AI Compute Costs Exceed Workforce Costs, Nvidia Executive Says

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The ongoing discourse on the impact of artificial intelligence on employment remains a subject of vigorous debate. In 2025, over 150,000 jobs were eliminated across 549 companies, and by the first quarter of 2026, more than 90,000 layoffs had already been recorded, according to the independent layoffs tracker Layoffs.fyi. 

Recent layoffs announced by prominent corporations such as Meta, Microsoft, and Oracle, among others, reflect the widespread concern, all undertaken with the intention of enhancing corporate efficiency.

However, AI replacing jobs isn’t applicable to all roles. Sometimes, instead of saving money on labor, AI can actually increase costs beyond what current manpower requires.

​“For my team, the cost of compute is far beyond the costs of the employees,” shared Bryan Catanzaro, vice president of applied deep learning at Nvidia, with Axios.

Catanzaro’s stance is backed by a 2024 MIT study, which found that AI automation would be economically viable in only 23% of roles where vision is a primary part of the work, with the remaining 77% requiring human work.

Despite there being no path-breaking evidence of AI improving productivity, tech giants have poured in exponential figures into AI initiatives, announcing $740 billion in capital expenditures for the year alone, according to Morgan Stanley, a 69% increase from 2025.

Are the spending figures translating into impactful systems? Or is it making companies rethink their budget and strategy?

“I’m back to the drawing board because the budget I thought I would need is blown away already,” shared Praveen Neppalli Naga, chief technology officer, Uber, with The Information after pivoting to AI coding tools, such as Anthropic’s Claude Code.

Keith Lee, an AI and finance professor at the Swiss Institute of Artificial Intelligence’s Gordon School of Business, notes that the cost of using AI has remained less efficient than that of human labor due to hardware and energy costs, which have raised operating costs for providers.

At its current pace, AI expenditures may reach $5.2 trillion by 2030, according to McKinsey data.

Lee noted that AI companies might be losing money because of their flat subscription model, as fixed fees aren’t enough to cover operating costs for heavy AI users.

“As a result, some firms are beginning to reevaluate AI not as a clear cost-saving substitute for labor, but as a complementary tool—at least until the cost structure stabilizes,” he said.

“It’s not just about AI becoming cheaper than humans,” Lee said. “It’s about becoming both cheaper and more predictable at scale.”