Hong warns that raw token consumption and internal usage leaderboards can reward activity without proving that AI has created customer value. He proposes trusted throughput instead: the rate at which a team turns AI-assisted work into reviewed, validated, maintainable and safely deployed outcomes.
The measurement should combine objective signals with human judgment. Merged pull requests, customer-facing improvements and cost trends can show useful movement, while engineering review and downstream outcomes reveal whether the work deserves trust. The same framework also makes it possible to compare adoption by team or workflow without treating every generated token as progress.
As code generation becomes abundant, the bottleneck moves into review, continuous integration and organizational confidence. Hong recommends investing in AI-assisted first-pass review, stronger test infrastructure and explicit controls such as budgets, quotas, anomaly detection and loop-step limits. Prompt caching and context pruning can also reduce waste without weakening the work itself.
The practical goal is not maximum AI usage. It is a learning loop in which teams observe where AI succeeds, capture repeatable patterns and feed those lessons back into engineering practice. Hiring and company promotion at the end of the talk are omitted.
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