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

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.

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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.

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