No Priors: Artificial Intelligence | Technology | Startups
00:20:33 · George Kurtz
[00:20:33] S0: Yeah, think there's a few things there. So first, what the labs are creating is incredible. I mean, where they've come from, what they created, it's unbelievable, right? And we also want to make that available to our customers. So the general theory is if you have a safe mind and you want to leverage a frontier model, great. We can leverage that. If you want to leverage our models, fantastic. Our models will be significantly cheaper. So just to give you an example, so for general remediation, if you use the highest caliber frontier cyber model, probably costs you about $10 Ours is like $03 So there is a huge cost difference, which is important. But then when you look at our model, because of the way we've post trained it on our data, a huge difference is we have the data. Like there is no Reddit of CrowdStrike security data. You can create synthetic data, but it's really not the same as what we have. We've got fifteen years. Well, maybe a little bit less because I started fifteen years ago, but call it over a decade of security data, including from our MDR, our Managed Detection Response Service, that has all of this sort of threat and vulnerability pairings and sort of the attack chains, and we've basically created this massive amount of data that we use to train it. We were getting great results on it, and I think it is very focused because of the post training. You know again, it's not going to cure cancer, but it's going to give you great results for security. That's number one. Number two is there is a safety and trust aspect. We have all of the data from our customers. We've been entrusted with that for the last fifteen years. We know how to protect it. We know what they want. We know how they want to use it. There's a real concern by customers. They don't want all this data going out to the labs. They don't want all their sensitive information. They don't want it. Their information being trained on, right? So we can provide a way to get them the trust and security that they want for their own security program. This sort of concept of AI sovereignty is very important to many of our customers. So again, we're not saying exclude the labs because they have incredible technology, but you can pick what you want or you can route between the various models, but ultimately we want to give customers choice.
00:22:55 · Elad Gil, George Kurtz
[00:22:55] S2: I think you've also talked a little bit about separation of church and state. Want to make sure that to some extent there's a neutral third party that can also interrogate some of the models that the labs are providing or some of the other open source out there, and it seems like CrowdStrike can definitely play that role as well. [00:23:12] S0: I think the number one factor in security is being trusted, being a trusted partner. You know, you can look at the vendors out there. There's obviously a few big guys that are out there. There's many security companies, but trust is built in drips and lost in buckets, and you know, customers, they don't want sort of cowboy activities that may put their data at risk. And again, everybody's trying to do the best they can. But our whole focus and I think focus is incredibly important in any business. Our whole focus is security, and our whole focus is keeping our customers safe. And we're not trying to solve the world's problems in AI and other areas. So I think that is certainly an advantage for us. [00:23:57] S2: If you were to think out two years from now, what do you think is the state of the security world? Like what do you think shifted from this point forward?
00:23:57 · Elad Gil, George Kurtz
[00:23:57] S2: If you were to think out two years from now, what do you think is the state of the security world? Like what do you think shifted from this point forward? [00:24:02] S0: Certainly within two years you're going to have local models that are all running on people's computers. Know you may have a mixture of local models plus sort of last mile routing into into frontiers in terms of what they look like. You're going to have a lot more open weight models. You know, you can just see the shift in the tokens, you know, over the last six months. [00:24:23] S2: What is the shift that you're seeing, and what's the order of magnitude there? How long do you think that continues for?
00:24:23 · Elad Gil, George Kurtz
[00:24:23] S2: What is the shift that you're seeing, and what's the order of magnitude there? How long do you think that continues for? [00:24:28] S0: I talk to CIOs, you know, almost every day. I had dinner with one tonight from big bank in New York, and basically the comment is, you know, we got into AI. We went all in great. The costs were out of control. You know token Maxing you know stopped when people got big bills and you know I said, well, how do you think about this? Like what are you going to do? They said the models are so the open weight models right now are so good. Even if they're three months behind, it's plenty for what we need. And one of the customers comments and they know I'm into Formula One. They said any Formula One activity that we need for the bank will go to the frontier. Like if it's Alpha and we're making money and it's like, you know, super important. We'll pay the money. No big deal. But for all the other things like it's really expensive and we're going to create all of our own open weight models, you know, and do more post training on them. Those sort of things that that's the way they're going to operate and I firmly believe that I'm seeing that in customer after customer that that's that's that's the way they're going to go. I think you know part of some of the challenges in the current environment is I think people are are all seeing that and there is some concern at the frontier level of what's going to take place.
00:25:46 · Elad Gil, George Kurtz
[00:25:46] S2: And so I guess on the open weight model, one of the statements that tends to be made is that they are at least in part distilled from the labs. In other words, you have Chinese open source players who are effectively training their models against that Anthropic or OpenAI model or other frontier models. Many of those labs are not trying to roll out anti distillation efforts. They're trying to build into the model the capability to not get distilled. Do you think it's just sort of a back and forth where the capabilities keep going almost like offense and defense in cyber, or what's your prediction in terms of the state of distillation in two years or, you know, within some reasonable time frame? [00:26:24] S0: I mean, I guess my first comment is what were the frontier models distilling when they got all that information? I mean, we know all of the, you know, sort of lawsuits around where the data came from, and how that unfolded, right? So we kind of start from that. [00:26:36] S2: So it's like a fine liquor. It's triple distilled, you know?
00:26:36 · Elad Gil, George Kurtz
[00:26:36] S2: So it's like a fine liquor. It's triple distilled, you know? [00:26:39] S0: It's triple distilled. Exactly. Like, so where does the distillation start from? You know? So I think it is what it is, but the labs certainly are focused on trying to prevent distillation. Obviously, you know, when you look at the Chinese models that are out there, there's a lot of distillation takes. There's factories of systems that are bouncing all over the Internet distilling this. I think the challenge is that the genie is out of the bottle. I mean, there's a lot of distillations already taking place. The models are very good, but we also have to give credit where it's due. If you don't have all the chips and you have to be resourceful and you still want to have top models. You know, I think there's some innovative things that are taking place outside of The US and we got to be mindful of that, right? So yes, it's hard. I think. It's going to be hard to put the Genie back in the bottle on distillation. They're going to do the best they can, but you know a lot of the models are are, you know, I think now you know what does it look like in two years? I don't know. You've got great models today. Can last for a while, and you have a lot of data that's already packed into them, so we'll see how it unfolds. I wish I had the crystal ball on that one.