Palantir CEO Alex Karp: "It's Like No Glove, No Love. No Ontology, No Love."

By the way, I always say this, it's, you know, you were you, we've been together since the beginning of our life in the public markets, when we first DPO'd, which is why so many people in America and across the world got to participate. But, you know, without you sending out missives about how we were misunderstood, probably millions and millions of people would not have participated. And yeah, it's Palantir, this kind of crazy roller coaster company where people can only understand the Palantir 5 years ago, not the Palantir that we have. And so there's a lot of work to do on the front line. I'm happy to have you with us.

Well, what happened by the way was like a revolution. It began with work we were doing with Nvidia and then going on television like that. It just exploded in a way no one could have really predicted. I think the explosion happened because people are being sold this thing they know isn't quite true, and then they paid a price. You're paying a lot of money for your enterprise for tokens that are really hard to see the value, and then you realize at the same time the alpha of your business is migrating to the person you're paying an absurd sum of money based on value. And then last not least, they're just behaving in the public sphere in a way that is blowing up the politics of America, making people really afraid, leading to people just wanting to pillage wealthy people but not help poor people. And you're sitting there and saying, you basically like, what is going on here?

So you have that backdrop. Then you have, and by the way, part of the reason for that backdrop, just to make this a little longer, is these models are excruciatingly effective when used with an application layer. Now, they all know that when used with an application layer, installed with what everyone's now copying from us called FDE, correct? So then I think part of the reason this revolution actually happened is technical participants in the market know one thing: Palantir gets it right. You think they're crazy, you think they're insane, Karp is obnoxious, and then it turns out you have to copy them, right? So now investors who are kind of just chillaxing in front of their screen producing AI slop on their computer and thinking 'I'm going to get rich and yeah, maybe poor people, no one else is going to get' They're like, 'Yeah, but that's just how the world works here. Good for me.' They've been a little bit tonedeaf to the technical thing. So they're very happy to watch the frontier. By the way, I always feel like I have to say this, you know, like

Well, people actually think I'm kind of not being like, I spend more time defending Dario than Dario and more effectively honestly, because they don't even talk to you if you're telling them the truth about what you think. Apparently, because like the views there, you know, and like I'm always trying to explain, you know, it's a worldview. This is essentially a world historic figure. He really did take the LLM revolution bring it to a foundation ontology with their models where they're doing it.

You would need compute. So that Nvidia primarily, and we're very strongly partnered with them. You would need an application layer because that while the and you need a way of installing them. So you need FDE, you need an application layer. People generally call that an ontology now after maligning it everywhere. Because you need to do it's and there's two points simply put: there's safety. So you know, we're now helping people and I think people are aware of the dangers of just you know like look that you know a lot of our clients are running around saying 'no ontology, no love.' Meaning no glove, no love. So like it's probably not exactly what I can say on TV but that famous word.

It's like they're like 'yeah, you look, no ontology no love.' It's like it's gone viral in corporate America. It's like crazy. So there's a safety function. If you're meeting someone you don't know and who may have a history of doing things and leaving certain products behind that you know you can't get rid of, you'd probably be insane not to have ontology protecting you. So that is the safety function. And that's that. So that's crazy important. By the way, with models coming from the whole world, that safety function is going to be even more important. But in the end of the day, businesses need to expand their tribal knowledge, their alpha while protecting it. And so then there's another term which is now being adopted essentially: orchestration. How do you actually deploy the models so that they can do exactly what they're capable of doing while not providing like chillaxing slop? So you get

Okay, well, within there, first of all, it's because that's where you get the maximum convergence between progress and safety because the open source model is not aspiring in any way obviously to export, although you do have our protective layer probably. So that data is not going back to your lab. Essentially, but then you also have the ability to fine-tune the model. The fine-tuning is very hard to do without dealing with the frontier lab. And when you're fine-tuning, you're basically explaining what is the alpha you're trying to create on top of the alpha that's deep in your business. What is the context of your enterprise? What is the specialized problem sets embedded in your data that you have a unique lever and insight into solving? So if you're building a drug to reduce the chance of cancer, you are actually building it on proprietary data and proprietary insights on how the human body works. And battlefield's obvious, but every single enterprise that is actually valuable has a specialized way of doing something a consumer wants, or in the government context has a specialized way of doing something that the end user does not want that protects your people from the end user. So to do that most efficiently, you would need compute, ontology both protective and alpha augmenting and creating, and you need to make sure it doesn't transfer. You then own those weights yourself.

Essentially, but then you also the allows you to fine-tune the model... you then own those weights yourself.

This by the way is not speculative. We've seen this already in the US government context where in contexts that are very hard to use frontier labs or where they're quite, I feel, unwilling to go. Like by the way, in what world does the Department of War, the only institution in America that is extremely high credibility, the institution that integrated before anyone else, that protects us every day, that allows us to export our technology, in what world are we outsourcing the moral veracity of that to a bunch of people sitting very far away who have never even met a man or woman in uniform and presumably don't care if they come home alive at least in practice? So there's also like, who makes these decisions? By the way, Palantir, despite we've always been part of the reason the sovereignty thing actually works, is technical users know for whether it was PG Foundry, ontology, Maven, now sovereign AI, we've always been on the side of protecting individual rights, protecting data, and protecting by logical extension your sovereignty. So that's actually where we should all be as a country and as the tech community. And then there's the reason though that this ended up being, you know, now I should say I'm wildly happy you have a lot of people entering and taking, you know, because this is actually where we should be. We don't like just because we basically have been at the forefront of launching many of these revolutions and it's commonly known in tech circles that we do this well. Building a broad, the reason you see this broad coalition is the customers, this is what they want and need. They're livid at the option that was provided and this way of doing things. In fact, currently the biggest problem at Palantir is just how do we scale this? So there's a lot of customers here.