Tokenmaxxing and the Agentic Barrier

There’s been a lot of talk lately about “tokenmaxxing” – the idea that the right metric for AI transformation success is simply using as many tokens as possible.

I get the appeal. It’s a crude metric, but it can be useful in a crisis... kind of like checking someone’s pulse tells you they’re not dead. But just like your heart rate, you want to be thoughtful about when and how you try to max it out.

This chart tells the broader story: pull request throughput and tokens per PR by token usage decile across ~7,500 engineers in Q1.

The PR throughput curve (purple) is encouraging: it climbs steadily from 0.77 PRs/week at the lowest token usage to 2.15 at the highest. More tokens = more output.

But look at the token cost curve (orange). Tokens per PR goes up exponentially. The median developer uses about 7M tokens per PR, versus 69M (!!) at the top decile. That’s roughly 10x more tokens for about 2x the throughput.

Tokens are like rocket fuel, and just like a rocket, going faster requires exponentially more of it.

The practical implication is that when it comes to tokenmaxxing, there’s a sweet spot. You’ll get way more bang for your buck by getting everyone in your org into the middle of the adoption curve (D4 through D6) than by pushing a small group into the stratosphere. Broad, moderate adoption beats narrow, extreme usage.

What about the promise of massive productivity gains from fleets of autonomous agents? Those can absolutely be unlocked, but they require serious investments in agent infrastructure, sandboxed environments, and context engineering, and most companies in 2026 are still in the early stages of addressing these challenges. Until those are overcome, there's still an "agentic barrier" – a speed of light you can't get past, no matter how many tokens you burn.