Rob Wiblin

Are we on track to, if we think crunch time is gonna start in six years, are we on track to have inference compute be a large fraction of our spending at that time?

Oh, totally.

I mean, I think this is a very natural institutional.

I think even beyond just being scared of making a mistake on this front, it's just that organizations have particular ways they do things.

And there's processes.

And right now, OpenPhil's process for grant making looks like usually someone fairly junior

gets an opportunity come across their desk, either through one of our open calls or through some contact they have.

And that junior person pulls together some materials to convince their manager it's a good fit.

And then that manager sort of convinces someone higher up that it's a good fit.

And you can have two layers or three layers or sometimes four layers of information cascading up

the decision-making process that we have in place as an org, and then it's approved.

And it's just like, if the right thing to do is to spend a billion dollars on some particular strain of work that's super automatable,

It just like that isn't even like you wouldn't trust some random junior person to make that call.

You need to you might need to have just a different process for that.

And I don't know what that process would look like, but I think that would be like one thing to figure out.

Yeah.

So I think there's two possibilities here.

One possibility is that by the time it's the right move to dump a bunch of money on crunch time AI labor, OpenPhil itself has already been largely automated.

And that's actually like an easy world because in that world, we just have a visceral sense that AIs are really helpful because they like, we've like, you know, maybe we've slowed down our junior hiring and like all our program associates are AIs right now.

And like