I think the EA/âAIS community has been remarkably successful at recruiting and deploying talent to address AI misalignment and misuse risks. Iâd like to reverse engineer their approach to see if we can apply a similar recipe to other âgrand challengesâ emerging from rapid AI progress.
Whatâs missing from my naive explanation for the recent success of the AIS community?
Conceptual Foundations (pre-2016): Early conceptual work established the importance of AI Safety and clarified the most valuable lines of action
Examples: Nick Bostromâs Superintelligence, The Sequences and Lesswrong, Amodei et al.âs Concrete Problems in AI Safety
Skill and career capital development (~2016-2021): Those producing conceptual work, along with their most engaged supporters, began doing direct work in the area. While much pre-LLM work may not have been directly impactful, it built crucial skills and career capital through organizations, connections, experience running successful programs, etc.
Examples: Open Philanthropyâs AI risks program, EA community consolidation (EAGs, university groups), AGI Safety Fundamentals course, CSET, CHAI, SERI MATS
Talent conversion (2022 onwards): When AI gained broader attention and impact opportunities became clearer, there was already a stock of arguments for AI Safetyâs importance and a community ready to address talent pipeline bottlenecks and enroll more people in useful work.
Examples: Open Philanthropyâs RFPs, MATS, BlueDot, Anthropic and DeepMind AIS teams, ARENA, AI-risk-focused events within EA
In this simplified story, if we want to draw more talent to newly formulated AI challenges, the most important things are:
Broadcasting the case for working in the area and identifying concrete lines of action
Developing a base of early practitioners with the skills and career capital needed to pave the way for future work
Creating a large influx of people with initial inclination to work in the area (e.g., from the EA community)
thatâs just false. alignment community even to this day is significantly less then 1000 people worldwide. i would not call that a success. iâm not blaming the people involved. theyâve done what they could. lets just not deceive ourselves here
Well, AI sentience probably has <100 people and Iâd be joyful if that increased to 1k. Iâm still interested even if the question is âHow can we fail less misserably at creating a talent pipeline?â