An Eighty-Year Shake-Up

America may be undertaking the most consequential reform of government science funding in eighty years. It is also creating enough uncertainty that some of its best scientists are considering leaving.

Near the end of my recent interview1 with , I asked which long-term reform would contribute most to American prosperity.

“We need to reform—and it’s so easy to do—our scientific funding system,” he said.

Samir favored creating multiple DARPA-style agencies led by accomplished scientists with broad authority to make unconventional bets. But he was also alarmed by the Trump administration’s current approach. Its real target, he had heard, was elite universities it disliked. Scientific research was becoming collateral damage.

Even after a project had passed peer review, Samir said, “some political committee” could decide that the research should not proceed. He was particularly troubled by “the withholding of money—and the capriciousness of that too.”

Eminent researchers he knew personally were already leaving for countries such as Canada. America’s postwar scientific leadership had been built partly by researchers driven out of Europe. Samir feared that the United States had begun reversing the flow that helped make it the world’s technological leader.

Coincidentally, only two days earlier, the Trump White House had released a 122-page report2 calling for the first comprehensive rethinking of America’s scientific enterprise since 1945.

Then, within the past week, The All-In Podcast released two extended interviews examining the same subject from sharply different directions. Michael Kratsios, the White House science adviser who wrote the report, defended the administration’s actions and explained its proposed reforms3. Eric Weinstein, one of the scientific establishment’s most uncompromising critics, argued that peer review should lose its dominant role and that government should fund exceptional people rather than carefully packaged proposals4. Together, the conversations exposed both the depth of the existing system’s failures and the risks of trying to overturn it.

The Question Kratsios Did Not Answer

Yet for all the ground covered by the report and the two interviews, the most immediate danger identified by Samir received remarkably little attention.

The report condemns grant requirements that forced scientists to conform to “the political fashions of the day.” It is right to do so. Scientific proposals should not have been judged by their alignment with progressive priorities involving diversity or other social objectives. But replacing one ideological test with another would not restore scientific neutrality. It would merely change who administers the test.

An elected government must decide how much public money to spend on science and which broad national objectives deserve priority. It may reasonably favor fusion, quantum computing, biotechnology, or fundamental physics over other fields. But setting priorities is different from withholding funding from individual projects because the administration is engaged in an unrelated political conflict with the university conducting them.

Neither the report nor the Kratsios interview establishes that distinction. When Friedberg pressed him about the canceled funding and scientists considering leaving the country, Kratsios largely dodged both questions, redirecting the conversation toward politicized grants and the need for broader reform. He did not confront the danger of making previously approved research dependent on political judgments imposed after scientific review. A government demanding greater accountability from universities and funding agencies must also make its own interventions transparent, consistent, and subject to evaluation.

A System That Does Not Study Itself

Those omissions are substantial, but they do not invalidate the report’s broader diagnosis. Science: A New Golden Age is unusually introspective for a government document. It does not simply celebrate past accomplishments or ask for more money. It questions whether the institutions built to support American science after World War II still fit the way discovery occurs today.

The report describes a system in which scientists can spend months preparing grant applications and nearly half their research time on administrative requirements. Peer review tends to favor established fields, institutions, and investigators, while federal agencies face little pressure to reconsider how they allocate money. Meanwhile, private industry has become by far the largest source of American research and development spending, eroding the old assumption that basic discovery happens in universities before moving in a straight line toward commercial application.

and 5 reached a similar conclusion a decade ago in a skeptical examination of National Science Foundation funding for economics. They accepted the conventional case for government support of science but argued that the relevant question was not whether public funding was better than no funding. It was whether each additional dollar produced more value than its alternatives. They found that more than half of NSF economics grants since 2005 had gone to just 11 universities and the National Bureau of Economic Research, while the agency appeared to have done little to determine which of its previous expenditures had produced the greatest marginal value.

Not every statistic or causal claim in the White House report can be independently confirmed. Some examples clearly reflect the administration’s political priorities. But its central concerns have also been raised by scientists, economists, philanthropists, and critics across the political spectrum. The evidence is strong enough to regard the diagnosis as at least directionally correct and probably substantially so.

Most important, the report treats these weaknesses as a call to action. It asks government to apply experimentation not only to the science it funds, but to the institutions deciding which science receives support.

Sputnik shows that disruption can revive institutions that have become complacent. But the American response did more than tear down an inadequate system: it created ARPA and NASA, expanded scientific education, and supplied new institutions and missions. Productive disruption identifies a weakness and builds something better. Arbitrary disruption creates uncertainty without establishing a replacement.

The need to allocate public money more intelligently has grown as government’s role has changed. In the decades after World War II, government supplied roughly 70% of American research and development funding and the private sector 30%. Today, those proportions have reversed. Kratsios called the current balance reasonable, and extraordinary private-sector advances in artificial intelligence, quantum computing, biotechnology, and medicine support his confidence.

But private research naturally gravitates toward discoveries capable of producing valuable products. Government remains essential where the benefits may be widely dispersed, impossible to own, or decades away. With a narrower and more distinctive role than it held in 1945, government has even greater reason to ensure that no single funding mechanism determines which discoveries receive support.

The report’s framework makes the emerging division of labor clearer. Universities, corporate laboratories, and federal laboratories each remain well suited to some kinds of work and poorly suited to others. New institutions should be designed specifically to occupy the gaps.

Source: White House, Science: A New Golden Age

More Than Peer Review

The White House report does not propose replacing the current system with one new model. Its more promising idea is to create a portfolio of funding mechanisms suited to different kinds of scientific work.

Traditional peer review remains useful for determining whether a proposal is rigorous and grounded in existing knowledge. But committees often struggle to distinguish exceptional ideas from merely good ones. Proposals that divide reviewers may be precisely those most likely to challenge an established field, yet consensus-driven panels favor work that everyone finds defensible.

One alternative is a “golden ticket,” which allows an individual reviewer to fund an unconventional proposal even when the rest of the panel is unpersuaded. Another is to support accomplished scientists for long periods rather than requiring them to specify each project in advance. The Howard Hughes Medical Institute has used this approach privately, while the NIH Director’s Pioneer Award has demonstrated that government can also fund people rather than proposals.

Other problems require different tools. Fast grants can provide modest funding before an opportunity disappears. Prizes can reward a clearly defined result without requiring the government to identify the likely winner beforehand. Regranting can place money in the hands of scientists close enough to the frontier to recognize talent before it becomes widely accepted. Independent research organizations can assemble permanent teams for projects too large for one university laboratory but too speculative for a commercial company.

Samir’s proposal for multiple DARPA-style agencies fits within this broader portfolio. DARPA gives individual program managers substantial authority to pursue ambitious objectives, assemble teams, and terminate projects that fail. Its success comes not from eliminating accountability, but from placing responsibility clearly on identifiable decision-makers rather than diffusing it across committees.

The common principle is not that peer review has failed. It is that no single mechanism should carry nearly the entire burden of deciding which discoveries deserve support.

Reform or Indiscriminate Cutting?

The administration’s actions must now be judged against the quality of its proposals. Friedberg estimated that approximately $3 billion remained unspent on frozen or terminated research grants. For perspective, that equals about 6% of the NIH’s roughly $47 billion annual budget, although the comparison is imperfect: the $3 billion apparently includes remaining balances from both NIH and NSF grants rather than one year’s spending by a single agency.

The more important question is what happened to the money. Was it permanently eliminated, temporarily withheld, or redirected toward the more promising mechanisms described in the report? Were individual projects evaluated on their scientific merits? Did the disruption produce better research, or did it interrupt existing work before any replacement was ready?

A reform agenda based on experimentation should be able to answer those questions. It must also account for consequences beyond the dollars withheld, including whether the manner in which grants were frozen or terminated created avoidable uncertainty and encouraged valuable scientists to leave the country. Without clear objectives, transparent criteria, and measurable results, even an intellectually serious reform risks becoming indistinguishable from indiscriminate cutting.

Make the Funder Scientific

The report’s most important proposal may be its least dramatic: every major funding agency should establish a metascience unit to study how science itself is funded. Different agencies could test peer review against golden tickets, fast grants, prizes, regranting, and long-term support for individual researchers. The results should be published, compared, and used to redirect money toward the approaches that work best.

A decade ago, Cowen and Tabarrok could not identify a serious NSF study determining which of its previous economics grants had produced the greatest marginal value. An institution devoted to advancing knowledge should be equally committed to learning from its own decisions.

No evaluation system will reveal every discovery that might have occurred or prevent politics from influencing national priorities. But it can force funding agencies, and the administrations overseeing them, to define their objectives, examine results, and change course when the evidence demands it.

Scientists must be free to undertake experiments that fail. Funding agencies must be required to undertake experiments from which they learn. The government should apply the scientific method not only to the research it funds, but to the institutions distributing the money and its own exercise of political power.

America’s scientific establishment needs disruption. Whether this moment produces renewal or decline will depend on whether we disrupt the institutions that resist learning—or the scientists whose discoveries make future prosperity possible.

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• Foundation.xyz: When it comes to protecting your Bitcoin, you shouldn’t have to simply trust the company that made your hardware wallet.

  Foundation believes in verifiability over reputation. Its firmware and apps are open source, its builds are reproducible, and both Passport Core and Passport Prime have undergone independent hardware security audits. The point isn’t simply that Foundation says its products are secure. It’s that you can verify that for yourself.

  Foundation is also expanding what users can control:

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  • Users can now add their own entropy. The latest Envoy and KeyOS releases allow users to introduce their own randomness when creating a seed, reducing reliance on the device itself to generate it.

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We also discuss Tim's own unlikely path into the industry: starting a podcast almost by accident, building 21-hour digital conferences, launching an in-person event without a budget, and learning his way into a business as the industry itself was being created.

Finally, we look ahead at MSCI, the risks facing Bitcoin treasury investors, the potential evolution of these companies into major financial institutions, and why curiosity and adaptability may be the real competitive advantages in a rapidly changing market.

Tim Kotzman: Bitcoin Treasuries Podcast / Bitcoin Treasuries Media

Subscribe to Exploring Prosperity for conversations and essays about Bitcoin, markets, economics, technology and human progress.

Thank you very much to our sponsors:

• Foundation.xyz: When it comes to protecting your Bitcoin, you shouldn’t have to simply trust the company that made your hardware wallet.

  Foundation believes in verifiability over reputation. Its firmware and apps are open source, its builds are reproducible, and both Passport Core and Passport Prime have undergone independent hardware security audits. The point isn’t simply that Foundation says its products are secure. It’s that you can verify that for yourself.

  Foundation is also expanding what users can control:

  • Passport Prime goes beyond Bitcoin. It can manage passwords and two-factor authentication codes, serve as a FIDO2 security key, and provide encrypted storage—all on a device you own and control. Foundation also plans to enable app sideloading, opening Prime to applications from independent developers.

  • Users can now add their own entropy. The latest Envoy and KeyOS releases allow users to introduce their own randomness when creating a seed, reducing reliance on the device itself to generate it.

  Learn more about Foundation → https://foundation.xyz/

• River Financial: If not cold storage for your Bitcoin, I’d highly recommend River for buying and holding- River is engineered to protect your Bitcoin over the long run. Use this link for discounts: https://river.com/signup?r=T7GGAF7G

Bitcoin treasury companies barely existed two years ago. has spent that time talking to the people building them.

He started by accident. As Bitcoin and MicroStrategy were rising in late 2024, Tim was working near a studio and the producer asked him to interview someone. That led to another and another. The producer asked what he wanted to call his show. “What show?” But soon he was interviewing executives and analysts simply because he wanted to understand what was happening.

He kept going. As Strategy’s success inspired dozens of imitators, Tim interviewed the current CEO Phong Le for his first-ever in-person interview, each of the executives launching the other firms, as well as the analysts studying them and the investors financing them. He watched a bull market make every new entrant a genius. But then a bear market began separating the experiments that worked from those that didn’t.

Along the way, Tim converted an afterthought activity into a remarkably valuable seat for himself: a front-row view of an industry being invented in real time.

Beyond Buying Bitcoin

Strategy proved that a public company could make accumulating Bitcoin the centerpiece of its capital strategy. But after watching the industry develop, Tim thinks 2026 may be remembered as “the year of digital credit” he told me. “Digital credit is the most important innovation in the Bitcoin treasury space, period.”

The idea is to raise capital from an entirely different class of investor. Instead of selling common stock to someone seeking Bitcoin’s upside, companies can issue preferred securities paying yields of 12 or 13 percent to investors seeking income. The company then uses that capital to buy more Bitcoin. One investor gets the yield he wants; another retains the leveraged exposure to Bitcoin he wants; and the company becomes the intermediary between them. The “model” works spectacularly if Bitcoin’s appreciation over time substantially exceeds the cost of that capital.

That evolution turns the Bitcoin treasury company into something far more interesting than a corporate holder of Bitcoin. Capital markets themselves begin to become the business.

The 30% Hurdle

Suppose Bitcoin appreciates 30 percent a year (it is at 36% since Aug 10, 2020). Tim posed a simple question: “If we walked out the studio door and started an operating business with the goal of earning 30 percent annually, how hard would that be?” How much capital would it take? How long would it take to get there? And even if we succeeded, how difficult would it be to sustain those returns?

That creates an unusual problem for a Bitcoin treasury company. Every use of its capital has to compete with an “expected” return from simply buying more Bitcoin. A very good operating business might not be good enough. Even among the extraordinary companies shown below, only Nvidia exceeded Bitcoin’s return over the past six years. Six years is a short window for judging long-term returns, but long enough to illustrate how high a 30 percent hurdle really is.

If Bitcoin continues to reach new highs over the long run, the economics can be extraordinary. The Bitcoin treasury industry has already raised tens of billions of dollars of capital and credit, demonstrating substantial demand for securities built around the belief in long term Bitcoin appreciation.

The Bear Market Test

The first real test came when the market turned. Falling Bitcoin prices compressed the premiums many treasury companies had enjoyed, made raising new capital less attractive and forced management teams to make choices they hadn’t faced during the ascent.

Tim calls it an “incredibly insightful” and constructive bear market. Companies that had looked similar began responding differently. Some sold Bitcoin. Others changed their strategies. Financing became more difficult and investors seemed to all but stop rewarding companies simply for owning Bitcoin.

The downturn forced these companies to demonstrate what their strategies could accomplish when Bitcoin wasn’t doing the work for them. The bull market accelerated the Bitcoin treasury industry. The bear market began testing which ideas within it might endure and it provided an important test for digital credit. Financing didn’t disappear; it repriced. Yields of 12 or 13 percent impose a real cost that issuers must overcome, but demand for these securities has persisted even as Bitcoin fell. Digital credit built around Bitcoin remains a rounding error beside credit markets measured in the tens of trillions. If Bitcoin continues to prove itself as reliable, appreciating collateral, there is plenty of room for this market to grow.

Why Retail Distribution Mattered

The early Bitcoin treasury companies could not rely primarily on institutional investors. Institutional investors require infrastructure, regulatory clarity and established ways to evaluate the risks of an emerging asset. Retail investors face none of those constraints. They were willing to tolerate Bitcoin’s volatility in pursuit of higher returns.

That made access to retail investors unusually valuable. Tim pointed to the prominence of Bitcoin personalities such as Jack Mallers, Dylan Leclair, and Anthony Pompliano (among others) in launching treasury companies. Their large audiences weren’t just useful for publicity. They provided direct distribution to investors who already understood the Bitcoin thesis.

As Tim put it, “We’re in an attention economy.” For an emerging industry trying to raise capital before many institutions were ready to participate, attention became part of the business model.

Walk a Mile, See a Mile

While Tim was watching a new industry develop, he was also building a business around it. Interviews led to a podcast, which led to an online conference. His first lasted 21 hours, 21 minutes and 21 seconds, with 46 interviews. Sponsors appeared, relationships grew and online conferences eventually led to live ones.

None of this was the original plan. Tim describes his approach as “walk a mile, see a mile.” Each step revealed an opportunity he couldn’t have seen from the one before it.

There is a broader lesson in his experience. We can identify the businesses and jobs that technological change threatens because they already exist. It is much harder to imagine the ones that will be created around industries that don’t exist yet. Tim didn’t set out to occupy his current seat. The seat was being created at the same time he was finding it.

Thank you very much to our sponsors:

• Foundation.xyz: When it comes to protecting your Bitcoin, you shouldn’t have to simply trust the company that made your hardware wallet.

  Foundation believes in verifiability over reputation. Its firmware and apps are open source, its builds are reproducible, and both Passport Core and Passport Prime have undergone independent hardware security audits. The point isn’t simply that Foundation says its products are secure. It’s that you can verify that for yourself.

  Foundation is also expanding what users can control:

  • Passport Prime goes beyond Bitcoin. It can manage passwords and two-factor authentication codes, serve as a FIDO2 security key, and provide encrypted storage—all on a device you own and control. Foundation also plans to enable app sideloading, opening Prime to applications from independent developers.

  • Users can now add their own entropy. The latest Envoy and KeyOS releases allow users to introduce their own randomness when creating a seed, reducing reliance on the device itself to generate it.

  Learn more about Foundation → https://foundation.xyz/

• River Financial: If not cold storage for your Bitcoin, I’d highly recommend River for buying and holding- River is engineered to protect your Bitcoin over the long run. Use this link for discounts: https://river.com/signup?r=T7GGAF7G

I have been revisiting some of my earlier work on the American university system, prompted by two recently published surveys and a small-group discussion on AI and education that Dartmouth professor of mathematics and computer science Daniel Rockmore and I led last week.

In a 2025 Pew survey, 70 percent of Americans said the higher education system was headed in the wrong direction, up from 56 percent in 2020.

Then, in June 2026, Gallup found that only 38 percent expressed strong confidence in higher education, down from 57 percent in 2015 and 42 percent in 2025.

Yet Americans have not rejected education itself. 71% percent of graduates say their education was worth the cost, and 93 percent would choose to earn a degree again.

Taken together, the surveys suggest a widening divide between confidence in education and confidence in the institutions providing it. AI could widen that divide or force universities to demonstrate more clearly what an education is for.

Our recent discussion examined how artificial intelligence is challenging one of the university’s oldest functions: the delivery of knowledge. AI can explain difficult material, personalize instruction, and help people acquire skills at negligible cost. It may also enable new educational ventures to compete with parts of the university system. Listening again to my March 2025 conversation with Pano Kanelos, then president of the newly founded University of Austin, I was struck by how directly his diagnosis anticipated the pressures AI is now intensifying. AI appeared briefly in that interview, although its implications for teaching and learning were not central to our discussion. Beneath our conversation about cost, conformity, and curriculum was a question that has since become more urgent: What should a university produce that technology alone cannot?

Kanelos identified three crises in higher education. The first was a crisis of principles. Universities cannot pursue truth without open inquiry, civil discourse, and freedom of conscience, yet too many have allowed intellectual conformity to displace serious disagreement. The second was financial. Costs have risen while families have become less certain that a degree justifies the burden. The third was curricular. The structure of majors, departments, and academic specialization was designed more than a century ago for a world that no longer exists. AI sharpens each problem. Generated information increases the need for intellectual independence, inexpensive alternatives place additional pressure on the value of a costly degree, and accelerating changes in work expose the limits of curricula organized around fixed occupational skills. Kanelos and his colleagues eventually concluded that building a new university was easier than changing an established one. “That says everything you need to know about existing universities,” he told me, because starting one from the ground up is hardly easy.

Describing the conditions that led him to help found UATX, Kanelos said he had seen students and faculty increasingly “sleepwalking through this thing we call education.” In an intellectually homogeneous environment, students encounter fewer challenges to their assumptions and become more attentive to the answers expected of them. He compared the mind to a muscle, strengthened by tension, opposition, and stress. “A truly successful class is when you begin the class with a set of assumptions and end the class with a different set of assumptions,” he said. “That’s learning.” His conception of education centered on developing prudence, wisdom, judgment, and discernment, capacities that allow someone to continue learning as circumstances change. Rockmore and I returned repeatedly to curiosity as the quality that leads a student to question an answer, follow an unexpected connection, and continue beyond the first plausible explanation. AI gives students extraordinary tools for doing all three. It also makes avoiding the intellectual effort through which judgment develops far easier.

At the time of our conversation, UATX had organized its curriculum around these ideas. Students began with a common intellectual foundation, examining enduring questions across literature, philosophy, science, mathematics, politics, and history. A four-year Polaris Project gave each student a larger purpose toward which that learning could be directed. One student wanted to teach 100,000 people to read ancient Greek. Pursuing that goal required her to study teaching, technology, communication, organization, and ways of measuring whether she was succeeding. The educational value lay in building, failing, adjusting, and trying again. The project gave her education a direction that accumulating credits alone could not provide.

Rockmore described AI as a “gut-check moment” for higher education, a time for universities to ask, “What are we doing here?” As technology assumes more of the work involved in delivering information and producing answers, institutions will have to clarify which parts of education remain essential and how those purposes should be served. My prediction is that this reconsideration will lead to a proliferation of experimentation and entrepreneurship in education. AI lowers the cost of instruction, tutoring, assessment, and curriculum design, allowing new ventures to compete with individual university functions without recreating the enormous structure of a university.

New ventures will drive some of this change, and established universities can drive it as well. Entrepreneurship is as relevant to institutions as it is to startups. Universities that experiment with how they teach and undertake a disciplined examination of the value they provide may emerge stronger. They retain considerable advantages, including faculty expertise, research facilities, recognized credentials, alumni networks, and physical communities. AI can help them strengthen those advantages by freeing faculty to devote more attention to mentorship, intellectual challenge, and shared inquiry.

Universities that resist this examination may find the education system reconfiguring around them more quickly than they expect. Kanelos hoped UATX would inspire “a hundred new universities,” each approaching education differently. AI may now provide both the pressure and the means to create them.

Note: Pano Kanelos later left the presidency of UATX, which has since changed leadership. Its current direction lies beyond the scope of this essay.

Thank you very much to our sponsors:

• Foundation.xyz: When it comes to protecting your Bitcoin, you shouldn’t have to simply trust the company that made your hardware wallet.

  Foundation believes in verifiability over reputation. Its firmware and apps are open source, its builds are reproducible, and both Passport Core and Passport Prime have undergone independent hardware security audits. The point isn’t simply that Foundation says its products are secure. It’s that you can verify that for yourself.

  Foundation is also expanding what users can control:

  • Passport Prime goes beyond Bitcoin. It can manage passwords and two-factor authentication codes, serve as a FIDO2 security key, and provide encrypted storage—all on a device you own and control. Foundation also plans to enable app sideloading, opening Prime to applications from independent developers.

  • Users can now add their own entropy. The latest Envoy and KeyOS releases allow users to introduce their own randomness when creating a seed, reducing reliance on the device itself to generate it.

  Learn more about Foundation → https://foundation.xyz/

• River Financial: If not cold storage for your Bitcoin, I’d highly recommend River for buying and holding- River is engineered to protect your Bitcoin over the long run. Use this link for discounts: https://river.com/signup?r=T7GGAF7G

Last week, Representative Greg Casar of Texas introduced legislation intended to protect American workers from what he describes as the threat of mass unemployment caused by artificial intelligence. The proposal would tax certain uses of AI associated with job losses and use the proceeds to support employment. The premise reflects an increasingly common fear: if machines can perform more of the work people do today, there may eventually be far less work left for people to do.

There is plenty of reason to take that possibility seriously. AI is already performing tasks that recently required highly educated professionals. But legislation designed to protect existing jobs raises a more difficult economic question: does identifying the work AI can eliminate tell us very much about how much work people will ultimately have?

A View From Inside the Disruption

Stephen Lofchie has an unusually informed perspective on that question. One of the country’s leading lawyers in financial-services regulation, he has spent decades advising businesses operating in some of the most complicated areas of law. He also concluded years ago that much legal work was unnecessarily inefficient, began building his own regulatory databases, and eventually saw AI become capable enough that he helped create Caffrey.ai, an AI company for legal and regulatory professionals. He continues to practice law while helping develop technology capable of performing some of the work lawyers have traditionally been paid to do.

Lofchie has no illusion that AI will preserve every legal task or job. Yet when I asked what the profession might look like in the future, his prediction was not that lawyers would have less to do. “I think lawyers will be doing actually more,” he said. Understanding how both things can be true may tell us more about the future of work than counting the jobs AI can replace.

When Cheaper Work Creates More Work

Much of law appears particularly susceptible to AI because the work is built around language. As Lofchie puts it, “It’s words on a page and words over the telephone.” Lawyers absorb documents, research how laws have been interpreted, apply them to facts, and communicate their conclusions. Judgment is required, but much of the work leading to it has traditionally been performed by junior lawyers. AI can now do much of that “extremely well.”

If fewer lawyers can produce the same amount of legal work, fewer lawyers would seem to be needed. What that arithmetic misses is that demand is not fixed. “I think the cost of producing good legal work will go down,” Lofchie says, and “as costs go down there’ll be a demand for more of it.”

Clip from the interview.

Ty Greenberg, Chief Revenue Officer of Caffrey, who is leading the firm’s go-to-market effort, points to AI, crypto, and prediction markets, where new forms of economic activity do not fit neatly within existing laws and regulations, creating novel questions for businesses, lawyers, and regulators.

None of this establishes that increased demand will employ as many lawyers as AI displaces, and Lofchie makes no such prediction. But productivity lowers costs, lower costs increase demand, and solving problems through innovation invariably creates new problems to solve. The amount of human labor required to perform today’s legal work therefore tells us surprisingly little about how much legal work there will be tomorrow.

The Businesses That Don’t Exist Yet

The larger economic effect may come from the new activity that cheaper legal expertise makes possible. Regulatory complexity imposes costs before a business ever earns a dollar. “Think about a new startup,” Lofchie says. Can it afford law-firm partners charging $2,000 an hour simply to determine how to launch within the law? He calls that expense “a tremendous drain on a new business and the possibility of a startup, or even a new desk at a big firm.”

That is part of the opportunity Lofchie saw in creating Caffrey, which combines AI with the regulatory databases and systems he began developing years earlier. Greenberg says customers may see a profitable new business opportunity but first need to determine how to pursue it within the relevant regulations. Reducing the cost of answering those questions makes more opportunities worth investigating.

The implications extend beyond law. Every business has ideas that are never pursued because the expected return does not justify the cost of investigating them. For an entrepreneur, lowering that hurdle can make a new company viable. For an established financial institution, it might mean testing a new trading desk or entering a new market.

We can count the work AI replaces, but we cannot count the trading desks that were never opened, the companies that were never started, or the ideas that were never tested because finding out whether they would work cost too much.

What AI Makes More Valuable

As AI takes over more routine legal work, the lawyer’s role shifts toward what the technology does less well. Lofchie says lawyers, particularly younger ones, tend to overestimate the “pure words on a page element of lawyering.” Clients need more than the right legal answer; they need someone who understands their business and can exercise judgment about what to do with it. Greenberg says that even as AI performs more research and document analysis, demand for senior legal expertise is rising. For “bet the company” litigation, he notes, clients never wanted a junior associate making the critical decisions—and they aren’t likely to turn them over to AI either. As intelligence becomes cheaper, judgment, experience and trust should become more valuable. The resulting work should be better as well, because AI can perform far more research and analysis within the economic limits of an assignment, leaving the lawyer to apply human judgment to a richer body of information.

Lofchie is dismissive of lawyers who resist AI because it sometimes hallucinates or makes mistakes. “You don’t think first and second year associates make mistakes?” he asks. The appropriate response in either case is verification. Paradoxically, Lofchie believes that may become more important as AI improves. When a tool produces consistently impressive work, the temptation to trust it grows, making the occasional error easier to miss. That leaves another important role for expertise. The lawyer may spend less time producing the initial analysis but still needs to know enough to recognize when an authoritative-sounding answer is wrong.

Lofchie believes AI will “really reward active learners.” A lawyer can use it to understand a client’s business more deeply, explore an unfamiliar area of law, or quickly learn how another jurisdiction approaches the same problem. The advantage extends well beyond law. When information becomes cheaper and easier to acquire, the value of what someone already knows should decline relative to the ability to learn, ask the right questions and apply new knowledge. AI will therefore favor the curious over the complacent. The most successful knowledge workers will not be those who use AI simply to perform today’s jobs more efficiently, but those who use it to expand the range of problems they can solve.

The political appeal of protecting workers from technological change is hardly new. Yet the United States has prospered in large part because it has repeatedly allowed innovation to disrupt existing ways of working and doing business. Maintaining that balance is particularly difficult with AI, whose effects may be unusually rapid and far-reaching. As Congress considers taxing companies for replacing workers with AI, it should remember that protecting today’s jobs can also prevent tomorrow’s from being created.

Full Interview.

BITCOIN TREASURIES CONFERENCE — NEW YORK CITY

Interested in Bitcoin treasury companies? Most of the executives and investors shaping this industry will be at the Bitcoin Treasuries Conference in New York on September 28. Use code BOBD10 for 10% off your registration at https://bitcointreasuries.net/conference#tickets

Thank you very much to our sponsors:

• A message from Foundation

  When it comes to protecting your Bitcoin, you shouldn’t have to simply trust the company that made your hardware wallet.

  Foundation believes in verifiability over reputation. Its firmware and apps are open source, its builds are reproducible, and both Passport Core and Passport Prime have undergone independent hardware security audits. The point isn’t simply that Foundation says its products are secure. It’s that you can verify that for yourself.

  Foundation is also expanding what users can control:

  • Passport Prime goes beyond Bitcoin. It can manage passwords and two-factor authentication codes, serve as a FIDO2 security key, and provide encrypted storage—all on a device you own and control. Foundation also plans to enable app sideloading, opening Prime to applications from independent developers.

  • Users can now add their own entropy. The latest Envoy and KeyOS releases allow users to introduce their own randomness when creating a seed, reducing reliance on the device itself to generate it.

  Learn more about Foundation → https://foundation.xyz/

• River Financial: If not cold storage for your Bitcoin, I’d highly recommend River for buying and holding- River is engineered to protect your Bitcoin over the long run. Use this link for discounts: https://river.com/signup?r=T7GGAF7G

brings together several of those disciplines into a different way of looking at the world.

“I’m basically interested in four subjects that are all interlinked in my head,” he told me. “Economics, physics, mathematics, and computer science.”

Four Subjects, One Lens

Whether discussing free will, artificial intelligence, capitalism, taxation, scientific research, or education, Varma approached each subject through the same interdisciplinary lens, asking whether an explanation remained consistent across disciplines and, ultimately, whether it survived contact with reality.

Varma traces that approach back to his childhood in India. He grew up wondering how a culture that celebrated wealth and entrepreneurship could also be one of the poorest countries in the world. The contradiction irritated him, and that irritation became a habit of mind.

“It’s all born out of irritation,” he said. “The irritation came from getting absurd answers to simple questions.” So he began asking why. That habit eventually became the organizing principle behind his writing as well as his book on free will, The Science of Free Will.

His standard for judging ideas is demanding. They should be tested against evidence, remain consistent with established knowledge, and explain more than the ideas they replace. As he put it, “The more radical the idea, the harder you really have to test it.”

That same standard leads him to distrust explanations that survive only within a single discipline. An economic theory should not contradict what we know about incentives. A theory of artificial intelligence should be consistent with physics and computer science. A philosophical claim should survive contact with mathematics. For Varma, understanding comes not from viewing a problem through one lens, but from seeing whether the same explanation continues to hold when examined from several directions at once.

Where Choice Comes From

His theory of free will illustrates the approach. Physics tells us that every particle in our bodies obeys the laws of nature. If those laws completely determine the future, where does choice come from?

His answer draws simultaneously from physics, computer science, and philosophy.

“The gap between the laws existing and making predictions from the laws,” he explained, “is the source of free will.”

The key idea is what computer scientist Stephen Wolfram calls computational irreducibility. Some systems are so complex that their future cannot be shortcut. Even if the underlying rules are completely deterministic, the only way to know what they will do is to let them unfold. Determinism, in other words, does not imply predictability.

Teaching Principles, Not Playing Whack-a-Mole

That conclusion extends naturally to artificial intelligence. If AI systems operate under the same physical laws and exhibit the same computational complexity, Varma argues there is no fundamental reason to believe they should be predictable either.

Importantly then, instead of trying to control increasingly capable AI by layering on more restrictions like whack-a-mole, he believes the better approach is to teach it guiding principles from the outset.

“You have to teach it principles,” he said. “The more you constrain a system, the less optimal it is.”

Incentives All the Way Down

The same framework shapes Varma’s views on economics and public policy. Rather than beginning with ideology, he begins with incentives.

“I think the biggest problem is that we don’t teach our students economics,” he said. “Do you really understand incentives? Incentives matter. All the rest is commentary.”

That perspective leads him to see a common thread running through many of today’s policy debates. Housing shortages, in his view, are less a failure of markets than of regulations that make building unnecessarily difficult. Slower economic growth reflects tax and regulatory systems that discourage investment and entrepreneurship. Political polarization stems from electoral rules that reward extremes instead of broad consensus. Even scientific progress depends on incentives: stable funding, intellectual freedom, and institutions that reward discovery over politics.

His views on taxation follow the same logic. Capital is not simply accumulated wealth. It is the stock of tools, equipment, and infrastructure that raises what each worker can produce. Taxing capital reduces the investment that builds this stock, and with it the living standards that follow. As he put it, “The more you tax capital, the less of it you have.”

The Long Game

Varma argues that many of the technologies that define modern life began as basic scientific research with no obvious commercial application. GPS, for example, depends on Einstein’s general theory of relativity, developed decades before anyone imagined satellite navigation. No private company could justify investing in research that might not pay off for seventy-five years. That, he believes, is one of government’s most important roles.

His concern is that the United States is beginning to weaken precisely the institutions that made it the world’s scientific leader. He points to funding uncertainty, political interference, and the loss of researchers to other countries as incentives that discourage discovery. Scientific progress, like economic growth, depends on institutions that reward long-term investment.

He sees this not simply as a question of research budgets but of national prosperity. Discoveries made today become the industries, companies, and technologies that raise living standards decades from now. Because those benefits often take decades to appear, they are easy for policymakers to undervalue.

That long-term perspective also shapes how he ranks America’s challenges. Rather than focusing primarily on more popular sources of pessimism and concern like healthcare or education, he believes artificial intelligence will improve both. The more important constraints, in his view, are housing and scientific research.

Many thinkers develop deep expertise within a single discipline. Varma connects disciplines that are usually treated separately and asks whether the same explanation survives each of them. As specialization continues to expand human knowledge, that kind of synthesis may become increasingly valuable. It is a perspective worth understanding.

Full interview.

Thank you very much to our sponsors:

• Foundation: Secure your digital life—from passwords, 2FA, files and Bitcoin with Passport Prime, an all-in-one personal security platform. Visit them at https://foundation.xyz/

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“Humanity is divine.”

That was Chris Sullivan’s answer when I asked how philosophy, political theory, monetary history, and spirituality shaped his worldview.

The answer also explains the tension running through our conversation. Chris has enormous faith in human creativity and very little faith in the institutions governing modern life. His argument is that those institutions have been distorted by the monetary system beneath them.

Most criticism of fiat money focuses on inflation. Chris’s critique is broader. Chris Sullivan’s critique begins much earlier. In his view, the modern central banking system is the root cause. Once money can be created without meaningful constraint, debt expands, incentives change, institutions adapt, and society gradually changes with them.

Debt makes political choices easier to postpone. Governments can expand programs without imposing the full cost through immediate taxation. Borrowing obscures tradeoffs and pushes obligations into the future. As Chris put it, “The ridiculousness and ludicrousness of the magnitude of debt ultimately only has one resolution.” Today's debt burden cannot realistically be repaid under the existing system. He envisions some form of global debt reset which he calls a debt jubilee followed by a monetary system no longer dependent on continual money creation. Bitcoin, in his view, is the leading candidate to provide that foundation.

His concern is not limited to the size of the debt. It is the behavior that develops around a system capable of producing it.

Government expands because financing is available. Bureaucracies survive without proving their value. Corporations become more focused on balance-sheet engineering. Capital flows toward businesses positioned to benefit from credit creation and government spending. Saving loses purchasing power while borrowing is repeatedly rescued.

Debt does more than finance activity. It changes which activities receive the greatest rewards. That distinction is central to Chris’s view of prosperity. Government spending may increase measured GDP, but he rejects the idea that spending itself creates wealth. “What they have created is concentration of wealth,” he said. In his framework, prosperity comes from entrepreneurship, competition, technological progress, and the productive use of capital.

This is also why Chris views Bitcoin as more than an investment. He first dismissed it as “magic internet money,” but later concluded that “Bitcoin could recapitalize the world to end the central banks.” He sees a fixed monetary supply, transparent settlement system, and asset outside political discretion as a possible foundation for a different incentive structure.

His conviction that the current system will imminently break is stronger than mine. Fiat systems can survive longer than their critics expect, especially when governments retain the ability to tax, regulate, borrow, and refinance. Collapse is not the only path to change. Monetary systems might actually be able to erode gradually as people adopt better alternatives, though that hasn’t typically happened with former global reserve currencies.

Chris nevertheless presents a serious question. If money influences incentives, and incentives influence institutions, then the quality of money may affect far more than prices or investment returns. It may shape the character of a society. While he worries about the correction process and the potential for revolutionary circumstances typical of fourth turnings, his conclusion is not as dark as much of his diagnosis.

“The great news is that we have immense prosperity and abundance already,” he said, and that if we do wind up suffering thrugh a transition period, what we emerge into will be a golden era. Human beings continue to build, invent, adapt, and solve problems even when institutions work against them.

Chris believes better money would release more of that potential. Whether Bitcoin ultimately plays the role he envisions, his underlying argument deserves consideration: prosperity depends not only on what people are capable of creating, but also on the incentives surrounding them.

Full interview.

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This week, two seemingly unrelated things intersected.

• A friend mentioned that his son decided he never wants to have children because he doesn’t want to bring them into a world like ours.

• ’s Substack arrived with the subject line, “A Philosopher’s One-Word Theory to Explain Why the World Feels So Weird.” It linked to his podcast conversation with University of Chicago philosopher , who is developing an idea she calls the “Unicontext.”

Thompson introduced the discussion by describing it as “a theory of everything that explains why people feel like everything sucks these days.”

That’s an ambitious description. The conversation lived up to it.

Callard’s central idea is the Unicontext. For most of human history, people lived in many different contexts. Home had its own norms. School had its own. Church, work, the neighborhood, and countless other communities all carried different expectations about how people should behave. Today, technology has increasingly merged those separate worlds into one enormous shared context. Much of modern life now takes place in a single global room where millions of people, ideas, tragedies, successes, arguments, and opinions compete for attention simultaneously.

The power of the idea lies in the range of questions it attempts to explain.

Why Bad News Travels Better Than Good News

Callard argues that goodness is often local while suffering is universal. One culture’s idea of a wonderful meal, a meaningful tradition, or a successful life may not translate easily to another. Violence, illness, grief, injustice, and death require no translation. In a global conversation, suffering becomes the language everyone understands. That helps explain why negative stories spread so easily and why they so often dominate public attention.

Why Identity Crowds Out Character

Character can only be revealed over time. Honesty, courage, generosity, and integrity become visible only after interacting with or knowing someone across many situations. Identity is immediately visible. In a world increasingly filled with strangers interacting across a single shared context, identity naturally becomes easier to discuss than character.

Why Comparison Never Ends

The Unicontext also helps explain why comparison has become so pervasive. When separate contexts merge, comparison becomes almost unavoidable. Callard illustrates this with the example of two school districts. As long as families attended their local schools, there was little reason to compare them. Once both schools become realistic options, everything starts to be measured: graduation rates, AP courses, college admissions, teacher quality, test scores. Rankings become increasingly important. Institutions begin optimizing for the same metrics, and over time they become more alike. The same dynamic extends well beyond education, encouraging businesses, careers, communities, and even people to converge toward whatever the world has decided to measure.

A Question Worth Exploring

One question remained after the conversation ended. If the Unicontext helps explain why humanity’s problems have become universally visible, does it also make humanity’s progress more difficult to recognize?

Progress usually arrives gradually. Tragedy is concentrated. It demands attention.

Perhaps the same framework also helps explain why genuine heroes seem harder to find. Progress is hard to see, and so, it seems, are the people driving it. There is no shortage of remarkable people accomplishing extraordinary things today, yet admiration often struggles to compete with comparison.

Back to My Friend’s Son

He isn’t wrong about what he sees. The suffering that reaches him is real. But the world he is judging isn’t the whole world. It is the Unicontext’s version of it, one that naturally amplifies the experiences every human being immediately understands: violence, injustice, illness, and death.

Progress spreads differently. It arrives quietly through millions of small advances and billions of everyday problems being solved: diseases eradicated, children surviving illnesses that once claimed countless lives, technologies that quietly improve everyday life, and acts of kindness that rarely extend beyond a family or neighborhood. Those stories simply don’t compete very well inside a global conversation where comparison and tragedy command our attention. That doesn’t mean the good isn’t there. It may simply mean it has become much harder to see.

Professor Callard’s theory offers a fascinating way to think about the modern world, and it raises questions that extend well beyond the conversation itself. For anyone interested in why modern life often feels so different from the world our parents experienced, Derek Thompson’s conversation with Agnes Callard is well worth an hour of your time.

Thank you very much to our sponsors:

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Thank you very much to our sponsors:

Foundation: Protect your Bitcoin using the latest off-line technology with easy-to-use Passport. Their next product, Passport Prime will protect all passwords off-line with even-better technology. https://foundation.xyz/

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https://river.com/signup?r=T7GGAF7G

Timestamps:
00:00 AI Isn't Thinking
02:00 Why AI Won't Replace People
03:08 AI Has No Motivation
04:53 Garbage In, Garbage Out
06:58 What Is Context Rot?
08:16 How Large Language Models Actually Work
10:23 Why AI Gives Different Answers
11:04 Why AI Hallucinates
12:40 Can You Trust AI?
14:18 Prediction vs. Reasoning
15:11 Why AI Only Sounds Smart
16:50 AI's Biggest Real-World Applications
18:04 The Spreadsheet Revolution Analogy
18:54 Will AI Replace Jobs?
20:31 Permanent Due Diligence
24:35 AI in Healthcare & Education
30:36 Personalized Education
32:24 How AI Could Improve Society

The Stress Test

For several uneasy weeks, the market appeared to deliver its verdict on one of the fastest-growing experiments in modern finance. Preferred securities issued by Bitcoin Treasury Companies traded sharply below their intended range.

Critics argued the business model had been exposed, and social media filled with predictions of Bitcoin’s demise. The question was no longer whether these companies represented financial innovation. It was whether the innovation had already failed.

I sat down with Strategy CEO Phong Le and Strive CEO Matt Cole just as both companies were responding to that turbulence. Both spoke candidly about what they had learned and how they intended to adapt. The more interesting story is how the market forced those conclusions in the first place.

Financial innovation rarely unfolds in a straight line. New products arrive with a set of assumptions, investors test those assumptions with real money, and management responds. Sometimes the process exposes a fatal flaw. Sometimes it produces a better product. Either way, markets discover information neither critics nor management possessed when the experiment began.

When Markets Disagree

One moment from our discussion captured that process well. Strategy had reduced its cash reserves to retire a significant amount of convertible debt, believing a stronger balance sheet compensated for the lower cash position. Investors saw it differently.

“We did not realize how important that U.S. dollar reserve was to our Stretch investors,” Le told me. Institutional investors, he said, placed considerably more value on readily available cash than management had expected, despite the company’s significant Bitcoin holdings.

Management believed one form of financial strength counted most. Investors preferred another. Neither side won the argument through debate. Prices settled it immediately. Strategy rebuilt its cash reserve to levels higher than before and earmarked those funds for dividend and interest obligations. Markets often become participants in product development, revealing assumptions that only real capital can test.

Innovation Happens in Public

Looking backward, successful innovations often appear inevitable. Looking forward, they almost never do. The first iPhone lacked an App Store. SpaceX reached orbit only after a series of visible failures. Early versions rarely survive unchanged, because markets reveal information designers could not fully anticipate.

Cole sees financial innovation through the same lens. “I don’t think it’s fair to expect V1 to be the best final form of innovation,” he said. “That is literally why innovation exists.” Products become durable, in his view, when companies absorb market feedback and rebuild around it.

Whether Bitcoin Treasury Companies eventually follow that path remains uncertain. The recent redesigns could prove to be meaningful improvements, or they could postpone larger problems. Markets have begun asking more useful questions than they were asking a month ago.

Looking Past the Labels

When I asked Le about the criticism that Bitcoin Treasury Companies are “merely financial engineering,” I expected him to distance himself from the phrase. Instead, he embraced it. “Financial engineering is the practical application of finance,” he said, comparing it to mortgage-backed securities, which connected homebuyers with investors seeking income. Like any engineering discipline, it can produce useful or harmful outcomes. The relevant question is whether it solved a real economic problem.

Cole reached a similar conclusion by way of Liar’s Poker, the book on the mortgage-backed securities market that shaped his early thinking. Financial structures create value, he said, by matching investors with different objectives. “If you are doing your job well in financial engineering, the sum of the parts are greater than the whole because you’re providing unique and valuable solutions to select investors.”

History offers reasons for caution too. Mortgage-backed securities became synonymous with the financial crisis once poor underwriting and excessive leverage overwhelmed them. Some innovations change markets for good. Others delay the recognition of risk.

Why Borrow? Why Lend?

The first two questions deserve particular attention. Why would a company borrow money to buy Bitcoin, and why would anyone provide the financing?

To many observers, the first question answers itself. Borrowing to buy a volatile asset sounds speculative. Yet borrowing to acquire assets expected to appreciate is one of the oldest activities in finance, from families buying homes to private equity firms acquiring companies. The calculation is the same each time: if the long-term return exceeds the cost of the financing, borrowing creates value. If not, it destroys value.

Le described Bitcoin Treasury Companies in similar terms. If Bitcoin is “a unique underlying asset that can appreciate thirty, forty percent a year,” he said, the next question is why public companies should not own it. Treasury Companies exist to provide exposure for investors who cannot or choose not to own Bitcoin directly.

The more interesting question may be why anyone would lend. The answer has less to do with enthusiasm for Bitcoin than in the diversity of capital markets. Pension funds, insurance companies, and endowments often operate under mandates restricting what they can own, some barred from Bitcoin directly, others required to hold publicly traded or income-producing securities.

As Cole noted, fixed-income investors ultimately care about total return, not whether that return comes through coupons, capital appreciation, or some combination of both. If Bitcoin-based securities eventually provide attractive risk-adjusted returns within those mandates, capital will continue flowing toward them. If they do not, it won’t. Markets will make that determination over time.

Bitcoin Doesn’t Need Them

The distinction between Bitcoin and the companies built around it is often overlooked. Critics frequently treat Bitcoin Treasury Companies as though they are essential to Bitcoin’s future. I asked both executives whether that premise was even true.

Neither executive hesitated. “No,” Le replied. “Does Bitcoin need us? No. Do our investors need us? Yes.” Companies like Strategy expand access for investors who otherwise could never own Bitcoin, because of mandates or regulatory constraints. Bitcoin would likely keep appreciating without them, though perhaps more slowly.

Cole answered from a different angle, “I don’t think Bitcoin needs the United States to have a strategic Bitcoin reserve. I think the United States would be stronger if it had a strategic Bitcoin reserve. And that similar principle applies to corporations.” Treasury Companies may strengthen Bitcoin’s adoption, but that differs from claiming Bitcoin depends on them.

Much of the public debate treats Bitcoin and Bitcoin Treasury Companies as though they succeed or fail together. These companies are wagering on Bitcoin’s future. They are not deciding it. If their assumptions prove correct, they may create substantial value for shareholders. If wrong, the companies will struggle regardless of how innovative their capital structures may be.

Time Is the Judge

The recent turbulence may eventually be remembered as a volatile chapter in the early history of a new asset class, or as the first sign that the business model contains weaknesses that grow more apparent over time. Neither conclusion is available yet.

What the episode has already revealed is how financial innovation evolves. Management teams begin with assumptions, investors test those assumptions with real capital. and prices reveal where expectations differ. Companies adjust, investors respond again, and the cycle repeats until markets settle whether a product deserves wider acceptance or a place among the ideas that failed to endure. That process is usually slower than either enthusiasts or critics prefer and Bitcoin Treasury Companies have not reached the end of it.

Only time will say which one Bitcoin Treasury Companies turn out to be.

Here’s the full interview.

Thank you very much to our sponsors:

• Foundation: Secure your digital life—from passwords, 2FA, files and Bitcoin with Passport Prime, an all-in-one personal security platform. Visit them at https://foundation.xyz/

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Read more

Thank you very much to our sponsors:

• Foundation: Secure your digital life—from passwords, 2FA, files and Bitcoin with Passport Prime, an all-in-one personal security platform. Visit them at

https://foundation.xyz/

• River Financial: If not cold storage for your Bitcoin, I’d highly recommend River for buying and holding- River is engineered to protect your Bitcoin over the long run. Use this link for discounts: https://river.com/signup?r=T7GGAF7G
  
  Timestamps:
  00:00 Introduction: Did We Solve the Wrong Problem?
  00:24 The Factory Model of Education
  01:07 Why Schools Were Designed This Way
  01:43 The Tradeoffs of Standardization
  02:26 The Problem of Passenger Learners
  02:39 Curiosity Is the Engine of Learning
  03:36 The Personalization Problem
  03:50 How AI Changes the Equation
  05:07 The Human Side of Education
  06:07 Unlocking Human Potential at Scale

This week, I wanted to recommend 's latest essay. It represents something increasingly rare in modern political debates: testimony from someone who has actually lived inside the alternative system.

Much of our political discourse consists of competing theories about how societies should work. Varma writes from experience instead. Having grown up in India during the final decades of the License Raj, he watched firsthand what happens when political systems place themselves between individuals and their ability to build, trade, compete, and adapt.

His conclusion is straightforward: prosperity arrived when institutions stopped obstructing the ingenuity that was already there. The essay is also a reminder that political systems often overestimate their ability to create prosperity and underestimate their ability to impede it.

That observation connects directly with something fundamental. Human ingenuity is the ultimate source of prosperity. The role of institutions is not to direct it but to create an environment in which it can operate with few constraints.

Markets, entrepreneurship, and competition then perform another essential function. They provide feedback. Entrepreneurs test ideas against reality. Investors allocate capital toward solutions and away from mistakes. Businesses survive or disappear based on whether they create value for others.

Unlike politics, which can postpone accountability for surprisingly long periods of time, markets eventually force a reckoning with reality.

Which leads to perhaps the most important lesson in the essay: arithmetic always wins. Incentives are real. Trade-offs are unavoidable. Economic laws can be ignored politically for a period of time but they cannot be suspended indefinitely.

The essay is here:

Samir Varma

ARGH!

I grew up in a country that decided, as a matter of high-minded national policy, that prosperity was vulgar…

Read more

2 months ago · 9 likes · 6 comments · Samir Varma

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A recent essay by Lyn Alden explored how humanity became prosperous through the interaction of energy, technology, institutions, markets, and information. One section focused on a challenge that many people already sense intuitively: advances in communication technology expanded humanity's shared reality, while artificial intelligence is making authentic information increasingly difficult to distinguish from synthetic information.

The essay caught my attention and a few days later Lyn was able to join me on Exploring Prosperity to discuss the implcations. One theme ran through both the essay and our conversation: what happens when confidence in information begins to erode?

Source: Lyn Alden https://www.lynalden.com/june-2026-newsletter/?mc_cid=2145880acd&mc_eid=e505233e70

The chart clarifies the concern. For most of human history, information remained local. People generally knew what was happening in their village or region and little beyond it. Over time, communication technologies expanded the radius of information and helped create a broader shared reality. Artificial intelligence introduces a new challenge. The cost of producing information is approaching zero while the cost of determining authenticity is high when it is even possible.

If this is so, how do societies determine what is true in the first place?

Science is one answer. Journalism, education, and democratic institutions are others. Yet prosperous societies rely on additional truth-determining systems that receive far less attention. Entrepreneurs, businesses, and markets spend every day testing ideas against reality.

An entrepreneur resembles a scientist more than many people realize. Both begin with a hypothesis, but neither knows the answer with certainty. The scientist conducts experiments and gathers evidence. The entrepreneur receives feedback through customer behavior, revenues, costs, and competition. In both cases, information is generated through testing. What was previously unknown becomes known.

This process sits at the heart of rising living standards. Prosperous societies do not simply accumulate knowledge. They create systems that continuously generate new knowledge by forcing ideas to confront reality.

Markets play a central role in that process. Product markets reveal whether customers value a solution. Financial markets help determine whether additional resources should be devoted to expanding it. One evaluates usefulness. The other evaluates scale. Together they form a decentralized problem-solving system that continuously tests ideas and allocates resources based on the results.

What makes these systems so powerful is that they do not require universal agreement. Customers and entrepreneurs may disagree about politics, culture, or current events. Investors and founders may consume entirely different sources of information. The system functions because participants can rely on feedback. Customers buy or they don’t. Revenues exceed costs or they don’t. Investments succeed or fail. Reality remains the scorecard.

Trust has always played an important role in this process. In many ways, the history of prosperity can be viewed as the expansion of trust. Human cooperation expanded from family to tribe, from village to city, from nation to global commerce. Markets, contracts, money, brands, and institutions allowed people to cooperate successfully with strangers. As the radius of trust expanded, so did the scale of human problem solving.

Economic historians have pointed to numerous examples. The Dutch Republic developed commercial and financial institutions that allowed merchants to transact across vast distances with greater confidence, helping fuel one of history’s great commercial expansions. Later, England’s institutional reforms following the Glorious Revolution strengthened property rights and confidence in long term investment, helping lay part of the foundation for the economic transformation that followed. More recently, Argentina’s recurring inflation and policy instability illustrate the opposite dynamic. The challenge was not simply declining trust in government. Money became a less reliable measuring stick. Long term planning became more difficult. Investment horizons shortened. Signals that businesses and investors relied upon became less dependable.12

The common thread across these examples is that trust improves the quality of feedback.

When people have confidence in contracts, property rights, money, and institutions, the signals they receive become more reliable. Better signals lead to better decisions. Better decisions contribute to higher living standards.

This brings us back to the challenge Lyn identified. Shared reality has immense value. Scientific discoveries spread more effectively when people trust the institutions that produce them. Markets function more efficiently when participants have confidence in the information they receive. Trust in information supports many of the systems that modern societies depend upon.

Entrepreneurship offers a useful perspective on this challenge. Entrepreneurs profit by identifying problems and creating solutions. If declining confidence in information becomes one of the defining challenges of the AI era, increasing resources will likely be devoted toward solving it. This would hardly be unprecedented. Banks helped people trust transactions. Brands helped consumers trust products. Auditors helped investors trust financial statements. Entire industries emerged because trust created economic value. In many respects, they became part of society's trust infrastructure.

During our discussion, Lyn pointed to Guatemala’s national election, where records were timestamped on the Bitcoin blockchain to create a verifiable record that could not be altered after the fact. It is an early example of how technologies designed to establish authenticity and provenance may become increasingly valuable in a digital world. If confidence in information continues to erode, products and services that help establish authenticity, credibility, and trust may become some of the most valuable offerings of the AI era.

None of this eliminates the challenge Lyn identified. A broadly shared reality remains enormously valuable, and technological solutions may never fully replace it. Yet the history of prosperity suggests that societies do not merely react to new problems. They develop institutions, technologies, and businesses that attempt to solve them. If confidence in information becomes scarcer, the same entrepreneurial system that has helped humanity solve countless other problems may increasingly be directed toward restoring it.

Prosperity has always depended on our ability to determine what is true. The next chapter may depend on how effectively we preserve the feedback systems that allow us to recognize it.

Full interview here:

Thank you very much to our sponsors:

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Is the future really getting worse? Let's find out.

A newsletter about what thought-leaders have to say about our American condition and whether we should be so pessimistic about the future.

By Bob Dewey

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https://river.com/signup?r=T7GGAF7G

Timestamps:
00:00 Introduction: The Shared Reality Problem- HIghlights
01:20 How Technology Changed Shared Reality
02:40 AI, Deepfakes, and the Verification Crisis
04:20 Will We Retreat to Local Sources of Truth?
05:40 Social Media Algorithms and Attention Economics
09:10 Sponsor Break
09:50 Can Bitcoin Help Verify Reality?
14:40 Hunter Gatherers, Agriculture, and Human Progress
19:00 Markets, Entrepreneurship, and Problem Solving
21:20 Debt, Governments, and Fiscal Dominance
24:00 Why Stocks Keep Rising Despite Growing Pessimism
27:40 Demographics, Deficits, and the K-Shaped Economy
30:20 Ray Dalio, Empires, and Avoiding Conflict
33:30 How Lyn Alden Is Positioning Her Portfolio
34:50 Why the Traditional 60/40 Portfolio May Be Changing
38:10 Orange Juice: Lyn's New Investment Venture
40:10 Writing Fiction, Building Ideas, and Long-Term Thinking
41:30 Closing Thoughts

The Hidden Phase of Progress

Every improvement in living standards begins as an idea. It may start with a scientist discovering a principle, an engineer developing a technology, or an entrepreneur launching a company. These moments attract most of our attention because they are easy to identify and easy to celebrate.

The larger challenge comes later. A technology does not transform society simply because it exists. Progress occurs when new ideas move from the margins into the systems that millions of people rely upon every day. That process unfolds through experimentation, investment, education, and implementation. Most of that work happens inside institutions.

This is one reason I was interested in speaking with Amy Oldenburg, Morgan Stanley’s Head of Digital Asset Strategy. At first glance, our conversation appeared to be about digital assets. By the end, I found myself thinking about a much broader question. How does a large institution learn?

Morgan Stanley manages trillions of dollars for clients around the world. Its reputation rests on trust. Clients expect systems to function. Regulators expect controls to work. Shareholders expect risks to be understood.

When an institution like Morgan Stanley begins exploring a new technology, the decision extends beyond opportunity. It becomes a question of responsibility.

An Unlikely Change Agent

Oldenburg occupies an unusual position in that process. Every major technological transition requires people who can operate in two worlds at once. They understand the emerging technology, but they also understand the institutions that must eventually decide whether to adopt it. Those people rarely receive much attention, yet they often determine whether innovation remains at the margins or becomes part of everyday life.

Oldenburg has spent much of her career in exactly that role. As a young professional she moved to San Francisco in 1999 and landed in the middle of the internet boom. She watched enthusiasm build around a transformative technology and then witnessed the collapse that followed. The experience left a lasting impression. New technologies can reshape entire industries, yet the path is rarely smooth.

After joining Morgan Stanley, she built her career investing in emerging markets. There she encountered another recurring theme. Countries with limited infrastructure often skipped directly to newer technologies. Fixed-line telecommunications gave way to mobile networks. Financial systems evolved differently depending on local conditions.

In some countries, preserving wealth and moving capital were daily realities. When Bitcoin emerged, some of the early buyers were people she knew through relationships she had built in those markets. They viewed it through a practical lens. They were interested in alternatives, optionality, and resilience.

For Oldenburg, digital assets fit naturally into questions she had already been asking throughout her career. That curiosity eventually led her to write a graduate paper called The Innovation Firewall.

The Innovation Firewall

Most people assume large organizations struggle with innovation because leaders are resistant to change. Oldenburg’s explanation was different.

Institutions are designed to produce reliable outcomes. Over time they build processes, governance structures, operating procedures, and risk controls that reduce the probability of failure. Those systems become increasingly important as organizations grow.

The result is a paradox. Trustworthy institutions accumulate processes and operating procedures over time. Changing any one part of the system becomes increasingly difficult because it is connected to so many others. The question is rarely whether a new technology is helpful. The question is whether it has advanced far enough to justify changing systems that already work.

During our conversation, Oldenburg pointed to the years following the Global Financial Crisis as an example. The financial industry has spent nearly two decades strengthening stability and resilience. Institutions invested heavily in controls, oversight, and operational robustness. Those investments were necessary, but they also increased the complexity of introducing something new.

This helps explain why technological adoption often appears slower from the outside than it feels on the inside. The challenge is deciding when it can be trusted.

What Institutional Grade Really Means

Oldenburg illustrated this distinction through a simple analogy. A local butcher can focus on a single product and optimize around it. Morgan Stanley operates more like Amazon.

Her point was that “institutional grade” is not a fixed standard. The standard rises when an institution begins depending on the technology itself. Once a technology becomes part of the infrastructure clients rely upon every day, the requirements change.

The Mobile Phone Lesson

When I asked Oldenburg where she saw the greatest opportunities emerging, she returned to an analogy from her own career.

She grew up in a world of fixed-line telephones. One phone sat in the house and everyone shared it. The monthly cost was modest. Over time, new features arrived. Call Waiting and Caller ID felt innovative. Each improvement appeared incremental and cost a little more.

Then mobile phones arrived. The first generation of devices did not reveal where the technology was heading. PalmPilots helped people organize information. BlackBerrys brought mobile communication to professionals. Early smartphones combined several existing functions into a single device.

The real transformation came later. Nobody predicted that the App Store would create entirely new industries. Nobody anticipated that phones would become cameras, navigation systems, payment platforms, entertainment centers, and business tools all at once. Today monthly costs are far higher than they once were, yet access to much of the world’s information sits in our pocket. The infrastructure came first. The broader opportunity emerged afterward.

That lesson shaped much of Oldenburg’s thinking about digital assets. The most important applications may not yet exist. The institutions investing time and resources today are not necessarily building toward a known destination. They are developing the capabilities needed to participate in a future that remains difficult to fully visualize.

This may be the hardest part of technological change inside a large institution. Leaders rarely receive certainty before they must act. By the time every opportunity is obvious, the opportunity itself is usually well understood by everyone else. The challenge is deciding when a technology has matured enough to justify investment even though its most important applications may still lie ahead.

Building Before You Know the Destination

Oldenburg maintained a measured approach and avoided sweeping predictions. When I asked whether tokenization would lower costs across the financial system, she did not offer a simple answer. Some costs will decline. New capabilities will emerge. Client expectations will change. Activity levels will increase. Entirely new services will become possible.

Those outcomes seem almost inevitable when a new technological foundation is introduced. What remains uncertain is where the value will ultimately accrue. As Oldenburg’s telecommunications example suggests, new capabilities often create opportunities that are difficult to foresee at the outset.

The areas attracting attention range from tokenized assets and private markets to collateral mobility and financial systems that could eventually operate around the clock rather than only during traditional market hours.

Oldenburg repeatedly returned to a similar conclusion. Institutions need to begin building. They need to learn. They need to experiment. Waiting for complete certainty is rarely a successful strategy when technologies are evolving rapidly.

The People Who Translate the Future

Progress is often described through the stories of inventors and entrepreneurs. Those stories deserve their place. Yet every transformative technology eventually enters a different phase. Someone must determine how the innovation fits into the systems that society already depends upon.

This is the role of the translator.

Amy Oldenburg’s role at Morgan Stanley provides a window into that process. She understands the emerging technology. She understands the institution. Her responsibility is helping each side understand the other.

Most technological advances do not require organizations to rethink how they operate. Digital assets may prove different. The potential implications extend beyond a new product or a new software tool. They reach into questions of market structure, settlement, collateral, private markets, and the movement of value itself.

No one knows exactly how that future will unfold. What is already clear is that institutions are investing time and resources to prepare for possibilities that may take years to fully emerge.

Every major technological transition creates inventors, entrepreneurs, investors, and skeptics. It also creates a smaller group of people whose role is less visible. They spend years building expertise, relationships, and credibility before those assets are needed.

Amy Oldenburg occupies that role today. Long before digital assets became a strategic priority at Morgan Stanley, she had already begun building the knowledge and relationships that would eventually help the institution navigate an uncertain future.

Institutions do not need a complete map of the future before they begin building. They need enough conviction to start learning. History suggests that this is often how technological progress unfolds.

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