Here is an excerpt:
America appears to be, at the moment, in a sort of benevolent hostage situation. AI-related spending now contributes more to the nation’s GDP growth than all consumer spending combined, and by another calculation, those AI expenditures accounted for 92 percent of GDP growth during the first half of 2025. Since the launch of ChatGPT, in late 2022, the tech industry has gone from making up 22 percent of the value in the S&P 500 to roughly one-third. Just yesterday, Meta, Microsoft, and Alphabet all reported substantial quarterly-revenue growth, and Reuters reported that OpenAI is planning to go public perhaps as soon as next year at a value of up to $1 trillion—which would be one of the largest IPOs in history. (An OpenAI spokesperson told Reuters, “An IPO is not our focus, so we could not possibly have set a date”; OpenAI and The Atlantic have a corporate partnership.)
Many people believe that growth will only continue. “We’re gonna need stadiums full of electricians, heavy equipment operators, ironworkers, HVAC technicians,” Dwarkesh Patel and Romeo Dean, AI-industry analysts, wrote recently. Large-scale data-center build-outs may already be reshaping America’s energy systems. OpenAI has announced that it intends to build at least 30 gigawatts’ worth of data centers—more power than all of New England requires on even the hottest day—and CEO Sam Altman has said he’d eventually like to build a gigawatt of AI infrastructure every week. Other major tech firms have similar ambitions.
Listen to the AI crowd talk enough, and you’ll get a sense that we may be on the cusp of an infrastructure boom. And yet, something strange is happening to the economy. Even as tech stocks have skyrocketed since 2022, the companies’ share of net profits from S&P 500 companies has hardly budged. Job openings have fallen despite a roaring stock market, 22 states are in or near a recession, and despite data centers propping up the construction industry, U.S. manufacturing is in decline.
It’s clear that AI is both drowning out and obscuring other stories about the wobbling American economy. That’s a concern. But even worse: What if AI’s promise for American business proves to be a mirage? What happens then?
The yawning gap between data-center expenditures and the rest of the economy has caused whispers of bubble to rise to a chorus. A growing number of financial and industry analysts have pointed out the enormous divergence between the historic investments in AI and the tech’s relatively modest revenues. For instance, according to The Information, OpenAI likely made $4 billion last year but lost $5 billion (making the idea of a $1 trillion IPO valuation that much more staggering). From July through September, Microsoft’s investments in OpenAI resulted in losses totaling more than $3 billion. For that same time period, Meta reported rapidly growing costs due to its AI investments, spooking investors and sending its stock down 9 percent.
Much is in flux. Chatbots and AI chips are getting more efficient almost by the day, while the business case for deploying generative-AI tools remains shaky. A recent report from McKinsey found that nearly 80 percent of companies using AI discovered that the technology had no significant impact on their bottom line. Meanwhile, nobody can say, beyond a few years, just how many more data centers Silicon Valley will need.
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Boom and bust can feel like two sides of the same coin: Consider also that if AI companies deliver on their massive investments, it would likely mean producing a technology so capable and revolutionary that it wipes out countless jobs and sends an unprecedented shock wave through the global economy before humans have time to adapt. (Perhaps we will be unable to adapt at all.) If they fail, there will likely be unprecedented financial turmoil as well.
The biggest lesson of the past two decades of Silicon Valley is that Meta, Amazon, and Google—and even the newer AI labs such as OpenAI—have remade our world and have become unfathomably rich for it, all while being mostly oblivious or uninterested in the fallout. They have chased growth and scale at all costs, and largely, they’ve won. The data-center build-out is the ultimate culmination of that chase: the pursuit of scale for scale itself. In all scenarios, the outcome seems only to be real, painful disruption for the rest of us.
If I had to put a number on it, I’d say I’m, I don’t know, maybe 85%–95% sure AI is in a bubble right now. The reason is that capabilities aren’t improving much, scaling the training of models is running into all sorts of problems (pre-training running out of steam, data running out, RL training being super inefficient), and fundamental issues scaling can’t overcome such as data inefficiency, poor generalization, lack of human example data in many domains, severe difficulties with models learning from video data, and a lack of continual learning or online learning.
Timing bubbles or timing the market in general is famously nearly impossible, so it’s hard to put a specific timeline on when I think the bubble will pop, but I reckon it’s gotta be within about 3 years, so by the end of 2028 or maybe a little further, but not much. In theory, a bubble could go on for quite a long time, but this is a huge bubble. The level of investment is immense, as described in the Atlantic article quoted above. It would be hard to sustain this level of investment for very long without delivering financial results, or delivering proxies for financial results like businesses’ pilot projects with AI going well.
I suspect when the AI bubble pops, for some people, suddenly the idea of imminent AGI will shatter like a dropped glass, for some people, it will be as if nothing happened at all, and for many people it will be somewhere in between. I can only hope that as many people as possible take it as an opportunity to return to fundamentals — go back to basics — and re-examine the case for near-term AGI from the ground up.