My intuition has been that two policy variables are decisive: keeping markets competitive and maintaining purchasing power through something like a negative income tax or basic income. Without competition, productivity gains get captured as rents rather than passed through as lower prices. Without redistribution, the collapse of labor's share of income leaves most people unable to participate in the economy even if goods are nominally cheap. You need both.

To test this intuition, I worked with Claude to build a general equilibrium model of the economy that lets you explore these dynamics interactively. Please go ahead and play with the model. I'd encourage you to try the different scenario presets and then experiment with the sliders yourself before reading on. DISCLAIMER: This model is entirely vibe coded and may be buggy!

Why a General Equilibrium Model?

Partial equilibrium arguments are what dominate this debate. The optimists say: "AI drives cost down, so prices fall." True in isolation. The pessimists say: "AI replaces labor, so wages fall." Also true in isolation. But these effects interact. Falling wages reduce demand. Concentrated capital ownership means savings flow disproportionately to those who already have capital. Market power determines whether cost reductions reach consumers or get captured as profits. You need a framework that accounts for all of these simultaneously.

What Exists in the Literature

Before building anything, we -- and by we I mean Claude -- surveyed the existing academic work. There is a lot of fantastic work to build on, but apparently nobody has combined all the elements needed to address both competition and redistribution at the same time (if someone has done that already I would love to see it):

The task-based framework comes from Acemoglu and Restrepo. Their 2022 Econometrica paper models production as a continuum of tasks that can be allocated between capital and different types of labor, with automation expanding the set of tasks capital can perform. This gives you displacement effects, productivity effects, and ripple effects through the wage structure. But it assumes competitive markets and has no redistribution policy.

For wealth dynamics, Moll, Rachel, and Restrepo's "Uneven Growth" shows how automation raises returns to wealth, generating endogenous wealth concentration through a heterogeneous-agent model. The April 2025 IMF working paper by Broadberry et al. builds on this to study AI diffusion. But again, no market power lever and no redistribution.

Korinek and Stiglitz (2019) provide the conceptual architecture closest to what we need. They explicitly discuss both anti-trust policy (lowering monopoly rents so competition passes cost savings to consumers) and redistribution (non-distortionary taxation to compensate losers). But their models are deliberately simple illustrations rather than a full GE model.

Saint-Paul (2025) comes closest to combining all the pieces. He models an economy where oligarchs who own proprietary technology choose between UBI and blocking AI to preserve the middle class as consumers. His key finding, that UBI plus AI can dominate blocking AI, resonates with my intuition. But his model is more political economy than full GE.

On the growth side, Aghion, Jones, and Jones (2017) and Trammell and Korinek (2024) provide frameworks for thinking about explosive growth and the Baumol cost disease constraint. Growth may be constrained not by what AI is good at, but by what remains essential yet hard to automate. Trammell and Korinek is especially useful as a comprehensive survey mapping the parameter space.

On the redistribution side specifically, Lopes (2024) builds a large-scale overlapping generations GE model to study UBI, finding that an expenditure-neutral reform can increase capital accumulation and reduce inequality but doesn't model AI automation.

And on the neglected question of market structure, Barkan (2024) demonstrates that under imperfect competition, AI productivity increases can actually decrease GDP, the opposite of what competitive models predict.

Based on this work Claude constructed a GE model that simultaneously includes heterogeneous capital ownership generating endogenous inequality, market concentration as a policy variable, and a negative income tax or basic income.

The Model Architecture

The model combines four building blocks:

1. Production uses a CES task framework (following Acemoglu-Restrepo). A fraction α of tasks are automated and performed by AI-capital with productivity A; the rest require labor. The elasticity of substitution σ is critical. When σ > 1, capital and labor are good substitutes, enabling the "everything gets cheap" scenario; when σ < 1, labor remains essential as a Baumol bottleneck.

2. Market power is modeled as Cournot competition with N symmetric firms. This gives a markup μ that ranges from near-monopoly to near-perfect competition. The markup compresses the effective labor share and creates a wedge between productivity and consumer prices. Competition policy — antitrust, regulation, open standards — is captured by the slider for N.

3. Capital dynamics are endogenous. Ten household deciles hold unequal capital stocks, with concentration governed by a parameter θ. Each period, households save a fraction of their post-tax income, with richer households saving more. Capital accumulates: k_{i,t+1} = (1−δ)k_i + s_i·y_net. This is the mechanism through which inequality compounds over time. The Piketty r > g dynamic emerges endogenously.

4. Negative income tax implements budget-balanced redistribution: y_net = (1−t)·y + t·ȳ, where t is the tax rate and ȳ is mean income. Below-mean earners receive transfers; above-mean earners pay. A labor supply elasticity captures disincentive effects.

The simulation runs for 40 periods, with automation ramping logistically from 30% toward the target level. AI productivity compounds at a user-selected growth rate. The model tracks output, prices, the Gini coefficient, real purchasing power by decile, capital accumulation, and the labor share over time.

What the Model Shows

The five presets tell the story concisely:

AI Dystopia (high automation, duopoly, no redistribution): The labor share collapses, the Gini climbs relentlessly over time, and the bottom decile's real purchasing power actually falls even as total output soars. Capital ownership concentrates as the rich save more and accumulate more capital each period. This is the permanent precariat.

Competition Only (high automation, 30 firms, no redistribution): Prices fall dramatically as competition forces cost savings through to consumers. The bottom decile does better than under dystopia because goods are cheaper. But inequality still compounds through differential capital accumulation. Competition helps with the price level but doesn't fix the income distribution.

Redistribution Only (high automation, duopoly, 40% NIT): The Gini compresses, but prices stay elevated because the monopoly markup prevents productivity gains from reaching consumers. The transfers are effectively subsidizing monopoly rents.

AI Utopia (high automation, competitive markets, moderate NIT): Output grows substantially, prices fall, the Gini stays low, and the bottom decile's real purchasing power rises alongside everyone else's. The combination of competition (driving prices down) and redistribution (maintaining purchasing power) produces broadly shared prosperity.

The dynamic charts make something visceral that static analysis obscures: inequality compounds. Even starting from moderate initial conditions, the differential savings mechanism means that capital ownership concentrates over time unless policy actively counteracts it.

What's Missing and Where to Go

This is a first-cut model meant to build intuition and invite discussion, not a calibrated forecasting tool. Also as stated above, it is entirely vibe coded, so YMMV.

Among the things worth considering:

Most importantly: checking whether the model appears to be correct. I would love to see other agents pointed at this for analysis.

The NIT currently taxes all income uniformly. This is obviously far from the current policy which taxes labor income more heavily than capital income. So effectively an additional policy choice is at present hardcoded.

The model treats the number of firms as static to simulate a policy choice on competition. There are more dynamic ways of modeling concentration and more sophisticated approaches to examining different types of regulation.

Capital ownership broadening (sovereign wealth funds, stakeholder ownership, broad-based equity participation) is arguably a third policy dimension beyond competition and redistribution. The model doesn't currently include it.

On the production side, the model doesn't capture the Baumol bottleneck dynamics that Aghion-Jones-Jones emphasize: sectors where automation is hard may constrain overall growth regardless of how productive AI becomes elsewhere.

Finally, the model doesn't include the emergence of a "human qua human" sector of the economy: jobs that could be automated but where consumer preferences choose the "artisanal" option instead (e.g. in hospitality).

I'm sharing this model because I think the debate about the AGI economy is too often conducted through competing anecdotes and vibes rather than through structured analysis. Please try the model yourself, find bugs in it, criticize the assumptions, propose extensions. What other policy levers should be in there? What dynamics are missing? You can fork the repo or make a pull request with suggestions.

My intuition that you need both competition and redistribution via a basic income or negative income tax, and that neither alone is sufficient seems to be supported by this model. Looking forward to feedback!

Unlike the dotcom bubble there is absolutely no demand constraint. People and agents are consuming as many tokens as are made available. There is no shortage of bandwidth or a lack of people willing to make online payments. Rapid revenue ramps into the billions are happening, with OpenAI and Anthropic setting a breakneck pace. And increasingly, AI agents are running 24/7, consuming tokens for research, analysis, and autonomous operations — a category of demand that barely existed a year ago and is growing fast.

This time round the issue isn’t demand. It is unit economics. Right now tokens appear heavily subsidized by equity and debt dollars. For example there are numbers out there suggesting that Anthropic may be losing thousands of dollars on $200/month max plans. Inference will have to become rapidly cheaper and/or labs will need to continue raising vast sums of money to continue advancing their models. The fierce competition which includes open source models and large companies which can divert cash flow from existing businesses doesn’t really allow for increased prices.

Now some might object that NVIDIA’s stock price looks reasonable based on growth and profitability. But that of course is only true as long as their customers can continue to plow money into hardware at the current rate or even faster. Any hiccup here could kick off an incredible contraction. AOL famously managed to merge with Time Warner only to implode as a business shortly thereafter when all the equity financed advertising dollars disappeared (including huge AOL investments which were 80% roundtripped back as advertising).

Where might such a hiccup come from? One candidate is the private credit market which is showing severe stress with multiple large funds having to limit outflows. Now some people claim that this is just a consumer stampede while institutions will continue to provide credit but I strongly doubt that credit will remain available for data center and energy buildout to the same extent that it has been. Another potential candidate are IPOs of labs such as OpenAI, Anthropic and SpaceXai. Many people seem to think that IPOs would be bullish but it was IPOs that ultimately broke the dotcom bubble. IPOs require financial disclosure. In the dotcom bubble they revealed meager revenues. Here they are likely to reveal massive cash flow hemorrhaging not just from CAPEX but also from negative unit economics. As a corollary, if SpaceXai can get away with it they will try not to break out segments and just show the company as a whole.

Meanwhile, if inference costs collapse and open source models remain competitive with proprietary ones, the labs face pressure from both sides — they can't raise prices because of competition, and they can't lower costs fast enough because of the hardware investment cycle. In that scenario the value shifts from the model itself to the ecosystem built around it. One specific way open source models can stay competitive is through distillation and reinforcement learning on subtasks for agents.

So where does all of this leave us? I think that there is about a 25% or so chance that a combination of genuine recursive self-improvement and massively cheaper inference will somehow make the economics work in time. Conversely I believe there is a 75% or so probability that we will see a major correction, possibly as early as this or next year.

As with the dotcom bubble the technology is entirely real even if the valuations aren't. The dotcom bust killed Pets.com but it didn't kill the web — Amazon and Google emerged from the wreckage to become dominant companies. Similarly, an AI contraction might devastate labs and their investors, but the capabilities won't disappear. Models will still work. Inference will get cheaper as distressed assets sell. Open source will keep advancing (at least as long as there is research).

The question isn't whether AI will transform the world — it will. The question is whether the current financial structure survives, or whether we will hit a painful reset along the way. I believe the latter is significantly more likely.

Sure we understand some causes, such as if you have a hiatal hernia or an H. pylori infection. As it turns out I appear to have neither based on the gold standard tests. Apparently most GERD cases are idiopathic. That’s a fancy medical word for a condition with an unknown cause. The GI specialist I went to said something along the lines of: “Most of the cases I see just start some day and we don’t know why. We treat them with Nexium and Pepcid and some of the time they go away.” WTF?

I was in disbelief but then hit the Internet and quickly found the Reddit GERD forum and the About GERD website of the International Foundation for Gastrointestinal Disorders. And sure enough, confusion reigns supreme. People trying all sorts of things. Tantalizing sightings of apparent cures amidst an ocean of suffering and frustration. How is it possible that this is the state of medicine on an issue of such prevalence?

As I have started to dig in, one thing has become clear. Way too many doctors seem to think this is somehow acceptable. I haven’t fully figured out what I want to do about it. My first step is to see if I can get better myself. To that end, I am looking into potential brain and biome connections. My early experiments with diaphragmatic breathing are so far quite promising.

I suspect though that I will also want to fund some research and/or invest in startups that are addressing GERD. So if you either have personal experience with improving GERD or are working on / aware of promising research or startups please let me know.

To everyone out there with GERD: this sucks. Let’s make it better!

But just because a society has time and resources it doesn’t automatically follow that its knowledge will improve. There is one crucial missing ingredient: the critical process. The critical process consists of proposing new ideas (“conjecture”) and pointing out flaws in existing ones (“criticism”).  Without the critical process knowledge becomes stagnant. Bad ideas remain unchallenged. Progress comes to a standstill. 

In the prior post on resources I illustrated a lack of resources through medieval palimpsests. That period is the perfect example of societies in which the church exercised such power that it suppressed new ideas, stunting progress for hundreds of years. Humanity almost experienced an absorbing event with the rise of the Plague which killed about a third of people. In general when the critical process is suppressed the result is a lack of progress combined with severe resource misallocation. The combination of the two makes absorbing events significantly more likely.

But even when permitted, the critical process faces a crucial challenge. How are good ideas separated from bad ones? In well-functioning market based economies, companies with bad products shrink and disappear, whereas ones with good products grow. Of course “well-functioning” is doing a lot of work here. As I have written about extensively in my book The World After Capital, in the digital world we don’t have well-functioning markets. 

Similar to markets, we have a decent yardstick in science. Here is what I wrote in a recent post titled “The Imperative for Moral Progress in the Face of Technological Acceleration”

In science, theories eventually get discarded if they lack explanatory power. This mechanism acts quite slowly at times, as we can see in the case of string theory. But over the course of history it has worked quite well. Nobody spends time on the phlogiston theory of combustion. It is at best of historical interest but doesn’t inform current science or engineering.

The problem, however, is that we appear to lack a mechanism for separating good ideas from bad ones in philosophy. My post continues: 

In philosophy on the other hand, texts continue to be studied, no matter how impractical, useless, or even detrimental the ideas have turned out to be for the lives of individuals, the functioning of communities and the progress of humanity overall. While something like string theory is an aberration in science, it is the norm in philosophy. Philosophy has turned out to be much closer to religion than to science.

Now one might think that some philosophical ideas have been successfully pruned because they are no longer politically palatable, such as the divine right of kings or justified slavery. But so far there seems to be no reliable way to assign these a permanent “falsified’ status. Instead, the most we can say is that they are “not currently” politically palatable. To see how dramatic backsliding can be one need look no further than the growing support for autocratic government in the United States.

Epistemic rather than political falsification is why scientific knowledge has been advancing so much faster than philosophical knowledge. Or more pointedly: it is why we have a robust set of universal physical laws while utterly lacking a universal moral core. My post goes on:

This has resulted in a profound asymmetry. With scientific theories pruned effectively we have been able to achieve rapid technological progress. But stuck in a morass of ever growing useless philosophy we have failed to achieve moral progress. This is an incredibly dangerous combination. E. O. Wilson perfectly captured this “The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions, and godlike technology.”

This then brings me to the crucial point which completes my argument. The right measurement for philosophical ideas ought to be whether they help humanity progress. What constitutes progress? Our growing ability to reduce tradeoffs. This definition of progress is an idea that I introduced in the post on values.

So here then is my proposal for a universal moral core. Three principles forming a North Star to guide our judgment in the selection of actions and in the evaluation of philosophical ideas:

1. Avoid absorbing states

2. Mobilize resources

3. Sustain the critical process

This universal core is quite minimal on purpose. It leaves as much as possible open for individuals, communities and even nations to figure out for themselves. The reason for this is implied by the first principle: prescribing too much detail risks resulting in a monoculture that lacks resilience to external shocks. On the other hand I believe that it is impossible to drop one of these three principles and still achieve progress by growing our knowledge. One can think of humanity as engaged in a potentially infinite game. The first principle (avoid absorbing states) says: preserve the game itself. The second (mobilize resources) says: field the strongest team. The third (sustain the critical process) says: keep improving our strategy. Drop any one and the game will be finite.

I plan to write a series of future posts in which I work out the practical implications of this moral universal core for decision making ranging from individual to communities all the way to all of humanity.

Post written with helpful feedback from Lumen (using Opus 4.5)

Post illustrated by Claude Sonnet 4.5

My first time in the United States was as an exchange student in Rochester, Minnesota. I was a junior at John Marshall High School. Early into the school year, some students missed an exam and I was told they would be given the same exam as make-up a week later. I was flabbergasted. Obviously they would get perfect scores because other students would tell them what was on the exam! But then I discovered that wasn't going to happen. Nobody was going to share. Sharing was completely unacceptable as it would undermine the integrity of the exam as a test of individual knowledge.

This was a profound cultural shock to me because the German school system in which I had grown up was based on students extensively helping each other to get good grades, including sharing answers mid exam. In other words, cheating. It was completely pervasive and if you had chosen as a smart student who knew the answers not to share you would have been considered a selfish weirdo and ostracized. As an aside, this attitude of cheating on tests may in part explain how German automotive executives talked themselves into the emissions scandal.

This was the moment where I realized just how pervasive and profound the influence of culture on behavior is. “Culture is what people do when no one is looking” is a great summary by Herb Kelleher, the founder of Southwest Airlines. This is true for the culture of a company and for the culture of a country. Ways culture gets formed is through the behavior of leaders and through the stories that are shared and celebrated.

What made America great was a culture that was a unique blend of self reliance with responsibility to family, community and country. The reason I felt instantly at home in America was because of this combination. You can just do things. And more than that, others will encourage you in that pursuit. No need to wait for someone else to do it for you. But in addition to self reliant action there was an expectation to contribute to your family, your community and there was a strong sense of country. This uniquely American equation has broken down profoundly.

Instead of a culture that celebrates builders who contribute, we have devolved into a culture dominated by selfish extraction and fakery. There is no singular cause for a change this profound, but we can identify some of the contributors. Milton Friedman’s 1970 essay in the NY Times “The Social Responsibility of Business Is to Increase Its Profits” marks a narrowing in the understanding of responsibility. It wound up combining in a toxic way with the Managerial Revolution that James Burnham had identified. The leading figure in this combination was Jack Welch who proceeded to destroy the once great GE, with his acolytes doing the same to other iconic American companies, including Boeing. I strongly encourage everyone to read David Gelles’s excellent “The Man Who Broke Capitalism.” One crucial aspect of the Welch approach was aggressive earnings management. In other words, cheating. The substance of the company was hollowed out to produce artificial and unsustainable gains in earnings. Outsourcing, offshoring, and financialization were spread throughout the economy as Welch wound up being celebrated on the cover of every business magazine. 

Now one might object that the robber barons weren’t exactly paragons of good behavior. Didn’t they use force to break the unions? Didn’t they intervene massively in politics? Yes absolutely. But there were two crucial differences. First, they were builders. They created factories, railroads, power plants and more. Second, many of them did give back in ways that lastingly made their communities better by funding hospitals, libraries, concert halls, etc. To be clear, I am not excusing their suppression of the labor movement, I am simply pointing out that there is a profound difference between creation and destruction. Incidentally this is why I am much more willing to see Elon Musk as a complex figure than a private equity raider or a high frequency trader.

What started in the 1970s found its way ever deeper into the culture. Much of reality television and celebrity culture became simply about selfish zero sum games. Put a bunch of people in a house and promote intrigue, with the most unscrupulous person emerging victorious and nothing built in the process. Similarly the financial markets, which were once crucial to the build out physical capital, became ever more self referential. Derivatives exploded in scale beyond anything that could possibly be justified as providing liquidity for hedging risks in the real economy. The proliferation of sports betting and meme stocks is just the logical conclusion of everything becoming a casino. Selfish extraction became the culture, replacing self reliant building and giving back.

I had been meaning to write about this decay in culture for some time, since I posted on the breakdowns in the rule of law (which is an integral part of shaping culture). Writing the post today was prompted by last week's debate over the role of Asian immigration in shaping American culture. Calling it a debate is somewhat generous because a lot of it was just people insulting the other side's motives or intelligence. There was very little of an attempt at actual understanding and responding.

Yes of course immigration can change culture. But the degree to which this is the case depends on the strength of the culture and the rate of immigration.  This is not unlike a natural ecosystem. A healthy forest will integrate new species as they arrive and they will find a niche that integrates into the overall system enhancing its biodiversity and resilience. A sick forest on the other hand can get taken over by new species and completely reshaped. When I arrived in the US, there was a healthy culture of not cheating. And so even though I came from a place of pervasive cheating in school I accepted and integrated myself into the system I found.

When you normalize cheating from schools to businesses to the highest levels of government then adding immigrants from countries in which cheating is pervasive will definitely not help. Now this explains a lot about the difference in experience: there are remnants of the old culture and as a result the US still attracts immigrants who want to build, whether that is a small local business, such as a restaurant, or a globally competitive tech company. As a result we are seeing many successful businesses at all levels founded and/or led by immigrants.

That’s why the wholesale vilification of immigrants by a government led by a liar and cheater is so profoundly wrong. It ignores that many immigrants (including quite a few who came here illegally) are in fact living the culture that made America great. All the while the government is further tearing down that culture with its own acts, such as sending masked and unidentified officers into the streets. There would be broad support for a sensible immigration policy along with rebuilding the culture that attracted me and many others to America in the first place.

More recently I have been vibecoding with Claude. And it is entirely clear that we have entered the age of automated software. What would have taken days, or weeks, or possibly months and even years, can now be accomplished in minutes, hours, or days. And all the necessary prompting takes place in natural language. No more remembering of esoteric assembly commands, or learning the syntax of a new higher level language. Just expressing one’s intent and then providing updates on what’s working and what is not. Writing software has gone from being difficult and slow to being fast and easy.

What does that mean for software businesses? First, when you make something cheaper you get a lot more of it. There will be more software created than ever before. Second, no matter how cheap you make something you wind up with more consumers than producers. Anyone can post on social media and yet way more people read or watch posts than create them. The same will be true for software, which means there will still be software businesses. Third, the era of high margin software businesses is largely over. There will be fierce competition including the now credible alternative of building it oneself. The recent rerating of software companies in the public markets is an early recognition of this new dynamic.

What will happen to the software industry? We are likely entering a period of maturity where margins will become similar to those in other competitive industries. Along with this will come a massive consolidation of the industry. We already had some prior rounds of this when there were platform shifts and older software businesses wound up getting consolidated into the likes of Computer Associates.

There are some important exceptions to this logic though. The most important are companies with network effects. We are seeing this in the continued strong profitability of Meta (with the caveat of financing very large capes for AI infrastructure). On the business side, something like LinkedIn, continues to perform well. Cloudflare has a less obvious network effect where they can detect malicious traffic on one part of their network which is then helpful to every customer. But the list of potential candidates for software businesses with strong network effects is quite small. One other area where there may be reasonably strong margins are databases. No matter how competitive code may become, the value of data is only going up and so is the willingness to pay for reliable and performant storage.

What about the tooling layer? Companies such as Cursor or more recently Conductor? It’s highly unclear that anyone can build a defensible business here. Most if not all the power seems to come from the underlying models. So the real question is more whether open coding models can stay competitive with the closed ones. If yes then there will be continued innovation and price pressure on models also. If not, then a lot depends on the competitive dynamics between the closed models. If there are multiple close coding models all trying to gain market share, then here too it will be difficult to operate at high margins. The long run equilibrium is anyone’s guess though.

In part it is difficult to figure out the long run equilibrium because it is unclear what will happen to open source and how that will matter. One of the reasons the coding models are so powerful is because they have read every open source line of code there is. So not only do they have the benefit of being able to invoke these libraries and build on these frameworks, they have also learned how to code from them. One possible implication is that open source will atrophy and potentially get swamped by code “slop” with the fraction of model generated code being contributed to Github rising rapidly. On the other hand it might be the case that we can truly figure out self play for coding and so the existence of prior code will be irrelevant.

There is another implication that is maybe counterintuitive. In the past it was great advice to not attempt a complete rewrite of a system. In 2013 I published a blog post on “Evolving your Technology as you Grow” in which I wrote that you should “never, ever [...] rewrite everything from scratch.” I pointed to a post by Joel Spolsky from 2000 that gives examples and a detailed argument for why a complete rewrite was a terrible idea that had killed several businesses. Well, automated software inverts this logic. If you have a big convoluted existing code base, you are now much better off, just using your data and having AI rewrite from scratch. You can use your existing application as the description of what you want! Keep in mind that any potential new entrant doesn’t have a legacy code base. In the past that might have meant it would take them years to build what you have but that’s just no longer true. The only thing you have that’s truly valuable is your existing data and customers.

Finally there are implications of automated software for the labor market. For several decades, learning how to code was a sure fire way to a high paying job. I financed my first car and my college degree by writing software. Programs such as Pursuit in New York have helped people go from minimum wage jobs to high paid software ones. Now there is a glut of coders and the only reason the labor market is not already horrendous is because large companies are notoriously bad at realizing productivity gains. That’s in no small part because the incentives tend to work against it – often one’s power inside a company is a direct function of how many people are in the department. By contrast, there may be more opportunities going forward in product management because defining the right thing to build is now ever more the constraint rather than actually building it.

If all of this sounds over the top to you, it probably means you haven’t coded with this latest iteration of models. It is genuinely difficult to get a sense for just how powerful they are without trying them out oneself.

I am approaching this moment with the clarity that comes from recognizing that one has failed. Over a decade ago I started calling for dramatic changes to how we organize society. I gave talks, wrote blog posts and even published a book. 

But I didn't make a dent. Instead, the existing societal machinery carried on. I knew all too well that it was falling apart. And yet I had hopes that it could be fixed.

The old order was clearly decaying. The rule of law had been undermined for years. Jack Welsh and his acolytes broke the economy. Private equity raided industries for short term financial gain. Everything got financialized. Government grew ever more bloated and ineffective. Well-meaning laws were weaponized to prevent anything from being built. 

Major problems weren't being addressed. Income and wealth inequality exploded. Atmospheric carbon skyrocketed. Infrastructure decayed. The misguided war on terror consumed immense resources and eroded civil liberties. To keep the public pacified gambling was allowed to grow boundless.

I kept arguing for an enlightened turn. For addressing our myriad problems head on all the while not giving up on core principles of democracy and rule of law. But I failed to build a sufficient audience or momentum for my ideas.

And in the meantime the forces that were willing to support a “blow it all up” approach kept growing. No principles. Oblivious to grift and corruption. Anything in the name of accelerating change. Might makes right. Along with so much else this is an echo of a century ago when futurism supported fascism. As Marx so brilliantly put it: history repeats, first as tragedy then as farce. We are getting the farcical version, but it is no less destructive.

So what now? There are two essential tasks at hand now.

The first task is to keep the bad from becoming extremely bad. To slow down the descent into madness. To create time for individuals, families, communities and even nations to prepare for what’s coming. The two approaches that I am partial to here are using what’s left of the old system as well as peaceful protest. Leveraging state and local powers and courts as long as they can assert themselves independently. And providing a surface for government overreach and brutality. 

To be clear up front: this will be an incredibly frustrating and costly process demanding great individual sacrifice. In Babylon Berlin, Gereon Rath and Charlotte Ritter never stop pursuing justice even as the Weimar Republic crumbles around them.  Many heroic acts of trying to uphold order, ultimately overwhelmed by the rise of evil. Another historic lesson is to anticipate the eventual use of “agent provocateur” tactics. 

Now maybe the United States has a stronger immune system so another reason to apply effort to this task is that it might just succeed here.

The second task is to invent what can come after the madness. This requires taking a long view. Establishing principles. Building a movement. Gathering strength. All while avoiding or surviving attacks from the agents of chaos. The traps along this task are a great many and unfortunately we don’t have the benefit of a Hari Seldon who has set all of this in motion for us already. Still we can take inspiration from the Foundation series. A movement that will be long term successful would do well to embrace the core principle that ultimately knowledge does underpin power.

A key challenge on this task will simply be to keep at it as the unfolding regression could last for generations. This is what I am committing myself to. I will continue to develop the ideas from The World After Capital and my blog. If I have one true strength,it is tenacity. Far from abandoning the quest for a “Knowledge Age,” now is the time to double down. That is exactly what Gigi and I are doing with our projects in the Hudson Valley, such as HudsonUp, The Spark of Hudson, Hudson Dots and Wally Farms.

Whatever we do, let’s not succumb to opportunism or worse become an active promoter of chaos and destruction. Instead let’s apply ourselves to one or both of these tasks. We need to buy as much time as possible and keep the regression as small as possible – all the while we are actively building a new and better world.

European Federation Manifesto

Europe needs to be strong – for itself and for the world.

China has become a superpower and the United States is struggling with the accumulated debt of neoliberalism and global intervention. Russia is waging a war at Europe’s doorstep. Europe can no longer rely on the support of the United States. Europe has fallen behind in its capacity to defend itself. Europe is rapidly deindustrializing.

The existing European Union has frittered away its credibility by turning into a costly bureaucratic morass. The European Union has lost the support of the people. At present course and speed the European Union will disintegrate and disappear into irrelevance. The same will be true for European nation states which are too small on their own.

It is time to reboot the European project as a European Federation.

The European Federation will focus on a minimal but crucial set of policies: defense, secure borders, abundant energy, frictionless commerce and dispute resolution between constituent nation states. Participating countries will maintain sovereignty in all other matters. As a first approximation the European Federation will be modeled on Switzerland. Like Switzerland, the European Federation will be neutral (i.e. not aligned with the United States or China). The European Federation will be committed to being non-offensive and non-interventionist. 

The European Federation will stand for humanist values. It will be tolerant but defend vigorously against intolerance. The European Federation will pursue a third path that provides an alternative to the Chinese government surveillance model and the emerging US might'makes-right model. The European Federation will leverage its existing strengths in research and manufacturing and attract scientists and entrepreneurs who want to drive forward progress in the freedom that arises from the balance of restrained and capable government with individual freedom.

The European Federation charter will be developed by a constitutional convention. Nations will send 2-5 delegates to the convention depending on size. The delegates shall be chosen by sortition among each nation's scientists, engineers and entrepreneurs. The delegates shall not include anyone who has been a professional politician for more than 3 years of their life. The delegates will be assisted at the convention by experts in constitutional law with a particular focus on federations.

The time to be bold is now. The time for the European Federation is now.

We visited Luang Prabang in Lao as a family and observed monks in the ritual morning alms walking down the street with locals and some visitors putting rice into their bowls. The alms bowl is one of only eight “requisites” which Buddhist monks are allowed to possess as personal property. This is obviously an example of an extreme restriction by morality on the acquisition of resources but it does serve to illustrate nicely that morality can have a substantial impact on the availability of resources.

A historical example is Europe, which was stuck in nearly a thousand year rut during the Middle Ages. Yes some magnificent churches were built during this time and some fabulous castles as well but beyond that societies were resource poor. Many people had virtually no personal possessions and often were reliant on alms for survival much like the Buddhist monks. In a particularly terrifying example, there wasn’t enough vellum produced so that religious texts were copied over older philosophical ones giving rise to palimpsests. In Europe this didn’t change substantially until the Enlightenment and the rise of Protestantism.

Having grown up in Germany, another example of different moralities (using this term broadly) having different success in mobilizing resources was the stark difference between the development of East and West Germany post World War II. After initially running neck-to-neck for some years, the market-based system in the West pulled away dramatically accumulating vastly more resources a significant fraction of which was mobilized for generating additional knowledge in different forms ranging from research universities to product innovation by private enterprises.

Now you might make the same objection as in the prior post: why should mobilizing resources be good? Isn’t that again a moral judgment? And yes, just like “longevity” isn’t a good in and of itself, resource mobilization isn’t either. Here too we need to consider the relationship between resources and knowledge. The operative question is: can we mobilize sufficient resources to advance knowledge?

Resource mobilization is necessary for knowledge creation. First, we need resources to free ourselves up from foraging initially and farming subsequently so that we can apply ourselves to learning, creating and sharing knowledge (what I call the knowledge loop). Second, many types of knowledge can only be found by building up the equipment necessary to explore and discover it. Without developing the microscope for instance, we might never have found that there is a microcosm.

This is my favorite interpretation of the biblical story of the fall from paradise. In paradise we were provided for by God. But once we had tasted the forbidden fruit of knowledge we were expelled and had to fend for ourselves. What knowledge did is that it let us accomplish certain things, such as farming. But those new capabilities resulted in a new set of problems. For example living close to animals gave rise to zoonotic viruses. So once you have embarked on knowledge you forever need to come up with additional knowledge. And that requires mobilizing additional resources.

The most recent and dramatic example here is global warming triggered by the burning of fossil fuels. Clearly fossil fuels were a huge energy unlock for humanity that made possible a ton of progress. But now we need even more energy to be able to deal with the consequences. So the mobilization of additional resources is necessary to allow humanity to continue solving problems. As I have pointed out in my book The World After Capital, the allocation of resources to problems such as global warming is downstream from attention. We have sufficient capital to address the issue but insufficient attention to make the necessary changes.

There is a clear link here to the need to avoid absorbing states. We require the resources to advance our knowledge sufficiently to fend off existential threats. And yes some of these are of our own making but certainly not all of them. Earth’s history is replete with examples of species – such as the dinosaur – and human civilizations – such as Easter Island – that did not have enough knowledge to deal with an existential threat. Both dinosaurs and Easter Island are also examples of misguided resources. The dinosaurs grew bigger and bigger, developing more muscle but not more brain. And Easter Island poured resources into the creation and movement of giant statues. 

Continuing to mobilize resources to advance knowledge and thereby protect humanity from absorbing states is thus part of what I consider the universal moral core. 

Claude Opus 4.5 made some helpful suggestions on the first draft of this post. Claude Sonnet 4.5 has drawn the illustration.

So what is Jevons Paradox? It goes back to English economist William Stanley Jevons who in the mid 1800s observed that as technological progress allowed coal to be used more efficiently, the aggregate demand for coal went up, instead of down as one might at first expect. What gives? If output stayed constant then using less coal per unit of output would indeed reduce total consumption. This is what one would call “ceteris paribus” (all else being equal). But Jevons observed that output was going up. Why? Because by using less coal, companies were able to make their products cheaper which in turn resulted in an increase in demand. In fact in some industries, demand increased by a higher percentage than the percentage reduction in the per unit use of coal thus resulting in a net increase in coal consumption.

In economics these types of counterintuitive outcomes are surprisingly common: second order effects often outweigh first order ones. A classic case is the increased fuel efficiency of cars which resulted in an increase in overall fuel consumption: people started driving a lot more as driving became cheaper. Now we can do some simple math to help us understand the effect better. Say cars become 10% more fuel efficient per mile, but people wind up driving 25% more miles, well then the total consumption of fuel will increase: 1.25 miles driven 0.9 gallons / mile = 1.125 gallons consumed (a 12.5% increase)! If on the other hand people only drive 5% more then total consumption goes down: 1.05 miles driven 0.9 gallons/mile = 0.945 gallons consumed (a 5.5% reduction). The technical term in economics for the magnitude of change in response to another change is “elasticity.” And we can see here that the elasticity of miles driven needs to be “high” in order for total consumption to go up.

Let’s now consider another example to hone our intuition here. Take food as an example and consider making farm production more efficient through increased automation, such as advanced combines.. Will the demand for food be highly elastic? Well not really because there is only so much people can eat (of course we are holding population constant here, something that Malthus had worried about a lot). So if people can’t eat a lot more then what will happen? Employment in farming will go down as labor is replaced by the more efficient machines.

So for Jevons paradox to result in an increase in labor as AI technologies are broadly deployed we need elastic demand for goods. But we actually need something else first which we have so far implicitly assumed. We need competitive markets. Suppose I am a monopolist. From the analysis of monopoly we know that monopolists charge higher prices and produce less quantity than would occur under competition. Here more efficient machines translate into higher profits for the monopolist. Today’s economy has many highly concentrated markets so the assumption of lower prices is not a given. And we also don’t know yet whether the use of AI will make markets more or less competitive.

For now let’s assume that prices will in fact go down as AI unlocks efficiencies. Can demand be expected to grow so much that it will offset the labor savings? The short answer is: we don’t know! The longer answer is: it’s really complicated. Why? Well first of all it should be quite clear that much of it depends on just how much more labor efficient AI becomes over time. Obviously in the extreme where AI and robots are so radically more efficient that you can produce output without humans at all there is no amount of demand increase that can offset this. It is quite useful to write out a formula:

Units produced * human labor / unit = Total labor

Consider a 90% labor savings and now ask how much would units produced have to grow to at least offset this?

X units produced 0.1 human labor / unit = 1 Total labor

X = 1 / 0.1 = 10!

So at a 90% labor savings from AI you need a 10x demand growth to just break even on labor demand. For many products that’s clearly not going to happen. On this ground alone we should reject a simplistic invocation of Jevons paradox. 

But there is another problem here. In a case like coal, we can maybe get away with what economists call a partial equilibrium analysis where we consider only one market (here the demand for coal). But AI will be everywhere and so what we really need is a general equilibrium analysis where we consider how all these changes ripple through the entire economy. This is especially true because there is a deep linkage between labor and demand for goods. If the demand for labor goes down and wages decrease, then incomes start to fall. And if people have less money they will consume less, not more. Of course here too we don’t know exactly what will happen because what if prices fall even faster than incomes? Well then maybe we can get demand growth after all!

It turns out to be easy for accomplished economists to write down models that will give you almost any result you want. You just need to fiddle a bit with the structure of the model or with the assumptions. And as we just saw nobody knows what assumptions to use because we have no idea just how powerful AI will turn out to be, how competitive markets will be, whether incomes drop faster or prices, and so on.

But that’s not even the worst part. Even if we could get the assumptions and model roughly right, economics is fundamentally descriptive. What we really need though is normative. We need a vision for the kind of society we want to live in with our growing technological capabilities. There is a coda to the Jevons paradox. Jevons ultimately turned out to be wrong in his predictions about the total demand for coal because he failed to take substitutes (such as oil and natural gas) into account. We are making much the same mistake today by assuming that there is no substitute for humans to have purpose other than work. I for one am firmly advocating for a Star Trek vision where we do in fact shrink labor. We should do to all work what we have done to farm work – automate most of it away. And instead enjoy lives of leisure, contemplation, community, exploration, etc. Plenty of ways for us humans to have purpose!

But IDEs are still code centric and code is going away. Well not away entirely but it will become increasingly hidden just as machine code has been hidden from us for a long time. I grew up at just the right moment in time where machine code and assembly language was still something you learned and occasionally used directly because computers were slow and if you wanted to write a game, core graphics loops had to be hand written in assembly. Since then entire generations of programmers haven’t seen assembly or machine code. It hasn’t gone away entirely but it exists at a layer that is just there and works. Now the same will happen with higher level code. 

Coding agents are doing to source code what compilers did to machine code: push the code below the interaction surface.

This creates an interesting challenge for what the AI native IDE should look like. All one has to do is look at recent iterations of Cursor to see how rapidly this is changing. Cursor started as a fork of Visual Studio (Microsoft open source!) and was immediately recognizable as such. The most recent version on the other hand has an entire section for just dealing with coding agents that looks nothing like a traditional IDE.

The question then is what should the right environment look like? I believe these will be Intent-based Collaboration Environments. In the “nothing is truly new” department it is worth remembering that the idea of intent based programming goes back to at least the 1990s and Charles Simonyi and others even started a company called “Intentional Software” in 2002. They had the right idea but were simply a bit too early. Now we have coding agents that can turn intents into code.

So what would I want to see in an Intent-based Collaboration Environment? Well for one a great way to express intent and keep revising that over time (so definitely version control for intent). And of course I want to collaborate on the intent with other humans and with agents. The second part that’s badly needed are better ways of assessing to what degree the system that has been produced meets the intents. Right now, I am doing a lot of screenshotting and then manually feeding that back to the coding agents. A lot of other stuff should happen completely under the hood, such as enforcement of code quality (starting with linting), feeding back error messages, writing and applying test cases, etc. I should be able to look at that if I want to but it really shouldn’t take up any of my attention (and hence screen real estate) unless something is going badly wrong.

This opportunity for building Intent-based Collaboration Environments doesn’t just apply to coding by the way. The same is true for other fields, such as engineering and science. Much more attention needs be on the intents and how they are being reflected in the results and much less on the underlying mechanics. Getting this right will unlock a huge amount of progress and I am excited to learn about projects pursuing such an approach.

The authors, however, miss a key point. We can identify some potent "oughts" with relatively clear relationships to "is." This leads us to identify an ongoing back and forth between morality and observable outcomes which is amenable to rational analysis. From there we can start to develop an explanation for the interaction between "oughts" and "is" over time and across societies. As an analogy: we cannot understand prices by only looking at demand, or only at supply, no matter how deep we try to dive into an individual buyer or seller. Prices emerge from the interaction between the two. And that understanding has allowed us to make predictions about markets. Similarly, if this program succeeds, then the explanation of the interaction between “oughts” and “is” will allow us to make predictions.

To start with, consider that both Judaism and Islam have strong injunctions ("oughts") against the consumption of pork. Where might this have come from? Different explanations have been advanced over time, but many of them are rooted in the specific characteristics of pigs. Pigs are difficult to raise in the climate of the Near East. Pigs are known to wallow in dirt, which is not exactly appealing. And pigs are the bearers of diseases, such as Trichinosis, which is often fatal. This risk point is strengthened by considering other banned foods, such as shellfish, which too are common bearers of fatal diseases. Maimonides in his wonderfully named Guide for the Perplexed notes this point although he seemed to believe that pork wasn’t dangerous.

In most religions and cultures there is a potent incest taboo. Here too we can see both an instinctual reaction and observed bad outcomes as contributing factors. Sexual reproduction among close genetic relatives tends to produce a much higher incidence of birth defects. Evolution has therefore provided many sexually reproducing species with mechanisms to reduce inbreeding, making it quite likely that humans have some inborn aversion to mating with close family members. Even without that the results are easily observable and were especially so at a time of high birth rates.

So we are beginning to see a pattern here: there are oughts that prevent an “is” from happening or at least reduce its frequency where the “is” results in death. Another obvious example is that many religions, including Christianity, consider suicide a sin or at least as a strongly negative act. 

Now you might rightly point out that this already implies a choice. The "ought" didn't spring directly from the "is" because someone could have decided that freedom is more important. After all there are Patrick Henry’s famous words: “Give me liberty, or give me death.”

But there is something special about death which gives it a privileged position. Death is (for now) irreversible. And so tradeoffs involving death are unlike say the tradeoff between exercising and reading. Death is an "absorbing state” from which there is no escape. Death prunes all future paths.

This privileged position for death applies not just to individuals but also at higher levels, such as societies, and even humanity at large. When the potential end of something is evoked, the stakes are raised immensely. We appear to have a strong built-in aversion to irreversible destruction. This would make a lot of sense from an evolutionary perspective. Oughts that help avoid irreversible destruction will have longer trajectories.

So this then is the beginning of a theory of “ought from is:” moralities that effectively reduce the likelihood of absorbing states will be longer lived than those that don’t.

We can also observe this in the stock market and among gamblers. Strategies that result in ruin are shorter lived. Ruin is a financial absorbing state. Nassim Taleb has written eloquently about the importance of the ruin problem throughout his work. More generally this maps to a mathematical concept known as ergodicity. I won’t attempt to explain this in today’s post other than to observe that processes with absorbing states are non-ergodic. And that matters immensely for which strategies are long run successful and hence which moralities are longer lived.

Now one might object that longevity of a human, or of a community, via morality isn’t necessarily a “good.” The criticism is that I have effectively reduced the question of good versus bad to the success of moralities and thereby implicitly equated a longer trajectory with good. Put differently, I have shown that moralities helping to avoid absorbing states will be longer-lived, but I have not yet established why longer-lived should matter morally. The answer, which I will develop in subsequent posts, connects to knowledge as the objective basis for values that I established earlier in this series. The logical chain runs as follows: knowledge grounds values; knowledge requires time to accumulate; absorbing states truncate that accumulation; therefore avoiding absorbing states is a foundational moral requirement derived from the primacy of knowledge itself. Longevity matters not because persistence is inherently good, but because it is the precondition for the knowledge project that gives our existence meaning and our morality its objective foundation.

As a reminder, the Philosophy Monday series is also an experiment in human-AI collaboration. The preceding paragraph, starting with “I have shown” was a completion written by Claude Opus 4.5. Illustration by Claude Sonnet 4.5 based on this post.

In philosophy on the other hand, texts continue to be studied, no matter how impractical, useless, or even detrimental the ideas have turned out to be for the lives of individuals, the functioning of communities and the progress of humanity overall. While something like string theory is an aberration in science, it is the norm in philosophy. Philosophy has turned out to be much closer to religion than to science.

This has resulted in a profound asymmetry. With scientific theories pruned effectively we have been able to achieve rapid technological progress. But stuck in a morass of ever growing useless philosophy we have failed to achieve moral progress. This is an incredibly dangerous combination. E. O. Wilson perfectly captured this “The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions, and godlike technology.”

Here is an exchange about the need for moral progress from a longer conversation I had with Jonathan Bi earlier this year for the Cosmos Institute:

What is the road forward? We need to pursue philosophy that is directly aimed at moral progress and rooted in our scientific understanding of the world. My blog series Philosophy Mondays is meant to make a small contribution to this. Together with Gigi,  we have also been supporting research (through the Eutopia Foundation) that is connected to important philosophical questions, such as finding firm underpinnings for human agency and integrating values in artificial intelligence.

If you are a fellow traveler on this journey we would love to hear from you. What are the best ideas on moral progress you have found? What projects have the most potential to accelerate moral progress? 

Before diving in, it is helpful to first examine what “abundance” might mean. Much as with the term “scarcity,” modern economics has done more to obscure than to clarify. There is a profound confounding between hypothetical technological capabilities and actually observed outcomes. In The World After Capital I argue for a definition of “scarcity” based on human needs (as opposed to based on prices). I also introduce a third term, which I call sufficiency. This stage is reached when we have enough of something so that we can satisfy human needs (even though we may not yet have figured out how to do it). A simple definition of abundance then would be “meeting human needs and wants” which is obviously a much bigger and potentially unlimited set. As such, achieving abundance is significantly harder.

One of the best descriptions of what an abundant world might look like can be found in the Culture Series by Ian M. Banks. And it was not a coincidence that Dario Amodei appealed to this in his essay Machines of Loving Grace. My critique of the AI abundance narrative is that it invokes AI as a Deus ex Machina for the world’s problem. In doing so it entirely eschews questions of power, including the power held by the “minds” themselves (basically taking the alignment problem as solved).

So what is my stance? I do believe that abundance is achievable in the long run. Humanity can become post economic and this is a beautiful goal. An enticing vision in this direction was also put forth in Star Trek. We would do well to re-embrace a version of that including energy too cheap to meter and fully automated production as a North star. But this is not the only possible future. There are many paths ahead of us and they do include utter dystopias (pick your favorite from Blade Runner, Elysium, The Matrix or even Wall-E). So the choices we make today as individuals, communities, nations do matter tremendously for what future will be realized.

It should be clear by now that uncritically reciting the “Lump of Labor Fallacy” doesn’t amount to analysis. We are headed into a world where growth will increasingly benefit the owners of capital. But capital ownership is already highly concentrated and becoming more so because of power laws and network effects. This is why we need to actively consider policies, such as economic freedom via Universal Basic Income, that spread the gains from automation broadly. We need to empower end users by giving each of us the right to be represented by an agent that is loyal to us (and not to say a corporation or the government).

My book The World After Capital is all about how to facilitate the transition out of the industrial age so that we set ourselves on a path to abundance. And that cannot and will not be accomplished by incremental changes to existing policies or by praying to the machine gods.

Gigi and I have been putting our money where our mouths are on this issue. We have funded a Basic Income pilot, we are setting up an affordable housing project, we are providing capital to a variety of steward ownership initiatives, we have built a Community Learning Center and we are funding research on values and AI. We will share more about these projects next year. All of them are designed to pull the future forward and point it in the direction of abundance.

Invoking the “Lump of Labor Fallacy” to shut down discussion of the labor market impact of AI and robotics is once again popular. Labeling something a fallacy is a powerful move. After all, who would want to hold “a mistaken belief, especially one based on unsound argument” (common definition for fallacy) and where one will be accused of zero sum thinking?

So what is the lump of labor fallacy? It is the belief that there is a fixed amount, a “lump,” of labor and so if machines take more labor, it must follow that there is less labor for humans. Those who deem this to be an unsound argument claim that automation of jobs allows the economy to grow which will over time create as many or more new jobs than were automated. To further strengthen this claim people tend to invoke historic episodes of automation that resulted in large economic expansion and gains in employment (such as introduction of mechanical looms and early industrial automation).

But it turns out there is a third option. It is entirely possible for the economy to grow as a result of automation and NOT deliver an offsetting amount of labor through growth. Just because that hasn’t happened in the past does not mean that it cannot happen. Believing that it must always happen is what I call the “Magic Employment Fallacy” (countering fallacy with fallacy). It is highly ironic when technology entrepreneurs unquestioningly invoked the “Lump of Labor Fallacy” because much of startup innovation is about making something work that hasn’t worked in the past!

The easiest way to see why growth doesn’t need to produce new employment is to conduct a thought experiment, one which Star Trek has already shown us. Imagine a device, the replicator, which can make anything from scratch with no human intervention. You can easily see massive economic growth as we think of all sorts of new crazy things to make, which in turn drives the demand for replicators. But how are replicators made? Well I am glad you asked. By replicators of course! So here we have a clear example of potentially massive economic growth without labor growth.

Now you might cleverly object: but what about people who want to buy something handmade because they value that? A replicator by definition can’t make that. In my book “The World After Capital” I call this “human qua human” jobs – a term that Yochai Benkler coined. And I can absolutely imagine a beautiful world of extremely high automation where we deeply value “manual” products and services.

This would be a radically different economic equilibrium from the one we are in today. Imagining such a world is the easy part, getting there from here is the real challenge. That of course is what my book “The World After Capital” is about so I encourage you to read that. I am thrilled to announce that the Second Edition is now available for reading online (it will soon also be laid out and available in print and as eBook download).

We are now in the second Trump term and the situation is considerably worse. Back then I wrote that

“... I deem it unlikely that Trump has a masterplan for becoming a dictator. Then again he probably didn’t have a masterplan for becoming president but he pulled it off nonetheless …”

I believe that still holds and yet here we are stumbling our way towards a dictatorship. Trump is a great improviser and his instincts have always been towards power.

His potent combination of bullying and corruption have made important societal groups fall in line. The US version of “Gleichschaltung” is more discombobulated than its inspiration but still the largest companies and their leadership have lined up to support the Trump administration. Big law firms have settled with him and now need to provide the administration with pro bono work. Universities, with few exceptions, have all but stopped criticizing the administration.

So what is the path from here to dictatorship? ICE plays a central role. Not only does it have a massive budget, but it is run and staffed by Trump loyalists or those whose loyalty has been purchased: in an otherwise tough job market ICE is adding nearly 20,000 new agents. There is widespread popular support for arresting and deporting illegal aliens with a record of major crimes, especially violent ones. But that hasn’t been the modus operandi of ICE.

Instead ICE appears to be going out of its way to generate active resistance. When you lose Joe Rogan and public support plummets to 34% on an issue where one could have 60%+ support, something is going majorly wrong. But if you look at it as an intentional strategy, then it is working as designed: which is to produce the kind of opposition that allows you to ratchet up your power grab. This of course was also a crucial ingredient to the rise of the Nazis. They intentionally fomented conflict with Communists and then used that very conflict to seize more power. In other words: this is a well established playbook. Even a staunch Trump supporter has identified this historic parallel. 

With ICE providing the pretext for a dictatorial power grab, what is the most important remaining part of society that needs to fall in line? Yes, it’s the US military. Appointing a pliable SecDef was the first step. Purges of generals and JAGs were the second. The third and final step is to make the military complicit. Operations like the ones against Venezuelan boats (and threatened land operations) are the perfect use case for this. You are slowly bringing the military into illegal territory all the while having a wonderful pretext. And if that is allowed to stand then it sends a clear message to the rank and file that anything goes.

So it was good to see six politicians record a video reminding members of the US military that they can disobey unlawful orders. There is unfortunately one thing deeply wrong with the video: all the politicians are Democrats with not a single Republican. Now in fairness Republicans probably weren’t asked to join but this should have been a bipartisan message.

President Trump went into overdrive after seeing it, labeling the statement seditious and calling for the arrest and trial of those who had made it. He also “retruthed” several other posts, including a now deleted one that read “HANG THEM GEORGE WASHINGTON WOULD !!”

Now why would the video elicit such a strong reaction? Is the video really a threat to United States military readiness? What exactly is the legitimate scenario that someone has in mind where soldiers would refuse an order because they had seen this video? Yeah no. This is a case of the Lady doth protest too much. You would only really go on overdrive if you in fact had upcoming unlawful orders in mind.

It is also worth noting that there is a long and sordid history in the United States of using the accusation of sedition to squelch legitimate criticism. The most relevant episode was the passing of the Sedition Act under John Adams. This was directly aimed at Thomas Jefferson and the Democratic Republicans. But as it turns out, Mark Kelly is not intimidated and gave a fiery speech instead of backing down.

Kelly and the others not bending to pressure seems to be helping more people find their spine. Rand Paul is speaking up against further escalation with Venezuela and several Republicans have called for an investigation into the second strike against crew members that had survived an initial attack on a Venezuelan boat.

If ever there was a moment to stop our stumbling into dictatorship this is it. Trump cannot be allowed to drag the military into an unlawful conflict with Venezuela. This attempt to compromise the US armed forces must be opposed.

But the real reason for writing less has been that I am experiencing a threshold crisis. I have been setting the bar too high which has made it impossible to get anything out. This has left me sitting on several draft blog posts, which is highly unusual for me. I am not exactly sure what to do about this but stating my dilemma clearly and publicly is a start. I need to force myself to write more loosely about what's careening around my mind in an attempt to dislodge the jam.

A lot of my brain is taken up with trying to understand where AI is headed and how to invest accordingly. In 2024 and for much of 2025 the "scale at any cost" approach was attracting all the dollars. And while scaling is still making progress there are now a bunch of scaled players but major issues such models going from incredibly smart to stupid in a heartbeat are unresolved. It seem increasingly clear that we need at least one and possibly several new tricks (something I had written about on the USV blog as far back as May 2024).

Understanding the AI trajectory is deeply intertwined with the profound transition between ages that we are finding ourselves in. One trend that I have underestimated is the decline in birth rates. I continue to believe that there are some built in corrective mechanisms but the scale of the trend warrants more attention. Stefano from Unruly Capital has an interesting blog post about population decline as a investment thesis.

Finally the intersection of physics and philosophy that I have gotten to in my Philosophy Monday series is genuinely difficult territory. I am particularly obsessed at the moment with what I consider to be the central role of irreversibility for informing morality. Much of morality appears to be about preventing absorbing states for individuals and for societies.

For now I just trying to get back in the habit of writing by being more willing to think out loud with potentially half baked ideas. Let's see how it goes.

I was utterly fascinated by the conversation between Dwarkesh Patel and Richard Sutton, which is worth listening to in its entirety. Quite early on Patel makes the point that humans are in fact different from other animals because we have knowledge. Sutton rejects the salience of this and says he’s more interested in the commonality asserting that “humans are animals.” Our commonality is likely relevant for understanding some basic learning mechanisms but the bulk of human learning occurs in an entirely different way from animals because we can learn from knowledge and they can’t (as they don’t have knowledge the way I have defined it). And of course access to knowledge is also what accounts for the surprising success of LLMs.

At a later point in the conversation, we have the following exchange

Richard Sutton 01:04:16
Are there universal values that we can all agree on?

Dwarkesh Patel 01:04:21
I don’t think so, but that doesn’t prevent us from giving our kids a good education, right? Like we have some sense of wanting our children to be a certain way.

Denial of universal values is a widely held position and yet I find it jarring to hear it expressed. In this series I have already stated that I am firmly in the camp that universal values exist. Our job is to find and elucidate them. And this is a job for us humans that we should not abdicate to the new gods we are trying to invoke, because we won't know what we would get.

I believe it is important to invert the question. What is the alternative to universal values? How will technological progress not end in ruin for humanity as a whole in the absence of some universal values? I recently went to China to give several talks about my book The World After Capital. And as part of the preparation I revisited one of the core concepts from the book which is the “space of the possible” which grows as we develop new technology. It occurred to me that as we grow this space we simultaneously shrink the planet. Hunter gatherers walked. In the Agrarian Age we added horses and sailboats. In the Industrial age we worked our way up to jet planes. Today we have a global network operating at the speed of light and have built hypersonic missiles that can reach anywhere on the globe in less than an hour. There is no alternative to universal values in a world of sufficient technological capability. It is quite easy to see this in the extreme when you consider a planet destroying weapon that would blow it to smithereens (picture Alderaan -- also the cover image for this post). It must be a universal value to not make use of such a weapon.

While this may seem obvious, where it gets tricky is when there is disagreement on what the potential effects of a technology might be. This is central to the discussion around the existential risk from AI as reflected by the title of Eliezer Yudkowsky and Nate Soares’s book “If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All.” While the universal value of not destroying the planet and all life on it makes sense in the context of a weapon designed specifically to do this, in the case of AI a lot depends on what you believe. There are some who reject the existence of existential AI risk outright – a position I won’t engage with here. What’s more interesting is a position such as Sutton’s, who seems perfectly fine with AIs succeeding humans, largely because he has concluded that “[...] succession to digital intelligence or augmented humans is inevitable.” And if something is inevitable then why worry about it?

Well because even if you believe that succession is inevitable, there are possible good and bad outcomes from the position of humans. And here too, the central pivot is around universal values. The reason to work towards those and to work towards ways of aligning (at least some) ASis with them is one of the few paths that could lead to good outcomes. The extreme view that alignment is impossible, is intellectually more interesting than the outright denial of existential risk. The only consistent follow-on argument though is that we urgently need universal values in order to stop the development of ASI. How else would we sustain a global and actually enforced slow down of ASI development?

One interesting objection that I have heard to universal values is that they cannot be converged upon because at that very moment there would be no more outgroups (only one gigantic ingroup) and cohesion requires an outgroup. I don’t think this is true for two reasons. First, there will be a small group of people who would blow up the planet (or at least kill all humans). While this outgroup is likely small it does exist. Second, we may not be alone in the universe and even if we make progress on “universal” values here on Earth, there are potentially large outgroups that we may have to defend against eventually.

So more to come in Philosophy Mondays.

P.S. I am currently reading an interesting book called “Science and the Good” with the subtitle “The Tragic Quest for The Foundations of Morality” – obviously of great interest given my program of establishing an objective basis for universal values.

Consider Dario Amodei’s essay Machines of Loving Grace. The essay is full of numbers and footnotes and could be read as an objective attempt to show how AI can contribute to making the world a better place. But underneath all this veneer of rational argument appears a deep longing for a submission to gods. The title, which is taken from a poem, strongly suggests a divine appeal. For fun I decided to ask Claude what person or entity it most associates with the words “loving grace.” Unsurprisingly it replied with Jesus Christ. Now one might dismiss this as simply an extravagant title choice but the essay concludes with two paragraphs about the Culture series by Ian M. Banks. If you are not familiar with the series you could read these paragraphs as simply being about values.

Amodei references what he calls a “well-known post” by Scott Alexander which is in fact a story titled “The Goddess of Everything Else.” Now Alexander is a central figure of the rationalist community and so this too could simply be seen as an allegory but it is worth noting that Amodei chose not to include the title of that post. Amodei also points to MLK’s “arc of the moral universe.” Here too it’s possible to view this as a kind of moral determinism but of course MLK was a preacher and in turn picked up this idea from other religious men including a sermon by Theodore Parker.

So yes Amodei at some level wants to believe in a rational concept of a moral determinism that leads us to a better future. But even in his most explicit statement in the concluding paragraph there is some doubt:

These simple intuitions, if taken to their logical conclusion, lead eventually to rule of law, democracy, and Enlightenment values. If not inevitably, then at least as a statistical tendency, this is where humanity was already headed. AI simply offers an opportunity to get us there more quickly—to make the logic starker and the destination clearer.

Moral progress is not inevitable, just a statistical tendency. And then comes the final sentence of the essay:

Nevertheless, it is a thing of transcendent beauty. We have the opportunity to play some small role in making it real.

Again the choice of words here is unlikely to be entirely accidental. “Transcendent beauty” has strong religious or spiritual connotations. And what is our “small” role? Nothing short of the creation of AI gods.

Our problems are wicked. We can no longer appeal to the old gods to help us solve them. So we must create new ones in their stead. Machines of loving grace as our salvation. Machina deus ut deus ex machina.

And now back to the Culture series. It describes exactly such a civilization. Benevolent machine gods called “minds” rule allowing humans and other species to flourish. The series is well worth reading exactly because it is quite subtle about what such a world feels like to humans. A paradise at some level but also a vague sense of being played with by an opaque layer of higher intelligence. As an aside, there are also some quite fun moments where that higher intelligence is revealed to have many of the same foibles as humanity itself.

I empathize with the desire behind all of this. At times I feel overwhelmed by the immensity of the forces of both nature and culture. Huge systems which we can not fully comprehend and for which any one human is insignificant. Even someone as brilliant as Dario Amodei cannot single-handedly bend the physics of the climate crises, or solve the intricate puzzle of infectious disease, or pull us back from the descent into tribal politics. If anything, better understanding deepens the sense of individual impotence. It is in those moments of despair that we most desire divine intervention. And yet I continue to believe that we should not rush to abdicate our responsibility. Gods, once called, may be impossible to get rid of.

One possible counterargument though is that we will soon have unlimited artificial attention. AI will give us machines with more knowledge than ever possessed by any human and thus attention will no longer be scarce. This is true but it brings the alignment problem into sharp focus. What will all this machine attention be directed at? Will it be human flourishing? And for what definition of flourishing? We are building a genie and our stories full with examples of wish fulfillment by genies gone terribly awry. Take global warming. Yes machines can pay attention to it but they might rightly conclude that humans are the cause of it leading to the extinction of many other species. What exactly might the conclusion from this insight look like? Or take the crisis of meaning that underpins so many of the current mental health issues. An intelligent machine might look at this and conclude that humans are better off drugged or need a new religion with AI as its god.

If we want to solve human attention scarcity with machines we must solve the alignment problem. And of course herein lies the great irony: we are not paying enough attention to alignment. Instead we are finding ourselves propelled into a full on rush towards artificial super intelligence fueled by the same economic and cultural systems that are causing attention scarcity in the first place. Markets are allocating vast amounts of capital this race because of the perceived payoff to the winner. And our geopolitical thinking is still stuck in the industrial age also where progress is seen as a race between nations.

At present course and speed we may well achieve abundant artificial attention but instead of solving our problems we are more likely to add to them. There is a parallel here to the end of the Agrarian Age. That age was marked by frequent warfare between nations. When industrial capabilities first came along they were not seen as requiring a new age but were instead harnessed to the old model. That ultimately resulted in not one but two world wars. We are repeating this mistake now. Instead of setting out to invent a new age, we are aggravating the problems of the existing system. I continue to be optimistic about where we can ultimately get to with these new capabilities, but for now it looks like it will get a lot worse first (including the potential for catastrophic outcomes).