Meta May Cap AI Token Budgets for Engineers

Instagram head Adam Mosseri said he can see a time in the future, perhaps only a year or two, when putting limits on Meta employees' AI token spend will become necessary. "I think that you can imagine, at least in a year or two … that the burn rate of a strong engineer might be the same as their salary, or their cost of employment. And in that world, you're going to probably need to put in some caps," the Meta executive said, while speaking on Lenny's Podcast.

AI token spend, a reference to the cost of processing AI prompts and responses, has been a much-buzzed-about subject in recent days. Meta shut down an internal AI token spend leaderboard after AI costs put the company on track for billions of dollars in 2026. Meta is not alone in rethinking its approach to AI experimentation.

Uber also had an AI reckoning after it blew through its 2026 AI coding budget by April. Soaring token costs saw Microsoft cancel Claude Code licenses, consolidating its engineers around its own Copilot CLI tool instead. Mosseri's belief, he explained, is that AI token costs will have to be managed just like any other resource, offering an analogy to things like payroll or operating expenditure (OpEx), which is the day-to-day costs of running a business.

"I think of it like…any other resource," Mosseri said. "I have to decide how to deploy capacity to my different teams because I have a limited number of GPUs and CPUs and storage and RAM etc. I have to decide how to deploy OpEx for labeling budgets across my teams.

I have to decide how to deploy payroll for headcount across my teams." Token budgets will be the same, he added, noting that the cap per engineer would have to be proportional to the company's trust in their ability to use the budget in an "ROI-positive" way. Meta doesn't currently have token caps for any employee, Mosseri said, but he believes that their use could be healthy in the future. Further down the road, he expects token costs to come down as the AI model makers enter a pricing war to attract people to use their tools over their competitors.

For now, the company has managed to rein in its token costs a bit by shutting down the "silly things" that it was doing, Mosseri noted — like that token spend leaderboard. "It's not that hard to build a token incinerator, and that doesn't create a lot of value," he said. Why this matters: The potential capping of AI token budgets for Meta engineers highlights the growing concern over AI costs and their impact on company finances.

As AI experimentation becomes more widespread, businesses are realizing that the costs associated with AI development can quickly spiral out of control. For developers and businesses, this means that AI token costs will need to be carefully managed and optimized to ensure a positive return on investment. The industry as a whole will likely see a shift towards more cost-effective AI solutions and pricing models, which could lead to increased competition and innovation among AI model makers.