The AI Hype is Real - And Also a Bubble
The money is funding amazing tech, but they are building faster than anyone can use what's built.
Every market commentator right now is asking the same thing: is this a bubble? Or is the payoff about to make us all rich?!
The bubble and the payoff are not rival forecasts to be settled in one side’s favour. They are the near and far side of the same situation, and the crash, when it comes, will hit the financing, not the technology.
The Two Rates
Underneath the current AI bubble argument sit two moving and dynamic numbers:
The Build Rate: how fast capital lays down capacity.
The chips, the data centres, the substations, the trenches and the cooling towers.
Capital moves in herds, on borrowed money, priced to the eventual prize.
The build rate right now is FAST.The Use Rate: how fast firms actually fold that capacity into the work.
They have to redesign the process around it, retrain the people, rewrite the contracts, get the auditor to sign.
The use rate runs on human beings changing what they do on a Tuesday morning, and so the rate is slow, lumpy, and almost impossible to hurry.
An overbuild is what you get when the market starts building more than is being used, and a bubble is exactly that gap, priced to a $ amount.
What a bubble prices is the speed at which the money tried to reach the answer, NOT whether the technology is real.
And the part the bubble debate keeps walking past is the one that matters: the capacity an overbuild leaves behind does not vanish when the financing does. It goes dark, and it waits, sometimes for years, and then, if the technology was actually real, it gets used.
The dot-com bust was a textbook ‘overbuild’
The cleanest precedent is the telecom buildout that ran from 1996 to 2000, and here it’s worth being specific about how the money moved, because the shape is eerily similar to what’s happening now.
Equipment makers (Lucent, Nortel, Cisco) lent their cash-strapped customers billions to buy their own gear. Lucent committed more than eight billion in such financing, and the SEC later charged it with manufacturing over a billion dollars of revenue this way.
Across the industry the practice ballooned, from $3.5 billion in 1999 to nearly $33 billion 12 months later. Their customers were mostly small carriers betting they could undercut the Regional Bell Operating Companies, and mostly they could not: forty-seven of them went bankrupt between 2000 and 2003, and the “revenue” went bankrupt with them.
Set that beside Nvidia today. It takes a stake in OpenAI; OpenAI commits the money back…to Oracle for compute, to Nvidia for chips. That same dollar gets counted more than once on its way around the ring, and the rhyme is exact. It’s not fraud (per se).
It’s what a frenzy looks like from the inside, every time: demand cannot keep pace with what capital wants to build, so capital finances the demand itself, and books the loop as growth.
And then it stops.
The NASDAQ peaked at 5,048 in March 2000. By the time it hit the bottom it had given back about 78% of its value — some $5 trillion dollars, gone.
Cisco fell almost 90%. A quarter-century later it still trades below that peak, even though its earnings had grown sevenfold in between.
Corning, which had bet the company on optical fibre, went from a $100 a share to about $1.
The retail layer was worse. Pets.com went from IPO to liquidation in 268 days.
Amazon fell 94%, and was widely expected to die.
Real people who had bought near the top and held lost most of what they had. The bust ended careers and wiped out savings, and it’s worth remembering the harsh truth plainly, because even in the best patient-optimist case…..everything doesn’t work out.
Survivors were not the firms with the best stories; they were the ones with the balance sheet to reach the far side.
Amazon raised $1.25 billion in convertible debt in early 1999, about a month before that window shut for three years, and ended 2000 with a billion in cash and an oxygen tank that lasted until the business turned. Pets.com and the rest had no such tank.
What the crash actually did was decide the winners and losers of that bull run, it said almost nothing about whether the technology was real. The honest answer to that question was not visible until years later.
The real tech was what the crash left behind
Back to the other half of the dot-com story. The one the crash hid.
Those carriers in their heyday and visionaries like Corning had actually laid something like eighty million miles of fibre-optic cable, real glass buried under highways and ocean floors, and after the bust somewhere between eighty-five and ninety-five per cent of it sat unused. Dark fibre. And five years on from the crash most of it was still dark.
Most of the companies that laid it had gone under, and their trustees sold the glass for cents on the dollar to the few survivors with cash. But that cheap dark fibre that had nothing to do, suddenly lit up when YouTube launched in 2005, and Amazon Web Services in 2006, and Netflix’s pivot to streaming in 2007. Suddenly, one of the most mocked excess of the boom became the floor the next decade stood on; the financiers took a big loss, yes, but that was never a reflection on the technology their money built.
The Answer: The J Curve
An economist at Stanford named Erik Brynjolfsson has spent more than a decade figuring out what happens if you plot the productivity gain from a genuinely new technology against time - it forms a (vaguely) J shaped curve.
The line, for years, goes down before it goes up. It bends and climbs only on a delay no one can quite predict, and the technology only then gets credit, in hindsight, for a transformation it had been quietly producing the whole time it appeared to be doing nothing.
What everyone in the bubble debate refuses to look at is the trough (which is also the part that does the work - and where we happen to be right now).
So why the fall then rise? Because we have to go through the expensive, unglamorous labour of the trough years is the rewiring of the business, the retraining, getting the new hires with the right skills and getting the old ones moved into the right new seats.
Using the latest LLM model is the cheapest part of any of this. The expensive part is the surrounding firm, partly torn down and rebuilt while it is still running, with people putting in hours that will not show up as output for years. The much awaited harvest, when it comes, is simply that output finally arriving.
Where AI sits on the curve right now
The evidence that AI’s aggregate curve is still in the trough is on the record. An MIT group called NANDA looked at hundreds of enterprise deployments and found 95% of them moved nothing on the company’s profit and loss. The bigger number underneath that one is that roughly 90% of the AI use actually having an effect was happening off the books. The Shadow Economy, NANDA called it. This is the small every day tasks and workflows which are genuinely helping people in their work, but is too unglamorous to be highlighted.
Two parallel surveys this year, from Oliver Wyman and S&P Global, found the same shape from the other side: the share of firms reporting they had hit their AI return targets fell from 38% to 27%, and the share planning to scrap most of their AI work more than doubled.
The sharpest version of the gap arrived this February, when Brynjolfsson himself wrote in the Financial Times that the harvest had begun! US labour productivity had run at 2.7 per cent in 2025, nearly double the prior decade. A few weeks later the Bureau of Labor Statistics released total factor productivity, the cleaner measure of an economy simply getting more efficient, and it had actually fallen. From 1.5% in 2024 to 0.8% in 2025.
The build rate, meanwhile, is sprinting. A couple of years ago Sequoia passed around a memo pointing out that the buildout was priced as though it would throw off six hundred billion dollars a year, and asking where that money was supposed to come from.
OpenAI is on track to do around $20-30 billion this year, against operating losses larger than that, and forward compute commitments north of a trillion dollars.
In this I’m actually in agreement with Goldman’s Jim Covello when he keeps asking what one-trillion-dollar problem AI is actually going to solve.
And Azeem Azhar, bullish on AI for a decade, wrote in November that revenue was running at about one-sixth of investment. That the “not yet” in “not yet a bubble” was doing a lot of work.
That is what early-trough looks like. Capacity over-built, workflows behind, the financing starting to creak.
The curve says the next thing is some version of the 2000-to-2002 sequence: a financing event that prices the gap, sorts out the over-extended balance sheets, and leaves the actual capacity sitting cheap for the firms that survive.
The curve doesn’t say AI is the bubble. It says the financing is.
What to watch
All of which points you and me at a sharper question than bull or bear: which rate is your company reacting to? The build rate or the use rate. And what are you building in the gap between them while the frenzy pays the bills.
The same NANDA study that found 95% of pilots failing also found the 5% that worked, and (surprise!) they had a lot of similarities.
In each of the 5% of success cases, the cutting edge tech was first deployed to a back-office process rather than the customer-facing flagship, and did the dull work of rewriting the surrounding workflow. Rather than dropping a new technology in beside the old one and hoping everything works out.
AI’s real dark fibre is not the chips. The chips are capacity in the financial sense, the thing being over-built and mispriced.
The dark fibre (the capacity laid down cheap in the frenzy and lit years later) is the organisational muscle being built right now in the 90% of AI use that NANDA calls the shadow economy:
the manager who has spent six months learning exactly what the model is bad at,
the analyst who quietly built an evaluation harness for his own team
the HR person who learned database structures to manage her workload better with an app she built
None of it shows on a profit and loss. But all of it is what the coming deployment wave will run on.
I will not pretend to know the schedule on which AI’s fibre gets lit, or that this buildout won’t turn out to have been over-built past the point of any sane return.
Daron Acemoglu (no one’s idea of a hype man) put the ten-year US productivity gain from AI at well under 1%, concentrated in fewer industries than the optimists believe, and he may be right!
That is the honest doubt, and it lives in the forecast. History is confident on one thing about our current situation: a financing event is not a verdict on a technology, and an overbuild’s wreckage is often the next decade’s foundation. What no forecast or oracle can tell you is whether this one actually lights up.
For that there is one number: the gap between what the buildout costs in capital expenditure and what it brings in as revenue. Right now that gap runs at roughly six to one. If it closes over the next three or four years, then we’ll know that the fibre got lit and the patient people were right.
If that never happens, then this was the overbuild that never got used. A bubble with no morning after. The first of its kind.
