Don’t Kill The Golden Goose: Rectifying AI Cost Without Stifling Innovation

Mark Williamson, CTO at Undo, builds agentic debugging to fix complex non-deterministic bugs — likes to remind computers who’s in charge.

​For two years, the AI story in software engineering has been simple: adopt fast, spend freely and trust the returns will follow. Engineering leaders were rewarded for getting developers onto AI tools, token spend became a badge of progress and the question of what it was producing was set aside.

That story is running out of road. According to TechCrunch, Uber exhausted its entire 2026 AI budget by April, primarily on Claude Code. Axios reported that an AI consultant’s unnamed client spent roughly $500 million on Claude in a month after failing to set usage limits.

CFOs are asking what the business got for that spend. Over the next 12 to 24 months, that scrutiny is going to turn into budget pressure for most engineering organizations. But there’s a danger in how companies respond.

The Same Mistake, In Reverse​

On the way up, the mistake was treating token consumption as progress. Companies built dashboards tracking prompts, tokens and active users. Some ran leaderboards ranking teams by usage.

​Google’s own usage figures illustrate how dramatically AI token consumption has grown: The company reported processing more than 3.2 quadrillion tokens per month in May 2026, compared with 9.7 trillion two years earlier. But usage volume alone doesn’t establish business or engineering value.​

For many companies, all those AI tokens don’t necessarily translate into better productivity. In fact, Gartner found that over half of developers say AI has improved team output by 10% or less. A team can double its AI usage without shipping any faster.​

Now the emerging mistake is treating token cost as the thing to control. Cap the spend, mandate cheaper models, push budgets onto individual teams and let them ration.​

Using The Cheaper Model

• Claude Sonnet, source code only spent $4.35 and gave a confident wrong diagnosis.