Nvidia executive: The cost of AI tools is ‘far beyond’ the cost of human workers

Meta announced last week in a memo that it plans to lay off 10% of its workforce, about 8,000 employees, as well as scrap plans to hire for 6,000 open positions. It’s part of an effort to “run the company more efficiently and to allow us to offset the other investments we’re making,” according to the memo. Microsoft has offered thousands of its own employees a voluntary buyout, the largest the company has ever offered.

Other tech headers, however, suggest that right now, AI isn’t saving companies money on labor; it’s actually costing them more than the humans they currently employ.

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

An MIT study from 2024 backs up Catanzaro’s experience. Analyzing the technical requirements of AI models needed to perform jobs at a human level, researchers found that AI automation would be economically viable in only 23% of roles where vision is a primary part of the work. In the remaining 77% of the time, it was cheaper for humans to continue their work.

On other instances, AI has proven to be fallible, with one engineer saying an AI agent destroyed his database and network as a result of what he called “overuse.” 

Despite no clear evidence on AI improving productivity and, according to the Yale Budget Lab, no widespread data to support the idea of AI displacing jobs, Big Tech firms have continued to pour money into AI, announcing $740 billion in capital expenditures this year so far, according to Morgan Stanley, a 69% increase from 2025. The magnitude of spending has caused some companies to rethink their budget altogether.

“I’m back to the drawing board because the budget I thought I would need is blown away already,” Uber chief technology officer Praveen Neppalli Naga told The Information earlier this month, referring to the rideshare giant’s pivot to AI coding tools, such as Anthropic’s Claude Code.

This increase in spending has coincided with more layoffs in the tech sector. According to data from Layoffs.fyi, there have been more than 92,000 layoffs in tech in 2026 so far across nearly 100 companies. The rate of these workforce reductions is already far outpacing last year, which saw about 120,000 layoffs over the year.

The continued AI spending and layoffs, even as human labor remains cheaper, expose a meaningful discrepancy in the economics of AI, said Keith Lee, an AI and finance professor at the Swiss Institute of Artificial Intelligence’s Gordon School of Business.

“What we’re seeing is a short-term mismatch,” Lee told Fortune.

The AI-labor cost balance

According to Lee, the cost of using AI has remained less efficient than human labor due to hardware and energy costs raising operating costs for providers. At its current pace, AI expenditures may reach $5.2 trillion by 2023, with $1.6 trillion from data center spending and $3.3 trillion from IT equipment, according to McKinsey data. Spending could surge to $7.9 trillion by 2030 at an accelerated pace. Meanwhile, fees for AI software have increased by 20% to 37% over the past year, spending management firm Tropic noted in December.

AI companies may also be losing money as a result of their flat subscription model, Lee noted, with fixed subscription fees failing to cover operating costs for heavy AI users.

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

While AI may cost more than human labor today, there will be warning signs of a tipping point toward AI’s economic viability. For one, Lee indicated, the cost of using AI will become significantly lower, with performing inference—how AI analyzes data—for a large language model with 1 trillion parameters plummeting by more than 90% over the next four years, according to a report last month from analyst firm Gartner. AI infrastructure will likely improve, and model designs and hardware supply will follow. AI companies will also likely change how they price their tools, switching from a flat subscription to usage-based pricing, Lee predicted. 

But the future of AI’s economic viability will also depend on if the technology proves its worth. It will have to prove itself reliable, with fewer hallucinations and a reduced need for human oversight, effectively integrating into a company’s infrastructure, according to Lee. Federal Reserve data shows about 18% of companies had adopted AI tools as of the end of 2025, a 68% growth in the adoption rate since September 2025.

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