Palantir CEO Alex Karp’s Remarks @WEF Davos 2026 (Transcript)

Get rich, company blows up, you’re on your beach in the Bahamas but shareholders are happy and it’s gone kind of thing.

The Reality of Software-Based Defense Systems

And what you’ve seen—so first of all you could say, well that’s changed. But germane to your question, the learning process in building how do you build this for sovereign governments is also a learning process for how do they adopt these technologies. So it’s not just the technology. Because if you’re building a tank like the British and then the French and then the Germans optimized tank technology, it’s easy to see how you would deploy it.

But how do you deploy a system that’s primary value is organizing parts on the battlefield without seeing the parts on the battlefield and seeing does it work? How much does it work? Is it much better than we had? Can we do things we couldn’t do in the past?

And then there’s a hidden thing about software AI that a lot of the value, interestingly, people always assume the value is from where you are to where you should be. And that’s obvious. But in most sovereign nations in the world, and we deal with almost all in one form or another that are broadly defined as in it, that would be, you know, it could deal with a company that’s, you know, American, the actual technological rigor of the enterprises has significant holes in it.

So it’s very dyslexic. I’m dyslexic. There are whole pieces of the enterprise that exist on a PowerPoint that when you go to battle, you will find out do not exist. And this, whatever country you’re in, in this room, if you are in the west, this is a problem. You have a day of battlefield, you will find out.

This is one of the advantages the Ukrainians had. They essentially started from nothing. And so there wasn’t—you didn’t have to rediscover that your enterprise didn’t work after infighting. And one of the huge advantages America has is just for better or worse, I believe it or not, think mostly for worse. I was always against interventions. I’m not a neocon. We had just all this experience on the battlefield so you could see what worked and what didn’t work.

But pulling it back to—so the first thing that sovereign nations really struggle with is can I identify which tech is better objectively? Can I even rate it? Can I rate—I’m looking at—

LARRY FINK: But don’t you need to know where you want to go to ask the right question?

ALEX KARP: You know, that’s actually—let me reframe it.

LARRY FINK: Yeah.

ALEX KARP: You have to know where you are to know where you want to go. Okay. So the point I’m making is with this exegesis is one of the most important things Palantir has done on the battlefield is be able to make up for the fact that half your enterprise doesn’t even work on the battlefield. It does work in a lab, on a PowerPoint only built by country.

LARRY FINK: It doesn’t work because of machinery or humans?

The Complexity of Modern Battlefield Operations

ALEX KARP: Well, because the conditions of a battlefield are variable. And for example, the modern battle—if you just take Ukraine as an example, just to make it empirical, you know, as everyone reads this and thinks, “Okay, well, how hard could it be to move a drone from A to B?”

Well, actually, first of all, you’re going to need to know where you want to put the drone. That’s going to require synchronizing all your data. You’re going to need to do that without transferring that data to your adversary, which means you’re going to have to know every single person who touched it. You’re going to have to obfuscate it to the final thing.

Then you’re going to want to do it presumably either within the accordance of your strategy or with your ethics. So where does it not go? You’re going to want to be able to correlate. Do you want to put the drone on your asset? No. Only two people in Ukraine may know that one of the generals is your asset. You can’t tell people that’s your asset. How do you make it look like to your soldiers that you actually were taking people out and you just missed your asset?

And then the war advances. So the Russians, which are often very underestimated for reasons I don’t understand, but they’re mathematically arguably the best in the world. And things they may not have in the beginning, they can cobble together. And so they began jamming electronics.

So now you have a completely different problem. But presumably your enterprise has to be the same or develop, because now it’s not a question of going from A to B. It’s a question of going through a completely jammed environment where you have no connectivity while actually collecting data while you go through that environment.

And every one of those things is a dynamic challenge, none of which were foreseen even before the Ukraine end. Every single battle zone in the world has a—by the way, the other thing is, then people fight differently.

So if you look at the big battlefields, I’m sure some people love our work here and some people hate it. By the way, we welcome all opinions at Palantir, even inside Palantir, we have people who love our work and people who are unhappy with the work. We welcome all—

LARRY FINK: That’s a spirit of dialogue.

ALEX KARP: It’s a spirit of dialogue with somewhat of a leader. And, but you know, if you look at how, you know, when the Ukrainians, it’s a small team of people and very courageous soldiers that are very technical. They have highly technical people that get built on top of our product, things that we don’t understand, and proprietary ways of using the product.

In Israel, they, according to rumors, use their intelligence. So most people fight military to military. This was intelligence in Iran, from what I can tell from the papers. And then in America you just have massive forces that no other country has, but they had to be integrated and the integration capacity.

So every one of those things is different. And so the twofold role of enterprise software on a battlefield is one, to make sure all the underlying things actually works and then two, to raise the level to a level no one else has in the world.

Translating Defense Technology to Commercial Applications

LARRY FINK: Let me translate this now. I mean, there’s so much technology was created from defense, whether it was the Internet, GPS. How do you envision this translating from defense and military to corporations, to businesses, to society?

ALEX KARP: Well, first of all, the fact that it’s basically a purely raw, naked environment means that you actually know the ground truth of what could work independent of what enterprises think can work. And the general is at a high level, it’s almost one to one translatable.

But precisely because just to take enterprises in general, not all enterprises tend to want to over time become like every enterprise. So if you take five, A enterprise and B enterprise and C enterprise, they’re in the same market, their tech infrastructure is trying to make them into the same enterprise. They have the same org chart, they have presumably roughly the same. They don’t have the same data and infrastructure.

And what you learn on the battlefield is that, and in life is that that’s not particularly valuable. What is very valuable is an enterprise can do something no enterprise in the world can do. And so that is the goal of every single military intelligence service. In fact, all these intelligence services, militaries have their own specialization.

And so when we went to commerce, what we’re saying is how can we make your insurance company the way you underwrite? How can we make that to your tribal knowledge about underwriting? How can we transform this to knowledge everyone has to a knowledge only you have and with efficiencies that no one else has.

So as an example, we on the battlefield, one of the most important issues is how do you acquire data, process it, and then put it into a framework where it can be actioned either from an intelligent perspective? Yeah. So just obviously, what does a business do in the end of the day, what is a business doing? I mean it is so for example, especially in anything underwriting, banking, hospital intakes—

LARRY FINK: It’s information.

The Information Advantage and Civil Liberties

ALEX KARP: It’s information. It’s sorting the information in a way that you have a distinct advantage over other people who are similarly situated and that that advantage can’t be eviscerated easily. And so for what you’re doing on the battlefield with our products, I’m happy to talk about what you’re doing with ontology and foundry in some cases on the battlefield, but definitely in commercial organizations across the world, but especially in America.

When you approach the underwriter or the hospital—we power tons and tons of hospitals, right?—they have an intake problem. They all have an intake problem. They all have a shortage of doctors and nurses. They are working in a low margin environment. But every single one has a different way of processing their patients according to what their specialty is and the kind of patients they don’t do well with. And how do you manage that?

And so the intake flow into your enterprise in a way that you can actually process these things 10, 15 times faster than you could before. That saves lives. It saves a lot of lives. It also interestingly, because you’re processing the LLM in an ontology, you have a structure for it all. Despite what people may want to believe, it also bolsters civil liberties because now you can say, well, I mean, just simple questions: Was someone processed based on economic considerations or were they processed based on their background?

Those things are impossible to see unless you have—there’s a huge civil liberties betterment side of this that typically people don’t believe we care about. But it’s actually exactly the opposite. We do care. And you know, showing is caring. It’s like we can granularly show why someone came in, why they were taken, why they were rejected. And we can do it in a way that makes business sense for the business itself. And then it leads to safety efficiencies.

LARRY FINK: And probably brings down costs.

ALEX KARP: Well, if you want to do a shorter kind of financial version, you basically, in the past, to do what we can do in the full light of a public market, you need to take the company private. And then you would take out the cost structure and you probably resell it. Well, now you can take out the cost structure, make the workers more important—so the actual workers, not the fat kind of in the middle—and then you can change the way they go to market.

LARRY FINK: So our hospitals need to process this information faster.

ALEX KARP: Presumably you’ll have much faster. And then the nurses—the nurses and doctors are also happier.

The Path to Adoption: Public vs. Private

LARRY FINK: You suggested that for companies that rapidly adopt, that they have to—best way to do it is going private, then they could restructure.

ALEX KARP: No, no, what I’m suggesting is they don’t have to do that anymore.

LARRY FINK: They don’t have to do that. What is the basic hindrance in the adoption of AI? Is it just legacy systems, legacy issues? How do we accelerate the adoption, that it’s good for humanity?

ALEX KARP: Well, I mean, our adoption accelerates beyond our capacity. So I think if you just buy large language models off the shelf and try to do any of this, it won’t work.

LARRY FINK: It’s a commodity.

ALEX KARP: Well, also it’s not precise enough. You can’t do underwriting with it. You couldn’t do any of these things that are regulated. So the problem with adoption at this point is people have tried things that just can never work, like buying—you borrow a large language model, you put it on your stack and you wonder why it’s not working.

And so what you’re going to see, especially in America, is people trying to do something like what we do with ontology, maybe by hand. Because once you build a software layer to orchestrate and manage the large language models in a language that your enterprise understands, you actually can create value.

Beyond the AI Bubble

And I think there’s a lot of discussions like, are we in the AI bubble? What is the meaning of the bubble? Well, I think what, if anything, we’re just in a lag where there’s a lot of AI. Some of it works. Again, if you go back to the battlefield context, most people, everybody in the world assumed this would not work, but now it does work. And so now the question isn’t, does it work? But how can we get it to work for my country?

And this is exactly what’s happening in companies. It’s like, oh, this company, it worked. Mine didn’t. What are you doing? For example, I mean, if you want to just make it parochial to us, Palantir barely has a sales force. In fact, it seems to be getting smaller every time I go see them. And you’re laughing because you know it’s true. Palantirians are here, but it’s getting smaller and smaller and smaller.

And it’s not because we’re trying to save on the unit economics, it’s actually because it is a low trust environment. In AI, people have tried lots of stuff, a lot of it hasn’t worked. But if you’ve delivered stuff that does work, why do you need a salesforce? It just sells itself. You have to say, “Hey, don’t talk to us.” And that’s on commercial and government at this point. It’s very hard to export simply because we have to train the people and we have limited bandwidth.

LARRY FINK: I was going to say that’s your limited bandwidth, the training once somebody takes on your software.

The Training Challenge

ALEX KARP: Well, in the government context, every country obviously has the equivalent of a security clearance, right? So to build Project Maven or Maven into your architecture, you’re going to need somebody with the highest level of clearance that also is technical. And most technical people unfortunately are not going after the highest level of clearance.

So there are very, very few people like that. So that resource is super scarce. And then they have to be trained and that can take a while. And you also have to have, like anything, you really have to believe in this and really think it’s important. And not everybody fits in that category.

LARRY FINK: How many people need to be trained to do this? I mean in a corporate level, does it have to be from the CEO down? How does this work? You’re talking about insurance underwriting.

ALEX KARP: Well, yeah, so insurance underwriting. The way it works is the best case scenario—you have the best and worst case scenario. Best case scenario, CEO is mathematically inclined, even though they may not know nothing about product, but they can impute a product working by looking at the math.

And then we probably need to train five or six people on there. In the beginning we do all of it and then we transfer that as much as we can to them or we’re trying to partner with people who can do that with us. But you need a small number of people. But we still need more than we have.

AI’s Impact on Economic Growth

LARRY FINK: You suggested repeatedly about how AI could strengthen foundations for an economy, especially we’re seeing that in the United States. How rapidly can AI change the growth trajectory? Because you mentioned that earlier about how it could improve the economies, the well-being of companies.

ALEX KARP: Well, in a lot of these things there’s a speed question, but I think with a lot of our companies, it’s like we can take out in the area we go in up to 80% of your cost and improve your top line dramatically. But it really depends on the use case and what we’re doing. And then there’s a speed function which is we can—in the past, five years ago, that would take us a year.

LARRY FINK: Sure.

ALEX KARP: Now it could take a week.

Jobs: Creation or Destruction?

LARRY FINK: Let me further that question though. I’m sure it’s on the minds of some people here. Is AI going to create jobs or destroy jobs overall?

ALEX KARP: I think one of the unfortunate things of the narrative in the West is it will destroy humanity’s jobs. Like, you went to an elite school and you studied philosophy. Use myself as an example.

LARRY FINK: I did too.

ALEX KARP: Yeah. Hopefully you have some other skill. That one is going to be hard to market.

LARRY FINK: But it was always hard to market.

ALEX KARP: It was hard to market. Very hard. It was a good education, a very, very strong education. If you can get a job, you might keep it. What I always thought is like, if I finally get a job, I’ll probably keep it and do well, but I’m not sure who’s going to give me my first job.

But technically, technicians—if you’re a vocational technician, like we’re building batteries for a battery company and the people who are doing it in America are doing roughly the same job that Japanese engineers are doing and they went to high school and now they’re very valuable, if not irreplaceable because we can make them into something different than what they were very rapidly and those jobs are going to become more valuable.

I mean, not to diverge in my usual political screeds, but there will be more than enough jobs for the citizens of your nation, especially those with vocational training. I do think these trends really do make it hard to imagine why we should have large scale immigration unless you have a very specialized skill.

The Shift from White Collar to Vocational

LARRY FINK: What about the foundation for white collar work in Europe and the United States has been through the universities. But I just heard you say we’re going to need more vocational men and women. And they may—they’re going to be. But are you also insinuating we’re probably going to need less white collar?

ALEX KARP: I think what we need to do is—yes, but I think we need different ways of testing aptitude. You know, there are a lot of people doing X that should be doing Y. Like if you could manage one of our systems, like just the person managing our Maven system in the U.S. Army is a former police officer who I think went to a junior college. And they’re doing very, very high end, very complicated targeting globally. And that person actually is irreplaceable.

And I think in the past, the way we tested for aptitude would not have fully exposed how irreplaceable that person’s talents are. And would they have been as talented if they had not gone to their college? Yes. But I think—I tend to, even inside Palantir, if you look at inside Palantir, what am I really doing all day? I’m walking around figuring out what is someone’s outlier aptitude. And then I’m putting them on that thing and trying to get them to stay on that thing and not on five other things they think they’re great at.

You know, everyone at Palantir, every engineer at Palantir—for maybe 18 years, everyone thought we were like a business joke. And now lots of business people want my advice. You know, the only people who don’t want my advice at Palantir about business are Palantir engineers. They’re like, “Hey, Alex, I have an idea about how we could just be a much better company.” And it’s always like, yeah, it’s like literally McDonald’s, but it’s like, we should have some titles and you should stop speaking in public. And I mean, there’s probably right about speaking in public sometimes.

LARRY FINK: I certainly admit that. I don’t think you did anything wrong today.

ALEX KARP: Yeah. So thank you for that high praise. One of the keys to success is setting the bar very low.

LARRY FINK: No, I don’t believe that’s how you operate Palantir. One last question.

AI and the Global Divide

LARRY FINK: Where is this? Where will the curve of AI go and the utilization in the United States and other developed economies? But what about the developing economies? How can they participate in this?

I mean, I read a research report yesterday that said the application of AI has been so dominant by societies of high education or companies of high education, and they’re seeing a very big divergence that is occurring already. And it’s so much based on the application of education and how that is being utilized. So is AI going to create a greater imbalance in our world in terms of growth?

The AI Divide: America, China, and the Rest

ALEX KARP: Well, I think the obvious first imbalance is it seems like America and China understand versions of making this work. And they’re different, but they both work and they work at scale. And I think that is very likely to accelerate way beyond what most people believe is possible.

Like the discount rate, I think not in the short term, but in the long term is way too high on what will be done and how this will impact every aspect of our society. And I would say especially on military. And then I tend to be a realist and I think, you know, you have wide divergences. It’s going to be hard to have the kind of discussions people want to have where two countries are and with maybe a third following of Russia on the like that because they’re so good at fighting.

Europe’s Structural Problem with Tech Adoption

And then I look, I spent—and I’ll get to the developing world—I spent a lot of my life, my most important years and my father’s family came from a part of Germany and I really care about Europe and especially the German parts of Europe where I had many of my best years. I still fantasize of going back to grad school. Not for the learning reasons. And you’re going to have more fun. I had so much fun. We won’t go into that, but it’s like endless. Sometimes when I’m traveling across the country I just think of grad school.

LARRY FINK: But.

ALEX KARP: The tech adoption in Europe is a serious and very, very structural problem. And what scares me the most is I haven’t seen any political leader just stand up and say we have a serious and structural problem that we are going to fix.

The Developing World: Pockets of Success and Failure

So that then you get to the developing world. I would imagine it also depends what you mean by the developing world. I would imagine with not enough knowledge you’re just going to find pockets that go very well and pockets that go very poorly.

As a generalization, like again, if you go back to this somewhat non-successful soliloquy I had about the underlying architecture. One way to look at the unfairness of AI is it pen tests, meaning it load bears on things. So societies and organizations and companies that can bear that load have a huge advantage.

The problem is if you can’t, if you’ve been pretending you’re bearing a load, you’re not, it collapses. And that’s where you have to start. And so if you go around and just say, “Okay, what societies and microcultures are going to be load bearing here?” I think you would find that parts of the developing world, certain communities in that are going to do very well.

You do need a realistic assessment of the load bearing. And there’s a certain honesty that is painful for all of us. And in this technology, large language models, however, implemented in software, you just cannot obfuscate what can bear the load and what can’t.

The End of Political Obfuscation

And then political structures are built to do just that. Like, “Yeah, I can’t fix anything, but I can give you some bullshit line that you want to hear that’s going to make you not care about how bad your life is and how much worse it’s going to be tomorrow.” I can give you that for free. And that stuff is, that is harder to get away with in this culture.

And you know, I still view myself as a card-carrying progressive and I think it’s the single most important thing a progressive could do is go around and say, “Yeah, but the revolution that’s coming is going to expose the actual market value of what you’re doing, whether we want it or not.”

Like, it’s like, I don’t even want to know the market value of some of this stuff, but it is over and over a relative rapid period of time. So next three years you’re just going to get market value, honesty in all sorts of character communities and micro communities.

And the best thing you can do if you are in a community, whether that is a large community like Germany or a large community, larger like America is, and you really care for the people you’re representing, is to say, “Yeah, but let’s, we have to kind of look closely at what load we can bear.”

LARRY FINK: Thank you, Alex.

ALEX KARP: Thank you, thank you.

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