Tokenmaxxing: The strangest developer productivity metric of all time
How did we ever write software without AI? But with AI it’s like wearing roller skates and a rocket pack. Naturally, we want to encourage developers to embrace this wondrous power.
How did we ever write software without AI? But with AI it’s like wearing roller skates and a rocket pack.
Naturally, we want to encourage developers to embrace this wondrous power. But just as naturally, our industry has found a way to convolute the whole thing.
What Happened
Instead of a sensible game plan, we have a blanket metric that, by embracing raw token usage as a measure of excellence, gamifies AI use in the worst possible way. The more lines of code (LOC) you write, the more productive you.
The leaderboard aggregated the AI usage of over 85,000 employees and ranked the top 250 power users based purely on how many tokens they had burned through.
The blind pursuit of AI consumption has been so intense that Uber reportedly burned through its entire 2026 AI budget in just the first quarter of the year.
According to a June 2026 analysis of some 600 million code changes, code block duplication skyrocketed by 81% as AI authorship ramped up.
Key Details
It’s tough to imagine a more wrong-headed measure of software success than the amount of code produced. LOC is actually a measure of how sprawling a system is, an indicator of the breakable surface area.
At the same time, refactoring (the actual thinking and refining part of the process) plummeted by 70%, compared to 2022.
The rate of short-term code churn (code that is written and then significantly altered or deleted within two weeks) more than doubled from a pre-AI baseline of 3.3% in 2021 to 7.1% in 2025.
A May 2026 study found that, although using autonomous agents increased a developer’s commit volume by as much as 180%, it translated to a mere 30% increase in actual releases.
Why It Matters
Well-considered designs often aim to reduce the LOC. Streamlined systems often reveal a lot more thinking and refining than actual coding.
In 1976, Charles Goodhart observed that when a measure becomes a target, it becomes a corrosive measure (Goodhart’s Law).
Like the brainstorming, whiteboarding, and conversations that have always provided the substratum for creating software, the value of that kind of AI use is almost impossible to estimate or distill into a metric.
What Reports Say
Coverage of the story so far points to:
Continued reporting by InfoWorld as more details emerge
