During the same week, Jeff Bezos shared his views on government. “If we ran Amazon the way New York City runs their school system – your packages would take six weeks to arrive.” The Amazon founder added, “We’d have to charge you a $100 delivery fee. And then when the package did finally arrive, it’d have the wrong item in it anyway.”

The fight is not really about billionaires or bureaucrats. It is about what happens when a society produces enormous, concentrated wealth faster than its institutions can absorb it.

Bezos and AOC provide convenient caricatures of two important arguments.

1. Markets enable some people to accumulate too much power, and that power allows them to prevent others from achieving the same success or earning their fair share.

2. Government is inefficient, and increasing taxes on the private sector would only lead to more waste and dysfunction.

California is the poster child of both arguments and a microcosm of the US as a whole. It is home to some of the world’s most phenomenal companies and entrepreneurs. Its companies, VC market, and talent networks are the envy of the world. At the same time, it has high taxes and governments that spend a lot but struggle to provide basic services or to build and maintain infrastructure.

California’s triumph and dysfunction are not separate phenomena; they are two sides of the same coin. Extraordinary wealth creation and institutional decay often go hand in hand.

There is a rich literature on how societies struggle to absorb a rapid increase in wealth, especially when it is concentrated in one sector. Economists call one version of this problem the “resource curse”: societies that suddenly discover oil, gas, or minerals often become richer on paper while their institutions become weaker, more corrupt, or more dependent on a single source of wealth.

This tension is about to intensify. As pointed out, the upcoming IPOs of SpaceX, OpenAI, and Anthropic will mint thousands of new multimillionaires and a bunch of new billionaires. Google and Facebook’s (Meta) IPOs minted far fewer millionaires, but even that was enough to make a noticeable impact on California’s state finances, income distribution, housing affordability, and broader economy. The next batch of large IPOs will have an even larger effect, against a backdrop of growing suspicion towards Big Tech and growing concerns about inequality and affordability.

Even that might only be the beginning. AI is the latest in a long line of technological changes that mess up the basic relationship between effort and reward. The more our economy depends on intangibles like software, networks, and content, the less fair or reasonable it feels. There are systematic reasons for this, but the point for now is that the AOC vs. Bezos debate is just warming up: Our economy will create more wealth and more unequal outcomes, and our government will become more eager to tax and more dysfunctional.

There will be populists supporting both sides of this divide. They will propose ideas that either kneecap the economy’s ability to create wealth or kneecap the government’s ability to address a growing list of challenges.

There is a better way, and it addresses the valid concerns of both sides. I mentioned the extensive literature on the adverse effects of rapid and concentrated wealth creation. This literature is focused on the discovery of natural resources such as gas and oil. But it offers solutions that apply to technology booms.

In essence, the US should treat innovation the same way Norway treats oil: encourage exploration, celebrate success, capture a share of the windfall, and put the proceeds into a fund that is insulated from short-term political spending and budget demands. The fund, in turn, invests the money for the long-term benefit of society, provides insurance for a rainy day, and can finance infrastructure and other public goods.

This does not require nationalizing AI companies or punishing founders. It could mean automatically dedicating a portion of capital-gains taxes, corporate tax windfalls, IPO-related receipts, and spectrum or data-center levies to a National Success Fund that operates under an independent, rule-bound mandate.

This arrangement addresses the two legitimate concerns represented by Bezos and AOC: It captures the upside and reinvests it in things that make everyone’s lives better, and it prevents the government from getting drunk on easy money. It gives higher taxes greater legitimacy and imposes greater discipline on politicians.

On its own, this solution will not solve all of America’s problems. But it should be one of the tools in our arsenal. And, beyond the specifics, it exemplifies the type of thinking we need to bring to the 21st Century: A synthesis that embraces economic growth and wealth creation AND acknowledges the importance of public institutions and the need for continued innovation on both fronts.

It’s time for the US to set up a National Success Fund. I wrote about this in 2023, but the need is more obvious and acute now. The choice is not between vilifying billionaires or vilifying government services. We can let the AI boom exacerbate our problems and tensions. Or we can use part of its upside to create a permanent national asset that strengthens America for generations to come.

Have a great week.

Best,

How will AI reshape our cities, companies, and careers?

My speaking schedule for the year is filling up. Visit my speaker profile and get in touch to learn more.

Click here to book a keynote or learn more.

People say we live in the “Information Age”. Indeed, our economy is increasingly anchored in the production of what economists call information goods: software, content, formulas, and other forms of intellectual property.

There are many differences between information goods and traditional physical goods. In Information Rules (1998), Hal Varian and Carl Shapiro point out one of the most important differences:

“Information is costly to produce, but cheap to reproduce.”

We understand this intuitively. To sell a billion cars, you need to actually produce a billion cars. But to sell a billion movie tickets, you only need to produce one movie. The same is true for software and other information-rich goods.

Software runs on physical infrastructure such as servers and communication networks. But the cost of setting up this infrastructure follows the same logic: a high upfront cost followed by a low cost of serving any additional customer.

To grow, a software business does not need to produce more physical things. By implication, it is not constrained by physical time and space and can grow at virtually unlimited speed to a virtually unlimited scale.

This has been largely true during the Internet and software boom of the past three decades. But the economics of AI seem different. For one, they require larger data centers filled with more expensive equipment and consuming more energy per square foot.

More importantly, the latest AI models consume significant additional resources to serve each additional customer query. When you ask ChatGPT 5.5 or Claude 4.7 a question, they do not simply deliver a cheap, pre-programmed answer. Instead, they reason; they send a series of back-and-forth queries in the background until they figure out what to tell you. From the user’s perspective, it might still feel like traditional software: you type in some input and get back some output. But from the machine’s side, the process is more elaborate, less predictable, and consumes much more energy and computing resources.

AI is not just information being copied. It is information being produced, again and again, every time someone asks a question.

As a result of the above, we are seeing something we’ve never seen before: Software demand is beginning to bump into physical constraints. The world is struggling to allocate sufficient land to build data centers and to produce and redirect the energy required to meet AI demand. Tech giants like Google, Amazon, Meta, and Microsoft are spending an unprecedented amount of money to build these new data centers, but they are approaching their financial limits. Google has recently partnered with Blackstone, one of the world’s largest landlords, to expand and expedite the construction of new data centers.

All this sounds like great news for real estate developers. Finally, order has been restored in the universe: If you want to grow your business, you need to pay more rent; the natural scarcity of land is asserting itself. Instead of software eating the world, it is now the world that is eating the free cash flow generated by software companies.

Not so fast.

It is easy to conclude that Silicon Valley will simply accept these constraints on its growth and profitability. But this is not going to happen. This is not what is already happening.

If buildings and energy on Earth are scarce, entrepreneurs will tap into them elsewhere. In recent months, multiple companies have begun allocating significant resources to developing data centers in space. SpaceX, Google, Nvidia, and Amazon are all in this game. Space has 24/7 sunshine, free air conditioning, no zoning laws or concerned neighbors, and lots and lots of empty space. Closer to earth, startups like Panthalassa are developing data centers that float in the ocean, seeking to capture similar benefits.

It will take some time for innovations of this kind to materialize. But they will. In the short and medium term, AI’s insatiable demand for land and energy is good for those who have a monopoly on these resources on earth. But in the longer term, this demand will fuel innovations that redefine our basic assumptions about the scarcity of land and energy.

In the past, humanity expanded to new continents because people wanted more sugar. In the future, humanity may expand beyond Earth because people want more slop. The joke is that slop may turn out to be more useful than sugar: the same machines that fill the internet with junk may also help us discover new energy, new materials, and new ways to loosen the oldest constraints on human ambition.

Have a great day.

Best,

P.S.

🗞️ I spoke to Fortune about how AI is and isn’t affecting the labor market.

🎧 I chatted with Jacob Shapiro about the nonlinear economy, careers, cities, China, Israel, and more. You can find the full conversation on Spotify, Apple Podcasts, YouTube, and other platforms.

📚 Check out Geoffrey Cain’s excellent new Steve Jobs in Exile (affiliate link), about Apple’s founders' years at NeXT, which shaped his thinking about the internet, software, managing unmanageable people, and managing himself.

How will AI reshape our cities, companies, and careers?

My speaking schedule for the year is filling up. Visit my speaker profile and get in touch to learn more.

Click here to book a keynote or learn more.

But cheap intelligence does not solve humanity’s basic problem.

The basic problem is not a lack of intelligence. In many cases, we already know what to do. We know how to reduce traffic. We know how to improve public health. We know how to build more housing. We know how to make organizations more efficient. We know how to prevent many avoidable disasters.

The problem is that knowing what to do is not the same as getting people to do it.

Human civilization is not limited only by intelligence. It is limited by coordination, trust, legitimacy, incentives, and consent. AI may help with some of these. But in many cases, it will intensify the underlying challenge.

Consider autonomous vehicles.

If every car became “super-intelligent,” traffic would not disappear. Each vehicle might drive better than a human. It might brake faster, choose routes more efficiently, avoid accidents, and react to its surroundings with perfect attention. But if each car is still optimizing for its own passenger, traffic remains a coordination problem.

Every vehicle wants to move forward. Every passenger wants the fastest route. Every local optimization creates effects elsewhere. A car that cuts through a side street may save its passenger three minutes while slowing down an entire neighborhood. A vehicle that inches forward at an intersection may behave rationally from its own perspective while contributing to gridlock for everyone else.

Traffic is not caused merely by drivers' lack of intelligence. It is caused by too many agents pursuing overlapping goals in shared space.

Now imagine a different system. All cars are connected. Their movements are coordinated. A central or distributed traffic intelligence assigns speed, spacing, routes, and right-of-way. Some cars are made to slow down even when they could go faster. Some passengers are routed away from the shortest path. Some trips are delayed so the whole system can flow better.

That might actually solve traffic. But would humans accept it?

The key improvement would not come from making every vehicle smarter. It would come from making every vehicle submit to a larger coordination system. The intelligence matters, but getting humans to submit to it matters more.

Autonomous vehicles illustrate a dynamic that recurs in many other human affairs. We already have enough intelligence to solve many of our stated problems. What we lack is agreement on who gets to decide, who has to sacrifice, who gets priority, and what happens to those who refuse. AI does not eliminate those questions. It sharpens them.

Most humans do not want to be optimized. They want to be respected, consulted, flattered, protected, or left alone. They want better outcomes, but not necessarily the discipline required to produce them. They want less traffic, but not to be told when they can drive. They want cheaper housing, but not always in their neighborhood. They want better public health and education, but not always the rules that make it possible. They want more efficient organizations, but not the loss of status, autonomy, or ambiguity that efficiency often requires.

This is why the promise of AI is both thrilling and terrifying. Its real advantage may not be that it knows more than we do. Its advantage may be that it can coordinate, persuade, monitor, and enforce behavior at a scale no human institution ever could.

Does that sound like a nightmare or a dream?

It depends on who controls the system, whose preferences count, whose sacrifices are considered acceptable, and what happens to those who refuse.

AI may make intelligence abundant. But the scarce resource is consensus.

Have a great week.

Best,

P.S.

I discussed human intelligence and coordination in my recent podcast with . You can find the full conversation on Spotify, Apple Podcasts, YouTube, and other platforms.

just published a piece about what AI-powered coordination could look like in practice. Money quote:

The internet made it easier for people to find one another, signal their preferences, and form temporary swarms. But it did not make it much easier to govern, compromise, allocate sacrifice, or act together over time. AI agents may help. They may give groups better memory, better interfaces, and better follow-through.

How will AI reshape our cities, companies, and careers?

My speaking schedule for the year is filling up. Visit my speaker profile and get in touch to learn more.

Click here to book a keynote or learn more.

The conversation ranged widely. Below are some thoughts on two related themes we discussed: What AI actually is and what it actually wants.

AI is a Medium, Not a Tool

Most of the conversation about artificial intelligence is stuck on the wrong question. We ask whether AI will replace this job or that job, whether it will make us more productive, and whether it will sit obediently on our desktops or run around performing things independently at our bidding. That framing assumes AI is a tool.

It isn’t. AI is a medium. And once you see that, the entire shape of the economy it’s producing snaps into focus.

A tool extends what you do. A medium packages what you are. The phonograph didn’t help singers sing; it took their performance, contained it, and broadcast it to a million people in parallel. Recorded music didn’t expand the number of singers in the world. It collapsed their income distribution into something violently unequal: a handful of stars, an ocean of baristas with guitars. The number of singers stayed roughly constant. What the technology dictated was a different income distribution, a different level of job security, and a different path toward success.

AI does the same thing, but for everything. As I told Jacob,

“AI can take me, my personality, my thinking, my ideas, and then serve a million people in parallel in the same way that a recording serves a million people in parallel, but in a much richer way, so they can actually interact with me rather than just listen to something that I did.”

This is why the “will AI replace my job?” question is a distraction. The more honest question is: how will AI change the income distribution of the game I’m playing, regardless of whether I’m a winner or a loser in it? Singers weren’t replaced by recordings. They were re-sorted. So were authors after the printing press, actors after film, photographers after the iPhone. The technology didn’t fire people. It changed who got paid, and how much, and how stable that pay was.

AI Doesn’t Want to Replace You. It Wants to Make You Interchangeable.

Here is the line that should sit on the desk of every white-collar professional in the world:

“The goal of capital is not to replace you because capital doesn’t want you to go on vacation. The goal of capital is to make you interchangeable, not to replace you.”

The history of work is a history of this drive. The artisan becomes the production-line worker. The creative becomes an office worker on a different kind of production line. The freelancer becomes a profile on a matching platform — Uber, Upwork, whatever — where if the client doesn’t like you, there are twenty others one click away. Each step preserves the work and dissolves the worker’s leverage.

Hollywood is the cleanest illustration. Early studios refused to print actors’ names on posters, a deliberate strategy to keep talent fungible. Once performers like Florence Lawrence and Mary Pickford became recognizable to audiences, their names became assets. Suddenly, actresses had pricing power. The studios paid up because stars made movies more likely to succeed. But as soon as they paid up, studio bosses started searching for ways to drive labor’s pricing power back down. The logical conclusion is what we have now: Marvel, Disney, and DC Comics, where the IP is the star and the actors playing Spider-Man or Wonder Woman are replaceable. The value sits in the asset, not the person.

AI is a machine that turns labor into capital — people’s work into work that can be done by computers and electricity. The goal of capital is to turn labor into a commodity. This is not a critique of capitalism; it’s just an observation of the inherent tendencies that drive our economy forward.

The good news is that capital can never fully succeed. Every time our economy figures out how to automate a profession or make certain skills interchangeable, humans come up with new things that other people want and cannot be described or measured well enough for a machine to do.

The other good news is that capital is not an independent entity. It is owned by people. So the question is not how to stop it from growing; it is how to spread it in a way that makes life better. The answer is not as simple as our politicians think.

More on this, soon!

On the podcast, Jacob and I covered a lot more than AI: the nonlinear economy and why all swans are now black, what’s happening to cities, why China’s volatility-suppression model has an expiration date, why the politics of both left and right are selling 1950s answers to a 2020s economy, and what the war in the Middle East reveals about all of it.

I’ll be unpacking those threads in the coming weeks. In the meantime, you can listen to my conversation with Jacob Shapiro on Spotify, Apple Podcasts, YouTube, and other platforms.

Have a great week!

Best,

Psst, are you a workplace executive?

Next month, I will be in London for a workshop about the future of work and workplaces. It’s a very cool gathering, in a very cool spot, with very cool perks. Would you like to join us? The organizers are looking for senior leaders & executives leading corporate workplace strategy based in the UK & Europe. If this sounds like you, drop me a line.

How will AI reshape our cities, companies, and careers?

My speaking schedule for the year is filling up. Visit my speaker profile and get in touch to learn more.

Click here to book a keynote or learn more.

Our GDP growth, stock markets, and job growth are overwhelmingly dependent on data center construction. Data center construction is dependent on the capital expenditures of five companies. The spending decisions of those five companies depend on the performance of two startups. The performance of these two startups depends on a handful of researchers. And those researchers do not fully understand what they are doing.

Let’s start at the end. Last week, OpenAI published a blog post about its effort to figure out why ChatGPT keeps talking about goblins:

Starting with GPT‑5.1, our models began developing a strange habit: they increasingly mentioned goblins, gremlins, and other creatures in their metaphors. Unlike model bugs that show up through a tanking eval or a spiking training metric and point back to a specific change, this one crept in subtly. A single “little goblin” in an answer could be harmless, even charming. Across model generations, though, the habit became hard to miss: the goblins kept multiplying, and we needed to figure out where they came from.

The post then explained why this may have happened and what OpenAI has done to stop it. You’re welcome to read the explanation in the original post, but it boils down to this: the people who design ChatGPT do not fully understand how it works and cannot predict how it will behave. In their own words:

Depending on who you ask, the goblins are a delightful or annoying quirk of the model. But they are also a powerful example of how reward signals can shape model behavior in unexpected ways…

And then there is the rest of the economy. The launch of ChatGPT kicked off an unprecedented investment boom. The first few months of 2026 saw more venture capital being poured into startups than at any previous point in history. Most of that money went to two companies: Anthropic and OpenAI.

Over the past three years, Anthropic and OpenAI have grown their revenue from zero to an annualized rate of around $40 billion each. This revenue growth signals strong demand from actual customers. It also fuels a massive demand for new data centers and the components that go into them.

That demand has fueled the revenue and valuation growth of a handful of giant tech companies such as Nvidia (which designs AI chips), Microsoft (which operates data centers), Amazon (which designs AI chips and operates data centers), Meta (which operates data centers and is developing a cutting-edge AI model), and Google (which designs AI chips and operates data centers and is developing a cutting-edge AI model).

These giant tech companies, in turn, have committed to investing more than $700 billion this year to build new data centers. That massive investment underpins America’s economic growth. As the Federal Reserve of St. Louis put it,

“recent investments in AI-related categories have contributed significantly to the real GDP growth in 2025. It has surpassed the contribution of IT components to the real GDP growth made during the dot-com boom, both in levels and as a share of GDP.”

When it comes to jobs, the demand for data center construction-related positions is now offsetting the decline of all other job listings combined.

In addition, about 10 of the largest tech companies account for more than 40% of the S&P 500's total value and most of its revenue growth. That means even investors who are “not investing in tech” are heavily dependent on the performance of a handful of tech companies.

To sum it up: Demand from OpenAI and Anthropic underpins the revenue growth of the largest tech companies, which in turn underpin the stock market, and whose spending underpins GDP and job growth. Meanwhile, the models developed by OpenAI and Anthropic behave in ways that their own developers cannot predict and do not fully understand.

In other words, our economy is now an upside-down pyramid held up by a goblin. The people building AI are not particularly reckless, and they are definitely not stupid. But they are doing something genuinely new: building systems powerful enough to move the world before anyone fully understands how they work.

Have a great week,

Psst, are you a workplace executive?

Next month, I will be in London for a workshop about the future of work and workplaces. It’s a very cool gathering, in a very cool spot, with very cool perks. Would you like to join us? The organizers are looking for senior leaders & executives leading corporate workplace strategy based in the UK & Europe. If this sounds like you, drop me a line.

🎤 How will AI reshape our cities, companies, and careers? My speaking schedule for the year is filling up. Visit my speaker profile and get in touch to learn more.

Click here to book a keynote or learn more.