“Tokenmaxxing” is making developers less productive than they think

There’s an outdated noticed in administration: What you measure issues. And, sometimes, you get extra of no matter you’re measuring.

Software program engineers have debated productiveness metrics for many years, beginning with traces of code. However as the brand new technology of AI coding brokers delivers extra code than ever, what their managers should be measuring is much less clear.

Monumental token budgets—primarily, the quantity of AI processing energy a developer is allowed to eat—have change into a badge of honor amongst Silicon Valley builders, however that’s a really bizarre method to consider productiveness. Measuring an enter to the method makes little sense whenever you presumably care extra in regards to the output. It’d make sense should you’re attempting to encourage extra AI adoption (or promoting tokens), however not should you’re attempting to change into extra environment friendly.

Take into account the proof from a brand new class of corporations working within the “developer productiveness perception” house. They’re discovering that builders utilizing instruments like Claude Code, Cursor, and Codex generate much more accepted code than they did earlier than. However in addition they discover that engineers should return to revise that accepted code way more typically than earlier than, undercutting claims of elevated productiveness.

Alex Circei, the CEO and founding father of Waydev, is constructing an intelligence layer to trace these dynamics; his agency works with 50 completely different clients that make use of greater than 10,000 software program engineers. (Circei has contributed to Trendster up to now, however this reporter had by no means met him earlier than.)

He says that engineering managers are seeing code acceptance charges of 80% to 90%—which means the share of AI-generated code that builders approve and hold—however they’re lacking the churn that occurs when engineers should revise that code within the following weeks, which drives the real-world acceptance fee down between 10% and 30% of generated code.

The rise of AI coding instruments led Waydev, based in 2017 to supply developer analytics, to completely rework its platform within the final six months to handle the proliferation of fast coding instruments. Now, the corporate is releasing new instruments that monitor the metadata generated by AI brokers, providing analytics on the standard and price of their code to supply engineering managers with extra perception into each AI adoption and efficacy.

Whereas analytics corporations have an incentive to focus on the issues they discover, the proof is mounting that enormous organizations are nonetheless determining the right way to use AI instruments effectively. Main corporations are noticing—Atlassian acquired DX, one other engineering intelligence startup, for $1 billion final yr, to assist its clients perceive the return on funding on coding brokers.

The info from throughout the business tells a constant story: Extra code is being written, however a disproportionate quantity of it isn’t sticking.

GitClear, one other firm on this house, revealed a report in January that discovered AI instruments elevated productiveness, but additionally that its knowledge confirmed “common AI customers averaged 9.4x greater code churn than their non-AI counterparts”—greater than double the productiveness positive factors the instruments supplied.

Faros AI, an engineering analytics platform, drew on two years of buyer knowledge for its March 2026 report. The discovering: code churn—traces of code deleted versus traces added—had elevated 861% beneath excessive AI adoption.

Jellyfish, which payments itself as an intelligence platform for AI-integrated engineering, collected knowledge on 7,548 engineers within the first quarter of 2026. The agency discovered that the engineers with the biggest token budgets produced probably the most pull requests (proposed modifications to a shared codebase), however the productiveness enchancment didn’t scale. They achieved two occasions the throughput at ten occasions the price of tokens. In different phrases, the instruments are producing quantity, not worth.

These sorts of statistics ring true whenever you discuss to builders, who’re discovering that code evaluation and technical debt are stacking upm at the same time as they revel within the freedom of the brand new instruments. One frequent discovering is the distinction between senior and junior engineers, with the latter accepting way more AI-generated code, and coping with a bigger quantity of rewriting as a consequence.

Nonetheless, at the same time as builders work to grasp precisely what their brokers are as much as, they don’t anticipate turning again anytime quickly.

“It is a new period of software program growth, and you need to adapt, and you’re pressured to adapt as an organization,” Circei instructed Trendster. “It’s not like it is going to be a cycle that may cross.”