AI Costs More Than the Workers It Replaced, Forbes Reports

Forbes is out with a piece arguing the AI trade has an uncomfortable math problem: the technology is currently costing companies more than the humans it was meant to replace, even as layoffs pile up to fund the spend.

For traders, the implication is simple. If AI compute bills keep outrunning the labor savings, the ROI story underpinning hyperscaler capex and AI-native valuations gets harder to defend.

The core claim

Companies are laying off workers to fund AI tools that currently cost more than the workers they just let go, all in pursuit of productivity gains that most studies cannot yet verify.

Bryan Catanzaro, Nvidia’s vice president of applied deep learning, said the cost of compute for his team now far exceeds what the company spends on the employees using it — an admission from the company that manufactures the hardware powering the AI buildout.

Uber burned its 2026 AI budget in four months

Uber’s CTO recently disclosed that the company burned through its entire 2026 AI coding budget in four months, with 84% of Uber’s engineers having adopted Claude Code by March and roughly 70% of committed code now originating with AI.

The corresponding value was murkier — Uber COO and President Andrew Macdonald conceded publicly that token usage didn’t seem to correlate directly with useful features shipped to users.

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The MIT and Goldman gut-check

An MIT study found that AI automation is economically viable in only about 23% of roles, and for the remaining 77%, humans remain cheaper.

Goldman Sachs’ chief economist has stated plainly that he does not view AI investment as strongly growth-positive. Sequoia Capital partner David Cahn has put a number on the gap: AI companies need roughly $600 billion in annual revenue to justify current infrastructure spending, and as of mid-2026 that gap is widening, not closing.

Capex keeps climbing anyway

Big Tech has announced $740 billion in capital expenditure this year, a 69% increase from 2025.

Jensen Huang has told the industry that a $500,000 engineer should be consuming at least $250,000 worth of AI tokens annually, and that Nvidia is working toward a $2 billion annual token budget for its engineering force — suggesting tokens should be a recruiting perk. That is the supplier telling customers to spend more, faster.

Options market and stocks to watch

Watch for AI-exposed names where the compute-versus-labor math is now front and center:

• NVDA: watch for reaction to any narrative shift on token economics — Nvidia executives are the ones publicly flagging that compute costs exceed headcount costs.
• UBER: watch for follow-up commentary on AI spend discipline after burning the 2026 coding budget in four months.
• MSFT: watch for capex commentary and any signs of tighter AI cost controls following reports of curbed coding assistants.
• META: watch flow around AI capex given ongoing layoffs framed as reallocation toward AI.
• GOOGL: watch for how hyperscaler capex guidance lands with investors already questioning ROI.

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