However, many of these people might not have a sufficient “toolbox” or research experience to have much marginal impact in short timelines worlds.

I think this is true for some people, but I also think people tend to overestimate the amount of years it takes to have enough research experience to contribute. 

I think a few people have been able to make useful contributions within their first year (though in fairness they generally had backgrounds in ML or AI, so they weren't starting completely from scratch), and several highly respected senior researchers have just a few years of research experience. (And they, on average, had less access to mentorship/infrastructure than today's folks). 

I also think people often overestimate the amount of time it takes to become an expert in a specific area relevant to AI risk (like subtopics in compute governance, information security, etc.)

Finally, I think people should try to model community growth & neglectedness of AI risk in their estimates. Many people have gotten interested in AI safety in the last 1-3 years. I expect that many more will get interested in AI safety in the upcoming years. Being one researcher in a field of 300 seems more useful than being one researcher in a field of 1500. 

With all that in mind, I really like this exercise, and I expect that I'll encourage people to do this in the future:

1. Write out your credences for AGI being realized in 2027, 2032, and 2042;
2. Write out your plans if you had 100% credence in each of 2027, 2032, and 2042;
3. Write out your marginal impact in lowering P(doom) via each of those three plans;
4. Work towards the plan that is the argmax of your marginal impact, weighted by your credence in the respective AGI timelines.

[Note: written on a phone, quite rambly and disorganized]

I broadly agree with the approach, some comments:

• people's timelines seem to be consistently updated in the same direction (getting shorter). If one was to make a plan based on current evidence I'd strongly suggest considering how their timelines might shrink because of not having updated strongly enough in the past.
• a lot of my coversations with aspiring ai safety researchers goes something like "if timelines were so short I'd have basically no impact, that's why I'm choosing to do a PhD" or "[specific timelines report] gives X% of TAI by YYYY anyway". I believe people who choose to do research drastically underestimate the impact they could have in short timelines worlds (esp. through under-explored non-research paths, like governance / outreach etc) and overestimate the probability of AI timelines reports being right.
• as you said, it makes senses to consider plans that works in short timelines and improve things in medium/long timelines as well. Thus you might actually want to estimate the EV of a research policy for 2023-2027 (A), 2027-2032 (B) and 2032-2042 (C) where by plicy I mean you apply a strategy for either A and update if no AGI in 2027, or you apply a strategy for A+B and update in 2032, etc.
• It also makes sense to consider who could help you with your plan. If you plan to work at Anthropic, OAI, Conjecture etc it seems that many people there consider seriously the 2027 scenario, and teams there would be working on short timelines agendas matter what.
• if you'd have 8x more impact on a long timelines scenario than short timelines, but consider short timelines only 7x more likely, working as if long timelines were true would create a lot of cognitive dissonance which could turn out to be counterproductive
• if everyone was doing this and going to PhD, the community would end up producing less research now, therefore having less research for the ML community to interact with in the meantime. It would also reduce the number of low-quality research, and admittedly doing PhD one would also publish papers that would be a better way to attract more academics to the field.
• one should stress the importance of testing for personal fit early on. If you think you'd be a great researcher in 10 years but have never tried research, consider doing internships / publishing research before going through the grad school pipeline? Also PhD can be a lonely path and unproductive for many. Especially if the goal is to do AI Safety research, test the fit for direct work as early as possible (alignment research is surprisingly more pre-paradigmatic than mainstream ML research)

if everyone followed the argmax approach I laid out here. Are there any ways they might do something you think is predictably wrong?

While teamwork seems to be assumed in the article, I believe it's worth spelling out explicitly that argmaxing for a plan with highest marginal impact might mean joining and/or building a team where the team effort will make the most impact, not optimizing for highest individual contribution.

Spending time to explain why a previous research failed might help 100 other groups to learn from our mistake, so it could be more impactful than pursuing the next shiny idea.

We don't want to optimize for the naive feeling of individual marginal impact, we want to keep in mind the actual goal is to make an Aligned AGI.