I'm eager to see Eliezer's planned reply to your "ETA2", but in the meantime, here are a few of my own thoughts on this...

My guess is that movement-building and learning are still the best things to do right now for AI risk reduction. CEA, CFAR, and GiveWell are doing good movement-building, though the GiveWell crowd tends to be less interested in x-risk mitigation. GiveWell is doing a large share of the EA-learning, and might eventually (via GiveWell labs) do some of the x-risk learning (right now GiveWell has a lot of catching up to do on x-risk).

The largest share of the "explicit" x-risk learning is happening at or near FHI & MIRI, including e.g. Christiano. Lots of "implicit" x-risk learning is happening at e.g. NASA where it's not clear that EA-sourced funding can have much marginal effect relative to the effect it could have on tiny organizations like MIRI and FHI.

My impression, which could be wrong, is that GiveWell's ability to hire more researchers is not funding-limited but rather limited by management's preference to offer lower salaries than necessary to ensure cause loyalty. (I would prefer GiveWell raise salaries and grow its research staff faster.) AMF could be fully funded relatively easily by Good Ventures or the Gates Foundation but maybe they're holding back because this would be discouraging to the EA movement: small-scale donors requiring the high-evidence threshold met by GiveWell's top charities would say "Well, I guess there's nothing for little 'ol me to do here." (There are other reasons they may be holding back, too.)

I think accelerating learning is more important right now than a DAF. Getting high-quality evidence about which x-risk mitigation efforts are worthwhile requires lots of work, but one thing we've learned in the past decade is that causes with high-quality evidence for their effectiveness tend to get funded, and this trend is probably increasing. The sooner we do enough learning to have high-quality evidence for the goodness of particular x-risk mitigation efforts, the sooner large funders will fund those efforts. Or, as Christiano writes:

To me it currently looks like the value of getting information faster is significantly higher than the value of money, and on the current margin I think most of these learning activities are underfunded.

And:

A relatively small set of activities seems to be responsible for most learning that is occurring (for example, much of GiveWell’s work, some work within the Centre for Effective Altruism, some strategy work within MIRI, hopefully parts of this blog, and a great number of other activities that can’t be so easily sliced up)

However, Paul thinks there are serious RFMF problems here:

A more serious concern is that there seems to currently be a significant deficit of human capital specialized for this problem and willing to work on it (without already being committed to work on it), so barring some new recruitment strategies (e.g. paying market wages for non-EAs to do EA strategy research) there are significant issues with room for more funding.

In contrast, I think there is plenty of room for more funding here, even without resorting to "paying market wages for non-EAs to do EA strategy research":

1. MIRI could run more workshops and hire some able and willing FAI researchers, which I think is quite valuable for x-risk mitigation strategy learning apart from the object-level FAI progress it might produce. But even excluding this...

2. With more cash, FHI and CSER could host strategy-relevant conferences and workshops, and get people like Stuart Russell and Richard Posner to participate.

3. I have plenty of EAs capable of doing the labor-intensive data-gathering work needed for much of the strategy work, e.g. collecting data on how fast different parts of AI are progressing, how much money has gone into AI R&D each decade since the 60s, how ripple effects have worked historically, more IEM-relevant data like Katja's tech report, etc. I just don't have the money to pay them to do it.

4. FHI has lots more researcher-hours it could purchase if it had more cash.

Finally, a clarification: If I think movement-building and learning are most important right now, why is MIRI focused on math research this year? My views on this have shifted even since our 2013 strategy post, and I should note that Eliezer's reasons for focusing on math research are probably somewhat different from mine.

In my estimation, MIRI's focus on math research offers the following benefits to movement-building and learning:

1. Math research has better traction than strategic research with the world's top cognitive ability. And once top talent is engaged by the math research, some of these top thinkers turn their attention to the strategic issues, too. (Historically true, not just speculation.)

2. Without an object-level research program on the most important problem (beneficent superintelligence), many of the best people just "bounce off" because there's nothing for them to engage directly. (Historically true, not just speculation.)

3. And of course, FAI research tells us some things about how hard FAI research is, which lines of inquiry are tractable now, etc.

Eliezer has not published a detailed explanation of his estimates, although he has published many of his arguments for his estimates.

Are these available? Are they the standard stuff (i.e., "Evidence and Import")?

~

For myself, I think the cause of AI risk reduction, in total and over time, has a worthwhile small-to-medium probability of making an astronomical difference on our civilization's future

How do you arrive at that conclusion? I'm less skeptical of the cause-specific claim than the organization-specific claim, but it's worth digging deeper into.