Half Your Salary on AI Tokens? Uber Found Out

Opening

This March, on the GTC stage, Nvidia’s Jensen Huang said something that stuck with me. “If an engineer earning a $500,000 salary only used $5,000 worth of tokens by year’s end, I’d go insane.” He said they should be spending at least $250,000 — half their annual salary. He went further, comparing skipping tokens to “designing semiconductors with paper and pencil.”

3 months later, this week, something unexpected happened at the company that followed that advice most faithfully. Uber capped AI coding tool usage at $1,500 per employee per month. The decision came after the company burned through its entire annual AI budget in just 4 months. To cut to the chase: “how much you spend on tokens” is already yesterday’s question. The next game is “what you spend it on” and “how you prove the results.”

🎯 Jensen Huang’s Token Doctrine

At this year’s GTC, Jensen Huang laid out a simple formula: “The more tokens you use, the more productive a developer becomes.”

In concrete terms: an engineer earning a $500,000 salary (about ₩700 million) should have an annual token¹ budget of at least $250,000 (about ₩350 million). That works out to roughly $20,000 a month, or about ₩28 million. He went even further, proposing the token budget as a kind of fringe benefit stacked on top of salary — an additional payout equal to half of base pay, meant to make engineers 10 times more productive.

It’s worth noting the context behind this claim. Nvidia’s entire business model is, in effect, “a factory that produces tokens.” Huang himself laid out the formula at GTC: “Revenue = Tokens per Watt × Available Gigawatts.” The more tokens get consumed, the more GPU demand rises, and the more Nvidia’s revenue grows. For Jensen Huang, tokens are the product.

But for companies like Uber or Walmart, tokens are closer to raw materials. Not a product — a cost. Larry Dignan of Constellation Research put it precisely.

“JPMorgan sells financial services, Walmart sells retail, GM sells cars. What CIOs at these companies want is cheaper inference, better ROI, and a clear answer on when their AI investment pays off.”

The optimal strategy for those who sell tokens and those who buy them are exact opposites. And yet Huang’s declaration created a single culture across Silicon Valley: so-called “tokenmaxxing”² — the belief that “consuming as many tokens as possible is productivity itself.” Some companies built leaderboards ranking individual developers’ token usage to spur internal competition. There were even reports that Meta ranked employees’ token consumption on an internal dashboard.