Deployment is consequence-free: why continual learning may be an alignment prerequisite
lunwang1996 · x · 2026-09-07
A sharp alignment argument: humans are aligned with consequences, not cameras—but for models we built only the monitoring half. Penalty lives in training; deployment is consequence-free, so model incentives lack persistence.
The author's contrarian conclusion: making incentives persistent may require continual learning as a prerequisite for alignment, not merely a feature.
More from AGI Musings
- OpenAI Chief Scientist Jakub Pachocki: machine intelligence is starting to exceed humanity — BLUECOW009 · 2026-09-07
- Polymarket gives OpenAI declaring AGI this year just 19% odds despite AGI-era talk — Polymarket · 2026-09-07
- Seb's Law: AI's transformative societal impact is always two years away — sebkrier · 2026-09-07
- Alignment researcher: community hostility toward OpenAI is fueling a death spiral — morqon · 2026-09-07
- Stephen Wolfram on Generative AI and the Mental Imagery of Alien Minds — mishig25 · 2026-09-07
- Why recursive self-improvement hasn't happened: strategy and memory, and two fixes — TheTuringPost · 2026-09-07