Zitron: AI sector's $110B revenue trails OpenAI's $122B raise

Ed Zitron's latest post at Where's Your Ed At puts a stark number on the AI economy: only about $110 billion in trailing twelve-month revenues across the entire industry. That is $12 billion less than the $122 billion he says OpenAI raised in March, and $145 billion less than what all AI startups raised combined in the first quarter of 2026. The gap between what is coming in and what is being poured in is the whole story.

Zitron's framing is that the industry debate has drifted to the wrong question. The public conversation is about job losses and productivity, while the actual problem is that infrastructure commitments keep growing faster than any revenue line that could support them. He notes that Anthropic's Head of Economics has said there is "no material increase in the unemployment rate to date," which cuts against the loudest displacement narratives being used to justify the spend.

He goes further and calls LLMs "a definitively niche technology," pointing at the occasional finding that they make people worse or slower at their jobs. He treats research arms like Anthropic's Economic Index and OpenAI's Economic Research Exchange as marketing operations more than neutral studies. The underlying economic bind, as he lays it out, is that providers need to charge substantially more for compute to reach profitability, and the customers most likely to use these tools cannot absorb those price increases.

The honest caveat is that this is one commentator's synthesis behind a paywall, and the headline $110 billion figure is his aggregate rather than an audited industry total. What the post does not give you is a stakeholder-by-stakeholder read of who breaks first if the funding tempo slows, or a clear ceiling on what enterprises would actually pay per token before walking. Those are the numbers the next few earnings cycles will settle.

If Zitron is even directionally right, the beneficiaries are the boring ones: smaller open-weight models that run cheaply, buyers who deferred multi-year AI commitments, and analysts who priced this as a capex cycle rather than a productivity revolution.