I’ve been wanting to write this post for months. The problem wasn’t having something to say. The problem was I couldn’t find the equation.
Two conflicting pressures have been running in parallel in the AI conversation. One says you’re not using enough: “AI-first” companies are mandating that engineers burn tokens worth multiples of their salary, and doing anything less signals you’re not serious. The other says you’re using too much: AI is consuming catastrophic amounts of energy, feeding a data center crisis, and responsible companies should feel conflicted about it.
Both of those stories are incomplete. And the only way to say that with any confidence is to actually check the numbers – which providers don’t exactly make easy.
The Tokenmaxxing Problem
The term “tokenmaxxing” entered the mainstream in early 2026, but the underlying idea had been building since OpenAI released its o1 model in late 2024 and introduced test-time compute scaling – the idea that spending more tokens at inference time produces better model answers. The logic drifted from there: if more tokens means better output from the model, then more tokens consumed by your organization must mean more productivity.
The statement that made this explicit came from Nvidia CEO Jensen Huang at GTC 2026. Huang proposed that engineers should receive token budgets on top of their base salaries and set a specific benchmark: “If that $500,000 engineer did not consume at least $250,000 worth of tokens, I am going to be deeply alarmed.” He compared an engineer not using AI to a chip designer insisting on paper and pencil instead of CAD tools. (Tom’s Hardware, CNBC)
It’s worth noting that Nvidia sells the hardware that runs those tokens. Jensen Huang’s benchmark is not a neutral observation about productivity.
Read this article in full here.
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