No Priors: Artificial Intelligence | Technology | Startups
00:19:14 · Maxim Bar Kogan
[00:19:14] S0: So, of I think Israel started maybe a bit late in the game, but it's catching up quickly. So, I think there's now amazing companies in Israel building world models, building AI infrastructure that's top of its glass, building chips. So, I think Israel, in general, is becoming very strong in AI, and we're proud to be a part of that movement. And I think you're right, our company has a very mixed DNA between cyber and AI, which kind of reflects mine's and Gil's backgrounds. Most of the people in our company, most of our research engineering come from a unit in the Israeli intelligence where we actually deal with math and cyber and the intersection thereof. And so, I think it is also reflected in kind of the type of talent that we bring in. I think it's important for a few reasons. The first and foremost is that we want to be more than just a security company long term. We think that to solve this problem well, it's going to require deep AI expertise, but then the problem is not just cybersecurity. The problem is how do we control advanced AI long term? And that problem, even if you just forget about enterprise security and the different gaps in various controls that they currently have, first principle is that problem just sounds very important to me. So, I think it will be crucially important. If you have AI companies that are $10,000,000,000,000 companies, we think you want a company that is not the vendor of the AI itself to oversee and help you control what AI is doing. And we think that's an opening that's a $100,000,000,000 plus opening for a really important company. And then if you think about what it's gonna take to control advanced AI long term, then we're just scratching the surface because long term, you're going to have to also understand much better what models are thinking, what's happening on the internals of these models as they're operating. That's also a lot of where our research is focused.
00:21:25 · Sarah Guo, Maxim Bar Kogan
[00:21:25] S1: So, the industry is quite divided on this issue, I mean, amongst the people who think about whether or not mechanistic interpretability or research into better understanding models is possible? Like, that's a question. So, it's something you believe in. [00:21:43] S0: We believe that there's been a lot of strong progress in that direction. We believe that understanding the internal weights and activations, what is the internal structure, the mathematical structure of these systems, is gonna be at least part of the solution. And in many ways, we think that, and this is maybe, we'll only know when we get there, but we think that for our level of intelligence, it's kind of difficult to understand very quickly what is the internal structure of a large language model. What is the internal structure of way Our it's an [00:22:27] S1: level, like, human intelligence or our level of your model's camera? Okay. Human intelligence.
00:22:27 · Sarah Guo, Maxim Bar Kogan
[00:22:27] S1: level, like, human intelligence or our level of your model's camera? Okay. Human intelligence. [00:22:30] S0: Oh, yes. Yeah. I think like yeah, exactly. I think as humans, it might still be very difficult to understand what weights and activations mean, and maybe mechanistic interpretability. It seems like, oh, maybe that's too hard or shouldn't be possible. But as we're starting to have models that are much smarter than us, at least in some important ways, we think that we'll be able to start tracking mechanistic capability much more effectively. And I think it's gonna be extremely rewarding, by the way, long term for understanding intelligence in general, like not just overseeing, but just understanding what intelligence is, how it works. What's the difference between the smarter model and the less smart model? [00:23:13] S1: I completely agree that the opportunity to understand and trust and secure and govern these super intelligent AIs is a very large opportunity. If we just scroll back today, the security person in me says, Well, then I have to give you all the permissions and understanding that I have to give these companies too. Like, how do you get customers or, you know, the Fortune 100s you're working with already or, you know, tech natives, everybody cares about their own security and business, to trust you now as a You're like less than 100 people, right?
00:23:49 · Maxim Bar Kogan, Sarah Guo
[00:23:49] S0: Right. I think it's one of those things that should not be possible. So, in theory, there's no reason why a Fortune 10 or 20 company would work with us because who are we? We're a two year old company. We're like a few people you know, who've done math and cyber. But I think it's an opening that only happens when their pain is very strong. So, their pain is so strong that they're gonna say, oh my god, I just saw this company come out of stealth, but it's a problem that I have daily. So, I'll give them a call. And suddenly, get inbound from these large customers, which is of course the best thing you could hope for as an entrepreneur. And I think it reflects, in my opinion, their understanding that a lot of the startups in this space are still small and new, but there's gonna be a huge company here, and we wanna find the right horse to bet on. So, we're gonna take a look at these companies. And number two, that if we don't do anything, then in a very short time, this will disable our business. At the end of day, security people are in the business of revenue preservation. They understand that this is between the two risks. They want to partner with someone that's promising and early rather than not do anything. [00:25:10] S1: The other thing besides agent actions across their surface area that every CSO I know is freaking out about and every engineering leader is freaking out about is the I would just describe it as the plummeting cost of vulnerability finding with these coding tools. [00:25:30] S0: Yes. Mythos.
00:25:30 · Maxim Bar Kogan, Sarah Guo
[00:25:30] S0: Yes. Mythos. [00:25:31] S1: And that has caused a number of issues for vendors that are being compromised. How do you think people should react to this other issue?
00:25:45 · Maxim Bar Kogan
[00:25:45] S0: I think mythos is really like, if you took me ten years ago, automated vulnerability research looked like a dream that would take twenty, fifty years to happen. Maybe it's because we were doing a lot of that in the Israeli intelligence, and we like to pat ourselves on the shoulder of how difficult the job is that we're doing. But it did look really far, and suddenly it's coming all at once. And so, I think that, first of all, the market is not overreacting. I think this is a huge change in what this means for security teams. If you're a pragmatic security person today, you understand that you need to move very quickly. Your strategy might look something like, I need to do the fastest quick fixes I can to mitigate the immediate risk. So, maybe I'll invest in whatever the vulnerabilities that have been found, let's try to mitigate for them, whether it is through patching or through mitigating controls. But then the real solution, and every security leader at large enterprise knows it, is that we need to have the foundational pieces in place to avoid those risks. And the foundational pieces are we need to have identity locked down. We need to have a firewall. We need to have endpoint detection. And for different asset classes in your enterprise, for different parts of your stack, there's a different foundational security mechanism that you need in place. For the AI attack surface that you now have, or for the AIs in your company, you also need a foundational security solution. That's kind of the role we play in that space. So, as part of your preparation for Mythos level models and beyond, you're going to need a lot of foundational security tools to fortify your different parts of the enterprise, and we're playing that part in the AI space.