Tokenmaxxing—excessive AI token usage—forced enterprises to confront the true cost of AI, according to the article. Amazon shut down an internal AI leaderboard after employees used AI solely to climb rankings, and Disney has dashboards tracking usage. One Disney employee used Claude 460,000 times in nine days, and top token users at some companies reportedly spent millions, not on business outcomes but on leaderboard rankings. This highlighted the difference between AI activity and AI value, exemplifying Goodhart's Law: when a measure becomes a target, it ceases to be a good measure. The article argues that the hidden cost of AI for most enterprises is not inference but everything required to make AI production-ready—retrieval, evaluation, governance, integrations, testing, and lifecycle management. While many optimize inference costs, the bigger issue is that organizations rebuild foundational infrastructure for each new initiative, with software compounding but AI resetting. Every new initiative should make the next one cheaper, but many rebuild enough of the stack that each project starts from near zero, accumulating across every team, project, and quarter.