Thanks for publishing this, Arb! I have some thoughts, mostly pertaining to MATS:

1. MATS believes a large part of our impact comes via accelerating researchers who might still enter AI safety, but would otherwise take significantly longer to spin up as competent researchers, rather than converting people into AIS researchers. MATS highly recommends that applicants have already completed AI Safety Fundamentals and most of our applicants come from personal recommendations or AISF alumni (though we are considering better targeted advertising to professional engineers and established academics). Here is a simplified model of the AI safety technical research pipeline as we see it.
   
   Why do we emphasize acceleration over conversion? Because we think that producing a researcher takes a long time (with a high drop-out rate), often requires apprenticeship (including illegible knowledge transfer) with a scarce group of mentors (with high barrier to entry), and benefits substantially from factors such as community support and curriculum. Additionally, MATS' acceptance rate is ~15% and many rejected applicants are very proficient researchers or engineers, including some with AI safety research experience, who can't find better options (e.g., independent research is worse for them). MATS scholars with prior AI safety research experience generally believe the program was significantly better than their counterfactual options, or was critical for finding collaborators or co-founders (alumni impact analysis forthcoming). So, the appropriate counterfactual for MATS and similar programs seems to be, "Junior researchers apply for funding and move to a research hub, hoping that a mentor responds to their emails, while orgs still struggle to scale even with extra cash."
2. The "push vs. pull" model seems to neglect that e.g. many MATS scholars had highly paid roles in industry (or de facto offers given their qualifications) and chose to accept stipends at $30-50/h because working on AI safety is intrinsically a "pull" for a subset of talent and there were no better options. Additionally, MATS stipends are basically equivalent to LTFF funding; scholars are effectively self-employed as independent researchers, albeit with mentorship, operations, research management, and community support. Also, 63% of past MATS scholars have applied for funding immediately post-program as independent researchers for 4+ months as part of our extension program (many others go back to finish their PhDs or are hired) and 85% of those have been funded. I would guess that the median MATS scholar is slightly above the level of the median LTFF grantee from 2022 in terms of research impact, particularly given the boost they give to a mentor's research.
3. Comparing the cost of funding marginal good independent researchers ($80k/year) to the cost of producing a good new researcher ($40k) seems like a false equivalence if you can't have one without the other. I believe the most taut constraint on producing more AIS researchers is generally training/mentorship, not money. Even wizard software engineers generally need an on-ramp for a field as pre-paradigmatic and illegible as AI safety. If all MATS' money instead went to the LTFF to support further independent researchers, I believe that substantially less impact would be generated. Many LTFF-funded researchers have enrolled in MATS! Caveat: you could probably hire e.g. Terry Tao for some amount of money, but this would likely be very large. Side note: independent researchers are likely cheaper than scholars in managed research programs or employees at AIS orgs because the latter two have overhead costs that benefit researcher output.
4. Some of the researchers who passed through AISC later did MATS. Similarly, several researchers who did MLAB or REMIX later did MATS. It's often hard to appropriately attribute Shapley value to elements of the pipeline, so I recommend assessing orgs addressing different components of the pipeline by how well they achieve their role, and distributing funds between elements of the pipeline based on how much each is constraining the flow of new talent to later sections (anchored by elasticity to funding). For example, I believe that MATS and AISC should be assessed by their effectiveness (including cost, speedup, and mentor time) at converting "informed talent" (i.e., understands the scope of the problem) into "empowered talent" (i.e., can iterate on solutions and attract funding/get hired). This said, MATS aims to improve our advertising towards established academics and software engineers, which might bypass the pipeline in the diagram above. Side note: I believe that converting "unknown talent" into "informed talent" is generally much cheaper than converting "informed talent" into "empowered talent."
5. Several MATS mentors (e.g., Neel Nanda) credit the program for helping them develop as research leads. Similarly, several MATS alumni have credited AISC (and SPAR) for helping them develop as research leads, similar to the way some Postdocs or PhDs take on supervisory roles on the way to Professorship. I believe the "carrying capacity" of the AI safety research field is largely bottlenecked on good research leads (i.e., who can scope and lead useful AIS research projects), especially given how many competent software engineers are flooding into AIS. It seems a mistake not to account for this source of impact in this review.