Yes, there is an AI bubble. AI-related stock prices are inflated, valuations of startups are inflated, there is too much marketing hype, data center construction is excessive, etc. All of these related bubblets will deflate, and probably soon. I’ll deal with the second question below, but the short answer is that we’ll be OK in the long run.

The AI Demand/Supply Mismatch

Why do I say there’s a bubble—and that it is likely to burst soon? The most important factor in the bubble is that demand is not keeping up with supply. All of the tech companies, both Magnificent 7 and AI startups, are on the supply side. The demand side is made up of non-tech industry corporations and individuals. Both are showing signs of weak demand. Some straws in that wind include:

• It’s pretty clear that bottom-up experimentation with generative AI in large organizations isn’t yielding much measurable value, which will eventually lower demand; the recent MIT NANDA study reports that 95% of companies are getting no measurable value from generative AI. While both the data and the methodology are problematic, I do think it’s directionally correct.

• The more enterprise-level use cases take substantially longer to implement than bottom-up experimentation, which will probably lower demand; This HBR article is a good summary of the reasons.

• For some of the more popular use cases such as code generation, it appears that the productivity gains are not quite was anticipated (primarily because of the need for human review); that should eventually lower demand;

• Some data suggest that the general public doesn’t use gen AI tools very often. Benedict Evans’ annual presentation about AI eating the world depicts several surveys in which daily use tops out at about 10%, and weekly use at 25-30%. And most of that is with one tool—ChatGPT.

There is also too much supply being developed on the data center front. While there are still some improvements in generative AI that will come from scaling training data and processors, it would appear that those benefits are leveling off, and credible alternatives to highly scaled models (e.g., the new DeepSeek V3) are still much more efficient than the primary US-based models in terms of compute.

If these trends continue, we’ll have many unused or under-utilized data centers, just as we had enormous volumes of dark fiber after the Internet bubble burst. While data center construction is so massive today that it is contributing more to the US economy than consumer spending, the pace of capacity creation isn’t sustainable and this bubble will surely burst too. The heavy use of carbon-based fuels for these data centers—nobody seems interested in powering them with wind or solar anymore, and nuclear will be slow to emerge—is also hardly sustainable for our atmosphere.

There is also a marketing bubble with generative AI, in that vendor company leaders are talking a lot about artificial general intelligence (AGI) while still being a long way from achieving it. For example, GPT-5 has some impressive capabilities, but it is hardly more intelligent than humans on every topic. Among many other shortcomings, the new LLM’s inability to draw an accurately labeled map of North America screams “not ready to beat humans.” This mismatch between marketing and actual performance doomed IBM Watson and will doom OpenAI if they keep it up.

Should We Care?

I suggested above that we shouldn’t care whether AI is in a soon-to-burst bubble state, but let me qualify that. From a financial perspective, many of us have benefitted from the U.S. stock market rise that has largely been fueled by AI. So from that narrow angle, we will care if the bubble bursts. I am not a stock market prognosticator, but I think the markets will eventually recover. There is likely to be pain in the short term, however.

From a general economic perspective, I would argue that even if the bubble bursts everything will eventually work out fine. The current AI bubble is a bubble of substance, as was the Internet and e-commerce bubble 25 years ago. Even if stock prices and startup valuations decline, AI will continue to advance and be used productively by companies and organizations. It’s probably better for long-term economic growth and for the maturation of AI if the general public isn’t discussing AI startup IPOs at cocktail parties and speculating over lunch about whether data center developers can get enough GPUs.

In my hometown of Boston we’re seeing the impact of a slowly-bursting biotech bubble. Instead of dark fiber or dark data centers, we’ve got plenty of dark lab space. There are very few biotech IPOs, venture funding is down, and startups are going out of business. There has been a mild drop in employment. Fortunately, the region’s economy is still fairly diverse and successful—including AI businesses—and we will eventually see the biotech industry boom again. But it’s perhaps an early indicator of what might happen with AI.