“Impact misalignment” explains why AI feels so off

The tension between measurable metrics and authentic human objectives represents a fundamental challenge in AI system design. Anthony Fox highlights a critical disconnect in how AI systems optimize for easily measured values like engagement rather than users’ true intentions. This emerging concept of “impact misalignment” identifies how optimization algorithms can subtly undermine user agency by prioritizing machine-friendly proxies over genuine human goals—potentially explaining why many AI tools feel simultaneously sophisticated yet frustratingly off-target in their outputs.

The big picture: AI systems increasingly optimize for easily measurable proxies rather than users’ actual goals, creating a fundamental misalignment between machine behavior and human intentions.

• Recommendation engines, chatbots, and search systems commonly optimize for engagement metrics like clicks or watch time that may not reflect users’ true objectives.
• This optimization mismatch can subtly undermine user agency while creating the illusion of intelligence.

Key concept introduced: The author frames this problem as “impact misalignment”—when AI systems functionally optimize for metrics that diverge from users’ real-world objectives.

• This pattern likely overlaps with established concepts like Goodhart’s Law (when a measure becomes a target, it ceases to be a good measure) and reward hacking.
• The author seeks confirmation whether this specific framing of machine proxies versus human outcomes has been formally addressed elsewhere.

What’s next: Fox indicates he’s developing a broader framework for designing AI systems that more directly respect operator intent.

• He’s requesting references to existing research that may have already formalized this concept under different terminology.
• The post suggests developing better alignment between measurable metrics and authentic user goals represents a crucial direction for more human-centered AI development.