サイバーセキュリティとAI駆動の科学を変革する――OpenAI社長 Greg Brockman 氏に聞く

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 a 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 cyber security are going to change. I think there's an opportunity to end up with a much more secure world. Hi, listeners. Welcome back to No Priors. Today, Ilad 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 Brockmuth. Greg, thanks so much for doing this. Thanks for joining us. Thank you for having me. 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? Look, I think that in AI, like I have this belief about how OpenAI operates, that there's a cycling quote of it never gets easier, you just go faster. And that's very much what happens is that there's just so much activity and that every time you think you're at kind of peak sort of, you know, impact and how things are happening, you realize that there's actually another level that's possible. And 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 improve 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 up 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 grand 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 just the machine just sort of gives you 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 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, if it'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. The rate of progress has been pretty amazing, too, because if I look at it, you know, you used to have problems with arithmetic, right? In early models, I remember people complained, oh, why can't it do basic math? And now you're solving these advanced theorems. As these things continue to advance and this starts to really impact areas of math, physics, materials, et cetera, 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? And so you kind of have this specialization that happens. Well, and this, by the way, is something that has always been, like, I came from a math background. I thought I was going to be a mathematician, and I was very inspired by mathematicians like Galois or Gauss, these people who were working on like a 100, 400-year time horizon, right?

And I think there's something deeply beautiful about mathematics being about sort of understanding the structure of the universe. And so it's not just about, and there's always these questions about is math invented or discovered, and that in my mind it's something that the beauty and the meaning of mathematics, it's not something that is objective in some sense. It's in the eye of the beholder. It's something that appeals to us, and there is some amount that's like kind of physical laws and it's about simplicity and, you know, compression theory, you know, complexity theory and all these things that are maybe more physics, but there's something that is also importantly human about it. And so to me it's not just about, I think, a lot of the conversation about mathematics. It's not just about here's a proof, no one understands it, great, we resolved the problem. It's really about what are the insights that are derived. And the thing that I think we will see, but I think there's promising signs of life on, is that there's beautiful insights within the work that these AIs have done.

And I think what we're seeing right now is that we published about almost 400 very important open problems that are now resolved. and the mathematical community is going through all of that to look for insights. And if you read some of the things that people have posted, you know, domain experts in these particular problems, and they say that it's like that there's these almost like sort of very surprising ways of applying ideas or incredibly like, wait, why do they use that result over here? And that what's coming out is not these incomprehensible theorem or even proofs, which, by the way, there is a history of incomprehensible proofs in mathematics. Like four-color theorem is a perfect example of this that is, it's like, okay, like we just have the machine check a bunch of cases. Like there's no beauty in that. And so I think we're into a different phase of machine discovery. I have some optimism that like as models also get to better theory of mind, like the ability to figure out like what we as humans know and how to get us to follow them on the path to new understanding will also be paved, right?

And so, like, for example, like, I'm a math person, but at some point in school I stopped learning math. And then I did Math Academy. And then I did, like, ChatGPT to help me learn new math. And so I just feel like the expectation for, you know, the rising tide of what knowledge, like, you can expect people to understand will also be there. I think that this is a really important point. and one of maybe the underappreciated benefits that I expect from and hope for and we're starting to see from this technology is helping people know more, understand more, understand each other better, understand the world better. And I think that there are technical mechanisms that can underlie the AI that's able to go and solve these problems, see all these connections between domains. How does it actually communicate to you? How do you understand it? And actually, there's even connections to AI safety and alignment on having AIs that were able to understand what they're doing. And so there are techniques that we've been talking about since 2017, such as debate and iterative amplification, which are kind of two sides of a similar coin,

where you really think about if you have AIs that are solving problems that are just too hard for humans to check. And even, for example, summarizing a book is an example of a task that's really hard to check, right? It's like, it's really hard to know the summary any good unless you've read the whole book. And so if an AI just gives you a summary of a book and says, this is a summary, is it a good summary or not a good summary? It's pretty hard, unless you have another AI who is trying to prove that the summary is a bad summary. And so then if that AI comes back and says, hey, like, here's this, like, paragraph in the book that I found. It's got a clearly a very important plot point, and that is not represented in the summary. You don't have to read the whole book to be like, oh, yeah, that's actually a very good argument. And so you realize that there are ways to train the AI that are actually very aligned with having humans be able to have oversight, to be able to be in the driver's seat, which is fundamental value, a fundamental thing that is important for the future from OpenAI's perspective. But also are very much aligned with what you're saying of can you make the results of an AI be something that means you learn something that is really tuned for the human rather than just sort of knowledge for some abstract knowledge sake.

For most people, advancements in math theory are still somewhat abstract, right? So there's like, wow, this is absolutely happening today. And then also, you know, AI for science, like there's a claim or a belief that it could solve all disease in some number of future years, right? How do you look for like one step in terms of what we might see in advancements in between? Well, I think that we've had a number of years in iteration of this technology to see the shape of what to expect. And I think that it's kind of like when you have an AI that is capable in a certain dimension, and if you understand why it got capable in that dimension, then all other fields of human endeavor that have similar properties, you should also expect similar results. And so, in my mind, why is mathematics something that we've managed to really transform in the way that we have had such incredible results? And part of it is that our AI is now at a certain level of capability and raw intelligence.

Part of it is that you can think of it as well mathematics is checkable but that not quite true anymore right We really started with very checkable problems of here an arithmetic problem there like some numeric answer and so you have an AI that you can train to just like check if it got the answer right because we can very much deceive is the answer that the AI is proposing the same as the actual answer, but for a proof, very different. Proofs are very hard to check. Is this actually accurate or not. Formalized proof, okay, yes, that is checkable, but natural language proof, which is what we're really producing here, much, much more difficult. But the thing that actually I think is the core insight is really that data, right? It's that you can get lots of data in mathematics. You get lots of data in theoretical physics. Can you get lots of data in biology? That's obviously something very data bottleneck, very difficult. Now, fortunately, we also have been working on lots of techniques to be able to work with less data. And if you think about what that means, It's like, okay, we now have smart models that if there's something where there's a lot of data,

and this is the fundamental physics of deep learning, right? Compute data algorithms equals capability equals results. We're able to do it in these hard domains where people have spent hundreds of years, our smartest people all trying to solve the problem, and now the AI can solve it. Amazing. When you transfer that over to biology, and clearly what we want is drug discovery. All these things that are like fundamental changes in biology and our understanding of it to improve quality of life, that's what we want to apply. The question will be, and it's not yet resolved, do we have the data? Do we have the ability to get the data? And so I think that what will happen is that we will take these, and we're already doing this, where we take these models, these same techniques, and apply them in the biological domain. And there are some areas that will fall quickly, right? That we'll just see a massive acceleration of things that used to be incredibly painful, now incredibly easy. But there will be other bottlenecks. And there'll be a question of, well, how do we alleviate those bottlenecks? And some of these, by the way, are not even really about deep biological theory. For example, regulatory filings, huge bottleneck, right?

It takes like a year to write up your whole sort of case for clinical trial and all these things. And it's just like, you can write those things with an AI much more easily. And so I think that we're going to have to look at every step of the drug discovery process. And there are going to be places where the fundamental data just does not exist. And so then innovations on wet labs, innovation on how you actually do simulations, All of this will become incredibly important. So I think that we will see these breakthroughs coming, but it's important to know there are things that are shared with mathematics. It's about scientific discovery and things that are very different in terms of where the data comes from. You mentioned biology, and I think that's a great example, where a lot of the early research, AlphaFold and things like that, were really focused on designing a molecule or understanding how something binds to each other. And to your point, it takes 15 years to develop a drug. It's $1.5 billion. $50 million or so tends to be preclinical, mouse studies, the molecule, and then $1.4 billion plus tends to be the clinical trial.

And so your point is, and it's also, you know, two, three years versus another 12 years. And so your point is really well taken in terms of a lot of the early emphasis of these models has been can the thing mind versus what are all the other things you have to worry about downstream to actually turn it into a productive thing for human endeavors. How do you at OpenAI currently stack rank the different areas of scientific endeavor in terms of where you're focused or where you want to generate more data or more results or more capabilities that others can benefit from? Look, I think that drug discovery is the most clear and obvious area, if you get to pick one, that you want to fully revolutionize. And just in places where you're going to really uplift everyone's quality of life, that is just like so purely important, and maybe the top one application of AI that everyone's been kind of thinking about for a long time, we clearly got to do it. And to your point, like there's this whole pipeline, it's just so long. and I think we're going to find that humans are incredibly bad at biology, incredibly bad at drug discovery and if you think about it there's this like property where for humans natural language

we're very well suited to it like it's something that you know very clearly it's very human it's like very well tuned to to how we have interacted with the world whereas biology it's totally foreign but for a neural net I don't really think there's any reason to believe it should be better at natural language, then it should be better, you know, compared to biology. And so the fact we're seeing such incredible results with neural nets in natural language, you're just like, from first principles, you should be able to really be able to crack the language of biology. And so to me, that is number one. It's like first principles should work in terms of what we're seeing. We're so close in so many actions. Yeah, it's very exciting. Yeah. And I think you can massively increase the parallelism. I think you can increase, I think you can decrease so many of these steps in terms of their duration. And I think, by the way, that there are many dimensions to drug discovery, for example, NF1 diseases. There's even, like, lots of people who get a drug to cure their particular condition exist. It's just out there somewhere in the literature that there's a paper, and if you can find that one paper

that's relevant to your conditions, then you'll be able to cure the person. And there's plenty of precedent for it, and if you massively scale up those kinds of operations. So I think that impact in the short run is actually very possible. But material science, another example, And I think there's a long tail of other domains where we should just see real changes in kind of what is possible for humanity to accomplish. It seems just we think about why a drug might take 15 years to go to market. Part of it is you're doing like successively more expensive trials that have a different set of safety considerations resulting in large scale human trials. And then these trials today are, they're ordained by regulation, right, for a good reason. It feels like one of the limiting factors, if you believe the technology will work and parts of the data can be acquired or generated, is going to be convincing regulators that we should change the expectations around what drugs are accepted and what the steps are. I think there is a moment here where, and there's more work to be done to reach this point,

But if you can really prove that the physics of drug discovery have changed, then of course it should trickle through to every part of the system. And if you think about, I mean, even just take a very different example, think about self-driving cars, right? We have so much of our, how roads operate and safety and all these things that are really tuned for human drivers. and if you have, if you reach a point where self-driving cars are 10 times or 100 times safer than human ones, right, that you've reached that point where AI is actually able to deliver the results in a domain that just are so unambiguously the thing that you want, of course you should then say, like, okay, how do we rethink the whole transportation system? Maybe you should have regulation that says you only are allowed to have self-driving cars in certain areas, like, that there are a lot of potential sort of changes to the the way that you think about what is even trying to come out of a system that needs to trickle back into regulation as well. And, of course, regulation is not uniform across the world, right, that there's very different regulations in Australia versus the U.S. when it comes to clinical trials,

and you're already seeing a lot of people moving their trials to places where the regulatory environment makes more sense for how things are, how they want to pursue and, you know, what people think is efficacious for them. And so I think that exactly how things should change, I think, will depend a lot on what we actually can prove, right? Is it the case you actually reach the point that you can get better results from a neural net than from actually running a clinical trial? Like, we're not there. Yeah. But you could imagine we will get there. But if they were more predictive on efficacy or tox or something, then obviously you should change it. That's it, of course. And I think that of areas where I think society should and I hope will be very sort of leaned into the what is possible has fundamentally changed and let's very quickly then reorient around it. And again, you have to do it. You have to really think through all the consequences, right, and all of the implications. And that one of the, you know, if you're thinking about long-term studies, people in the question of, like, you know, is this neural net actually, in terms of what's predicting the simulation, actually accurate for long-term outcomes?

It's pretty hard to not have at least had, you know, one long time tick to correspond to that. But I think that we will see, like, 80% of what is possible come to fruition very quickly. You are a little ways out from the Astro release and Dev Day now. Is there anything in terms of how people internally or externally are using some of the new products or capabilities that surprises you? Well, I think Dots has really changed how many of us work. And the thing that Dots represents is... You described Dots briefly. So Dots is a proactive agent. It's always on, always working. You stop working, it keeps working. and the kind of thing that my dots does, right? So I have a dot that's looking out for work that I was supposed to do, like messages I was supposed to send, things I said I would do that I haven't gotten to yet. And I always wake up in the morning, there's like a list of things that I was like, oh yeah, this is my to-do list.

And it often notices things that I never even ask it to notice. If there's like a conflict on my calendar in terms of scheduling, it'll point that out. If it's like, hey, you actually need to leave this meeting earlier in order to get to the other thing. And so it's like so contextually aware. It knows what I'm doing with my day. It understands the project we're working on. It understands, I sort of, you know, it knows what's in my inbox, knows what's in my Slack, and is able to integrate across these. And it knows the questions I've been asking about, so it knows what areas are most of the top of mind for me And I think that having like in some ways what dots is is to change to the form factor not the capability right It like it an Astro level model It got its own cloud computer but the change in that form factor in a workplace setting with this level of intelligence, it's a game changer. And do you have multiple dots running or you just have one that's sort of covering all your background tasks? I've got one. I've got one. It's a little owl is the, if I can ask to change the icon. And it's just sort of doing everything across my work life.

OpenAI obviously has research efforts and then a consumer and an enterprise and developer business, platform business today. Do you think that there's going to be a separation between, let's say, assistants that help you at work, like DOTS, which is how I primarily think of it, and then like consumer assistants, like the instincts of the world? And there is some, I think these things are going to converge, no question. And I think that there are some pressures that are very different in different environments. So for a mass market consumer product, really figuring out how to scale and to provide things for free or at very low cost is very important. Whereas in the workplace, if you're productivity, if you're the executive, individual contributor, whatever your role is, then it is much easier to say peak intelligence. And really, as long as you're getting returns to paying more, it's worthwhile to throw more compute at it. And so I think that that is the main distinction is really around cost. And we've seen this in chat GBT, right? that ChatGVT, we put in a lot of effort to have the largest scale free tier,

and bringing things like memory to all of our free users was an incredible endeavor, like a real huge engineering effort. And it's just very easy to implement the same functionality at much higher costs for a very small number of people. And so I think that what we're seeing in the personal assistant workspace is that there are going to be these different market segments where different amounts of compute are going to be expended fundamentally. And so sometimes when there's a different level of compute, it then means the product can look a little different, right? It can be that there's just more functionality that's embedded in one versus the other. And there's, of course, different use cases. You think about things like, you know, payments and travel and things like that are much more for the consumer. But I don't think there's any fundamental difference, right? It's like really just like an AI that's doing things and it just like happens to be like slight sort of pressure in one way or another. And so one of the strengths of OpenAI is the fact that we have 1.2 billion consumers on Chachi BT, and we have also a leading coding and enterprise business, and no one else is both in-house.

And so a lot of our strategies is, say, let's unify these so we're not sort of competing on two fronts. It's really competing on one front. And I think that this is also the general direction of travel of AI is to blur the distinction between what is consumer, what is enterprise. We're going to have this renaissance of entrepreneurs. Like, we're already seeing it. I mean, imagine you're seeing this very concretely. I know people who are quitting their job to go start companies who say, I never would have done it if I didn't have an AI that I knew I could rely on. It could take care of so much of the normal barriers to entry. And so I think that we're going to see, just like the distinctions have held for so long, I think they're going to change. So one of the things that traditionally has separated the enterprise and consumer world is how you think about UI. Obviously, there's your point on cost and underlying ability to deliver something for somebody at a certain level of fidelity. But the other part of it is just the user interface. And often the user interface in an enterprise setting or how you access it is very different from the consumer setting. To your point, you're looking towards unifying these layers. You've also been thinking quite a bit about dynamic UIs.

And that's always been kind of one of the things people have talked about quite early is how do you just dynamically generate a UI that's useful for a user? Or in some cases, these things kind of move into the background. It's just part of your chat. How do you think UI will evolve for these different use cases, or how do you think about that as a problem as you sort of address both of these segments? So, first of all, I think that the best UI is no UI. And I think in AI, that is where we're going. Just one thing you can talk to, and you can speak to it verbally, you can talk to it over Slack, iMessage, any of the services you can call it, and that's literally what you can do with dots, like literally today. but there are some fundamental differences between personal setting and a workplace setting and the workplace you have an admin right you have kind of your company security team you have just more stakeholders and it's not just sort of a ai that's for you that's representing you that you own and you own its outcomes and so i think that that is the main distinction i see between

the enterprise and the home use case. And of course, this leads to product differences as well. And sometimes they're very small things. Like, for example, when we release a new feature, the enterprises want control over when it's introduced. And so we have admin controls and toggles. And so you can have an experience where there's just less functionality available to you as a result of the fact that it is in the workplace. And that does then change the, well, we need some way to surface that to you. And this comes back to, well, the whole point was that there was no UI, right? So it's like, what does it mean to be functionality that's missing? How do you even surface it and let people know that that's the case? So that's an ongoing challenge, and I think that that will not magically go away. I think that you need to lean into that complexity. But, yeah, to me, fundamentally, it's kind of crazy and beautiful if you think about it. The chat-GBT, the text box, right? That's where it started. It was a text box. And what we're heading to, there isn't even a text box, right? It's just like, that is the AI we were promised. When I think about what might make a product that doesn't have a UI better, there's, like, you tell me, but I think there's, like, how much compute can I spend on it?

How good is the model itself? There's a memory system. There's, like, I don't know, decisions about behavior. How good are models at being calibrated on, you know, how hard they should think about something? So that's something we're getting very good at. and with the release of GPT-6 to everyone, that we have the ability now for the AI, if it's going to go think hard about a problem, it actually can give you a quick answer and then keep thinking and then give you the more fulsome answer. And also to the point of no UI, and to the thing you were saying earlier too, is that we introduced intelligent UI. So now the AI can do generative UI on the fly. And so I think that even though there's no fixed UI, there's no buttons, there's no menus to learn, having an AI that can actually craft the result to be vividly exactly what you want. Like if you think about even something like flight booking, you want kind of a complex UI there, right? So you can see all the different options and that for any question you might ask, like how do I assemble a bicycle or something, you might want some very sort of bespoke app

that normally a person would have gone off and written for a month. And of course, you're never going to hire someone to write a one-off app for you to help assemble a bike, but totally fine for an AI to go do that for you in real time, in the moment you need it. And so I think that there is something about these, like we're now in a world where the AI is molding itself much more to you, right? That the user interface is becoming exactly the thing you need at the moment you need it. And that is a lot of our focus, and we're doing that across every aspect of how we think about the product. One of the biggest shifts in the discourse around AI recently has been around cyber. And how do you build secure systems that are resistant to AI-driven cyber approaches? And so I'm just a little bit curious how you're thinking about that, how you're building guardrails, or how you're approaching the different aspects of cybersecurity. Well, first of all, I do think the physics of cybersecurity are changing, and we're in what we call the defender's window. And the way that I think about this window is that we have peak frontier AI capabilities that can help defenders secure themselves.

They're not broadly diffused yet, but the broadly diffused capabilities are ratcheting. And you can see this from each model release to model release from providers across the world. the capabilities on cyber that are available to everyone are going up. And you're starting to see the leading edges of some of the impacts, right? You're starting to see, for example, in South Korea, that there were some high-profile hacks of banks that were driven by different models. And I think that the picture that is so critical is that defenders need to use this time. And so we have been working very hard to be able to responsibly give access through our Daybreak program to defenders to be able to use our peak intelligence or use very capable models to find and fix vulnerabilities in their systems. And one thing that is very important to realize is these flaws have existed, right? It's every line of human written code in some ways that you have to ask, is this secure? Was the developer trained in the right way?

And you just start to realize that the way in which you built systems were kind of fundamentally insecure, and we always knew it, right? There's never been a CISO who's been over-resourced. Yeah. I mean, you can look at XKCD in the comic of, like, all the world's infrastructure reliant on, like, one open-source project written by one dude in his basement, right? Or Heartbleed, right? Exactly. Or anything that predated, like, all these AI issues. Exactly. And so I think that, to me, and I think very importantly, it's very important to realize that these are really human issues, right? That these are the way that we chose to build the Internet. it was security got kind of bolted on, right? And we had high profile incidents, right? You think about early worms and viruses. And then people started to realize, well, we need virus scanning software, right? And we need to have encryption for requests over HTTP, should be over HTTP. Yes all of these mechanisms were ones where I think that we kind of took the slower route in order to add security on afterwards Like the internet really been built for resilience not necessarily for security

And I think that what we're going to see now is a moment where a lot of that debt, a lot of that tech debt is going to come to the surface. It's time for us to pay it down. And that as a company, we are really leaning into trying to help with this moment. So for example, we have a billion-dollar commitment that we've made to help frontline defenders get access to our models and utilize them for defense, that we're studying and trying to find different vulnerabilities out there, and then thinking about how do we help the world respond? How do we coordinate incident response? How do we work with government? How do we work with the community in order to land these as well as possible and to help people navigate through? but I think that people have not yet really internalized how much the physics around cybersecurity are going to change. I think we will end up, I think there's an opportunity to end up with a much more secure world, right? You think about the fact that AI lets you pay down your tech debt in a way that was just previously unthinkable. The fact that you could do things like formal verification of software.

Like these models are obviously great at proving things. Why not prove security properties of software? That's something that the technology has kind of been there for people, but it's just so much. Full niche expenses. Exactly. No one does it for anything significant. And so I think all of that can change. And I think the world we're moving to right now, and we've done this internally within OpenAI, is every time a new model drops, you need to apply it to secure yourself, find your vulnerabilities, and then saturate. And so, for example, we took about 25% of our production engineers and said, all of your projects are on hold. You are just securing now. and took our peak intelligence model at the time, had it poke at all of our infrastructure, read a bunch of things, built some tools so that it's easy to do it again over time. And we found a number of issues, we fixed it. And at some point we saturated the issues that this model was capable of finding. And that's a very good story, right? It's like, I think that the thing to realize is that we're gonna have a next model. We're gonna have to repeat that process and that that will continue. But at some point, again, I think if you think about,

well, the code that's being written, So we're actually, so much of it now AI-assisted, we can actually train these models, and we are training these models to write secure code from the beginning. And so I think we can have sort of fundamentally more secure systems through the use and application of these AI models. So it sounds like basically there's going to be a period of time where maybe we're going to see more of these cyber attacks, more incidents, et cetera, as humanity or as people sort of catch up in terms of their core bases getting more and more secure, using more AI-generated code that's sort of preset to be more secure. and so we'll probably end up with some transitionary period over time where, you know, we'll have more offense and defense and then defense will catch up. I mean, that's kind of the consensus I've been hearing in the industry in general and seems like the most likely Ford case. Yeah, look, I have a lot of optimism about how we can navigate this moment. I think it does require a coordinated response, right? It really requires defenders to internalize how the physics of cybersecurity are changing, that there is this window where that window is not going to last forever, right?

And that you should think about we're shifting to this world where the security of an organization is related to both the fundamentals that they've gotten right, which is kind of one-time investment, but also the level of cyber capability they've used to defend themselves. And as we shift to that world, I think that we can be in a world where defenders are able to outstrip attackers, but it is going to take a lot of change, right? It's going to take a sort of change in terms of how we think about every organization operating and how we collectively think about critical infrastructure and resourcing those that have sort of not had those resources to date. I think your example, I mean, your description of this is like, let's say, like, internet-wide tech debt, and then your response to it internally is a really good one, because I've been part of companies where, you know, tech debt is very painful to address. and to say we're going to take 25% of the engineers or, God forbid, you know, we're going to do a feature freeze for a quarter until we fix some stuff that really needs to be fixed.

That doesn't feel like the state that the world has decided on quite yet. When you think about where the entire world is from a security investment perspective, traditionally instead of this like, oh, maybe it's like low single digits percent of the IT budget, Like, what do you think it should be at this moment of time? And, like, is it all of this other stuff that you were investing in from a cyber tooling perspective, network, endpoint, traditional corporate security is less relevant, or we just need to go spend twice as much? I have a feeling that cyber spend, security spend, should actually vastly outstrip software spend. Like, I really think that... Well, that's a statement. Yeah, I really believe it. And I may not be right, but when I look at the world we're going to be in, where software in some ways is becoming like the barrier to entry. Exactly. Free-ish. Exactly. It's becoming so cheap. Yeah. And so, of course, making sure you don't want software, right? You want software that is reliable, that is secure, that you can believe in.

And so, to me, the resilience of that software is the sort of… That's the value. Exactly. That's going to become even more important. And so I think that we have this moment, we have this opportunity to, you know, to your point of the XKCD with the little block at the bottom of some open source maintainer. Yeah. Like, that never made any sense. And the reason we had that world was because of some of this physics, right, of kind of how hard it is to write software and to write reliable software and that everyone has the supply chain. And so few companies audit their supply chain, right? If there's, we've seen this with supply chain vulnerabilities where someone uploads a malicious package and a number of companies get hit. All of that needs to change, and it's totally impossible to change with human software developers because there's so few people who have this expertise in this very obscure thing of, like, you know, sort of knowing different packages and, like, different statistics. Third-party software now, like Socket and others, will do this as an example, where they'll scan through your open source supply chain and try and find vulnerabilities and identify them for you. Or, you know, there's the human element of it where maintainers will suddenly take over a project

and then install something that would be malicious relative to people using that packet. So it's really interesting to see that shift in terms of the third-party ecosystem as well, developing to support the efforts that OpenAI and others are doing in terms of this market. Exactly. And I think it's so important. So we're doing this with the community. We're partnering with companies like CrowdStrike. And I think that it is, again, a coordinated response. But we really view it as a core priority for OpenAI to lead, right, for us to really, because we have the frontier model, we have the peak intelligence model, certainly in cybersecurity. We think that there is transformative potential for the good possible, but that means that we as a company, and we've been going through a security transition, right, that over the past couple of months, we've talked about safety security alignment of our development process, but we're also thinking about that for the world of how to really help the world become more fundamentally secure and help people through this whole period. One of the things about OpenAI is that it's always been very pro-human and very pro-human agency, human progress, let's use AI to better everyone. I think the original premise of OpenAI

was let's have something that can counterbalance other companies in terms of creating AI that can be used very broadly and universally in a positive way. Has that ethos continued at OpenAI, or how do you think about that in the context of the AI community? I think that ethos is very core to our mission, right, to ensure AGI benefits all of humanity. And the humanity there is very key. We think that humans should be firmly in the driver's seat, should have control over the future, and that this technology should amplify human agency. Even as models surpass humans in capability. And I think this is absolutely critical, right? That I think that the picture of, and keep in mind these models are very jagged, right? That there are places where obviously these AIs are more capable than anyone today, right? Even GPT-4, you know, is far more than any person. But the thing that is so important, and I'm reminded, for example, of the, there's the story of why Steve Jobs put the handle on the iMac, right? So that way people could feel that they could control the machine, right? That you can always pick it up and throw it out the window. And I think that the same spirit needs to apply to AI.

The fact of the spirit of humans are in the driver's seat, that these human judgment, human values. And maybe you should think of how humans will operate in certain fields, in certain dimensions, in the future, whatever it is, is become more like CEOs or like board members. But it's important that we have that level of agency and that I think that we're going to build machines just like if you're a CEO, you're clearly going to have domain experts. You're clearly going to have teams that are going to have knowledge across so many things and maybe even better ideas than you on many different axis. But fundamentally, the question is, are you happy? Is this what you wanted? Is this what makes your life better? and that that to me is something that we should all collectively, it's not just about AI, it's about technology. Technology is to lift up human civilization. It's the single most fundamental driver of improvement in the quality of life and that is what we should be seeking from AI. That is the belief of open AI.

Thank you so much for doing this, Greg. Yeah, thanks for the time. Thank you both. Appreciate it. Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces follow the show on Apple Podcasts Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts