Summary

This post explores the cost-effectiveness of field-building programs for students, specifically the Atlas Fellowship (a rationality program, with some AI safety programming), MLSS (an ML safety course for undergraduates), a top-tier university student group, and undergraduate research stipends.

We estimate the benefit of these programs in ‘Quality-Adjusted Research Years’, using cost-effectiveness models built for the Center for AI Safety (introduction post here, full code here). Since our framework focuses on benefits for technical AI safety research exclusively, we will not account for other benefits of programs with broader objectives, such as the Atlas Fellowship.

We intend for these models to support — not determine — strategic decisions. We do not believe, for instance, that programs which a model rates as lower cost-effectiveness are necessarily not worthwhile as part of a portfolio of programs.

The models’ tentative results, summarized below, suggest that student groups and undergraduate research stipends are considerably more cost-effective than Atlas and MLSS. (With many important caveats and uncertainties, discussed in the post.) Additionally, student groups and undergraduate research stipends compare favorably to ‘baseline’ programs — directly funding a talented research scientist or PhD student working on trojans research for 1 year or 5 years respectively.

Program	Cost (USD)	Benefit (counterfactual expected QARYs)	Cost-effectiveness (QARYs per $1M)
Atlas	9,000,000	43	4.7
MLSS	330,000	6.4	19
Student Group	350,000	50	140
Undergraduate Stipends	50,000	17	340
Baseline: Scientist Trojans	500,000	84	170
Baseline: PhD Trojans	250,000	8.7	35

For readers who are after high-level takeaways, including which factors are driving these results, skip ahead to the cost-effectiveness in context section. For those keen on understanding the model and results in more detail, read on as we:

Disclaimers

This analysis is a starting point for discussion, not a final verdict. The most critical re...

Cost-effectiveness Models for AI Safety