A year ago, the smart move inside a big company was to use as much AI as humanly possible. Some firms even ranked employees on leaderboards by how many tokens they burned, a status game that earned its own name: tokenmaxxing.
That era is ending. The same companies are now capping AI use, and the new buzzword is its mirror image: tokenminimizing.
The latest is AT&T, which has started limiting some employees’ access to GitHub Copilot, according to The Information. Meta is reportedly reining in staff spending on Anthropic and other AI tools, a sharp reversal from the months when workers raced each other to consume the most.
The bill came due #
The trigger is simple: the spending got frightening. The most AI-obsessed firms now spend $7,500 per employee per month, and agentic tools that call a model over and over have tripled enterprise AI bills even as per-token prices collapsed.
Uber blew through its entire 2026 AI coding budget by April and now caps employees at $1,500 a month per tool. Walmart has capped use of its in-house AI agent. Amazon scrapped the internal leaderboard that ranked staff by AI usage after people gamed it, sending compute costs up.
Even individual engineers were a problem: Microsoft found some spending $500 to $2,000 a month on Claude Code tokens alone.
Cue the ‘I told you so’ #
Some companies are enjoying the moment. “We never celebrated tokenmaxing,” Box chief executive Aaron Levie said. “We never had leaderboards, so we didn’t get ahead of our skis on… incentivizing the wrong thing.”
Not everyone is pulling back. At Databricks, an engineering leader said the AI budget for engineers is still unlimited, “so tokenmaxxing still exists”, a sign that firms confident their staff use AI efficiently see less reason to ration it.
That is the tension under the trend. Caps control costs, but they can also throttle the productivity gains that justified the spending in the first place.
The real winners are the cost-cutters’ tools #
The more lasting shift is what tokenminimizing pushes companies toward. To cut bills without cutting use, firms are swapping expensive frontier models for cheaper or open-source ones on simpler tasks.
That hands an opening to the plumbing. Microsoft and Databricks have launched ‘gateway’ tools to monitor and cap staff AI spending, and Nvidia-backed Factory, valued at $1.5bn, just launched a model router that shunts cheaper tasks to cheaper models.
Satya Nadella captured the mood in a weekend essay, arguing AI models should be swappable rather than dominant. “The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see,” he wrote. Coming from the boss of a company whose software is under pressure from the very labs it depends on, it is also a tell about where this is heading.
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