# [英語インタビュー] パランティアCEOAIが戦場と企業を変える | Alex Karp| アレックス・カープ|日本語字幕 | 英語字幕 |

*Gariben TV* · https://www.youtube.com/watch?v=yzkBY4BgJZg

## Summary

Palantir co-founder and CEO Alex Karp discusses how AI and enterprise software support decision-making on the battlefield, using Ukraine as an example, and how the same principles apply to businesses like hospitals. He argues that AI adoption fails when large language models are deployed without an orchestration layer, and that AI will create enough jobs, especially for those with vocational training. He closes by saying AI exposes which organizations and societies can actually bear the load, forcing a painful honesty that political structures often avoid.

## Key points

- Palantir's battlefield software compensates for the fact that half of an enterprise's systems don't actually work outside a lab.
- Moving a drone in Ukraine requires synchronizing data, protecting sources, and adapting to Russian electronic jamming.
- Ukrainian soldiers built proprietary tools on top of Palantir's product that even Palantir doesn't understand.
- Businesses, like hospitals with intake problems, can use the same data-processing frameworks to work 10-15 times faster.
- AI adoption fails when companies just put a large language model on their stack without a software layer to orchestrate it.
- Palantir can remove up to 80% of costs in some areas; work that took a year five years ago can now take a week.
- Karp argues there will be enough jobs for citizens, especially those with vocational training, questioning large-scale immigration.
- AI 'load bears' — it exposes which organizations and societies can actually bear the load, punishing those that pretend.
- The person managing Palantir's Maven system in the US Army is a former police officer who went to a junior college and is irreplaceable.

## Notes

### Overview
Alex Karp, co-founder and CEO of Palantir, is interviewed about how AI supports decision-making in defense and how that translates to corporations and society. The interviewer notes Palantir's compounded return since going public is 73%, versus his own 21% at BlackRock.

### AI on the Battlefield
- Karp says one of Palantir's most important contributions is making up for the fact that half of an enterprise doesn't work on the battlefield — it only works in a lab or on a PowerPoint.
- Using Ukraine as an example, he explains that moving a drone from A to B requires synchronizing data without transferring it to your adversary, knowing everyone who touched it, and protecting sources — for example, hiding that a general is your asset.
- When the Russians began jamming electronics, the problem changed to navigating a jammed environment with no connectivity while collecting data.
- Ukrainian soldiers, small teams of courageous and highly technical people, built proprietary tools on top of Palantir's product that Palantir itself doesn't understand. America's massive force, by contrast, needed integration.

### Defense Technology in Business
- Karp compares the battlefield problem of acquiring, processing, and framing data to what businesses do: sorting information to gain an advantage that can't be easily eviscerated.
- Hospitals all have intake problems, doctor and nurse shortages, and low margins, but each processes patients differently; the right software can process intake 10-15 times faster.

### AI Adoption
- Adoption accelerates beyond capacity. People try things that can never work, like putting a large language model on their stack and wondering why it fails.
- Once you build a software layer to orchestrate and manage LLMs in a language your enterprise understands, you create value — similar to what Palantir does with Anthropic.
- Palantir can take out up to 80% of cost in some areas; work that took a year five years ago can now take a week.

### Jobs and Aptitude
- Karp believes there will be more than enough jobs for citizens, especially those with vocational training, and questions large-scale immigration without specialized skills.
- The person managing Palantir's Maven system in the US Army is a former police officer who went to a junior college, yet is irreplaceable — traditional aptitude tests would not have exposed this talent.
- Karp describes his own role as walking around identifying people's outlier aptitude and keeping them focused on it.

### AI and Fairness
- AI 'load bears': organizations and societies that can bear the load gain a huge advantage, while those pretending to bear it collapse.
- This forces a painful honesty that political structures, built to obfuscate, struggle with.

## Transcript

[00:00] One of the most important [music] things Palantir's done on the battlefield is be able to make up for the fact that [music] half your enterprise doesn't even work. On the battlefield. It does work in a lab, on a PowerPoint. [music] For example, if you just take Ukraine as an example, how hard could it be to move a [music] drone from A to B? >> It's my pleasure to introduce Alex Karp. Um I want to just start off on something that's more intimate between he and I. I mean, I'm pretty proud of what I created at BlackRock, but my total return since I've been a CEO has only compounded at 21%. Uh since Alex and Palantir went public, uh his compounded return is 73%. So, um congratulations, Alex. As co-founder and CEO of Palantir, uh

[00:54] Alex has closely worked with defense and government private private organizations to apply how AI can be used, and and it's really important. And I must say, my conversations that I've had in with Alex over over the last year has really enlightened me. So, I'm looking forward to this, Alex. So, let me just start off from your perspective, how is AI supporting decision-making in defense and security? >> One of the most important things Palantir's 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. For example, if you just take Ukraine as an example, how hard could it be to move a drone from A

[01:41] to B? Actually, first of all, you're going to need to know where you want to put the drone, and that's going to require synchronizing all your data 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 where does it not go? Do you want to put the drone on your asset? No one Only two people in Ukraine may know that one of the generals is your asset. You can't tell people that your asset. How do you How do you make it look like to your soldiers that you actually were

[02:17] taking people out and you just missed your asset? And then the war advances. So, the Russians they began jamming electronics. So, now you have a completely different problem. 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 like a dynamic challenge. None of which were foreseen even before the Ukraine. And by the way, the other thing is then people fight differently. The Ukrainians, it's a small team of people and very courageous soldiers that are very technical. They have highly technical people that have built on top

[02:57] of our product things that we don't understand and proprietary ways of using the product. And then in America you have just have massive force but they had to be integrated. So, every one of those things is different and so the the twofold role of 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. >> There's so much technology was created from defense whether it was the internet GPS. How do you see envision this translating from defense and military um to corporations to businesses to society? >> On the battlefield, one of the most important issues is how do you acquire data, process it,

[03:45] and then put it into a framework. In the end of the day, what is a business doing? 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. 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

[04:23] flow and into your enterprise in a way that you can actually process these things 10, 15 times faster than you could. >> So what is the basic hindrance in the adaptation of of the of AI? >> Our adoption accelerates beyond our capacity. The The problem with adoption at this point is people have tried things that just can never work. Like by 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 Anthology maybe by hand because once you build a software layer to orchestrate and manage the large language models in in a language that your enterprise understands, you

[05:08] actually can create value. And again, if you go back to the battlefield context, like 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 it how can we get it to work for my country? And this is exactly what's happening in >> In companies. >> In companies. It's like, oh, this company it worked, mine didn't. What are you doing? >> How rapidly can AI change the growth trajectory? >> In a lot of these things there's a speed question. I think like with a lot of our companies 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.

[05:51] In the past, 5 years ago, that would take us a year. Now, it could take a week. >> Week, yeah. Is AI going to create jobs or destroy jobs overall? >> There are will be more than enough jobs for the citizens of your nation, especially those with vocational training. I do think these these trends really do make it hard to imagine why we should have large-scale immigration unless you have a very specialized skill. >> Are you also insinuating we're probably going to need less white-collar? >> You know, there are a lot of people doing X that should be doing Y. It's like just the person managing our Maven system in the US Army is a former police officer who I think went to a junior college. And they're doing very

[06:31] high-end, very complicated targeting globally. And that person actually is irreplaceable. And I think in the past, the way we tested for aptitude uh would not have fully exposed how irreplaceable that person's talents are. And would they been as talented if [clears throat] they had not gone to their college? Yes. If you look at inside Palantir, what am I really doing all day? I'm one 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 the five other things they think they're great at. >> So, is AI going to create greater imbalance in our in our world in terms of growth? >> One way to look at the unfairness of AI

[07:18] is it pentests, meaning it load bears on things. So, societies that can and organizations and companies that can bear that load have a huge advantage. The problem is if you've been pretending you're bearing a load you're not, it collapses and that's where you have to start. And there's a certain honesty that is painful for all of us in in this technology. It you just cannot obfuscate what can bear the load and what can't. And then political structures are built to do just that. Like I can't fix anything, but I can give you some 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. And the best thing you can do and you

[08:02] really care for the people you're representing is to say let's You have to kind of look closely at what what load we can bear. >> Thank you Alex. >> Thank you. >> everyone. >> [applause]