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UPDATED 11:51 EDT / AUGUST 08 2026

AI

Forecasting the AI bubble: When scarcity turns to surplus

BREAKING ANALYSIS by<br>Dave Vellante

Artificial intelligence can be technologically transformative and still produce a capital bubble. Those two ideas are not in conflict.

The bubble bursting does not require AI to fail. It only requires deployable supply and capital commitments to grow faster than monetizable demand. When productive, revenue-producing AI capacity takes longer to materialize, pricing will normalize and financing will no longer bridge the gap. That’s when the capital cycle resets.

But here is the good news for investors: The AI supply chain remains constrained by high-bandwidth memory, advanced packaging, network fabric, power and site readiness. These bottlenecks not only slow deployment, they also delay price discovery (the point at which buyers have more choice); and they postpone the moment when the market discovers whether it has overbuilt.

Welcome to this week’s Breaking Analysis . We’ve titled it: “Forecasting the AI bubble: When scarcity turns to surplus.”

In this episode, we will build a framework for understanding what could delay the bubble popping, what could trigger it and which indicators will reveal when scarcity in supply becomes surplus that impacts the market.

We will also test that framework against the Oracle, OpenAI and Stargate buildout, which to us is the clearest current example of capital commitments racing ahead of deployment, utilization and cash flow.

Our premise today is the following:

The bubble will not burst because AI stops working. It pops if scarcity clears before utilization and cash flow catch up.

And the best place to begin is with the number that makes this cycle look almost unstoppable:

A projected $1.5T semiconductor market

Let’s begin with the sheer scale of the demand picture.

In 2024, David Floyer and I forecast that an expanded silicon ecosystem would approach $1 trillion by 2028. The scope of that model was broader than the WSTS semiconductor-product market shown here, so this is not a perfect apples-to-apples comparison. But directionally, the market is moving much faster than we anticipated.

Global semiconductor revenue approached $800 billion in 2025 . The WSTS forecast shown here puts 2026 revenue at approximately $1.51 trillion – nearly double in one year .

So it’s clear that the underlying AI demand is substantial. Nvidia reported $75.2 billion of data-center revenue in its latest fiscal quarter, Broadcom reported $10.8 billion of AI semiconductor revenue, and AMD generated $5.8 billion in data-center revenue. Those are substantive proof points that AI-factory deployment and customer spending is unusually strong.

But the composition of the forecast is where it gets interesting.

More than half of the projected 2026 market comes from memory. So the revenue curve is being driven by two forces at once: enormous structural AI demand and extraordinary scarcity pricing.

That does not minimize the demand picture. It means the revenue line is rising faster than the physical unit and deployment curves.

And that is where the bubble analysis begins.

There’s little question that strong AI demand exists. The question is whether demand will continue absorbing capacity as HBM, packaging and other constraints ease – and as scarcity premiums begin to normalize.

That is why we need to understand two separate memory curves: physical bit growth, and the price and margin curve.

A decline in memory pricing alone would not mean the AI bubble has burst. The more concerning indicator would be prices falling while physical bit demand, deployment and productive monetization also begin to weaken.

To understand when that could happen, we have to understand why the supply constraints are not clearing simultaneously. The bottleneck moves across the system.

The AI bubble has a bottleneck clock

To forecast when scarcity could flip and become a surplus, we have to understand why the breaking point is likely to be delayed. AI infrastructure is not one market. It is a chain of interdependent gates as shown here.

A graphics processing unit allocation without sufficient high-bandwidth memory is not deployable capacity. HBM without advanced packaging does not become a working accelerator. Racks require network fabric to operate as a cluster. And a cluster still requires power, a ready site and capital before it becomes revenue-producing capacity.

The least available layer governs the output of the entire system.

But the key point is that the binding constraint moves. Early in the cycle, the dominant shortage was accelerator availability. It then shifted toward HBM and advanced packaging. As those constraints ease, the pressure moves outward toward networking, power, site readiness and...