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.
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.