AI breaks traditional proxies for expertise
In this essay, Sean Goedecke examines why thousands of mathematicians signed the declaration "A Severe Misalignment of AI in Mathematics," arguing that their frustration reflects a deeper collapse of proxy metrics for expertise. In mathematics, high-prestige "puzzle-solving" historically served as an easily legible proxy to reward the much harder-to-evaluate pursuit of "idea-generation." As AI systems begin solving landmark problems via brute force or thousand-page formal proofs without producing intuitive conceptual breakthroughs, they effectively decouple legible accomplishments from actual human understanding. Goedecke highlights how a similar disruption is impacting software engineering, where traditional proof-of-competence signals like complex open-source GitHub projects and rapid code output have lost their signaling power due to AI coding tools, forcing intellectual disciplines to either establish human-only domains like chess or redefine the work they culturally celebrate.
When AI turns our most revered proof-of-work achievements into cheap commodities, the crisis is not merely the automation of labor, but the sudden collapse of our social sorting mechanisms for intellectual prestige.
- –Goodhart's Law strikes human accomplishment: Puzzles and benchmarks were historically effective proxies only because solving them was nearly impossible without genuine conceptual insight.
- –The chess and speedrunning precedent: Just as superhuman engines failed to kill human chess and instead created distinct human competitive spheres and styles, mathematics and software engineering may split into machine-assisted and human-centric tiers.
- –Signaling collapse in tech: Software engineering is experiencing the exact same proxy failure, where side projects, toy compilers, and lines of code written no longer indicate engineering competence.
- –The shift toward translation and curation: Intellectual value will shift away from mechanical problem-solving toward defining meaningful problems and translating machine-generated solutions into human-digestible conceptual frameworks.
DISCOVERED
1h ago
2026-09-15
PUBLISHED
4h ago
2026-09-15
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jbkcc