Jensen Huang has a check for whether or not an engineer is price conserving, and it comes with a token price range connected. On the All-In Podcast at the shut of GTC 2026, the Nvidia chief government stated that if a US$500,000 engineer’s annual AI token consumption got here in beneath US$250,000, half their wage, “I’m going to be deeply alarmed.” Nvidia, he confirmed, is working towards a US$2 billion yearly token invoice for its engineering power.
It’s a memorable provocation from the man who sells the compute. It’s additionally a tidy description of the trade-off now being made in company budgets in all places, normally with much less candour: cash that after paid folks is more and more being paid for tokens. The query the business has been slower to ask is whether or not that commerce is really working, and the trustworthy solutions arriving from the corporations that moved first counsel it usually isn’t.
The place the cash went
The reallocation itself is not in dispute. The 4 largest hyperscalers have guided roughly US$700 billion in mixed 2026 capital expenditure, almost double final 12 months, whereas Gartner projects AI agent software program spending will attain US$207 billion, up 139%. On the different aspect of the ledger, Challenger, Grey & Christmas data exhibits AI as the most-cited purpose for US job cuts for a report fourth straight month, with tech accounting for 31% of first-half layoffs.
An inside Meta memo obtained by Reuters described Could’s cuts of 8,000 roles as offsetting the firm’s substantial investments, at the same time as income grew 33% that quarter. Oracle’s filings present headcount down 21,000 as financial savings feed its knowledge centre buildout. These are extremely worthwhile corporations. The layoffs aren’t survival measures. They’re financing.
Andy Challenger’s abstract of his agency’s knowledge is the plainest accessible: “Corporations are shifting budgets towards AI investments at the expense of jobs.” The duty a employee carried out could not have been automated in any respect. The price range that paid for it has merely moved.
What the cash purchased
Right here, the report turns awkward. Gartner surveyed 350 executives at corporations with over US$1 billion in income, all deploying AI brokers or automation, and located roughly 80% had lower headcount, with no correlation between the cuts and improved returns. Analyst Helen Poitevin’s verdict: “Workforce reductions could create price range room, however they do not create return.”
Her analysis discovered the organisations that did enhance ROI have been these utilizing AI to amplify their folks relatively than take away them. The token aspect of the ledger has its personal reckoning underway.
Uber gave 5,000 engineers AI coding instruments in December and exhausted its complete 2026 AI price range by April. Chief working officer Andrew Macdonald conceded that regardless of 70 per cent of dedicated code being AI-generated, the connection to something clients expertise is lacking: “That hyperlink is not there but.” Uber’s engineers are now capped at US$1,500 a month in AI spend.
Walmart imposed related token rationing on its inside assistant after demand blew previous projections, Bloomberg reported. One thing is clear in that element. When tokens exceed the price range, they get capped. When folks exceed price range, they get severance.
The admission
No firm has travelled the full circle extra publicly than Klarna. The fintech large changed roughly 700 customer support roles with an OpenAI-powered assistant, froze human hiring for greater than a 12 months, and made its AI-first mannequin a part of its pitch to public market buyers.
Then buyer satisfaction fell, complaints rose, and chief government Sebastian Siemiatkowski went on Bloomberg to say what few executives have stated aloud: “We targeted an excessive amount of on effectivity and price. The outcome was decrease high quality, and that’s not sustainable.” Klarna is hiring people once more, and its CEO now argues that investing in the high quality of human help is the firm’s future.
Gartner expects the Klarna sample to generalise, predicting that by 2027, half of the corporations that lower customer support employees for AI will rehire, usually beneath new job titles. Its separate survey of 321 customer support leaders discovered solely 20% had genuinely lowered staffing due to AI in the first place, which suggests a lot of the reducing was atypical price self-discipline carrying an AI costume.
OpenAI’s Sam Altman has acknowledged as a lot, conceding some “AI washing” in company layoff bulletins, and enterprise capitalist Marc Andreessen, co-founder of Andreessen Horowitz, calls AI the “silver bullet excuse”. The displacement narrative, in different phrases, is partly theatre. The price range shift beneath it is actual, and so is the injury.
Who absorbs the experiment?
The verified hurt lands on the folks least ready to take in it. Stanford HAI’s 2026 AI Index discovered that employment for software program builders aged 22 to 25 fell almost 20% from 2024, at the same time as older cohorts saved rising. Corporations are successfully eradicating the backside rung of the ladder whereas nonetheless anticipating senior engineers, the ones directing all these tokens, to exist in 5 years.
The worldwide arithmetic is harsher nonetheless. Huang’s thought experiment assumes a US$500,000 engineer, a compensation bracket that covers maybe 2 to 5% of American software program engineers and vanishingly few wherever else. Apply his half-salary token ratio to a typical engineer in Kuala Lumpur, Manila or Jakarta, and the token price range prices greater than the individual.
In the markets the place a lot of the world’s software program work and buyer help really occurs, the trade-off he describes doesn’t amplify employees a lot as value them towards a machine, utilizing ratios set in Santa Clara.
What Klarna discovered at the price of 700 jobs and a dented model is roughly what Gartner’s knowledge exhibits in combination: the returns comply with corporations that spend on individuals who use AI, not on AI that replaces folks. The CFOs now capping token budgets after burning via them in 1 / 4 have began to rediscover one thing the business spent two years unlearning. Expertise was by no means the line merchandise holding the enterprise again.
(Picture by kate.sade)
See additionally: Per-token AI charges come to GitHub Copilot
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