Part II of VIII
The pace isn’t explained by technology alone. A self-amplifying financial loop has formed where every major actor profits from the others’ growth, and where slowing down is structurally costly, even where individual actors prefer caution.
Building AI at scale isn’t primarily a software problem. It’s an industrial one: specialized chips, data centers the size of city blocks, electricity bills that rival mid-sized utilities, thousands of engineers competing for the same narrow labor pool. That costs billions per year. Watch what the industry built to sustain it: a financial structure where the same companies invest in each other, sell to each other, and depend on each other’s growth. The result is a self-amplifying loop, and it has no clear natural brake.
Consider what it means to commit more than twenty times your annual revenue to infrastructure. The Stargate Project, OpenAI’s joint venture with SoftBank, Oracle, and MGX, announced roughly $500 billion for US AI infrastructure in January 2025.7 A separate figure (OpenAI’s total compute spending target of $600 billion through 2030, reported by CNBC in February 2026) is not an upward revision of that number; it is actually a downward reset from the $1.4 trillion in infrastructure commitments Altman had touted in late 2025.7a No tech company has committed at a ratio like this. Against OpenAI annualized revenue of about $6 billion exiting 2024,6 the original Stargate commitment ran to 80× annualized revenue. Even at the annualized revenue run-rate of over $20 billion confirmed by early 2026,6 the $600 billion compute target still stands at roughly 30×, closer in scale to national infrastructure projects than to ordinary software spending. This isn’t a bet on a product. It’s a bet on explosive, sustained growth. That growth may come. Either way, the structure commits every actor in the loop: each profits from the loop continuing, and restructuring costs fall hardest on whoever stops first.
The difficulty here is not moral. It is mechanical. You cannot stop an eighteen-wheeler with the brakes of a sedan. The industry built this structure out of necessity first.
What happens if AI revenue doesn't catch up to the commitments?
OpenAI’s internal projections reportedly target “hundreds of billions” by 2030. If revenue doesn’t scale to meet that, the lab tier faces the kind of restructuring that hit telecom companies in 2001. The major hyperscalers are better insulated; they fund capital spending from cash flow generated outside this loop. The risk concentrates in the lab tier and the neocloud layer (CoreWeave and similar firms) that runs on GPU-backed credit. A revenue miss at the lab tier doesn’t necessarily slow AI. It restructures who controls it.
Part 3 asks the obvious question: if every actor in this loop profits from it continuing, who exactly is positioned to slow it down?