Rob Wiblin

And part of the responsibility of being the person in charge of this program area is that you investigate those renewals and make decisions about whether we should keep the grantees on or not.

And those grants, I tried to...

follow like what an OpenPhil canonical decision making process would be there.

And so I tried to pursue kind of a barbell strategy for a while where like on the one hand, there were either renewals or people who like knew us, who reached out to us to ask us to consider grants, where I wouldn't hold myself to the standard of like really

on the technical merits, like understanding and defending the proposal, but would lean more on heuristics like, this person seems aligned with the goal of reducing AI takeover risk, this person has a broadly good research track record, and so on, and try to make those grants relatively quickly.

But then I would also be trying to develop a different funding program or some grants that I really wanted to bet on where I would try and hold myself to that standard and try and really write down why I thought this was a good thing to pursue.

And it turned out that the second thing basically turned into making a bet in late 23 to mid 24 of AI agent capability benchmarks and other ways of gaining evidence about AI's impact on the world.

Yeah, yeah.

So I launched this request for proposals, which OpenPhil has done technical safety requests for proposals before, but this was by far the narrowest and most sort of like deeply justified technical RFP that we had put out at that time, where I was like,

We are looking for benchmarks that test agents, not just models that are chatbots.

And these are the properties we think a really great benchmark would have.

And these are examples of benchmarks we think are good and not so good.

And we had a whole application form that was in some sense sort of guiding people or trying to elicit...

the information about their benchmark that we thought would be most important for determining whether or not it was really informative.

And mostly this was just be way more realistic, have way harder tasks than existing benchmarks.

Even if you think your tasks are hard enough, they're probably not hard enough.

There was a lot of push in that direction.

So it was a very opinionated and very detailed and very narrow RFP.

And we ended up making $25 million of grants through that, and then another two to three million from the companion RFP, which was just a broader, like, all kinds of, like, information from RCTs to surveys about AI's impact on the world.

And I'm, like, pretty happy with how that turned out.