TokenPost.ai
As Big Tech and startups race to embed generative AI into products and workflows, a more sober question is increasingly dominating boardrooms: not whether AI works, but whether it pays. What was once framed as a story of rapid capability gains is now becoming a debate about 'token economics'—and in this case, tokens as an operating cost that can quietly burn through budgets faster than productivity can catch up.
The shift was thrown into sharp relief after Andrew Macdonald, president and chief operating officer of Uber ($UBER), warned in a recent podcast appearance that AI spending is becoming harder to justify. Macdonald said usage of AI tools across engineering teams has surged, but clear proof that the spend is translating into better products or sustained productivity gains remains thin. He described the phenomenon as 'tokenmaxxing'—a kind of token overconsumption where companies pay for more model output without reliably getting more business value back.
The remarks drew attention precisely because they did not come from an AI skeptic. Uber has been among the large global platforms eager to operationalize AI in software development and internal processes. The timing also added fuel to the debate: they followed reports that Microsoft ($MSFT) had recently scaled back internal access to Anthropic’s Claude Code for some employees, citing rising usage-based charges, and nudged engineers toward GitHub Copilot as a more controllable alternative.
At the heart of the conversation is a new definition of 'tokenomics.' In crypto, tokenomics typically refers to how tokens are issued, distributed, and potentially burned. In AI, tokenomics has come to mean something more mundane and potentially more painful: billing mechanics. Every query, code generation, and agentic workflow consumes tokens, turning experimentation into a metered expense line—often before organizations can measure the return.
Several data points illustrate the intensity of the cost pressure. Uber reportedly exhausted its 2026 budget for Claude Code in roughly four months. Around 5,000 engineers used the tool, with monthly adoption rates cited in the 84% to 95% range. Internal billing per engineer was said to range from roughly $150 per month to as high as $2,000, depending on usage. One internal demonstration by the company’s chief technology officer reportedly consumed about $1,200 worth of tokens in just two hours—an anecdote that has circulated as a stark example of how quickly costs can compound when advanced tools are used at scale.