Ethan Mollick: “AI has blurred lines between jobs.”

One of our big findings in our study at Procter and Gamble was that AI blurred the lines between jobs. Now OpenAI has a similar finding.

Organizational boundaries are becoming porous, the walls thinning. Companies are going to need to think about division of labor in a new way. pic.twitter.com/OcNtL3OdIH

— Ethan Mollick (@emollick) July 31, 2026

This is not optional, things are getting chaotic now, and not dealing with this change won't make it go away. Plus, this could be a huge boost for both individual satisfaction & firm performance if done properly

Our paper: https://t.co/9HjE0W1Ti8
OpenAI: https://t.co/21fOey4a3b

— Ethan Mollick (@emollick) July 31, 2026

An AI blizzard is headed our way [Yikes!]

Adam Satariano, Paul Mozur, Jacqueline Gu and Cade Metz, The Impending, Inescapable Deluge of A.I. NYTimes, July 30, 2026.

Boom....

From the American Midwest to the Persian Gulf, hundreds of major data centers now under construction will be turned on in the coming years. They are set to deliver an avalanche of computing power to develop and run A.I. that has no equal in the history of the technology industry, with breakthroughs that once felt revolutionary likely to become increasingly routine.

Behind each leap in A.I. are corresponding jumps in computing power. Today, there are about 20 million A.I. chips crammed into the data centers that underpin the technology’s growing abilities and usage worldwide, according to the research firm Epoch AI. [...]

In size and ambition, this moment compares to the building of the railroads in the 1800s, President Franklin D. Roosevelt’s New Deal in the 1930s, and the Manhattan Project to create an atomic weapon in the 1940s, technologists said.

“This is the largest scale infrastructure build-out in the history of humanity,” said Rob Wachen, a co-founder of the microchip firm Etched, which has raised more than $1 billion to meet the growing demand for A.I. components.

Peter DeSantis, who leads foundational A.I. models at Amazon — which provides computing power to the A.I. firms Anthropic, OpenAI and others — said the Seattle company has doubled its computing capacity since 2022 and would double it again by next year. “It’s hard to get your mind around the scale,” he said. [...]

Confidence in the Scaling Laws has led A.I. leaders to make ever bolder predictions. Dario Amodei, the chief executive of Anthropic, has said that if these laws hold for another year or two, A.I. will be able to perform huge amounts of white-collar work. Demis Hassabis, the head of Google’s A.I. lab DeepMind, wrote recently that A.I. could usher in “10x of the Industrial Revolution at 10x the speed.”

Bust?

Economists and investors have raised concerns that tech firms are spending faster than they can profit from A.I. Past infrastructure booms have been followed by downturns before the benefits of the technology were realized. The railroad boom in the 1800s, electrification in the 1920s and the dot-com bubble in the late 1990s were punctuated by economic recessions and a stock market crash as companies that overspent went out of business.

“Each time you’ve had a technological revolution, this kind of bubble bursting happened,” said Philippe Aghion, who won the Nobel in economic science in 2025 for research on innovation-driven economic growth. “A.I. is like the fourth industrial revolution and it has this aspect to it that generates a bubble.”

Moreover:

With more computing power coming online, geopolitical divisions are only set to widen.

The United States, home to about 5,500 data centers, about 10 times the next closest country, is far ahead of the rest of the world, including China. U.S. companies like Amazon, Google, Microsoft and Meta control about 80 percent of global computing power that drives A.I., according to Epoch AI. Google alone is believed to have four times as many A.I. chips as all of China’s companies, which are racing to catch up by developing new semiconductors and A.I. infrastructure of their own.

The race is on...

The article then goes on to discuss the huge increase in total chip count distributed over a growing collection of ever larger data centers under construction or proposed. We're in an AI arms race. The article then goes on to discuss the race with China, currently a fairly distant second (by an order of magnitude) to the US in chip count and gigawatts.

Mr. Nanos said the U.S. data center lead over China would likely grow over the next four to five years, before China’s domestic chips are produced at scale. After that, China should begin closing the gap.

“The advantage will run out,” he said.

The U.S.-China race threatens to leave the rest of the world behind. France, Germany and other nations are trying to encourage data center construction across the European Union, which has 5 percent of global A.I. computing power, according to a report by A.I. developers and policy experts in the region. Europe has been hampered by electricity and land access, permitting and financing.

Changes in the labor market:

“There’s going to be millions of jobs destroyed, millions of jobs created,” said Erik Brynjolfsson, an economist who is the director of Stanford University’s Digital Economy Lab. “That’s going to be very difficult. Even if new jobs are created, they’re not the same jobs.”

The last three paragraphs are about recursive self-improvement.

Whoops! Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.65 trillion in debt that doesn't appear on their balance sheets

🦔A Nikkei investigation found that Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.65 trillion in debt that doesn't appear on their balance sheets, more than the $1.35 trillion they officially report. These are GPU contracts, data center leases, and joint ventures that… pic.twitter.com/QbkzlenQAv

— Hedgie (@HedgieMarkets) July 20, 2026

America and China in the World, a quick note

Is that where we’re headed, to a bipolar world dominated by America and China? I just asked Google Search: “Is China the largest economy in the world?” Its reply:

China is the world's largest economy when measured by Purchasing Power Parity (PPP), but the United States is the largest in terms of nominal Gross Domestic Product (GDP).

• By Purchasing Power Parity (PPP): China is the world's largest economy, with an estimated output exceeding $44 trillion. This metric adjusts for the cost of living and the price of local goods, reflecting a higher real volume of economic activity.
  
• By Nominal GDP: The United States holds the top spot. In current market exchange rates, the U.S. economy is valued at approximately $32 trillion, while China's nominal GDP is roughly $20 trillion.

So that’s one thing. The other is AI. America and China are in a race for dominance in AI. America has a technical lead, but with respect to Frontier LLMs, that lead is measured in months, not years. And China is making its models open weight while the most advanced American models (Anthropic, OpenAI, Google) and not open. It’s not at all clear how that will shake out.

With the Trump administration authoritarianism is on the rise in America. While the Democrats may well win the next presidential election, it’s not at all clear what that means for America’s already precarious democracy. The problem is that a great deal of power is concentrated in the hands of political and business elites, so much that America’s democracy looks like a chess game played by oligarchs where ordinary Americans are the pawns.

Is that what the world will become in 2040, a competition between American and Chinese oligarchs in which everyone else is a pawn, with Homo economicus triumphant over all?

Henry Farrell on "The political economy of billionaire derangement"

Henry Farrell, The political economy of billionaire derangement, Programmable Mutter, July 15, 2026.

He opens with two somewhat different quotes about billionaires, Tyler Cowen more or less in favor of them and Tim O’Reilly deeply skeptical of them. He suggests we breed them together:

To be clear: this hybrid would be notably different to Tim’s argument, and likely actively objectionable to Tyler. I alone am to blame. But surprisingly, there is inadvertent support for some of its key elements from Peter Thiel, who is a surprisingly valuable chronicler of the conditions that give rise to billionaire derangement, as well as a living example of it (the Antichrist is here!).2 Here, I’m drawing both on lectures that Thiel gave at Stanford, as summarized by his amanuensis Blake Masters, and the resulting co-authored book, Zero To One. These sources have a lot to say about the connections between entrepreneurship, kingship, and personal eccentricity.

The conclusion I draw is that the forces that Tim identifies, combined with specific aspects of the political economy of Silicon Valley, help explain the derangement of certain Silicon Valley billionaires and their epigones. Old style princes were notorious for their tendencies to deranged behavior, which came not just from their inbreeding, but their power, and the unwillingness and inability of others to contradict or check them. So too, modern princes.

Not too long ago, many people, including Tyler, hoped that the advance of classical liberalism would go hand-in-hand with the growing power of tech billionaires, advancing both causes at once. Now, politically influential tech billionaires have visibly lost contact both with reality and with anything that could plausibly be described as classical liberal values. See the screen shotted quote above. Rather than making snide asides about billionaire derangement syndrome, it might be time for such people to confront what actual billionaire derangement means for the ideological straddle that they have relied on for so long. That is even more so, in a world where markets as well as market-makers are being devoured by the passions.

To summarize the particulars of my theory: Thiel’s lectures and book provide good, if incomplete evidence that the princely passions described by Hirschman didn’t disappear, but went underground. Commerce and power were fused into a new ideology of entrepreneurial virtù that became highly influential among the founder community in Silicon Valley. This combined with Silicon Valley’s (and popular culture’s) tendency to connect genius with eccentricity, not simply selecting people who seemed strange, but compounding their strangeness through self-reinforcing feedback loops. Finally, this all happened in an intimate social environment of founders and funders. Billionaires know each other and measure themselves and their success against those they consider peers, in a dense entangled clique that commingles high degrees of mutual influence with rivalries and jealousies.

Farrell then goes on to quote Thiel’s book at some length, analyzing while going along. And so:

In short then, Thiel - both as reported by Masters and in collaboration with him - suggests that Silicon Valley is a place where being a founder is tantamount to being a king. You have a greater chance of succeeding if you are weird, and if you succeed, your weirdness will likely feed upon itself in feedback loops of positive reinforcement. Finally, it is a closely interconnected social system where the key people are conjoined in a dense tangible clique. They observe each other all the time, and are observed by others for cues of what you need to do to become a made man. [...]

SpaceX perhaps marks a culmination of the kingly ideal, a moment in which, as Tim puts it, someone like Musk can “raise enormous amounts of capital while freeing himself from any restraints from those who provide it, so that he can spend the proceeds on Mars, humanoid robots, artificial intelligence or whatever next satisfies his ambition.” But it is also the moment in which someone who is visibly profoundly disturbed has briefly become the world’s first trillionaire. And it is one in which others want to copy him.

And then a bow to Keynes’s “animal spirits” in the market place:

I’ll finish by noting that this is just one aspect of a greater change. Deranged billionaires like Musk and Thiel are both partly responsible for this transformation, and notable symptoms of it. Still, they are not the whole of it. The reason that the SpaceX IPO temporarily succeeded, even though the numbers make no sense whatsoever, is that investment markets too are ruled by vibes. Tyler’s co-blogger, Alex Tabarrok, devoted years of his life to making the case for prediction markets, arguing that they would distill market wisdom into a more general source of knowledge on a multitude of topics. Now they are here, but all too often it’s spirits, not wisdom, that they seem to be distilling. Even more so for crypto. Matt Levine makes a joke about “Bleebzorx Tokens” to explain the worthlessness of memecoins, and someone else invents Bleebzorx Tokens to make a tidy profit.

There’s more at the link.

OpenAI has been declared a financial risk

OpenAI is a risk.

In this case to Oracle.

S&P formalizes what I have been saying since 2023. https://t.co/aMcjEViLLS

— Gary Marcus (@GaryMarcus) July 12, 2026

Big AI has bet on the wrong business model.

David Wallace-Wells, Did We Make the Wrong Bet on Big A.I.? NYTimes, July 8, 2026.

Last week, the Palantir chief executive Alex Karp made one of his more remarkable television appearances in what is quickly becoming a notorious run of televised rants.

“Something has gone completely wrong,” he declared on CNBC, in an appearance so vivid and spastic it was widely described online as a “crash out.” He was referring to the whole structure of the A.I. industry, which had been built on top of a value proposition that looked to him like a dead end. The big labs, such as Anthropic and OpenAI, have been overhyping their own closed-source models, he argued, hoarding their value rather than empowering their clients and partners with them. More than that, he seemed to say the labs were exploiting those clients and partners — private companies and individuals but also militaries and intelligence agencies — by making use of their research and intellectual property. Open-source or open-weight alternatives, which allow considerably more in-house customization and control, were obviously preferable, he suggested, for almost all users. “The jig is up,” he announced. [...]

This is one reason it was so striking for Karp to be yelling that A.I. was heading in the wrong direction — a presumptive ally openly bashing the big A.I. labs and the business proposition they represent. Karp had been softly floating his critique for some time, but the CNBC event looked like a proper coming out. Just one day earlier Palantir had published a kind of manifesto devoted to what it described as the all-important principle of “A.I. sovereignty.” The central argument: Companies should seek to build their own A.I. tools, not just customize those on offer from the frontier labs. This might mean relying on open-source L.L.M.s rather than the proprietary ones on which the A.I. boom has mostly been built in America, but it would amount to a liberating declaration of independence from Big A.I., which in Karp’s estimation was sucking up much more value than it was generating.

Karp isn’t exactly a disinterested observer here. [...] France has announced that its intelligence service is cutting ties with Palantir. The future of the firm’s partnership with Britain’s National Health Service also seems to be in jeopardy. Karp was on TV to promote a new partnership with Nvidia that would allow Palantir to develop and sell a distinct set of products to compete with those on offer from the frontier labs — which is to say, in railing against the Big A.I. business model, he was undeniably talking his own book.

Questioning the hype:

The basic idea was that at a certain point, competition would somewhat naturally come to an end, when the technology would grow so powerful that it could quickly and dramatically engineer its own successor models, producing an exponential liftoff leading quite quickly to what is often called “artificial superintelligence.” [...] These days, as A.I. boosters have cooled their talk of a jobs apocalypse, you also hear a little less about artificial superintelligence, now typically short-handed as “A.S.I.” But the ongoing A.I. investment cycle is still built on the same underlying paradigm: that historic levels of capital expenditure are justified because the returns from winning the race would be unthinkably enormous.

But can the race even be won? Can any lab open up an enduring advantage over the others, let alone one sufficient to justify a monopolistic claim on A.I. revenue?

Over the last year or so, this logic has come to seem a lot more questionable, in part because, though progress has continued, no model has retained a long-lasting advantage, and plenty of those cheaper, open-source alternatives have kept a pretty close pace with the best-in-class versions.

And thus

a growing number of A.I. watchers have begun emphasizing that however impressive the models were, the ultimate impact of A.I. will be determined as much by what is sometimes called “diffusion”: how quickly, widely and capably those tools will be embedded in a broader social and economic ecosystem still directed by humans and full of many human bottlenecks. If that alternative perspective is right, it will make the leading A.I. labs considerably less central to the A.I. future than they have seemed for so long. A draft internal analysis prepared by Treasury Department analysts has reportedly warned that the size of the big A.I. companies represents a systemic risk to the country’s economy and financial system, though higher-ups have publicly criticized the report. [...]

But as we move further into that A.I. future, it no longer looks so clear that we are heading toward convergence like we used to read about in science fiction. Instead, what we have is a more unsettled landscape, which some have called decentralized and democratic and others simply more competitive. The meaning of this technology is not limited to its market impact, of course, and the trajectory could change again. But that is just another reminder of how early in this story we are — that such fundamental propositions about the shape of what’s to come might change so profoundly in the space of just a year or two.

And this doesn't even take into consideration the criticisms made by Gary Marcus, Subbarao Kambhampati, Yann LeCunn, Melanie Mitchell and others to the effect that the big labs have bet on the wrong technology.

SpaceX has become a securitized narrative, a meme stock

Know why $SPCX is so volatile?

Because it’s a meme stock with a rocket company thrown in.

So the price is being driven by vibes, not news.

— Gary Marcus (@GaryMarcus) June 26, 2026

From the S-1, p. 30:

Our mission is to build the systems and technologies necessary to make life multiplanetary, to understand the true nature of the universe, and to extend the light of consciousness to the stars. To do this, we have formed the most ambitious, vertically integrated innovation engine on (and off) Earth with unmatched capabilities to rapidly manufacture and launch space-based communications that connect the world, to harness the Sun to power a truth- seeking artificial intelligence that advances scientific discovery, and ultimately to build a base on the Moon and cities on other planets.

In other words, to boldly go where no one has gone before.

This very expensive AI infrastructure depreciates fast; if you don't make profits soon, you won't make them ever.

“If there is a deflation of the AI bubble, the optimists say that the new infrastructure will remain even if the companies do not — just as railways survived the 19th-century railway bust. However, this fails to reckon with the reality of depreciation (few pieces of silicon hold… pic.twitter.com/TaiMJqUzne

— Gary Marcus (@GaryMarcus) June 26, 2026

In 2025 OpenAI spent $35B and lost $21B (Whoops!)

OpenAI spent roughly as much last year as the US government spent on the FBI and NASA combined.

The return on all that? A $21 billion loss. 👇 https://t.co/kN8wuFvmb9 pic.twitter.com/PAIzJPXehl

— Ed Elson (@edels0n) June 25, 2026

“The way down is the way up” may work for mystics, but not for business.

Big AI vs. Big Government in the 21st Century

Ross Douthat, The Battle With Anthropic Is the Start of a New Kind of Conflict, NYTimes, June 16, 2026.

The nature of the Anthropic conflict can be swiftly summarized even if the details are in dispute. Two months ago the company declined to publicly release its latest model, Mythos, citing various safety concerns (and hyping the model’s revolutionary power). After previewing Mythos to the U.S. government and certain corporate actors, Anthropic then released Fable, a version of the model with various safety guardrails. Amazon, an Anthropic investor and client, discovered a way to bypass some of those guardrails. This was reported to the White House, Anthropic’s response was deemed unsatisfactory, and the administration used its export-control power to forbid the use of Fable by any foreign national inside the United States and anybody at all outside it — a rule that Anthropic treated as a requirement to shut the new A.I. model down.

That’s where we are now, with the company and the administration negotiating over how to bring back Fable while ongoing leaks to the press paint one or the other side as unreasonable or reckless or ideological and clueless about tech.

Two facets of the conflict:

But beyond the specifics of why, say, the libertarian tech people in the Trump administration distrust the effective-altruist tech people running Anthropic, the kind of conflict we’re seeing here is overdetermined by the trajectory of the A.I. models: There is too much potential power here not to have ongoing, escalating struggles over who actually gets to rule.

The war over Fable previews the two broad forms that this conflict will take. First there is a private-public struggle, where governments grope for a regulatory sweet spot that allows them to maintain a meaningful veto over the A.I. behemoths without killing off their innovative power, while the A.I. companies try to maintain control over their own models and influence over how governments use their innovations.

There is a path here that leads to nationalization in all but name and a path that leads to a kind of de facto corporate takeover of the government, or at least a too-big-to-fail symbiosis. And along the way there may be not just conflicts between presidents and A.I. executives but also increasingly ruthless corporation-on-corporation action, out of fear that the A.I. landscape is winner-take-all to an extent we’ve never seen in capitalism before. [...]

Then alongside the struggle to control A.I. power within American borders, there is the geopolitical struggle to maximize global power (where the only real players are probably the United States and China) and maintain sovereignty (where everyone else is likely to be scrambling to maintain some independence). The use of export controls to shut down Fable presumably reflected U.S. fears of Chinese access to a jailbroken version of the model, but it was also a warning to every other country in the world: If we end up with economy-permeating A.I. models that are made and regulated in America, the American government will control the on-off switch.

There's more at the link.

John Quiggin on the SpaceX IPO

At Crooked Timber, June 15, 2026.

The SpaceX IPO, valuing a motley collection of dubious business at over a trillion dollars, marks the abandonment of the Efficient (financial) Markets Hypothesis, one of the zombie ideas I criticised in the wake of the Global Financial Crisis. Not only do financial markets fail in the task of valuing assets accurately, but the institutional structures that are supposed to make them work have given up trying.

This was prefigured by the rise of Bitcoin and other forms of crypto. Revealingly, no one any longer uses the term “cryptocurrency” – these assets are never used as currency in ordinary transactions, and even their illicit uses seem to have faded. Rather, Bitcoin is valuable solely because it is valued. As I pointed out back in 2018, (free from paywall here) once this logic is accepted, it can be applied to financial assets more generally, and particularly to stock markets. [...]

It’s only with SpaceX that we can see the complete abandonment of any pretence at rationality. In the case of SpaceX, I was struck by Dave Karpf’s observation that Musk’s wealth in 2020 was “only” $24 billion. Everything of value in his career (Tesla cars, batteries, Starlink) had been achieved by then, and everything he has touched since then has been a disaster (Xitter, Cybertruck, robotaxis, Starship). Yet his wealth has multiplied 50 times over).

In support of the IPO, Goldman Sachs has put its name to the claim that the company will grow 100 times over by 2030. This is patently absurd. Nothing in Musk’s ragbag of assets has this kind of potential. [...]

The comprehensive corruption of the financial system confirms me in the view that the USA is one big grift. Not just the financial system, but politically and militarily as well. But that’s a topic for another day.

A sophisticated take on the AI bubble argument

GeometricInvestor has a longish analysis of money flow and investment in the AI business, The AI Capex Ledger: Who Pays, Who Earns, and What the Bond Market Is Missing, June 12,2026.

The opening paragraphs:

The AI debate is stuck on the wrong question.

The question is not whether AI is a bubble. Nor is it whether NVIDIA is expensive, whether Michael Burry is early again, or whether productivity gains will eventually lower inflation. The better question is an accounting one: who needs to earn what return for the AI capex cycle to make sense?

At the bottom of the stack, GPU, HBM, networking, power, and cooling suppliers are already earning. The capex is real. The checks have cleared. That was the first phase of the trade.

The harder question sits one layer above. Hyperscalers and neoclouds are converting capital into compute. Compute becomes tokens. Tokens must become revenue. Revenue must become gross profit after depreciation, power, financing, and model costs. And finally, the buyers of those tokens must earn a return high enough to keep spending.

Only then does AI become a true macro productivity shock rather than a capital-spending boom with better branding.

Then comes the long analysis, followed by these concluding remarks:

The AI cycle will not be resolved by asking whether GPUs are expensive or whether chatbots are useful. It will be resolved by a chain of returns.

Can infrastructure suppliers earn margins without oversupply?

Can hyperscalers and neoclouds sell enough tokens to cover depreciation, power, financing, and obsolescence?

Can enterprises earn more from those tokens than they spend?

Can the economy convert those firm-level returns into productivity growth?

And if it can — does the bond market understand that higher productivity may mean a higher neutral rate, not just lower inflation?

That is the real AI macro debate. Each layer has a hurdle, and each hurdle has a date with evidence. The next time an AI headline crosses your screen, skip the bubble question and ask the ledger question: which layer is this, and what return does it need?

The framework carries its own falsifier, as it should: monetizable AI revenue clearing the hurdle band for years while aggregate productivity and corporate operating leverage outside the supplier base stay flat. That would mean buyers funding sellers indefinitely without a return — the one thing this piece says cannot last.

The first phase was about capex. The next phase is about returns.

H/t Tyler Cowen.

Ezra Klein contra (extreme) Homo economicus [Don't torture pigs]

Ezra Klein, What the Cult of Efficiency Costs Us, NYTimes, June 14, 2026.

The opening:

Chris Murphy, the Democratic senator from Connecticut, offered the graduates of Wesleyan University wise counsel in his commencement speech a few weeks back. “You are about to step out into a world that prizes efficiency and the annihilation of drift and friction above all else,” he said. “Our entire economy is built on rewarding companies that are efficient at making a profit, not based upon how they treat their workers, the social value of their product or the impact they have on the community.”

“You didn’t design this world,” he continued. “You didn’t choose it. But you will live with the consequences of this cult of efficiency. And you will have to choose which side you are on.”

Efficiency inflicted on pigs:

In 2016 and 2018, voters in Massachusetts and California passed ballot initiatives banning, among other things, the sale of pork from pigs confined in gestation crates. These crates confine breeding sows — large animals, often 400 to 500 pounds — in two-by-seven-foot cages in which they cannot so much as turn around, much less root or socialize. Because sows are often reimpregnated about a month after their piglets are born, they can spend years of their lives in these crates.

I watched, in the interest of fairness, a video from an arm of the National Pork Board on why gestation crates are good for pigs. It features row upon row of sows penned between bars so narrow they cannot turn around. It is no way for any animal to live, particularly not one as smart and as social as a pig.

There are studies I can cite on the psychic and physical violence these crates inflict on pigs — the elevated cortisol levels, the sores, the obsessive biting of metal bars — but I think the way Kristof puts it is simpler and more honest: “Think of your dog enduring what pigs face, and you realize that the moral cost is incalculable.” The difference between dogs and pigs is neither their intelligence nor their sentience. It is our willingness to admit their intelligence and their sentience. It is our decision to extend them our compassion and concern.

Homo economicus:

In traditional economics, prices are the informational lifeblood of an economy: They reveal the cost of materials and labor, the balance of supply and demand. But much can be hidden in prices. Perhaps it is artificially low because waste is being dumped into the rivers or workers are being robbed of their wages or the burden is borne by animals that will spend years of their lives without the comfort of their herd or the ability to feel grass beneath their hooves or turning around when curious about a sound. When that happens, we have sacrificed compassion for cost.

Most of us know by now that the lives animals lead in factory farms are often hideous. A 2019 survey by the Johns Hopkins Center for a Livable Future found that a majority of Americans wanted stronger oversight of confined animal feeding operations and a plurality wanted a ban on new ones. Those numbers were even higher when the same pollsters asked Iowans and North Carolinians, where majorities favored a ban on new concentrated animal feeding operations, in part because these operations often impose terrible costs on the human beings who live near them.

There's much more at the link.

Burton “Random Walk” Malkiel on SpaceX’s IPO and the poor upside potential for the stock

Burton Malkiel, best known for his 1973 book, A Random Walk Down Wall Street, has some interesting remarks in the NYTimes for those who are wary of being stuck with a stake on SpaceX (and perhaps OpenAI and Anthropic as well) as a consequence of having index funds in their 401(k).

Given that so many millions of Americans are suddenly having SpaceX shares foisted upon them, I understand why some financial experts are criticizing the practice of index investing itself. Right now, just a handful of A.I.-related stocks represent almost half the value of the total stock market index. If A.I. stocks collapse, so will the worth of your index fund.

This is the paragraph I find particularly interesting:

Unlike prior initial public offerings, SpaceX shares are already so expensive there isn’t a lot of upside potential left. When Facebook, now Meta, went public in 2012 with a valuation of $100 billion, shareholders were able to benefit financially from its growth to a $1.5 trillion giant. Amazon went public with a generous (for 1997) valuation of $440 million, and shareholders profited as it grew into a $2.5 trillion behemoth. Not so SpaceX. Because it was owned by private investors for so long, much of the gain will immediately be handed off to its venture and private equity backers rather than preserved for new investors.

He goes on to point this as well:

Moreover, unlike other public companies, SpaceX is employing a dual class share structure that gives Elon Musk essentially complete control with no independent oversight. Public shareholders will, comparatively, have no voice in corporate decisions. Mr. Musk controls multiple related corporate enterprises, raising the possibility of conflicted transactions within the Musk ecosystem. Many investors will be uncomfortable giving him so much power and holding an index fund in which he has so large a share.

He then says:

These are all legitimate reasons to worry. But in my view, it would be a mistake to abandon an indexing strategy. Timing the market is impossible. Yes, the stock market is unusually concentrated today, and it is likely to get even more so over the next period with Anthropic and OpenAI looking to go public soon.

You can read the rest of the article, if you wish. But it's that paragraph about upside potential that caught my attention.

What about these upcoming tech/AI IPOs? [Crazy, man, crazy]

David Wallace-Wells and Natasha Sarin, Wall Street’s A.I. Bet Is About to Become Yours, NYTimes, June 10, 2026.

SpaceX, Elon Musk’s rocket, satellite and A.I. company, is about to go public at a record-breaking $1.77 trillion. This summer, Anthropic and Open A.I. will follow suit, also with sky-high valuations. Are they worth it? The Opinion writer David Wallace-Wells and the contributing writer Natasha Sarin, an economist and law professor, tackle that question and discuss what these I.P.O.s mean for the American economy in the near future and beyond.

Well into the conversation:

Wallace-Wells: Well, I think, at the moment, a lot of Americans look at the A.I. companies and do see an especially vivid illustration of the plutocratic structure of our society, right? They see these five companies [SpaceX, Anthropic, OpenAI, Google, Microsoft]; they’re run by these five visible people. They’re all worth an unbelievable amount of money. And to the extent that we are imagining futures being dictated by the companies themselves, that can be quite scary.

And, to some degree, going public and having government stakes in the companies both address that problem to a certain extent. It would mean that the country, as a whole, is invested in the success of these labs and may benefit to some degree — although at what scale is an open question — from the success of the company. But there are other ways in which some of these approaches — public offerings and/or government investment — don’t change the dynamic. Which is to say — maybe, most notably — if this is a bubble then it’s the public that is left holding the bag. [...]

Sarin: You know, part of what makes me somewhat nervous — and should make everyone nervous — is that it’s not like you and I are alone in our view that, oh, we might be on the verge of a bubble, a bubble might be on the horizon. Last summer, Sam Altman was asked some version of, “Is this an A.I. bubble?” And he said: “Are we in a phase where investors as a whole are overexcited about A.I.? My opinion is yes.”

And another thing that should make us somewhat nervous is: If we look at history, if we look at every large technological innovation that has changed the way that humans work and the way that we all live — most recently the internet, but if we go back to railroads, whatever moment you want to look to — there is a very predictable, in some sense, cycle that you see, in terms of what happens to the economy at those moments of technological change.

Everyone sees the emergence of this new technology and gets really excited about it and its potential for massive change. Investors see that, too, and money rushes into this new technological prospect. And it rushes in productive ways, but it also rushes in ways that ultimately don’t end up being that productive. So, this is, if you think of examples during the internet bubble, like the growth of everything, every company that had .com attached to it. That doesn’t take away from the fact that the internet actually did change all of our lives.

But ultimately, what happens is that the bubble bursts and a bunch of debris is left behind, and that isn’t just about a couple of companies that ultimately fail. It is about what that means from the perspective of the broader economy that we all inhabit — in that, often, those corrections come with deep economic downturns and have the consequence of having large-scale unemployment, having an economy that isn’t growing quickly, having the need for the government to step in as a potential backstop.

And so, from my perspective, the question isn’t are we in a bubble or will the bubble burst? The question is: When?

Wallace-Wells: Yeah, one thing that I think about in this moment, when thinking about the I.P.O.s and what justifies these massive, massive valuations, is: These are five companies, and three of them are going public. In the public imagination, they do dominate the A.I. landscape. But of course, they are only providing one set of products, which is to say access to their L.L.M.s; and they’re providing it in different ways at different price points, at different tiers. But it seems to me like the massive boom story that they’re trying to tell is one that’s a little bit of a holdover from an earlier era of A.I. thinking, in which the companies and the people who are designing the products often talked about artificial general intelligence, artificial superintelligence, and they said that these products are improving so much that at some point they’re going to be able to improve themselves recursively without human interference.

And at that point, there’s going to be a kind of a takeoff in which the products themselves, the companies that made them — and to some extent the economy as a whole — would be rendered almost unrecognizable to people living on the other side of it. Some people call this “the singularity.”

But I wonder how much that still feels true today. And what I mean by that is, I was just looking at some data today, that just over the course of this calendar year, 2026, the amount of use of Chinese open-source A.I. models has tripled, while the use of the American A.I. products has basically flatlined. We see a lot of companies — Uber was maybe the most high-profile one — saying, “We’re actually winding down our employees’ use of A.I. because it was too expensive, given what we were getting out of it.”

And so, if we think about a future in which there’s going to be a superintelligent Borg running the whole economy, then yes, racing to be the biggest, best monopolistic A.I. company is hugely important and it does justify these absolutely gargantuan valuations if you believe that, for instance, Anthropic will be the one to win.

But if you’re thinking about a world in which, yes, A.I. is everywhere, yes, everyone is using it, but it’s not totally clear how many people think it’s super important to pay a huge premium to buy the absolute best-in-class model. And how many more people are likely to think, “I can use this open-source product from China that’s 80 percent as good as Anthropic’s first-rate model and pay only 5 percent of the price.” That’s a very different world.

The A.I. companies used to talk about building a moat — what they could do to secure their advantage. And they thought that getting to something like A.G.I. or ASI faster was the main way to do that. In a world in which that’s at least not imminently on the horizon, and we have all of this low-price competition from below, isn’t it the case that these companies are at some real risk of expecting much, much higher returns than they are likely to get in the medium term?

Sarin: Yes, 100 percent. And I will say something that has given me a fair bit of nervousness around A.I. and the ultimate possible profitability of these companies. ChatGPT was, as you were pointing out, launched in the fall of 2022, which feels like yesterday, but was less than four years ago, you know? But I guess it’s all relative —

Wallace-Wells: It’s both at once. It’s like a whole different era and the same.

Later:

Sarin: And flip side, for a while we were all talking about, and we were hearing a lot about, the idea of singularity or A.G.I. as this gold star that was coming right on the horizon. And now you have people — I’m using Sam Altman because he’s spoken publicly about this recently in ways that have gotten a fair bit of attention, but he’s not the only one saying this — where they’re talking about A.I. and describing it, even internally themselves, as not really all that useful of a term; and kind of describing it as not some sort of magical switch that’s going to flip on at some moment in the short horizon, but instead as the idea that these models are over time going to continue to get better and more useful and more transformational. But that’s not something that’s going to happen instantaneously.

Wallace-Wells: But even the way that you’re talking about these questions is illuminating to me, because you’re talking about, on the one hand, the big A.I. companies, and then the firms that are using them. And when you’re talking about productivity, you’re focusing on the firms that are using them. But these are two separate questions, right? If OpenAI and Anthropic are going to justify trillion-dollar valuations, or even larger valuations, they’re going to have to make a lot of money, too. Even if tons of people are making money on A.I., it has to be in these companies to justify the value.

And when I hear Sam Altman talking about the possibility that, in the future, A.I. will be like a utility in the same way that we pay for our electricity, I think to myself: The electric utilities are not worth a trillion dollars. This is a technology which absolutely has huge transformative potential, but to me, the question is: How much of that is captured by these companies?

Sarin: It feels like both an unanswered question, and an inherently, frankly, unanswerable question. But also, it should make you even more nervous about this bubble conversation that we were having because — and Ray Dalio said a version of this last week — if you’re thinking about it from the perspective of these firms, you have to spend a ton of money and justify these valuations, not just because you’re worried about, like, is this a good way to deploy resources, but because you’re worried about losing market share.

If you’re of a view that the way this all shakes is that there’s going to be one, two, maybe three large players that are able to capture the market, you have to try to be one of them. And that results in, frankly, the incentive structure to spend a lot, and to look like you are doing a lot, in ways that might ultimately not be tied to fundamentals with respect to investment opportunities and what is profit maximizing from the perspective of the firm.

So, you should be worried about that. But there’s another piece of this, which is that the companies themselves are asking public investors to pay prices at valuations that assume that A.I. is going to reshape the economy; and to pay those prices at the same time as these companies themselves haven’t figured out how to stop losing money; and at the same time, as these companies themselves haven’t figured out how they are going to be the ones left standing at the moment when A.I. ultimately is a developed technology with a developed set of market players that we all have grown with and understand. And I think that is something that is just so striking about this moment.

There’s more in the conversation. Bottom line, no one knows what’s going on, what’s going on. More than anyone’s willing to say out loud, it’s a crapshoot.

Some of my more skeptical articles about AI:

• Aye Aye, Cap’n! Investing in AI is like buying shares in a whaling voyage captained by a man who knows all about ships and little about whales. 3QD, Dec. 4, 2026
• Trapped in Work Mode: The Real Challenge of AI is not Technical, It’s Conceptual, Mythic, and Institutional, 3QD, June 22, 2026.
• The Shock and the Narrowing: How ChatGPT's Success May Have Compromised AI's Future, New Savanna, Mar. 0, 2026.
• The Paradox of Contemporary AI: Engineering Success and Institutional Failure, 3QD, Mar. 26, 2026.

Dealing with the Devil, John Malone and American business

Matt Stoller, A Billionaire Explains Why American Business Now Feels like the Mafia, BIG, June 5, 2026.

The article opens:

In 1981, a consultant named Elmer Smalling, an expert in pay-TV systems, was negotiating on behalf of Jefferson City, Missouri, to see about better prices for residents. Across the table was an executive for a giant corporation named TCI, which had 25% of the U.S. cable market and was known for the hard-charging tactics of its CEO, John Malone. The negotiations were not going well, because the city was thinking of taking away the franchise and going with someone else. Paul Alden, one of Malone’s subordinates, lived up to that reputation.

“We know where you live, where your office is and who you owe money to,” he told Smalling. ”We are having your house watched and we are going to use this information to destroy you. You made a big mistake messing with T.C.I. We are the largest cable company around [.] We are going to see that you are ruined professionally.”

The bulk of the article is a review of Malone's autobiography, Born to Be Wired: Lessons from a Lifetime Transforming Television, Wiring America for the Internet, and Growing Formula One, Discovery, Sirius XM, and the Atlanta Braves, 2025.

Stoller observes:

And this book, while not honest, is as close as I’ve ever seen to getting to the core of how the billionaires who took over American society really think. Malone’s book helped me understand the generation of media billionaires, before the tech oligarchs, who had to contend with the dying embers of New Deal regulations. And they knew a world where it wasn’t ok to do what they were trying to do, and yet they did it anyway, with energy, creativity, and a malevolent zeal to make the world safe for capital.

I'll give you three paragraphs from the review:

At age 27, he presented to the AT&T board of directors something that was then novel in corporate governance. He argued for them to buy back their own stock, which would reduce the total number of shares and thus increase earnings per share. The Chair of AT&T at the time, Fred Kappel, rejected the suggestion, while congratulating Malone on a fabulous presentation. Kappel told him, “If in your whole career, you can do a single thing that changes the Ma Bell system in even the smallest way, you would be very successful.”

It was a jarring moment for Malone. Though a rising star at AT&T, he realized he’d have to leave Bell Labs because it wouldn’t satisfy his need for control. But it’s also, unwittingly, the moment we see a young aggressive corporate leader’s reaction to that of an executive who was genuinely engaged in caretaking of a vast enterprise. Bell Labs had violated what would become a cardinal rule for Malone, which is that its leaders valued the corporation itself over the efficient use of capital in maximizing returns. Malone told his colleagues that Bell Labs, the legendary scientific lab, was a bureaucratic mess, a dinosaur, headed for extinction.

When he left Bell Labs for McKinsey, Malone writes as if it is an act of rebellion. “I wondered whether I had just jumped from the biggest, safest vessel I would ever board,” he wrote, “I felt ill.” This kind of odd faux rebellious streak courses through the book.

That middle paragraph is about a man who's sold his soul to Homo economicus.

There's much more in the article.

Mathematicians are concerned that exploitation by the AI industry threatens the long-term intellectual interests of the field

Siobhan Roberts, As A.I. Makes Strides in Mathematics, Mathematicians Urge Caution, NYTimes, June 2, 2026.

Mathematicians issue a declaration:

On Tuesday, a group of 16 mathematicians, in consultation with colleagues and math organizations worldwide, published the Leiden Declaration on Artificial Intelligence and Mathematics. It aims to “frame the conversation about future directions,” said Dame Ursula Martin, one of the authors, and a mathematician and computer scientist at Oxford.

This effort comes as A.I. models have been making headlines with successful results in research-level mathematics. In late May, OpenAI, the maker of ChatGPT, announced that one of its models had disproved a notable 80-year-old mathematics conjecture in the field of combinatorial geometry.

The conjecture is one of some 1,200 problems posed by the Hungarian mathematician Paul Erdos. While some of these “Erdos problems” are considered throwaway questions of narrow interest, others have proved influential and field shaping. Along with a research paper describing the proof, OpenAI released a companion paper by several independent mathematicians. Jacob Tsimerman of the University of Toronto, an expert in the adjacent subfield of number theory, commented: “This is a really impressive piece of work, and I would accept it for any journal without hesitation.”

Potential problems:

Among the potential threats that the Leiden Declaration authors articulate are accuracy and reliability: Journal editors are already complaining about a flood of plausible seeming A.I.- generated papers and proofs that have turned out to be incorrect, and in ways that are difficult for mathematicians to discern.

Perhaps most pointedly, the authors raise the question of whether the many A.I. companies tackling mathematics — major players such as OpenAI, Google DeepMind and Anthropic, or start-ups such as Harmonic, Math, Inc. and Axiom Math — are keeping the field’s best interests in mind. “Technology companies’ involvement in research,” they write, “raises the risk that research questions are prioritized and incentivized because of their amenability to A.I. methods and models, rather than their deeper significance to understanding.” In turn, they point out, this disadvantages researchers who choose not to use the technology, and those who do not have access to it.

For Rodrigo Ochigame, a historian and anthropologist of computing and artificial intelligence at Leiden University in the Netherlands, and one of the statement’s authors, the latest OpenAI proof illustrates why this sort of collective reckoning in the discipline is necessary. “The story follows the same pattern as many other announcements by commercial A.I. developers,” Dr. Ochigame said. “The A.I. model is proprietary and unavailable to anyone outside the company. We get a flashy promotional video, while basic information needed to assess the scientific meaning of the result is kept secret. The company disclosed nothing about the methods, human-written prompts, training data, or computational resources consumed.”

Much of the article consists of a videoconference and email dialog with Dr. Ochigame, Dr. Martin and mathematician Michael Harris of Columbia University:

MARTIN: What OpenAI has done is throw a great deal of resources at Erdos problems, and got lucky with this one. That’s remarkable, and impressed the experts. We are not told about the model’s failures. [...]

To think of mathematics in terms of precise and neatly stated problems, like high school exams or the list of Erdos problems, is to misunderstand and diminish what makes mathematics so powerful and significant. Mathematics is not just about solving problems — it is also the cultivation of ideas, understanding, judgment, and human insight.

HARRIS The purpose, from my perspective, is to recover control of the narrative about the values and the goals of mathematics from the A.I. industry. Mathematicians are concerned that the values of the profession are being misrepresented, not intentionally but due to the media campaign on the part of the industry, which seems to want to promote the belief that they are in a position to transform mathematics — “the A.I. revolution in math,” as one headline put it not long ago. [...]

We want to affirm certain values that have characterized the profession: openness, honesty, giving credit where credit is due, sharing, transparency about methodologies, and access for independent verification of results.

An aspect of mathematics that is cherished by mathematicians is that it is one of few successful examples of a gift economy — that is to say, its economy is somehow an island of idealism in our society.

OCHIGAME Several A.I. companies are investing in dedicated teams focusing on mathematics, using problems as benchmarks and publications as training data. They are training their models to prove theorems not because they want to advance mathematical knowledge, but because they hope that such training will improve the models’ reasoning abilities more generally. [...]

MARTIN It’s important not to lose sight of the fact that what the A.I. companies are doing, what you can achieve with this technology, is absolutely extraordinary. I don’t think we’re challenging that. We’re challenging the framing, we’re challenging the behaviors around it.

I share the concern that these mathematicians express, that the commercial exploitation of mathematics is inimical to long-term research interests.

There's more at the link.

AI Skeptic: This Business Makes No Sense

Ed Elson is joined by Ed Zitron to discuss the state of the AI industry (read: bubble). Zitron argues that every AI startup is unprofitable at its core. Then James Kynge breaks down what to expect from President Trump’s visit to China. Finally, Ed digs into data from the producer price index and what it could signal for inflation and the broader economy.

Ed Zitron is the author of the Where’s Your Ed At Newsletter, and the Better Offline Podcast. James Kynge is the host of the Prof G Media’s China Decode podcast and Senior Research Fellow at Chatham House.

Timestamps
00:00 - Intro
00:27 - Today's Number
00:47 - Market Vitals
01:30 - AI with Ed Zitron [interesting!–BB]
26:35 - Ad Break
29:00 - Trump Visits China (ft. James Kynge)
38:38 - Ad Break
40:08 - PPI
43:04 - Credits

AI’s New Trillion Dollar Mission (is BS)

YouTube:

This week on Prof G Markets, Scott Galloway and Ed Elson discuss the growing belief in Silicon Valley that AI won’t just replace workers, but managers too. Then, they break down the proposed pied-à-terre tax in NYC and why they believe taxing luxury second homes makes sense. Finally, they unpack why alcohol stocks are struggling while GLP-1 drugs are booming, and what that says about the future of American consumer behavior. [...]

Timestamps:
00:00 Preview
00:26 Today's number
01:04 Today's episode
05:23 AI's new mission
24:35 Ad break
26:55 The wealth tax debate is heating up
46:10 Ad break
48:41 The death of the night out
01:08:56 Week ahead
01:11:11 Scott's prediction
01:12:51 Ed's prediction
01:14:05 Credits

Starting at about 20:08, Scott Galloway:

And that is there is something to be said of and there's a balance here. I've in my companies, I'm doing some virtue signing right now. I've always said there should be two or three people and I've always had small companies, right? They they start at zero. Once we have a someone in HR or CFO, I either step step down from the CEO role or become the chairman because I I don't have those skills to scale a company and I don't want to deal with that stuff.

But until then, I've always said we should have two or three people that are one or two bad decisions away from living in their car. They're not, you know, they're they have bad judgment. They're they do stupid shit all the time. They're not what I'd call there's no way they're leaving us for Google. Let me put it that way. A little bit down on their luck maybe. And guess what? The business can be a great means of a little bit of social good.

And also the notion this is basically the notion that part of an organization if you think of stakeholders and I didn't get this. So, I always thought my goal was to pay people less than market and figure out other tricks of the trade to get them to stay and retain them. And then what you realize as you get older is that what is more rewarding is to build a profitable company and slightly overpay people. And if there's some fat in the organization and if there's a few people who quite frankly are, you know, not going to get a job anywhere else but work, you know, work hard or good people and maybe they're not, you know, amazing. Okay, that's okay too. And in some countries, the objective of a lot of the owners is to increase employment. Now, you have to balance that with making sure the organization can survive and has profitability.

But this is again this singular messiah complex that is nothing. There's only one stakeholder and it's shareholders. and I can figure out technology to replace people and we can all work singularly and then eventually the AI will take out those singular teams and replace them and then there will just be one. It'll be Jack Dorsey and Elon Musk who each own 49% of the world and do a lot of ketamine and if they're good enough they will provide UBI for all of us such that we don't uh rise up and kill them.

I I I'm not a fan, Ed. I'm not a fan of this whole line of thinking. I think it's [ __ ] and I think it's unhealthy and I think it's nihilistic.