The AI Bubble — No One's Happy

In November 2025, at the Wall Street Journal’s Tech Live event, OpenAI’s chief financial officer Sarah Friar was asked how the company intended to finance its chip and data center commitments. Her answer was specific: OpenAI was looking for an ecosystem of banks, private equity, and a federal “backstop” or “guarantee” that could lower financing costs and increase the amount of debt the company could take on. The interviewer pressed: a federal backstop for chip investment? Friar confirmed.

The retraction arrived within twenty-four hours, from three directions. Friar herself, on LinkedIn: “I used the word ‘backstop’ and it muddied the point.” Sam Altman, on X: “We do not have or want government guarantees for OpenAI datacenters.” David Sacks, Trump’s AI and crypto policy czar: “There will be no federal bailout for AI.” [1]

Three people denying something in twenty-four hours is unnecessary unless the thing they are denying is the plan. She has since been excluded from key financial meetings, her absence described as “notable and awkward.” [2] The person who said the quiet part out loud was not corrected. She was sidelined.

To understand why a federal backstop is the plan — and to understand what Friar was looking at when she said it — you have to look at the financial architecture she was asked to finance. It has a topology that will be familiar to anyone who remembers what happened to telecommunications.

The gap

This is not an argument that AI does not work. AI is useful. In specific domains it is genuinely productive. Some agentic tools are delivering real value — automating well-defined workflows, accelerating code review, compressing research cycles. The software wrapping LLM calls is evolving, and is already proving it’s use.

But the capital expenditure being committed is not priced for a technology that is useful. It is priced for a technology that is transformative — for something approaching artificial general intelligence, arriving on a timeline measured in months rather than decades. The problem is that the underlying architecture cannot deliver that. Large language models predict the next token; they do not model the world, plan across steps, or reason about consequences. The people who built them have begun saying so publicly. Ilya Sutskever, the OpenAI co-founder whose work established the scaling hypothesis the industry runs on, said in late 2025 that pre-training scaling is “essentially tapped out” and that another 100x of compute “won’t get a qualitative change in capability.” [3] Dario Amodei at Anthropic predicted powerful AI “as early as 2026” in October 2024. [4] By February 2026 he was saying “I don’t believe we’re basically at AGI” and acknowledging that if his revenue forecast was off by a year, “there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt.” [5] The commitments that underpin the industry were made against the 2024 timeline, not the 2028 one. The estimates have moved. But the money has only increased.

What the field requires, by their own account, is research — not half a trillion dollars in concrete and copper. The buildout is an answer to a question the technology has not yet resolved, built at a scale that forecloses the possibility of changing course when the research points somewhere else.

J.P. Morgan, modeling what the buildout would need to earn to clear a ten percent return on current capital expenditure, arrived at roughly six hundred fifty billion dollars per year in AI-sector revenue — the equivalent of thirty-five dollars per month, in perpetuity, from every iPhone user on earth. The current run-rate is about twenty-five billion. The gap is twenty-six-fold. [6] Goldman Sachs’ chief economist concluded that AI had contributed “basically zero” to U.S. economic growth in 2025 and observed that “FOMO, not ROI, is driving hyperscaler capex.” [7] The San Francisco Federal Reserve’s February 2026 consensus: “While GenAI and related applications are useful, they are not the innovation that spurs broad-based reorganization of the economy.” [8]

The current architecture will not close this gap. It will not close it because the capabilities the spend assumes — autonomous reasoning, reliable multi-step planning, self-correction without human oversight — are not properties of next-token prediction at any scale. The people who built the systems have said so.

The flagship products themselves are evidence of the deficit. ChatGPT, Claude, and Gemini are not LLM calls — they are complex engineering systems that use the model’s output to orchestrate conventional software: search engines, code interpreters, calculators, file systems, external APIs. The LLM produces text; the application routes that text through tools that do the actual work. The industry is compensating for the limitations of the architecture with the same software engineering the architecture was supposed to replace. If the models could...