The next enterprise AI bottleneck is not model capability. It is whether leaders can tell the difference between a team using AI well and a team running up a number.

Over roughly a quarter in 2026, several of the largest engineering organizations in the world took down the dashboards they had built to prove their AI investments were working. Amazon shut down KiroRank, the internal leaderboard that ranked developers by how many tokens they consumed on its Kiro platform. Meta also dismantled a near-identical board, Claudenomics, that tracked token usage among its heaviest AI users. The reason was the same everywhere. Once usage became the number that mattered, engineers found ways to run up usage, and the costs arrived long before the value did.