AI is already useful. It writes and reviews code, summarises documents, helps answer customer enquiries, analyses data and is steadily appearing inside the software businesses already use every day. We use it ourselves, and we think it is going to have a significant impact on the way businesses operate.
But believing AI is useful is not the same thing as believing that every investment being made around AI makes financial sense.
There is an enormous amount of money flowing into AI companies, chips, data centres and the energy infrastructure needed to power them. Stanford's latest AI Index found that global corporate AI investment more than doubled in 2025, while investment in generative AI grew even faster. At the same time, the biggest technology companies are committing extraordinary sums to the infrastructure they believe will be needed to satisfy future demand.
That demand may well arrive. In fact, there is plenty of evidence that some of it already has. The problem is that today's investment does not merely assume that AI will be popular. It assumes it will become valuable enough, quickly enough, to justify hundreds of billions of dollars being spent ahead of it.
That is where the bubble starts to appear.
What do we actually mean by an AI bubble?
What do we actually mean by an AI bubble?
Calling something a bubble can make it sound as though the underlying thing is fake. That is not really what a financial bubble means.
Strictly speaking, economists generally use the term for a situation where the price of an asset moves beyond what its underlying fundamentals can reasonably justify. Bubbles can become self-reinforcing: rising prices attract investment, that investment creates further growth and confidence, and the apparent success encourages even more money to enter. Eventually the fundamentals have to catch up with the expectations. If they don't, the process can reverse.
With AI, we are using the word slightly more broadly. The concern is not simply that a handful of AI shares are expensive. It is that valuations, infrastructure spending and business plans across an entire industry increasingly depend on very optimistic assumptions about how much AI customers will eventually consume and how much they will be prepared to pay for it.
That distinction matters because a technology can be genuinely transformative and still have a financial bubble built around it.
The dot-com boom is the obvious example. Investors were right that the internet would change the world. Where they went wrong was assuming that almost every internet company, at almost any valuation, would become a great business. When that optimism collided with reality, the Nasdaq fell around 77% from its 2000 peak to October 2002 and many internet companies disappeared. The technology itself continued to develop.
AI could follow a similar pattern.
Where the AI bubble is actually forming
Where the AI bubble is actually forming
The easiest way to see the scale of it is to follow the money into infrastructure.
Microsoft said in July that its calendar-year 2026 capital expenditure expectation was around $175 billion after an accounting-related lease change. It spent $41 billion in its latest quarter alone, with roughly two-thirds going towards relatively short-lived assets such as CPUs and GPUs. Amazon has said it expects to invest about $200 billion across the business in 2026, while its latest results said the large increase in property and equipment purchases primarily reflected AI investment. Meta expects capital expenditure of between $130 billion and $145 billion this year.
These aren't obscure AI startups gambling venture capital. They are some of the largest and most profitable companies in the world, building physical infrastructure at an extraordinary pace.
And to be fair, there is real demand behind it. AWS revenue grew 37% year-on-year in Amazon's latest quarter, Microsoft said Azure revenue grew 43%, and Microsoft 365 Copilot has passed 30 million paid seats. This is one reason we don't buy the argument that AI is simply a fad waiting to disappear.
The more interesting question is whether the eventual demand is large enough to justify the amount being built for it.
There is also an unusual dynamic inside the AI economy. The companies building the infrastructure are not always simply selling computing power to independent customers. They are also investors and strategic partners in some of the AI companies consuming huge quantities of that infrastructure. Microsoft's financial results, for example, now separately discuss the effects of its OpenAI investment, while Amazon's latest quarter included a very large accounting gain from its investments in Anthropic.
That doesn't make the revenue imaginary, nor does it prove that the whole market is circular. But it does make the industry more complicated than the simple story of millions of ordinary businesses suddenly buying hundreds of billions of dollars' worth of AI.
The argument in Ed Zitron's recent piece about an "AI demand bubble" takes this much further, arguing that the industry is dangerously dependent on a relatively small number of heavily funded AI labs buying enormous amounts of computing capacity. We wouldn't treat the analyst estimates used to make that case as established fact, particularly because the cloud companies do not disclose enough detail to verify them. But the broader question it raises is a sensible one: where does the money eventually come from once you follow the chain all the way back to the end customer?
OpenAI and Anthropic are projected to account for more than 70% of Amazon’s AI revenues. Source: Barclays research, via Where’s Your Ed At.
The awkward question is what customers will actually pay for
The awkward question is what customers will actually pay for
Business adoption of AI is unquestionably growing. The more difficult part is proving what that adoption is worth.
A recent NBER study surveyed nearly 6,000 senior executives across the UK, US, Germany and Australia. It found that 69% of firms were already using AI in some form. But 89% of executives said AI had made no measurable difference to labour productivity at their business over the previous three years. Among the minority reporting an impact, the researchers estimated the average productivity increase across all firms at just 0.29%. Executives expect substantially larger benefits in the years ahead, which may turn out to be correct, but that gap between what AI has delivered so far and what businesses expect it to deliver next is exactly where the financial risk sits.
That feels reasonably consistent with what we see in the technology industry. There are AI products that are genuinely excellent and already difficult to imagine giving up. There are also a lot of software companies that appear to have added an AI assistant because somebody decided the product needed an AI button.
Eventually, customers become less interested in whether something contains AI and start asking whether it saves enough time, makes enough money or improves something enough to justify another subscription.
There is another complication. Generative AI has a real usage cost. Model providers charge according to tokens, calls, generated media and other forms of consumption because every interaction ultimately requires computing resources. That is different from software where adding another active user can sometimes cost very little. AI can certainly become cheaper to run, and it already has in many cases, but falling inference costs are a double-edged sword: they make AI easier to deploy while also making it harder to justify premium prices for products that are little more than a thin layer over somebody else's model.
This means the industry has two things to prove at once. AI needs to become valuable enough that customers will pay materially more for it, while providers need to make it efficient enough that serving those customers produces attractive margins.
That is achievable for some products. It is much harder to believe it will be achievable for all of them.
What would the bubble bursting actually look like?
What would the bubble bursting actually look like?
We don't think there will necessarily be a morning where everybody collectively decides AI was a mistake.
A more believable version is much less dramatic. Growth remains strong, but not quite as strong as investors expected. Businesses become more selective about the AI tools they pay for. A few heavily funded AI companies struggle to justify another enormous funding round. Some data-centre projects are delayed. Investors start demanding profits rather than promises of future scale.
Once expectations change, the same feedback loop that helped inflate the market can begin to work in reverse.
Private AI businesses that could previously raise money based on what they might become in five years suddenly have to explain how they make money today. Dozens of companies selling broadly similar AI products consolidate or disappear. Features that once commanded a £20 or £30 monthly add-on get bundled into existing software. Model providers compete harder on price, pushing the underlying technology towards becoming a commodity.
The infrastructure companies have more room to adjust than is sometimes implied. Microsoft explicitly told investors in July that if demand changes it can slow purchases of shorter-lived CPUs and GPUs and stagger data-centre build-outs. That flexibility reduces the chance of a single catastrophic collapse, but the fact that investors are already asking Microsoft how it would respond to AI overcapacity shows that the possibility is no longer a particularly fringe concern.
So a burst may look less like AI suddenly disappearing and more like a fairly brutal repricing of who actually captures value from it.
There would still be GPUs running, data centres operating and businesses using AI. There would simply be fewer companies able to justify enormous valuations on the assumption that they will eventually own a large part of the market.
The dot-com comparison is useful, but not for the usual reason
The dot-com comparison is useful, but not for the usual reason
The lesson of the dot-com bubble isn't "new technology gets overhyped and fails".
It is almost the opposite.
The internet really did transform commerce, communication, entertainment and almost everything else. But being correct about the importance of the internet did not automatically make investors correct about which businesses would win, how quickly the opportunity would develop or what those businesses were worth in 1999.
A huge amount of capital was destroyed when those assumptions were reset. At the same time, the infrastructure, knowledge and consumer habits developed during the boom did not vanish. Companies that built genuinely useful businesses on top of the internet went on to become far larger afterwards.
That is the comparison we think makes sense for AI.
It is completely possible that today's AI boom is simultaneously producing one of the most important technologies of our generation and a spectacular amount of bad investment.
In fact, those two things often go together. A genuinely important technological shift makes an especially convincing investment story because there is something real underneath the hype. Nobody has to invent the potential of AI. You only have to exaggerate how quickly that potential turns into revenue, or assume that your particular company will be one of the businesses collecting it.
AI after the bubble could be much more useful
AI after the bubble could be much more useful
If there is a correction, we suspect the AI industry that comes out of it will look considerably less exciting from the outside.
There will probably be fewer standalone AI products doing essentially the same thing. The underlying models will become cheaper and more interchangeable. More AI functionality will simply be included in the software businesses already use, rather than sold as an entirely separate category.
Most importantly, companies will gradually stop caring about whether they are "using AI".
They will care that a process that used to take three hours now takes twenty minutes. They'll care that staff can find information more quickly, that a salesperson spends less time updating the CRM, that customer enquiries are dealt with faster or that somebody no longer has to copy information between three different systems.
That is when AI starts becoming infrastructure rather than a marketing category.
It is also why we don't think businesses should respond to talk of an AI bubble by avoiding AI. The sensible response is almost the opposite. Experiment with it, use it where it solves a genuine problem and measure whether it actually improves something. Just don't assume that every company with "AI" in its pitch deck will still exist in five years, or that every new AI subscription deserves a permanent place in your software stack.
There is plenty of evidence that demand for AI is real. There is also evidence that the measurable economic benefits are still catching up with the extraordinary level of investment being made in anticipation of them.
Our view is that those two things eventually have to meet somewhere in the middle. Expectations will come down, weaker businesses will disappear, infrastructure spending will become more disciplined and the companies left standing will have to prove that their products create enough value for customers to keep paying for them.
That would be painful for plenty of investors and AI companies. For everyone who actually wants to use the technology, though, it might be one of the healthiest things that could happen.
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