The BigAI Slowdown: a Voluntary Market Correction
Right at the moment when the financial market shows signs of the AI bubble bursting, the captains of the industry together propose a slow down. Big Capital never refrains from anything out of responsibility, so we must look elsewhere for the explanation.
My intuition is leading me into thinking that a call for slowdown can be a sign that the industry has begun to recognize that the financial dynamics surrounding AI have become dangerous enough that some form of controlled deceleration may be in its own interest.
The contemporaneous reporting is unusually revealing. On September 14, AI-linked stocks fell sharply after Dario Amodei, Sam Altman and Elon Musk all endorsed slowing the pace of AI development; Nvidia fell, AMD fell even more, and the selloff spread through semiconductor and infrastructure stocks. Reuters also notes that investors are increasingly worried about debt, circular financing, enormous capital expenditures and rising yields behind the AI buildout.
Why now?
I think there are at least three layers to what is happening.
1. The technological argument may be the surface justification
The public argument is safety: cyberattacks, misuse, autonomous systems, loss of control, etc. Amodei has been particularly explicit about this, and Altman and Musk have now endorsed the basic proposition that the race needs to slow down. I don't dismiss this argument. There are genuine technological reasons for caution.
But the timing matters enormously.
If the industry were merely discovering an ethical problem, you would expect the argument to emerge independently of financial-market conditions. Instead, the safety argument arrives at precisely the moment when investors are becoming increasingly uncomfortable with the economics of the AI investment cycle. That makes the financial interpretation impossible to ignore.
2. The AI economy has acquired a reflexive character
The current AI boom isn't a simple model:
investors finance companies → companies produce AI → AI generates profits.
It looks like:
investors finance AI infrastructure → AI co.'s buy big infrastructure → infrastructure co.'s revenues and valuations rise → higher valuations → more capital → still more AI infrastructure.
That is a reflexive financial system. And when expectations become sufficiently optimistic, the distinction between real economic demand and investment demand generated by expectations of future demand becomes blurred.
This is why the recent discussion about circular financing and debt is so important. Reuters specifically reports that investors are increasingly scrutinizing debt and circular financing in the AI ecosystem. Ruchir Sharma has made essentially the same point from a broader market perspective: he argues that AI already displays several classic bubble characteristics - overvaluation, excessive ownership and overinvestment - while the financing requirements are becoming enormous relative to present revenues.
That doesn't mean AI is "fake." It means a revolutionary technology can simultaneously be real and be the object of a financial bubble. The railways were real. Electricity was real. The internet was real. Housing was real. And yet bubbles formed around all of them.
3. A "soft landing" hypothesis becomes interesting
I think this is the strongest part of my arguments. Suppose the people running these companies can see that expectations have become excessive. They have two alternatives:
Option A: Continue accelerating.
Everybody keeps announcing bigger models, bigger data centers, bigger chip orders, bigger capex projections and increasingly heroic future revenue assumptions. That can push valuations still higher, but eventually the numbers have to reconcile. If they don't, the correction is brutal.
Option B: Introduce a narrative of restraint.
Suddenly the market has a reason to lower expectations:
- slower model development;
- slower chip demand growth;
- lower infrastructure spending;
- longer time horizons for monetization;
- greater regulatory scrutiny;
- more emphasis on safety;
- delayed IPOs;
- less frantic competition.
The extraordinary thing about Option B is that it can allow valuations to decline without requiring anyone to say "the AI boom was a mistake." That's the potential soft landing: a voluntary deflation
4. There is a paradox here
If the CEOs really wanted to protect their stock valuations, publicly calling for a slowdown seems counter-intuitive. Yet that's precisely why I find the development interesting.
The market reaction has been immediate. Nvidia and other chip stocks came under pressure; semiconductor companies around the world declined; SoftBank was hit particularly hard.
In other words, the announcement itself has begun doing some of the work of a correction. That's potentially useful to the industry. Imagine you are sitting on an enormous investment program predicated on the assumption that AI capability and AI infrastructure demand will grow exponentially.
If you announce:
"Everything is going fantastically well; we're going to accelerate even further."
you increase the expectations you will eventually have to satisfy.
But if you announce:
"We need to slow down because safety and responsible deployment require it."
you have created an intellectually respectable explanation for why growth may moderate. The market can reprice the sector without the industry having to confess:
"We may have overbuilt."
5. Another signal to watch
OpenAI's IPO delay.
Reuters reports that Altman said OpenAI would not proceed with an IPO this year, citing safety concerns. Whether safety is the entire reason is another matter.
An IPO at the top of an exuberant market is extraordinarily attractive to insiders and early investors. But an IPO also forces a company to expose its economics to public scrutiny. And the economics of AI are precisely what investors are beginning to question. So delaying the IPO can have another interpretation: don't crystallize the valuation while the market's assumptions about AI economics are becoming unstable.
That's not proof of anything. But it is an intriguing coincidence.
6. The dangerous variable
This may be the most important part of the story. The AI boom has been built in an environment where investors were willing to capitalize extremely distant future earnings at very high valuations. That becomes much harder when interest rates and bond yields rise. Reuters reports that U.S. Treasury yields are approaching 5%, while markets are expecting further monetary tightening.
This matters because AI is, in many respects, a duration trade. You are spending enormous amounts today for profits that are supposed to arrive years in the future. The higher the discount rate, the less valuable those distant profits become. And therefore you can get a strange situation: AI technology continues improving rapidly while AI stocks fall dramatically. The question is whether the price paid for future AI has become unreasonable.
7. No conspiracy
The AI industry's leaders may have reached the point where slowing the technological race is becoming financially rational, even if their public justification is primarily safety.
They don't have to conspire. They don't even have to agree about the markets. They simply have to recognize the same underlying problem: exponential expectations are becoming increasingly difficult to sustain. And once several industry leaders independently reach that conclusion, you can get a coordinated-looking slowdown without an actual conspiracy.
8. There is an even darker possibility
The slowdown could also be an attempt to protect the incumbents. Think about what happens if the AI race continues indefinitely. The companies have to spend extraordinary sums on:
- GPUs;
- data centers;
- electricity;
- networking;
- memory;
- talent;
- model training;
- inference infrastructure.
The financial barrier to entry becomes enormous. Eventually, even the largest technology companies have to finance increasingly gigantic capital expenditures. At some point, the question changes from:
"Who has the best model?"
to:
"Who can finance the next $500 billion of infrastructure?"
That's a financial arms race. A slowdown could therefore benefit the established players by reducing the rate at which they have to spend capital merely to avoid falling behind. This is one reason I would be cautious about taking the "responsibility" narrative completely at face value.
9. The distinction I would make
There are really three bubbles, not one.
The technological bubble
The belief that AI capabilities will continue improving exponentially forever. That may burst somewhat, but AI itself remains enormously important.
The investment bubble
The belief that essentially every dollar invested in AI infrastructure will generate extraordinary returns. This is much more vulnerable.
The valuation bubble
The belief that companies exposed to AI deserve almost unlimited multiples because they sit at the center of the next technological revolution. This is the bubble I think is most susceptible to a soft landing. And importantly, the collapse of the third doesn't require the collapse of the first.
My base case
If I had to put the pieces together today, I would expect something like this:
AI doesn't crash. AI valuations correct. AI capex growth slows. Margins become more important. Weak AI companies disappear. Infrastructure projects get postponed. The surviving giants continue investing, but more selectively.
In other words: the technology keeps advancing while the financial narrative becomes much less euphoric. That would actually be a very healthy outcome. The dangerous alternative is the opposite: AI capability keeps accelerating, expectations keep accelerating, leverage keeps accelerating, and everybody discovers simultaneously that the expected cash flows aren't going to arrive quickly enough.
Then instead of a soft landing you get a liquidation. And there is evidence that investors are increasingly thinking about precisely this problem: the latest selloff came not because AI suddenly stopped working, but because expectations about the pace and economics of AI development changed.
So I think my instinct can be right in one important respect: the most interesting story may not be "AI leaders suddenly became responsible." It may be that the people closest to the machine have realized that the machine is running at a speed that is becoming financially, and perhaps, technologically unsustainable.
And if so, "slowdown" is a remarkably convenient word. It can mean safety. It can mean prudence. It can mean less capex. It can mean lower expectations. And, potentially, it can mean "let's bring the market back down to earth without making it look like we've admitted there was a bubble."
That last interpretation is speculative, but in my view it is absolutely worth watching.
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