Ex-OpenAI interpretability lead: most neurons in trillion-param models are now legible
CatAstro_Piyush · x · 2026-10-16
A former OpenAI mechanistic interpretability contributor argues interpretability may be the most important scientific problem today — alignment is unsolvable if we don't know what we're building. He co-authored early automated interpretability work at OpenAI and came away pessimistic: primitive tools could legibly explain only 2% of neurons, and networks looked like arbitrary tangled knots. With modern agentic tooling and closer study, the picture flipped: in models up to trillions of parameters, the vast majority of neurons and their circuits are now legible. Quote-tweeters responded with a playful poem about wasted neurons and angry loss curves.
More from AGI Musings
- Ex-OpenAI interpretability lead: most neurons in trillion-parameter models are now legible — NinaDSchick · 2026-10-16
- Pennsylvania residents reject $10,000-per-household offer for massive Amazon data center — Polymarket · 2026-10-16
- Claude dev Felix Rieseberg's tutorial: 4 ways to be more ambitious with AI — daniel_mac8 · 2026-10-16
- A 1968 newspaper predicted today's AI art debate almost word for word — mtizard · 2026-10-16
- Curing AI-era FOMO: define what 'fulfilling your potential' means to you — dejavucoder · 2026-10-16
- "An LLM doesn't understand anything" gets the perfect parody reversal: neither do you — VoidStateKate · 2026-10-16