Meta is implementing strict spending controls on employee AI usage after internal token consumption surged to levels projected to cost the company billions in 2026. The initiative, communicated via internal memos on June 13, addresses a rapid, unmonitored increase in daily artificial intelligence utilization across the workforce. Internal telemetry reveals that Meta personnel consumed 73.7 trillion tokens over a thirty-day window. A significant portion of this activity was quantified through Claudeonomics, an internal leaderboard that ranked teams by their utilization of Anthropic Claude. Chief Technology Officer Andrew Bosworth formally condemned this practice of tokenmaxxing, stating that raw usage volume does not indicate meaningful productivity. Meta will immediately discontinue the leaderboard and redirect engineering workflows toward MetaCode, its in-house coding assistant, to lower third-party API dependencies and internalize model spending. To operationalize the cost cap, Meta will deploy an AI Gateway dashboard within weeks. The system will aggregate real-time consumption data across departments, flagging anomalous spending patterns through automated alerts. Formal token budgets and allocation mandates will become enforceable in early 2027. The policy restructuring impacts roughly 6,000 employees and signals a strategic pivot from earlier directives that encouraged unrestricted AI adoption to a framework of metered resource governance. The corrective measures reflect a widening industry confrontation with artificial intelligence operational expenditures. Although Meta has pledged up to 135 billion dollars for AI infrastructure through 2026 and 600 billion dollars for data center construction through 2028, internal token bills constitute a newly identified operational drain. Comparable budget breaches have emerged at peer technology firms. Uber exhausted its 2026 AI coding allocation within four months before enforcing a 1,500 dollar monthly per-tool limit, while industry surveys indicate fewer than twenty-seven percent of enterprises maintain full expenditure visibility. Goldman Sachs projects a twenty-four-fold expansion in enterprise token consumption by 2030, validating the necessity of Meta’s intervention. These governance adjustments add a critical cost-management dimension to Meta’s heavy capital expenditure strategy. Equities markets have recently factored in investor skepticism regarding large-scale AI spending, contributing to a downward correction in Meta’s valuation. The shift toward structured token oversight will determine whether centralized accounting enhances engineering efficiency or restricts the internal AI integration that executives have promoted as a core competitive differentiator. Complete budgetary enforcement is targeted for the first quarter of 2027, setting a precedent for corporate AI financial discipline.