Stanford Seminar - Entrepreneurial Thought Leaders: Stephen Cohen of Palantir

I was thinking it would be fun to come back to Stanford, my alma mater, and tell a few of the stories, basically the path that led from where you all are sitting right now to the stage up here and Palantir and all the other things that have happened in the last eight years. So firstly, how many grad students do we have in the room? Okay. And how many undergrads? All right. And of the undergrads, how many SLEEK kids do we have in here? Do we have any SLEEK kids? Okay, that's actually kind of funny unto itself because I was going to start the story with the story of SLEEK, my beginning here at Stanford. I was a SLEEK kid. It's actually pretty funny there aren't too many in here. So I was fortunate enough to grow up around here in Fremont, and during high school, I don't want to date myself too much, but I worked on an online gradebook software company during the first dot-com bubble. In those formative adolescent years, I was able to jump outside the narrow high school bubble and see a bit about how the world worked in the world of entrepreneurship. You can probably take a few guesses at how much I knew about what I was doing, attacking K-12 while still being in K-12. Education isn't exactly the most lucrative market, although it's gotten a lot better since then. But it was very helpful to see things from an outside perspective, see what actually worked and what didn't work. I snuck in here, went to a few lectures at Stanford, BASES had a business plan competition, I jumped into that, and I don't think I ever won a single business plan competition. I learned business plans are not my forte. But I also learned that Stanford had the most intense concentration of traditional talent I'd ever seen anywhere. There's a special energy, an aliveness to everyone here, and an openness towards the world that I was so excited about being a part of. My number one consuming desire was to take the talent here and work on changing the world, transforming things. So those first few weeks freshman year, always daunting, show up there in the SLEEK dorms in Florence Moore, was in Faison, and you talk to the kids and they're just as brilliant as you imagined. Traditional talent is overwhelming how much is there. But I had this nagging feeling: whereas the traditional IQ was incredibly high and incredibly concentrated, what I was really looking for was this type of worldly wisdom about how to take the world of ideas and actually connect it to the real world, to actually generate an impact with all this. This is what draws so many of us here to Stanford in the first place. I was struck by how little of that knowledge existed within the institutions. I've learned since then that so much of entrepreneurship simply can't be taught. We have formats like this, and I think they're fantastic for sharing a little bit that can be shared. So much of it just has to be learned through experience. But this recognition that there is a difference between traditional IQ and the entrepreneurial disruptive force, and that the latter category needs to be learned outside the traditional bounds of institutions like Stanford, was a very important starting point for how I went about doing things here. Hopefully that may help inform how you do some of this stuff. So, recognizing there was no single linear path that would lead to an entrepreneurial outcome, I immediately tried to jump to non-linear paths, which for me meant working on products. I love working on products. Of course, since my last one was a K-12 grade book, I don't exactly know if my choice of product at this age was particularly great, but I had some fun ones in those opening years. This was the beginning of wireless internet taking over. Everyone had these routers, no one had any idea where to place them. So I had this idea that I would hack the network card in my laptop. I got this old crusty cart from my house in Fremont. I played on the varsity tennis team in high school, and I got an old tennis container and a tennis ball, put a laser mouse on top of it so I could track 2D coordinates with this cart, and then I hacked into the driver in this laptop so I could pull out the signal strength readouts and effectively build a 2D topological model of the signal strength in a room like this. Now, what did this actually amount to? Well, I was this freshman a few months into Stanford, and I've got this cart that's roughly sized like this, and I'm walking all around Tressider and everywhere else with this tennis ball duct tape contraption, probably looking like a crazy person, just back and forth mapping out all this signal. Obviously, that idea didn't exactly evolve into Palantir. But after that, I started working on an augmented reality toolkit. It was actually part of my senior project work. What it did was take webcams and find these elements in a scene and basically project a 3D matrix onto that so it could determine the 3D coordinates of what it was seeing. Anyways, you could rig this thing up to build a 3D mouse. So I wrote a 3D mouse, connected it to Quake 2, which was just recently open sourced, and there you have it. You have this new way of interacting with video games with your computer. You can just move around and move your hands to do things, kind of Minority Report-ish. I figured this could be the next big thing. I talked to a good venture capitalist friend of mine, Ashmit Sudana, who I think now is a partner at Foundation Capital, and he said, 'Stefan, it's a great idea, but it can't impact the real world. The entrenched players in this market, these console manufacturers, they're the ones who control the distribution, and because of this essential feature of the marketplace, it might be a good product, it might be a good idea, but it probably won't be a reality.' So of course, this really bummed me out. This is not what you want to hear. Any entrepreneur has plenty of stories like this where they get the bad news. But sure enough, years later, how many of you have used a Connect before? Right, you've probably played with PlayStation 3, a variant of it. We got that exact same technology, and sure enough, it came from the console manufacturers who controlled most of it. So he was right. But the story I'm trying to paint here is one of working outside the traditional bounds of the institution here at Stanford while still leveraging the resources that were available, and still longing to find the way of connecting that world of ideas and products to the actual real world. The next kind of story in this adventure was doing research with Andrew Ng, jumping in and learning as much about AI as possible. How many computer scientists in the room? Could you raise your hands? So what is this, like 15, maybe 10 percent? So the computer scientists here can particularly appreciate this. I grew up watching Star Trek: The Next Generation. We've got Commander Data, we have artificial intelligence in the ship's computer. It's everywhere, and it's this incredibly powerful idea that computers will eventually catch up with generalized human intelligence. So as a computer scientist, when you first start learning about modern AI, statistical machine learning, at least for me, the very first thing you notice is this stuff is very, very hard. Anyone who's taken CS229 knows that feeling in particular. Andrew Ng is the best teacher for it. Highly recommend doing anything you can with that guy. He's great. As I learned AI, I tried to mine that treasure chest to find something, an idea so powerful that it could punctuate from that world and transcend into ours and have a big impact. I had the same kind of nagging sensation I had that freshman year in SLEEK: these kids, despite being really smart, there's something that may be missing. And with the AI research, I think modern artificial intelligence has these brilliant, elegant models, these kind of topological visual analogy models for how to do classification, how to divide up problems. But they're incredibly quantitative and they tend to deal with problems that have a very well-defined structure. There are some very clever algorithms, and there's been a lot of clever algorithmic breakthroughs in the last 15 years. But the nagging sensation that I ultimately couldn't kick is that in the final analysis, AI is probably more 'A' than it is 'I'. It's a little more artificial than it is intelligent. The problems are particularly artificial, and the solutions are not quite exactly what I would call intelligent. They tend to be a little more linear. They at least don't analogize to generalized intelligence in the way that one would hope. So once again, frustration. It didn't lead where I was hoping it would. But I decided to switch my game up a bit. I'd been trying to find brilliant ideas and then find people at Stanford to help work on them with me, and I decided to invert the model and try to find the brilliant people to work with and see what ideas they had. So I went around and asked all my friends who the most brilliant people they knew were, tried to meet them, and asked them the same question, and followed the trail of brilliant people. There were a lot of people pointing at this guy, Peter Thiel, as the smartest person they knew. He seemed to be at the top of the list. So I made it my goal to find this guy and talk to him and see what could get done there. One of my good friends, Joe Lonsdale, who I think just did an interview in The Daily, if any of you saw it, he ended up co-founding Palantir with me along with some other folks as well. But he was working with Peter at Clarium. Clarium was Peter's global macro hedge fund. He put it together right after he sold PayPal. Peter was one of the founders and the CEO of PayPal before it was bought by eBay. And sure enough, Peter was undoubtedly, and still is, the highest IQ guy I've ever met in my life. He's incredibly brilliant. Everyone was right. But the thing that was striking, this is back in 2003, the PayPal Mafia still hadn't started too many of its companies. I mean, the PayPal Mafia as a term wasn't even that well-known at the time. And this whole network, they all had this energy, and they were all putting together the wisdom of how to actually transcend that world of ideas and punctuate the actual world. This wisdom was being put together in ad hoc fashion. It's been formalized now more through these talks. You can listen to Max, you can listen to all of these guys talk about their experiences, and you can learn a bit about that kind of grab bag of tricks. But they were focused on the same problem, and they recognized it as the problem it is. I think you can define the smartest, you could call it traditional or standard IQ, it's defined by what I would call a competence, a clarity, and then a gracefulness in the execution when given a really tough problem. Whereas entrepreneurial talent or entrepreneurial potential is almost always defined by being able to summon this incredible interpersonal intensity and commitment to work on really hard problems for sometimes sustained amounts of time. So when you see someone demonstrating that, that's when you say that's going to be a good co-founder. And if they're brilliant too, well then hey, you're really lucky. You definitely want to work with that person. So next thing I knew, I was interning at Clarium 40 hours a week. I was trying to graduate early, which by some stroke of luck I actually managed to finish a few quarters early here. So I'm doing a 40-hour job, 20 units of CS, and everything else that we all do here at Stanford as undergrads. The only way to make it work, quite frankly, was to get in this habit of just blitzing three days in a row. For one quarter, that last quarter at Stanford, with the exception of the first and the last week of the quarter, Tuesday through Thursday I just wouldn't sleep. I would literally just work. I mean, it was wild. I couldn't even imagine doing it now. Now if I don't get my eight hours of sleep, I'm quite a grumpy guy. But anyways, these Tuesdays through Thursdays, I would just, around two or three in the morning, go down El Camino to the Denny's. It's no longer there, it's now a Su Hong, but I would go to that Denny's and I would say hi to Vladimir. He was the late night waiter. We became best friends. Embarrassingly, got to know each other so well that he would just let me go behind the kitchen to the back and get the food myself, which is quite something. But honestly, it felt natural at the time. It didn't even feel like that much of a push. It just felt like what needed to get done. So I think it's critical while you're here, if you find opportunities to engage, if you feel a resonance with an idea or a project or a set of people, just run with that. This stuff is so much about opening your mind and your heart to the possibilities of doing it, of being that engaged with an idea. And even if it doesn't work, you just gain so much from that experience. But this strategy of focusing on finding the smartest people and surrounding yourself with them, I want to caveat it with an important footnote here. I don't think a people-first strategy in isolation ever really works. I don't think that's successful. One of the core reasons is that you want to surround yourself with brilliant people, but also people who are getting stuff done and doing interesting things. In general, those people only got that way because they love doing interesting things and they've learned from those experiences and they've changed them. So if you take this pro-networking argument to an extreme of just trying to find the brilliant people, if you're lucky you'll actually get in front of them, you'll talk to them, but when you're there, you're not going to have anything substantive to discuss. You're not going to be in the world of ideas the same way that you would be if you really cared about them and focused on the substance first. So your best case scenario is having a shallow interaction with them where nothing truly substantive can come about from it. But if, on the other hand, you care and you're deeply passionate about these ideas, about the products, about the things that can change the world, and then you also focus on finding the brilliant people who want to work in those spheres, I think this is really the right recipe. This was essentially the recipe that led to the early days of Palantir. So when Peter finally sat down, I guess this was like midway in 2004, and said, 'I've got this idea, Stefan. Let's take some of the ideas from the PayPal fraud platform and let's try to generalize them to solve the country's counter-terrorism problem. And we'll start by selling it directly to the US government. And while we're at it, let's solve the generalized enterprise information management platforms that are out there. Let's help big enterprises use a Silicon Valley approach to understanding their data.' In this kind of context of being around these brilliant folks but also deeply caring for the ideas and the products, this was obviously a winner. This is what I had been looking for that whole time at Stanford. It was always right next to the Stanford institution but never quite linearly connected. So away we went. Those staying up Tuesday through Thursday, pushing and pushing. I went from my last CS final, which I totally bombed. I think I got a C minus or something for the first time in that class. I went from that last final to a little office at 3000 Sand Hill, where Peter had actually started PayPal, and that was the first day of anyone working full-time on Palantir. We had very little time, only about eight weeks before a killer meeting with a guy named Gilman Louie, who was at the time the president of In-Q-Tel. In-Q-Tel, for those who haven't heard of it, is the intelligence community's venture capital fund. They do a great job opening doors for companies in Silicon Valley and elsewhere that want to work on the US government's challenges. So we have this meeting, it's only two months away, and we don't exactly have too much product at the time. So it was prototype crunch time. About eight weeks to put together a basic prototype. I'd have to say, CS people in the room can understand this, it would be a little admittedly light on the back end elements, the initial prototype a little more focused on the lights and the fireworks and the front end. But it was a success. Gilman liked it, and he began to open the doors for us in the US government. But before he left that meeting, he said the most funny thing, which was, 'If you guys can't help us fix our counter-terrorism problem, you've got a bright future in video games.' So I took that as a ringing endorsement. I'll share a few funny stories from those times, a few illuminating ones. There we were, crunching away, and every other week basically flying out to Washington DC, trying to meet with folks inside the government, get as close to our potential customers as possible, and just showing them our platform, showing them the prototype, and asking them, 'What do you want? What do you need?' But the real information came from looking at the points of resonance with the platform, getting them in front of it and seeing what they used, what did they like, what were their eyes attracted to, what was actually going on, where were the real opportunities to add value. So we would go out there, gather this feedback, and then drink a whole bunch of these things and crunch and get it integrated and do it again. One of the points that I think demarcated an element of success was when my government sponsor was introducing me at one of these meetings and he said, 'Everyone, I want to introduce you to Mr. Two Weeks. He can build anything you ever want in two weeks. So ask away.' Needless to say, that's what you want to hear. It's what Peter would say is the right kind of problems to have. But it definitely increased my Red Bull consumption. In this process of fleshing out the product and finding the exact market fit, finding exactly how these pieces fit together to fulfill that vision that Peter had originally outlined, there's no easy path through and there's no linear path through. It's the same thing where you throw the entrepreneurial intensity at it and then you see what sticks. But you can look, and I think it's very important to look for the demarcation points that actually indicate you are succeeding. The final demarcation point where Palantir went from more of a provisional idea to a full conviction for me personally, when I knew that we really had an opportunity to change things and impact the world, was at one of these meetings. I get the Mr. Two Weeks introduction, and it was a conference room with maybe 30 people in it. These are all government folks, all in suits. Government meetings are not terribly exciting. They tend to go at quite a slow pace. But this one was more exciting. People were energetic and they were really excited with what we had, and they could see the disruption. At the end, there was a moment where the meeting was breaking, everyone was walking away, and out of the corner of my eye I see at the other end of the room two senior government executives, fully in their suits, grown men who've been in government service for over 20 years, stand up and give each other high fives. So when your government guys are doing the high fives with each other, saying to themselves, 'This is going to change things,' that's when you've done it. I mean, not done it, but you're in the position to do it. That's when I knew that if we could just follow through with this, if we can make it work, we could really change things. That moment of founder conviction was a very important moment. Roughly around that time, the conceptual vision for the product stabilized. Those are the moments where you start to gain clarity on what the heart and soul is of what you're doing. For Palantir, the fundamental aspiration of the platform is basically to enable humans to perform the analytical reasoning that for whatever reason machines can't seem to replicate. There's a certain form. The simplistic version of this is to enable the ideal human-computer symbiosis, but I think that doesn't quite do justice to some of the more subtle aspects of this. The idea here is firstly, accepting as computer scientists and engineers and people who are in the technology business that there's actually a lot computers can't do. It could be for practical empirical reasons, could be for theoretical reasons. I find the theoretical possibilities quite interesting themselves. But accepting that there are these realms of reasoning that computers are particularly bad at without human help, like for instance figuring out what the right framing is for a problem. You can't even really describe a problem to a computer without a framing on it that pre-exists, so you certainly can't hope for the computer to just tell you, 'Hey, here's the right framing.' Computers are very bad at finding patterns in data unless there's an incredibly long instance of a very well-constrained, well-understood problem. Then it can begin to find certain patterns. For the vast majority of human analytical problems, it's just not. It's sparse, it's isolated, things are connected in ways we have a stronger intuition for, we have a rational reason. So because of all these facts, really the right kind of future economic relationship for man and machine is one that deeply respects the capabilities of what only man can do, or just at least for now what only man can do. And also, of course, you want the computer scientists who are recognizing this: the machines can do the absolute best of what they can do, the algorithmic reasoning, the computational processes that they can execute trillions of times faster than we can. So this is the pound to your idea, the ultimate pound to your aspiration. But I think that this idea is actually just one part of a much broader opportunity I wanted to talk about here before we open this thing up to questions. That opportunity, well, I'll sketch an image. Take the entire domain of human economic activity, all the things we do, all the things we want to do, all the things that make that up. Now draw a line that divides, on one hand, the precisely definable, and on the other hand, everything that's not quite that, all the related phenomenon that for whatever reason can't quite be precisely pinned down. So we can probably precisely pin down yes or no, am I hungry? But we can't precisely pin down how hungry am I, what does that feel like? We can assign a number, we can make an approximation, but that phenomenon is at best exactly that, an approximation. The fundamental fact of it is it exists in something that's much less precise but just as real and just as tangible. I think that that line that separates the precisely definable from that which is not, this is basically the line where algorithms can... Let me backtrack for a second. This may just be obvious from a technology entrepreneur's standpoint, but I'll say verbatim: I really believe that with all the advances we've had in computing, computers will do all things computers can possibly do. So if you accept the extrapolation there, then the next question naturally becomes, what can computers do? If the answer is everything, then we can expect computers to be coming and taking away a lot of what we do day-to-day. But I strongly believe it's just not. Actually, there's a lot that computers really can't do. I'm also very interested in the reasons why. Quantum computing, I hope we don't have any administrators in here, but I spent last quarter sneaking into the History Corner Tuesdays and Thursdays auditing the quantum computing course that Stanford just started teaching, because it's deeply fascinating stuff. Potentially in quantum computing, there are some answers to why classical computational algorithms might not be able to get certain human reasoning tasks done. But nonetheless, recognizing the depth and the subtlety of the qualitative domain and recognizing it as disjoint and separate from the quantitative domain, this lets you start seeing problems a little differently. There's a lot of interest in Salesforce and other CRM products, a lot of web analytics, and these are all essentially domains where we are collecting, we're building a quantitative universe. The space of all big data is one where we build a quantitative universe and then we study it. One approach to this is to say, let's get as much data as possible and then let's develop the most sophisticated algorithms possible for finding patterns in the sea of quantitative data. But I ultimately don't believe that will be terribly successful. I think the much more important questions are: let's study the human aspects of this, the qualitative aspects, the problem. What are we trying to get done? What's actually happening? What are the subtle aspects of this process that when we actually clarify, then we can actually learn? That's where we want to collect the data, that's what we want to analyze, that's what we want to figure out. Through the study of the qualitative phenomenon that dovetails right next to this quantitative phenomenon in the actual universe, through this we can then optimize how we use our computers to actually do what we want them to do. This place, this is where I think we're going to see a lot of technology companies in the 21st century. I think this is much closer to the actual 21st century big data analysis problem, at least much more so than getting more and more fancy algorithms to do the same problems we've already seen. So with that, I will turn it over to you all for some questions.

Yeah, so the question is how does this perspective shine on the natural language processing problem? That's a great question. The immediate thing is that you probably don't want NLP. The stuff you do, like good NLP, will probably be a lot like Google, where it's disconnected from the intentional aspect, the actual direct value creation aspect that us humans are actually focused on. It will probably remain more of a tool than a full turnkey technique. I'm less bullish on translation technology that can capture all the subtlety and complexity of grammatical constructs. I believe that all that will converge at this natural translation of meaning, then more of the brute force stuff like we'll do a lot of synonyms for some phrases and then hopefully the other smart human mind can put together the rest of the context. I think the latter is where we'll see mostly advances. But it's a question worth studying.

Yeah, this is a great question. You've picked up on the subtlety. This explanation that at least the computer scientists in the room will probably find very displeasing, distasteful. I have a hard time with this idea unto itself because of exactly this. The problem with... Yeah, so the question was, in a nutshell, the line that divides quantitative phenomenon from qualitative phenomenon, must it be, doesn't it inherently have to be a qualitative line? And then further, is there a hope of developing algorithms that could perhaps figure out where this line is or maybe pull some of the qualitative problems into the quantitative domain? I think the line's constantly moving, it's fuzzy. But the logical issue here that you highlight is that I'm defining qualitative negatively, not positively. Because if we could define qualitative phenomenon in a very tight precise description, then of course we could just turn it into a quantitative phenomenon and we can use a computer to solve it. So it's one of these things. It touches a bit, I warned you all I was a SLEEK kid here before I started talking, so you could hear a little bit of a SLEEK, a little Markman Suzanne here. But in essence, there are these kind of ambiguous aspects to a lot of these human phenomena we experience all the time, including subjective phenomena that, because they defy that precise logical description, we cannot turn them into algorithms. I don't see that phenomenon as being fundamentally tackled by these quantitative approaches, but we can definitely chip away at it. So the real invitation with this framing is to look at problems, to dive into those qualitative subtle aspects. I was talking about going back and forth to DC, working on developing our product. There's all these product development formalisms, agile, you've probably studied a whole bunch of them in some MS&E courses here. But the framing I had just reflecting on that problem is: there's a lot of really deep subtle stuff. The users ultimately don't know what the product is they want. I mean, how could they? That's why you're the entrepreneur. If they knew, they would just make it themselves, they wouldn't really need you. The only way you actually seek satisfactory answers to these questions of what's the right product, what's the right feature, is to look at these subtle aspects and study them in their own right, and really appreciate the depth of them as a phenomenon unto themselves, not just crassly reify them into a logical conception that resembles a much more quantitative-friendly truism. If that makes sense.

Yeah, that's a great question. To sum it up, it's reflecting on this qualitative quantitative thesis. What makes the best user for Palantir? How does that relate to it? The strongest users we've had, they actually have some of those entrepreneurial qualities. They really want to get things done. In their enterprise, they care deeply about solving the actual problem, accomplishing the mission. So they have that entrepreneurial quality, and they almost always categorically have another feature: they're incredibly frustrated with the organization's existing technology base, the inability to use all these wonderful quantitative tools to actually impact the mission. So starting from that base of frustration and deep concern for the mission, they see Palantir as a lens through which they can better understand what's actually happening in their organization, what's happening on the ground reality. At the end of the day, they're essentially respecting the deep and complex subtleties that are actually out there in the real world. But then by appreciating those complexities, they are able to map a lot of that information back into Palantir and then take advantage of our facilities to enable collective learning, collective pursuit of the objective there. So the relation, I don't know if I can break it down into what classes people should take or what folks should study. I wouldn't dare jump into that debate anyways. But in essence, I would say it's a deep appreciation for the complexities and subtleties of the ground reality, but then also just a deep desire to get leverage on it collectively as an institution by pulling it into Palantir, being able to model it in an accurate way that does justice to those underlying truths.

Yeah, so the first question, what do we look for at Palantir? What's going on in these interviews? I like to try to meet every single person we hire, perform the last interview in the Palantir process, sometimes earlier interviews. There's a lot of subtlety and complexity to it too, so I don't want to pretend to break it down into a basic rubric or anything else, it won't do justice to it. But the essential qualities that we look for, even though they come in many different forms of different people in different positions in the company, is the highest concentration of talent possible. That's number one. Number two, long-term time horizons. And number three, what I like to call generative personalities. I'll break those down. Talent, as I was mentioning before, I think talent can really be identified by, firstly, a competence in solving whatever problems that talent is kind of directed at. And then in the competent problem solving and in the competent execution, looking for answers, there's actually just a deep gracefulness. So it's not just that the person knows how to answer the question, it's that they're even graceful in their process of answering it. This almost always means they add clarity. They clarify the problem structure and the solution structure. In algorithms problems and whatnot, this is at least easier to identify because the problems are much more well-constrained, but they're very hard. So the people who are able to confidently answer the question, clarify the basic construct of the problem, and then execute it in a graceful way, those are always the most talented. But it applies just as much to domains outside of computer science. In a sales context, in engineering contexts, you want a good salesperson to be able to clarify what the actual problem was, what was the tactical ground reality, how did they proceed through it, what were they trying to accomplish, how did they do it, what did they do right, what did they do wrong. This is what you want on the talent side. Second thing is long-term time horizons. This one's critical, and it's another one that no one hit me over the head with when I was here. When you have that talent, it's very hard to create a substantial store of value unless you really want to work on the same thing, unless you really are willing to dedicate your talent to focus it through some duration of time on a particular task. What we'll find out there is, it's in many ways a self-directed construct, how you look at the world, whether or not you focus on a handful of problems and derive your satisfaction from solving them longitudinally, versus if you're always jumping from one thing to the next and you actually don't like to stick with anything for the long term. I don't mean for this to suggest that you should just do one thing and not vary it. If anything, you just heard from this talk I was doing a hundred different things. But you can see there's still some common threads to it, and that commonality, that's the long-term time piece. That's what we look for at Palantir. The third and last trait is the generative personality. You can be very talented, you can have very long-term time horizons, but some of you might even have a professor that does this: they apply the talent and long-term time horizon to showing why a lot of ideas don't work or why they're silly, and they're more hesitant to construct and be generative about proposing solutions. It's very important in any startup to generate as much momentum as possible. So that means you want to take that talent and long-term time horizon and focus it in the most creative, energetic way possible. Does that make sense? Yeah, that's what we look for.

Can you repeat that one more time? How do you bridge between engineering and sales so that you've got that gracefulness that comes out in your salespeople when they're speaking to your clients?

Oh yeah, no, this is a very good question. Basically, how do we reconcile the engineer and the engineering aspects with the sales aspects, especially in a way that constitutes some gracefulness of execution? I would say the first thing is we probably focus a little less on the gracefulness of execution and more on this kind of substance-forward perspective. We like to get the engineers and people who actually add value as close to the front lines as possible, interacting with the clients. It's basically we want to share as much of our fundamental substance with each and every party we work with as possible. Probably have time for one more here. Yeah, go ahead.

I mean, I don't... Counterterrorism was an initial impetus for the company, but it's a very, very small aspect of what we do these days. With the principal customer being the US government, there's not unlimited leeway in other parties you work with, and there's a lot of scrutiny. We as founders care about it very, very deeply. So although there's always legal aspects, you get good lawyers, they help you out with these things. What really matters more, I think, are the moral aspects, the mission and the purpose. We focus the most on that and making sure that we're having a positive impact. All right, well, I'm going to leave that time for you all to come up here and address questions individually. But thank you very much.