Building a School Where AI Models Learn About Humanity

**Edwin Chen** (0:00)
We are building this kind of school for AGI, where AI models come to learn about humanity, where we teach them how to run the world. It almost seems like there's nothing that humans can do that AI won't soon be capable of. I could see it happening within the next five years.

**Dan Shipper** (0:17)
AI may be able to do it better than us, but someone told the AI to go do that. They're being built to be means to tasks that humans want them to do, right?
Every is the only subscription you need to say at the edge of AI. If you care about being on top of the latest models and using the latest tools, you have to subscribe to Every to separate out the signal from the noise. Go to every.to slash subscribe today. Edwin, welcome to the show.

**Edwin Chen** (0:54)
Hey Dan, thanks for having me.

**Dan Shipper** (0:56)
For people who don't know, you are the founder and CEO of Surge. You all provide data environments and evals for the model companies, but you do it in this very interesting way. You have this, even on your website, like this emphasis on taste and expert judgment I find like really interesting and compelling. You talk about raising, like you use the word raising AGI, which I feel like is a very distinct type of word using data.
You also famously got to about a billion in revenue without raising money, which is wild. I feel like data is this new game that a lot of companies are playing and probably more are going to be playing soon, when you guys are this sneaky giant. Tell me how that's going, because I think it's been a little while since we got the last update on how things are going.

**Edwin Chen** (1:49)
Yeah, I mean, I think it's going amazing. The way I often think about this is that we are building this kind of school for AGI, the school where AI models come to learn about humanity, and yeah, where we teach them how to run the world.
And it's almost like their models are children, where they arrive unformed and then yeah, they leave smarter and more creative and more thoughtful and ready to operate in the messiness of your world.
So I think a lot has changed the past year, like in the same way that the things that you teach children when they're in preschool or in middle school or in high school is very different from what you're teaching them when they're in college. And it's not just that they're more advanced, like it's not just that you're teaching them a more advanced form of what they did before. It's like, okay, now we are teaching you not just arithmetic, but how do you parse these ambiguous math questions? Or how do you teach people not just grammar, but taste and poetry and beauty? So yeah, I think there's a lot that's been changing in the past year, especially in enterprise. And yeah, it's been a crazy time.

**Dan Shipper** (2:55)
What would be like a specific example of what the frontier of teaching was a year ago versus what the frontier is now?

**Edwin Chen** (3:05)
Yeah, so a couple of years ago, actually, we created our first math benchmark with OpenAI, and it was called GSM 8K.
And this was actually just testing models on their abilities to do middle score math. And even then, the GPT models of the time, they could barely score, I think, like 20 percent.
And then a year ago, the models were like something they became a lot more capable at solving IMO problems. But there was still this open question, okay, can they actually do research level mathematics? Like, can they move beyond these sort of competition-only, sort of contrived, very closed problems into doing things that are actually useful in the real world? And so, yeah, a couple of months ago, we released an updated benchmark called RemindBench, which actually tests models on their ability to do research level mathematics. And what's crazy is that this is actually what we're starting to see from these models. Like, I think in the past few months, they've started to solve a lot of these open Erdős problems. Like, a couple of weeks ago, OpenAI published a new result where the models had disproved an open conjecture from Erdős. And the way it went about disproving this was actually a fairly sophisticated level of mathematics, I think, like using a bunch of very novel algebraic geometry techniques. And so, yeah, it's just very, very different from the types of things that we were doing a year ago, where, sure, like, IMO problems, they're hard, but they're still sort of close-ended and solvable in theory by a high schooler. And now suddenly you have these algebraic geometry results that, you know, even top professors in the world were kind of amazed by.