the AI trade-off failing CFOs

Jensen Huang has a test for whether an engineer is worth keeping, and it comes with a token budget attached. On the All-In Podcast at the close of GTC 2026, the Nvidia chief executive said that if a US$500,000 engineer’s annual AI token consumption came in under US$250,000, half their salary, “I am going to be deeply alarmed.” Nvidia, he confirmed, is working toward a US$2 billion yearly token bill for its engineering force.

It’s a memorable provocation from the man who sells the compute. It’s also a tidy description of the trade-off now being made in corporate budgets everywhere, usually with less candour: money that once paid people is increasingly being paid for tokens. The question the industry has been slower to ask is whether that trade is actually working, and the honest answers arriving from the companies that moved first suggest it often isn’t.

Where the money went

The reallocation itself is not in dispute. The four largest hyperscalers have guided roughly US$700 billion in combined 2026 capital expenditure, nearly double last year, while Gartner projects AI agent software spending will reach US$207 billion, up 139%. On the other side of the ledger, Challenger, Gray & Christmas data shows AI as the most-cited reason for US job cuts for a record fourth straight month, with tech accounting for 31% of first-half layoffs. 

An internal Meta memo obtained by Reuters described May’s cuts of 8,000 roles as offsetting the company’s substantial investments, even as revenue grew 33% that quarter. Oracle’s filings show headcount down 21,000 as savings feed its data centre buildout. These are highly profitable companies. The layoffs aren’t survival measures. They’re financing.

Andy Challenger’s summary of his firm’s data is the plainest available: “Companies are shifting budgets toward AI investments at the expense of jobs.” The task a worker performed may not have been automated at all. The budget that paid for it has simply moved.

What the money bought

Here, the record turns awkward. Gartner surveyed 350 executives at companies with over US$1 billion in revenue, all deploying AI agents or automation, and found roughly 80% had cut headcount, with no correlation between the cuts and improved returns. Analyst Helen Poitevin’s verdict: “Workforce reductions may create budget room, but they do not create return.” 

Her research found the organisations that did improve ROI were those using AI to amplify their people rather than remove them. The token side of the ledger has its own reckoning underway. 

Uber gave 5,000 engineers AI coding tools in December and exhausted its entire 2026 AI budget by April. Chief operating officer Andrew Macdonald conceded that despite 70 per cent of committed code being AI-generated, the connection to anything customers experience is missing: “That link is not there yet.” Uber’s engineers are now capped at US$1,500 a month in AI spend.

Walmart imposed similar token rationing on its internal assistant after demand blew past projections, Bloomberg reported. Something is clear in that detail. When tokens exceed the budget, they get capped. When people exceed budget, they get severance.

The admission

No company has travelled the full circle more publicly than Klarna. The fintech giant replaced roughly 700 customer service roles with an OpenAI-powered assistant, froze human hiring for more than a year, and made its AI-first model part of its pitch to public market investors. 

Then customer satisfaction fell, complaints rose, and chief executive Sebastian Siemiatkowski went on Bloomberg to say what few executives have said aloud: “We focused too much on efficiency and cost. The result was lower quality, and that’s not sustainable.” Klarna is hiring humans again, and its CEO now argues that investing in the quality of human support is the company’s future.

Gartner expects the Klarna pattern to generalise, predicting that by 2027, half of the companies that cut customer service staff for AI will rehire, often under new job titles. Its separate survey of 321 customer service leaders found only 20% had genuinely reduced staffing because of AI in the first place, which suggests much of the cutting was ordinary cost discipline wearing an AI costume. 

OpenAI’s Sam Altman has acknowledged as much, conceding some “AI washing” in corporate layoff announcements, and venture capitalist Marc Andreessen, co-founder of Andreessen Horowitz, calls AI the “silver bullet excuse”. The displacement narrative, in other words, is partly theatre. The budget shift underneath it is real, and so is the damage.

Who absorbs the experiment?

The verified harm lands on the people least able to absorb it. Stanford HAI’s 2026 AI Index found that employment for software developers aged 22 to 25 fell nearly 20% from 2024, even as older cohorts kept growing. Companies are effectively removing the bottom rung of the ladder while still expecting senior engineers, the ones directing all those tokens, to exist in five years.

The global arithmetic is harsher still. Huang’s thought experiment assumes a US$500,000 engineer, a compensation bracket that covers perhaps 2 to 5% of American software engineers and vanishingly few anywhere else. Apply his half-salary token ratio to a typical engineer in Kuala Lumpur, Manila or Jakarta, and the token budget costs more than the person. 

In the markets where much of the world’s software work and customer support actually happens, the trade-off he describes doesn’t amplify workers so much as price them against a machine, using ratios set in Santa Clara.

What Klarna learned at the cost of 700 jobs and a dented brand is roughly what Gartner’s data shows in aggregate: the returns follow companies that spend on people who use AI, not on AI that replaces people. The CFOs now capping token budgets after burning through them in a quarter have started to rediscover something the industry spent two years unlearning. Talent was never the line item holding the business back.

(Image by kate.sade)

See also: Per-token AI charges come to GitHub Copilot

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