AI costs now exceed employee salaries at Nvidia as compute expenses reshape corporate budgets

At Nvidia, compute already costs more than employees. The rest of corporate America is catching up

Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. Bottom line: Escalating demand for artificial intelligence is beginning to reshape how companies allocate their technology budgets, with compute costs in some cases outpacing traditional labor expenses. Executives and engineers working closely with large-scale AI systems say the balance between human and machine costs is shifting in ways that would have seemed unlikely just a few years ago. At Nvidia, that shift is already visible. "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, told Axios. A similar pattern is emerging outside core AI vendors. At Uber, spending on AI coding tools has ramped up so fast that it has exhausted the company's planned 2026 AI budget. According to The Information, Uber's CTO has already used up the ride-hailing firm's 2026 AI budget, largely because of token costs from heavy model use. Token-based pricing for many large models has turned inference usage into a metered, recurring operating cost. Because these token charges rise with every request, costs can spike as more teams use the tools, and they are harder to forecast than standard software licenses. At least some leaders see that trade-off as acceptable, positioning AI spending as a way to grow output without adding headcount. Amos Bar-Joseph, CEO of Swan AI, made that point in a widely circulated LinkedIn post, writing, "We're building the first autonomous business - scaling with intelligence, not headcount." Spending forecasts for the wider IT market show the same trend. Gartner links the increase to strong demand for AI infrastructure, software, and cloud services, covering both new deployments and recurring usage costs. Uber has already burned through its 2026 AI budget, as token costs from heavy use of coding models quickly piled up Even so, the speed of that spending growth is drawing more scrutiny. Large enterprises, particularly those accountable to shareholders, face increasing pressure to demonstrate that AI investments translate into measurable gains. Companies are being pushed to show productivity gains and other hard metrics that tie AI spending to business results. "The tone is shifting a bit more into what is the true value of a worker... human or digital?" said Brad Owens, vice president of digital labor strategy at Asymbl, a company focused on workforce orchestration. That question is becoming more urgent as AI costs shift and scale. Changes in pricing by major model providers are already influencing how companies evaluate different platforms. Anthropic, for example, has adjusted its pricing in response to rising demand. Competition between AI labs is now as much about cost efficiency as raw capability, with investors watching how much work each model can deliver per dollar spent. One OpenAI investor told Axios this shift could favor the company, arguing that Codex uses tokens more efficiently than rivals like Claude Code and can cut usage costs. Those choices feed back into how enterprises structure their overall technology budgets. If compute costs keep climbing with usage, AI spending could increasingly resemble a core operating expense, subject to the same kind of cost controls as payroll. In that context, pricing decisions by major AI providers can ripple quickly through corporate budgets. If prices keep climbing, heavy AI spending could shift from a bragging point to a balance-sheet headache, especially for companies that scaled usage fast without strong limits.

Bosses Are Blowing More Money on AI Agents Than It'd Cost Them to Just Pay Human Workers

Can't-miss innovations from the bleeding edge of science and tech Mindlessly unleashing AI agents to take over employees' jobs can be pretty costly, it turns out. Some companies are learning the hard way that paying for the incredible volume of AI agent requests is costing more than what they'd pay their human employees, Axios reports. AIs can perform all sorts of tasks, ranging from the rote to the complex. But one of the most popular ways it's being used in the workplace is to generate mountains of code at a pace far greater than a human could achieve. Sometimes, software engineers will even run multiple AI agents at the same time, all working on different tasks in the background without supervision. Each of these tasks costs tokens, and the bill can quickly add up. "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, told Axios. The problem has become harder to ignore as organizations are increasingly reliant on using AI tools and agents -- including the organizations building them. "Pretty much 100 percent" of Anthropic's code is now AI-generated, the company's head of Claude Code Boris Cherny claimed earlier this year. Google and Microsoft's bosses claim that this share is around a quarter of their companies' code. Meta employees performance reviews are now partly based on how much AI they use, showing that a lot of the push towards using AI is coming from the top. It probably doesn't help that many tech workers are treating their token bills as member-measuring contests, using millions of tokens in a single day. The slang for this, we regret to inform you, is "tokenmaxxing," with some power users racking up monthly token bills north of $150,000. "I probably spend more than my salary on Claude," Max Linder, a software engineer in Stockholm, told The New York Times last month. Uber engineers using Claude Code have already blown through the company's entire 2026 AI budget, The Information reported. Tech leaders' attempts to grapple with the situation can sound nearly as comical as the dilemma itself. In March, Nvidia CEO Jensen Huang proposed giving software engineers AI tokens equal to roughly half their base salary, something he said could be used as a recruiting tool. Why be wooed by a signing bonus, when if you work for us, you get to use more AI? At the same time, it's a clear money-making opportunity for AI providers. One OpenAI investor told Axios that the concern over token costs could benefit them, since they believe Codex uses tokens more efficiently than Anthropic's Claude Code. Anthropic, meanwhile, has cashed in by raising its pricing. In all, the token costs are just one of many major question marks over AI automation. The jury's still out on whether using error-prone AIs is more efficient and worth the potential havoc they can wreak internally -- as evidenced by incidents at Meta and Amazon, among others -- while numerous studies suggest that forcing workers to use AI tools could actually be making their jobs harder.

'The cost of compute is far beyond the costs of the employees': Nvidia exec says right now AI is more expensive than paying human workers | Fortune

The recent tech layoffs would initially appear to indicate the great labor shift from human workers to AI may already be happening. 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. 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."

Are Humans Actually Cheaper Than AI? Why 'Digital Workers' Are Blowing Up 2026 Budgets

The promise of AI replacing human workers was supposed to save companies money. For many, it's doing the opposite. Deploying AI agents to replace workers without a clear cost strategy can be a costly miscalculation and with worldwide IT spending expected to reach over 6 trillion in 2026, up 13.5 percent from 2025, the stakes have never been higher. "As AI workloads scale, data center investment is ramping rapidly, which in turn is driving increased demand for high‑performance compute," Gartner's distinguished vice president analyst John-David Lovelock said. "This dynamic is creating meaningful growth opportunities for companies delivering AI‑optimized processors, accelerators, and enabling technologies." According to The Information, Uber's chief technology officer reportedly exhausted his entire 2026 AI budget ahead of schedule due to token costs.

AI is costing more than employee salaries? A new report reveals shocking truth

Companies are working on cheaper, more efficient AI to reduce costs. AI is no longer just a futuristic promise but is something that companies are using on a daily basis to quietly reshape how work gets done and how money gets spent. For years, AI firms burnt through investor funding to train and run powerful systems, and with each passing day that phase is now beginning to shift. As funding slows, these companies are now turning to customers to recover the run costs. Businesses, which depend heavily on AI for coding, operations and automation, are starting to feel the strain, and what once looked like a productivity boost is now becoming a growing expense that leaders cannot ignore or easily justify in their budgets. Uber's Chief Technology Officer Praveen Neppalli Naga recently revealed that the company exhausted its annual AI budget within just a few months of 2026. Moreover, the surge was driven largely by heavy use of AI coding tools such as Claude Code. Startups are facing somewhat similar challenges, as Amos Bar-Joseph, CEO of getswan.com, shared that his four-person team ran up an AI bill of 113,000 dollars in a single month. This clearly shows how even the small teams can generate large costs when relying heavily on advanced AI systems. Also read: Using AC in extreme heat? Follow these 5 steps to avoid fire risks and blast The concern is not just limited to end users; even the companies building AI infrastructure are feeling the pinch. Nvidia executive Bryan Catanzaro noted that for his team, compute costs have gone far beyond employee expenses. This change shows that AI has become very important and costly in today's work. Industry forecasts show the trend is only growing. Gartner estimates global IT spending will reach 6.31 trillion dollars in 2026, marking a 13.5 per cent increase from the previous year. A large part of the increase in costs is due to spending on AI, including building it and paying for subscriptions. Also read: WhatsApp to stop working on older Android phones from September: Check if your device is affected Not only the big companies but the regular users are also feeling the heat, as many paid AI tools have strict limits, so even subscribers cannot fully use them unless they bear the cost incurred with them. Also, the best AI features are often locked behind paywalls, which creates a gap between people who can afford them and those who cannot. Even though costs are rising, companies are trying to fix this. Some are making better, more efficient hardware, and others are building AI models that use fewer resources. Over time, this could lower the costs and make AI easier to use for everyone.