Paths and waystations in AI safety

I think it's worth bringing in the idea of an "endgame" here, defined as "a state in which existential risk from AI is negligible either indefinitely or for long enough that humanity can carefully plan its future".

Some waypoints are endgames, some aren't and some may be treated as an endgame by one strategy, but not by another.

Executive summary: This essay presents a structured approach to solving the AI alignment problem by distinguishing between the technical "problem profile" and civilization’s "competence profile," emphasizing three key security factors—safety progress, risk evaluation, and capability restraint—as crucial for AI safety, and exploring various intermediate milestones that could facilitate progress.

Key points:

1. Defining the Challenge: AI alignment success depends on both the inherent difficulty of the problem ("problem profile") and our ability to address it effectively ("competence profile").
2. Three Security Factors: Progress requires advancements in (1) safety techniques, (2) accurate risk evaluation, and (3) restraint in AI development to avoid crossing dangerous thresholds.
3. Sources of Labor: Both human and AI labor—including potential future enhancements like whole brain emulation—could contribute to improving these security factors.
4. Intermediate Milestones: Key waystations such as global AI pauses, automated alignment researchers, enhanced human labor, and improved AI transparency could aid in making alignment feasible.
5. Strategic Trade-offs: Prioritizing marginal improvements in civilizational competence is more effective than pursuing absolute safety in all scenarios, which may be unrealistic.
6. Next Steps: Future essays will explore using AI to improve alignment research and discuss the feasibility of automating AI safety efforts.

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