SPEAKER_00: it's, it's honestly sad. What quantity do you think is a large quantity of missiles to order in a year right now? And like in the US, if you're talking about a reasonable size, like long range

SPEAKER_03: missile, what is a large quantity? Four to five figures, thousands to tens of thousands would be

SPEAKER_05: a large quantity of missiles. Way less. Hundreds? Way less. Way less. Dozens?

SPEAKER_00: Our highest end systems, we're not at the capability of producing dozens per year, but that is like their ultimate eventual output when everything is working.

SPEAKER_10: Yes. It's pathetic. This Week in Startups is brought to you by Squarespace. Turn your idea into a new website. Go to squarespace.com slash twist for a free trial. When you're ready to launch, use offer code twist to save 10% off your first purchase of a website or domain. LinkedIn jobs. A business is only as strong as its people and every hire matters. Go to linkedin.com slash twist to post your first job for free. Terms and conditions apply. And AdQuick. If most of your advertising dollars are going to digital ads, it's time to diversify. Out of home advertising like billboards offer low cost, high value reach. AdQuick makes it easy to plan, buy and measure all in one place. Visit adquick.com slash twist and mention twist to get

SPEAKER_12: $1,000 off your first campaign. Hey everybody, welcome back to This Week in Startups. Very excited

SPEAKER_13: for our next guest. His name is Byron Hargis, and he's got a startup in the defense tech space. It's called Castellion. And welcome to the program, Brian.

SPEAKER_15: Hey Jason, thanks for having me.

SPEAKER_13: All right. Uh, I like the American flag back there. I love defense tech and, uh, our researchers were really excited, uh, when they found your startup, uh, to have you on the program. So we're

SPEAKER_19: really excited to have you here. Maybe tell the audience a little bit about what you're building.

SPEAKER_20: Absolutely. So, um, Castellion is really focused on bringing back deterrence through strength. And

SPEAKER_00: if you kind of subscribe to the, the Reagan air piece through strength, like how do you, how do you maintain peace? You have to have actual strength against the adversary and adversary has to know that. So we're focused on building very affordable, uh, long range strike munitions, specifically hypersonic munitions, uh, to get after kind of the current problem set that we see with having very limited, uh, options against pure adversaries, such as China.

SPEAKER_25: Hmm. Let's take a moment here just to level set with the audience. We hear hypersonics all the time. Now, I think we all assumed that inter-ballistic missiles were going at a very high rate of speed. Yeah. We see things come out of battleships and submarines. They look like they're going really fast, but you know, for civilians, uh, like ourselves, I, I think sometimes we don't

SPEAKER_26: understand what, what hypersonic means. Let me, uh, let me take that piecewise. Cause it is,

SPEAKER_00: it is kind of a confusing topic. So the hypersonic itself, like the kind of like general term is usually like associated with going five times the speed of sound or greater. So Mach five, um, the actual technical details of that are even more complicated. It's really that, um, you're getting into compressible flows. You're in, you're not in equilibrium. So Navier Stokes becomes much harder to actually compute when you're trying to figure things out. And generally what it means is that if you don't, if you don't actually do testing, it's extremely hard to model a hypersonic system. And you are correct that, um, when you look at things like ballistic missiles or like a re-entering space vehicle, um, they all do enter hypersonically. So like typically, like if you're at orbital speeds and you're re-entering the atmosphere, you're coming in at least at Mach 25. And so ballistic missiles, you know, space capsules, everything is, they're all start hypersonic when they come back into the atmosphere. Now, when you hear like the Department of Defense or like a company like ours talk about hypersonics, it, we're really actually referring to a subset of that. And so very specifically, like a ballistic missile follows a ballistic trajectory. In other words, it's a very predictable, controlled trajectory that usually goes through space. It launches from the surface or what have you goes into space and then re-enters and comes back and it follows like the same arc that you would throw like a baseball, um, hypersonic weapon systems. Uh, what, what the DOD typically refers to is really like when looking at how it flies, it typically flies a very different looking flattened trajectory. And so you're not leaving the atmosphere. You're flying at a very prolonged distance horizontally at very high rates of speed. The reason that hypersonic systems are kind of like all talked about right now is really when you're trying to get after, um, say like a pure adversary, such as, such as China, they, they put in tremendous investment, um, to basically negate American capabilities, uh, regionally close in, close into the coast of, of China. Um, a hypersonic system fundamentally, um, when viewed at it from the, from the other side, it doesn't look like a ballistic missile. And the reason that's important is traditionally ballistic missiles usually also, uh, imply that you might have a nuclear warhead on the front. And so you don't typically start launching ballistic missiles at other nuclear armed countries for fear that they might confuse what

SPEAKER_25: you are doing. Oh, that's fascinating. Yeah. Okay. So just to recap here, these things go, well,

SPEAKER_26: five times the speed of sound that's Mach five airplanes go under Mach one, even right. We, we fly below the speed of sound in commercial airlines, but there's boom, uh, a new, uh, a member of the twist 500 and the supersonic passenger plane and the Concorde obviously would break Mach one, but we're talking about five times the speed of the Concorde. The second note is these things tend to fly closer to the ground. They're not inter ballistic. They don't go into outer space or

SPEAKER_25: into the upper atmosphere. When you go into the upper atmosphere, you have less air, so you can go a little bit faster. Yeah. So these things are fighting against wind and they're going five times as fast. And I think the reason this is important or the introduction of the, this capability is so important is that you can't defend against hypersonics or it's incredibly hard to defend against hypersonics. In other words, the iron dome, if Hamas or, you know, whoever was dropping bombs on

SPEAKER_06: Israel, the iron dome wouldn't catch a hypersonic. Am I correct that that's the reason this is so important?

SPEAKER_37: It's not impossible. It is much more difficult. All right. And we have a video here. What are we seeing?

SPEAKER_38: Uh, maybe you could sports cast this. Many people are listening. Absolutely. So this is some of the

SPEAKER_41: the team. We, we develop a lot of the hardware in house, um, very, very uniquely in aerospace. Most of

SPEAKER_00: aerospace, like you typically hear the primes as integrators. We do actually a lot of the manufacturing of all the systems that are inside the missile ourselves. Um, that's actually our, our Marine MKR. Um, it's a, it's a heavy duty truck that can basically pick up a shipping container, put it on the back. We, we made a launcher for that to make a mobile launcher to be able to do, um, accelerated testing obviously has applications for the army and the Marines. Um, but here we're, we're actually testing, um, a prototype of, uh, the, the upper stage of a hypersonic weapon system.

SPEAKER_42: Got it. And so are you, which piece of this puzzle are you building? Are, are you a provider

SPEAKER_06: to other people building the hypersonics? Are you building the full set or TBD? What's, what's the

SPEAKER_41: plan here? We're doing both. So, um, very concretely, we are planning to provide full all up rounds,

SPEAKER_00: uh, especially at the lower end of the cost scale. And, and I view that as absolutely necessary that as a country, we'd be able to do that. Cause one of the kind of key tenants of like American defense is we make the most exquisite systems, but they're very expensive and we tend not to have a lot of them. Um, when you're looking at an adversary like China, um, that's put a lot of effort into manufacturing and their defense defensive capabilities, we need to actually have sufficient quantity, um, to actually deter them. Cause they're, they're not going to be scared of having like a few

SPEAKER_46: very high end missiles. What will these cost? Do you think, what do you think they're going to cost

SPEAKER_00: ballpark? Like for, for our like smallest weapon system, they'll probably be on the order of one third to one quarter of what, uh, a much less capable, but comparable in size system currently costs the U S government. If you compare them like on a capability basis, like what could this weapon

SPEAKER_03: do versus like our weapon? It's probably one 10th of the cost. Okay. So I have a, I'm sorry, a very naive question. Cause I didn't serve in the military and, uh, you know, I've been behind a desk here,

SPEAKER_13: uh, doing podcasts. Why didn't the military industrial complex make, uh, more affordable missiles, more affordable tanks, more affordable planes over the last couple of decades where we saw in

SPEAKER_26: consumer and business technology, uh, a massive decrease in price. All we've seen on the other side is a massive increase in price. Now I'm sure that these providers were adding features,

SPEAKER_52: but we all know, you know, you can buy a smartphone today for, but a hundred, 200, $300 in Android phone, obviously. And you could have capabilities that are literally quite literally a hundred X what somebody had, but 20 years ago. Me, I mean, it might even be a thousand X, uh, you know, based on the camera and the whole, the whole set. I'm kind of getting at why economically, politically systems, what, what's the issue here of this opportunity suddenly emerging

SPEAKER_55: to take 90% out of the cost structure.

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SPEAKER_64: that we used year after year at an affordable price. And we really appreciate your partnership.

SPEAKER_00: Fundamentally, like the incentive structure that the traditional primes have worked under for a very long time and has generally been accepted by the government has not driven them to those types of improvements that you've seen in commercial industry. Like it, if you had, if you had a traditional prime build your cell phone, probably be over a hundred thousand dollars, maybe a million dollars. Only a select few people would have one. And that's good because that's all they would be able to build. Um, it really is like you got cost plus contracting. That's one aspect. There's basically no commercial penalty in terms of like taking a long time or it being expensive because all of the, all of the expenses are paid for. You have essentially a, uh, kind of like peacetime posture where because these systems take a very long time to develop and they are expensive, like the government isn't buying any. And of course, as you know, if you don't, if you're never going to sell, but you know, a handful, of course they are going to end up being more expensive. So like you have to, it's, it's not just like, I don't want to just blame the primes cause it's, it's not a hundred percent on them. Uh, it's really just the kind of traditional aerospace, I'd say market has been very backwards. In fact, like the space market, which I'm very familiar with was like this pre SpaceX on both launch, launch vehicles, satellites, uh, you name it. It's this exact same problems. Uh, there's just been a new entrant that fixed it. And I, I think that in this market, a new entrant can also fix these problems. There there's literally enough margin with different, different techniques

SPEAKER_71: and processes to bring that much cost out. So, uh, if I'm summarizing you correctly,

SPEAKER_25: a paradigm shift has occurred. The, um, old paradigm was this cost plus paradigm and there was very little competition. There was competition, but it was competition from a handful of players, maybe, you know, count them on one hand. And their incentive was to give the government what they wanted how they wanted it, not to look at it and say from first principles, what's the best thing I could

SPEAKER_26: make for the lowest price. Whereas in Silicon Valley, we have a rabid competition with a large customer base. There's only one customer here in the United States. Am I understanding this paradigm

SPEAKER_41: shift correctly? Yes, there's absolutely a paradigm shift. And I'm going to give you one more,

SPEAKER_00: like very concrete example. Like it's just one part of a huge problem, but like, even with what you said, typically, yes, the government writes the requirements and they are buying to like the requirements, the system needs to do this. Um, and you know, the, the major primes are building to that. If you're under a cost plus contract, it's not like, it's not like most folks working that are thinking like, just how can I pump up the price, um, and, you know, stiff the taxpayer. But fundamentally, if a customer asks for something and it's actually very difficult, like it hurts manufacturability, it causes, it's going to increase the cost greatly. If you're planning to sell these things under a firm fixed price model or, or commercially, you will fight to the death that this is a bad idea. And you're going to go literally generate conflict between you and your customer, which is always like uncomfortable. But in a cost plus environment, why do that? I'll just agree to it. Oh, look, oh, the schedule is now longer, it's going to cost three X as much, but this is what you want it. And I, I told you the consequence and you said, do it, but you don't fight. And I think like, that's just like a small example of like

SPEAKER_74: what, what has been part of the problem. It's kind of like, you know, your dad or your grandpa goes

SPEAKER_76: into the, I'm kind of the dad in this now, but you know, your, your grandpa, your dad, like they like to go to a certain restaurant. They like to eat like this New York strip steak. They get their, whatever New York strip steak. They like their martini, like the side of mashed potatoes. And the chef's like, Hey, I want to do something a little more interesting here. And the customer's like, yeah, I like my steak and potatoes and my martini. Thank you very much. You kind of, you're kind of stuck there if you are the provider, because they told you what they want. You kind of hinted at something. Maybe you want to try a seared tuna. They don't want it. They want the steak.

SPEAKER_26: That's what they always knew. And so you're kind of left with having to just build these things here's the capability. Here's the cost. Might this interest you? That's how the industry is

SPEAKER_00: kind of moving now. You think it is? I mean, obviously the space is now venture backable, like we're venture backed. That's relatively new. Like, honestly, Andrew's success unlocked just an enormous potential in the fact that now these types of companies are venture backable.

David Friedberg: And they're standing on the shoulders. I assume you would agree of SpaceX, which showed, you know, Hey, you can, you can have the government as a client and it will work out pretty well. And you can actually make things that kick ass that they didn't ask for that they will eventually buy from you. And all my friends who are SpaceX, venture capital, his investors, founder fund, et cetera, they're feeling pretty good about the investment now. So it's kind of a big

SPEAKER_00: unlock. Yeah. Yeah, totally. And so like the SpaceX thesis, right, there's still like a commercial aspect to it. Andrew is definitely more like defense only, but there was a very big software focus. We're down here, like we're making hypersonic missiles. There's not a obvious, like, you know, what's our SAS subscription software play? There isn't one. And that literally was uninvestable probably like three years ago. And so like this market is now unlocking. And the reason I say that is like, if you are, if you're in this like traditional kind of market with selling to the defense department, everything you do, like if you don't have outside capital that you can go raise,

SPEAKER_13: you have to find funding for it. How long will it take you to go from raising your first venture dollars, you know, outside investment, uh, let's say not your own C capital, but out from

SPEAKER_89: first day for outside capital to first day revenue in from the government. What's the number of months

SPEAKER_92: or years do you think? Oh, no, it was months. It was months in our case. Um, wow. So you picked

SPEAKER_89: something that they really needed. So in six months, nine months, you had, you had money coming in from the government in the bank account. Correct. How is that even possible? I thought the government

SPEAKER_41: moved slow and this took years. What happened? Explain it. I started my career as a, an engineer

SPEAKER_00: working in defense and aerospace and, uh, worked my way up into doing government sales, which is extremely esoteric. Um, and really the reason I did that was I actually wanted to be part of driving. What are we working on? Cause typically as an engineer in aerospace, like you're told what you will work on. Um, and so like, I found like being part of the sales process actually helped, you know, like pick what we are working on, what we're trying to solve. This is like, there's two very

SPEAKER_25: important founder lessons here. Um, and we like to always point them out when we're talking to Chris founders. If you want to be the CEO, eventually the sales team, especially in the early days of a

SPEAKER_26: startup, um, they're the closest to the customer and they really start to understand what the problem set is, what the needs are, what the willingness to pay is, what the quantity is. And you know, that, if you look at just that subset of tasks, it sounds like the CEO's job sounds like the founder's job.

SPEAKER_25: The sales job is the founder's job. Eventually, you know, you have to get a customer and that customer has to be delighted. So this is actually a really interesting lesson here. I think is don't look down on the sales job. If you are a young person and you can get into sales, you know what?

SPEAKER_97: I guarantee you the CEO is going to come talk to you at some point. They're going to be like,

SPEAKER_37: Hey, how's customer X, Y, and Z doing? Yeah. I mean, to your point, it's not, it is sales. And

SPEAKER_00: like, yes, you're trying to make sure that you're getting to a product that you can actually make money from, but sales fundamentally, before you make your sale is really like product development. Like what absolutely building the right thing. Is this what the feedback is? Is this what we should be building? Are we building something no one will buy? And everybody is already telling me there's no way in hell they're going to buy that. Like you are, you're the voice to the rest of the

SPEAKER_93: company. And if you're doing sales correctly, you are doing product work as well.

SPEAKER_26: Yeah. And, and this changes over time, but this is such an astute point. If you look at your first couple of sales executives as product discovery, uh, and, and customer discovery individuals, then you can take in your mind half of their salary and comp and put it towards product development and half towards the sales process. And that will make it easier for you. Now, of course, once you have a product completed, well, then you're doing consultative sales. You're, you're, you're saying, Hey, here's what we have. What are your needs? And you're kind of matching it up and

SPEAKER_06: closing the sale. And then if you really have a great product like SpaceX does now, the third phase

SPEAKER_60: is you're picking up the phone and taking orders. All right. We all know if you're a founder or even if you're on a small business, you're thinking about your company 24, seven, 365 days a year. That's the life of a founder. This is not clock in clock out nine to five gig for you as the business owner. So when you're hiring, you want a partner that's as equally as committed as you are. And that's, of course, LinkedIn jobs. LinkedIn jobs is like your co-founder. They're going to make it so simple for you to post your jobs for free on LinkedIn, where there are 1 billion members. You're going to be able to share what you're posting and actually keep all the promising candidates organized in one place. And also LinkedIn is going to help you quickly write a job and get it in front of the right people, whether you want to post for free or use some promotion to get it in front of even more qualified applicants. So do me a favor. Don't take my word for it. I mean, you should. I know what I'm talking about is where I find my great people, but just understand that 72% of small businesses using

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SPEAKER_52: But this is so important because to our previous discussion about the paradigm shifting, I think what I'm taking from this is a really important insight, which is you can build exactly, the sales team can get in there and build exactly what the customer wants. But then there's also room for creativity and the founder saying, I want to build something that they don't know they need.

SPEAKER_25: So I want to get to China. Everybody is scared about China's, um, low cost, uh, missiles, low cost production and the, and the, the velocity of their production. And the thesis I hear from my friends, you know, in, uh, the deep state and who are in and around it is we have an adversary that can manufacture stuff at a fraction of the cost and at a multiple of the speed. What do you think is going to happen? You, you look at the, um, the field in Ukraine and what that has wrought, which is basically

SPEAKER_52: they're not even fighting with industrial military complex, uh, items. They're zipping drones around with grenades strapped to them. This is a whole different warfare. Um, so maybe your thoughts on

SPEAKER_25: where is China right now? How far is American manufacturing behind them? And how do we close

SPEAKER_00: the gap when in the munitions space, uh, when a lot of the defense manufacturing space, honestly, I, my, they're not ahead. China's not ahead in technology. Um, in most areas, non hypersonics, they are actually ahead. I would view them as ahead of the U S which is a weird place for the U S to be. But in terms of manufacturing of those systems, um, they are generally, uh, doing quite well. It's very hard to win a fight if you are the first side to run out of munitions and weapons. Um, and so like kind of what you're seeing in Ukraine is like, I'm sure they would like to fight further distance from each other and push the other side back. But when you run out of those type of munitions, then you engage closer and what you use to do that changes. What's the path to winning

SPEAKER_13: if they're using quite literally slave labor in some of their factories, um, with the Uyghurs, uh, or absurdly low compensated wages that works six days a week, 12 hours a day. How does the United

SPEAKER_41: States compete with that? We don't have to compete on a dollar per dollar

SPEAKER_00: parity level. Um, we're a very wealthy nation. We can't afford to pay more, but we do need to produce in a quantity and at a, at a sustainable rate that allows you, um, to, you know, basically, you know, atrophy their capabilities. Warfare is always like a back and forth. We do something, they do something to counter it. You have to do something to counter that. You need to just do that process faster than the other guys. And then you need to produce it at a quantity that overmatches anything they have. And that's where we're behind and what we are trying to fix.

SPEAKER_115: So we need more factories and it. Absolutely. It seems to me more factories. They don't have

SPEAKER_00: to get down to the same price as a slave labor, but you, you do need to produce the systems that can counter, uh, Chinese capabilities and at a quantity that matters. Now, if your system is much more capable than theirs, then you don't have to maybe make as many to counter those capabilities. So there's also like that factor that has to be looked at as well. So

SPEAKER_118: it doesn't even have to be exactly equal numbers, but you have to counter like their, uh, potential

SPEAKER_71: to hold you at risk. The good news is we have slave labor coming to the United States. This is

SPEAKER_89: a little known fact, but we have unlimited slave labor coming in the form of robots, uh, figure.

SPEAKER_119: And I know that was like, you're like, where's he going with this one? I was like, oh, dear God,

David Friedberg: dear God, who's the enslaved robots. So automation of factories, obviously that's been a trend in our

SPEAKER_25: lifetime, but having actual robots who can fill in what the, you know, singular arm robots that are fixed, you know, into a conveyor belt, uh, and, and, uh, and a production line that's going to fill in the gap where maybe we're short on humans. It's, it's honestly sad. What quantity do you think

SPEAKER_00: is a large quantity of missiles to order in a year right now? And like in the US, if you're talking about a reasonable size, like long range, what is a large quantity?

SPEAKER_03: Four to five figures, thousands to tens of thousands would be a large quantity of missiles.

SPEAKER_05: Way less. Hundreds? Way less, way less. Dozens?

SPEAKER_00: Um, it, our highest end systems, we're not at the capability of producing dozens per year, but that is like their ultimate eventual output when everything is working. Yes. It's pathetic.

Chamath Palihapitiya: These missiles, the missiles you're building, all due respect, the complexity of building a missile

SPEAKER_96: and a cyber truck, these don't seem, yeah, I mean, I didn't want to say it's a way less complicated

SPEAKER_00: problem than building a car. It's a way less complicated problem than building a satellite. So when you're talking about bringing in like a robotic workforce, we're, we're so far away from like the scale that where that would actually pay back. Like we're talking about, let's just automate some of the task because you're still like a thousand is a very large order.

SPEAKER_25: Okay. Well, since we're going there and we're going to be super candid here in this interview, I like having candid guests. Thank you so much for educating us. Um,

SPEAKER_60: would, what is the skill level? I'm going to try to be, um, delicate here,

SPEAKER_52: the skill level to put together a hypersonic missile. I'm sure you, let's say if you need, you know, X number of people per missile, you know, to, to build a missile or to build 10 missiles a day or whatever it is. I mean, it seems like building 10 missiles in a factory should be pretty easy task. Uh, that's only a 3000 missiles out of your factory a year. So I'm just picking something easy. Let's even dump it a hundred hypersonic missiles a day in a factory. That should be possible. Yeah. 30,000, 40,000 a year from a factory. How many of those people

SPEAKER_39: need to be engineers, college educated? Most of our manufacturing base is going to be

SPEAKER_00: technicians and they do need to be highly skilled in certain areas. Like some of the, some of the things that we're doing are, are, are dangerous if they're not performed, uh, correctly, like when you're dealing with energetics. So you, you do have to have highly specialized training and skills, but it's something that, you know, you can teach to anyone. Um, it doesn't have to be like, they don't have to have a four year degree to understand how, um, to handle this process safely and what makes it unsafe, like don't do this. And really you need, you need, when you're looking at manufacturing at rate, like you're really trying to control, um, your process and quality. And so those procedures and processes, that's not like the technician's job to develop it, but they're an integral part of giving you feedback of like, Hey, this is working, this is not working.

SPEAKER_26: Educated associate's degree down to even high school educated, able to think logically and,

SPEAKER_144: and be thoughtful for an eight, 10 hour shift is enough. They're going to get paid 30, 40,

SPEAKER_37: 50 bucks an hour. What do you think? I don't like, if you don't have a college degree, but you have the right attitude and the ability to learn, I don't care. Like that, who cares?

David Friedberg: This makes me feel a lot better talking to you and really getting into, again, just like first principles, which is just a fancy way of, you know, basically building the model from the bottom

SPEAKER_25: up. Well, we wish you great success. If you were an engineer and you wanted to work at the firm, where we, where would we send the, the amazing engineers, uh, developers, smart people who want to help, uh, build the defenses of the country, uh, so that we can have more peace and prosperity.

SPEAKER_152: Castellian.com and we're also on LinkedIn under Castellian Corp. So please do, uh, find us and

SPEAKER_25: submit a resume. All right. Continued success. And we appreciate the effort you're doing and we do

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SPEAKER_156: we got an awesome twist 500 interview coming up. It's with a company called crunch space. Now I'm sure you've heard of crunch base. It's the well known online database of all things, private markets, startups, venture capitalists, funds, all of that fun stuff. I'm adding them to the twist 500 because they recently released a suite of AI powered features that I think are honestly pretty awesome. I love data. I love charts. I love analytics. This is my jam. And I think it's going to help the company grow and possibly have a big exit. So it's twist 500 material. That said, I used to work there a couple of years back in the day. I helped set up the crunch based news team, a project that I'm still very, very proud of, but because I was an employee, I didn't have options, which I partially exercise. So now I actually own stock in the company. Normally I try to avoid any sort of conflict of interest because I don't like them as a journalist, but as Jason says, no conflict, no interest. So I figured let's just be honest, let's add them to the twist 500. And so in that vein, I have Jagger McConnell, the CEO of crunch base and my former boss Jagger. Hey, how you doing? How's it going? Good to see you. So, I mean, it's, first of all, it's been, it's been a little while. Um, and since, you know, we were in touch crunch base has watched the AI wave explode. And when I think about crunch base, this, you know, amazing repository of private market information and the importance in AI of having proprietary data, it seems like a match made in heaven. So can you just take us back to when

SPEAKER_163: country race was like, Oh, we're going to go in this direction. Sure. I, and I remember very clearly because I was using chat to PT and I was like, Oh, this is so cool. And everyone had a sort of like

SPEAKER_164: moment of like, this is going to change everything. And I was like, Oh, this is going to change everything. Um, and the, the, the challenge was, I said, well, we're great. We're a data company. And everyone's saying, Oh, congratulations. Data is the new oil, all of this. I was like, yeah, kinda, except once our data goes into the LLM, it's going to never come out again. And then, and then the oil is being made somewhere else that we're not a company anymore. So it actually felt like an existential threat to me where I said, well, we can't just rely on historical data now, like historical data. In fact, and this isn't just for crunch base, every company in the world that relies on historical data or had to mix a business model off of it is now completely host, in my opinion. Um, and the reason is because once it goes in, you're done. And the insights that those that AI is going to do on top of the, um, the, the data is going to be way better than anything you're going to be able to do as a company. So what do you do about it? So that was where, that was the wait, what do we do? And I was like, well, there is data that we have that no one else has. And can we use that somehow? Those are two things. It's every edit that's ever been made in crunch space overall time, right? You can't crawl your way there and find that. And then the other piece is, um, do you have our engagement data, right? What can I do with engagement data anonymized to sort of figure out what's going to happen, um,

SPEAKER_167: next in crunch space? So can you go and start?

SPEAKER_156: So historical data is looking back. It's knowing that crunch space raised 106 million dollars that it's history. It's everything that's kind of in the rear view mirror. But your point is that crunch space knows so much about how people have interacted with that data that it provides almost like meta context on top of the raw information itself. That's right. Like if you, if you go and

SPEAKER_164: look at a profile now, you'll see the history of the company, but what you don't see is what, how does profile change over the last 17 years to get to where it is? And then how, how's traffic slow changed on that profile of time? Are there more investors or less investors looking at this profile now than it was before, um, or new corp dev people or recruiters, whatever happens to be, or is the entrepreneur engaging with these profiles more or not? All of that is this engagement data and historical edit data, um, that when we looked at it and we, again, said, well, the whole this unstructured data, what do we do? We just pumped it into ChatGPT and sort of some of these, uh, open source ML models. Can we figure out that it can predict patterns? And the answer was a hard yes,

SPEAKER_156: which is great. So this really feels like a transition at the company away from, we have a ton of data. You can give us some money to access it, pull it out via the API, do whatever you want to do with it too. We are now going to, instead of offering access to the data, offer intelligence that the data, um, gives rise to, thanks to ML models put on top of it. Is that

SPEAKER_164: fair? That's a hundred percent correct. In a way like ChatGPT is, is, is like this. It took all the historical data of the public web and it's not trying to predict the next word that makes sense. Right. And we're doing the same sort of thing in a smaller context of Crunchbase saying all the data we have unstructured, can we go and figure out what is going to happen next in a company that, that, that first prediction of whether it be funding or acquisition or whatever it happens to be.

SPEAKER_156: So how many different things are you predicting now with the new system you've put in place,

SPEAKER_158: which came out earlier this week, I believe actually. And, and frankly, Jagger, how accurate

David Sacks: are you in these predictions thus far? Yeah, it's, it's, it's, the first question is easy. The second one is harder to answer. Uh, the first one we did 18 insights and predictions.

SPEAKER_164: Um, so it's, it said we're shifting the focus of, it's not just historical now, it's what's happening now at the company and what's going to happen. And that's insights and predictions by definition on the predictions side, you've got funding prediction acquisition prediction. Um, are they going to close? Is the business going to close? Uh, are they going to do layoffs? Are they growing? Um, so there's a number of these different angles and some of these things we're not going to put onto the website. Like you're not going to go and see, like, are they going to do a layoff? But that is an interesting prediction, um, that, that we're using more on the risk side, which is more on the API side. So if you're an API customer, you might have access to like,

SPEAKER_156: the riskiness of companies. Uh, but do you know, as a person who has made most of his money working for other people, I think I would love to know if a major layoff was coming as a signal. So are you not putting that one in public just in case you, you accidentally worry people about their

SPEAKER_164: employment prospects? Yeah. I mean, the, the reality is, it's like none of these predictions are gonna be a hundred percent perfect. And that's when you get to the accuracy conversation in a second, but they're, but some of them are not perfect and we don't want to unnecessarily worry people when we say, Hey, it's a 60% chance of, of a layoff. Like, wait, what does that mean exactly? Um, but for some of our customers, um, you know, if you're thinking about like, know your customer, that increasing risk for like a credit score, it kind of says,

SPEAKER_171: well, maybe I should, um, take a pause and maybe do a little bit more due diligence here

SPEAKER_186: to dig in deeper. Um, and that's more the intent of it.

SPEAKER_156: So these are much more directional versus absolute predictions about what the company's going to do. 60% chance of layoffs is useful if you're selling per seat SaaS. And you might think, oh, they're reducing headcount, not a great place to go do a sales call, but it's not like, oh, I have an 84% chance of being laid off in the next 74 days.

SPEAKER_189: Yes, that's correct. That instruction, when you add that time scale to any of these predictions, I guess very tricky. So our fundraising prediction, um, we have an incredibly high level of precision and recall on whether or not a funny round is going to occur. When you say, well, when is it going to occur? Now it's a different, that's a different question. If you're saying, well, Jagger tomorrow, what funny rounds are going to happen in my precision and recall go very close to zero. But what's interesting is it's way closer to 100% than you guessing on your own,

SPEAKER_190: because we've got more signal than you do. It's still a very, very small number, right?

SPEAKER_156: So, you know, people that I talk to still sometimes have kind of an old Crunchbase model in their heads, and I don't know how long it's going to take to educate people that it's been growing and changing and improving for a long time now. Thanks, by the way, I use Crunchbase all the time. But if you think of Crunchbase as a wiki back in the day, it's cool because everyone got to participate. And it sounds like the new Crunchbase with AI technologies is still predicated in a way on how people show up, access and interact with the data. So it's still, in a way,

SPEAKER_193: community powered is my read of the situation.

David Sacks: Yeah, certainly the 80 million people using Crunchbase is an important part of how Crunchbase

SPEAKER_189: works. But yeah, one of my biggest frustrations is when I go and say, you know, someone says, oh, I know exactly how questions work. Why don't you tell me? They're like, well, it's a wiki. And I'm

SPEAKER_156: like, no, well, it used to be in 2014. That's 11 years ago now. That's right. But 2014 is a useful data point because you joined in 2015 and took over as CEO when it was spun out as a private company. For those folks who don't know, Emergence, Mayfield, Omerz. And then most recently, Jagger, you raised from, as I scroll through my notes frantically, you can build me alignment growth. Yeah, there you go. I had arrangement growth in my head and I'm like, I know that's not correct. Yeah. But I bring that up because that's a lot of capital. You guys have raised 106.5 million according to crunchbase.com. What did that last $50 million unlock for the company? Because that

SPEAKER_158: was in 2022. So in and around the point when everyone started to talk more about AI.

SPEAKER_207: Yeah, that's right. Honestly, the market shifted pretty dramatically at the end of that year. You might remember a lot of the data companies out there took a beating as prospecting became less important to the sales prospectors of the world. So we also took that opportunity to do this pivot. So, so chat to get to came out. This is when we had this sort of aha moment. And we said, let's not blow all this capital just going and trying to sell. Let's go and actually build something that is very materially different than what's in the market today. So that's why we went and started focusing on this new pivot towards predicting the future rather than just doing better

SPEAKER_156: historical data. A lot of people are using proprietary closed source models. And I was just kind of curious what, what did crunchbase pick as it's kind of like model paradigm? Are you guys using, you know, Meta's llama models, or have you built something internally? What's the underlying brain

SPEAKER_164: for all the new AI stuff? Yeah, I mean, it's a combination of a bunch of tech, honestly. And some of it is our own stuff. We use open source, like TensorFlow to go and do a lot of the ML side,

SPEAKER_189: we certainly are using open AI to go and handle a lot of the unstructured data. And just in our

SPEAKER_207: testing, it seemed like the best. And but we're still to the way that we can kind of move in and out, depending on what's what's better, and what's cheaper, honestly, so sort of combination. So like,

SPEAKER_189: when DeepSync came out, we're certainly like, that's interesting. They're like, maybe not. So an ongoing conversation. But the nice thing is, I think every every software and data company that uses

SPEAKER_217: the stuff needs to be thinking about how to how to make it interchangeable. So you can sort of go

SPEAKER_183: where the wind blows if something becomes better. Yeah, I think the phrase is model agnostic.

SPEAKER_156: I'm sorry. Yeah. Okay. Does AI reigning compute that whole kind of bucket of cost? Does that now take up a much larger portion of Crunchbase's OpEx than like the old AWS bills back in the day did? I'm trying to get a handle for like, what is it like to change your company towards kind of an AI first model? And how does that shake up your profit and loss

SPEAKER_207: statement, frankly? Yeah, it absolutely does. There's a short answer. Certainly, it's a huge line item that didn't exist two years ago. And we're watching the people using our Crunchbase

SPEAKER_228: Scout today to understand how much that bill might be going up. So it's an interesting pricing time. And

SPEAKER_163: how do we think about passing those prices on to our customers ultimately, right? Because at the end

SPEAKER_207: of the day, I can't just go negative and become an unprofitable company again. So it's how do we balance the costs? And obviously, as we see the prices going down in the sort of LLM world, that also helps

SPEAKER_175: us out. So there's a lot of weight to see right now in terms of numbers, but it is hard to sort of plan for. Certainly, I think that's a big challenge for CFOs. Well, it's kind of cool because I feel like right

SPEAKER_156: now, if you build something that uses modern AI techniques that is too expensive, you should do it because in 12 months, it will cost 10% as much. And if you capture that market, you can I can see a reason for the cash burn in many circumstances. This is not 2021. People are not just taking bricks of cash and heaving them out of windows. So right.

SPEAKER_236: And as long as it's like, the challenge though, again, is like, you can do that, but then you're

SPEAKER_189: completely vulnerable to someone saying go make the exact same thing to an AI agent that goes and builds it later. So so again, at the end of the day, like, and what is the most valuable thing? It's the data no one can have access to. Even if you have historical data behind a paywall, I think they're still vulnerable, because I think there's gonna be AI in the future that goes and social engineers its way in and gets its data. Like we go and say, AI go and have all the data there is in the world.

SPEAKER_242: And it says that at any cost, like that AI is going to go and make phone calls.

SPEAKER_156: It's going to create six different email addresses, rotate its IPs, use three different agents from four different models and six different companies to go and it's going to get around it. I wasn't going to bring up copyright, but I mean, I think it does play here. I know Crunchbase does some work to prevent scraping if memory serves, but I presume that that's gone, you know, a hundred X in the AI era. So how have you guys managed to defend the fort, if you will, and have

SPEAKER_183: AI companies come to you and said Jagger, we'll give you, you know, 20 million a year for the whole data set up, we can just ingest it into our model. Yeah, we certainly have had some of those sorts of

SPEAKER_164: offers. Like we are, we are avoiding those. We, of course, do our best to block crawlers. We use a

SPEAKER_207: lot of tools to do this. Like Primer X is probably one of the best known where they're going in and blocking and that's our whole core competency, but they're still not a hundred percent. And that is why you need to be careful about what data you put out onto the internet. I will never put engagement

SPEAKER_189: data obviously onto the, onto the internet. So no one will have access to that. But we, if we are using forward looking stuff, you can call that all day long. Predictions change every moment. So you'd

SPEAKER_207: not be calling us constantly. And there's, and, and at least today, there's no way to like simultaneously crawl every single page that Crunchbase has and pull it all down simultaneously. Like our servers can handle that anyway. That's a DDoS attack. Yeah. So we, exactly. So we've got,

SPEAKER_189: we've got plenty of protection there in the sense that it isn't technically possible. Um, so the, the,

SPEAKER_207: the, the predictions are so dynamic is so live that we think we're safe there. Okay. So there's kind of

SPEAKER_156: three major things that Crunchbase just rolled out. We've talked a little bit about the predictive company profiles, essentially here's the company, here's the information. And then here's what Crunchbase thinks is going to happen next. And here's kind of the state of the business. There's also a private market homepage. Now I've gotten to play with this a little bit, but for folks who haven't seen

SPEAKER_162: it Jagger, can you just tell them what that is? Yeah. So basically the idea here is, is there's a lot

SPEAKER_164: going on in crunchface that you don't know about, right? You, you, where now we depend on you doing a search or looking at a specific company, you're totally missing other things that you might really care about. So let's make a homepage that essentially has a feed of all the cool stuff that's happening, whether that be predictions, insights, what's trending, all that's available now. And, and the part that I actually like more is the for you section where you can go and specify, these are the industries I care about. And these are the predictions and insights that I care about. So if you say, Hey, I want to know every time a key hire gets made at a certain type of company,

SPEAKER_189: you can go and do that. And it will just pop up on your feed or a new prediction for funding,

SPEAKER_207: whatever happens to be. And you can click into it, see some details and see the details

SPEAKER_156: that are driving that below it. That feels very much like an analog to CNBC to me, but it's slightly different data. Cause you know, private, private markets are real time, live, everyone can see the information, but I go to CNBC to tell me, what do I need to know from this massive ocean of data? And it feels like in a machine learning context, you're doing that with crunch space now with this new homepage. Yeah, I think that's right. Internally, what we're

SPEAKER_163: saying is like, what's the TikTok equivalent for crunch, you know, like how can we create a sort of stream of interesting stuff? And that's like the dream. I don't think we're there yet to speak

SPEAKER_257: clear, but maybe someday. So I'm going to see you doing a dance on my, my private market homepage. I see. Okay. AI generated version of me. That's right.

SPEAKER_262: So that way you can stay on beat. There's another thing though. You mentioned it earlier,

SPEAKER_156: but I haven't gotten to touch on it in particular. It's a crunch based scout, which is from my experience, kind of like, um, an AI agent that I send forth to do tasks. And I presume you've stuck with the traditional crunch based dog branding because it fetches things and brings them back.

SPEAKER_164: It's your, your associate that can help you do things. It's going to get better and better over

SPEAKER_250: time. Right now you can say things like make a chart, um, comparing funding of these two different companies. It'll go and do that for you. Or the other cool thing about it, it has all the information from the public web as well. So if you want to merge those two things together, um, what companies may

SPEAKER_207: be affected by policy changes? Um, you know, like those sorts of things can start to happen. It can go reach out, do its own searches against the news and then integrate that with the crunch based data

SPEAKER_263: that it has access to. And I think there's, um, a lot of opportunity hiding in there.

SPEAKER_156: Everything you just described is kind of what I expected the words to be that came out of your mouth. But to me, just a black box behind the scenes in terms of how many different technologies had to be put together to make that happen. Did it take a long time to get the first version of it that was, that felt right? Like, I I'm just curious, cause you're a product guy. So I'm kind of curious, like the process to get this from like, okay, we're going to do this too. Now it's good enough

SPEAKER_189: that we can begin testing. It's probably one of the most complicated parts of the other stuff that we've launched, um, to make it feel right. Um, it's, there's a lot of technology involved. And, and if you think about it, it's like, how do you scope the conversation to the stuff that is in crunch based? I I don't want people asking who is our favorite baseball team and having like strong answers there. So, uh, so how do we scope it to crunch space? How do we give it access to the crunch space? So to merge those two things together, um, is a very complicated problem. Um, and how do you

SPEAKER_207: know when to use the public web and when not to use the public web? Um, and, and, and you can't rely on AI just to figure that out for you. So you have to sort of put those rules in place around it. Um, what, what do you give it access to? What do you not give it access to? Uh, and then even like,

SPEAKER_175: there's a cost involved, right? Every time someone is asking a question, it's costing crunch

SPEAKER_272: space dollars. Um, so wait, wait, not, not dollars. I mean, pennies and dollars.

SPEAKER_274: I was like, dude, I did a lot of testing. Should I send you a check?

SPEAKER_278: Like, uh, but it is maybe more expensive than you think it is. So, so it is, it is a, um,

SPEAKER_189: sure. How do we, how do we make that efficient and still keep the user experience good? Um, it's something that we're, we're thinking about. And of course, um, now that it's live,

SPEAKER_207: you know, like we're learning every minute about how people are using it, what's not working right. And we're just saying, make it better. And that refinement is actually where the most

SPEAKER_156: amount of work is going to be put in. Going back in time. So the round from, uh, 2022, the series D you guys were talking about having, you know, over 60,000 customers, uh, thousands of S and Bs. And you had dropped some really interesting notes about how the company was performing. Um, saying that in the first, uh, half of 2022, I believe you'd added like 9 million in net new ARR with only $2 million in burn earlier in this conversation, you said you don't want to become unprofitable. So how has growth been? And it does seem quite a lot. Like you shot for, we're going to get in the black because the market's uncertain. So can you just fill me

SPEAKER_233: in with like, um, I don't know what the last couple of years of financial progress?

David Sacks: Yeah, sure. So, so again, we got hit, um, with that, uh, sort of pain stick at the end of 2022 as

SPEAKER_189: well. Um, growth slowed down pretty, pretty quickly. And because we were, we were really pushing hard and selling to sales prospecting. Like that was really our focus at the time.

SPEAKER_207: Um, in 2023, we made a hard decision to do some layoffs. Um, and there, and we, what we did was we pulled back on go to market. We said, look, we need to go build AI. We need to go and build this new direction. That's going to take dollars. We don't have the money to do everything simultaneously. So we have to go and pull back and go to market. So, um, we did, did that layoff in the middle of 2023. Um, and then in, uh, and, and so from there, our growth slowed down. Um, and it was actually a business decision to do that. So we said, look, growth isn't the thing that's important right now. The thing that's important is this tech. You've got to be profitable while you're in that mode. Like if you're going to make that cut,

SPEAKER_284: you can't just go and burn the cash until you build the thing. Um, so if, if slow growth,

SPEAKER_156: then profit and then faster growth later. Oh, okay. That doesn't sound unreasonable. It's not

SPEAKER_163: unreasonable. Um, so with this launch, we certainly are, are, are burning more, right? We want to make

SPEAKER_189: sure there's a big splash. We want to make sure that everyone has seen, um, and is aware of this

SPEAKER_207: big change for crunch space. Um, and we now are reinvesting in growth again. So, but we're doing it in a more measured way. Um, so that we can make sure that we don't get ahead of our scheme. So we're going to try to stay as close to even a positive as we can. Um, and, and as we dip down, um, and then, uh, later this, uh, year where we should hopefully fly right back, uh, to where we were.

SPEAKER_180: I, I'm putting you on the spot just a little bit coming off of some time when you were not focused on

SPEAKER_156: growth. How fast can you hope to reignite that for the company this year? Is that like a, we're going to go 15% or is that like, fuck it, we're going to go 50% this year and go hog wild. I just don't have a good vibe for like, what does a startup that's going back into growth set for expectations that will be aggressive, but possible. Yeah. And, and it is a fantastic, especially trying

SPEAKER_250: to predict that and put that in a model for 2025, it gets very, very challenging. Um, our prediction model isn't that good yet, unfortunately. Um, but the, the, the good news is that, you know, we, we're, we feel pretty comfortable that we're going to have double digit growth. The question is, is that's a w like, which part, which end of that scale, you know, it's a wide range. 11 or 99. That's right. That's right. But we're, we're feeling pretty comfortable that we can reinvigorate growth. Like we've got plenty of leads. Um, the question is, is how, how much will people pay for predictions that they've never seen before and that are better than they, they, they probably

SPEAKER_207: believe it to be, um, with, with the precision of recall numbers that we have, it's, it's, it's a little, like, it's hard to get your head around how good we're at. Like, how do we even do this? You know? And that's, so we spend a lot of time explaining even how we do the forecasts or the

SPEAKER_208: predictions so people can understand and start to believe a little bit that maybe we are onto something

SPEAKER_303: here. Well, it's a little bit more complicated than the old crunch based score, which back in the

SPEAKER_233: day, the algorithm was like, I mean, let's call it basic, you know, basic. And that it wasn't,

SPEAKER_156: it wasn't trying to be more, it was a very basic system that was stood up and worked for a while. But I mean, it was easy. Cause you're like, it's three things like, oh, whatever it was, versus now we're applying much more complicated AI tools to a growing dataset.

SPEAKER_189: There's thousands of feature vectors that we're looking at across a company to go and make predictions or there's, we're thinking about, you know, how does investor flow code or profile, right? And, and, and how recent is that from an edit that entrepreneurs made? And then is it about time for them to go and raise funding? Do the entrepreneurs look back at those same investors? They both searching for each other and finding each other on the platform, like all of that is just on the funding, funding predictions and the acquisition predictions. And there's a lot of power that's hiding here to make the accuracy or really the precision

SPEAKER_207: of recall of, of, of, and the funding example, 95%, 99%, um, just it, it, it's just, it's wild to

SPEAKER_310: sort of see, like, and so when we, when we got the results, we're like, no, one's going to believe how good this is. I mean, that's, that's pretty exciting. Well, it's good to have that versus the

SPEAKER_156: opposite, which is no, one's going to believe how bad this is. Oh my Lord. Now, what are we going to

SPEAKER_180: do? One last one before I let you go, Jagger, you know, we're talking a lot about M&A, a lot about

SPEAKER_156: IPOs and crunch base is by definition, a late stage company now. And I'm kind of curious about just how you're thinking about building versus selling versus eventually going public. Like what's, what's the vibe from your view on what's the next kind of like major financial step for

SPEAKER_314: crunch base. Yeah. I mean, I, I go to my country's profile and I refresh the, uh, acquisition prediction,

SPEAKER_189: uh, all the time to sort of see what, what the system's like. Um, you know, it's right now, I think it says we're probable that we're going to get acquired and we're unlikely to go IPO.

SPEAKER_315: No, that's probably accurate. That's probably accurate. Um, so, you know, I think I like,

SPEAKER_317: I'm gonna just leave it to my presentation. I want to do the talking for me at this point.

SPEAKER_259: Um, well, I, I, I, I looked up, I, I don't do it often, but I did log onto my card account and I was

SPEAKER_156: like, yeah, it's still there. So, you know, feel free to pay for my children's private school, because I learned what that costs recently, and I wanted to cry and vomit. So, um, literally wishing you all the best. And with my journalist hat back on, thank you very much for coming on Jagger. I appreciate it. And for folks who want to try all this stuff out, um, where should they

SPEAKER_233: go on the great wild internet? Crunchbase.ai. That's the place. That's the place.

SPEAKER_157: All right, Jagger. Well, I appreciate it, man. I'll talk to you soon. Thank you. And, uh, best of luck this year.

SPEAKER_162: All right. Talk soon. Take care.

SPEAKER_320: Vlad from Robin hood and Raul from superhuman. Is that correct? Producer Matty. Oh, wow.

SPEAKER_25: There it is. We got a, got a yes chef. We have two amazing guests on Wednesday and then news, uh, will be there on Monday and Friday. Have a great weekend, everybody. Bye-bye.