Well, what are the specific use cases for which like, uh, an obliterated cyber offensive model could be used in ways that are good by people?

Yeah, so like I said, so, uh, the point is, is that you can test your own system before some bad actor, uh, tests your system for you.

So like threat modeling, right? Like if you wanna say, "Okay, this is what a typical threat would look like," or, "This is what, uh, a high-scale threat would look like," you're, you're making threat modeling s- simpler and easier for, uh, defenders to see, right? So they, if they're seeing a, a wider variety or more complexity of threats internally, then, then they won't be surprised when they see them externally.

So for the most part, it's for people that want to test their system and, like you said, agents as well, right? Like all these critical infrastructure companies are rolling out agents everywhere, right? Like banks and airlines, right? But you need to red team these agents, right? [chuckles] Because if you don't, then you're gonna roll them out to the real world, and they'll be an untested system.

So the first time they'll be tested is when a bad actor tests them.

And so the, the whole point is for all these systems is can good actors test their systems before rolling them out to the real world?

On the model layer at least.

[laughs] Yeah.

How different are they in reality?

Yeah.

So I think, I don't know if you saw Anthropic just wrote a blog, right? About how capable GLM 5.3 is, especially the obliterated version, like similar to what we offer is in cyber.

So that gap is closing.

And I think that some of the, the outrage in the industry today, yeah, exactly, is that that gap is closing faster than I think people predicted.

Even the term, I kinda don't even like that term, right? Frontier models, right? It just has this connotation that it's like some set, stack ranking that they'll always be ahead and they'll never that that window will never close but it seems to be closing relatively faster and faster right the maybe before it was a year they were a year behind I would say today they're maybe around three months behind so I think around the absolute frontier so I think They're catching up.

I think sometimes as these companies have these huge valuations, you have to worry about commoditization of models.

take both of those issues and then that specific case