Amazon deletes devs’ tokenmaxxing leaderboard to minimize costs

The market takeaway is not that AI adoption is slowing, but that enterprise AI monetization is running into a classic principal-agent problem: users are rewarded for visible activity, while buyers care about net productivity and cost per task. That creates a near-term asymmetry where usage metrics can rise faster than realized value, pressuring the vendors most exposed to consumption-based pricing and compute pass-through. In the next 1-2 quarters, investors should expect more internal governance, tighter rate limits, and anti-gaming controls, which can dampen headline usage growth even if underlying workflow automation continues.

AMZN is the cleanest read-through because the issue directly hits internal compute discipline and could raise scrutiny on AI tooling economics inside the retail/cloud stack. The second-order effect is more important: if enterprises become skeptical of raw token metrics, procurement cycles may lengthen as buyers demand ROI-linked pricing, slowing net-new AI seat expansion for software vendors with weak measurement. CRM is especially exposed if customers push back on broad AI add-ons that cannot be tied to labor savings, while META is less directly vulnerable but still exposed to a broader re-rating of AI engagement metrics versus monetization quality.

The contrarian view is that the current backlash is actually healthy for the platform winners: it filters out low-value use cases and accelerates consolidation around vendors that can prove task-level savings, not just model access. Over 6-12 months, the leaders should be those able to move from token pricing to outcome pricing, because that lowers churn and increases enterprise stickiness. The trade is therefore not to short AI broadly, but to fade names where AI is still marketed as a usage story rather than a productivity contract.