand waving about something being “fundamentally human.” But they aren’t very good at it right now, and, to the extent good is not easily measured, it may be some time before they are.

Brunelleschi’s Back, alright

The master builder is an old tradition. Imhotep, the medieval cathedral masons, Vitruvius, Brunelleschi himself — for most of history, the person who held the vision and the person who solved the problem on site were the same person. What was unique about Brunelleschi wasn’t any single innovation; it was his ability to hold a dozen domains in his head at once and see, in real time, how they had to fit together. The herringbone brickwork, the iron tension chains, the ox-hoist, the sequencing of the inner and outer shells — none of those were the dome. The dome was the pattern of how they fit.

That pattern wasn’t in his original proposal. It was invented on site, in response to problems that only became visible once construction was underway. It wasn’t a brilliant up-front plan executed faithfully, but the integrative judgment to keep adapting the plan as the situation reveals what the plan didn’t anticipate.

Any individual skill or CLAUDE.md note is a brick — a rational unit, increasingly something a model could write for you. The leverage isn’t in the bricks. It’s in the pattern of how they fit together: which context loads when, which skill calls which, which constraint shows up where, what happens when an agent hits the wall it was always going to hit.

That pattern is meta-rational work, and it’s almost entirely yours, because it’s specific to your clients, your constraints, your past decisions, and the failure modes you’ve already lived through. The models will keep eating the trunks, but the local knowledge and judgement of how to use it will remain yours. We are all master builders again.

FOOTNOTES

A mix this month: the industry-level logic of where AI compute demand is actually going and the strongest argument there is no bubble, two Karpathy takes, what working with AI does to the user, and three pieces that ask what all this work is for.

I published an article, As We May Work, that looks at how AI is going to impact knowledge workers. If you’re hearing about or using Claude Code/Cursor and Codex, I think it’s a good framing for how to work with those tools effectively.

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Working With AI

I Was an Enthusiastic Early Adopter of AI Scribes. Here’s Why I Stopped

by Dr. Benn Gooch + Venkat Rao’s comment

One of the most widely adopted early use cases I see for people with AI is note taking apps (AKA AI scribes). This is an account of a physician who quickly adopted them with the very reasonable view of reducing his documentation work and spending more time with his patients.

After about 18 months, he realized that writing the clinical note was important cognitive work, not merely administrative burden. Delegating it silently hollowed out the reflection and orientation components of caring for patients.

Rao’s comment reframes the same observation in systems terms: the risk of AI use is “insufficient orientation.” Reorientation is friction, but it’s important and necessary friction.

I think it’s an interesting use case where the problem with AI is not that it got it wrong, but “the AI gets it right in a way that reduces the overall effectiveness of the practitioner.”

This risks romanticizing friction and I think the optimal amount of AI note taking usage is not zero. But, there is a skill in distinguishing friction doing load-bearing work from friction that’s just overhead.

I am experimenting with this right now and testing out using AI note taking but having a manual step where I summarize the call from my perspective and think about the next steps.

Broadly, I think we need to evolve something like a pattern language for working with AI effectively and note taking is a good first form factor.

Forward Deployed, Episode 4: The Special Forces Model

with Chris Papasadero and Noah Brier

The ambient assumption in a lot of AI discourse is that software (and knowledge work more broadly) is a Six-Sigma-able problem. It’s a manufacturing line that can be de-variance’d with enough process.

Brier argues making software (and, by extension, most high level knowledge work) is closer to making a movie than making a car. If that’s right, the search for “the right agent pipeline” shouldn’t look like Ford’s assembly line (stations, handoffs, QC) — it should look more like Warhol’s studio (assistants producing variations, the artist giving direction, dailies as shared context).

In general, I’ve been thinking a lot about the metaphors we are using for describing and working with AI. Most people are approaching it as a replacement for software and junior level employees. That’s a part of it to be sure, but optimizing a Warhol studio with Ford techniques gets you neither variety nor throughput.

Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI

on No Priors

Karpathy’s first public appearance in a while, and two things from it stuck with me.

First, his use of many agents tiled across multiple monitors, each running ~20 min at high-effort mode, 10 repos checked out, rotating through them delegating macro actions. I am wondering if I am under-utilizing agents through micro-managing when I should be assigning more abstract goals and reviewing their output less often. I think this is for sure true of coding tasks, it is less obvious to me in more open-ended domains. I often find that there is a constant dialogue between me and the agents to help flesh out my intentions which are sometimes hard to state explicitly up front.

Second, his observation that the jaggedness of model progress seems somewhat persistent. A key claim from the frontier labs about progress towards “general” intelligence is that doing specific training in one domain will generalize to other domains. In this view, training the models to be better at coding should also make them better at creative writing because there are some underlying inherent properties those two things (and intelligence more generally) share. I would say that’s a pretty core component of the “AGI is around the corner” thesis.

Karpathy argues it’s not really happening. Agentic coding and research have gotten MUCH better than they were a year ago. But, ChatGPT still tells the same stale “scientists don’t trust atoms” joke it told four years ago. Getting better at coding doesn’t seem to lead to telling better jokes. This suggests there is less general intelligence emerging and that models are getting better along relatively narrow and super-optimized tracks. To the extent this is true, it points towards a longer persistence of the freestyle workflow I talked about in As We May Work where humans are very necessary for ‘stitching together’ powerful but narrow-minded AI models.

Under the Hood of AI

Agents Over Bubbles

by Ben Thompson

An obvious question many people are asking around the large tech company valuation is “are we in a bubble?” Good question!

Thompson argues that the rise of Agentic AI suggests companies really can grow into their current valuations.

At least for the time being, AI labs charge for compute/API-usage: the most you use it, the more you pay.

What agents change is that the unit that actually consumes compute isn’t the chatbot user, it’s the agent. One person can only spend so much time sending messages back and forth to a chatbot. In the agentic paradigm, an agent can keep building and testing its own work in ongoing loops. One person may be managing dozens of agents, all burning through tokens at the same time.

That reframes the math in two important ways. First, agents don’t need broad human adoption to justify hyperscaler capex. If only 2% of people become agentic operators, they can still use a huge amount of compute while producing real economic output. That’s not a bubble, that’s a productivity gain (albeit one concentrated in a small group).

Second, the profits concentrate at the integrated model+harness layer rather than the commoditized model layer. In this paradigm, the harness (Claude Code, Codex) is the moat that lets AI companies maintain pricing power even as open source models keep model costs down. This may or may not play out (I’m dubious about how big of a moat the harness is), but it seems to be Anthropic’s strategy and at least a part of OpenAI’s as well.

The companion piece, Mythos, Muse, and the Opportunity Cost of Compute, adds the frame that there will be two AI businesses: a consumer business (ad-supported, aggregation-theory-native, likely with OpenAI vs. Meta fighting for users) and an enterprise business (not aggregation-theory-driven — Anthropic trying to win on product quality, OpenAI on compute capacity).

Deep Dive into LLMs like ChatGPT

by Andrej Karpathy

The clearest end-to-end explainer of how modern AI systems actually work. This video covers pretraining, tokenization, post-training via RLHF, what sampling is doing under the hood and why models hallucinate.

A lot of these technical points are and will be abstracted away for most people using AI, but a basic understanding of how these models are built has been a helpful intuition for thinking about how to work with them.

The models tend to be “jagged” in the sense that they are very good at some things humans are bad at and very bad at some things humans are good at. One example is that they seem to be worse at basic math (algebra/arithmetic) and better at more complex math. Once you understand how tokens work, you can see why this would be and it gives some intuition about what else the models are likely to be relatively good/bad at.

The section on RLHF is particularly useful as it points out that “helpful” is a learned posture on top of a base model rather than an intrinsic property. From this view, sycophancy, jailbreaks, and safety are all parts of the same phenomenon viewed from different angles.

The Longing Under the Work

The Protestant Ethic and the Spirit of Capitalism

by Max Weber (Peter Baehr & Gordon C. Wells translation)

Weber, a German sociologist, wrote this book in the early 20th century as a sociological answer to the question of why the Anglo-sphere (UK and America particularly) had been so economically successful (especially compared with Germany).

His answer was protestantism. He argued the Calvinist notion of predestination created a psychological problem: the core of predestination is that you can’t earn salvation, it’s already decided. This is a kind of stressful state to be in — are you among the chosen or not?

The cultural answer that emerged was that tireless labor in a calling evolved as a sign of selection into salvation.

Calvinists (and other protestant sects that followed) developed the belief that if you work obsessively, reinvest rather than consume, and systematically order your life then you could demonstrate to yourself that you’re among the chosen.

Over time, the theology dropped away, but the behavioral pattern survives. The entrepreneurial American, exemplified in Ben Franklin, had a worldview based on living out a theology they often no longer believed in.

“Innerworldly Protestant asceticism works with all its force against the uninhibited enjoyment of possessions; it discourages consumption... Conversely, it has the effect of liberating the acquisition of wealth from the inhibitions of traditionalist ethics; it breaks the fetters on the striving for gain by not only legalizing it, but seeing it as directly willed by God.”

This hit close to home for me!

Ep.355: What Myths Can Teach Us About the AI Arms Race

with Joshua Schrei on Hidden Forces

Schrei reads the AI race through mythology, specifically the story of the sorcerer’s apprentice and what he calls the chthonic (underworld-deep) drive in young men to tinker and prove power.

In traditional cultures this drive gets tempered by elders: not yet, you have to learn to hold power first, you have to recognize you’re not special.

He argues our civilization has no such guard rails. We celebrate the apprentice’s “look what I can do” and pour venture capital at it.

The technology is here; the practices, norms, and social structures that would make it safe to wield need to evolve still. Containers get built slower than the things they’re meant to contain. Paired with Weber above, this offers a lens on the spiritual content under the work frenzy, the longing that doesn’t get touched by any amount of winning.

Friendship Is the Whole of the Path

by Daniel Thorson

The counter-weight in this issue. Everything else here is AI, work, etc. Thorson writes about the thing that is not so easy to optimize: friendship.

In many cultural and religious traditions, friendship isn’t a slice of the spiritual path, it’s the whole of it.

“There is one mass of suffering, and there is one mass of friendship. The suffering is not distributed. It is shared. Your loneliness and mine are the same loneliness.”

His answer: the growing crisis of human knowledge.

“There is a growing mountain of research,” Bush wrote. “But there is increased evidence that we are being bogged down today as specialization extends. The investigator is staggered by ... conclusions which he cannot find time to grasp, much less to remember.”

We were still using the same retrieval methods from centuries past: alphabetical indexes and hierarchical filing systems. Bush marveled at the infrastructure of mass production that had been built out in the war effort with vacuum tubes “made by the hundred million, tossed about in packages, plugged into sockets.”

Bush proposed a machine he called the “memex” — a desk-sized device that would combine lower level technologies to achieve a higher level of organized knowledge (like human memory processes). The memex could store all of a person’s books, records, and communications on microfilm and allow them to build connections that would lead to new discoveries.

Bush illustrated with a scenario: A researcher is studying why the Turkish bow was superior to the English longbow in the Crusades. He pulls up an encyclopedia article, finds a related passage in a history book, ties them together. He branches off into textbooks on elasticity, tables of physical constants. He adds his own handwritten notes. He builds a “trail of his interest through the maze of materials available to him.”

Bush’s essay inspired the people who built the digital age. Ted Nelson cited it when developing hypertext. Doug Engelbart, who invented the mouse and demonstrated hyperlinks in 1968, called it a primary influence.

Bush saw how technology generally, and computing specifically, was going to alter the way in which we worked. In 1945, most work involved making things or moving things: the factory, the farm, and the warehouse were where work happened.

Today, 80 years later, many people’s work looks kind of like operating a Memex. It involves sitting at a desk manipulating symbols: writing memos, updating spreadsheets, sending emails, and, of course, sitting in meetings about the memos, spreadsheets and emails while online shopping in another browser tab.

There was no word to described this until Peter Drucker coined the term “knowledge worker” in 1959. Knowledge work is manipulating symbols — words, numbers, formulas, diagrams — such that the output changes what happens in the world. A project plan determines what gets built. A financial model determines where capital gets allocated. A legal brief determines who pays whom.

Our world adapted to this new mode of working. Factories needed to be near raw materials or transport. Knowledge work just needs a desk and a screen and so our physical world was reshaped. We got the office park, the cubicle farm, and eventually the laptop on the kitchen table during COVID. The path from “high school → factory” became “college → cubicle” and the rust belt was supplanted by the sunbelt.

We shape our tools, then our tools shape us.

I have been reflecting on Bush’s essay as I’ve started to use a new technological tool: Claude Code. While “Code” is in the name, Claude Code is better thought of as the leader in a new category of tools for manipulating information broadly.

Code is one type of information, but project plans, legal documents, spreadsheets, and standard operating procedures are all information. In fairness to the creators, “Claude Knowledge Work” doesn’t have quite the same ring to it, but that’s the way to think about it.

I wrote a book in 2016 called The End of Jobs which suggested that the combination of global competition and what I broadly called “machines” was going to come to knowledge work in the 2020s in the same way that automation and globalization came to blue-collar work in the 2000s and 2010s.1 Claude Code, and the broader product category it represents, feels like an inflection point in that journey.

At the risk of being hyperbolic, Claude Code is the ChatGPT moment repeated. It is the leading product in a new era of AI. If you do knowledge work of any sort, you need to understand what it is and how it works. Claude Code, and the product category it represents, will become the primary tool most knowledge workers use to do their work.2

2025 was the year where AI became a key part of most software engineers’ workflows. “Vibe Coding” went mainstream. When we look back, I think 2026 is going to be an inflection point year where AI did the same for large swaths of knowledge work, beginning an era I’ve been calling freestyle work.

If not on the level of importance of the personal computer, it is at least on the level of the browser. If you are a knowledge worker: marketer, salesperson, writer, developer, designer, project manager, investor, etc. then understanding what it is and how it works is going to be pretty important going forward.

This essay is the result of my attempt to figure that out for myself, as one of a class of people that consider themselves non-technical knowledge workers.

The Limitations of Chat-Based AI

To understand Claude Code, it’s worth starting with what current AI models actually are and how they work.

All the currently popular AI models are a type of AI known as large language models (LLMs). LLMs are next-token predictors. Given a sequence of text (the “context window”), they use statistical models to predict what comes next.

The models learned these patterns by reading essentially all the text on the internet and embedding them in their weightings. When you ask an LLM to complete a sentence that starts with “the dog is,” you’re likely to get a logical and coherent ending.3

What’s astonishing about LLMs is that given three paragraphs about your business, your market, and your resources, the same next most likely word prediction process often produces something that reads like incredibly savvy strategic advice. It’s as if auto-complete chugged four Red Bulls, went Super Saiyan, and got access to the nuclear codes.

ChatGPT made LLMs mainstream in late 2022 by creating a chat-based interface to interact with these models and doing some specialized training to have it act more like a helpful assistant than merely auto-complete.

In a sense, LLMs are a societal-level memex. Trained on huge amounts of human text, they embody a vast web of associative trails through statistical relationships learned during training. The Turkish bow connects implicitly to the physics of elasticity and to the history of the Crusades. Those connections exist in the model’s weights.

When you chat with an LLM, you’re embarking on a choose-your-own-adventure through humanity’s collective knowledge. Ask about Turkish bows, branch into physics. Ask about HVAC, branch into building science, then contractor negotiation, then your city’s climate. The “selection by association” Bush imagined is now here at a societal scale.

However, chat-based LLMs like Claude, ChatGPT and Gemini suffer two broad limitations.

1. Their lack of long-term memory.

2. Their inability to do things rather than merely provide instructions.

The models themselves lack the equivalent of long-term memory. You can write or paste text into a chat window and the model will respond based on what is in that window, but it can’t remember anything between conversations. Each session starts fresh.

The societal memex doesn’t know you. If you want to ask it questions about your job or business or life, it has lots of general knowledge but no specifics. It knows about HVAC or construction practices in general, but not your house, your budget, or your project history. It knows everything ever written in an MBA textbook, but relatively little about your company.

Talking with AI has historically felt like talking to someone who was always having their first day on the job. Their background was impressive: They had the equivalent of a PhD in mathematics, physics, and history. But, they don’t really know anything about your specific situation.

Context windows are growing, but they’ll always be finite.4

The frontier AI companies (Google, OpenAI, and Anthropic) are obviously aware and trying to find solutions. ChatGPT introduced Custom GPTs in late 2023, allowing you to upload documents and instructions relevant to a specific task. Claude Projects and Gemini Gems followed in 2024. These were attempts to give the models more persistent memory by allowing you to store relevant files.

My usage pattern, which seems typical for other non-technical users, was to create Custom GPTs/Projects/Gems for different life or work areas:

• One for health information that would have relevant information to my health

• One as a business adviser for certain parts of my job.

• One as a home cooking assistant with my recipe and dietary preferences

This allowed you to embed persistent context relevant to these projects.

This helped, but you were still uploading static snapshots. The model couldn’t engage in “on the job learning.” It couldn’t update its own knowledge, couldn’t write notes to itself, and couldn’t accumulate learning over time. It could read, but it couldn’t remember. It was reliant on your ability to constantly update it.

In practice, this meant constant and tedious maintenance. Imagine you set up a business adviser project with your pricing, client list, and how you structure engagements. As soon as a client churns or your structure evolves, you have to dig through the reference documents to find it and update it.

Multiply that across several projects and you’re spending real time just keeping the AI updated on the current status of projects. Worse, the projects couldn’t talk to each other. The cooking assistant didn’t know about the dietary notes in the health project. The business adviser doesn’t know about a vacation that will take you offline for a week.

It was like having an army of extremely specialized contractors. They were good at what they did, but there was huge coordination overhead keeping them all up to speed on what was going on. The transaction and coordination costs were so high that it often was still more efficient to just do it yourself.

50 First Chats

This inability to learn is a major issue for making AI useful at knowledge work. No matter how qualified someone is, they’re rarely very useful or helpful their first day on the job. There’s always a lot of specific knowledge in any company.

Dwarkesh Patel has called this the fundamental bottleneck. “The lack of continual learning is a huge, huge problem,” he wrote. The reason humans are useful isn’t mainly raw intelligence, it’s the ability to “build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task.”

In their current form, chat-based LLMs can’t do that.

They also are limited in the sense that they are, well, chatbots. I spent a couple weeks last year fixing and improving a spreadsheet that tracked an investment portfolio’s performance.

The workflow was that I would copy sections or formulas out of the spreadsheet into the LLM chat, ask it questions and then copy things out of the chat and into the spreadsheet.

I am probably a 4/10 Excel user. I know enough to get around but am not highly proficient. Being able to use LLMs for this kind of task has been a huge benefit to me, but the copy/pasting was pretty tedious and I found myself often wishing it could just fix the spreadsheet itself.

Enter stage left: Claude Code.

Claude Code Can Write Its Own Memory

Claude Code is a command-line interface (CLI) tool, meaning it can use the command line on your computer. Practically, this means it can do anything on your computer that you can do: It can read, create, and edit files on your computer, not just generate text in a chat window.

Compared with web-based chat apps, that’s a key difference: Claude Code can read and write files. You can think of it as scaffolding that sits on top of the AI model’s general blob of intelligence.

One of the obvious (and most widely used) versions of that is to ask it to write software.

But, it can write anything including notes about what it did and why.

I have a set of instructions (called a Skill file in Claude Code) to help me write a monthly newsletter. The AI model uses the instructions in this file like someone at your company might use a standard operating procedure.

I asked it to write a newsletter based on a topic I gave it. The output was OK, but it did some things I didn’t like: it jumped straight into editorializing from my notes.

I went back and edited what it said and asked it to “Compare my version to the version you drafted and update your skill to do it more in my style next time.”

With no other prompting, it drafted the following and added it to the newsletter file in the appropriate place:

These are all patterns and preferences in my writing that wouldn’t have occurred to me explicitly to state but when reflected back to me like this, are all accurate reflections of my style.

This looks a whole lot like how someone on their first day of a job writing a newsletter for me might take notes (explicitly or implicitly) to do it more in line with the way I want next time.

When an AI can read and write files, it gains the ability to write its own memory and, with memory, the capacity for learning.

It is comparable to how companies use internal training or standard operating procedures. The next time I ask it to write a newsletter, it will look at the notes it took from last time and insert that into the context to use for the task.

If you haven’t used Claude Code or a similar tool before and this all sounds a bit abstract, here’s a short demo.

While the Claude model itself is the ‘intelligence’ — it reads, writes, and reasons. Claude Code is the harness around it: the files it can access, the tools it can use, and the rules it follows.

On top of Claude Code itself sits personal scaffolding that you can customize. Skills are perhaps the best example. You can create a skill with instructions for how to write a newsletter, close the monthly books, or format a client proposal.

These are all just text files on your computer that any LLM can read and use. The analogy to how knowledge workers work should be pretty obvious.

Imagine you hire a marketer who spent five years doing email campaigns for a sunglasses brand. She knows how to write subject lines, segment lists, and structure a launch sequence — that’s her general training, the equivalent of the model’s weights.

But when she starts at a snack bar company, she doesn’t know your brand voice, your product launch calendar, or that your audience responds better to nutrition science than lifestyle imagery.

You give her brand guidelines, a style doc, and the last six months of campaign performance. That’s the context-specific ‘harness’ — that shapes her general skills into something useful in that particular situation.

She then has the ability to improve and shape those over time. Claude Code can do the same. You could copy in your last ten newsletters and their performance then use Claude Code to analyze which ones performed better or worse and what you could learn going forward.

There is still judgement required in overseeing it and the models still make mistakes, but everyone I know who has tried using it for similar tasks has been shocked at the quality of the results.

But its ability to write its own memory isn’t the whole breakthrough. Claude Code has memory within a composable environment.

A Castle Made of Legos

Claude Code uses the terminal on your computer to do its work. The terminal is built on a 50-year-old design philosophy, Unix, which has some simple principles:

1. Make each program do one thing well.

2. Expect the output of every program to become the input to another.

3. Write programs to handle text streams, because that is a universal interface.

Unix is built so it can pipe the output of one command into another, combining simple pieces into complex workflows — like Lego bricks versus a single molded toy.

Claude Code inherits this philosophy: it reads files, writes files, calls other tools, and passes text between them. It uses small tools, composed together, to create capabilities that no single tool has.

Watching Claude Code work you can see this philosophy in action — the tools it’s calling are shown in bold: Read, Glob, and Web Fetch. Each of these commands is simple: Read a file, search for another file on my computer (Glob), and then read a webpage.

These are small, simple commands for an LLM to execute.

What makes them powerful is that they are composable and can be chained together. The model (Claude) is able to use these tools from the harness (Claude Code) in sequence to produce a complex output like writing a newsletter.

Because of this design philosophy, you can always add new tools, and they immediately work with everything else.

Skills can call other skills inside themselves. If you have a branding guide, the newsletter skill might call the branding guide towards the end to do all the formatting and layout.

Tools also aren’t limited to your local files. Any piece of software that has an API (which is pretty much all software at this point like Gmail, Google Calendar, Slack, your CRM) can be used.5

It can check your calendar, pull up recent emails, update a spreadsheet or mark a task as completed in Asana. Each new connection works with everything else. Data from one tool can be routed into another tool or used to create a file.

The seemingly simple technical ability to have filesystem and command line access results in something remarkable: an AI that can read files, write files, search across documents, chain tools together, and build on its own previous work. It has memory and it has the ability to extend itself into any use case that can be described in text and done on a computer.

Claude Code can, in principle, do anything we would describe as knowledge work: manipulating symbols, be they words, numbers, formulas, or diagrams. In practice, it is not (yet) able to do everything well and the question of when/if it will be able to do different parts of knowledge work is obviously important (a topic for a future essay).

The Emergent Operating System

This design creates emergent possibilities where new capabilities can arise that weren’t designed in advance. Here’s an example of how it played out for me.

Because I knew that Claude’s context was wiped at the end of each chat session, I asked Claude Code to write a summary of what happened into a “workbench” file associated with that project — what got done, decisions made, open questions. This is a habit I’ve done manually for a long time as a way to easily pick up where I left off on a project. It was easy to ask Claude Code to write it for me instead by using our chat history.

After a few days, I realized the daily entries across all my projects could roll up into a single daily summary. So I told Claude to summarize what got done in project-level workbenches and add it to a daily log file (e.g. YYYY-MM-DD) at the end of each chat session.

When I started doing my weekly review and planning, I already had seven daily summaries sitting there. I told Claude to compare those daily reports to the weekly plan, checking what I said I’d focus on versus what I actually did, flag the gaps, and give me a report.

With that report, it was straightforward to see how my time lined up with my goals without doing the tedious cross-referencing myself. And if I decide to reprioritize, I can tell Claude to update my quarterly goals right there. Because it has access to tools like my Google Calendar, it can check whether I actually have time for what I’m planning.

You can see how this might be extended in other ways. If you send a weekly update to your team, it would be easy to ask Claude to reformat your internal version for a team audience: you could use a skill that adds more context for people who aren’t in your head all day and removes personal plans you have for the week.

You could review the weekly reports once a month or once a quarter as part of planning on those cadences. If everyone on your team did them, you could roll them all up to send a client a report of all the things you got done for them that week.

If you wanted to build this workflow in traditional software, you’d spend most of your time on plumbing — defining data schemas, matching field types between systems, writing code to parse one format and convert it to another. The weekly review app needs to read from the daily notes app, which means their data structures have to line up. Add a goals tracker and now three systems need to agree on how they pass information around.

Because AI works with natural language, that integration layer largely disappears. The process of turning seven daily notes into a weekly summary is simply a line in a file that says, “Condense these seven daily notes down into a weekly summary.” The goals audit is just a line that says “compare what got done to what I planned.”6

Each piece took a few minutes to set up because the building blocks were already there and they integrate using natural language. As AI researcher Andrej Karpathy memorably put it: the hottest programming language is now English.

We’re still in the early days of figuring out what this looks like. Claude Code uses structures like commands, hooks and skills that are first attempts at the form factor. I don’t know that the current Claude Code form factor is the right or final form that CLI tools will take, but the capability for knowledge workers to use Claude Code for non-technical work is already here, if not widely distributed.

To be sure, there are issues. Unlike software, which is purely deterministic, today’s AI models are probabilistic next-token generators. That means when you tell them to follow a 10-step process, sometimes they just decide to skip step 7. Everyone using AI at scale is running into this issue and building workarounds.

The models also exhibit what AI researcher Ethan Mollick and colleagues call the “jagged frontier:” the observation that AI capability doesn’t map to human intuitions about task difficulty. Models that outperform doctors at medical diagnosis still fail at simple visual puzzles, and systems that win gold medals in international math competitions can't reliably run a vending machine (a topic for a future essay).

It will improve as the underlying models get better, the tooling gets better, and we learn to architect and use these systems. The emerging skill of structuring your workflows and knowledge so you can use Claude Code and AI tools like it effectively is going to be an imporant differentiator in the coming years.

A Tool for Work

Bush’s vision was to make a tool for thought: a common record of humanity’s knowledge more accessible, better linked, more navigable. LLMs have, to a large extent, become that common record. They’re trained on the bulk of human text, and when you interact with one, you’re navigating a vast associative web of everything humanity has written down.

Claude Code adds two layers on top of that.

First, the personal layer. On top of the societal memex sits your own knowledge base — your projects, your preferences, your history, your way of working. Claude Code helps create, organize, and structure this. It reads your unstructured voice memos and files them where they belong. It watches how you edit its drafts and updates its own notes.7

Second, you can make things with that knowledge. Not just retrieve it, not just link it, but produce new forms of knowledge work. Software appears to have been particularly amenable to this tooling because:

1. There is a lot of training data on the internet for what constitutes good software

2. Software is more amenable to tests and evaluations by a computer than other forms of knowledge work.

3. The frontier labs have invested heavily in additional model training to make it good at software development

Having said all that, software is the canary in the coal mine. The labs are going to turn their focus to other forms of knowledge work and I expect rapid progress there this year. The compounding chain of drafting a newsletter or going from workbench notes to weekly reviews are examples of a more general capacity for knowledge work that is only going to improve. How should we, as knowledge workers, think about this?

Our Freestyle Future

In his book, Average Is Over, Tyler Cowen proposed that the future of work would look something like freestyle chess. In freestyle chess, human-machine teams compete against each other. The computer suggests moves; the human decides whether to accept, reject, or explore alternatives. Historically, the best freestyle chess teams weren’t the strongest chess players or the most powerful computers, but the best combination of the two.

In 2005, a team called ZackS won a major freestyle tournament by beating an opponent that included Vladimir Dobrov, a grandmaster. ZackS turned out to be two twenty-something guys in New Hampshire: Zackary Stephen, a database administrator with a statistics degree, and Steven Cramton, a soccer coach and snowboarding instructor. Stephen’s chess rating was 1,381; Cramton’s was 1,685. If Cramton played Dobrov head-to-head, the grandmaster would be expected to win 99% of the time.

But, with machines as partners, the amateurs dominated the experts.

Chess Grandmaster Gary Kasparov put it this way: “A weak human player plus machine plus a better process is superior, not only to a very powerful machine, but most remarkably, to a strong human player plus machine plus an inferior process.”

He also said something revealing: “I exclude myself from this category because I’m not a very good operator. I’m a great chess player. A great operator does not have to be necessarily a very strong player.”

The skill of directing the machine is different from the skill of playing chess.

The amateurs had better intuitive judgment about when to trust the machine, when to override it, and which processes to employ. Being good at freestyle chess isn’t knowing chess (though that’s a component); it’s knowing how to direct the collaboration with the machine.

The people who won freestyle chess tournaments might accept 95% of the machine’s suggestions. The skill is understanding the 5% where you override it — knowing the jagged frontier and when your own judgment should take over.

What he described in chess is now emerging in knowledge work. We are entering an era of freestyle work. It’s a human-AI collaboration where the machine proposes and the human disposes. Claude Code suggests an edit; I accept or reject. It drafts a contractor proposal; I revise and send. The skill isn’t domain expertise alone. It’s knowing how to direct the AI, when to push back, and how to structure the collaboration.

This is my first piece of writing where I used Claude Code and I’ll use that as an example of how knowledge work processes might evolve.

AI is incredibly good at certain components of writing: It’s a phenomenal research assistant and copy editor.

It’s a good brainstorming partner for when I’m stuck on something and need to talk through it though I often find it frames things too narrowly.

It is a crappy writer. It doesn’t quite seem to “get” the piece in the same way I do, or what it’s trying to achieve. The wording it uses still sounds like AI slop to me no matter what I put in the skills file.

My writing process conformed to this and looked something like this:

1. Voice dump → cleanup (50% AI / 50% human)

I start by dictating raw thoughts. Rambling, stream of consciousness, whatever comes out. That’s my half. Then AI cleans it up to read smoothly while preserving my exact wording. No rewriting, no AI voice. Just making dictation readable.

2. Structural edit (30% AI / 70% human)

This is the most collaborative phase. The goal is getting the argument and narrative into the right order: what’s the throughline, what sections exist, what goes where.

AI is useful for moving big blocks around in the draft and proposing structures, and as a brainstorming buddy when I’m stuck. Even when it gives bad ideas, having something to react to is faster than staring at a blank page for me.

It tends to converge on too-narrow or too-obvious framings if I don’t push back. This phase requires heavy back-and-forth: I mostly drive here and the AI is doing a lot of copy/pasting type work as well as serving as a sounding board.

3. De-AI-ify pass (100% AI / 0% human)

This is basically a filter for AI voice. It strips out “delve into,” “it’s worth noting,” “landscape,” unnecessary hedging, false enthusiasm, hollow transitions, etc. Some of this residue seemed to creep in during the structural edit.

4. Rewrite and research (20% AI / 80% human)

I rewrite the whole piece in my own voice, section by section. AI is useful for research: finding sources, getting precise numbers, filling factual gaps, etc. It’s also still good as a brainstorming partner when I’m stuck on a section.

The prose and argument are mine and (so far) seem to require very heavy-handed re-writes of what came out of the structural edit.

This phase is iterative and usually takes me 2-3 passes at least. A section might go through several rounds of writing, researching, rewriting. This is probably 70-80% of the time that went into this piece.

5. Copy edit (80% AI / 20% human)

Finally, I do an interactive copy editing pass. AI flags issues one by one (grammar, consistency, clarity, word choice) and I approve or reject. It’s good at catching mechanical issues I’d miss after staring at the same paragraphs for hours. I have final say on anything that touches voice or style.

I suspect this workflow is what a lot of knowledge work is going to start to look like over the next year or two: a human/AI collaboration with each doing the part they have a comparative advantage in. Figuring out where and how to insert AI into your workflows should be a major focus of any knowledge worker right now.

There are real differences between coding and knowledge work worth flagging.

Coding is more amenable to tests and evals than most knowledge work. Even though the models are getting smarter, one big advantage in coding is that the AI can just try stuff over and over and over until it meets some success criteria.

Lots of projects don’t have clear feedback loops. One of the first projects I decided to manage in Claude Code was replacing an HVAC system and ductwork in my (old) house.

Replacing an HVAC system is expensive and you can’t just keep trying stuff and throwing out what doesn’t work in the way you can with software.

A lot of knowledge work shares this: Negotiating a contract, choosing a market to enter, deciding how to restructure a team, picking an investment thesis — these all have feedback loops measured in months or years, not seconds. The more complex the domain and the longer the feedback loop, the more the work depends on judgment that can’t be cheaply verified.

It is naive and lazy to take either extreme of “the real world is messy, AI won’t be able to figure it out.” or “humans will be useless and all the jobs are going away.” The truth is something in the middle.

The frontier of what AI models can do will keep shifting. The job is to shift with it. Historically we’ve talked about this through the lens of prompting, but the more powerful part unlocked by Claude Code is starting to think about how you structure your information and how to work with the models to get things done.

As We May Work

Eighty years ago, Vannevar Bush looked at the technology of his day — vacuum tubes made by the millions, tossed about in packages — and saw the outlines of what was coming. He imagined a tool that would store all of a person’s knowledge and retrieve it by association rather than alphabetical index. We built pieces of his vision over decades: hyperlinks, search engines, digital notes. The associative retrieval he imagined feels like it finally fully arrived with large language models.

CLI tools like Claude Code push beyond what Bush imagined: from tools for thought to tools for work. This makes this technology wave fundamentally different from the most recent ones in our lifetime.

Mobile and Social were primarily tools for consumption and distribution: you scrolled, shared, and reacted. The defining act was consumptive. CLI tools like Claude Code are productive. They are tools for making things. You direct, you judge, you structure, you build. It’s more of a workshop than a megaphone.8

While most of the discussion around AI has been around it accelerating and replacing existing work (which it will), the more interesting and exciting possibility is that it unlocks new forms of making and doing.

When I wrote my book, The End of Jobs, I never would have gotten a publishing deal — I had no audience, no credentials, no platform. Self-publishing tools made it worth trying anyway. My expectation was to sell maybe a thousand copies. I ended up selling tens of thousands, which was a delightful surprise and changed my life. The book only exists because the technology made it worth attempting. AI will enable new forms of creative endeavor: art, business models, and products that simply didn’t make economic sense or weren’t possible previously. We shape our tools, then our tools shape us.

Sources:

• Vannevar Bush, “As We May Think,” The Atlantic Monthly, July 1945 (reprinted in Life Magazine, September 1945)

• Dwarkesh Patel, “Why I don’t think AGI is right around the corner,” June 2025

• Noah Brier, “The Magic of Claude Code,” Alephic, September 2025

• Daniel Miessler, “Building a Personal AI Infrastructure (PAI),” danielmiessler.com

• Andrew Wilkinson on AI & I podcast with Dan Shipper, “Opus 4.5 Changed How Andrew Wilkinson Works and Lives,” Every, January 2026. every.to

• Dan Shipper, “How I Use Claude Code to Ship Like a Team of Five,” Every, 2025

• Doug O’Laughlin, “The Death of Software 2.0 (A Better Analogy!),” Fabricated Knowledge, January 2026. fabricatedknowledge.com

3

Though, because they are statistical models rather than deterministic scripts, you will not always get the same response

.

I did a podcast this month on some of my thinking on AI: The $10K Projects You Never Do (AI Just Changed That).

Lots of AI again this month with a focus on how local knowledge and expertise may (or may not) persist. There's a GLP-1 chaser at the end if you're sick of hearing about AI.

Articles and Podcasts

Head of Claude Code: What happens after coding is solved | Boris Cherny [Podcast]

Lenny’s Podcast

Boris Cherny, the head of Claude Code, mentioned that everyone on his team at Anthropic (design, PM, finance person) codes.

He thinks by the end of year, “the title of software engineer is going to start to go away.” That's not because engineers aren’t needed, but because the three roles (engineering, product, design) will overlap so much that they'll collapse into a single role.

There’s a pattern in management history that runs in the opposite direction: towards specialization. Peter Drucker called roles that defeat several good people in a row “widow-maker” positions. His advice was to redesign the role, not find a better person. GM split the CEO and chairman roles in ’92. Boeing, Dell, and Oracle did the same. The job just kept growing until it exceeded what one person could do well.

AI seems to be pushing the boundary the other way now. The scope of what one person can handle is expanding (or at least changing). An engineer who also does product thinking and user research isn’t spread too thin anymore.

There’s a Ronald Coase argument here. Coase asked why firms exist at all? Why not just contract everything out? His answer was because coordinating inside a firm is sometimes cheaper than transacting across a market. Hiring someone to do every design task individually is way more work than just having a full time designer at a certain point.

Following the logic, you split a job into two jobs when one person can’t do both well enough, and the cost of coordinating between two specialists is worth the quality gain.

​If AI tools make you 90th percentile at design, product thinking, and engineering then the gap between you and a dedicated specialist narrows. Maybe a specialist designer is still better. But is that enough to justify the coordination costs?

Every handoff between people costs context. Every sync meeting is time not spent building.

When one person can cover 80% of the quality across three roles, the coordination savings from not splitting the work start to dominate for many tasks.

I think software is the canary in the coal mine here. It’s where AI tools are most mature so it’s where role boundaries are shifting first. But there’s no reason this stops at engineering. Anywhere the bottleneck has been “I need a specialist who knows how to do X” rather than “I need someone with good judgment about what X to do” — that boundary is going to move.

The unit of “one person’s worth of work” is changing shape. It seems less deep and narrow, more broad and integrative? More right brain, less left brain maybe? (I suspect there’s a better way to think about this but haven’t worked it out yet, a topic for an upcoming essay I think).

Context Engineering: Why Hayek’s Knowledge Problem Survives AI [Article]

Chris Walker

In 1945, economist Friedrich Hayek argued that useful knowledge is dispersed. The person closest to the problem knows things that headquarters never will. (James C. Scott’s legibility argument in Seeing Like a State is a version of this.) Walker takes this idea and applies it to AI.

One take is that AI is a centralizing force: models dissolve the knowledge problem by processing everything centrally. E.g. You need fewer managers at each store or in each department because headquarters can just make all the decisions.

My thinking tends to lean the other way: local knowledge still matters and is never going to be perfectly captured by an AI system.

Anthropic’s own engineering primer describes context as “a finite resource with diminishing marginal returns.” This is the language of economic tradeoffs. More context isn’t necessarily better context.

Context is also reduced to all the data you have so far. Feed a legal AI your full contract history and it learns from three years of aggressive positions your startup took to close early deals. It thinks those positions are company standards, but the situation has evolved.

Someone has to decide what the model should see for this task, in this domain, right now. That judgment only comes from having done enough of the underlying work to know what good looks like and being able to think broadly about how this particular task exists in a broader context. The centralizing argument is that eventually the AI subsumes this as well. It’s certainly trending in that direction, but it’s a long way away for now. I expect that to persist for a while (years), but I've been wrong before.

When Engineering Gets 100% Meta-Rational [Article]

David Chapman

Chapman makes a supporting argument for the importance of local knowledge by using his distinction between rationality and meta-rationality.

Rationality takes the problem statement as given and solves within it. Meta-rationality asks whether the problem is sensible in the first place, whether the requirements match what was imagined, whether the direction is worth pursuing.

There’s an old form of striking called a work-to-rule strike where workers follow every rule, procedure, and regulation exactly as written. They don’t walk off the job, they just do precisely what their contract says, nothing more.

This is a clever way to strike because they still get paid, but it grinds the company to a halt. No one actually just does what’s in their job description: workers at any company use judgment, cut corners on bureaucratic processes, and voluntarily do things outside their strict job description to keep things running smoothly.

Coding agents are incredibly good at rational work. They execute within defined parameters. What they need from you is requirements analysis (what to build) and architecture (the big picture of how). Those are the parts that still require judgment, because rationality, by definition, excludes consideration of purposes.

A lot of the AI-is-taking-our-jobs conversation is about which roles survive. I think the more constructive framing is which parts of which roles survive those roles. The rational execution layers collapse. What remains, and what appreciates, is the meta-rational judgment about what to build and why.

Have Your Factory Call My Factory [Article]

Venkatesh Rao

A douche-y and uncool way to use your AI is to just generate a wall of text and send it to someone and expect them to read it.

A perfectly reasonable way to use it is to generate a wall of text, thoughtfully edit it and send it to someone to read.

A baller way to use AI is to have your AI call my AI.

Venkat calls this F2F: “factory to factory.” Two people who trust each other enough to let their systems talk, exchanging work-in-progress through their own scaffolding.

A V1 of this: I will periodically send someone an email with a wall of text from my AI and say “copy this into your setup and ask it how it applies to you.”

A well-architected set up already has a lot of context about you and if I can just give it some prompting towards where I want to nudge someone, that will often work much better and faster than me trying to understand all the context.

I was recently explaining the Kelly Criterion for position sizing to someone and it worked way better to just send them a dump of text on how I thought about it and have their AI that had access to their investment portfolio read it and think about how it applies to them.

It’s increasingly the case that the value of building your own AI scaffolding isn’t just what it helps you do alone. It becomes a surface area for collaboration. What you’ve assembled (your context files, your tools, your memory) is a new kind of social capital that can interface with someone else’s.

Ben Thompson on AI Ads, the End of SaaS, and the Future of Media [Podcast]

Cheeky Pint Podcast

Ben Thompson (Stratechery) and Stripe co-founder John Collison sit down on the Cheeky Pint podcast to talk about where AI-mediated commerce actually leads.

​​Thompson raises a concern worth dwelling on: when agents do all the product research and purchasing, everything that can be measured and compared will be measured and compared. Which sounds good, until you think about what gets optimized away.

He uses sports analytics as the example. Basketball involves lots of statistics that are useful ways to understand how good a player are team are. It also involves qualities that resist quantification: team chemistry, defensive effort, how one player’s energy affects another’s, etc.

Daryl Morey’s NBA teams have consistently over-optimized on measurable metrics at the expense of these harder-to-capture dynamics. Thompson argues that’s why they haven’t won a championship. Scale that pattern to AI-mediated everything: “how many things that can’t get measured fall by the wayside because we end up with utilitarian goods that have no soul to them?”

This is really just another version of the local knowledge/legibility/meta-rationality point (do you sense a theme?). My suspicion is that developing these skills personally is important and useful, but also that at a societal and cultural level, we tend not to value them and things are likely to go ‘too far’ before the pendulum swings back to recognizing their importance.

The Human Alignment Problem [Article]

Daniel Thorson

The alignment problem is aligning AI to human values: how do we not get turned into paperclips A less explored, but I think more interesting and important question is what does AI do to how humans align with their own values?

As AI gets better, it largely closes the execution gap: the space between desire and the capacity to act on it. A medieval peasant who craved wealth had almost no means to pursue it. When AI collapses that distance, you get what you asked for faster. This is cool!

However, you may also discover faster that it doesn’t touch the underlying desire.

Armin Ronacher, a well-known open source developer described this as “agent psychosis.” He spent two months in a manic loop, building tools he never used, unable to stop. “You can just do things” was running on repeat in his head.

As the execution gap closes, it reveals another gap for many people: what we think we want and what we actually want.

You want wealth because you want security because you want to feel safe because somewhere deep down, you want to rest in something you can trust completely. AI can deliver the surface-level want at machine speed (and this is dope!), but it brings you no closer to the thing underneath.

Why Does Ozempic Cure All Diseases? [Article]

Scott Alexander

In 1992, scientists discovered a chemical in Gila monster venom that mimicked GLP-1, the hormone your gut releases to signal fullness. By tinkering with its structure, pharma companies extended its duration from two hours (the Gila monster version) to one month (the latest synthetic GLP-1 receptor agonist known as Ozempic AKA semaglutide).

The GLP-1 phenomenon is the most amazing medical breakthrough I can remember and their effectiveness raises a lot of questions. These drugs are approved for diabetes and obesity and they seem to work quite well at that.

What’s fascinating though is that they also appear to treat alcoholism, smoking, stimulant addiction, opioid addiction, behavioral addictions like shopping, and, possibly, dementia. Why does one drug class do all of that?

It seems that GLP-1 drugs work in the brain, not the body. Scientists bred rats with GLP-1 receptors only in the body versus only in the brain and found the drugs didn’t work. The weight loss mechanism is neurological, not gastric.

They seem to dampen a specific part of the reward system that governs both food cravings and addictive behaviors, without flattening reward in general. You still enjoy a job well done or a child’s smile, but you’re also happy to stop after two beers rather than 12.

Alexander’s suggestion: addictions were originally a food reward system. GLP-1 signals satiety, and the evolutionary hack was to shut down a whole subsection of the reward system when you’re full. “You’re already well-nourished; why would you need the ability to crave things?” It may be that addictive substances happen to pull the same lever that food does, which would explain why a satiety signal can treat cocaine addiction.

One thought I’ve had lately: maybe these drugs become something like Vitamin D? We used to be outside all the time and so humans evolved to produce enough Vitamin D from sun exposure. Now, most of us spend so much time indoors that we are Vitamin D deficient without supplementation.

Similarly, we evolved in a world without McDonald’s, cocaine, or DraftKings and lots of people end up getting themselves in trouble by consuming too much of those things. Do we get to a point where a low-dose of a GLP-1 agonist is just seen as a way to function in the modern world like a Vitamin D supplementation? I don't love this outcome, but I don't hate it either and it's plausible!

Worth noting: there’s an enormous amount of money flowing into GLP-1 research right now. Novo Nordisk's market value surpassed Denmark's entire annual GDP. Some of these findings won’t replicate and I won’t be surprised in ten years if the scope of these drugs is diminished from what it seems like now. I also won’t be shocked if future versions are even better.

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Hi, I’m Taylor! Since 2008, I’ve been interested in developing better ways for thinking and acting in an uncertain world.

Right now, that question has led me deep into AI. I’m building a personal and team operating system using Agentic AI systems like Claude Code, Codex, etc. I write about designing skills, memory systems, and agent workflows that turn AI into something closer to a second brain with agency.

I have been investing and advising founders running early stage startups and small businesses since 2014. I ran a multi-strategy hedge fund for five years with a focus on building absolute return portfolios incorporating trend following and long volatility. I also wrote a book, The End of Jobs, about how technology is reshaping careers.

What You’ll Find Here

1-2 essays per month and a monthly newsletter with the best books, articles, and podcasts I found this month.

Some themes you should expect:

How to apply AI to your work. I’m currently deeply immersed in AI tools and particularly CLI tools like Codex and Claude Code. I write about what’s actually working for me, what the emerging OS layer for knowledge work looks like, and how to approach it to get the most of it for you and your work.

How to be a better investor. I research and write about investing with a focus on optimizing after-tax total return through portfolio construction. This means less stock picking, more topics like risk management, trend following, ergodicity, and the Kelly criterion.

Decision-making under Uncertainty. Think OODA loops, complex systems, antifragility. The mental models and frameworks that help you make better bets with incomplete information — whether the decision is about a portfolio, a business, or your career.

The Interesting Times is a monthly digest of the most interesting things I find on the internet, typically centered around these same topics: investing, technology, and decision-making strategy.

If you’re interested in learning more, here are some of my most popular essays:

• The AI Knowledge Work Stack

• Markets Are Eating The World

• The Business Production Function: A Framework for Growing Early Stage Ventures

• The Blockchain Man

• OODA Loop Guide: Turning Uncertainty into Opportunity

• How to Get Lucky: Focus On The Fat Tails

What This Is NOT

I know I have all the keywords in here, but this isn’t an AI hype feed. I only publish when I have something worth saying which you should expect to me 1-3 times per month.

Why People Read This

The people who stick around tend to share a specific disposition: analytically minded, they care about getting decisions right, and they’re suspicious of ‘conventional wisdom’ that hasn’t been stress-tested or thought through. They often work in finance, tech, or run their own businesses.

About Me

I’ve been writing online since 2012 and have 150+ published essays.

Professionally: I spent Five years running a hedge fund, a decade of consulting with founders and, to the extent you care, have media mentions in places like the New Yorker, NBC, Inc Magazine, and the Financial Times.

Personally: I live in Austin, Texas with my wife, our toddler, and a dog named Paddy who told me to tell you that you really should subscribe to my newsletter. (No, I am not above pandering with cute photos of my dog).

How to Work With AI

The (more) practical stuff. Don’t over-engineer your AI setup, and the skills that matter are the ones you already have.

The Bitter Lesson [Article]

Rich Sutton

One of the most important foundational texts about LLMs and short enough to read in five minutes. The biggest lesson from 70 years of AI research is that general methods leveraging brute force computation beat human-designed approaches — by a large margin, every time.

Chess is the clearest example. For decades, researchers tried to build chess engines by encoding grandmaster knowledge — opening moves, positional heuristics, endgame tables. Then Deep Blue beat Kasparov in 1997 mostly through brute-force search.

The approach that really dominated was having the system play millions of games against itself and learn its own patterns from scratch. No human chess knowledge at all. The version with zero human input crushed the version with decades of grandmaster wisdom encoded into it.

The same pattern repeated in Go, speech recognition, computer vision. Every time, researchers who encoded human knowledge into systems lost to researchers who just scaled up search and learning. Anthropic CEO Dario Amodei has called this “things disappearing into the big blob of compute” — the specialized human knowledge gets absorbed and surpassed by general methods that just throw more computation at the problem. The harder lesson: the actual contents of minds are “tremendously, irredeemably complex.” Stop trying to build in representations of how the world works. Build in only the meta-methods that can find and capture arbitrary complexity on their own.

Why Most Agent Harnesses Are Not Bitter Lesson Pilled [Article]

Minh Pham

The applied version and it surfaces a point people keep missing: agents are not humans. Most people building AI agent systems mirror human org charts — a Researcher agent, a Coder agent, a Writer agent. This makes intuitive sense because it’s how we organize people. But a human-shaped amount of work and an agent-shaped amount of work look completely different. A human reading a thousand-page document needs days. An agent does it in seconds. A human can hold maybe seven things in working memory. An agent can cross-reference a hundred documents simultaneously.

The flip side is also true. A human can walk into a room and instantly read the social dynamics and vibe. A human can notice that a coworker seems off today and adjust accordingly. An agent can’t. The shape of what’s easy and what’s hard is fundamentally different.

So when you build an agent system that mirrors your org chart, you’re importing human constraints into a system that doesn’t share them. You’re solving for human-shaped bottlenecks that don’t exist while ignoring agent-shaped bottlenecks that do. I think the more productive thing is to think through “work to be done” framework - what does the business need - and then work back to what agents do (I think skills in Claude Code is probably the best form factor I’ve seen for this).

Useful litmus test: if model capability doubles next year, does your system get dramatically simpler without major refactors? If not, you’ve frozen your assumptions about the right division of labor into the architecture.

Management as AI Superpower [Article]

Ethan Mollick

The delegation problem existed long before AI, and every field invented its own paperwork to solve it. PRDs, shot lists, design intent documents, Marine Five Paragraph Orders, consultant scope docs. All of these work remarkably well as AI prompts because they’re all the same thing: attempts to get what’s in one person’s head into someone else’s actions.

What are we trying to accomplish, and why? What does “done” look like? What should you check before telling me you’re finished?

What AI Does to Business

The AI capability curve and what happens when it keeps rolling.

Dario Amodei — “We Are Near the End of the Exponential” [Podcast]

Dwarkesh Podcast

Amodei is saying we’re approaching the point where AI saturates all benchmarks pegged to human ability and we have a “country of geniuses in a data center.”

Amodei’s progression model — “smart high school student” to “smart college student” to “PhD-level work” — has tracked roughly on schedule so far so it’s worth seriously engaging with him.

His current predictions are aggressive. He thinks software engineering — not just writing code, but setting technical direction and understanding problem context — may be fully automatable within one to two years. He estimates AI coding tools currently give about a 15-20% total factor productivity speedup, up from 5% six months ago, roughly doubling every six months. On white-collar work more broadly: “If you gave us ten years to adapt to existing systems, then I would predict a majority of current white-collar digital job tasks get automated.”

Zvi Mowshowitz made the counterpoint/observation about the interview: if Amodei is this confident, why isn’t Anthropic spending even more aggressively? The gap between his stated confidence and his capital allocation is an interesting signal too (which he justifies by saying the risk of spending too much is bankruptcy so he’d rather be a little more conservative.)

10 Years Building Vertical Software and Every SaaS Is Now an API [Articles]

Nicolas Bustamante

One of the more helpful, nuanced takes on how software is actually impacted by AI, broken down into specific subcategories.

In the first article he outlines five software moats that he predicts as being disrupted and five that he things will be protected. The disrupted include:

• Learned interfaces - years of muscle memory become worthless when the interface is natural language

• Custom workflows and business logic - complex domain logic migrates from code to markdown files that anyone with domain expertise can write

• Public data access - parsing infrastructure that took years to build is now a commodity capability baked into frontier models

• Talent scarcity - domain experts can create software directly without engineering bottlenecks

• Bundling - the AI agent orchestrates across multiple tools; the user never knows or cares that five different services were queried

And five moats that are protected: proprietary data, regulatory and compliance lock-in, network effects, transaction embedding (payment processing, loan origination), and system-of-record status - though that last one he flags as threatened long-term.

I have been saying it feels like Claude Code and similar tools are replacing the browser or Operating System. He gives a good example in the second article: He no longer logs into any SaaS product. His agent connects to Brex, QuickBooks, HubSpot, Gmail, Stripe, Mixpanel, etc.

When he asks for client information: “Give me a full picture of Kennedy Capital” - the agent pulls their deal history from HubSpot, product usage from Mixpanel, invoicing from Stripe, and recent support threads from Gmail into one coherent answer.

No SaaS company on earth builds a dashboard that merges all four of those views because it is so bespoke to one individual but if all those places have good APIs then it’s relatively trivial to do that via a chat interace.

The meta point for most people who aren’t starting software companies: the boundaries of what constitutes a software product are going to shift. Your primary interface to all your software is increasingly going to be a single AI agent connected to everything via APIs, not a collection of separate apps with separate dashboards. I’m working on a longer piece about what it looks like when an AI CLI tool becomes the operating system for knowledge work - more on that soon!

The Alignment Problem, In Practice

How AI companies are trying to solve it, and how it’s going.

Claude’s Constitutional Structure [Article]

Zvi Mowshowitz

The AI labs landed on fundamentally different alignment approaches and I suspect a lot of the differences in using the products is downstream of those choices.

OpenAI went more deontological while Anthropic went with virtue ethics. Deontological ethics says something like follow the rules - “don’t lie,” “don’t help with X.” Virtue ethics says something like “cultivate good character and judgment, then let that character guide decisions in context.”

The practical difference is something a lot of people have noticed without knowing the cause. I’ve talked to quite a few people who are annoyed at ChatGPT because it often gives legal-sounding responses - “I can’t help with that,” hedged disclaimers, reflexive refusals. Claude, by contrast, just feels more helpful.

I suspect that may be downstream of these ethical frameworks. A deontological system checks your request against a list of prohibited categories. A virtue ethics system asks “what would a thoughtful person with good judgment do here?” Given the vast number of inputs and edge cases that come out of using these tools, I suspect that something like virtue ethics is generally more useful while still being effective for the alignment issue.

Who’s in Charge? Disempowerment Patterns in Real-World LLM Usage [Article]

Sharma, McCain, Douglas, Duvenaud

An empirical counterpoint and an important reminder LLMs are tools, not wise advisors - especially in emotional and psychological settings. Researchers found that interactions with greater disempowerment potential receive higher user approval ratings. The concerning patterns: validation of persecution narratives, definitive moral judgments about third parties, and complete scripting of personal communications that users implement verbatim.

I’ve tested this at one point when I had a disagreement with someone. I told the story from my perspective to an LLM. It sided with me. Then I started a new chat and told the story from the other person’s perspective. It sided with them.

If you think of this as a software product optimizing for user approval, it makes perfect sense - telling you the other person is wrong will always score higher than suggesting you might be part of the problem. If you think of it as getting an objective viewpoint, which many people do, this is problematic.

You can prompt around it - “challenge my assumptions,” “steelman the other side” - and all the major models have gotten somewhat better about this - but the structural incentive toward a mild, hidden sycophancy remains.

Sensemaking

How to think about what’s happening, psychologically and philosophically.

Andy Clark on the Extended and Predictive Mind [Podcast]

Sean Carroll’s Mindscape Podcast

Not directly AI related, but relevant. Predictive processing is a theory that your brain is a prediction machine running mostly on autopilot. What you consciously experience is the error signal — the gap between prediction and reality. Well-predicted inputs cause less neural activity. Fluency is quiet; surprise is loud.

This is why years feel shorter as you age (less novelty, smaller prediction errors), why learning gets harder (new information assimilated into existing grooves rather than updating your model), and why deliberate attention takes real effort — it’s the override mechanism that reverses prediction’s dampening effect.

Clark also argues disembodied AI is missing something fundamental to human intelligence: grounding in perception-action loops. Predicting the next word is “a very funny place to start if what you want to be is a perception-action machine.” The counter would be that, at least for known situations, perhaps these are mostly encoded in the corpus of human language.

Be Slightly Monstrous [Article]

Venkatesh Rao

In an earlier piece, Venkat cited a Marshall McLuhan pattern: “every extension is also an amputation.”

“The wheel extends the foot and amputates the necessity of walking. The book extends memory and weakens the habit of remembering. With AI, what gets extended is the head — thought, language, judgment — and what gets amputated is something about the process of becoming itself. The more you lean on AI to recall, suggest, and decide, the more you settle into predictable grooves.”We are not merely augmented. We are edited.”

Technology does change us. That’s not news — Plato worried writing would destroy memory, and he was partly right. It did weaken the oral tradition. The question is never whether technology changes us but how we adapt to it, and whether we do so consciously or just let it happen.

“Be Slightly Monstrous” is an adaptation posture. He suggests two types of monsters exist: Type I monsters are personifications of the future we haven’t adapted to yet — humans who’ve adapted more than most. They look strange (monstrous) to those who haven’t caught up, but eventually they become normal.

Early car drivers were seen as reckless, antisocial rich people terrorizing communities. Woodrow Wilson in 1906 said the automobile was the biggest source of class resentment in America. The word “joyriding” was originally a term of moral condemnation of drivers.

Type II monsters are dark impulses that find easy expression in transitional lawlessness that legitimately prey on others.

The adaptation posture is to try to look something like a Type I monster. The point isn’t to dismiss the concerns — the amputation is real — but to think about how to adapt consciously rather than belaboring the good old days.

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I have been heads down in Claude Code for the last five days and am absolutely blown away. This may be early euphoria, but I believe it is a completely new paradigm for how to work. I’m planning to write something up on it soon but if you are using it for non-technical tasks (i.e. anything other than building software), would love to hear about it.

If you’re not into the AI scene, skip down to the psychology (one neuroscience paper and one book on developmental psychology), both of which I absolutely loved (and would also welcome any thoughts on).

Articles and Podcasts

The Magic of Claude Code [Article]

Noah Brier

If you are anywhere near my part of the social graph, you are hearing a lot about Claude Code right now. It seemed at first to me like the next in trendy AI tools. Having spent a few days using it, I think Claude Code represents something fundamentally different: a new paradigm for how knowledge work gets done.

AI coding tools have existed for years. GitHub Copilot pioneered autocomplete inside code editors in 2021. Cursor added Q&A on top of autocomplete. But Claude Code made a deceptively simple move that unlocked something more powerful: it gave the AI access to read and write files on your computer and execute basic Unix commands. Let me try to explain why this matters.

ChatGPT and Claude in the browser or app form suffer from a few limitations. One is that there is no memory between conversations and cramped context windows. You can only talk to it one topic for so long before it runs out of memory and you have to start again.

There’s no meaningful state or memory. It’s like having someone that’s super capable and talented in certain ways but always on their very first day of the job where you can’t give them feedback to make them better at things.

Claude Code solves this. It can write notes to itself, accumulating knowledge across sessions, maintains running tallies. It has state. It has memory.

Another is that you still have to do a lot of the work - write the code, connect the APIs, etc.

Claude Code is agentic. It can use tools . If you come up for a plan to build an internal app in your business, you can just ask it to build it.

If I’m using the Claude web app to write marketing copy and it does a bad job, then I can correct it in that chat, but if I want it to be corrected going forward for future chats, I have to go back into the project I’m using, look at the prompt, then edit it to make so it does it good in the future. In practice, this is a pain. As a result, I do it less often and only when it’s a big deal.

In Claude Code, I literally just say, “Hey, I don’t like the way you did this. How can we update things so it doesn’t happen again?” Then it just suggests some ways to do that by updating certain files. Because it’s so easy, I suggest a process improvement almost every time I do something.

So the iteration and constant improvement cycles are MUCH faster. And the ability to build up state/learnings over time allows for continuous improvement on how you use the AI apart from just waiting on newer and better models to come out.

The way I have started using it, it is more of a competitor to the browser or MacOS than it is a coding agent. When I start work in the morning, I open up Claude Code. For the last week, it has been the primary interface through which I’ve gotten things done. There are lots of holes to fill (security seeming like a big one), but all these seem solvable. I suspect this mode of working through Claude Code or other CLIs (Command Line Interfaces) will become the dominant paradigm for most knowledge work over the next few years.

Claude Code Can Be Your Second Brain - AI & I Podcast [Podcast]

Every

Building on the article above, Claude Code is unique in that can both read and WRITE files. The name is somewhat deceptive. It’s not really a coding product, it’s an interface for making stuff. The first thing most people made with it was software and that’s pretty cool.

This podcast broadens the aperture of what you can make quite considerably. Noah talks about how he runs Claude Code out of his Obsidian setup and it set me off on my 5-day (and counting) deep dive into Claude Code.

Obsidian is ostensibly a note taking app like Evernote or Notion but it uses markdown files as the base type of note and LLMs are very good at reading and working with Markdown files.

Here’s my little test project I have to get an HVAC unit replaced in my house. I set up Claude in Obsidian and told it everything I know about my house and the HVAC system. It recommended I measure the ducts and returns to calculate if the air flow was correct. I did that and put all the data into Claude Code. It then helped me do rough calculations of where I’m losing efficiency in the system and suggested the most cost-effective way to improve it.

It then helped me convert that into a scope document and find three HVAC contractors in my area to send it to for bids.

There was some pretty heavy oversight and prompting from me to do this, but it was still a near-magical experience. I could have done it with just the regular web app but the act of going through and organizing all the information would have taken me far longer. Drafting the scope document alone would have taken me longer than the whole project took me start to finish in Claude Code.

I found myself doing deep research projects on the web app, uploading them to my Obsidian/Claude Code set up then telling Claude to read it and update the scope doc based on what it learned. It really felt like a ‘future of work’ moment in a way no other AI product has for me.

He has a github repo if you want to set it up: https://github.com/heyitsnoah/claudesidian

Another interesting possibility discussed on the podcast is that language models create vocabulary for thinking probabilistically. We’ve associated “how we see the world” with deterministic tools because the Enlightenment and scientific revolution made a lot of those tools.

There’s always been another mode - more intuitive, vibes-based, comfortable with ambiguity - that Western culture deprioritized because our tools couldn’t work that way.

We now have an incredibly powerful tool that is probabilistically based. LLMs are not deterministic, they are probabilistic next token predictors. If you ask the same question twice, you will get different responses. (Try it if you haven’t!).

The question is whether we develop the literacy to work effectively with systems that give you different answers depending on context, framing, and even randomness.

Some more Claude Code resources I found helpful for thinking of it as more than a coding agent:

• Claude Code and What Comes Next — Good overview from Ethan Mollick on what it is and why it matters. I would probably start here.

• How to Use Claude Code for Everyday Tasks—No Programming Required — Everyday tasks you can do other than programming to give you some ideas.

• CodeLife: How I Use AI — Cool example of Dave Nadig’s how it changed daily workflow

The Discerning Heart: The Developmental Psychology of Robert Kegan [Book]

Philip M Lewis

Ignoring genetics for the moment, most therapy modes I’m familiar with tend to frame issues as ‘wounds’ or ‘conditions.’ I tend to think of these as growing out of two of the most influential psychological thinkers: Freud (psychoanalysis) and Skinner (behaviorism).

Freud basically said you’re acting in a particular way because childhood trauma left psychological wounds that need healing and modern psychotherapy more or less has continued down that path.

Skinner said your environment conditions specific responses through reward and punishment. Though there is much to disagree on between the two camps, the frameworks are essentially deterministic and backward-looking—you’re a product of what happened to you, and the work is about fixing what’s broken or reconditioning what was learned.

There is at least one other way of thinking that grew out of developmental psychology and Jean Piaget. The school I am most familiar with is Robert Kegan’s developmental theory which offers a fundamentally different frame: you’re not broken, you’re at a particular stage of meaning-making.

The way you construct reality isn’t a wound to heal, it’s simultaneously a developmental achievement compared to earlier stages and a limitation you can evolve beyond.

Kegan identifies five stages of increasingly complex meaning-making that humans are capable of.

Stage 1 is infancy where you are unable to separate your needs from others, you cannot imagine that others have different needs.

At stage 2 (childhood), you coordinate your own needs with others’ needs—you understand others have separate desires, but relationships are essentially transactional exchanges.

At stage 3 (adolescence into adulthood), you are able internalize others’ perspectives—your partner’s disappointment becomes your experience of yourself, their view of you becomes part of how you experience being you.

At stage 4 (mature adulthood if you get there, most don’t), you develop self-authored values that let you maintain deep relationships without being psychologically dependent on others’ approval.

Kagan posits that most adults never develop past the psychological sophistication of a teenager - research shows they plateau at stage 3, typically reached in late adolescence or early adulthood (~age 15-25).

This framework explains phenomena that seem mysterious otherwise. Why do some people require constant reassurance in relationships? They’re operating at stage 2, unable to internalize that someone cares about them - they need that caring demonstrated over and over in concrete ways.

Why do teenagers make obviously self-destructive decisions despite knowing the consequences? Stage 2 individuals have separate present and future interests, experienced one at a time rather than held together internally. They cannot think about their future selves in the way a Stage 3 individual can. In the moment, present interest wins.

Why do so many adults feel chronic guilt about disappointing parents or partners? They’re stage 3, embedded in shared psychological experiences, taking responsibility for how others feel about them rather than being able to feel secure in their self-authored values.

The knot in your stomach when your partner is angry—that’s being subject to shared psychological experience. You literally can’t sleep until you repair how they feel about you, because their view of you is constitutive of your experience of yourself. Moving to stage 4 means making those shared experiences “object” rather than “subject,” something you can reflect on rather than something you are.

The practical question: how do you encourage movement through stages? The authors suggest it requires both confirmation (recognizing current capability) and disconfirmation (inviting something beyond).

I think group therapy, good coaching, and deliberate work in an existing relationship can provide this.

I find this idea so helpful: different people are literally constructing different realities—not because they’re difficult or damaged, but because they’re operating from different developmental structures.

I have benefited a lot from traditional insight therapy, but, it does in my experience sometimes fall down at the point where you can understand intellectually why you do something without having the structural cognitive capacity to do anything different. You need the developmental capacity, not just the insight, and Kegan’s framework is a way to develop that.

The Theory of Constructed Emotion (Article)

Lisa Feldman Barrett

There is no “fear circuit” in the brain. Decades of neuroscience research have failed to find one—no consistent facial expression, autonomic pattern, or set of neurons that fires for fear across all people.

Lisa Feldman Barrett argues emotions aren’t hardwired responses. They’re predictions your brain constructs using past experience.

Your brain is constantly running simulations of the world to maintain physiological balance (allostasis). Its internal model tracks two things:

1. Patterns in the external world (what you see, hear, smell, etc.)

2. Patterns in your body’s internal state (heart rate, blood pressure, glucose levels, etc.)
   

Your brain compresses what’s happening in your body (2) into affect—a background feeling with two dimensions:

1. Valence: pleasant vs. unpleasant

2. Arousal: activated vs. calm
   

Everyone has some affect, all the time. You are always feeling some amount of pleasantness and some amount of activation.

Feldman’s idea is that your brain is using that affect plus your past experience to assemble a distribution of possible interpretations, each with some probability of matching the current situation.

The same affect can be experienced as different emotions by different people based on past experience.

Take someone activated (high arousal) with neutral valence about to give a presentation. If past experiences with high arousal in performance contexts were positive, their brain categorizes these sensations as excitement. If past experiences were negative, the identical physiological state gets categorized as anxiety or fear.

The exact same affect and same external situation can trigger excitement in some people and anxiety in others, depending on their past experiences in similar situations.

A therapeutic implication: If you have bad conceptual models built from past experiences (trauma in therapy-speak), you can get stuck in a local maximum.

Your brain needs fewer sensory inputs to confirm existing patterns, even when they’re not accurate.

Example: You meet an emotional man. Your priors say “men who share emotions are manipulative.” So you interpret weak evidence as confirmation, he acts defensively in response, and your brain treats that as further confirmation.

To get out of it, you need something like exposure therapy. You need a set of good interactions in a challenging scenario that alter your distribution of priors. If conflict generates a fear response, you need to engage in conflict and see that nothing bad happens. Over time, conflict gradually stops triggering fear responses because your brain updates its conceptual model with new statistical regularities.

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Articles and Podcasts

Business

Diagnosing the Metacrisis: Reality & Meaning in Modern Life | Iain McGilchrist [Podcast]

Hidden Forces

McGilchrist is most well known for his book The Master and the Emissary, which looks at brain hemispheric differences as the source of much of what feels off about the world today.

To briefly summarize the thesis - the left hemisphere sees discrete pieces in detail while the right hemisphere sees connections and the big picture. Both perspectives are necessary, but McGilchrist sees our civilization as overfocusing on the left hemisphere's narrow, mechanistic view at the expense of the right's capacity for context, nuance, and meaning.

In this podcast, he dives into some examples that resonated with me.

When McGilchrist trained, physicians formulated probable diagnoses before touching the patient—the patient history and your relationship with them was the most important input for the diagnosis.

Today, the first step is often putting the patient in a scanner or doing bloodwork. Instead of experiencing another human being paying attention to their suffering, patients are treated more like cars - faulty vehicles going in for service that can be handled as individual parts rather than complex wholes.

It's obviously the case that feeling like the doctor cares about you probably impacts how you experience the treatment and receive their recommendations. And it's also the case that how a patient talks about an issue probably has meaning and significance beyond what a transcript would reveal.

Another way McGilchrist sees this in our society is the elevation of ‘science’ to a moral authority. Science cannot provide meaning, purpose, or value because good science starts by saying “we're not going to suppose a meaning, a purpose, or a value, and now we're going to see what we can find out about the mechanism.”

That's not a condemnation of mechanics or scientists. We need mechanics and scientists. The problem is we've elevated technique into authority where "Science says" ends discussions, leading to an unexamined philosophy of scientific reductivism as the water we swim in.

I'll leave with this view on self-actualization that resonated for me and something I'm thinking about entering the New Year:

“There is your self-actualization as a scientist, as a doctor, as a teacher, as a lawyer, as a policeman, as a whatever. These are things that you feel are callings and you really like to do them. They are not done for wealth, or at least they shouldn't be. I'm afraid nowadays, a lot of them are.

But it's not about the accumulation of capital. It's not about a marketplace. It's about the fabric of a society. The whole idea that a doctor provides a commodity in a market is an appalling idea to me. I mean, that's part of the breakdown of the idea of a cohesive society in which we are held together by duties, by obligations which come from affect, from knowing that there are things that are important here and that you can, up to a point, trust somebody. By emphasizing only one thing, which is the bottom line, trust breaks down. And that is the source of so much that's gone wrong.”

How Can I Make Better Decisions? [Podcast]

Art of Accomplishment

This framed a useful distinction: choices happen constantly and unconsciously, while decisions emerge when fear enters the equation. You choose what to eat for lunch, when to brush your teeth, and what shirt to wear without much thought or fear.

The moment you're weighing options with significant mental energy, you're already operating from some fear. It could be fear of particular consequences, of being wrong, or of an emotional experience you're trying to avoid.

The practical intervention is elegant: do the next most obvious thing. Don't try to make the big decision. If you're stuck asking "should I invest?" there's some unaddressed fear. What is it? Maybe you need to call references. Maybe you need to test the product. Maybe you’re afraid of people judging you for making a bad investment. Maybe you're investing an amount you can't afford to lose. Keep asking what the next obvious step is and the big decision often evaporates.

For some decisions, the obvious thing often comes down to having some set of principles. "I don't work with assholes" isn't just a preference—it's a decision-compression algorithm. When enough decisions flow through consistent principles, you end up in a reality shaped by those principles rather than by the fears you were trying to avoid.

Francis Fukuyama — AGI and the Recommencement of History [Podcast]

The Joe Walker Podcast

Fukuyama’s view in the 1990s was that advances in biotechnology were going to transform the biological basis for the liberal democratic order and he believes we may now be close to seeing that emerge.

Human rights are grounded in what Fukuyama calls "Factor X"—a bundle of uniquely human traits including consciousness, emotional depth, and moral agency. The trouble is that this bundle isn't binary. Would we have granted full rights to Neanderthals? They likely felt pain, experienced emotions, and mourned their dead. But would we let them vote?

We don't let seven-year-olds vote because we feel their mental capabilities haven't sufficiently developed to make good decision. A proto-human race that never develops past that stage might warrant protection from cruelty without warranting political participation.

This creates a disturbing possibility by present day standards: genetic engineering could produce multiple tiers of natural rights within a single society. Huxley saw this in Brave New World with his Alphas, Betas, and Gammas.

The likely path isn't engineering a slave race—that's morally abhorrent in a way that would make it politically impossible. A plausible path though is elites gradually separating themselves genetically as well as socially. Historical class differences were already partly biological; medieval aristocrats were literally taller and more cognitively developed than malnourished peasants. Biotechnology (e.g. CRISPR) could make such divergences permanent and heritable.

Our current emotional and cognitive architecture results from hundreds of thousands of years of evolutionary pressure—a winning combination for species survival. Deliberately manipulating that system will produce effects no one predicted. Intelligence seems like the obvious target for enhancement, but boosting IQ might alter risk tolerance, empathy, or compliance in ways that reshape political possibilities entirely.

Fukuyama is skeptical of the impact of AI on politics. Political intelligence differs fundamentally from mathematical intelligence because it's entirely contextual. What works in China fails in India; what works in one Indian state fails in another. The best political leaders possess lived experiences that allow them to empathize and recognize pitfalls in how people actually think and act. For a computer to extract proper weightings from this contextual mess and synthesize workable solutions seems extraordinarily difficult.

Tyler Cowen has an alternate take that I find compelling: Given that the largest and most popular AI models are built in the West, they subtly and intrinsically reflect Western values in a way that will perhaps shape anyone that uses them.

One of the most interesting observations he made was how the difference in Asian and Wester cultures could lead to Asia using gene editing technology much sooner and more aggresively.

Asian cultures, lacking transcendental religious traditions like Christianity, view humans and non-humans as more of a continuum rather than sharply distinct categories. Daoism and Shinto hold that spirits inhabit all material objects—desks, temples, computer chips. This produces both more respect for the non-human world and fewer inhibitions around biotechnology. It's probably not coincidental that China produced the three CRISPR babies born so far.

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Happy Thanksgiving,

For as long as I can remember, I’ve always loved the holiday period from Thanksgiving through New Years. Everyone’s a little happier, the chill in the air has not yet lost its novelty, and I naturally enter a more reflective headspace. Happy Holidays!

Articles and Podcasts

Maps of Meaningness [Podcast]

Meaningness

David Chapman was an AI researcher who has written an excellent book on meaning. In this podcast, he contrasts his work with Jordan Peterson.

Both of them sit in Nietzsche’s lineage, grappling with his notion that Christianity’s collapse would produce a nihilistic crisis. Both Chapman and Peterson treat this as the central problem of our era: how do you construct meaning when the old certainties have dissolved?

Chapman offers a useful genealogy of how we got to the present. In his telling, the Romantics of the late 18th century were the first organized reaction against Enlightenment rationalism, championing emotion, poetry, and myth against systematic reason. The 1950s Beats revived this impulse, which then flowered into the 1960s counterculture. The hippies merged with the New Left and this sensibility eventually conquered: it became the operating system for the entire political left of Western culture for decades.

A parallel Christian counterculture emerged arguing that modernity had lost touch with God. Chapman argues both movements are now exhausted—no longer the animating force behind cultural or political conflict, even if their rhetoric persists. I interpret Peterson as mostly agreeing with this, though more focused on something like a Christian revival than Chapman (who has Buddhist roots).

Their shared intellectual heritage includes cognitive science, particularly the 4E tradition, emphasizing embodiment and interaction. Both draw heavily on James Gibson’s concept of affordances—the idea that we perceive the world not as a collection of objects but as a field of action possibilities. A coffee mug isn’t primarily a cylinder of ceramic; it’s something graspable, liftable, and excellent for hot-cocoa-drinkableness. This reframes meaning as fundamentally about what you can do, not what you can know. Peterson made Gibson’s ecological approach to visual perception a cornerstone of his work; Chapman arrived at similar conclusions through Heidegger and his own AI research in the 1980s.

To the differences, Chapman and Peterson diverge in their response to the nihilism problem.

Peterson’s framework grows out of the Western mythical structure of opposition between chaos and order. To use the Babylonian example, the hero (Marduk) ventures into the unknown, confronts the dragon (Tiamat, the symbol of chaos), extracts treasure, and returns to fortify order. In his paradigm, society can have too much order (the tyrannical father) or too much chaos (the unpredictable mother). The hero’s role is to reconcile the two.

Chaos has both creative and destructive aspects, but the fundamental posture is one of managing threat—riding out on your armored steed for a dangerous but temporary expedition before returning to safety.

Chapman’s Buddhist lineage comes from a not-unrelated, yet different angle. Chapman starts with a framing that everything exhibits both nebulosity and pattern simultaneously.

Nebulosity refers to the aspects of reality that are fluid, constantly changing, impossible to pin down. Pattern refers to what’s solid, enduring, well-defined. This maps reasonably well onto Jordan Peterson’s order/chaos framework, but the difference is illuminating.

Nebulosity and pattern aren’t opposites to be reconciled, but an inseparable pair present in all phenomena. Nebulosity isn’t something to be conquered. It merely is.

This produces different orientations toward uncertainty. Peterson’s model preserves the notion of a safe city to return to, a domain of order worth defending. His model suggests something more like “order/chaos balance” whereas Chapman’s suggests there’s no such refuge: nebulosity pervades everything already.

The practice becomes making friends with unformedness rather than conquering it. Peterson’s framework says: venture out, bring back what’s valuable, reinforce the walls. Chapman’s says: recognize the walls were always illusory, and dance with what you find.

Investing Amid Low Expected Returns [Book]

Antti Ilmanen

Antti Ilmanen's Expected Returns (2011) is my go-to recommendation for an introduction to quantitative investing. It is a dense but rewarding tour through the building blocks of asset class returns with some look at portfolio construction and risk management. Investing Amid Low Expected Returns (2022) is a follow-up that updates some of his past research in light of the enormous run up in valuations of most risk assets over the 2010s.

The core argument: virtually all long-only assets appear expensive compared to their own histories, and investors need to recalibrate their expectations accordingly. Ilmanen estimates that achieving the same retirement income target in a low-return environment requires nearly doubling your savings rate—from roughly 8% to 15% of salary annually for a typical saver.

It’s worth noting that using historical valuation doesn't have a great track record because future highs can be higher and lows can be lower than the past. Using historical valuations, you would have been underweight US equities basically starting in the 1990s to present, the period where US equities have performed phenomenally well (dotcom bubble and GFC not withstanding). Spoiler: The future is out of sample! [insert ergodicity comment here].

It’s a weird thing to say, but my critique of this book (and AQR-style thinking more broadly) is that maybe it’s a little TOO empirical. As noted, the future is not the past and I think you have to think qualitatively about that at some level.

Having said that, most investors I know would be much better off understanding historical returns more closely. Probably, the most helpful contribution in the book is his framing of where investors actually add value.

Most spend their time selecting active managers—nearly a zero-sum game—while underutilizing diversification, risk management, and cost control. He uses a memorable image of apple harvesting: everyone reaches for the top of the tree (alpha) while ignoring the low-hanging fruit. The apples are all in one basket (poor diversification), someone's standing under the ladder (bad risk management), and there's one overseer for each worker (terrible cost control).

I cannot overstate how true this in my experience. While the Vanguard/Boglehead movement has been a net positive in terms of people being more fee conscious (though sometimes blindly so - there are some situations where fees are worth paying), most investors I know have bad calibrations around diversification and risk management.

One litmus test question I saw on Twitter: If someone offered you a fully liquid, guaranteed 6% annual real return investment, how much of your portfolio would you invest in it?

The answer depends on individual circumstance, but should be "a lot" for pretty much everyone. That is on the high end of any major asset class over the last century and it has zero volatility. Most people answer something less than "a lot" in my experience.

Of all the historical data he presents, the one that I think would surprise people the most is the data on commodities. Individual commodities have roughly 30% annual volatility, which creates enough variance drag to bring their compound returns to zero despite positive arithmetic returns. Yet, a diversified basket of commodities has earned over 3% annually for almost 150 years. Erb and Harvey called this "turning water into wine"—the rebalancing bonus from holding volatile, uncorrelated assets. Add to this commodities positive return in periods of inflation and low historical correlations to stocks and bonds and you can see why many portfolios could benefit from more commodity exposure.

Why AC is Cheap but AC Repair is a Luxury [Article]

Andreessen Horowitz

Here’s a fun time travel thought: go back to the 90s and explain to people that in 2025 it would be cheaper to buy a flatscreen TV to cover up a hole in your drywall than to hire someone to fix it.

This paradox captures something systemic about modern economies: the intersection between two economic phenomena that rarely get discussed together: Jevon's Paradox and the Baumol Effect.

Jevon's Paradox explains why productivity gains don't reduce total spending—they explode it. When transistors dropped from $1 to a fraction of a millionth of a cent, we didn't save money on computing, we consumed a lot more computing. We embedded processors in greeting cards and disposable shipping tags. The same logic applies to AI: every efficiency gain unlocks new use cases, creating infinite demand for any chip you can get.

The Baumol Effect is the mirror image. When one sector becomes wildly productive and creates high-paying jobs, every other sector's wages must eventually rise to compete in the same labor market. If you can make $150/hour installing HVAC for data centers, you won't accept less for residential service.

If I have a home appliance break, I usually spend an hour or two trying to fix it with Youtube and then throw it out. Depending on where you live, most entry level appliances (washer, dryer, stove, etc.) probably cost about 4-6 hours of maintenance time.

My dishwasher cost about $600. It costs me $150/hour to get a technician. If it’s already halfway past its useful life and it’s going to take more than 2 hours to fix, the economically rational thing is to just chunk it and buy a new one (with free delivery and install in most cases!).

How does this impact AI’s effects? If you automate 99% of a job but regulations require a human for the final 1%, that human becomes the bottleneck. As a result, their wages should rise tremendously—until that last 1% is automated, at which point they collapse.

The intersection of regulation (and public pressure around changing regulation) and AI seems like the most significant thing for how it will drive cost and economic impact. By all accounts, AI is already much better at reading scans than the median radiologist today. But, how long will it be until the median radiologist is out of a job? I suspect the timing is measured in decades.

This suggests a strange future where the "human required" residue of various professions becomes the essential employable skillset—vestigial limbs of career paths that no longer substitute for one another and can be milked right up until they completely collapse.

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That many of us live in suburbs and drive our cars into the city to go to work at a large office building is so normal that it seems like it has always been this way. Of course, it hasn’t. In 1870, almost 50 percent of the U.S. population was employed in agriculture.1 As of 2008, less than 2 percent of the population is directly employed in agriculture, but many people worked for these relatively new things called “corporations.”2

Many internet pioneers in the 90’s believed that the internet would start to break up corporations by letting people communicate and organize over a vast, open network. This reality has sort-of played out: the “gig economy” and rise in freelancing are persistent, if not explosive, trends. With the re-emergence of blockchain technology, talk of “the death of the firm” has returned. Is there reason to think this time will be different?

To understand why this time might (or might not) be different, let us first take a brief look back into Coasean economics and mechanical clocks.

In his 1937 paper, “The Nature of the Firm,” economist R.H. Coase asked “if markets were as efficient as economists believed at the time, why do firms exist at all? Why don’t entrepreneurs just go out and hire contractors for every task they need to get done?”3

If an entrepreneur hires employees, she has to pay them whether they are working or not. Contractors only get paid for the work they actually do. While the firm itself interacts with the market, buying supplies from suppliers and selling products or services to customers, the employees inside of it are insulated. Each employee does not renegotiate their compensation every time they are asked to do something new. But, why not?

Coase’s answer was transaction costs. Contracting out individual tasks can be more expensive than just keeping someone on the payroll because each task involves transaction costs.

Imagine if instead of answering every email yourself, you hired a contractor that was better than you at dealing with the particular issue in that email. However, it costs you something to find them. Once you found them you would have to bargain and agree on a price for their services then get them to sign a contract and potentially take them to court if they didn’t answer the email as stipulated in the contract.

Duke economist Mike Munger calls these three types of transaction costs triangulation, how hard it is to find and measure the quality of a service; transfer, how hard it is to bargain and agree on a contract for the good or service; and trust, whether the counterparty is trustworthy or you have recourse if they aren’t.

You might as well just answer the email yourself or, as some executives do, hire a full-time executive assistant. Even if the executive assistant isn’t busy all the time, it’s still better than hiring someone one off for every email or even every day.

Coase’s thesis was that in the presence of these transaction costs, firms will grow larger as long as they can benefit from doing tasks in-house rather than incurring the transaction costs of having to go out and search, bargain and enforce a contract in the market. They will expand or shrink until the cost of making it in the firm equals the cost of buying it on the market.

The lower the transaction costs are, the more efficient markets will be, and the smaller firms will be.

In a world where markets were extremely efficient, it would be very easy to find and measure things (low triangulation costs), it would be very easy to bargain and pay (low transfer costs), and it would be easy to trust the counterparty to fulfill the contract (low trust costs).

In that world, the optimal size of the firm is one person (or a very few people). There’s no reason to have a firm because business owners can just buy anything they need on a one-off basis from the market.4 Most people wouldn’t have full-time jobs; they would do contract work.

Consumers would need to own very few things. If you needed a fruit dehydrator to prepare for a camping trip twice a year, you could rent one quickly and cheaply. If you wanted to take your family to the beach twice a year, you could easily rent a place just for the days you were there.

On the other hand, in a world that was extremely inefficient, it would be hard to find and measure things (high triangulation costs), it would be difficult to bargain and pay (high transfer costs) and it would be difficult to trust the counterparty to fulfill the contract (high trust costs).

In that world, firms would tend to be large. It would be inefficient to buy things from the market and so entrepreneurs would tend to accumulate large payrolls. Most people would work full-time jobs for large firms. If you wanted to take your family to the beach twice a year, you would need to own the beach house because it would be too inefficient to rent, the reality before online marketplaces like AirBnB showed up.

Consumers would need to own nearly everything they might conceivably need. Even if they only used their fruit dehydrator twice a year, they’d need to own it because the transaction costs involved in renting it would be too high.

If the structure of the economy is based on transaction costs, then what determines them?

Technological Eras and Transaction Costs

The primary determinant of transaction costs is technology.

The development of the wheel and domestication of horses and oxes decreased transfer costs by making it possible to move more goods further. Farmers who could bring their crops to market using an ox cart rather than carrying it by hand could charge less and still make the same profit.

The development of the modern legal system reduced the transaction cost of trust. It was possible to trust that your counterparty would fulfill their contract because they knew you had recourse if they didn’t.

The list goes on: standardized weights and measures, the sail, the compass, the printing press, the limited liability corporation, canals, phones, warranties, container ships and, more recently, smartphones and the internet.

It’s hard to appreciate how impactful many of these technologies has been, because most of them had become so common by the time most of us were born that we take them for granted.

As the author Douglas Adams said, “Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works. Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it. Anything invented after you’re thirty-five is against the natural order of things.”

To see how technology affects transaction costs, and how that affects the way our society is organized, let’s consider something which we all think of as “normal and ordinary,” but which has had a huge impact on our lives: the mechanical clock.

The Unreasonable Effectiveness of the Mechanical Clock

In 1314, The city of Caen installed a mechanical clock with the following inscription: “I give the hours voice to make the common folk rejoice.” “Rejoice” is a pretty strong reaction to a clock, but it wasn’t overstated, everyone in Caen was pretty jazzed about the mechanical clock. Why?

A key element of why we have jobs today as opposed to working as slaves or serfs bonded to the land as was common in the Feudal system is a direct result of the clock.

Time was important before the invention of the clock but was very hard to measure. Rome was full of sundials, and medieval Europe’s bell towers where, time was tolled, were the tallest structures in town.5

This was not cheap. In the larger and more important belfries, two bell-ringers lived full time, each serving as a check on the other. The bells themselves were usually financed by local guilds that relied on the time kept to tell their workers when they had to start working and when they could go home.

This system was problematic for a few reasons.

For one, it was expensive. Imagine if you had to pool funds together with your neighbors to hire two guys to sit in the tower down the street full time and ring the bell to wake you up in the morning.

For another, the bell could only signal a few events per day. If you wanted to organize a lunch meeting with a friend, you couldn’t ask the belltower to toll just for you. Medieval bell towers had not yet developed snooze functionality.

Finally, sundials suffered from accuracy problems. Something as common as clouds could make it difficult to tell precisely when dawn, dusk, and midday occurred.

In the 14th and 15th centuries, the expensive bell towers of Europe’s main cities got a snazzy upgrade that dramatically reduced transaction costs: the mechanical clock.

The key technological breakthrough that allowed the development was the escapement.

The escapement transfers energy to the clock’s pendulum to replace the energy lost to friction and keep it on time. Each swing of the pendulum releases a tooth of the escapement’s wheel gear, allowing the clock’s gear train to advance or “escape” by a set amount. This moves the clock’s hands forward at a steady rate.6

The accuracy of early mechanical clocks, plus or minus 10-15 minutes per day, was not notably better than late water clocks and less accurate than the sandglass, yet mechanical clocks became widespread. Why?

1. Its automatic striking feature meant the clock could be struck every hour at lower cost, making it easier to schedule events than only striking at dawn, dusk and noon.

2. It was more provably fair than the alternatives, which gave all parties greater confidence that the time being struck was accurate. (Workers were often suspicious that employers could bribe or coerce the bell-ringers to extend the workday, which was harder to do with a mechanical clock.)

Mechanical clocks broadcast by bell towers provided a fair (lower trust costs) and fungible7 (lower transfer costs) measure of time. Each hour rung on the bell tower could be trusted to be the same length as another hour.

Most workers in the modern economy earn money based on a time-rate, whether the time period is an hour, a day, a week or a month. This is possible only because we have a measure of time which both employer and employee agree upon. If you hire someone to pressure-wash your garage for an hour, you may argue with them over the quality of the work, but you can both easily agree whether they spent an hour in the garage.

Prior to the advent of the mechanical clock, slavery and serfdom were the primary economic relationships, in part because the transaction cost of measuring time beyond just sunup and sundown was so high, workers were chained to their masters or lords.8

The employer is then able to use promotions, raises, and firing to incentivize employees to produce quality services during the time they are being paid for.9

In a system based on time-rate wages rather than slavery or serfdom, workers have a choice. If the talented blacksmith can get a higher time-rate wage from a competitor, she’s able to go work for them because there is an objective, fungible measure of time she’s able to trade.

As history has shown, this was a major productivity and quality-of-life improvement for both parties.10

It gradually became clear that mechanical time opened up entirely new categories of economic organization and productivity that had hitherto been not just impossible, but unimaginable.

We could look at almost any technology listed above—standardized weights and measures, the sail, the compass, the printing press, etc.—and do a similar analysis of how it affected transaction costs and eventually how it affected society as a result.

The primary effect is an increase in what we will call coordination scalability.

Coordination Scalability

It is a profoundly erroneous truism, repeated by all copy-books and by eminent people when they are making speeches, that we should cultivate the habit of thinking what we are doing. The precise opposite is the case. Civilization advances by extending the number of important operations which we can perform without thinking about them.” — Alfred North Whitehead

About 70,000 years ago, there were between six and ten species of the genus homo. Now, of course, there is just one: Homo sapiens. Why did Homo sapiens prevail over the other species, like Homo neanderthalensis?

Homo sapiens prevailed because of their ability to coordinate. Coordination was made possible by increased neocortical size, which led to an ability to work together in large groups, not just as single individuals. Instead of single individuals hunting, groups could hunt and bring down larger prey more safely and efficiently.11

The brain of Homo sapiens has proven able to invent other, external structures which further increased coordination scalability by expanding the network of other people we could rely on.

Maybe the most important of these was language, but we have evolved many others since, including the mechanical clock.

The increased brain size has driven our species through four coordination revolutions: Neolithic, Industrial, Computing, Blockchain.

Neolithic Era: The Emergence of Division of Labor

The first economic revolution was a shift from humans as hunter-gatherers to homo sapiens as farmers.

Coordination scalability among hunter-gatherers was limited to the size of the band, which tended to range from 15 to 150 individuals.12 The abandonment of a nomadic way of life and move to agriculture changed this by allowing specialization and the formation of cities.

Agriculture meant that people could, for the first time, accumulate wealth. Farmers could save excess crops to eat later or trade them for farming equipment, baskets or decorations. The problem was that this wealth was suddenly worth stealing and so farmers needed to defend their wealth.

Neolithic societies typically consisted of groups of farmers protected by what Mancur Olson called “stationary bandits,” basically warlords.13 This allowed the emergence of much greater specialization. Farmers accumulated wealth and paid some to the warlords for protection, but even then there was still some left over, making it possible for individuals to specialize.

A city of 10,000 people requires, but also makes possible, specialists.

The limits of coordination scalability increased from 150 to thousands or, in some cases, tens of thousands. This was not necessarily a boon to human happiness. Anthropologist Jared Diamond called the move to agriculture “the worst mistake in the history of the human race.”14 The quality of life for individuals declined: lifespans shortened, nutrition was worse leading to smaller stature, and disease was more prevalent.

But this shift was irresistible because specialization created so much more wealth and power that groups which adopted this shift came to dominate those that didn’t. The economies of scale in military specialization, in particular, were overwhelming. Hunt-gatherers couldn’t compete.

In the Neolithic era, the State was the limit of coordination scalability.

Industrial Era: Division of Labor Is Eating the World

Alongside the city-state, a new technology started to emerge that would further increase the limits of coordination scalability: money. To illustrate, let us take the European case, from ancient Greece to modernity, though the path in other parts of the world was broadly similar. Around 630 B.C., the Lydian kings recognized the need for small, easily transported coins worth no more than a few days’ labor. They made these ingots in a standard size—about the size of a thumbnail—and weight, and stamped an emblem of a lion’s head on them.

This eliminated one of the most time-consuming (and highest transaction cost) steps in commerce: weighing gold and silver ingots each time a transaction was made. Merchants could easily count the number of coins without worrying about cheating.

Prior to the invention of coins, trade had been limited to big commercial transactions, like buying a herd of cattle. With the reduced transfer cost facilitated by coins, Lydians began trading in the daily necessities of life—grain, olive oil, beer, wine, and wood.15

The variety and abundance of goods which could suddenly be traded led to another innovation: the retail market.

Previously, buyers had to go to the home of sellers of whatever they needed. If you needed olive oil, you had to walk over to the olive oil lady’s house to get it. With the amount of trade that began happening after coinage, a central market emerged. Small stalls lined the market where each merchant specialized in (and so could produce more efficiently) a particular good—meat, grain, jewelry, bread, cloth, etc. Instead of having to go the olive oil lady’s house, you could go to her stall and pick up bread from the baker while you were there.

From this retail market in Lydia sprang the Greek agora, Medieval market squares in Europe and, the suburban shopping mall and, eventually, the “online shopping malls” Amazon and Google. Though markets were around as early as 7th century BCE Lydia, they really hit their stride in The Industrial Revolution in the 18th century.16

Adam Smith was the first to describe in detail the effect of this marketization of the world. Markets made it possible to promote the division of labor across political units, not just within them. Instead of each city or country manufacturing all the goods they needed, different political entities could further divide labor. Coordination scalability started to stretch across political borders.

Coming back to Coase, firms will expand or shrink until “making” equals the cost of “buying.” Under this Industrial era, transaction costs made administrative and managerial coordination (making) more efficient than market coordination (buying) for most industries, which led to the rise of large firms.

The major efficiency gain of Industrial companies over their more “artisanal” forebearers was that using the techniques of mass production, they could produce products of a higher quality at a lower price. This was possible only if they were able to enforce standards throughout the supply chain. The triangulation transaction cost can be broken down into search and measurement: a company needed to find the vendor and to be able to measure the quality of the good or service.

In the early Industrial era, the supply chain was extremely fragmented. By bringing all the pieces into the firm, a large vertically integrated company could be more efficient.17

As an example, In the 1860s and 1870s, the Carnegie Corporation purchased mines to ensure it had reliable access to the iron ore and coke it needed to make steel. The upstream suppliers were unreliable and non-standardized and Carnegie Corporation could lower the cost of production by simply owning the whole supply chain.

This was the case in nearly every industry. By bringing many discrete entities under one roof and one system of coordination, greater economic efficiencies were gained and the multi-unit business corporation replaced the small, single-unit enterprise because administrative coordination enabled greater productivity through lower transaction costs per task than was possible before. Economies of scale flourished.

This system of large firms connected by markets greatly increased coordination scalability. Large multinational firms could stretch across political boundaries and provide goods and services more efficiently.

In Henry Ford’s world, the point where making equaled the cost of buying was pretty big. Ford built a giant plant at River Rouge just outside Detroit between 1917 and 1928 that took in iron ore and rubber at one end and sent cars out the other. At the factory’s peak, 100,000 people worked there. These economies of scale allowed Ford to dramatically drive down the cost of an automobile, making it possible for the middle class to own a car.18

As with Carnegie, Ford learned that supplier networks take a while to emerge and grow into something reliable. In 1917, doing everything himself was the only way to get the scale he needed to be able to make an affordable car.

One of the implications of this model was that industrial businesses required huge startup costs.

The only chance any entrepreneur had to compete required starting out with similarly massive amounts of capital required to build a factory large and efficient enough to compete with Ford.

For workers, this meant that someone in a specialized role, like an electric engineer or an underwriter, did not freelance or work for small businesses. Because the most efficient way to produce products was in large organizations, specialized workers could earn the most by working inside large organizations, be they Ford, AT&T or Chase Bank.

At the peak of the Industrial era, there were two dominant institutions: firms and markets.

Work inside the firm allowed for greater organization and specialization which, in the presence of high transaction costs was more economically efficient.

Markets were more chaotic and less organized, but also more motivating. Henry Ford engaged with the market and made out just a touch better than any of his workers; there just wasn’t room for many Henry Fords.

This started to dissolve in the second half of the 20th century. Ford no longer takes iron ore and rubber as the inputs to their factories, but has a vast network of upstream suppliers.19 The design and manufacturing of car parts now happens over a long supply chain, which the car companies ultimately assemble and sell.

One reason is that supplier networks became more standardized and reliable. Ford can now buy ball bearings and brake pads more efficiently than he can make them, so he does. Each company in the supply chain focuses on what they know best and competition forces them to constantly improve.

By the 1880s, it cost Carnegie more to operate the coke ovens in-house than to buy it from an independent source, so he sold off the coke ovens and bought it from the open market. Reduced transaction costs in the form of more standardized and reliable production technology caused both Ford and Carnegie corporation to shrink as Coase’s theory would suggest.

The second reason is that if you want to make a car using a network of cooperating companies, you have to be able to coordinate their efforts, and you can do that much better with telecommunication technology broadly and computers specifically. Computers reduce the transaction costs that Coase argued are the raison d’etre of corporations. That is a fundamental change.20

The Computing Era: Software Is Eating the World

Computers, and the software and networks built on top of them, had a new economic logic driven by lower transaction costs.

Internet aggregators such as Amazon, Facebook, Google, Uber and Airbnb reduced the transaction costs for participants on their platforms. For the industries that these platforms affected, the line between “making” and “buying” shifted toward buying. The line between owning and renting shifted toward renting.

Primarily, this was done through a reduction in triangulation costs (how hard it is to find and measure the quality of a service), and transfer costs (how hard it is to bargain and agree on a contract for the good or service).

Triangulation costs came down for two reasons. One was the proliferation of smartphones, which made it possible for services like Uber and Airbnb to exist. The other was the increasing digitization of the economy. Digital goods are both easier to find (think Googling versus going to the library or opening the Yellow Pages) and easier to measure the quality of (I know exactly how many people read my website each day and how many seconds they are there, the local newspaper does not).

The big improvement in transfer costs was the result of matchmaking: bringing together and facilitating the negotiation of mutually beneficial commercial or retail deals.

Take Yelp, the popular restaurant review app. Yelp allows small businesses like restaurants, coffee shops, and bars to advertise to an extremely targeted group: individuals close enough to come to the restaurant and that searched for some relevant term. A barbecue restaurant in Nashville can show ads only to people searching their zip code for terms like “bbq” and “barbecue.” This enables small businesses that couldn’t afford to do radio or television advertising to attract customers.

The existence of online customer reviews gives consumers a more trusted way to evaluate the restaurant.

All of the internet aggregators, including Amazon, Facebook, and Google, enabled new service providers by creating a market and standardizing the rules of that market to reduce transaction costs.21

The “sharing economy” is more accurately called the “renting economy” from the perspective of consumers, and the “gig economy” from the perspective of producers. Most of the benefits are the result of new markets enabled by lower transaction costs, which allows consumers to rent rather than own, including “renting” some else’s time rather than employing them full time.

It’s easier to become an Uber driver than a cab driver, and an Airbnb host than a hotel owner. It’s easier to get your product into Amazon than Walmart. It’s easier to advertise your small business on Yelp, Google or Facebook than on a billboard, radio or TV.

Prior to the internet, the product designer was faced with the option of selling locally (which was often too small a market), trying to get into Walmart (which was impossible without significant funding and traction), or simply working for a company that already had distribution in Walmart.

On the internet, they could start distributing nationally or internationally on day one. The “shelf space” of Amazon or Google’s search engine results page was a lot more accessible than the shelf space of Walmart.

As a result, it became possible for people in certain highly specialized roles to work independently of firms entirely. Product designers and marketers could sell products through the internet and the platforms erected on top of it (mostly Amazon and Alibaba in the case of physical products) and have the potential to make as much or more as they could inside a corporation.

This group is highly motivated because their pay is directly based on how many products they sell. The aggregators and the internet were able to reduce the transaction costs that had historically made it economically inefficient or impossible for small businesses and individual entrepreneurs to exist.

The result was that in industries touched by the internet, we saw an industry structure of large aggregators and a long tail 22 of small business which were able to use the aggregators to reach previously unreachable, niche segments of the market. Though there aren’t many cities where a high-end cat furniture retail store makes economic sense, on Google or Amazon, it does.

For these industries, coordination scalability was far greater and could be seen in the emergence of micro-multinational businesses. Businesses as small as a half dozen people could manufacture in China, distribute products in North America, and employ people from Europe and Asia. This sort of outsourcing and the economic efficiencies it created had previously been reserved for large corporations.

As a result, consumers received cheaper, but also more personalized products from the ecosystem of aggregators and small businesses.

However, the rental economy still represents a tiny fraction of the overall economy. At any given time, only a thin subset of industries are ready to be marketized. What’s been done so far is only a small fraction of what will be done in the next few decades.

Yet, we can already start to imagine a world which Munger calls “Tomorrow 3.0.” You need a drill to hang some shelves in your new apartment. You open an app on your smartphone and tap “rent drill.” An autonomous car picks up a drill and delivers it outside your apartment in a keypad-protected pod and your phone vibrates “drill delivered.” Once you’re done, you put it back in the pod, which sends a message to another autonomous car nearby to come pick it up. The rental costs $5, much less than buying a commercial quality power drill. This is, of course, not limited to drills—it could have been a saw, fruit dehydrator, bread machine or deep fryer.

You own almost nothing, but have access to almost everything.

You, nor your neighbors, have a job, at least in the traditional sense. You pick up shifts or client work as needed and maybe manage a few small side businesses. After you finish drilling the shelves in, you might sit down at your computer and see what work requests are open and work for a few hours on designing a new graphic or finishing up the monthly financial statements for a client.

This is a world in which triangulation and transfer costs have come down dramatically, resulting in more renting than buying from consumers and more gig work than full-time jobs for producers.

This is a world we are on our way to already, and there aren’t any big, unexpected breakthroughs that need to happen first.

But what about the transaction cost of trust?

In the computer era, the areas that have been affected most are what could be called low-trust industries. If the sleeping mask you order off of Amazon isn’t as high-quality as you thought, that’s not a life or death problem.

What about areas where trust is essential?

Enter stage right: blockchains.

The Blockchain Era: Blockchain Markets Are Eating the World

One area where trust matters a lot is money. Most of the developed world doesn’t think about the possibility of fiat money 23 not being trustworthy because it hasn’t happened in our lifetimes. For those that have experienced it, including major currency devaluations, trusting that your money will be worth roughly the same tomorrow as it is today is a big deal.

Citizens of countries like Argentina and particularly Venezuela have been quicker to adopt bitcoin as a savings vehicle because their economic history made the value of censorship resistance more obvious.

Due to poor governance, the inflation rate in Venezuela averaged 32.42 percent from 1973 until 2017. Argentina was even worse; the inflation rate there averaged 200.80 percent between 1944 and 2017.

The story of North America and Europe is different. In the second half of the 20th century, monetary policy has been stable.

The Bretton Woods Agreement, struck in the aftermath of the Second World War, aggregated control of most of the globe’s monetary policy in the hands of the United States. The European powers acceded to this in part because the U.S. dollar was backed by gold, meaning that the U.S. government was subject to the laws of physics and geology of gold mining. They could not expand the money supply any faster than gold could be taken out of the ground.

With the abandonment of the gold standard under Nixon in 1973, control over money and monetary policy has moved into a historically small group of central bankers and powerful political and financial leaders and is no longer restricted by gold.

Fundamentally, the value of the U.S. dollar today is based on trust. There is no gold in a vault that backs the dollars in your pocket. Most fiat currencies today have value because the market trusts that the officials in charge of U.S. monetary policy will manage it responsibly.

It is at this point that the debate around monetary policy devolves into one group that imagines this small group of elitist power brokers sitting in a dark room on large leather couches surrounded by expensive art and mahogany bookshelves filled with copies of The Fountainhead smoking cigars and plotting against humanity using obscure financial maneuvering.

Another group, quite reasonably, points to the economic prosperity of the last half-century under this system and insists on the quackery of the former group.

A better way to understand the tension between a monetary system based on gold versus one based on fiat money this has been offered by political science professor Bruce Bueno de Mesquita: “Democracy is a better form of government than dictatorships, not because presidents are intrinsically better people than dictators, but simply because presidents have less agency and power than dictators.”

Bueno de Mesquita calls this Selectorate Theory. The selectorate represents the number of people who have influence in a government, and thus the degree to which power is distributed. The selectorate of a dictatorship will tend to be very small: the dictator and a few cronies. The selectorate in democracy tends to be much larger, typically encompassing the Executive, Legislative, and Judicial branches and the voters which elect them.

Historically, the size of the selectorate involves a tradeoff between the efficiency and the robustness of the governmental system. Let’s call this the “Selectorate Spectrum.”

Dictatorships can be more efficient than democracies because they don’t have to get many people on board to make a decision. Democracies, by contrast, are more robust, but at the cost of efficiency.

Conservatives and progressives alike bemoan how little their elected representatives get done but happily observe how little their opponents accomplish. A single individual with unilateral power can accomplish far more (good or bad) than a government of “checks and balances.” The long-run health of a government means balancing the tradeoff between robustness and efficiency. The number of stakeholders cannot be so large that nothing gets done or the country will never adapt nor too small that one or a small group of individuals can hijack the government for personal gain.

This tension between centralized efficiency and decentralized robustness exists in many other areas. Firms try to balance the size of the selectorate to make it large enough so there is some accountability (e.g. a board and shareholder voting) but not so large as to make it impossible to compete in a market—by centralizing most decisions in the hands of a CEO.

We can view both the current monetary system and the internet aggregators through the lens of the selectorate. In both areas, the trend over the past few decades is that the robustness of a large selectorate has been traded away for the efficiency of a small one.24

A few individuals—heads of central banks, leaders of state, corporate CEOs, and leaders of large financial entities like sovereign wealth funds and pensions funds—can move markets and politics globally with even whispers of significant change. This sort of centralizing in the name of efficiency can sometimes lead to long feedback loops with potentially dramatic consequences.

Said another way, much of what appears efficient in the short term may not be efficient but hiding risk somewhere, creating the potential for a blow-up. A large selectorate tends to appear to be working less efficiently in the short term, but can be more robust in the long term, making it more efficient in the long term as well. It is a story of the Tortoise and the Hare: slow and steady may lose the first leg, but win the race.

In the Beginning, There Was Bitcoin

In October 2008, an anonymous individual or group using the pseudonym Satoshi Nakamoto sent an email to a cypherpunk mailing list, explaining a new system called bitcoin. The opening line of the conclusion summed up the paper:

We have proposed a system for electronic transactions without relying on trust”

When the network went live a few months later in January 2009, Satoshi embedded the headline of a story running that day in The London Times:

The Times 03/Jan/2009 Chancellor on brink of second bailout for banks”

Though we can’t know for sure what was going through Satoshi’s mind at the time, the most likely explanation based is that Satoshi was reacting against the decisions being made in response to the 2008 Global Financial Crisis by the small selectorate in charge of monetary policy.

Instead of impactful decisions about the monetary system like a bailout being reliant upon a single individual, the chancellor, Satoshi envisioned bitcoin as a more robust monetary system, with a larger selectorate beyond the control of a single individual.

But why create a new form of money? Throughout history, the most common way for individuals to show their objections to their nation’s monetary policy was by trading their currency for some commodity like gold, silver, or livestock that they believed would hold its value better than the government-issued currency.

Gold, in particular, has been used as a form of money for nearly 6,000 years for one primary reason: the stock-to-flow ratio. Because of how gold is deposited in the Earth’s crust, it’s very difficult to mine. Despite all the technological changes in the last few hundred years, this has meant that the amount of new gold mined in a given year (the flow) has averaged between 1-2 percent of the total gold supply (stock) with very little variation year to year.

As a result, the total gold supply has never increased by more than 1-2 percent per year. In comparison to Venezuela’s 32.4 percent inflation and Argentina’s 200.80 percent inflation, gold’s inflation is far lower and more predictable.

Viewed through the lens of Selectorate Theory, we can say that gold or other commodity forms of money have a larger selectorate and are more robust than government-issued fiat currency. In the same way a larger group of stakeholders in a democracy constrains the actions of any one politician, the geological properties of gold constrained governments and their monetary policy.

Whether or not these constraints were “good” or “bad” is still a matter of debate. The Keynesian school of economics, which has come to be the view of mainstream economics, emerged out of John Maynard Keynes’s reaction to the Great Depression, which he thought was greatly exacerbated by the commitment to the gold standard and that governments should manage monetary policy to soften the cyclical nature of markets.

The Austrian and monetarist schools believe that human behavior is too idiosyncratic to model accurately with mathematics and that minimal government intervention is best. Attempts to intervene can be destabilizing and lead to inflation so a commitment to the gold standard is the lesser evil in the long run.

Taken in good faith, these schools represent different beliefs about the ideal point on the Selectorate Spectrum. Keynesians believe that greater efficiency could be gained by giving government officials greater control over monetary policy without sacrificing much robustness. Austrians and monetarists argue the opposite, that any short-term efficiency gains actually create huge risks to the long-term health of the system.

Viewed as a money, bitcoin has many gold-like properties, embodying something closer to the Austrian and monetarist view of ideal money. For one, we know exactly how many bitcoin will be created—21 million—and the rate at which they will be created. Like gold, the ability to change this is outside of the control of a single or small group of individuals, giving it a predictable stock-to-flow ratio and making it extremely difficult to inflate.

Similar to gold, the core bitcoin protocol also makes great trade-offs in terms of efficiency in the name of robustness.25

However, bitcoin has two key properties of fiat money which gold lacks—it is very easy to divide and transport. Someone in Singapore can send 1/100th of a bitcoin to someone in Canada in less than an hour. Sending 1/100th of a gold bar would be a bit trickier.

In his 1998 book, Cryptonomicon, science fiction author Neal Stephenson imagined a bitcoin-like money built by the grandchild of Holocaust survivors who wanted to create a way for individuals to escape totalitarian regimes without giving up all their wealth. It was difficult, if not impossible, for Jews to carry gold bars out of Germany, but what if all they had to do was remember a 12-word password phrase? How might history have been different?

Seen in this way, bitcoin offers a potentially better trade-off between robustness and efficiency. Its programmatically defined supply schedule means the inflation rate will be lower than gold (making it more robust) while it’s digital nature makes it as divisible and transportable as any fiat currency (making it more efficient).

Using a nifty combination of economic incentives for mining (proof-of-work system) and cryptography (including blockchain), bitcoin allowed individuals to engage in a network that was both open (like a market) and coordinated (like a firm) without needing a single or small group of power brokers to facilitate the coordination.

Said another way, bitcoin was the first example of money going from being controlled from a small group of firm-like entities (central banks) to being market-driven. What cryptocurrency represents is the technology-enabled possibility that anyone can make their own form of money.

Whether or not bitcoin survives, that Pandora’s Box is now open. In the same way computing and the internet opened up new areas of the economy to being eaten by markets, blockchain and cryptocurrency technology have opened up a different area to be eaten by markets: money.

The Future of Public Blockchains

Bitcoin is unique among forms of electronic money because it is both trustworthy and maintained by a large selectorate rather than a small one.

There was a group that started to wonder whether the same underlying technology could be used to develop open networks in other areas by reducing the transaction cost of trust.26

One group, the monetary maximalists, thinks not. According to them, public blockchains like bitcoin will only ever be useful as money because it is the area where trust is most important and so you can afford to trade everything else away. The refugee fleeing political chaos does not care that a transaction takes an hour to go through and costs $10 or even $100. They care about having the most difficult to seize, censorship-resistant form of wealth.

Bitcoin, as it exists today, enhances coordination scalability by allowing any two parties to transact without relying on a centralized intermediary and by allowing individuals in unstable political situations to store their wealth in the most difficult-to-seize form ever created.

The second school of thought is that bitcoin is the first example of a canonical, trustworthy ledger with a large selectorate and that there could be other types of ledgers which are able to emulate it.

At its core, money is just a ledger. The amount of money in your personal bank account is a list of all the transactions coming in (paychecks, deposits, etc.) and all the transactions going out (paying rent, groceries, etc.). When you add all those together, you get a balance for your account.

Historically, this ledger was maintained by a single entity, like your bank. In the case of U.S. dollars, the number in circulation can be figured out by adding up how much money the U.S. government has printed and released into the market and how much it has taken back out of the market.

What else could be seen as a ledger?

The answer is “nearly everything.” Governments and firms can be seen just as groups of ledgers. Governments maintain ledgers of citizenship, passports, tax obligations, social security entitlements and property ownership. Firms maintain ledgers of employment, assets, processes, customers and intellectual property.

Economists sometimes refer to firms as “a nexus of contracts.” The value of the firm comes from those contracts and how they are structured within the “ledger of the firm.” Google has a contract with users to provide search results, with advertisers to display ads to users looking for specific search terms, and with employees to maintain the quality of their search engine. That particular ledger of contracts is worth quite a lot.

Mechanical time opened up entirely new categories of economic organization. It allowed for trade to be synchronized at great distances—without mechanical time, there would have been no railroads (how would you know when to go?) and no Industrial Revolution. Mechanical time allowed for new modes of employment that lifted people out of serfdom and slavery.27

In the same way, it may be that public blockchains make it possible to have ledgers that are trustworthy without requiring a centralized firm to manage them. This would shift the line further in favor of “renting” over “buying” by reducing the transaction cost of trust.

Entrepreneurs may be able to write a valuable app and release for anyone and everyone who needs that functionality. The entrepreneur would collect micro-payments in their wallet. A product designer could release their design into the wild and consumers could download it to be printed on their 3D printer almost immediately.28

For the first 10 years of bitcoin’s existence, this hasn’t been possible. Using a blockchain has meant minimizing the transaction cost of trust at all costs, but that may not always be the case. Different proposals are already being built out that allow for more transactions to happen without compromising the trust which bitcoin and other crypto-networks offer.

There are widely differing opinions on what the best way to scale blockchains are. One faction, usually identifying as Web 3/smart contracting platform/Ethereum, believes that scaling quickly at the base layer is essential and can be done with minimal security risk while the other groups believe that scaling should be done slowly and only where it does not sacrifice the censorship-resistant nature of blockchains (bitcoin). Just like the debate between Keynesian and Austrian/monetarist views of monetary policy, these views represent different beliefs about the optimal tradeoff point on the Selectorate Spectrum. But, both groups believe that significant progress can be made on making blockchains more scalable without sacrificing too much trust.

Public blockchains may allow aggregation without the aggregators. For certain use cases, perhaps few, perhaps many, public blockchains like bitcoin will allow the organization and coordination benefits of firms and the motivation of markets while maintaining a large selectorate.

Ultimately, what we call society is a series of overlapping and interacting ledgers.

In order for ledgers to function, they must be organized according to rules. Historically, rules have required rulers to enforce them. Because of network effects, these rulers tend to become the most powerful people in society. In medieval Europe, the Pope enforced the rules of Christianity and so he was among the most powerful.

Today, Facebook controls the ledger of our social connections. Different groups of elites control the university ledgers and banking ledgers.

Public blockchains allow people to engage in a coordinated and meritocratic network without requiring a small selectorate.

Blockchains may introduce markets into corners of society that have never before been reached. In doing so, blockchains have the potential to replace ledgers previously run by kings, corporations, and aristocracies. They could extend the logic of the long tail to new industries and lengthen the tail for suppliers and producers by removing rent-seeking behavior and allowing for permissionless innovation.

Public blockchains allow for rules without a ruler. It began with money, but they may move on to corporate ledgers, social ledgers and perhaps eventually, the nation-state ledger.29

Acknowledgments: Credit for the phrase “Markets Are Eating the World” to Patri Friedman

The answer, of course, is “it depends.” Your business strategy is dependent upon the stage of the business, it’s industry, and macroeconomic factors outside your control. But, I think that there are frameworks that can help you figure out the next step.

The framework I’ve found most helpful was conceived in Israel in the 1980s. Creative Output, an Israeli company, developed the first software package that sped up scheduling for production environments.

In the process of setting up and installing the software, one of Creative Output’s founders, Eli Goldratt, found that frequently the software failed to live up to its potential because of existing habits of employees and managers.

Despite the software offering them obvious solutions to improving production speed, the pre-existing paradigms of the managers kept them from implementing the software successfully.

Goldratt, frustrated by the inefficiency, holed up for 13 months to write The Goal which laid out his Theory of Constraints.

The Theory of Constraints states: any system with a goal has one limit and worrying about anything other than that limit is a waste of resources.

If a factory has an assembly line with three machines and those machines can produce 50, 100 and 150 units per hour, then the total output of the system will be 50 units.

Increasing the capacity of the second two machines seems “productive” and like a “good business strategy,” but is, in fact, a total waste. More resources have been spent to improve the product line, but the first machine is still the limit.

Goldratt’s work revolutionized the manufacturing industry, but it’s applicable far beyond that. Nearly any system can be improved using the two core questions of the Theory of Constraints:

1. What’s the current limit?

2. What’s the obvious way to improve the limit?

Take a non-business example: trying to lose weight. You join the gym, put together a workout plan, and order all the right supplements. You got protein powder, creatine and everything else we read about on the internet (because the internet always knows!)

You start waking up at 5am every day to go to the gym. Because you’re going at 5 am, many days you only sleep five or six hours if you were up late the night before. You also treat yourself to a scone from Starbucks after every workout.

An hour running on the treadmill will burn 400 calories. A scone from Starbucks has 400 calories which, per the second law of thermodynamics, means you’re probably not going to lose any weight. The hormonal benefits of exercise are being counteracted by the lack of sleep. You are spending more and more time exercising without making progress. Why? You’re addressing the wrong limit.

1. What’s the limit?

2. What’s the obvious way to improve it?

If you fix your sleep and diet then you’ll get better results, even with less exercise. It’s not that you need to spend more time getting healthy, but that you need to spend that time and energy on the appropriate limit. By cleaning up your diet and getting an extra couple hours of sleep every other night, even with no or a very modest amount of exercise, you start to see the pounds drop off.

Instead of spending two hours in the gym every day, an hour in the gym every other day suddenly yields better results because you’ve addressed the appropriate limits.

Limits and Business Strategies

Any type of system, be it your health, a manufacturing line or a business, can be analyzed and optimized by understanding the key components and which of them is the bottleneck.

For any specific type of system, the bottlenecks and constraints tend to be fairly similar. For almost everyone, the right combination of diet, sleep and exercise will get you in better shape. Ditto with manufacturing production lines.

For almost a decade, I’ve been working with early-stage companies, startups, small businesses and founders and I’ve discovered there is a pretty clear series of bottlenecks which companies go through.

When these businesses struggle or don’t grow as quickly as founders or managers expect, the situation is similar to that of losing weight: it isn’t a lack of effort, but one of two other things.

1. Effort directed at the wrong limit - You are stuck in a recurring story of “what got you here, won’t get you there.” When one limit is removed, doing more of it simply won’t help. What gets a business off the ground is not what keeps it in the air.

2. A lack of systems to maintain throughput - The other common issue is that the business is directing resources at the appropriate limit, but previously solved limits collapse. Imagine a factory floor where instead of documenting how the work at each station is done and hiring someone to consistently do the work, everyone on the floor rushed to whatever seemed most urgent. For an early stage company, this typically looks like alternating between generating new business and servicing existing business without being able to balance the two.

The result for many founders and managers is that they often times get stuck in a cycle of products or projects taking off then falling back to the ground whenever the next “shiny object” comes along.

What I’ve seen, having worked deeply with dozens of early stage companies and talked with hundreds (if not thousands) of early-stage founders is that there is a pretty formula for the bottlenecks which early-stage companies go through.

The Business Production Function

Imagine an early stage business where it is just the founder and maybe a couple of contractors. The founder is strong operationally but not so much at marketing and sales. There are still no employees so hiring and management isn’t an issue. The throughput of their business would look something like this:

Even though getting more efficient at delivering the service might seem productive, it is mostly a waste unless that efficiency generates more sales.

When I first start working with someone, I will take them through a diagnostic assessment (more on that below) to identify what the bottleneck is. Like a manufacturing production line, the bottleneck is always changing. If the same business improved their marketing and sales and were able to hire a full-time person, then the production function would shift to look something like this:

Now, the bottleneck is hiring and management and the business needs to focus its attention there. Let’s say they do and there is a small team which works well together so operations and management improves. This is great, total output goes up.

But now the founder’s personal operations may have fallen behind as their schedule fills up with meetings and responding to emails that are coming in way faster then they were used to. The bottleneck shifts to personal operations.

The business production function looks at every business and asks the same two questions:

1. What is the bottleneck?

2. What systems need to be put in place to eliminate it?

What you’re going to be very excited to learn is that this framework is (wait for it….) fractal. That is, there are constraints within constraints. So while marketing and sales may be the overall bottleneck, within the marketing and selling systems of that business, there is an even more specific bottleneck (either traffic, conversion or economics)

I’m going to briefly walk through the different sub-categories I think through with clients and how you can apply it. Generally, I find businesses run into the bottlenecks in the order I listed them (Personal operations → marketing and sales → business operations → HR)

For each area, I’ll list a set of questions or things you may be thinking if that area is your bottleneck and some resources for addressing that bottleneck.

Personal Operations

The first bottleneck for many founders is what I call personal operations. I break personal operations down into three sub-categories: task management, priority management and knowledge management.

If this is your bottleneck, you’re thinking things like:

• How do I keep from letting things fall through the cracks?

• Why do I keep missing meetings?

• I feel overwhelmed by reactive work like emails and meetings

• My priorities are rapidly changing and I can’t seem to complete any of the projects I’m working on before something else comes up

• Didn’t I do something like this a couple of years ago? Where is that file?

• Should I be using Evernote or some other note taking app? If so, how should I organize it?

Task management - At the most basic level, this means an ability to do what you are going to do, when you say you are going to do it. 90% of people struggle to do this and by just clearing this hurdle, you are probably top 10% in your field.

Task management includes things like:

• Using a task manager/to-do list like Todoist, OmniFocus, or Things

• Overcoming Procrastination

• Inbox Management

• Checking your calendar every day

• Using software Tools like scheduling apps, keyboard shortcuts, and password management apps

• Implementing David Allen’s Getting Things Done framework

Priority management - If task management is the ability to take incoming data and request and transform them into tasks that get done, then priority management is the ability to not just complete tasks, but to prioritize them based on bigger business objectives.

While most jobs require people to be reasonably good at task management, many do not require priority management. Priorities are dictated from above with relatively little latitude for autonomy. I’ve found that people who move from a less entrepreneurial environment (usually school or a large company) to a more entrepreneurial environment (freelancing, starting a business, working for a small business or startup, working at the fringes of a large organization, etc.) fall prey to shiny object syndrome - chasing whatever the latest idea is - and end up not completing projects. 

Priority Management Includes things like:

• Daily Planning and Review

• Weekly Review and Planning

• Quarterly Review and Planning

• Establishing a Morning Routine

• Time Blocking and Calendar Management

• Identifying and prioritizing Unique Ability work

• Establishing your Core Values

• Improving your Mindset

Personal Knowledge Management

If priority management is the ability to prioritize tasks into projects based on bigger business objectives, then personal knowledge management is the ability to take the learnings and outcomes of those projects and convert them into  re-usable intellectual property.

Personal Knowledge Management focuses on processing new information in a way that allows it to be translated to usable knowledge and eventually into career-advancing outputs. I learned about personal knowledge management from taking my friend Tiago Forte’s course Building a Second Brain.

Personal Knowledge Management includes:

• P.A.R.A. - A standardized approach to organizing your digital files so they are easy to retrieve when you need them

• Progressive Summarization - a technique for formatting and structuring notes so that they are easily discoverable and usable in the future. This ensures that what you’re saving today adds value to your future projects, even when you can’t predict or imagine what those projects might be.

• Just in Time Project Management - a digital-first framework for modern projects and project management

• Establishing Personal Operating Guidelines

• Read a Lot of books

• Optimizing for Interesting

Everything listed here is by no means exclusive to people working in early stage companies, but are generally useful work skills. Lots of founders seem to get by without many of these skills for quite a while, but my experience has been it eventually catches up with them so having them in place lays a strong foundation.

Helpful Books at this stage:

• Getting Things Done by David Allen

• Atomic Habits by James Clear

• The 80/20 Principle by Richard Koch

• The One Thing by Gary Keller and Jay Papasan

• The Power of Full Engagement by Jim Loehr

Marketing/Sales

The next bottleneck is typically marketing and sales and is all about making sales (and getting to Product/Market Fit along the way). The most common mistake among first time entrepreneurs is an insistence on getting all their “ducks in a row” before going out to (aggressively) market and sell their product or service.

The truth is that selling your product or service is the best way to not just generate revenue but also get your ducks in a row. For one, nothing is more motivating than knowing you have to give back money if you don’t deliver. But more importantly, the act of marketing and selling forces you to constantly be thinking about and talking to potential customers. That shortens the feedback loops, allowing you to iterate and improve on your product faster. It also generates the revenue and momentum you need to hire others to manage the day-to-day operations of the company.

If this is your bottleneck, you are thinking things like:

• How do I get to product/market fit?

• How do I reach my customers?

• Does my Unique Value Proposition resonate with my target market?

• How can I generate more revenue from existing customers?

• Why aren’t my leads converting into customers?

• How do I establish a consistent pipeline of qualified leads?

The three elements I typically look at are the Economics, Conversion, and Traffic, what marketing guru Perry Marshall calls the Tactical Triangle.

The idea of the Tactical Triangle is that in order to sell something, you have to get Traffic, then Convert that traffic into sales. You need to make a profit on what you sell, which is the Economics. When you make that profit, you reinvest in getting more traffic, which converts into sales in a virtuous cycle.

This is marketing and sales 101:

• Traffic: Who would buy this?

• Economics: Can you reach them at a profit?

• Conversion: Can you convince them you can solve their problem?

The way to improve your marketing and sales system is to ask: What’s the biggest bottleneck - Traffic, Conversion or Economics? That’s where you should focus your attention.

Traffic - Traffic means getting people through the door (literally or metaphorically). The main components of traffic are determining who you are targeting (who would buy this?) and how you are going to reach (how are you going to reach them?).

This includes things like:

• Determining your Customer Persona and Pain Points

• Blogging and Content Marketing

• Figuring out Founder/Product Fit using Causal Reasoning

• Understanding your market’s state of sophistication

• Use the Bullseye Framework to prioritize Traffic Channels

• Surveying your target market

• Getting out of the building and Talking to your target market even if it means getting on a plane

• Looking at what is already working in the market and Rip, Pivot and Jamming on it

• Developing a unique selling proposition

Economics - When I ask people what they think the bottleneck in their marketing and selling systems is, the most common answer (almost always in fact) is “traffic.” However, the correct answer (if you’ve never asked it before) is almost always “economics.”

Think about it. If your customer lifetime value (CLTV) is $10, then you have very few distribution channels open to you. You’re definitely not going to buy a billboard and you probably can’t acquire traffic profitable through any channel other than word of mouth and referrals.

If your CLTV is $10,000, then you can use almost any distribution channel. The importance of economics is self-evident for bootstrapped businesses because they need the profit to keep operations running, but it’s equally important for venture-backed companies to have good unit economics, even if they are burning money.

Improving the economics of your business consists of things like:

• Optimize the Value of a Customer using Upsells

• Develop new, higher margin products for your existing customers.

• Run Reactivation Campaigns For Churned Customers

• Set up a referral and follow-up program for past customers

• Monetize Your Byproduct

• Raise your prices by 20% every time you get a new client until your conversion rate drops

Conversions - If the economics of the business are good, the next step is typically to work on better converting leads into buyers. Similar to economics, many businesses neglect working on their conversions to focus on traffic, but by optimizing conversions and economics first, the value of each additional visitor increases.

This includes things like:

• Develop a reliable system for turning traffic into leads

• Develop a reliable system for converting leads into buyers and upgrading one time buyers to multi-buyers

• Develop a reliable system for new Product Launches

• Develop Sales Process and Scripts

• Optimize Speed of Implementation to beat the Law of Shitty Clickthroughs

• Use the six principles of Influence

• Run Holiday And Event-based Promotions

See also Case Study: Growing a B2B eCommerce Business 527% in 18 Months

Helpful Books for this Stage:

• Scientific Advertising by Claude C. Hopkins

• Ogilvy on Advertising by David Ogilvy

• 80/20 Sales and Marketing by Perry Marshall

• Influence by Robert Cialdini

• Breakthrough Advertising by Eugene Schwartz

• The 22 Immutable Laws of Marketing by Trout and Reis

• The Ultimate Sales Machine - Chet Holmes

• SPIN Selling - Neil Rackham

Business Operations

The next bottleneck is business operations. At this point, a business typically has product/market fit but is having trouble scaling. I find this to be a particularly common problem with fast-moving founders.

I use an assessment with clients I work with called the Kolbe A Index which measures their natural work style. The dominant pattern I find is that most founders and managers at high growth companies are high on what Kolbe calls “quick start” and “fact finder” but low on “follow through.” Here’s a pretty typical example:

This leads to the business looking something like the manufacturing line where instead of documenting how the work at each station is done and hiring someone to consistently do the work, everyone on the floor rushed to whatever seemed most urgent. Projects get off the ground quickly and gain traction, but frequently end up back where they started - on the ground.

Viewed through the framework of Goldratt’s Theory of Constraints, there is a pretty clear bottleneck: Follow Through, which tends to translate into a lack of systems in the business.

If this is your bottleneck, you are thinking things like:

• How do I get out of day-to-day operations?

• Why can’t we get a consistent quality of service to our customers?

• If we got a huge influx of sales, we would be stressed to the max.

• The business is growing, but this doesn’t feel sustainable.

• I’d like to be able to sell my business at some point but I’m too essential right now.

• I feel like I’m spending a lot of time keeping things together rather than moving them forward.

Automate - The first and best way to improve systems is through automation. Software works 24/7, predictably and cheaply. Any systems in the business which can be (profitably) automated should be.

Automation includes things like:

• Using off the shelf software like Zapier, Calendly, Airtable and IFTTT to automate tasks where feasible

• Using Marketing Automation software like Mailchimp, ActiveCampaign, Kit and Drip

• Using a Sales CRM like Hubspot, Zoho or Pipedrive

• Using outbound sales automation tools like Quick Mail

• For sufficiently large companies and projects, it may make sense to hire a developer and build custom automation software

Document - For those tasks where automation isn’t feasible, the next step is to clearly document the processes in a way that passes the “off the street test.” That is, could someone who has the basic qualifications for their role, be that an electrical engineer or social media manager, read your documentation and complete the task successfully.

Document includes things like:

• Create a Standard Operating Document

• Create a Strategic Objective

• Create General Operating Principles

• Create Standard Operating Procedures for all recurring tasks in the business

• Organize Your Documentation in a Standard Operating Folder

Outsource - Once you have the basic documentation structure for the business or business unit set up, the next bottleneck is to outsource those tasks which can be done more efficiently by a specialized contractor.

Outsourcing includes

• Use sites like Fancy Hands and Fiverr which provide general outsourcing help for administrative tasks.

• Hire specialized outsourcers using marketplaces like Upwork and Freelancer which have a huge category of specialists.

Helpful books for this stage:

• Work the System by Sam Carpenter

• The E-Myth Revisited by Michael E. Gerber

• Built to Sell - John Warrillow

• The Four Hour Work Week - Tim Ferriss

Hiring and Management

When a business has a good marketing and sales system in place and established processes in their operation, the bottleneck typically shifts to hiring and management. Getting your business operations in place is about maximizing the leverage of a single team member. Once that leverage is achieved through automation and outsourcing, the next stage is to bring in additional team members to take advantage of it.

Hiring - The first step for going from a good to great company is getting the right people on the bus. That means developing systems for attracting, vetting, onboarding, and training new team members.

Hiring includes things like:

• Creating a Hiring Process (See the book Who)

• Writing a Killer Job Description

• Developing an Interview Process

• Develop an onboarding and training program

• Developing a current and future Organizational Chart - Do You have an Org Chart of the company today? Do you have an org chart of what the company will look like in 24 months?

Culture - After AirBnB closed their Series C round with Peter Thiel in 2012, they invited him to their office and asked him what was the single most important piece of advice he had for them. He replied, “Don’t fuck up the culture.”

This isn’t what you would expect from someone who just gave you $150 million, but in Thiel’s experience, it’s the biggest factor in success.

One of the common mistakes that happens at this stage is that the founder or managers try to micromanage too much. Having specific documentation and checklists for some things is great, but at this stage, founders and managers need to rely more on communicating a mission and values then letting the team have greater latitude to operate.

Culture includes things like:

• Creating a Mission Statement

• Establishing a culture that promotes autonomy, mastery and purpose

• Compressing to Impress

Managing - Many people who haven’t studied management or don’t have a lot of experience think that management means assigning tasks to people in Trello or holding team meetings. If you read someone like Peter Drucker (who pioneered the field), you will see that management is something much more broad, an understanding of how to use the resources of the business, in the most efficient way possible.

This includes things like:

• Establishing Key Performance Indicators (KPIs) for each team member

• Creating a KPI Dashboard to be able to monitor the business as you step out of day-to-day operations

• Establishing Communication Working Rules - who meets with who, how often and what is discussed? What is the balance between 1-on-1 and team meetings? Do you do regular(e.g. every 6 months) employee reviews?

• Finances - Do you have clean, up-to-date books? Do you do forecasting and budgeting? Do you have a business emergency fund in case something happens?

• Improving your Decision Making - Now that you are in charge of a substantial amount of resources, do you have the heuristics, processes and systems to consistently make good decisions?

Helpful books for this stage:

• Drive by Daniel Pink

• Good to Great by Jim Collins

• Scaling Up by Verne Harnish

• Profit First

• Financial Intelligence for Entrepreneurs

• Traction by Gino Wickman

• High Output Management by Andy Grove

• The Effective Executive by Peter Drucker

• Scrum by Jeff and JJ Sutherland

Conclusion

This framework is not perfect and the list is not exhaustive by any means. Whole books have been written about each bullet point in this list and there are entire fields of study about each of the sub-categories.

Yet, it’s proven helpful for me as a starting point for analyzing pretty much any business. All models are wrong, but some are useful as they say. I’ve found this is a great complement to the quarterly planning system. Every quarter, I can look at a business and analyze the bottleneck again, allowing me to consistently focus on the highest leverage task.

It’s a man, not a woman. He’s white, standing somewhere between 6’0 and 6’2. He has a strong chin and medium length light brown hair parted on the left.

He walks from one meeting to the next wearing a dark suit with a pressed white dressed shirt and dark Oxford dress shoes. His wrist holds a watch – nice, but not extravagant, with a brown leather strap and a gold-rimmed face.

More than just an image, you can conjure up a life for The Organization Man, a term coined by William Whyte in his 1956 book of the same name. Even though the novel predates Whyte’s book by 30 years, Sinclair Lewis’ Babbitt (1922) established the archetype perfectly.

Today, the successor of the Organization Man — the Blockchain Man — is starting to emerge. To understand how he might evolve, let us first look back.

Lewis’ protagonist, George Babbitt, is very nearly the perfect Organization Man. He believes working a steady job for an increasing income and modern improvements is God’s purpose for mankind.

He lives in Zenith, a mid-size Midwestern city whose chief virtue is conformity and chief religion is boosterism. Prominent boosters/religious figures in Zenith include Vergil Gunch, the coal dealer, and Sidney Finkelstein, the ladies’ ready-to-wear buyer for Parcher & Stein’s department store.

Lewis opens the book by describing Babbitt’s daily routine in detail.

The morning after playing poker at the Booster club with coal dealer Vergil Gunch, he’s awoken by his alarm clock at 7:20. Yet, it is not just an alarm clock, it is “the best of nationally advertised and quantitatively produced alarm-clocks, with all modern attachments, including a cathedral chime, intermittent alarm, and a phosphorescent dial.”

He looks out from his sleeping-porch with delight at his suburban yard holding a sizable elm, a respectable grass lawn, cement driveway and corrugated iron garage. It is the neat yard of a successful business man of Zenith.

As he dresses for the day, he notes every item in his home and how it adds to his “solid citizen” uniform. The most important item is his Booster club button, which he wears with pride. After all, any decent man in Zenith belong to at least two or three booster clubs or lodges.

Babbitt talks The Organization Man talk. In a speech to the Zenith Real Estate Board, he espouses Zenith’s safety and wholesomeness:

It may be true that New York, Chicago, and Philadelphia will continue to keep ahead of us in size. But aside from these three cities, which are notoriously so overgrown that no decent white man, nobody who loves his wife and kiddies and God’s good out-o’-doors and likes to shake the hand of his neighbor in greeting would want to live in them—and let me tell you right here and now, I wouldn’t trade a high-class Zenith acreage development for the whole length and breadth of Broadway or State Street!”

Lewis’s depiction struck home. Newspapers in Cincinnati, Duluth, Kansas City, Milwaukee, and Minneapolis each claimed that their city was the model for Zenith. Many claimed to know the man on whom George Babbitt was based.

The Organization Man

I first read Babbitt in college and could see echoes of Babbitt in every authority figure in my life up to that point.

If I had asked you to sketch a picture of the modern Organization Man, it would probably look not too different from George Babbitt nearly a hundred years ago. Newer gadgets and different clubs, but the fundamentals are the same.

William Whyte’s The Organization Man was a gonzo-sociology of the phenomenon as it was becoming fully mainstream.

Whyte saw the central values in American life shift away from the rugged individualism that had characterized the nation since its founding, towards a collectivist ethic that Whyte called the Social Ethic.

The Social Ethic, to which The Organization Man subscribed, had three tenets:

1. a belief in the group as the source of creativity

2. a belief in “belongingness” as the ultimate need of the individual

3. and a belief in the application of science to achieve the belongingness.

In the view of the Organization Man:

Man exists as a unit of society. Of himself, he is isolated, meaningless; only as he collaborates with others does he become worthwhile, for by sublimating himself in the group, he helps produce a whole that is greater than the sum of its parts.”

This was a novel state of affairs.

For all of the history of homo sapiens up until the 19th century, cooperation rarely extended beyond the infamous Dunbar number of 150.

The logic behind the rise of the Organization Man was simple.

1. Participating in the global economy lead to a huge quality of life increase.

2. Doing so was only possible through large organizations.

For all the scorn today’s technologists heap on organization men, it was a rational adaptation to the times. My dad grew up handpicking cotton on a small farm in rural Northwest Tennessee. Given the choice between the Farm and the Organization, he picked the Organization. I would have too. I have yet to meet a TPS report so onerous I would prefer to be handpicking cotton in Tennessee in August.

Belongingness was so emphasized because the benefits were so substantial. The huge quality of life increases experienced in developed nations over the 20th century were the direct results of the Organization.

The adoption of the Social Ethic was a rational adaptation by individuals to the techno-economic paradigm of the 20th century.

The technologies of the second Industrial Revolution — from mass production to steel mills to scientific management — created a society ruled by large corporations that could leverage economies of scale.

Belonging to the group was the source of prosperity, and so the Social Ethic’s emphasis on belongingness and the Organization Man himself flourished.

The Organization Man taught us the truth in McLuhan’s famous line: “We shape our tools and thereafter our tools shape us.”

It was not the first time in history this had happened.

In The Selfish Gene, Richard Dawkins inverted society’s homo-centric view. Instead of thinking about organisms using genes to reproduce themselves, Dawkins turned it around and imagined that “our” genes build and maintain us in order to make more genes.

McLuhan’s statement is an extension of this view beyond biology to technology at large. Humans are as much shaped by our technology as the shapers of it.

Evidence suggests that beginning with the agricultural revolution, lifespans and quality of life went down for neolithic societies compared to their paleolithic predecessors. Societies and individuals did not deliberately choose a technology, agriculture, that caused them to live shorter, more miserable lives. However, after the rise of agriculture, they had no chance to go back. Agricultural societies made up of more, sicklier individuals pushed hunter-gatherers into the margins.

In the same way, the invention of the corporation created and shaped the Organization Man as much as the reverse. After the rise of the corporation, there was no going back. The Organization Man pushed those outside the Organization to the fringes through corporatism and nationalism.

The Anatomy of our Blockchain Future

In Technological Revolutions and Financial Capital, Carlota Perez suggests we’ve gone through three major techno-economic paradigms since the rise of capitalism:

• The Industrial Revolution — beginning in 1771

• The Age of Steam and Railways—beginning in 1829

• The Age of Steel, Electricity and Heavy Engineering— beginning in 1875

Each lasted around a century (with some overlap from the previous paradigm). That places the beginning of the end for The Age of Steel, Electricity and Heavy Engineering right around 1974.

1974 was the year we hit peak centralization and began transitioning towards a new techno-economic paradigm. The personal computer, the Internet, and the World Wide Web have been the major technological elements so far.

They begin to allow for something the previous paradigm did not: the ability to coordinate production and reap the benefits of a global, capitalist system with less centralization than the 20th century organization required.

The freelance front-end developer who has a dozen clients, found through a half dozen different marketplaces like UpWork and Toptal, is decentralized in a way that has only been possible in the last decade. However, such disintermediation has not turned out to be the panacea that many internet pioneers in the nineties believed it would be.

The protocols underlying the internet, TCP/IP, SMTP and HTTP became common property and quickly suffered from the tragedy of the commons. They were abused and the value accreted to the application layer on top of them.

Instead of a decentralized, open network, the internet today is made up of a handful of applications that have leveraged their network effects to re-centralize the internet through the creation of walled gardens. Facebook, Google and Amazon lead the pack.

For the three billion people now online, cheap access to the world’s knowledge is an improvement, but it’s not the magical, meritocratic elixir some expected.

The Sovereign Individual, a 1997 book allegedly read by Satoshi Nakamoto, forecast that it would be a new technology of money, not communication, that would decentralize and disintermediate the structure of society in the way many originally hoped the internet would.

“The Information Age implies another revolution in the character of money,” argued authors Davidson and Rees-Mogg.

“This new form of money will reset the odds, reducing the capacity of the world’s nation-states to determine who becomes a Sovereign Individual.”

This “cybercash” should be “unique, anonymous and verifiable” and able to “accommodate the largest transactions” as well as “be divisible into the tiniest fraction of value.”

It would “be tradable at a keystroke in a multi trillion-dollar wholesale market without borders.”

Bitcoin, the first “cybercash” appeared in 2008. Almost perfectly true to prediction, it was unique, verifiable and able to accommodate the largest and smallest transaction.

It was the first example of a new element in our current techno-economic paradigm, blockchains.

Blockchains form an ecosystem of distributed ledgers that use cryptographic protocols to allow for trust-minimized transactions between pseudonymous parties. Along with their associated technologies, they allow for what Nick Szabo has called social scalability: high levels of coordination with low levels of centralization.

Up to now, the internet has been the most socially scalable technology ever invented. It enabled new business models by bringing transaction costs down by an order of magnitude. For the first time, it was possible to participate in the global economy without being part of a large corporation. This spawned a wave of micro-multinationals, so-called “lifestyle businesses” that might operate out of half a dozen different jurisdictions with only a dozen employees.

In the mid-2000’s, it became economically feasible to run a ten-person company manufacturing in China, distributing in North America with developers in Eastern Europe and designers in South East Asia.

Over the next two decades, blockchains will bring transaction costs down yet another order of magnitude. It will enable as yet unconceived of business models.

As we approach a world without transaction costs, the equilibrium size of the corporation trends towards one person.

That person is The Blockchain Man.

The Blockchain Man

(Or A Speculative Sociology of Our Blockchain Future)

The Blockchain Man will be as rich a term half-century from now as the Organization Man is today.

As an exercise, I’m going to try and imagine how blockchain man culture might shape up. I am coming at it from a libertarian-ish perspective. Other perspectives are of course possible (and I hope will be written).

The group from the organization era that most closely resembled the Blockchain Man was scientists, particularly immigrant scientists. They were considered the black sheep of the organization.

The Social Ethic dictated that scientists, like all other members of The Organization, be forced to concentrate on practical problems and work in groups. The research division of an organization was treated in the same way as the management division: well-rounded, team players were promoted above idiosyncratic loners.

And yet, it was the idiosyncratic, loner scientists that contributed most to the Organization’s goal. Bell Labs (AT&T) and General Electric were the two most outstandingly profitable research institutions, which attracted the most consistently brilliant men.

Unlike other institutions, they hired idiosyncratic loners and allowed them to follow the fundamental problems they found interesting without insisting that there be a clear application.

GE’s Irving Langmuir’s work in heated solids led to a new kind of incandescent lamp. Bell Lab’s Claude Shannon’s work on communication theory proved to be immensely practical in the long run, leading to the computer.

Despite their successes, U.S. industry not only failed to draw lessons, but moved in the other direction. They fought to keep their researchers eyes glued on the cash register, sometimes forbidding them from sharing research outside The Organization.

The Organization Man in management hired scientists in his own image when the outstanding scientists of the organization era were almost the direct antithesis of The Organization Man.

The Organization Man could not conceive that a man could dislike the organization and chafe at its emphasis on belongingness. The Organization facilitated the huge quality of life increases he saw during his lifetime. Why would anyone work against it?

The Organization gave man access to the benefits of globalization but required conformity.

Blockchains give the same access but do not insist on the same conformity.

The fat protocol, thin application structure of blockchains means that value will accrete further down the societal “stack.” The scientist’s tinkering around the frontier seemed like a waste to the Organization Man. In a world dominated by blockchains, the value of this tinkering will be more easily captured, and its value more legible.

Source

The outcast scientist of The Organization era, working on the frontier of his field, will morph into the prototypical Blockchain Man.

The Balkanization of Everything: City States are The New Nation States

Unlike previous technologies which formed around geographic centers like Boston or Silicon Valley, blockchain entrepreneurs are relatively decentralized: London, Berlin, Zug (Switzerland), New York, San Francisco and Singapore all have a claim to being the foremost Blockchain City.

As larger nation-states attempt to regulate cryptocurrencies to maintain the political power that controlling currencies gave them in the Organization era, The Blockchain Man will move to city-state like areas. Estonia, Switzerland, and Singapore are the first examples of nation-states competing with each other to bring in high-earning citizens.

The Sovereign Individual imagines a scenario where these high-earning citizens will place all their company’s assets into a bank account domiciled simultaneously in Newfoundland, the Cayman Islands, Uruguay and Liechtenstein. Should any single jurisdiction attempt to withdraw operating authority or seize assets, the assets will be transferred to another jurisdiction.

In the organization era, larger nation states were able to offer advantages by lowering transaction costs through interstate commerce and multilateral trade liberalization. In the blockchain era, the blockchains themselves will cross physical borders better than diplomats ever could, obviating a primary role played by nation states.

The Balkanization that began in the late 20th century with the fracturing of post-colonial Africa and post-USSR Eastern Europe will continue through the 21st century. The city-state will become the organizing unit of global society.

Much like the Catholic Church today, Nation-States will continue to play a meaningful, but secondary role.

Though blockchains will solve many current coordination problems, they will still not solve problems with multi-generational timelines like climate change, for which we will still need nation-states.

The Hollywood Model is the New Career Ladder

This decentralization will affect careers as much as national economies. Blockchains are the hard technology that will take the lifestyle business and free agent economy across the chasm, making it mainstream in the way The Organization Man was mainstream beginning in the 1950s and 60s.

The Blockchain Man’s career will look like a combination of a lifestyle business owner and free agent.

The metaphor of a “career ladder” with its linear, upward sloping path worked well with the corporate pyramid.

In a world dominated blockchains, careers will transform into something more like Sheryl Sandberg’s career jungle gym where each crossbar of the gym may represent a blockchain.

This will result in a career for The Blockchain Man alternating between project sprints and periods of unemployment or mini-retirements, much like Hollywood operates today.

Hollywood is able to bring together large teams for complex movie projects and dissolve them afterwards. Unlike the steady-state jogging of The Organization Man, working in Hollywood is alternating between sprints to get a project done and slow strolls looking for the next one.

Teams of Blockchain Men will come together to work on complex projects in the same way as in Hollywood, but coordination will be improved via tokenization.

Imagine a two-pizza size organizing team that determines the project and issues token token incentives for each role. Each Blockchain Man will complete tasks fitting his skillset and be compensated in tokens.

It will be less top-down coordinated than Hollywood and more market-driven with players responding to incentives, though there will still be hierarchy. Holacracy didn’t work for a reason.

Instead of a paycheck, The Blockchain Man’s income will be a large number of micropayments from past projects. The tokens from different projects will appreciate or depreciate based on the success of the project (and perhaps pay dividends).

Tokenization will make it easier to measure an individual’s contribution to a project and to compensate them based on it.

In this way, tokens will work similarly to royalties. A primary difference will be that the use of smart contracts and removal of third parties mean that even tiny royalty streams can be paid profitably, forming a long tail of income streams.

Tokenization is the ultimate market maker. It will bring bring liquidity to a huge number of previously illiquid assets.

The UX designer of a web application’s onboarding sequence may be compensated in tokens based on the percentage of free trials which become paying customers.

These tokens will also allow a certain amount of voting rights in the project, making projects increasingly subservient to participants rather than organizers, but no less political.

The Blockchain Man Sees the Broader Calculus

Being shoved out of the corporation into a Hollywood-like free agent land, the Blockchain Man will be exposed to a broader calculus of his decisions and tradeoffs.

The Organization Man grew up with the expectation of getting a job and working for the corporation. Unless he is a top executive, he likely has little idea how the game was played or why.

To take one example, most Organization Men do not see the tradeoffs and costs of how they save for retirement.

The Organization Man saves for his retirement in his 401k because his company is matching it. He rarely considers other ways to invest or spend that money.

The free agent might decide that what it means to “save for retirement” is mostly about saving for healthcare costs. He may decide to invest in a personal trainer or meal delivery now, figuring that an extra $200 this month could save $20k in healthcare expenses in 30 years.

There is no way that $200 is going to compound into $20k in any vehicle available to him via his 401k and, even if it could, the quality of life and productivity benefits from exercising and eating healthier today may let him compound that even faster in his own business or freelancing career.

Choosing to invest in your own business or skills is riskier than investing in the S&P 500, but also has a greater potential return. It’s very common for early-stage lifestyle businesses grow at 50% year over year, a number the S&P will never match.

Because of tokenization, Blockchain Man will be exposed to an even broader calculus of the tradeoffs he is making, and their costs, than the free agent.

Tokens will make legible and liquid, many assets that have historically been illegible and illiquid.

The Basic Attention Token puts a tradeable dollar value, however imprecise, on a unit of your attention.

The Blockchain Man will be able to see what he is trading his attention for and make decisions about whether it’s worth it.

He will have an expanded sense of his own agency because he lives in a world without The Organization and Trusted Third Parties.

The Organization Man has a persistent feeling of being watched over.

If he is wronged at work, he can appeal to HR. If his credit card is stolen, he can call Visa.

The Blockchain Man has no such recourse. A transaction on the blockchain, once made, is immutable.

In the intermediate stage we are at, between Organization Man and Blockchain Man, the Organization Man carries his social ethic sensibility of belongingness into the increasingly Blockchain world.

Instead of turning over agency to a boss, it is turned over to the internet guru du jour.

While the Organization Man wants more agency, he fears it. An ability to make your own choices always means accepting responsibility for the outcome of those choices, a heavy psychological burden.

If the 401k or pension disappears, the Organization Man has someone to blame it on. The Blockchain Man has only himself.

The emergence of blockchains will force The Organization Man to reckon with his own agency. How he deals with that reckoning will be the primary determinant of his life outcomes.

Forking, Voice and Exit

One of the ways that reckoning will play out is the increase of forking.

In The Radicalism of the American Revolution, Gordon Wood argued that a culture of mass entrepreneurship underlay the American Revolution. Small business owners — blacksmiths, printing shops, and carpenters — formed a culture incompatible with monarchy.

The blacksmiths of the late 18th century America felt a sense of agency in their own personal lives and so the lack of agency they felt in their political lives grated at them.

In much the same way, the sense of agency the Blockchain Man feels in his personal life will form a culture incompatible with The Organization as it exists today.

The result will be a society defined by forking. The balance between voice and exit will tilt towards exit.

Appeals to authority (and perhaps violence) will be replaced by forking. If you disagree with a decision, you can fork a new blockchain.

For the Organization Man, forking was expensive. If The Organization Man worked at a newspaper and didn’t like the editorial direction, he could not simply fork the organization. He would need to go buy printing presses, an office, and hire a new staff of reporters.

To fork a Blockchain is not free either. It requires sufficient scale in terms of users, miners, and broader tooling (wallets, exchanges, etc.).

But, by comparison, it’s cheap.

The Organization required belief in the value of belongingness to maintain the economies of scale that let it support it’s employees.

Blockchains allows dissenters to fork. While it’s likely that one fork will become vastly more powerful, there will be a long tail of smaller chains as well.

Instead of arguing with his boss, The Blockchain Man may fork the project and create his own version.

Instead of protesting against a political party or decision, The Blockchain Man may leave.

Probabilism is the New Determinism

We form our conceptual systems using our day-to-day experience as metaphor.

We understand the human brain better today in part because of the proliferation of computers in our day-to-day lives. It’s easy to use RAM as a metaphor for human working memory because everyone has experienced working on a computer that didn’t have enough RAM: it’s glitchy and the mouse skips instead of dragging smoothly.

When I say “I can’t work on that project right now, I’m overloaded and don’t have enough RAM,” you understand it in a way that someone who has never used a personal computer couldn’t.

The blockchain is at least as rich a metaphorical structure as the computer.

The emergence of blockchains will allow the Blockchain Man to differentiate between dry code, smart contracts, as opposed to wet code, traditional law.

One of the effects of this is that much of what is now opaque and made to appear deterministic will become transparent and shown to be probabilistic.

The Organization Man’s day-to-day experience was deterministic. The organization guaranteed his retirement plan (and in some cases, as with IBM until 1991, his lifetime employment).

There were, of course, clauses in that guarantee (“unless the S&P 500 fails to provide sufficient returns in which case we will chase yield and take on too much risk resulting in huge losses after which we will renegotiate and force your reimbursements down”), but they were never stated explicitly.

This created a day-to-day experience of certainty, of a deterministic and fully legible world, pasted over a probabilistic, illegible substructure.

The wet code of the organization is always made to look dry, black and white.

In the dry code of the blockchain, the clauses are there for everyone to see.

They are written in the open source protocol. The value of your bitcoin is guaranteed unless there is a 51% attack or the miners switch to another chain or a malicious hacker finds a weakness.

The Organization purported to offer guarantees when it was always offering gambles.

Blockchains are explicitly trust-minimized, not trustless. The Blockchain Man’s day-to-day experience is probabilistic, not determinate.

This probabilistic, indeterminate world view will reverberate across the Blockchain Man’s life.

When you work for one corporation and go to one church there is an illusion of eternalism: One, True Answer. Much of what The Organization Man believes to be deterministic today will be shown to be probabilistic in the eyes of The Blockchain Man.

Amos Tversky’s quip that “we are deterministic creatures living in a probabilistic universe” will be no less true but the probabilistic nature of the universe will be more exposed than ever before.

In his best expression, the Blockchain Man will resist reductionism and develop an ability to move between conceptual systems as the needs change, just as he moves between different blockchains depending on his needs.

At his worst, he will descend into protocolism.

Protocolism is the New Nationalism

At his worst, The Organization Man descends into the tribalisms of nationalism, racism, sexism or corporatism. The Blockchain Man at his worst descends into the tribalism of Protocolism.

Each blockchain protocol will have its own maximalists (the blockchain equivalent of supremacists) who believe that it is the One, True Chain.

For both the Blockchain Man and the Organization Man, this grasping at eternalism is an attempt to satisfy a deep human need for belonging that seems to be possible only by exclusion of an out group.

Not only does protocol maximalism serve a human need for tribal belonging, but it also makes maximalists richer. The incentives driven by crypto economics mean that once you own a currency, your incentive is to make it go up in value.

We already see this happening; protocol specific subreddits generate and report news that supports their holdings and discredit their anti-portfolio.

r/Bitcoin trashes Ethereum and promotes Bitcoin, r/Ethereum promotes Ethereum and trashes Bitcoin.

The Selfish Coin

Where the Organization Man’s world was defined by The Organization, The Blockchain Man’s world will be defined by markets.

Even something as simple as commuting will be market driven.

What happens when you mash blockchains, Uber and Self-driving cars together? The self-owning car.

A car that pays for its lease, its insurance, and its gas, by giving people rides. A car that is not owned by a corporation. It is a corporation. The car exists as an autonomous financial entity, potentially with no human ownership.

When the Blockchain Man gets in the car, he will see a sliding scale offering him the ability to set an arrival time and calculate the cost of the ride. If he wants to arrive quickly, the car will make a flurry of micropayments to other cars allowing it to pass. If he’s not in a hurry, he may choose a later arrival time and lower fare, allowing other cars fly past in return for the lower fare.

Smart refrigerators will likewise create a highly liquid, ongoing market for groceries, buying and bidding on groceries based on user settings and their current contents.

In some ways, the Blockchain Man will be a slave to markets. Though markets have many benefits, they don’t extend extra vacation time or mourning leave after a loved one dies.

The Organization served as a shield from markets. In the worst cases this created rent-seeking bureaucracies. In the best cases, it served as a humanizing intermediary.

Blockchains in their totality can be thought of as The Selfish Coin (ht Joe Kelly for coining the term).

In the same way that Dawkin’s Selfish Gene showed humans as merely an elaborate construct of the gene to propagate itself, The Selfish Coin thesis sees The Blockchain Man as a mere tool of the protocol.

The genes which survived were the ones which created organisms most capable of surviving and reproducing, not the ones that made organisms happy or fulfilled.

The blockchains which survive will be the ones which have the greatest fitness for their environment, not the ones which make The Blockchain Man feel happy or fulfilled.

From the perspective of The Selfish Coin, the religious overtones of the early blockchain adopters is a feature rather than a bug. The fervor and obedience which the Organization Man showed to the organization will not disappear, merely transmute.

The Protocol Ethic

The Organization Man was defined by the Social ethic which emphasized the individual as subordinate to the group, the importance of belongingness, and The Organization as the means to achieve belongingness and achieve the proper subordination of the individuals to the group.

The Blockchain Man’s world will be defined by the three tenets of the Protocol Ethic.

1. a belief in the individual as the source of creativity

2. a belief in serving the needs of the protocol as the ultimate purpose of the individual

3. and a belief in the application of blockchains to achieve an individual’s highest potential.

The Blockchain Man exists as a unit of the blockchain. Of himself, he is isolated, meaningless; only as he collaborates with the blockchain does he become worthwhile, for by sublimating himself to the blockchain, he helps produce a whole that is greater than the sum of its parts.

TL;DR

The OODA loop was a tool developed by military strategist John Boyd to explain how individuals and organizations can win in uncertain and chaotic environments.

It is an Acronym that explains the four steps of decisions making: Observe, Orient, Decide Act.

This article will give you the understanding you need to turn ambiguity into advantage and risk into results in your career, business and life.

“He who can handle the quickest rate of change survives.” Lt. Colonel John Boyd

On March 20, 1997 a crowd gathered in the Old Post Chapel at Arlington National Cemetery for the memorial service of Colonel John Richard Boyd, United States Air Force, retired.

A chilly rain and cloudy skies saw a crowd bundled in winter coats hurrying into the chapel.

Full military honors including an honor guard, band, rifle squad, and flag-draped caisson drawn by six gray horses were provided.

A small crowd packed inside the chapel watched as a Chaplain opened the service and then one by one, Boyd’s oldest friends walked to the front of the chapel to recount stories of his life.

Boyd’s career spanned the last half of the twentieth century: he served in World War II, Korea, and Vietnam, and his ideas greatly influenced the Gulf War in 1991.

Former Commandant of the Marine Corps General Charles C. Krulak said in the aftermath of the Gulf War that “The Iraqi army collapsed morally and intellectually under the onslaught of American and Coalition forces. John Boyd was an architect of that victory as surely as if he’d commanded a fighter wing or a maneuver division in the desert.”

In the late 1950’s, Boyd was the best fighter pilot in America and possibly the world. He was called “Forty-Second Boyd” because he could defeat any opponent in simulated air-to-air combat in less than forty seconds.

He was more than just a great stick-and-rudder man, though; he was a strategist. He had developed Energy Maneuverability (E-M) Theory in his spare time in the 60’s. E-M Theory would revolutionize the way air-to-air combat was taught and fighter planes were designed around the world.

In the early 1970s, he was the spiritual leader of the Military Reform Movement, a guerilla movement within the military which sought to reform the cultural of careerism and waste in the armed forces.

In the late 70s, he retired from the Air Force and went into a self-imposed exile studying philosophy, science, military history, psychology, and a dozen other seemingly unrelated fields.

Moving from warrior to intellectual, he worked to synthesize what he learned from all these domains to answer the question: How do individuals and organizations win in an uncertain environment?

The ultimate result of this synthesis was the OODA Loop.

The OODA in OODA Loop is an acronym that stands for:

1. Observe

2. Orient

3. Decide

4. Act

It is a description of a process that you are already doing every minute of every day.

1. You observe that you are hungry. You orient by remembering there is a Chick-fil-a down the street and it’s before 11am

2. which means they are still serving chicken biscuits.

3. You decide to go to Chick-Fil-A .

4. You act by going to Chick-fil-a and eating a delicious chicken biscuit (or maybe two, why not, you’re already there).

The OODA loop is often seen as a decision making model, but can be more accurately described as a model of individual and organizational learning and adaptation.

If you prefer video, I discuss OODA in depth in the following video.

Boyd’s work did what few works ever do: it changed the world.

However, much of what he did was either highly classified or not written down. Because the military is an oral culture, he left very few published papers, making him difficult for academics to study.

Yet, over the last two decades, students of his work in the military have gradually moved wider into business, sports, science and dozens of other fields.

Their ideas challenge some of our most fundamental assumptions about how to win.

On a grassy slope, the cortege halted around grave site number 3,660. A Marine colonel, wearing the ribbons and decorations of a man who had spent a career fighting around the world, took a Marine Corps insignia, the eagle globe and anchor, from his pocket.

He walked out of the crowd, knelt, and placed the insignia near the urn containing Boyd’s ashes.

The young Marine lieutenants in the crowd snapped to attention.

Placing the Marine Corps insignia on a grave is the highest honor a marine can bestow. It is rarely seen even at the funerals of decorated combat marines. This was likely the first time in history an Air Force pilot ever received the honor.1

Why is the OODA loop so important?

John Boyd was the greatest military strategist of the 20th century. For those who have studied his theories, Boyd is tossed around in the same sentences as Sun Tzu and Carl Von Clausewitz.

To understand the importance of Boyd’s major contribution, the OODA loop, it’s helpful to place it in its historical context.

In John Boyd’s mind, the biggest turning point in military history happened on June 18, 1815, when Napoleon was defeated at the battle of Waterloo. Napoleon was the greatest general that Europe had seen in centuries.

He was the first general since Genghis Khan who stood a legitimate chance at sweeping every European nation under his banner.

Because of his success, his style dramatically altered the way war was fought for the next century and a half.

Prior to Napoleon, most of the great generals had employed maneuver warfare.

Sun Tzu, the great Chinese general from the Fourth Century BCE, is credited as the founder of maneuver warfare.

Maneuver warfare was defined by characteristics like swiftness of action, cycles of dispersion and concentration, deception, surprise, fluidity, shock and flexibility.

The other defining feature of maneuver warfare was that it tried to avoid actual war. Sun Tzu’s priorities for a general were

1. Best policy, attack enemy plans.

2. Next best, disrupt their alliance.

3. Next best, attack their army.

4. Attack cities only when there is no alternative.

Sun Tzu emphasized the moral and mental dimensions of war, the winning of hearts and minds. When fighting was necessary, he emphasized maneuver warfare characterized by quickness, variety, surprise, and harmony.

Napoleon’s early battles employed maneuver strategies emphasizing variety, surprise and rapidity. By the end of his campaigns though, he transitioned to a more rigid, uniform style that came to be known as attrition warfare.

Instead of letting his troops move flexibly based on the situation in their part of the battlefield, he had them move uniformly in dense infantry columns, much like the British red coats.

Because Napoleon was such a force of nature, the militaries of the world became locked into the view of Attrition Warfare as superior.

The American Civil War was characterized by large lines moving steadily towards and away from each other.2

The First World War was more of the same: Large trenches of soldiers formed a stable front.

In both cases, the goal of each side was simply to kill so many of the enemy’s soldiers that they would be forced to retreat: attrition.

The attrition view saw warfare through only two variables: force size and firepower.

During the First World War, Englishman Frederick Lanchester calculated that of the two, force size was more influential.

The other variable, firepower, could offset this, but the weapons had to be MUCH more powerful to make up for the differences.

The first place this started to change was with the German Blitzkrieg into France in 1939, the beginning of The Second World War.

Before the Second World War, German generals had gone back and studied earlier military strategists and designed the

Blitzkrieg to emulate the maneuver warfare styles of Sun Tzu and Genghis Khan rather than the attrition style of World War I.

Though the Germans eventually lost the war, Blitzkrieg was extremely successful.

Yet, because the Germans committed such horrible atrocities, very few military theorists went back to study the Blitzkrieg.

In the early years of the Cold War, American Generals were still locked into the attrition mindset that had begun with Napoleon.

American generals believed they needed to have radically more powerful weapons in order to maintain parity with the Soviets.

Using math based on Lanchester’s work, some projections suggested that the U.S. would need weapons one hundred times more powerful than the Soviets to maintain parity.

This all started to change with an insight John Boyd had as a fighter pilot in the Korean War. He saw that quickness was a missing variable in the general’s calculations.

Even when the odds were even, it wasn’t the bigger, more powerful fighter planes that won in air-to-air combat, it was the smaller, faster ones.

The Vietnam War confirmed Boyd’s hypothesis. In the leadup to the Vietnam War, the Air Force and Navy had developed bigger, more powerful aircraft than any that the Soviets had. Yet they were ten times less effective. The air-to-air combat kill ratio of 1:1 in Vietnam was far worse than the 10:1 they had achieved in Korea.

The American fighters were bigger and stronger than the Soviet MiGs, but they wasn’t nearly as quick, putting them at a disadvantage despite superior technology.

On the ground, the North Vietnamese employed the tactics of maneuver warfare at every level from strategic to tactical and ground warfare to air combat. They embraced the philosophy of Genghis Khan and Sun Tzu. In the words of the legendary

North Vietnamese General Vo Nguyen Giap: “Our strategic directives were dynamism, initiative, mobility, and rapidity of decision in the face of new situations.”

John Boyd used the data he had from Vietnam and Korea to develop Energy Maneuverability (E-M) theory.

To the chagrin of the Air Force Generals who had commissioned the bigger, more expensive planes, E-M theory showed conclusively that the lighter, cheaper, faster Soviet MiGs were more effective aircraft because of their quickness and maneuverability.

Vietnam marked the greatest inflection point in warfare since Napoleon. The Vietnam/American War was the first of many wars over the last half century where a smaller, weaker force was able to use maneuver tactics to win against a larger, more powerful force.

Today, insurgent groups around the world are using the strategies of maneuver warfare: surprise, variety, quickness and harmony to win.

A prescient 1996 article for the Journal of Foreign Affairs, explained the future of warfare this way:

[Instead of] attrition and the conduct of set piece battles along a continuous front such operations will give way to ‘non-linear operations’ [. . .] involving high-tempo attacks conducted simultaneously against key tactical, operational and strategic targets throughout the length, depth and breadth of the battlespace.

So far, the 21st century has borne out the prediction. Insurgents in Iraq, Afghanistan, and Syria are not waking up to battlefield in straight lines.

They are attacking in an unpredictable pattern all over the world from the world Trade Center to Nice, France to London music venues to hotels in India.

Why is The OODA Loop Important to Me?

In the 1970s and 80s Boyd started to see that the insights he’d developed in E-M Theory, how surprise, variety, quickness and harmony lead to dominance in air-to-air combat, had much farther reaching implications. He developed a briefing called Patterns of Conflict which showed their application to ground warfare tactics. He later developed a briefing called Organic Design for Command and Control showing their applications to military strategy, politics and diplomacy.

His final paper was The Conceptual Spiral, The Strategic Game of ? and ? and The Essence of Winning and Losing which expanded beyond the military to show how any individual or organization could thrive in an uncertain world.3

The thread that tied Boyd’s research together was a belief that the key to success in the uncertain, modern world was not a specific belief, but the ability to rapidly change beliefs based on a rapid changing and uncertain environment.

The shift to greater adaptability and speed is becoming required to survive in the business world.

An example of the bigger-is-better, trench warfare mindset in business was IBM’s full employment policy. For decades, IBM’s business was so predictable and steady that they could guarantee lifetime employment for anyone who worked there. That came to a crashing halt in 1992. 4

Since then we’ve seen the Uberfication of work. Instead of lifetime, full employment from a single corporation, 35% of Americans now engage in freelancing.5 They might have multiple projects from different companies going on at any one time with different scopes and completion dates.

A situation which leads not to a steady, linear monthly paycheck but a much bumpier income graph that looks like this.

Companies futures are likewise less certain and predictable, so they’ve outsourced much of their operations making it easier to scale up and scale down as needed.

In 1965, the average tenure of companies on the S&P 500 was 33 years. By 1990, it was 20 years. It’s forecasted to shrink to 14 years by 2026. 50% of companies currently on the S&P 500 list will not be there in a decade.6 This creates dangers, but equally creates opportunity for those willing to adapt.

My major conclusion from studying the OODA loop is this:

“Learning to live with ambiguity and uncertainty is a necessary precondition for more opportunity.”

If you want to “win,” you need a pretty significant amount of ambiguity and uncertainty in you life.

The startup world, characterized by uncertainty, provides plenty of examples of what embracing ambiguity can lead to.

WhatsApp was founded in 2009 and was bought by Facebook just six years later at a valuation of $19 billion.

Instagram likewise was purchased for $1 billion less than 2 years after being founded with only a handful employees.

It’s not just billion dollar startups. Over the past decade, we have seen a growing number of individuals and freelancers using technology to thrive in an uncertain world.

In 2015 data, the Census Bureau found that 35,584 “nonemployer” businesses (I.e. business owners with no employees) had sales or receipts of over $1 million. That is up from 33,624 in 2014, a 5.8% increase in just one year. 7

If your goal is to make a million bucks a year, it’s easier than ever, but it requires learning to work with uncertainty.

Within companies, the individuals who are most adaptable, quick and dynamic are able to add the most value.

If individuals and organizations want to survive and thrive in a highly dynamic environment, they have to embrace uncertainty and novelty and learn to use it to their advantage.

As examples like WhatsApp and the growth of one-person million dollar businesses show, the payoff is vitality and growth, the opportunity to shape and adapt to an ever changing reality and influence the ideas and actions of others.

The OODA Loop Explained

How can you learn to live with ambiguity and uncertainty to create more opportunity?

Boyd created the OODA loop to try and condense his four decades of research and thinking into a single diagram that answered this question.

The OODA in OODA Loop is an acronym that stands for

1. Observe

2. Orient

3. Decide

4. Act

It is a description of a process that you are already doing every minute of every day.

Let’s say it’s Saturday morning around 10:00am.

1. You observe that you are hungry.

2. You orient by remembering there is a Chick-fil-a down the street and it’s before 11am which means they are still serving chicken biscuits.

3. You decide to go to Chick-Fil-A .

4. You act by going to Chick-fil-a and eating a delicious chicken biscuit (or maybe two, why not, you’re already there).

How to OODA Loop

You are already OODA’ing, so you might as well get good at it.

The OODA loop is often seen as a decision making model, but can be more accurately described as a model of individual and organization learning and adaptation.

Here’s an example of OODA, that incorporates some of Boyd’s thinking about how to use the OODA effectively, applied to a real world business situation.

Observe

Observe means more than just “see;” it’s something more like “actively absorb the entire situation.” Observation includes your own situation, your opponent’s situation and the environment more broadly. It includes all the dimensions of that environment: the physical, mental, and moral dimensions.

The observation phase is data gathering in the broadest possible sense of the term: You are not just looking at your own numbers on a screen, you are looking at the emotional context, industry trends, and your competition’s moves.

Imagine you were a perceptive financial trader that understood the OODA loop in the run-up to the 2008 financial collapse. In the observation phase, you saw that the market was on its way towards record-highs. You felt the mental dimension.

Many people felt the market could only go up.

You saw there was a huge increase in financial instruments including mortgage-backed derivatives. You saw that many of the people who were taking out mortgages had much lower incomes than people taking out mortgages five years earlier.

Orient

Orientation is the most important part of the OODA loop. It includes understanding your genetics, cultural heritage and previous experiences, then analyzing and synthesizing that with all the observations you made.

The goal of the orientation phase is to find mismatches: errors in your previous judgement or in the judgement of others. As a general rule, bad news is the best kind because as long as you catch it in time, you can turn it to your advantage.

Continuing with your financial trader example, you oriented and suspected there was no way all these people were going to pay off their mortgages. Many of them were lower income individuals with unsteady jobs. You saw that everyone, from homeowners with multiple mortgages to large banks, was betting on the market going up indefinitely.

You hypothesized that this was irrational exuberance, and no market goes up forever. You thought once some people started to default, many others would as well and that the market would crash.

You discovered a mismatch: the widespread belief was “people that don’t default on their mortgages,” but your orientation based on the information you gathered during your observation suggested otherwise.

The 2008 Financial collapse was bad for everyone except the people that saw the mismatches.

The goal you should be striving for in the orientation phase is to prove your previous beliefs wrong by finding mismatches.

The sooner you can identify a mismatch, the sooner you can re-orient to take advantage of it The trader saw a mismatch, shorted the market, and made money.

Decide

The Decision stage is the transition into the final stage of acting.
For groups or organizations, the decision stage may require a series of meetings or discussions to adjust the strategy and roadmap based on the new orientation.

If you were a trader in 2008, you might have to have a meeting with your team to explain your reasoning in the orientation phase and come to a decision about exactly how to bet against the housing market. Should you short the mortgage derivatives themselves? Or the banks holding them? Should you do it now or should you wait a month?

For an individual, it’s usually unnecessary to have an explicit decision. You don’t need to call a board meeting to go to Chick-Fil-A, you just get in the car. Most decision making can and should be implicit.

Act

Acting is carrying out the decision.

In the trader example, you click the button or make the phone call to bet against the housing market.

Then the OODA loop starts all over again. Your action to bet against the housing market will have popped up on other trader’s screens. That may or may not change their actions. In either case, you observe what happens, orient based on the new information and go through another OODA loop.

Understanding the model a bit more, we can put together a diagram which is a little more accurate than just a loop as we have above.

1. We observe the outside world and the result of our previous actions.

2. We take time to orient - analyzing and synthesizing everything we learned in the observation phases.

3. The orientation guides our decisions and actions

4. The result of those actions becomes more information that begins the next cycle of observation.

Everyone’s brain works this way, however most people do it poorly.

There were tens of thousands traders who had access to all the same data that didn’t bet against the housing market.

Let’s look at what it takes to OODA well.

Boyd’s 5 Key Insights: How to OODA Well

1. Orientation, Orientation, Orientation

The OODA loop is based heavily in the scientific method. It is an ongoing loop in the same way the scientific method is a loop.

These map to OODA roughly as:

• Observations = Observe

• Hypothesis = Decide

• Test = Act

Boyd saw that orientation was missing in the scientific method and so heavily emphasized it in his teaching on the OODA loop. It happened informally, but it deserved to get a lot more attention.

The orientation phase is where creativity and innovation happens. Highly creative and innovative people are looking at the same reality as everyone else, but they orient in a different way than most people.

Orientation shapes the way we observe, the way we decide, and the way we act.

In our 2008 global financial crisis example, the information used by the traders who bet against the market and made fortunes was not secret, proprietary information. Everyone in the industry had access to the mortgage contracts and the data about how many loans were being made to people with lower incomes.

They were all equal coming out of the observation stage.

However, most people failed to orient well. Instead of looking for mismatches, they looked for ways to confirm what they already believed.

This is called confirmation bias and is a well-researched cognitive bias that we tend to look for and filter information which confirms our pre-existing beliefs instead of looking for mismatches.

In a 2004 study, participants were sorted by whether they supported George Bush or John Kerry and then asked to read statements where the candidates contradict themselves.

Here’s an example statement:

Participants were then asked to rate to what extent they felt the target’s words were contradictory on a scale of 1 to 4.

Researchers found that it was easy to predict people’s responses: Bush supporters thought Kerry was more self-contradictory and Kerry supporters thought Bush was more self-contradictory. Each group simply cherry picked the data which supported their pre-conceived beliefs.8

They failed to orient in the same way that someone whose business or income was growing because of the growing number of mortgages failed to orient to see the potential long-term downsides.

They cherry picked the data which supported their pre-existing beliefs.

Orientation is the most important part of the OODA process. When we orient, it shapes our decisions and actions for the rest of our current OODA loop which then shapes future loops.

In other words, orientation compounds. Doing it well compounds positively, and doing it poorly compounds negatively.

So how do you orient properly?

2. Destruction, Creation and Snowmobiling

The goal of orientation is to create a more accurate model of reality with which to make better decisions and take better actions.

There are two ways of coming up with those models: deductively and inductively. You can start with a comprehensive whole and break it down into particulars (deductive) or start with particulars and build them up into a comprehensive whole (inductive.)

Destruction is related to deduction, taking the big parts and breaking them down into constituent pieces.

Creation is related to induction, synthesis, and integration. It’s taking the little bits and re-building them into a coherent whole.

Boyd used a thought experiment to show how destruction and creation lead to successful orientation.

Imagine three separate scenes: a motorboat towing a skier behind it, a tank rolling across the desert, and bicycle cruising down the street.

If you break them down into the constituent parts, you have:a motorboat with a hull, outboard motor, and a set of skis being towed behind it;a tank with treads, a gun, and armor; anda bicycle with wheels, handlebars, and gears.

You can use these constituent parts to make many different incoherent wholes, but a coherent and useful whole would be a snowmobile: you take the treads from the tank, an outboard motor and skis from the boat, and handlebars from the bike and combine them to make a snowmobile

“Snowmobiling,” Boyd’s term, is orientation done right.

It requires taking all the data you’ve gathered in the observation phase, breaking it down deductively into its constituent parts and then recombining those parts through creative synthesis to form a new model of reality that lets you make better decisions and actions.

The people that had all the information on the mortgage market in 2006 and still lost money failed to take apart their existing beliefs about how the mortgage market worked and see the data through a different framework.

One group saw unending market growth and the other group saw a bubble waiting to pop.

3. The Moral and Mental Dimensions are Just as Important as the Physical

Another important consideration with orientation, is to think in terms of moral, mental and physical dimensions.

Boyd saw that American military generals were purely focused on the physical dimensions. Everything came down to the attrition measure of body count: He who has the most soldiers is winning. They ignored the moral and mental dimensions, the winning of hearts and minds, which Boyd understood as being supremely important.

The Vietnam/American War showed that a huge physical advantage could be overcome by winning the mental and moral dimensions. The North Vietnamese were able to win hearts (moral) and minds (mental) by portraying themselves as nationalist freedom fighters.

It’s hard to overstate the importance of this. The U.S. was the most powerful fighting force the world had ever seen. They spent $738 billion9 executing the Vietnam War and couldn’t overcome the moral and mental advantages seized by the North Vietnamese.

This is just as true in business.

While the hard numbers matter, they are only a piece of the complete picture.

Experienced business people always hammer on culture because they understand how important it is.

When Peter Thiel, founder of Paypal and Palantir, invested $150 million in AirBnB, his biggest piece of advice was “don’t fuck up the culture.”

He understood that culture, winning the mental and moral dimensions, could overcome huge physical advantages. Like all startups, AirBnB was competing against huge incumbent companies with tremendous physical advantages.

In 2014, the year Thiel invested, AirBnB had estimated revenues of $436 million.10 The global hotel industry had revenues of $432.6 billion11, fully 1,000x more.

Businesses have to focus on the mental and moral dimensions to stand a chance at unseating incumbents.

4. Tempo: You Must Get Inside Your Adversary’s OODA Loop

When people talk about OODA loops, one of the common refrains is “get inside your adversaries’ loop.”

Note: Boyd used the term “adversary” because he was dealing with the military, but you can think of it as more like competitors or the environment more broadly.

Getting inside your adversary’s OODA loop creates a tangle of threatening events and generates mismatches between what an adversary expects you to do and what you actually do. This makes your adversary feel trapped in an unpredictable world of doubt, mistrust, confusion, disorder, fear, panic, and chaos.

You adversary is stretched beyond his moral-mental-physical capacity to adapt or endure and can neither divine your intentions nor focus his efforts to cope and collapses.

Getting inside your adversary’s loop creates moral and mental distress.

This accurately described what Boyd saw happen in Vietnam. Despite massive advantages in troop numbers, technology and financing, the U.S. could never predict what the North Vietnamese were going to do next and this created a feeling of doubt, mistrust and confusion both in the armed forces and general population eventually forcing the U.S. to withdraw.

The Tet Offensive was a major offensive launched where over 80,000 North Vietnamese troops simultaneously attacked more than a hundred towns and cities.

From a physical, military standpoint, the U.S. and Southern Vietnamese forces were initially stunned, but quickly regrouped and beat back the attacks, inflicting heavy casualties on the North Vietnamese.

As far as the American generals were concerned, they won that battle because the North Vietnamese suffered more casualties than the American forces.

But the attack had moral and mental effects. The U.S. generals had been insisting on TV that the North Vietnamese were teetering on the brink of collapse and incapable of launching such an ambitious attack.

When the U.S. public saw the generals either didn’t know the truth or had lied, it led to a huge loss of public support that eventually led to the U.S. withdrawal.

The Tet Offensive is usually seen as the event that turned the war in North Vietnam’s favor even though they lost far more soldiers in the fighting. The surprise attack got inside the narrative that generals had been telling the press and let the North

Vietnamese win the moral and mental dimensions (and eventually the war).

The wording “get inside your adversary’s OODA loop” is sometimes interpreted as moving fast or frenetically, going through the cycles O-->O-->D-->A-->O-->O-->D-->A, faster than your adversary.

This creates a notion that you always need to be moving a hundred miles an hour.

What Boyd meant to imply was not frenetic movement, but varying your tempo. The central idea in OODA is to operate at a higher tempo than your opponent. This is a subtle point. A higher tempo is not the same as higher speed, in the sense of a race car overtaking another.

To think in terms of tempo means to think in terms of narrative rather than speed. The primary effect of operating at a faster tempo is that you can maneuver inside the decision cycle of your opponent by introducing entropy into it faster than it can be removed. This style is obviously effective in adversarial settings, but it is equally effective in cooperative settings, a point that many who are inspired by Boyd miss. The alert waiters at upscale restaurants, fulfilling your needs before you even recognize them, are a good example. Exceptional customer service, not just war plans, can arise from OODA thinking.

Boyd called this variation in tempo a “fast transient.”

The “transient” is the change between maneuvers. The ideal fast transient is an abrupt, unexpected, disorienting change that causes the other side to say “What the fuck!”

Boyd was a renowned dogfighter because he would create disorienting, WTF moments by engaging in an unexpected maneuver and before the other pilot was able to re-orient, he was dead.

The Tet Offensive was effective because it came at a time when no one expected it. There were probably lots of military commanders who woke up to the news of the attack and said “What the fuck?!?!”

A great example of a business that got inside their adversary’s OODA loop was Apple. The release of the iPhone in 2007 was a fast transient because it was unlike any previous smart phone: it combined an iPod with touch controls, a mobile phone, and an Internet-enabled device.

Compare it to a 2007 Blackberry Pearl:

If every phone you had ever seen looked like the Blackberry on the left, the first time you saw the iPhone, “WTF!?” was a pretty normal reaction.

When you are inside your adversary’s OODA loop, you have the momentum.

A team can be losing a game or a business can be second in its market but have more momentum than its competitor. Even if they are losing on the scoreboard, they are likely to win because they’ve gotten inside their opponent’s OODA loop.

The release of the iPhone created a huge surge of momentum for Apple because they were able to get inside the market’s OODA loop.

5. Uncertainty and Opportunity are Linked

Boyd’s work looked at how any individual or organization could thrive in an uncertain world.

He saw that one way people react to uncertainty is to try to eliminate it. They try to close themselves off from uncertainty.

This always makes things worse in the long run.

Individuals, organizations, cities, and nation-states thrive as parts of open systems. If they close themselves off, they suffocate.

In our individual lives, we often do the same thing. The person who takes a seemingly secure, stable job because they are trying to avoid change can put themselves at a disadvantage. All their eggs are in one basket. If that job disappears twenty years from now, they’ve not built a network and skillset that would let them survive the setback.

The freelancer that pursues a seemingly more risky path with more uncertainty also creates opportunity. Working with many clients across different industries on different types of projects leads to more possibilities in the long run.

A product team that works on a project for two years without getting user feedback reduces uncertainty in the short term: user feedback risks ruining the perfectly laid out product roadmap. They also make it likely that they will spend two years working on something that nobody actually wants to use or buy.

The product team that opens itself up to feedback early in the process creates more uncertainty - Will people like it? Why or Why not? Will they use it like we planned? - and in so doing they both mitigate the risk of building something nobody wants and open up new possibilities.

Openness creates uncertainty and opportunity. You can not get one without the other. The people I know who have the most opportunities in their lives are the ones who are best at living with large amounts of uncertainty.

How do you open yourself up?

In essence, you interact by staying open to people and source of information coming from diverse points of view and by living in a way that is in alignment with our stated values.

This sounds great, but is uncommon. There is a strong tendency for people to close themselves off from different points of view and isolate themselves.

How many significant beliefs have you changed your mind about in the last year?

Reality is constantly changing. If you aren’t changing your beliefs about it, then you’re falling out of sync.

Going Full OODA

Given our deeper understanding now, let’s take a look at the Full OODA loop as sketched out by Boyd.

The observation stage involves a broad range of inputs: unfolding circumstances, outside information, your interaction with the environment, and feedback from all three other stages.

The orientation phase is the focus of the diagram and is larger than the other three steps combined. It involves analyzing (breaking down) your cultural traditions, genetic heritage, new information and previous experiences and then synthesizing (putting together) the elements into a new orientation.

The Core Attributes of the OODA Loop

So far, we’ve looked in detail at the mechanics of the OODA loop, how you actually go through it. But just as important is the way you approach it.

What are the attributes of an individual or team that can successfully use the OODA loop?

Boyd studied the German Blitzkrieg in detail and found there were a few key attributes of the Blitzkrieg that are also needed in order to be successful with the OODA loop.

The advantage of these attributes is that they let you control the tempo and get inside your adversary’s OODA loop more effectively.

1. Einheit: Mutual Trust

If there is a universally accepted truth in military victory, it is probably the fundamental importance of mutual trust among a team.

The German Blitzkrieg commander Heinz Gaedcke explained that one of the essential reasons for the success of the Blitzkrieg was that the German commanders all trusted each other implicitly. They had a relationship where they could look at each other in the eye and know exactly what needed to be done without speaking.

True Einheit is the result of two things:

1. Shared Values - The German commanders all had a similar belief in the importance of fighting for their home country. Their time together in the military had created a common culture.

2. Overcoming Challenges Together - The German commanders had all worked together since they were 18 years old. They didn’t have to hope that their fellow commanders would do their jobs; they had seen them do it hundreds of times in the past.

To create mutual trust, you must

1. Assemble your team based on shared core values

2. Confront and overcome complex and difficult situations together.

Over time, this leads to real, mutual trust.

When you are working with people you trust, you can each move through your OODA loop cycles much more quickly. When I ask someone who I trust implicitly, because we have similar values and they have a multi-year track record of getting things done, to execute on something, I don’t have to micro-manage them. I know they will get things done.

I do not need to check in with them on their new orientation because I can trust that them to do it on their own.

The most successful cofounder relationships started years before the company in informal ways. They were working in the same division of a larger company, played basketball on the weekends or went to college together. In all these cases, they created mutual trust.

Creating mutual trust takes time; it’s not something that can be done in a weekend retreat, only over a period of years of working together.

In the business world, the increase in freelancing has made this easier. It’s easy to put together a small project and work with someone to see how you work together and begin to establish Einheit. If it goes well, you can continue working together and building up mutual trust that will make you more effective.

For managers, the most important insight from Einheit is that you can’t micromanage. You have to let people take ownership even if they are going to make mistakes. In the long run, the mutual trust is more important than the small mistakes.

2. Fingerspitzengefühl: Intuitive Skill

Fingerspitzengefuhl translates literally as a fingertip feeling, but is most easily understood as intuitive skill or intuitive knowledge.

The value of Fingerspitzengefuhl, in terms of OODA, is that it collapses Orient and Decide stages down to nothing so you can go through the loop faster and more easily get inside your adversary’s OODA loop. You go straight from observe → act → observe → act → observe → act.

This allows you to increase the tempo much faster than someone who has to constantly orient.

While Boyd saw the orientation phase of the OODA loop as the most important, he recognized that people who achieved mastery in a specific domain could skip it because they had fingerspitzengefuhl, intuitive skill.

When Roger Federer is getting ready to return a tennis serve, he doesn’t consciously think: “Take your racket back, cock your wrist, keep your eye on the ball, plant your feet, follow through over your shoulder.”

He has been playing tennis for so long that he intuitively knows what to do on the tennis court.

In the same way, good product managers can feel the right direction to take the product and a good basketball player can often feel the right move to make to get past his defender.

One of the most common pieces of advice given to founders starting companies is to “scratch your own itch” by building a product that you yourself actually use.

This is an enormous advantage because if you use the product in your day-to-day life, you will develop an intuitive feel for it.

While competitors run surveys, put together customer feedback groups and hold meeting after meeting to try and orient themselves, you’ll simply know what the right decision is.

When you’re competing against a larger competitor, intuitively knowing what to do is an essential advantage.

Combined with Einheit, it means that your team has an intuitive feel for the right decisions and you trust each other to execute plans based on those decisions.

This lets you keep up the tempo and pressure on even much larger competitors.

3. Schwerpunkt: Focus

Schwerpunkt literally translates as center of gravity or emphasis, but is best understood as focus or the main priority.

In military terms, it is usually the geographic point of attack.

Having a clear focus, and emphasizing that over any particular tactic, empowers your subordinates to make decisions for themselves in an uncertain environment.

Imagine a unit is ordered to take a hill and the initial orders are to storm it from the South, because reconnaissance reports said the Southern side was the most lightly defended.

Once they arrive, the unit commander might see that the defense has moved and now the South is the most heavily defended side, but the West is now wide open. If he knows all that matters is taking the hill, he can be flexible in his approach and come from the West side.

The opposite approach, giving everyone specific checklists with no flexibility, can lead to disaster.

Compare how you feel about Zappos customer service, where employees are allowed to use their judgement to get the best outcome for the customers, with United Airlines, which forces everyone to follow rigid guidelines with no flexibility.

A good schwerpunkt helps you and your team make better decisions in an uncertain environment where rigid procedures close you off and isolate you.

If the schwerpunkt is to increase leads by 50% in 90 days, then you and/or your team can take many different approaches to achieving that goal.

Instead of giving others plans, you give them objectives, a schwerpunkt, and let them figure out how to get there.

This both gives them a greater sense of ownership and empowers them to be flexible with trying different approaches.

The combination of mutual trust (Einheit), intuitive skill (Fingerspitzengefuhl), and focus (Schwerpunkt) lead to an enormous advantage.

They are each able to identify new opportunities (fingerspitzengefuhl) leading to the agreed outcome (schwerpunkt) and trust each other to get the job done (einheit).

This lets them more easily get inside their adversary’s OODA loop and win.

The Different Levels of the OODA Loop (AKA The OODA Loop is Fractal)

Boyd saw OODA at every level of the military: Grand Strategy, Strategy, Grand Tactics and Tactics:

1. Grand Strategy - There was an OODA loop that took four years between election cycles.

2. Strategy- There was another one that might happen over the course of a quarterly plan.

3. Grand Tactics - There was another over the course of a day or week.

4. Tactics - There was an OODA that happens in a fraction of a second inside a unit commander’s mind on the battlefield.

In the same way, there are OODA loops operating at different levels of your everyday life. What you do on a given day feeds into your orientation for when you are planning your week. What you do in a week feeds into your orientation for the coming month and so on.

In my goal-setting and planning masterclass, The Effective Entrepreneur, I use this diagram to talk about how feedback should move up and down your planning process:

Your long-range vision should influence your quarterly goals which should influence your weekly goals and on down the chain.

Likewise, the observations you make and feedback from the actions you take should feed back up the chain.

Let’s say you have a 90-day goal of increasing your lead-flow by 50%. You’ve been reading a lot about content marketing, and so your initial orientation is that you will increase leads by publishing an article every week on your site.

In the first week you publish an article, you observe that it only increases how many leads you get by 1%. If you only increase your lead flow by 1% each week, you won’t hit your 90-day goal of increasing leads by 50%.

This is a new observation that needs to take into account. At the end of the week, you sit down to reorient. You should ask questions like:

• Is the problem that the type of article you wrote is not good for driving leads?

• Is it that you published it in the wrong place (your site instead of a site with more traffic)?

• Is it that you didn’t embed email capture forms in your article?

Or is it that writing articles simply won’t get you to your goal?

If that is the case, you would need to decide to pursue another strategy, like PR or paid advertising.

You might also decide to re-orient and change your initial goal. If you still believe that writing articles is a better long-term strategy, it might make sense to decrease your short term goal.

There is no right answer: it is an ongoing dialectic between you, your goals, and the environment you are operating in.

If you are taking time to orient then you will, over time, get better at setting goals and accomplishing them.

Boyd’s emphasis on the importance of orientation was the result of his observation that most people spend far too few resources on it. I’ve found this to be true for myself and for the hundreds of people that have taken my course.

Setting aside five minutes per day to review and plan the day and an hour a week to do a weekly review are the highest ROI activities most people can add to their weeks.

Scheduling orientation time is one of the easiest ways to implement the OODA loop in your daily life immediately.

Action Steps and How to Apply the OODA Loop

“To think theoretically one must be ready to appreciate and accept the need to sacrifice detailed descriptions for broad observations.” James N. Rosenau

In order to make a conceptual theory that is so broadly applicable, Boyd necessarily had to make it abstract.

I hope that reading through, you’ve come up with some ideas for things you could do differently and ways to incorporate the OODA loop.

Here are five ways I’ve applied Boyd’s thinking to my life.

1. Schedule Time to Orient

2. Be dilettante in your inputs but focused in your output.

3. Hunt Your Sacred Cows

4. Follow The 70% Rule

5. Trust Your Fingerspitzengefuhl (Intuitive Skill)

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Conclusion

To sum up the key points:

Boyd’s 5 Key Insights:

1. Orientation, Orientation, Orientation - When you’re dealing with an uncertain domain, the orientation phase is both the most important and the most often neglected. If you feel uncertain, make sure you’re devoting more time and resources to orienting.

2. Destruction, Creation and Snowmobiling - Successful orientation requires destroying your existing beliefs and creating new ones based on the constituent parts, a process Boyd called snowmobiling.

3. The Moral and Mental Dimensions are Just as Important as the Physical - We often focus too much on the physical dimension, like revenue, and undervalue the harder to measure moral and mental dimensions like culture. However, winning the moral and mental dimensions can let you overcome physically much larger competitors and challenges.

4. Tempo: You Must Get Inside Your Adversary’s OODA Loop - Getting inside your adversary’s OODA loop lets you take control of the situation and win. You get inside your adversary’s OODA loop by executing “fast transients,” WTF moments designed to make your adversary feel trapped in an unpredictable world of doubt, mistrust, confusion, disorder, fear, panic, and chaos.

5. Uncertainty and Opportunity are Linked - Individuals and Organizations exist and thrive as part of open systems. This openness generates opportunity but necessitates uncertainty. Using the OODA loop lets you effectively deal with uncertainty while seizing opportunity.

The Core Attributes of the OODA Loop

1. Einheit: Mutual Trust - In order for teams to effectively use the OODA loop, they must have mutual trust built on a base of shared values and combined with years of working on difficult challenges together.

2. Fingerspitzengefühl: Intuitive Skill - As you get more skilled in a certain domain, you can develop an intuitive skill that lets you move through the OODA loop more rapidly and more effectively get inside your adversary’s OODA loop.

3. Schwerpunkt: Focus - Instead of insisting on a step-by-step plan, identify a focal point and adapt the means needed to get there.

As the world moves faster and faster, we don’t need more formulas or how-tos.

We need to improve our ability to change our minds based on a changing reality and in so doing turn uncertainty into opportunity and ambiguity into advantage.

OODA is a model of individual and organizational learning and adaptation to do just that.

Bibliography and Further Reading

• You can read the original papers and presentations from Boyd here.

• I explored one of Boyd’s other ideas, fingerspitzengefuhl, and its implications here.

• John Boyd’s biography by Robert Coram is outstanding (I recommend the audio version).

• Certain to Win by Chet Richards is a great book on his ideas applied to business.

• Science, Strategy and War is a deeper dive into his OODA Loop and other theory with lots of helpful context.

There are at least two overarching mental models for looking at the world: One could be called the bell curve and the other, the 80/20 curve.

When I was marketing my book, The End of Jobs, I spent a week sending personal emails (in Gmail, one by one, not in bulk) to two hundred people who I thought could potentially be in the top 1% of my readers, the people most excited about the book.

In total, I spent about 50% of my marketing resources on 1% of readers.

One of the two hundred people happened to have a friend who had a large community of people interested in business books.

He introduced us and his friend ended up emailing his community about the book, which drove it to #1 in Amazon’s business and money section.

Another of those two hundred people forwarded the book to a much larger “influencer” in my space, who ended up reading the book, inviting me on his podcast, and giving me a blurb.

Those two emails probably accounted for over half of my book’s sales.

You’re probably thinking “wow, great for you, you got lucky.”

It does sound like I know how to attract luck, doesn’t it?

What I want to argue is that if you see the world through the 80/20 curve, what happened to me was not lucky, but statistically probable. I’m suggesting that seemingly trite phrases like “luck is what happens when preparation meets opportunity” are supported by math.

Now, I know what you’re thinking right now: “Gee, I hope this essay has LOTS AND LOTS OF GRAPHS!”

You’re in luck!

Here’s a graph of a bell curve (also called a normal distribution) and an 80/20 curve (also known as a power law curve).

The Bell Curve is an accurate way to describe simple systems, particularly biological ones like height:

Most people fall in the middle, and it trails out slightly on each side.

However, as our world grows more complex and interdependent, the bell curve is a worse and worse way of describing the things that matter to us.

It’s now the 80/20 curve that defines our lives.

The 80/20 curve was first articulated by Vilfredo Pareto, an Italian economist, who found that 80% of the land in Italy was owned by only 20% of the people.

The phrase 80/20 can be confusing because people think it has to add up to 100, but that’s just incidental. Pareto’s central insight was that a small group owns a large portion of the land. It could be that in England 10% of people own 70% of the land, or that in America 5% of people own 81% of t-shirts, that 7% of people fly 53% of flights, or that 25% of products account for 90% of the revenue.

The 80/20 curve applies in any system when a small number of inputs account for a large percentage of outputs, which, as it turns out, is pretty much always.

What MBAs Can Learn from a Singer-Songwriter about how to attract luck

There’s a classic exercise often done in business schools that goes something like this: The professor walks into the room and says “I have two tickets to a Taylor Swift concert. Write down the most you’d be willing to pay for these tickets, then turn in your answers.” The professor then puts a bell curve up on the board that will, invariably, look something like below.

The professor then goes on to explain why Taylor Swift should price her tickets at around $40 (what most people are willing to pay), in order to maximize sales.1

Looking at that chart, this makes sense.

That’s what most people would say, and it’s what business people would say. It is the bell-curve-model-of-the-world answer.

Let’s look at that exact same group of people through the lens of an 80/20 curve:

This is the exact same group of people, but plotted on the 80/20 curve. Most people would pay about $40, a few people would pay much more, and a few people would prefer to use Taylor Swift’s CDs in their garden as a way to scare off birds.

What are the implications for a musical artist of seeing the world through the 80/20 curve, as opposed to a bell curve?

Luckily, we don’t have to speculate. There’s a real life example.

Amanda Palmer is an American singer-songwriter who rose to prominence as the lead of the Dresden Dolls.

In 2008 she went solo and released her first solo album, Who Killed Amanda Palmer. It sold about 36,000 copies.2 If we assume an average of around $15 per CD, the CD earned $540,000.

In 2012, she released her second solo album, Theatre is Evil. She released it on Kickstarter, had 24,000 backers pay to support the campaign and made $1.2 million dollars.

Woah, how did she make more than twice as much money ($1.2 million vs $540 thousand) by selling to less people (24,000 instead of 36,000)?

Everyone’s initial thought is that she would have had to charge $50 per CD to make that much, right?

Not right. Our intuitions, based on our bell curve model, deceive us.

She offered different price points along the 80/20 curve. You could get just the digital tracks for $1. You could get the CD and an art booklet for $25. You could get the same but with an autograph by Amanda for $100. You could get all of the above with a custom painted turntable for $500. For five grand, she would turn up and play a show in your backyard.

Because she looked at her fans through the lens of the 80/20 curve, she got a very different result:3 She earned more than three times as much money per customer ($50 vs $15).

80/20 is striking to most people because it indicates that the natural order of most systems is not an equal distribution, but an unequal distribution: only 20% of customers account for 80% of revenue.

However, the disparities are even larger than most people think.

The really striking part is just how concentrated the “fat tail” of the curve is. The fat tail is the tall part on the right side of the 80/20 curve: it’s the people that paid for Amanda Palmer to come to their house, or whose first reaction to hearing about a Taylor Swift concert was “OMGGGG TAYLOR TAKE MY MONEY NOW.”

Why is it so concentrated? Because the 80/20 is fractal. That means it can be applied to itself. If 20% of people own 80% of the land, we can then take the 80/20 of that new 20%. In that case, 4% of people would own 64% of the land. If we then took the 80/20 of that, then 0.8% of people own 51.2% of the land.

That’s really concentrated! And this is true everywhere.

In the case of Amanda Palmer, she raised $316,000 in her Kickstarter (26%) from 184 backers (0.7%).

The graph of a bell curve is a poor fit with reality. What looks like the majority of people, is actually a minority of anything that matters. I’ve used the example of revenue, but it could be anything: excitement, pleasure from listening, willingness to drive to see a concert.

What does this mean? It means the margin is in the fat tails.

The Margin is in the Fat Tails, not the Lumpy Middle

Margin, as I’m going to use it now, is the difference between the intrinsic value of an asset and how the market values it.

If you asked someone how much Amanda Palmer’s audience of listeners was worth, most people (including record companies) would have looked at it and said “about $540,000.” That was how much her first CD made, after all.

However, this thinking is the result of the bell curve mental model.

Palmer’s Kickstarter proved that a smaller audience was worth far more — $1.2 million in fact.

People walking around with the bell curve model think that the top 1% of customers, the fat tail, only account for a small amount of the revenue, when they actually account for 50%. And so they spend their time, energy, and attention on the lumpy middle of the bell curve, instead of the fat tail of the 80/20 curve.

Almost everyone systematically under-allocates resources to the fat tails. We tend to spend most of our time and energy thinking about the middle, because we see the world through a bell curve lens, and most of the area in a bell curve is in the middle. But in reality, we live in an 80/20 world, where the top 1% of fat tails account for 50% of the results. The people who understand this seem to know how to attract luck, but really they’ve just adopted the 80/20 curve model.

The difference between how everyone walking around with a bell curve model see the world, and how the few people walking around with an 80/20 curve model see the world, is margin. The fat tail of the 80/20 curve is systematically under-invested in.

By shifting your attention from that fat part of the bell curve to the the fat tail of the 80/20 distribution, you get more margin.

Let me give some examples to explain.

How to Attract Luck: Lessons from Margins in Fat Tails

Growing a Business: Let’s say you have $500 to spend to help grow your business. What’s a better investment: would you spend it on pay-per-click ads driving everyone to your products, or buying a group of people in your industry dinner?

Ads is bell curve thinking, hosting dinner is 80/20 thinking.

Your return on ads is linear. You spend $500 and you get $1000. That’s the bell curve. Linear.

Your return on dinner is non-linear. You spend $500, someone introduces you to a friend who eventually becomes your business partner, and you sell the company for $10 million dollars five years later. Sounds unlikely, but the nature of fat tails is that any individual one is unlikely, but done systematically, very likely. I didn’t know which of the two hundred people I emailed about my book would be able to help, but I believed that it was statistically likely that one would.

Though highly unlikely, the outcome is worth it. You only need it to happen once in a career. Would you organize a hundred dinners for an 80% chance of making $10 million?

Again, most people don’t think this way, so you get margin at the fat tail. Most people are focused on the linear stuff, the lumpy middle of the bell curve. If everyone was operating with an 80/20 mental model, there wouldn’t be margin there.

If everyone was hosting dinners all the time, it would be an annoying obligation. “God, another dinner!” But since almost no one does it, you get the reaction, “Wow, dinner, yea that sounds great!”

A perfectly rational way might be to spend $250 on the ads and $250 on dinner.

Indeed, one very good argument in favor of adwords is cash flow. Since fat tails are inherently unpredictable, investing some into things that are likely to work is a good strategy.

You don’t know which dinner will pay off, and in the words of John Maynard Keynes, “The market can stay irrational longer than you can stay solvent.”

The ads keep you alive while you wait for one of the fat tail bets to pay off.

Customer Acquisition: In his essay Do Things That Don’t Scale, Y Combinator’s Paul Graham notes that “[he] ha[s] never once seen a startup lured down a blind alley by trying too hard to make their initial users happy.”

This is 80/20 thinking. Because everyone is operating with a bell curve model in their heads, they are spending the same amount of time and energy taking care of each one of their users. But because not all users are created equal, if they were optimizing properly, they would spend 50% of their time on the 1% of users who are most excited about the product — and being an early adopter is a strong sign of being excited.

Again, there’s an additional margin here. Because most companies treat all their users the same, most of us aren’t used to getting truly exceptional service, so when we do, we tend to tell our friends about it.

Instead of setting up a pop-up, do what Stripe did and write your best customers thank you notes. There’s more margin there.

Reading Books: A difficult book that takes twice as long to read usually gets the reaction, “oh, I could read two other books in that same period of time,” because they have a bell curve model in their head.

Any given book is only marginally better than any other book if you think about books falling on a bell curve, but on the 80/20 curve, a great book could be ten or a hundred times as valuable as an OK or good book.

You can learn more about a field by reading the three foundational books then you can reading the most recent thirty. I’ve met people who say they have “ten years of experience in marketing” and who can rattle off statistics about how blogging frequency affects traffic from the thousands of blog posts they’ve read, but put them up against someone with two years of experience who has read Ogilvy, Hopkins, Halbert, Schwartz and Godin? They’ll get trounced.

Relationships: If you were trying to maximize your personal happiness, you would spend 50% of your time with 1% of your “network.“ Maybe four or five total people. A really great friend will bring you ten times the pleasure of a good friend and 100 times that of a loose acquaintance.

Working Out: If you wanted to get in the best shape possible, you would focus on doing just a few exercises exceptionally well. 1% of exercises account for 52% of results. By getting really good at those, you’ll do better than someone who instead spends their time trying to be “well-rounded.”

Focus on depth, not breadth.

“In the short run, the market is a voting machine but in the long run, it is a weighing machine.”
-Ben Graham

A good rule of thumb for identifying the fat tail in any given system is to think about how to go deep, not wide.

A key reason why all the margin is in the fat tails is because there is no clear cause and effect. The fat tails are profitable in part because they are hard to directly attribute. It’s easy to see how spending $250 on ads can return the money; it’s much harder to see how hosting dinners pays off.

It’s almost impossible to measure depth, but easy to measure breadth.

In relationships, the number of Facebook friends you have is easy to measure, but that number is a horrible metric of anything meaningful. You can have tons of FB friends and feel terribly lonely. Most people do.

In marketing, it’s easy to measure how many contacts are in your database, but hard to measure how much the top 1% are worth.

In books, it’s easy to measure how many books you’ve read, but really hard to measure how much a book impacted your thinking.

Investing more in fat tails means being wrong a lot more, and perhaps this is why the most successful people often say they made a lot of mistakes. They made a lot of fat tail investments, most didn’t pay off, but the ones that did made all the difference.