No Priors: Artificial Intelligence | Technology | Startups
00:29:11 · Sarah Guo, Maxim Bar Kogan
[00:29:11] S1: Do you see in large enterprises any holdouts? Right? And I would say I actually haven't spent a bunch of time talking to people about this recently. But I remember a year and a half, two years ago, there were large companies that just said like, we're going to ban all of this stuff until it's safe. [00:29:34] S0: Yeah, I hardly see that anymore. I think in the financial sector, there's some companies that are more opinionated on what they allow. They still allow agents, but there may be like more granular as to like, maybe we're only gonna allow these two tools. I personally think that the companies that are gonna do well are the companies that are gonna allow a lot of different tools because the landscape is changing so quickly. If you bet on OpenAI a year ago, that would have been the safest bet in the world, but suddenly Anthropic has much better models and better tools, and potentially a year from now, there's someone else with much better tools. So, I think there is a price to pay, but I think if you're a large company, your risk profile is and should be different. When you're a startup, you want to have your agents do everything for you because you have everything to gain and you have nothing to lose. When you're a large, we're in JPMorgan. You have so much to lose, and you can maybe take a bit more time to gain what you can gain from AI. And by the way, JPMorgan is the adopt a guy very quickly. I think it is okay for companies to have a nuanced view the bigger they are on how they're adopting AI.
00:29:34 · Maxim Bar Kogan, Sarah Guo
[00:29:34] S0: Yeah, I hardly see that anymore. I think in the financial sector, there's some companies that are more opinionated on what they allow. They still allow agents, but there may be like more granular as to like, maybe we're only gonna allow these two tools. I personally think that the companies that are gonna do well are the companies that are gonna allow a lot of different tools because the landscape is changing so quickly. If you bet on OpenAI a year ago, that would have been the safest bet in the world, but suddenly Anthropic has much better models and better tools, and potentially a year from now, there's someone else with much better tools. So, I think there is a price to pay, but I think if you're a large company, your risk profile is and should be different. When you're a startup, you want to have your agents do everything for you because you have everything to gain and you have nothing to lose. When you're a large, we're in JPMorgan. You have so much to lose, and you can maybe take a bit more time to gain what you can gain from AI. And by the way, JPMorgan is the adopt a guy very quickly. I think it is okay for companies to have a nuanced view the bigger they are on how they're adopting AI. [00:30:46] S1: How do you think about that question for yourself? Risk profile, pace, the environment's changing very quickly, and then, you know, you see a lot of problems growing the scope of the product, and the research thesis here is already quite large.
00:30:46 · Sarah Guo, Maxim Bar Kogan
[00:30:46] S1: How do you think about that question for yourself? Risk profile, pace, the environment's changing very quickly, and then, you know, you see a lot of problems growing the scope of the product, and the research thesis here is already quite large. [00:31:04] S0: We are kind of in luck in the AI security space because, yes, there are lot of vendors. There's a lot of new technologies that are coming up, but the two core pillars of how 2026 AI works have not changed in the last few years. So, we're still using largely LLM foundation models that are not entirely dissimilar to how they were a few years back. And we're still building agents in pretty much the same way where we have an LLM decide what are the tool calls that we're going to make and generate those. And so, that does allow a company today like us to scale to a lot of different applications that are utilizing these two primitives while still keeping the core technology that we're developing fairly lean and focused. Now, of course, there's always a risk that tomorrow there's a completely new LLM paradigm that could happen, or a completely new edge paradigm that could happen, And that's why we do try to We have strong opinions loosely held about what does AI look like in 2027. We maybe have a good picture for 2026, but for 2027, we're very open minded, and we think that's the right stance to be for the next two years until we see what does AGI, ASI look like.
00:32:36 · Sarah Guo
[00:32:36] S1: Do you see the set of problems you're addressing, trust in the models, and governance of them, as something that the labs could ever do, or do you think it's a structural thing? I ask because the number one question amongst the startup ecosystem in the Bay Area today is, you know, if you assume capability improves or, you know, when the labs just gets hungrier from their already currently ambitious stance, why wouldn't they do this too? And so, I ask you the same question.
00:33:08 · Maxim Bar Kogan
[00:33:08] S0: Today, if you're a private person or if you're a security buyer, there are some places where you don't want to trust the same person that you're buying it from. So, you know, maybe, you know, if you're buying a car, you're not going to have the same guy that you're buying it from certify that the car is good. You're maybe going to have someone else do it. And if you're a security team, you're not going to trust the vendor of a product to tell you that this product is not going to mess your environment. You're going to want to have an independent party whose whole business depends on telling you that this thing is correct and being right, that this thing is legitimate and being right. So, there's biorep psychology in this space that I think really goes in our favor. And then I think there's the core problems, like why are models even making mistakes? Why are agents even making mistakes? So, I would broadly categorize it into two things. One is, there's the jagged intelligence of these models, and there's sometimes very silly mistakes that they make. And I think that problem will go away. I think we're heading for much smarter models that make less silly mistakes, and our role is not going to be to prevent silly mistakes. That will be taken care of by the model vendors because they're very incentivized to do it. I think what is the other fast growing category of things that we're seeing malice do wrong is places where they're actually not making a thing that is like a silly mistake, but more, I would say, have an independent, you would even say, semi aware or semi conscious perspective on what should happen, and that perspective might not always align with your perspective. And I think that is a problem that we've seen grow hand in hand with models getting smarter. Maybe just the way it is that as you get smarter, you have more independent thoughts and you're more conscious. And I think that problem is actually seemingly very hard to tackle today, even for the large vendors, and one of the key things that are making it easier for us to understand and detect these things versus the other vendors is that we're allowed to do certain things that they're not.
00:35:29 · Maxim Bar Kogan
So, for example, we're allowed to look at a lot of historical data of how these agents have behaved, but enterprise today are not willing to have Anthropic or OpenAI give that historical data because they know these are very data hungry companies that will want to train on that data. And so, I think there are some ways in which you are given more context and more latitude to know if something is happening that is wrong compared to the past, compared to how these agents typically behave and so on that the vendors don't have, and is really important in solving this problem. And the last thing I'll say is that you're not dealing with one vendor. So, we're heading for a world where there's a multitude of different vendors for many reasons. You're going to have, for cost reasons, open source models that people are going to use because it's cheaper, and you're to have models that are better at different tasks and at different cost profiles. And so, it is going to be unrealistic to expect all the vendors to provide the same level of security and to assume that as you're trying to adopt technology very quickly, especially coming from new vendors that obviously have not yet built out all of that. So, I think these are the reasons why I think it would be very difficult for this problem to be just completely solved by the large labs.