# Tokenmaxxing is Goodharting


Coding agents and reasoning models let individuals consume many more LLM tokens
than they could a year ago. It's now easy for a single engineer to spend
thousands of dollars in daily token usage. This is being actively encouraged
through the recent memetic spread of "Tokenmaxxing" -- the idea that if you
consume more tokens, you're more "AI native" and therefore producing more
valuable output.

**Tokenmaxxing is not The Way.** Plainly, it's a textbook instance of
Goodharting. Token leaderboards come from an understandable short-term instinct
to shift habits towards more AI usage, but direct optimization in this fashion
inevitably overshoots into wasteful spending. Token-usage-as-target means token
consumption ceases to be a useful metric.

Per-engineer token usage is, admittedly, useful as a diagnostic when engineers
are dramatically and systematically underusing AI. However, the leaderboard
version of token usage is likely actively harmful. This is analogous to how
“lines of code merged” or “PRs merged” or “design docs written” can be
interesting aggregate diagnostic numbers when used _directionally_, but
obviously a leaderboard of “design docs written per quarter” would produce the
wrong incentives.

Operationalizing this into predictions over the timespan of the next 6 months:

- Weak claim: Prominent executives / thought-leaders start publicly criticizing
  Tokenmaxxing / raw token usage as a bad metric.
    - This is basically already happening. My prediction: 90%.
- Stronger claim: There is a noticeable cross-company narrative shift toward
  discipline on AI spend with an emphasis on ROI measurement and skepticism of
  using raw token as a proxy for productivity.
    - I'm more tentative on this[^1]. My prediction: 60-70%.

Reasoning:

- There was a sharp bend in the curve of agent adoption around December '25 /
  January '26.
- Increased enterprise spending will start showing up in Q1’26 OpEx financial
  results, but will likely show up as a sharp increase in Q2’26 results.
- Since January, there has been increased pressure at many companies for
  engineers to "Tokenmaxx". This is an incredibly easy (and costly) leaderboard
  to game.
- See, for example:
    - [The Pulse: 'Tokenmaxxing' as a weird new trend](https://blog.pragmaticengineer.com/the-pulse-tokenmaxxing-as-a-weird-new-trend/)
    - [More! More! More! Tech Workers Max Out Their A.I. Use](https://www.nytimes.com/2026/03/20/technology/tokenmaxxing-ai-agents.html)
- "Tokenmaxxing" is fun and memetic, but likely has a short lifespan before you
  see the median engineer try to game the leaderboard numbers -- or at least,
  change their decision-making on the margin to make less efficient use of
  tokens.

In the short term, I'd bet that some companies will start imposing soft token
budgets on engineers, while others will continue to soft-allow unlimited spend
-- either for legitimate reasons (more effective/efficient uses are discovered;
frontier labs have abundant compute which changes the
[marginal return calculus](/blog/2026/04/30/Thoughts-on-Marginal-Token-Spend/)),
or for memetic/signaling reasons.

There are innumerable ways to make positive-value use of AI. In the short term,
Tokenmaxxing -- especially the explicit encouragement of Tokenmaxxing in the
absence of clear definable output value -- is not such a way.

[^1]:
    The reasons for my hesitancy: primarily the argument of "market can stay
    irrational longer than you can stay solvent". Spending on engineers' use of
    AI only really becomes a "problem" if companies need to start becoming more
    conscious of spending (i.e. there is pressure to reduce it). With sufficient
    macroeconomic exuberance, this pressure could be long-delayed.