Alex Karp is here. He is the co-founder and CEO of Palantir Technologies. The company's data mining software is used by various government agencies in the United States, including the CIA, FBI, and NYPD. It is also used by hedge funds and other financial firms. Karp is a Stanford

graduate and co-founder of the company with ex-PayPal engineers. I am pleased to have him here at this table for the first time. He does not do many television interviews, and for that, I'm also pleased to have him here. Welcome. Thank you for having me. Let me talk a

bit about the background, which is interesting. Stanford Law, wasn't it? It was Stanford Law, uh, and then, and then you went to Frankfurt? Then I went to Frankfurt, PhD in neo-classical, uh, social philosophy, which is a fancy way of saying a quick path to being unemployed.

Certainly, to be the highest educated, least earning person on the planet. And why did you do that? Um, well, the same reason I, I got involved in founding, founding. I, I was really passionate about the issues. I largely wasn't putting money ahead of what I thought was

important. Um, some of the ideas I dealt with were actually quite important. How, what does it mean to know something? How does it, what does it mean to communicate it? Um, what is the foundation of Western society? Uh, can we participate as citizens? And how I was very passionate

about those ideas, just like I'm passionate about, uh, my business, Palantir, and, and also about anti-Semitism and other issues that you thought needed to be written about and discussed. I published widely on both technical issues, uh, kind of issues involving the convergence of neo-con philosophy and

Freud, and on discrimination, and on civil liberties, and on anything that anyone would read, uh, which is what you do when you're an aspiring academic with, uh, essentially an interest in writing, um, and uh, and then I migrated into a business context. Um, and what caused you

to do that? Well, you know, the interesting thing about, um, academia is that it's more interesting to the people in it than it is to anyone else. Uh, you know, and uh, the impact, yes, you end up fighting about small things that may be fairly unimportant to other people. And I

I thought that while the ideas were important, that the actual impact they were going to have was, was going to be very small. I mean, the kind of work I, I, I did was intelligible to probably 30, 40 people in the world. So, um, it, it seemed

more interesting and compelling to do something else, get involved in business and see how that worked out. And some of the issues in business are actually, from an academic perspective, also quite compelling. So, where did you migrate to? Well, it turned out I had a facility for

I was pretty good at making money, and, um, I was very bad at getting paid. So, I basically turned the ability to make money and get paid into a business where I look for other people who were good at making money and bad at getting paid, and

I taught them how to make money quicker and to get paid fairly. This was kind of a venture capital kind of operation, or, um, basically my own, uh, business which I built, and uh, we went, I went around looking for, uh, people who had academic proclivities and were interested in

business and I would approach them and say why don't you build a business, I'll help you, and uh, and I did that for a couple years and uh, and then I reconnected with Peter, Peter, Peter Thiel um, and uh, we began along with a couple

other guys to build this company and while there you had the idea for Palantir. Yeah, well I, you know, post 9/11 I think the idea again it was Silicon Valley ought to be involved in in in fighting terrorism and protecting our civil liberties um, and uh, and you know

terrorism is asymmetric, asymmetry presupposes software because you're finding needles in haystacks, actually using an approach that's not exactly like data mining. It was a critique on some of the data mining approaches uh, that had been used at PayPal and we thought that that approach would be very

effective in this context and would do two things. It would allow humans to find needles in haystacks, so make the data intelligible to you and me, which it's not. And by doing that it would allow them to find bad people trying to

destroy our society and could be used also to protect civil liberties by making the data sets transparent so that you could it was very clear what the government is doing and how they're doing it, which is a particular passion of our company. So the the basic idea is

that the West or Western values will win if we and the West believe in what we're doing. There's no point in having a war on terrorism if civil liberties are being undermined to the extent that we aren't willing to fight that war and this is particularly true in the cyber

In context, it turned out that the approach used at PayPal was essentially an anti-data mining approach. It was anti-data mining in the sense that data mining uses algorithms across large datasets, and what we do is we use what legal scholars call predicate-based search.

So, we would look at you, and then we would go out and say, 'Oh, there's lots of different things in your life that may be indicative of someone being involved in bad behavior.' But it would also be very clear how the government looked at you. It wouldn't be a wide net

cast into a sea of data that brings back all of our, all the innocent citizens that are touched by that net. It would be, it's a very precise, very precise operation, and each step in that operation is documented. How much of your business is finding terrorists for the

US government or whoever wants to hire you? Most of our business is in government, and it all involves both sides of this equation, uh, finding uh people who are up to essentially bad things, both in the anti-terror area, in cyber, in financial malfeasance,

mortgage fraud, but also making sure the data is tagged in a way that allows agencies to collaborate with one another. Which is one of the big findings in the 9/11 Commission: why weren't people able to collaborate? People tend to focus on the policy issues, but there are also

massive technical issues that had to be solved, and solved in a way where you can just install it. We do that. And the second piece is how do you do it so that when the government is looking at you or me, it's clear that they're looking at

you and me for a reason. If they bring a case against us, what is the case? Where did the evidence come from? Did it come from sources they were allowed to say? Did they migrate from the source of one piece of evidence into another source? These are the kind of questions that

that Americans care a lot about. Why can't the government do this themselves? We, first of all, the go, we are a piece of this. The hardworking and tremendously impressive people that you meet when you're in this business are the most important part. Our part is to

provide world-class software. This is something America, by the way, is very good at. Silicon Valley is especially good at, and that will continue in your judgment in America's leadership in creating software. I think software is probably an area where America will continue to lead. I think Silicon

Valley will be at the tip of that, of that movement. Uh, it's, it's, it's the place we've excelled at. Of the software products you like to use, your audience likes to use, they're mostly American, and they don't use it because it's built in

America. They use it because it's the best. And why is that? Well, you know, the thing is, we, it's, of course, we have enormously talented people, but what makes a software product work is an ability to build a business around a compelling idea. And that's that while

this seems obvious in America and especially in Silicon Valley, in almost every other place if you want to build a business, people will say, well, how are you going to make money tomorrow? In Silicon Valley, we build businesses around an idea, and then we figure out how we're

going to make money and this is incredibly important because if you want something to really work, something really complicated, you cannot hire people who are motivated by a paycheck. They have to live and breathe it, they have to do it over a long period of time. You have to be willing to...

took 3 years to build before we even went to market. 3 years, no revenue. In any other part of the world, people would laugh you out of the room. You go, "But not in the Venture Capital world, but not in Silicon Valley." It's a rare place

where somewhere else it doesn't have to be Silicon Valley for Venture Capital to say, "You're not going to make revenue for 3 years, and but then we believe that you have the seed to grow into something big." Venture Capital exists everywhere, but the companies that have invested using

that capital exist in America by and large, and particularly in Silicon Valley. And it's, it's, I believe it's, it's not because we're smarter, it's because we work better together, because we're more likely to be compelled by a big

idea and put monetization second. And this is, this is the secret to our company. And you know, even when we went to market, we didn't hire sales. We still don't have salespeople. We have no salespeople working for us at Paler. We sell

the product by exposing clients to it and saying, "Compare us to what you have. So what's the compelling business idea here?" The compelling business idea is, uh, if you want to make data, if you want to interact with data in a way that's intelligible to you, so the data that's

around you and you're the government, it's a very hard thing to do at scale. So let's just say you go to the market and you buy something, you talk on your cell phone, you send an SMS, you send it every little kind of, you write a report, all

those are data at massive scale. It's very hard for you to see that as a pattern. Well, if I'm looking at the government trying to figure that out, it's very hard to do. What's done currently is they hire a team of computer scientists who write algorithms

that none of us understand right, and then they come back and say, 'Oh well, maybe'. Do you believe the computer scientists they hire or would you rather see it yourself? And that's what we offer. We offer a way to just plug on our platform and you can be a leading expert

in finding patterns in large data sets, whether those patterns are terrorists, they're cyber attackers, they're people again in financial malfeasance, or we the people who want to look at what the government is doing in one form or another, either through Congress or through our attorneys, we can then see

what the government is doing. This is very important. We also allow people to collaborate, which is an enormously important problem when, when people look at what happened on 9/11, the conclusion was it happened because people weren't collaborating, and they focus on kind of

especially the CIA and FBI agencies did not effectively collaborate, and people focus on the policy and inability to work together, but there's also a massive technical issue: how do you work in a massive data set and take out the two pieces of data that the FBI is allowed

to see it's very tough and that's something we we turn we have a turnkey approach to that you can put our product on top of your servers and you can collaborate tomorrow what patterns have you discovered about terrorists well you know obviously you know we don't discuss

exactly what we've done in in a classified context nor am I allowed to but but for example in the syar case which is a case that was discussed in the public uh we took a data set that lots of people looked at some of the

world's experts and we found that there was a group of people that lived in a village that was responsible for most of the attacks in Iraq and we found out they responsible for most of the money and most of the attacks and that's very valuable and highly actionable

information if you know if you know all the real attacks are coming from One Source there's a lot you can do about that and this is typical for the kind of things or when we looked at the Dalai Lama um a case which we've got a lot of

public attention we partnered with Citizen Lab in Canada and um we discovered that the Dalai Lama had been infiltrated by one of the world's most sophisticated and uh uh largest uh cyber networks and we discovered that working at the service of someone else working

at the service of somebody else a government that part we were not clear about but we are clear one of the very interesting things about cyber is this whole issue of attribution um I think the metaphor that one could use in talking about cyber is maybe less cyber

War, but the democratization of spy techniques, so in the '80s and the '70s, only large governments could pull off spy operations. Now, teenagers in a basement with a computer can pull off a spy operation. And it's very unclear if

it's them or someone else. And the diagnostics you use are very, very complicated. It's not like someone breaks into your home, they leave a footprint or a handprint or they leave a hair behind, or you take a picture of it. It's very

very tough to find out who they are. But what we can allow people to do is get a much better sense of what the evidence is, where it points to. The reason the simple reason why simple data mining doesn't work, even leaving aside the moral issues it raises, is it uses a

static algorithm against a non-static and highly entrepreneurial, uh, adversary. That would be the terrorist. That's the terrorist. And, and so what you need to do is have a platform that allows you to interact with the data and see the patterns before they realize they're

giving off the patterns. So it's non-algorithmic, it's not static. And what we allow people to do is we allow the, the actual person doing the work, the analyst, the targeter, to get ahead of the terrorist, the C patterns the terrorist

doesn't realize they're giving off. And this is, this is what, now, how good is the terrorist to, there you have to think of terrorists as entrepreneurs, and they tend to be, I think, very good at what they do. And my view is we need to put

our best entrepreneurs against their best entrepreneurs, that's how we win because they all stopped using cell phones the way they used to, and all those kinds of things. You can think you can think of any approach you use as a static approach, they will figure that out. They, this person gets taken away,

they do a diagnostic, how did that person get caught, and they change their patterns immediately. What we need to do is is game them, is be ahead of whatever their technique is. And that requires people who are very flexible, agile, the kind of people that build weird companies in

the valley. You put put them against these people, and that's by the way, in these pockets in government, that's what you find, entrepreneurial people going after their entrepreneurs. What do you fear the most? If we as a country understand what the danger is, and we as a country

understand the kind of uh, that it's possible to fight this without giving up our civil liberties, we win. If we underestimate the danger, or we perceive this as a war that will be costly to who we are, we'll lose. Democracies tend to win when the country gets behind it. We

are kind of the greatest democracy in the world, and we tend to win wars where the people believe in what they're doing, where the people think that there's a trade-off between civil liberties and fighting cyber terrorists. It's going to be very hard to win as long as we have

computers that are turned on, there will be people that penetrate them every time. We find a way to block them, they'll find a way in. The danger is is real, and it's going to be with us the rest of our life. Now the question is, if the danger is

real and it's with us for the rest of our life. What can we as citizens do, we as Americans do, that allows us to experience the liberty we want and stop them? And I think unless that educational piece is really addressed directly, uh, we will have a problem.

educating the public about the threat, both the threat and the necessity of taking certain approaches. Obviously, I'm biased, and I think you know, P's ability to tag data and show what the government has done is enormously important, but you have to explain if it's if this is

explained where there's a contradiction between our liberty and protecting ourselves, we're going to have a real problem. It's just the same way in any other war where if it's we don't believe in what we're doing, we can't win. Where Americans believe in what we're doing, we

tend to do very well. But then are you arguing if they believe in what you're doing, they're willing to give up civil liberties? Well, that part of the argument you're making, no, my my, well, I believe one of the reasons why I actually talk

in public, um, is there are approaches like ours which allow the people to make sure the government is actually doing what it claims to do. So if you understand what the government is doing to protect and you understand what the limits are, and you know those limits and

they can be enforced, that's one thing. If it's data mining where they're casting a massive net under the ages of protecting us, well, that's not fine. Uh, if the CIA can hire people like you, like paler technologist, uh, why can't bad guys hire

People, not you, but people like you. Well, I I think actually they don't exist. No, no, no, no. I I I think the bad guys do get people of equal talent to what we can get. I think that we in America are very good at organizing ourselves so that we

outperform the bad guys they get. But I think they get very talented people, and the cyber context is a great example. You have what are probably bands of teenagers taking down major organizations. These are highly talented, highly motivated. That's happening today, or the potential to do that is

there if what we found with the Dalai Lama and our other kind of open source operations uh is indicative of what's out there. It's out there, it exists today. There are many cases where we don't know what's happening and we are all capable of being targeted. Take it down software

and technology has democratized espionage. So what would have taken a large governmental organization now a now can be done with two or three teenagers in a in a c in a coffee shop. The biggest question, this is really scary, the biggest question that you as a

president would have to, if you were a president, you would have to decide an organization in America is being taken down. Okay, we know it's being taken down. What is the response? That person has got gotten inside an organization with significant security and been able to

obfuscate who they are. Doesn't that sound like the Larry novels in R yeah? So but but now you don't need to be a governmental organization. You can be a single teenager now. You as the president to go back to the example, how do you

respond well if it's a government organization you call up and say look you either stop or we're going to come after you, but what if it's a teenager and before you've solved that issue you cannot respond. And this is a major issue that, okay, but it's part of your ability

to see up to see around the corner. Then in terms of before something before they take it down you can see patterns of people and our actions and our behavior. Well what we do is we help people like we did with the Dalai Lama figure out if you actually have been infiltrated, so

what kind of data is leaving and they had been they had been and in a massive way, but then two we say these people are likely to actually be this. They appear to be teenagers but they are teenagers that are likely being handled by this organization or they actually are

teenagers or more likely when you're done with our diagnostic we say we are not sure who they are but we are sure they are in this building. Why don't you send someone to go find out who they are? Why isn't this going to collide head-on

into uh privacy and civil liberties? Well it would if there was a contradiction between finding terrorists and protecting civil liberties, but the exact same transparency you can use to find terrorists you can use to see what the government's using. The central question in my view of civil liberties is how

it's not if the government has data because let's assume the government has as much data as a health insurance company. It's how is that data being used? Is it being used in a way that's lawful, meaning do they have the right to use it?

and is it being migrated into places it's not allowed to be used? We allow the we allow the enforcement of rules that the government ought to enforce. And by the way, my experience is they want to enforce, or at very least know they need to enforce. And if it's framed in that

way, then you can have you can have what is the ultimate silicon Valley solution? You remove the contradiction and we all march forward. It's extraordinary stuff. Thank you for coming.

현시점 가장 주목받는 회사 (팔란티어)

BZCF | 비즈까페

알렉스 카프와 팔란티르 기술 소개

팔란티어 테크놀로지스의 공동 창립자이자 CEO인 알렉스 카프를 소개합니다.

팔란티어의 데이터 마이닝 소프트웨어는 미국 정부 기관과 금융 회사에서 사용하고 있습니다.

카프의 배경: 스탠포드 법학, 신고전주의 사회철학 박사.

중요한 문제에 대한 카프의 열정은 그의 학문적 탐구의 원동력이 되었습니다.

그의 학문적 연구는 지식, 커뮤니케이션, 서구 사회, 시민권에 중점을 두었습니다.

카프는 학계의 영향력이 생각보다 크지 않다는 것을 알게 되었습니다.

그는 더 큰 영향력을 발휘할 수 있는 비즈니스 분야로 전환했습니다.

그는 돈을 버는 데는 재능이 있었지만 비즈니스 감각이 부족했습니다.

그는 비슷한 기술을 가진 다른 사람들이 자신의 작업으로 수익을 창출할 수 있도록 돕는 비즈니스를 구축했습니다.

이후 그는 Peter Thiel과 파트너가 되어 Palantir를 설립했습니다.

대테러 및 시민 자유에 대한 팔란티어의 역할

9/11 테러 이후, 카프는 실리콘밸리가 대테러에 기여해야 한다고 믿었습니다.

팔란티어의 접근 방식은 기존의 데이터 마이닝에 대한 비판입니다.

데이터를 이해하기 쉽게 만들고 투명성을 통해 시민의 자유를 보호하는 것을 목표로 합니다.

핵심 신념은 투명성과 시민의 자유를 준수하는 서구의 가치가 우세하다는 것입니다.

팔란티어는 광범위한 데이터 마이닝에 비해 정밀한 접근 방식인 술어 기반 검색을 사용합니다.

팔란티어의 비즈니스 모델과 영향력

팔란티어의 비즈니스는 대부분 정부를 대상으로 하며, 악성 행위자를 찾아내고 데이터 협업을 보장하는 데 중점을 두고 있습니다.

9/11 위원회의 핵심 조사 결과인 정부 데이터 협업의 기술적 문제를 해결합니다.

시민의 자유를 보호하기 위해 정부 데이터 사용의 투명성을 보장합니다.

팔란티어는 미국 혁신의 강점인 세계적 수준의 소프트웨어를 제공합니다.

성공은 당장의 이익뿐만 아니라 매력적인 아이디어를 중심으로 비즈니스를 구축하는 데서 비롯됩니다.

팔란티어의 기술적 접근 방식과 장점

팔란티어는 대규모 데이터 세트를 이해하기 쉽게 만들어 패턴을 파악할 수 있도록 도와줍니다.

불투명한 알고리즘과 달리 전문가가 데이터에서 패턴을 찾을 수 있는 플랫폼을 제공합니다.

데이터 분석에서 협업을 촉진하여 9/11 위원회에서 강조한 핵심 문제를 해결합니다.

정부 기관의 데이터 협업을 위한 턴키 솔루션을 제공합니다.

팔란티르의 작업과 첩보 활동의 민주화 사례

시아르 사건의 데이터 분석을 통해 이라크 공격의 배후를 파악한 그룹.

정교한 사이버 네트워크가 달라이 라마의 네트워크에 침투한 것을 발견했습니다.

쉽게 구할 수 있는 기술을 통한 스파이 기술의 민주화.

팔란티어의 접근 방식은 비알고리즘적이고 동적이어서 진화하는 위협에 적응합니다.

진화하는 위협에 대응하기 위해 기업가적 접근 방식을 일치시켜야 합니다.

두려움에 대처하는 방법과 공교육의 중요성

위협에 대한 대중의 이해의 중요성과 균형 잡힌 접근 방식의 필요성.

민주주의는 시민들이 대의를 이해하고 지지할 때 성공합니다.

사이버 위협의 지속적인 특성과 효과적인 대응의 필요성.

위협에 대처하고 자유를 지키는 데 있어 공교육의 중요한 역할.

자유와 보안 사이의 균형을 설명해야 할 필요성.

개인정보 보호 및 시민의 자유에 대한 우려 해결

팔란티어의 접근 방식은 정부의 조치를 감독하여 남용을 방지할 수 있습니다.

숙련된 인재를 고용하는 적들의 능력과 이에 대응하는 미국의 능력.

고도로 숙련된 개인과 그룹이 제기하는 심각한 위협.

소프트웨어와 기술을 통한 첩보 활동의 민주화.

식별하기 어려운 개인이나 그룹의 공격에 대응해야 하는 어려움.

팔란티어는 패턴과 잠재적 위협이 현실화되기 전에 이를 식별하는 능력을 갖추고 있습니다.

팔란티어는 조직이 침입 여부를 판단하고 잠재적인 위협을 식별할 수 있도록 도와줍니다.

팔란티어의 접근 방식은 개인 정보를 침해하지 않으면서 위협을 정확히 찾아내는 것을 목표로 합니다.

데이터의 단순한 존재가 아니라 데이터가 어떻게 사용되는지에 초점을 맞추고 있습니다.

팔란티어는 데이터 사용과 관련된 규칙과 규정을 시행하는 데 도움을 줍니다.