What shape is a bubble? – Negroni Venture Studios

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What shape is a bubble?

Liz Upton

27 July 2026

Nobody’s going to call you shocking or subversive if you point out that we’re experiencing an AI bubble. And as with all financial and cultural phenomena, the temptation is to map the shape of the current boom (and its predictable end) against boom and bust cycles we’ve seen before: the dot-com bubble, the subprime mortgage crisis.

There are a couple of problems with the impulse to pattern-match here. First, we tend to misdiagnose what went wrong in previous financial crises; and some of the diagnoses which may well tip our current boom into a bust are not necessarily the ones we think they are.

Let’s put AI aside for a moment (I know, it’s hard, we’ve all become disquietingly reliant on it) and look at some of the things we think we believe about other bubbles.

If you ask Jim in the pub what caused the subprime mortgage crash, his likely response will be that house prices fell, so borrowers defaulted. Jim (and received wisdom) is wrong here. Subprime mortgage delinquencies actually started to turn upward in 2006. This happened before the crash came in 2008, not after it; mortgage defaults were rising while American house prices were also still rising.

The canonical subprime product, the 2-28 adjustable-rate mortgage, was built to be refinanced rather than repaid: you’d get two years of teaser rate, then a reset that both the borrower and the lender assumed would never arrive, because consistently and eternally rising prices would magic up the equity for the next refinance. Nearly four in five subprime hybrid ARMs written in 2003 had been refinanced away by the end of 2006. The ratchet ran on appreciation, and specifically on appreciation accelerating.

House price growth did not crash. All it had to do to break this cycle was to slow. It did: house prices were still appreciating and were still positive, but they stopped appreciating so quickly. And that was enough: the refinancing window narrowed and the defaults began. The crash arrived a year later. Jim in the pub owes me a pint.

Acceleration is incredibly important in this instance. Any quantity has a level, a velocity, and an acceleration. Markets instrument the first two obsessively: sell-side models forecast levels, momentum funds trade velocity. Almost nobody positions on the acceleration, which is a mistake: it’s where you’ll find regimes changing. When financing embeds a growth assumption (a reset that presumes refinancing, a lease sized to expansion), the assumption holds only while growth continues at the assumed rate. Revenue up 40% satisfies the headline while breaking a structure built for 70%. Between acceleration rolling over and growth going negative, there’s a window of borrowed time: every chart still points up, but the carefully calibrated machine is already broken.

Figure 1: One quantity, three numbers. The level can stand at a record while its acceleration has already turned negative. Growth peaks and starts to fall; the second derivative crosses zero first. The shaded window is borrowed time: the period in which everything still looks fine and the structure is already broken. This is the shape of subprime in 2006. Is it the shape of AI capex now?

What about the dot-com boom? Does the AI boom look like that?

2000 was a fairly straightforward (and horrible, for those of us who got caught up in it) capital budgeting equity event, where dot-com stocks were overpriced and balance sheets were thin. Again, ask Jim in the pub what he thinks about the shape of the AI industry, and he might well tell you it looks like the same thing: a technology cycle that has resulted in a number of overpriced stocks in the sector accompanied by thin balance sheets.

And while that’s true to a degree, there’s a lot more to this current cycle. Sure, stocks are monstrously overpriced and balance sheets are just weird, but AI capital expenditure is more like the subprime mortgage crisis specifically because it’s not about capital budgeting: it’s about loans and debt.

The AI sector runs on take-or-pay capacity contracts, GPU-collateralised term loans, and asset-backed notes sold to insurers: debt-financed construction, leased to tenants, with supply arriving years after commitment. This is not a technology cycle. It’s a commercial property cycle that happens to be going on in compute, and property cycles break the same way every time: demand growth decelerates into the supply the boom has just finished building, while demand itself keeps growing.

The different ways in which the two biggest players, OpenAI and Anthropic, have structured their business models around debt and leasing point to two very different outcomes for the two organisations.

OpenAI looks a lot like the subprime mortgage borrower who will be unable to pay, and can only keep refinancing. As of the date of writing, their customer mix is not ideal:...