Regarding Jackson's comment, I agree that 'dumping' money last-minute is a bit silly. Spending at a higher rate (and saving less) doesn't seem so crazy - which is what it seems you were considering. 

 Why do you believe that capital won't be useful?

My guess the modal outcome from AGI (and eventual ASI) is human disempowerment/extinction. Less confidently, I also suspect that most worlds where things go 'well' look weird and not not much like business-as-normal. For example, if we eventually have a sovereign ASI implement some form of coherent extrapolated volition I'm pretty unsure how (we would want) this to interact with individuals' capital. [Point 2. of this recent shortform feel adjacent - discussing CEV based on population rather than wealth].

Thanks for the list! Napkin AI is particularly cool. 

Your LibGen link isn't working for me. Its Wikipedia page usually has live (& safe) links in the infobox. Same for Sci-Hub.  

One recent service I've found is Readwise. It's similar to Pocket/Instapaper in adding web-pages to read later, but you can also upload your own EPUB and PDFs.  

I thought about this a few years ago and have a post here.

I agree with Caleb's comment on the necessity to consider what a post-superintelligence world would look like, and whether capital could be usefully deployed. This post might be of interest.

My own guess is that it's most likely that capital won't be useful and that more aggressive donating makes sense.

The consequence of this for the "spend now vs spend later" debate  is crudely modeled in The optimal timing of spending on AGI safety work, if one expects automated science to directly & predictably precede AGI. (Our model does not model labor, and instead considers [the AI risk community's] stocks of money, research and influence) 

We suppose that after a 'fire alarm'  funders can spend down their remaining capital, and that the returns to spending on safety research during this period can be higher than spending pre-fire alarm (although our implementation, as Phil Trammell points out, is subtly problematic, and I've not computed the results with a corrected approach).

Thanks again Phil for taking the read this through and for the in-depth feedback.

I hope to take some time to create a follow-up post, working in your suggestions and corrections as external updates (e.g. to the parameters of lower total AI risk funding, shorter Metaculus timelines). 

I don't know if the “only one big actor” simplification holds closely enough in the AI safety case for the "optimization" approach to be a better guide, but it may well be.

This is a fair point.

The initial motivator for the project was for AI s-risk funding, of which there's pretty much one large funder (and not much work is done on AI s-risk reduction outside of people and organizations and people outside the effective altruism community) though this result is entirely on AI existential risk, which is less well modeled as a single actor.

My intuition is that the "one big actor" does work sufficiently well for the AI risk community given the shared goal (avoid an AI existential catastrophe) and my guess that a lot of the AI risk done by the community doesn't change the behaviour of AI labs much (i.e. it could be that they choose to put more effort into capabilities over safety because of work done by the AI risk community, but I'm pretty sure this isn't happening). 

For example, the value of spending after vs. before the "fire alarm" seems to depend erroneously on the choice of units of money. (This is the second bit of red-highlighted text in the linked Google doc.) So I'd encourage someone interested in quantifying the optimal spending schedule on AI safety to start with this model, but then comb over the details very carefully.

To comment on this particular error (though not to say that other errors Phil points to are not also unproblematic - I've yet to properly go through them), for what it's worth, the main results of the post suppose zero post fire alarm spending^[1] and (fortunately) since in our results we use units of millions of dollars and take the initial capital to be on the order of 1000 $m, I don't think we face this problem of smaller $\eta$ having the reverse than desired effect for 

In a future version I expect I'll just take the post-fire alarm returns to spending to use the same returns exponent $\eta$ from before the fire alarm but have some multiplier - i.e. $x^{\eta }$  returns to spending before the fire-alarm and $k{x}^{\eta }$ afterwards.

1. ^{^}

   Though if one thinks there will many good opportunities to spend after a fire alarm, our main no-fire-alarm results would likely be an overestimate

Strong agreement that a global moratorium would be great.

I'm unsure if aiming for a global moratorium is the best thing to aim for rather than a slowing of the race-like behaviour -- maybe a relevant similar case is whether to aim directly for the abolition of factory farms or just incremental improvements in welfare standards.

This post from last year - What an actually pessimistic containment strategy looks like -  has some good discussion on the topic of slowing down AGI research.

Thanks for the transcript and sharing this. The coverage seems pretty good, and the airplane crash analogy seems pretty helpful for communicating  - I expect to use it in the future!

I agree. This lines with models of optimal spending I worked on which allowed for a post-fire alarm "crunch time" in which one can spend a significant fraction of remaining capital.

I think "different timelines don't change the EV of different options very much" plus "personal fit considerations can change the EV of a PhD by a ton" does end up resulting in an argument for the PhD decision not depending much on timelines. I think that you're mostly disagreeing with the first claim, but I'm not entirely sure.

Yep, that's right that I'm disagreeing with the first claim.  I think one could argue the main claim either by:

1. Regardless of your timelines, you (person considering doing a PhD) shouldn't take it too much into consideration
2. I (advising you on how to think about whether to do a PhD) think timelines are such that you shouldn't take timelines too much into consideration

I think (1) is false, and think that (2) should be qualified by how one's advice would change depending on timelines. (You do briefly discuss (2), e.g. the SOTA comment). 

To put my cards on the table, on the object level, I have relatively short  timelines and that fewer people should be doing PhDs on the margin. My highly speculative guess is that this post has the effect of marginally pushing more people towards doing PhDs (given the existing association of shorter timelines => shouldn't do a PhD).

I think you raise some good considerations but want to push back a little.

I agree with your arguments that
- we shouldn't use point estimates (of the median AGI date)
- we shouldn't fully defer to (say) Metaculus estimates.
- personal fit is important

But I don't think you've argued that "Whether you should do a PhD doesn't depend much on timelines."

Ideally as a community we can have a guess at the optimal number of people in the community that should do PhDs (factoring in their personal fit etc) vs other paths.

I don't think this has been done, but since most estimates of AGI timelines have decreased in the past few years it seems very plausible to me that the optimal allocation now has fewer people doing PhDs. This could maybe be framed as raising the 'personal fit bar' to doing a PhD.

I think my worry boils down to thinking that "don't factor in timelines too much" could be overly general and not get us closer to the optimal allocation.