Revolutionizing Cybersecurity and AI-Driven Science with OpenAI President Greg Brockman

Greg Brockman (0:00)

To ensure AGI benefits all of humanity, we think that humans should be firmly in the driver's seat, should have control over the future, and this technology should amplify human agency. When I look at the world we're going to be in, where software in some ways is becoming, like the barrier to entry, it's becoming so cheap. You don't just want software, right? You want software that is reliable, that is secure, that you can believe in. And so cyber spend, security spend, should actually vastly outstrip software spend. I think that people have not yet really internalized how much the physics around cybersecurity are going to change. I think there's an opportunity to end up with a much more secure world.

Sarah Guo (0:39)

Hi, listeners. Welcome back to No Priors. Today, Elad and I talk with Greg Brockman, the co-founder and president of OpenAI.

We discuss OpenAI's latest math results, Astra, and why Greg believes AI can now generate genuinely new knowledge. We get into drug discovery dots, and his case for why security spending should actually vastly outstrip software spending, which is quite a claim. Here's our conversation with Greg Brockman.

Greg, thanks so much for doing this.

Elad Gil (1:06)

Thanks for joining us.

Greg Brockman (1:07)

Thank you for having me.

Elad Gil (1:08)

So every week in AI feels like a year, and a lot's happened this week for OpenAI. You rolled out GPT-6 to over a billion people, which is pretty amazing. There's some really exciting news that you have on the math side.

There's all sorts of things undoubtedly that are happening internally. What are you most excited about this happening right now, both at OpenAI and in the broader AI world?

Greg Brockman (1:29)

Look, I think that in AI, I have this belief about how OpenAI operates, that there's this cycling quote of, it never gets easier, you just go faster. I think that's very much what happens, is that there's just so much activity, and that every time you think you're at peak impact, and how things are happening, you realize that there's actually another level that's possible. It's not just about any one company, it's not just about any one technology, it's really about we as a world and society are all coming together to adopt this technology, get the most out of it to prove everyone's lives. And so, I think to me, the math results this week represent a real milestone, right? It's a real moment where the, if you think about like the fundamental driver of human quality of life improving over the past thousand years is technology, right? It's scientific advancement. And I think that we now have the ability to massively accelerate scientific discovery. And there's always been this question of, is AI actually able to come with new knowledge?

And at this point, it's just impossible to deny, right? It is definitively, yes, possible. Now, there's still more to go.

I think we're not yet coming up with whole brand new theories, but we are able to resolve these hundred year old math problems. And to me, the thing that's very exciting is it's not just about the sort of, you know, sort of the just, the machine just sort of gives an answer, right? It's really, what can a human equipped with these tools achieve that was unthinkable before? And I think it's almost like that the leverage of humans to do whatever you want to do, it's going way, way up, right? It's now that you can, if you have a math problem you want to go solve, you can solve it, but what about taking that same capability, applying it to biology? What about if you're a small business owner and you want to be able to operate your business well, like that level of intelligence that's tuned for that domain, it's just going to mean that almost this sort of unthinkable results that are aligned with your goals, with your desires, is now going to be within the reach of everyone, and that to me is so exciting.

Elad Gil (3:32)

The rate of progress has been pretty amazing too, because if I look at it, you used to have problems with arithmetic, right? In early models, I remember people would complain, oh, why can't I do basic math? Now, you're solving these advanced theorems. As these things continue to advance and this starts to really impact areas of math, physics, materials, etc., and various applications, is there some need if these models get so smart to focus on how to communicate that effectively to people? Or how do you think about the actual communication side of this as you do a very advanced theorem? Because if you look at any field of math, only a very small number of people can ever really adjudicate a theorem that's produced by a person. Yes. Right? So you have this specialization that happens.