Nvidia exec says AI is more expensive than human workers as compute costs explode across tech

Nvidia exec says AI is more expensive than actual workers -- yet some companies don't see the extra costs as a negative

It's easy to point and laugh, but the picture might be more nuanced than it seems. The viability of the circular economics of the AI world has long been debated, but most discussions are about the movement of cash within the industry. Slowly but steadily, though, reports are coming in from executives, such as an Nvidia executive and Uber's CTO, that they are starting to realize that the price of tokens may well outweigh that of plain ol' human brain cells. In the face of mass layoffs to make room for AI agents, that fact is seemingly so ironic that Alanis Morrisette is probably writing a song about it. There are all sorts of enterprise pricing arrangements for LLMs, but for most standard users, the price of a standard AI assistant is $20 a month for a standard plan, and $200 for the pricier, fully-featured version. Token-based pricing is where the real spend is, usually in the form of coding assistants like Claude Code or GitHub Copilot, as well as automation agents with planned tasks of varying complexity that usually run repeatedly on a schedule. The continuous nature of those sessions requires a constant trickle of money, as many firms' bean counters have come to realize. Bryan Catanzaro, Nvidia's VP of applied deep learning, recently told Axios that "For my team, the cost of compute is far beyond the costs of the employees", quite an interesting statement from the company selling the shovels for the gold rush. That perspective is shared by Uber's CTO Praveen Naga, who "[went] back to the drawing board because the budget [he] thought [he] would need is blown away already" as of two weeks ago. Likewise, Swan AI's Amos Bar-Joseph posted a while back on LinkedIn about how proud he was about a $113k bill from Anthropic (makers of Claude) for a four-person team. Oversimplified math pins that amount that at $28k per person per month, which is likely more than each person's monthly wages. Jokes abound right now that "companies have discovered jobs again," and the humor is backed up by a 2024 MIT study stating that 77% of the time, it was preferable to have humans do the work. And yet, the popular sentiment of "I told you so" may be partially misguided. Many CEOs see these bills as a good thing, as it means that their employees are making progress on large-scale automation -- in short, driving innovation, at least supposedly. Uber's Naga said that 11% of its live updates to code are written by AI agents, and reportedly envisions said agents taking on the roles of software engineers. To wit, "the vision for [him] as a CTO is to transform from software engineering to [AI] agent software engineering." Nvidia's own Jensen-Huang seemingly believes that his engineers' productivity is measured by their spending in AI tokens, wanting a $500k salaried engineer to spend at least $250k worth of tokens per year. It is true that many companies are probably finding out the hard way that tokens are more expensive than the workers they're supposedly replacing. However, a business spending additional millions in tokens in order to permanently automate the majority of its workflows might net a long-term win, one that likely leads to job cuts as said automation nears stability. "Just hire more people" would be an easy reply to that, but people don't work tirelessly 24/7. The third scenario is a failed investment in AI automation, either due to a lack of a business structure, unsuitability of tools for the tasks, or simply an inability of the business to properly instruct the clankers. Recent studies have shown that the vast majority of companies rushing to implement AI without a strong plan end up experiencing massive losses on those initiatives. After all, any developer will tell you that it's really easy to build a product if the customer can describe it accurately and the specifications don't change, a fact put succinctly by Edward Berard a long while back: "walking on water and developing software from a specification are easy if both are frozen." Whether all this extra spending on tokens in addition to workers is a temporary tandem expense next to salaries as AI learns the ropes and takes over, or whether it's a complementary expense as AI becomes a force multiplier for those employees, will remain to be seen, and it's likely contextual. But it's almost certain that layoffs will continue as companies feel out, and finance, this new era of tech. Follow Tom's Hardware on Google News, or add us as a preferred source, to get our latest news, analysis, & reviews in your feeds.

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.

'The cost of compute is far beyond the costs of the employees': Nvidia continues to stress importance of human workers - but how long can we all hang on?

* Nvidia exec says compute cost outweighs human workers - for now * Fears over AI taking jobs continue to prevail, especially among younger workers * Nvidia CEO Jensen Huang has taken a key role in trying to play down fears Despite a barrage of recent reports that the AI workplace revolution is underway, and human workers are doomed, a senior Nvidia executive has claimed using technology is still the more costly option - at least for now. Speaking to Axios recently, Bryan Catanzaro, vice president of applied deep learning at Nvidia, noted that, "for my team, the cost of compute is far beyond the costs of the employees." This is in spite of many firms cutting back on human workers in favor of AI technology, which does not require as much investment and continual monitoring as the new tools. Human vs AI - or both? Catanzaro should know what he's talking about - he leads an incredibly senior team at Nvidia which looks to find, "new ways to use AI to improve projects ranging from language understanding to computer graphics and chip design". His beliefs also run in time with CEO Jensen Huang, who has understandably looked to allay fears about AI taking people's jobs, despite Nvidia being at the very forefront of the latest developments in the technology. At the company's recent Nvidia GTC 2026 event, Huang revealed he actually finds himself "getting busier and busier" as AI processes speed up workflows across his business. "A lot of people are saying AI is coming, we're going to run out of jobs - but it's exactly the opposite," he noted. The theme was continued at the recent Adobe Summit 2026, where Huang spoke more on fears that artificial intelligence might replace skilled professionals, noting that AI lets us frame roles differently, replacing human labor in terms of the tasks, but it frees up workers to align outcomes with their true purpose. Huang also recently told Democratic California Congressman Ro Khanna that he thinks "the narratives of AI destroying jobs is not going to help America...First of all, it's just false. Of course, with every technology, and every single day that goes by, jobs of the past are changed." "The purpose of your job and the tasks that you do in your job are related but not the same," he added. "Using myself as an example, if they were the same, then somebody would observe that what Jensen does really for a living is typing and talking. And typing and talking have both been automated to a superhuman level by AI. And yet I'm busier than ever." All this may still fail to reassure workers, particularly those in the early part of their careers, finding that AI has taken entry-level jobs which are often so useful for building up experience. Recent research from Randstad claimed Gen Z workers are the most concerned about AI displacing human roles, despite being strong users, with only one in five saying d they feel their job is immune from AI. And a Forrester report along with data from banking giant Goldman Sachs also claimed humans remain the main blocker to widespread workplace AI adoption, with many workers saying they feel threatened by the technology, especially against a backdrop of continuous tech and AI-induced layoffs. So it remains to be seen just how long this cost-effectiveness balance can be continued, although human workers (including myself) will be hopeful AI doesn't take over just yet. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

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.

Nvidia VP Says AI Costs 'Far' More Than Human Employees

Despite unclear productivity gains and high costs, big tech companies have committed around $740 billion to AI-related expenses this year, a 69% jump from 2025. A key Nvidia executive says that AI isn't reducing labor costs -- right now, it's actually more expensive than the human workers that companies already have. "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. A 2024 MIT study supports this view. Researchers looked at what it would take for AI systems to match human performance across different jobs and found that automation made financial sense in just 23% of roles that rely heavily on visual tasks. In the other 77% of cases, keeping human workers was still the more cost-effective option. There are also examples of AI making costly errors. In one case, an engineer said an AI tool wiped out his database and network. Despite the drawbacks of AI, big tech companies are still investing heavily in it. According to Morgan Stanley, tech firms have already committed about $740 billion to AI-related spending this year, a 69% increase from 2025. Over just the past year, fees for AI software have also gone up sharply, increasing by 20% to 37%, according to spending management company Tropic. AI spending is rising quickly. Based on McKinsey estimates, it could reach $5.2 trillion by 2033, including about $1.6 trillion for data centers and $3.3 trillion for IT hardware. The scale of that investment is so large that some companies are now reconsidering how they allocate their budgets. For example, Uber's chief technology officer, Praveen Neppalli Naga, told The Information earlier this month that the company's shift toward AI coding tools is driving up costs. "I'm back to the drawing board because the budget I thought I would need is blown away already," he said. As companies increase AI spending, layoffs across the tech industry have been rising. Data from layoff tracker Layoffs.fyi shows that more than 92,000 tech workers have already lost their jobs this year, spanning nearly 100 companies. That pace is much faster than last year, when roughly 120,000 layoffs occurred over the entire year. Keith Lee, an AI and finance professor at the Swiss Institute of Artificial Intelligence's Gordon School of Business, told Fortune that companies are spending huge amounts on AI, even though human workers are currently cheaper for many tasks. There's a gap between what makes financial sense on paper and what companies are actually doing. "What we're seeing is a short-term mismatch," Lee told the outlet. AI may be more expensive than human workers right now, but that could change. Lee says the economics will shift as the cost of running AI models drops and infrastructure improves. He added that AI will only become truly cost-effective if it proves reliable and needs less human supervision. "It's not just about AI becoming cheaper than humans," Lee told Fortune. "It's about becoming both cheaper and more predictable at scale."

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.