AI Spending Starting to Outpace Human Labor for Companies

What Happened?

While AI had been hailed by many as a better, more efficient way to streamline operations while cutting labor costs, many companies are finding the opposite to be true. At Nvidia, vice president Bryan Catanzaro said the computing cost for his team now exceeds total employee costs, reflecting how expensive large-scale AI systems have become when they are used continuously.

These expenses are tied to processing power, cloud infrastructure, and token-based pricing models that charge based on usage rather than fixed contracts. This pattern is showing up across industries. At Uber, the company’s chief technology officer reportedly exhausted the entire 2026 AI budget early due to high token costs. That suggests companies are underestimating how quickly usage-based pricing can scale when teams integrate AI into everyday workflows.

Startups are leaning into the trend as well. Amos Bar-Joseph, CEO of Swan AI, publicly highlighted his company’s spending on Anthropic models, framing it as a sign of progress toward building an ‘autonomous business’ that grows without increasing headcount. According to Gartner, global IT spending will reach $6.31 trillion in 2026. A large share of that increase is tied directly to AI infrastructure, cloud services, and software subscriptions needed to support these systems.

Why It Matters

This level of spending is forcing companies to confront the question of whether AI is truly more efficient than human labor, or simply more scalable. The basic assumption behind most of the investment in artificial intelligence is that automation will reduce long-term costs.

However, rising computing costs are complicating this assumption. Unlike salaries, which tend to be relatively predictable, AI costs can fluctuate, typically spiking when usage itself increases. Every query, generation, or automated workflow adds incremental cost, and those costs compound at scale…

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Financially, there’s going to be pressure from investors, as justifying soaring tech budgets without any clear returns may be a hard sell. Executives will need to show tangible productivity gains and new revenue directly tied to their new AI systems. Without that, AI begins to look like an open-ended expense rather than a smart investment.

Some executives are already trying to get ahead of the curve. Brad Owens at Asymbl pointed to a growing focus on evaluating the value of a worker, whether human or digital. It’s an important distinction that shows a change towards treating AI systems as a part of the workforce, with tangible expectations around output and efficiency.

How It Affects You

The ripple effect of companies spending more on AI than people will be felt everywhere. At entry-level and mid-skill positions, there are likely to be fewer openings, resulting in longer job searches and more competition, as companies try to push more volume through the AI systems they’ve already paid for. Even if jobs aren’t eliminated outright, employers may expect one person to do the work of two or three, with AI filling in the gaps.

Cost is a factor as well. AI isn’t free, and companies won’t be content to just absorb rising expenses. If computing costs continue to rise, these extra expenses will be passed along in the form of higher prices, subscription fees, and even reduced service quality. And if companies start to come to the conclusion that AI costs more than it saves, you can expect cost-cutting measures in the short term. Stop-and-start-style investments like this breed instability rather than efficiency, and workers tend to be the ones to feel it first and hardest.

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