A month ago, I set out to answer a deceptively simple question: Is AI a bubble?
Since 2024, people have been asking me this as I’ve spoken at events around the world.
Even as Wall Street bankers largely see this as an investment boom, more people are asking the question in meeting rooms and conference halls in Europe and the US.
Some have made up their minds.
called it a “peak bubble.” The Atlantic warns that there is a “possibility that we’re currently experiencing an AI bubble, in which investor excitement has gotten too far ahead of the technology’s near-term productivity benefits. If that bubble bursts, it could put the dot-com crash to shame – and the tech giants and their Silicon Valley backers won’t be the only ones who suffer.” The Economist said that “the potential cost has risen alarmingly high.”The best way to understand a question like this is to create a framework, one that you can update as new evidence emerges. Putting this together has taken dozens of hours of data analysis, modeling and numerous conversations with investors and executives.
This essay is that framework: five gauges to weigh genAI against history’s bubbles.
I studied and lived through the dot-com bubble. I was an investor and felt its effects firsthand.1 Like many of you, I was also active during the Global Financial Crisis. For this reason, I’ve invested considerable thought and analysis into a robust framework to understand what’s going on. I’ll share it with you today.
My thinking is grounded in the study of Carlota Perez, Bill Janeway and other giants in the field, as well as financial analyses of major banks and analysts. But what I haven’t seen anywhere else is a framework that translates those theories into a practical dashboard for AI today: a set of parameters you can read, compare across past bubbles and use to orient your decisions.
We will make the underlying data and methodology available to members of Exponential View in the coming weeks.
I usually reserve my deep dives for paying members of Exponential View. But this question is too important, and the public debate clouded by posturing, for me to keep it behind a paywall. So today’s piece is free for everyone to read. If you want to access this analysis as a PDF, you can download it here.
If you are an investor or executive navigating these questions, I am opening a very limited number of consultation sessions to work directly on them. This is a bespoke opportunity to stress-test your strategy with the same analytical framework I’ve developed. You can apply here.
The meaning of a bubble
Bubbles are among the oldest stories of capitalism. They’re parables of excess, belief and collapse. But bubbles are not just financial phenomena; they are cultural artifacts. They return again and again as morality tales about greed and folly. Tulip mania, often misremembered as a frenzy of bankrupt weavers and drowning merchants, was less disastrous than legend suggests. It was confined to wealthy merchants and left the Dutch economy largely unscathed. But the myth has endured, and that is the point: bubbles become stories we tell ourselves about the dangers of optimism.
Some bubbles are financial: the South Sea frenzy of the 1720s, the roaring stock market of the 1920s, Japan’s real-estate boom in the 1980s and the housing crash of 2008.2 Some are technological. In the 1840s, railways were hailed as the veins of a new industrial body. They were. But a body needs only so many veins, and tracks were soon laid in places commerce could not sustain. Telecoms in the 1990s promised a wired utopia, only for 70 million miles of excess fiber to lie dark underground. The dot-com boom gave us visions of a new economy, much of which did eventually materialize, but not before valuations evaporated in 2000.
The funny thing is that there doesn’t seem to be an academic consensus on what an investment bubble is. Nobel laureate in economics Eugene Fama has gone so far as to say they don’t exist.
I want to go beyond the notion that we know a bubble when we see a bubble. There are two interrelated systems at work. The first, the most visible, is when stock markets become absurdly overvalued and collapse. The second is whether the quantity of productive capital (going into capital expenditure or venture capital) deployed collapses. The two are related, of course. A collapse in equity prices makes investment flows more expensive. And a secular decline in productive capital investment may be read by equity markets as a harbinger of a slowdown.
But to frame this: we see a bubble as being a 50% drawdown from the peak equity value that is sustained for at least five years. In the case of the US housing bubble and the dot-com, that trough was roughly five years long. Full recovery to pre-bubble peaks took 10 years for US housing, and 15 for the dot-com. Alongside, we would expect a substantial decline in the rate of productive capital deployed, once again 50% from peak.
For the purposes of this analysis, by “bubble” I’ll address both of those competing dimensions. Ultimately, it means a phase marked by a rapid escalation in prices and investment, where valuations drift materially away from the underlying prospects and realistic earnings power of the assets involved. Bubbles thrive on abundant capital and seductive narratives, and they tend to end in a sharp and sustained reversal that wipes out much of the paper wealth created on the way up.
A “boom,” by contrast, can look very similar in its early stages with rising valuations and accelerating investment. But the crucial distinction is that, in a boom, fundamentals eventually catch up. The underlying cash flows, productivity gains, or genuine demand growth rise to meet the optimism. Booms can still overshoot, but they consolidate into durable industries and lasting economic value.
Between the two lies a gray zone: periods of exuberance when it is genuinely hard to tell whether capital is building the foundations of a new economy or merely inflating prices that will not be sustained. It’s like being in the eye of a storm: you can feel the wind, rain and pressure, but you don’t yet know whether it will clear the air or flatten the house.
This brings us to the present question: is AI another bubble? What unsettles many observers are the numbers. Since ChatGPT’s release in late 2022, hyperscalers have more than doubled their annual data center capex, betting heavily on the infrastructure needed to train and run ever-larger models. When Sam Altman floated a $7 trillion investment requirement last year, it sounded like hubris. Today, investors aren’t laughing; they’re wondering whether the scale of spending is sustainable.
Bubbles are impossible to diagnose in real time. Only in retrospect do we know whether exuberance was justified or delusional. Rather than making a definitive call — which borders on unhelpful speculation — it’s better to benchmark today’s boom against history to spot where bubble-like dynamics may be creeping in.
We’re thinking of this as flying a plane. Pilots don’t rely on a single instrument. They monitor a cluster of gauges to understand how the flight is going. We’ll use five of them here:
Gauge 1 – Economic strain: Is investment now large enough to bend the economy?
Gauge 2 – Industry strain: Are industry revenues commensurate with the deployed capex?
Gauge 3 – Revenue growth: Is revenue rising/broadening fast enough to catch up?
Gauge 4 – Valuation heat: How hot are valuations? Compared to history, are stocks excessively overpriced?
Gauge 5 – Funding quality: What kind of money is funding this? Is it strong balance sheets, or fragile, flighty capital?
In the rest of the essay, I look at each gauge to explain why it may be green, amber or red, and at the end I pull them together into a full dashboard view.