Tech · TechCrunch
‘Tokenmaxxing’ is making developers less productive than they think
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There’s an old saw in management: What you measure matters.
Key facts
- Jellyfish, which bills itself as an intelligence platform for AI-integrated engineering, collected data on 7,548 engineers in the first quarter of 2026
- Major companies are noticing — Atlassian acquired DX, another engineering intelligence startup, for $1 billion last year, to help its customers understand the return on investment on coding agents
- GitClear, another company in this space, published a report in January that found AI tools increased productivity, but also that its data showed “regular AI users averaged 9.4x higher code churn
- Faros AI, an engineering analytics platform, drew on two years of customer data for its March 2026 report
Summary
But as the new generation of AI coding agents delivers more code than ever, what their managers ought to be measuring is less clear. It might make sense if you’re trying to encourage more AI adoption (or selling tokens), but not if you’re trying to become more efficient. They’re finding that developers using tools like Claude Code, Cursor, and Codex generate a lot more accepted code than they did before. Alex Circei, the CEO and founder of Waydev, is building an intelligence layer to track these dynamics; his firm works with 50 different customers that employ more than 10,000 software engineers. (Circei has contributed to TechCrunch in the past, but this reporter had never met him before.
Faros AI, an engineering analytics platform, drew on two years of customer data for its March 2026 report. Jellyfish, which bills itself as an intelligence platform for AI-integrated engineering, collected data on 7,548 engineers in the first quarter of 2026. One common finding is the difference between senior and junior engineers, with the latter accepting far more AI-generated code, and dealing with a larger amount of rewriting as a consequence.