Companies are now spending more on AI tools and computing power than on employee salaries, raising new questions about the real cost of automation.
Bryan Catanzaro, Nvidia’s vice president of applied deep learning, told Axios that compute costs for his team are now far higher than employee costs. Axios also reported that Uber’s chief technology officer has already used the company’s full AI budget for 2026 because of token costs.
The rising cost of AI use is becoming a bigger concern as companies deploy tools for coding, research, support, and daily operations.
Amos Bar Joseph, chief executive of Swan AI, also drew attention online after posting about his Anthropic bill and saying the company was building an autonomous business by scaling with intelligence instead of headcount.
The examples show how AI expenses can increase quickly when companies rely heavily on paid models and token-based usage.
Worldwide IT spending is expected to reach $6.31 trillion in 2026, up 13.5% from 2025, according to Gartner. The research firm said the increase is being driven by strong demand for AI infrastructure and software.
Gartner also expects data center systems spending to grow 55.8% in 2026, while software spending is forecast to rise 15.1%.
The higher spending may create pressure for companies to show clear returns from AI investments.
Businesses with large IT budgets will likely need to prove that AI tools are improving productivity, reducing costs, or helping generate revenue. That pressure may be even stronger for public companies that have to answer to shareholders each quarter.
Brad Owens, vice president of digital labor strategy at Asymbl, told Axios that the conversation is shifting toward the real value of a worker, whether human or digital.
Rising AI costs may also affect enterprise spending at major AI labs.
Axios reported that an OpenAI investor believes the shift could benefit the company if Codex uses tokens more efficiently than Claude Code. The report also noted that Anthropic has changed pricing to respond to higher demand.
The larger issue is clear: as AI labs raise prices and usage grows, heavy AI spending could move from a sign of innovation to a financial risk.
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