It is a bit early to tell and seems hard to accurately measure, but I note some concrete examples at the end.

Concrete examples aside, in plan making it's probably more accurate to call it purposeful practice than deliberate practice, but it seems super clear to me that in ~every place where you can deliberately practice, deliberate practice is just way better than whatever your default is of "do the thing a lot and passively gain experience". It would be pretty surprising to me if that mostly failed to be true of purposeful practice for plan making or other metacognitive skills.

As a concrete example, as far as I can piece together from various things I have heard, Open Phil does not want to fund anything that is even slightly right of center in any policy work. I don't think this is because of any COIs, it's because Dustin is very active in the democratic party and doesn't want to be affiliated with anything that is even slightly right-coded. Of course, this has huge effects by incentivizing polarization of AI policy work with billions of dollars, since any AI Open Phil funded policy organization that wants to engage with people on the right might just lose all of their funding because of that, and so you can be confident they will steer away from that.

Thanks for sharing, I was curious if you could elaborate on this (e.g. if there are examples of AI policy work funded by OP that come to mind that are clearly left of center). I am not familiar with policy, but my one data point is the Horizon Fellowship, which is non-partisan and intentionally places congressional fellows in both Democratic and Republican offices. This straightforwardly seems to me like a case where they are trying to engage with people on the right, though maybe you mean not-right-of-center at the organizational level? In general though, (in my limited exposure) I don't model any AI governance orgs as having a particular political affiliation (which might just be because I'm uninformed / ignorant).

Do you have any data on whether outcomes are improving over time? For example, % published / employed / etc 12 months after a given batch

I agree! This is mostly focused on the "getting a job" part though, which typically doesn't end up testing those other things you mention. I think this is the thing I'm gesturing at when I say that there are valid reasons to think that the software interview process feels like it's missing important details.

This might look like building influence / a career in the federal orgs that would be involved in nationalization, rather than a startup. Seems like positioning yourself to be in charge of nationalized projects would be the highest impact?

Your GitHub link is broken, it includes the period in the url.

I
Love
Interesting
Alignment
Donferences

I spoke with some people last fall who were planning to do this, perhaps it's the same people. I think the idea (at least, as stated) was to commercialize regulatory software to fund some alignment work. At the time, they were going by Nomos AI, and it looks like they've since renamed to Norm AI.

+ the obvious fact that it might matter to the kid that they're going to die

(edit: fwiw I broadly think people who want to have kids should have kids)

Hmm, I have exactly one idea. Are you pressing shift+enter to new line? For me, if I do shift+enter

>! I don't get a spoiler