Legora Chief Technology Officer Jacob Lauritzen has publicly criticized tokenmaxxing, the practice of consuming excessive artificial intelligence tokens to inflate internal usage metrics and performance reviews. Speaking on the 2VC podcast released Saturday, Lauritzen described the tactic as counterproductive, noting that organizations measuring AI adoption primarily through token volume incentivize wasteful behavior rather than meaningful output. Instead, Lauritzen advocates for structured engagement models such as corporate hack days and technical demonstrations. He argues that companies should reward employees for demonstrable efficiency gains and high-quality deliverables rather than raw token consumption. While emphasizing that skipping AI integration carries significant opportunity costs for fast-growing firms, he maintains that productivity gains must justify the resource expenditure. Lauritzen’s remarks reflect a broader industry recalibration as technology firms transition from unrestricted token adoption to strict financial controls. Several major companies have recently curtailed internal AI spending after tracking dashboards produced diminishing returns and escalated costs. Uber implemented a strict limit of $1,500 in monthly token expenditures per AI platform following earlier budget overruns. Amazon recently decommissioned an internal leaderboard that monitored tool utilization, with company representatives clarifying the system was never designed to drive usage for its own sake. Industry leaders are increasingly vocal about the financial impracticality of unlimited access. Andrew Feldman, chief executive of semiconductor provider Cerebras Systems, stated at a recent Bloomberg conference that providing employees with unrestricted token allocations was fundamentally flawed. He advised organizations to adopt cost-effective open source alternatives for routine tasks, comparing the approach to purchasing groceries at wholesale rather than utilizing premium services for everyday needs. The collective shift underscores a maturing enterprise AI strategy. Organizations are prioritizing measurable productivity improvements and disciplined budgeting over arbitrary engagement metrics. This transition aims to align artificial intelligence deployment with tangible business outcomes while containing operational expenses.