There’s a new term picking up steam: “tokenmaxxing.”
The idea is simple: encourage engineers (and increasingly, non-engineers) to consume as many AI tokens as possible. Meta employees built an internal leaderboard to track it. Cleo’s CEO says he spent over $36,000 on tokens in a single month. Jensen Huang says he’d be “deeply alarmed” if a $500K engineer didn’t consume $250K worth of tokens.
The critics are right that raw token consumption is a vanity metric. Spending tokens isn’t the same as shipping value. As one VP of engineering put it: what matters is value created per token, not volume.
But here’s where I think the more interesting conversation lives.
If you’re a team or an organization that hasn’t yet figured out how AI fits into your work, you have to start somewhere. And starting means experimenting. Experimenting means burning some tokens that don’t produce immediate, measurable output.
That’s not waste. That’s tuition.
The engineers who are getting genuinely faster—compressing idea-to-production timelines, catching bugs earlier, automating the tedious parts—didn’t get there by reading about it. They got there by using the tools daily, making mistakes, learning what works and what doesn’t in their specific context.
Companies are structuring AI access differently: flat monthly budgets, usage-based metering, enterprise plans with spend caps. There are good reasons for all of these, especially in industries where governance and cost discipline aren’t optional. The structure matters less than the intent behind it: is the goal to enable exploration, or just to contain cost? The best setups do both.
Tokenmaxxing as a leaderboard competition is silly. But tokenmaxxing as a cultural signal that says “we expect you to be investing in learning these tools”? That’s worth paying attention to.
The gap between teams that are deeply engaged with AI and teams that are still watching from the sideline is widening fast. The tokens you “waste” learning today are cheaper than the competence gap you’ll be paying for later.