AI 开始制造新问题：企业如何治理 AI 垃圾、成本、岗位与价值衡量

The central enterprise question is shifting from whether to use AI toward how to govern the work patterns it creates.

Cheap text generation can flood organizations and platforms with plausible writing that has little informational value.

Institutions and communities develop immune responses when low-quality automated content begins to damage trust.

Companies should not label ordinary cost cutting as AI transformation when the work still has to be rehired or manually recovered.

Maximizing tokens and minimizing tokens are both poor strategies when neither is tied to meaningful business outcomes.

AI value should be measured per unit of intelligence by combining useful output, review time, rework, and total cost.

The strongest adoption pattern lets domain experts carry their existing expertise farther instead of replacing it with generic output.

An AI writing policy should define accountability, disclosure, and review requirements rather than merely banning recognizable stylistic patterns.

Organizations need model allocation rules that match expensive intelligence to high-value tasks and cheaper models to routine work.

The sophistication of operational questions is evidence that AI has moved beyond novelty into real organizational infrastructure.