AI safety researcher at LessWrong ponders, “When should humans quit the field?”

A researcher on the AI safety forum LessWrong is questioning when humans should abandon technical AI safety careers as AI systems become capable of conducting their own safety research. The post explores whether continued human training in alignment research makes sense if AI will soon outpace human capabilities in this field, potentially rendering human expertise obsolete within months or years.

The central premise: The author assumes it’s possible to create “SafeAlignmentSolver-1.0″—an AI system that can safely and effectively conduct alignment research at a scale that makes human efforts redundant.

Key considerations for career decisions: Several factors should influence whether aspiring researchers continue pursuing technical safety work.
• If SafeAlignmentSolver-1.0 could be deployed within 12 months, training programs like MATS (Machine Learning Alignment Theory Summer program) may become pointless.
• The most valuable contributions would come from ensuring frontier AI companies actually develop, deploy, and implement solutions from alignment-solving AI systems.
• Research fleet management positions would likely be limited and reserved for experienced professionals, not newcomers.

Timeline uncertainty: The author acknowledges that while SafeAlignmentSolver-1.0 won’t be deployed tomorrow, certain milestones could signal when human training becomes obsolete.
• AI systems may soon be “inventing and testing new control protocols” independently.
• There could be a transitional period of “weeks to years” where humans work alongside machines before becoming unnecessary.
• Weaker systems like “BenchmarkDesigner-v1” might arrive within 12 months, serving as early indicators.

The bigger question: Potential researchers need to evaluate whether their efforts will have time to impact the world before AI systems take over these functions entirely.

What the author is asking: The post seeks community input on specific warning signs that would indicate when to pivot away from technical safety careers toward other ways of contributing to AI safety and governance.

The bottom line: AI safety research involves developing methods to ensure advanced AI systems remain safe and aligned with human values as they become more powerful. The author is essentially asking when aspiring AI safety researchers should give up on learning these skills and focus on other ways to help, since AI might soon do this work better than humans ever could.