Unpopular Ideas That Became Billion-Dollar Businesses

Nine out of ten people might tell you you're crazy. The tenth might see what you see. Garry, Harj, Jared, and Diana talk about contrarian bets — the ideas that look impossible until they work. From Uber and Coinbase to DoorDash and Flock Safety, they share how founders find opportunity where others see dead ends.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Garry Tan?

  If you only want to work on things that are hot, you're going to find yourself working on derivative ideas that end up being obvious, that end up having five, 10, 100 competitors. It's great for that number one, number two, but guess what? Like, number three through number 98 of all the people in that market, the startups are going to die. Nine out of 10 people might tell you you're stupid or crazy, but then one out of 10 people might be exactly the person who believes what you believe. Run out and try to find things that humans really desperately want and need, and then you'll figure out the rest.

• 0:43Garry Tan?

  Welcome back to another episode of The Light Cone. As Peter Thiel says, competition is for losers. And as we look at all the AI startups we're working with, increasingly, AI competition is back. So how do you actually deal with it? You know, well, I think if we go back to 01 again, what we would say is, we're looking for, how do you think from first principles? How do you actually deal with that competition by being contrarian and right? Harj, how do you think about it?

• 1:14Harj Taggar?

  Yeah, something I've been thinking about recently is, probably just over a year ago, we talked about how we were finding it easier than ever to fund companies that were looking for a startup idea and that they could pivot and find an idea. And it felt like the two causes of that were, one, there was just so much greenfield. Like, AI was new. There were so many verticals to go after that hadn't been picked over yet. And the models themselves were changing. Like, there was such a, there was a step function increase in new models every few months that just caused the idea space to expand. So you could both, there was greenfield, and you could always count on a big change coming that would shake things up and create more ideas. And I think clearly, we've seen the benefit of that.

• 1:56Harj Taggar?

  Like, there are so many vertical AI agent companies that are doing tremendously well. But I kind of feel like the vibe is shifting a little bit now, where when I'm talking to founders, doing off hours, trying to help them find ideas, it's not as easy as, hey, like, go figure out like a A vertical where there's like a workflow to automate like insurance or banking because there's multiple startups in each of these verticals now. And there actually hasn't been like a model that's shaken things up for a while now. And so I think it's becoming more important to think about what's your actual like unique insight? That is going to enable you to find a good idea and what's like the contrarian bet you're going to make. So that you can actually stand out from all of the competition.

• 2:38Garry Tan?

  So how do you actually discover a secret, if you will, you know, something that you know and believe that nobody else believes yet?

• 2:46Harj Taggar?

  Maybe we could do some case studies. Maybe like, why don't we talk about some of the companies we've seen? That made a contrarian bet when they were coming up with their idea initially. And like, maybe that will shake out some things people can learn from.

• 2:57Jared Friedman?

  I think a helpful comparison to the previous technology shifts that we've had in in the world. Like, if you look at the history of when we invented the internet, when we invented the smartphone. Each time there is a, you know, roughly two year window where it was really easy. There is, there is essentially like a, like a modern day gold rush, right? Where like the, a whole new class of startup ideas like just opened up. Everybody rushed in, launched all the obvious ideas. And then, you know, roughly two years after that, the obvious ideas begin to have been picked over. And you have to like, look deeper for a secret.

• 3:35Garry Tan?

  I mean, I think it's deeper than merely like, is it obvious or non-obvious? Non-obvious sounds like in your body might feel like, you know, neutral. But actually, non-obvious feels dangerous and scary. Like, I could devote my life to this, waste 10 years on this and have no outcome. And, you know, I guess the way that manifests, I think, is that there are just like ways of thinking that are out there that we pick up in media or in like sort of the conversations we have with the people around us that we don't examine. So, you know, I was doing office hours recently with someone in the marketing space. And they came to me and said, you know what, like, nobody

• 4:17Garry Tan?

  has ever made a company large enough doing like just what we were doing. The weird thing was, yeah, we have AI now, like this is sort of the perfect moment where no one's doing it. That means there's no competition. In fact, everyone else who has been in the space, you know, prior to the AI revolution has failed. So there are just so many dead bodies stacked up around this idea. How do you know that you're not the right person to go and actually blow this wide open? Especially because there is still this giant new capability. You know, we have age of intelligence, the rocks can talk, they can think, and they can actually do the work of real human beings.

• 5:00Garry Tan?

  You know, you might be one of the only people who has the guts to go into that space and actually apply this. And his numbers are going up. The customers are sort of pulling it out of him already. Sometimes I'll talk to people about his startup and people say, oh, I need that tomorrow. So, you know, this is like the perfect example of, you know, sometimes the market will be giving you indications of product market fit. But then you'll be using these mental models from, you know, are people on X talking about it? Or is TechCrunch talking about it? Or, you know, what will my friends say at a party? They'll say like, oh, you're working on a tar pit idea. So it sort of interacts with this idea that like competition is back and competition is bad.

• 5:43Garry Tan?

  It's that like the obvious things, you know, you're just going to pile up 10, 20, like, you know, some of these spaces seem like they're like five or six, or maybe a dozen, or like two dozen different startups, all of which have a real shot at it. And then now sort of the moment to again focus on the secret again.

• 6:02Harj Taggar?

  Yeah, Jared, to your point, the like new tech platforms create this like two year window. And so we were talking about this earlier, like both mobile, like iPhone launches, Android comes soon after it. In the immediate like year or two before that, like there are a lot of like sort of obvious ideas to go after, like photos, Instagram sort of grew out of that era. But like the actual big winners turn out to be companies like Uber, DoorDash and Instacart.

• 6:27Jared Friedman?

  Those were so non-obvious, like folks who weren't around then don't remember how non-obvious that was. Like when the iPhone came out, there was like a million articles, a million like social media posts about like what kind of companies you could build with the iPhone. And like, I don't think a single person thought that like Uber would be the consequence.

• 6:45Harj Taggar?

  I think the one in this contest is really interested about actually might be DoorDash. Because sort of essentially what we're saying is right now, competition is back. There is more competition to find a good idea, AI idea than ever. DoorDash entered a very crowded space, like food delivery. Food delivery in general had been around for a while. Mobile was probably like a catalyst for even more food delivery apps. So by the time DoorDash launched, you already had Postmates, iterations of it. Actually, I was just talking to-

• 7:12Jared Friedman?

  Grubhub and Seamless were huge companies.

• 7:15Harj Taggar?

  There's actually a YC company, Order Ahead, that Gary and I worked with that was actually doing quite well at the time. Wasn't actually delivery. It was let you pick food up from the restaurant, which at the time-

• 7:26Garry Tan?

  Yeah, just pick up.

• 7:27Harj Taggar?

  It seemed like a bigger market maybe than food delivery at the time.

• 7:29Garry Tan?

  They were farther ahead, like they had way more locations, for instance.

• 7:32Harj Taggar?

  Yeah.

• 7:33Garry Tan?

  I mean, interestingly, like I think it happened even before that with, it was Lyft before and they were called Zimride before. And then funny enough, YC had a company called Ridejoy that was exactly a competitor against Zimride.

• 7:46Jared Friedman?

  And they were like neck and neck.

• 7:47Garry Tan?

  Neck and neck, yeah. And Zimride decided to try to do peer-to-peer local rides. You know, Zimride and Ridejoy were sort of both picking off people from Craigslist. And the idea there was like, oh, I'm driving to LA this weekend. You know, this platform would match you with other people and help you get, you know, a ride to Tahoe from SF or LA. And then suddenly Zimride realized, well, wait a second. Like, what if we actually did this at a much smaller scale? Because we have smart, everyone, you know, 70, 80% of people out there started having smartphones. So instead of having this like long, drowned out email back and forth to like meet at this time.

• 8:29Garry Tan?

  So we could get a ride and then, you know, I'll pay up for gas money. It became something that could be used every single day for short haul rides. And that was like sort of the first moment that people thought, well, maybe you could have a lot of people, more or less like a mobile workforce, entirely driven by the phone. And then I think that's, that happened like sort of contemporaneously to Uber with their black cars. But the funny thing was, and- Maybe this rhymes with the often the office hours that we have to do all the time. It's like, I remember a meeting with the Ritejoy guys and I was like, hey, this Zimrite thing seems like it's working pretty well.

• 9:06Garry Tan?

  We're seeing a lot of other companies that, you know, I think we had just met a poor Vameta at Instacart and he was doing it for grocery delivery. Basically, the market was sort of pulling it out of all of these startups. And that was exactly the moment where interesting, interestingly, I think those founders said, we're worried about the laws. It seems illegal and we don't want to do anything illegal.

• 9:30Jared Friedman?

  And they weren't wrong. I remember talking to the Lyft founders the week before they'd launched Lyft. And they were extremely worried that they would go to jail and they decided to like roll the dice and launch this thing anyway. I think a big reason why other people didn't launch Lyft and Uber before was utterly that. It's like it was basically illegal to do that and they were worried that they would go to jail.

• 9:48Garry Tan?

  What's funny now is it does seem like the world will sort of change the laws, as it were. If it turns out that the end user, the end consumer wins by that much.

• 9:58Jared Friedman?

  Totally. I mean, a lot of great startup ideas are sort of in this gray area of like, the law is not totally clear. It's a little bit murky, whether it's legal or illegal, even OpenAI is like that, right? I mean, they they crawled the entire web without permission. You could argue that that's fair use, or you could argue it's like massive copyright theft.

• 10:16Garry Tan?

  I guess all of these sort of buttress this idea that like non-obvious is not merely like, you know, purely intellectually, you know, not clear that this is going to work. It actually is a little bit more subtle than that. It's like, oh, I feel like that's might be a little dangerous, like there might be something that, you know, I don't feel comfortable doing. And then really, really great founders sort of sense that as actually a signal.

• 10:39Jared Friedman?

  Coinbase was like this as well, where the Coinbase folks, like, I mean, that's always operated in a gray area of legality.

• 10:47Harj Taggar?

  Coinbase to me feels a little bit less, I don't feel like crypto was well understood enough for it to be clearly illegal. Actually, they actually couldn't take that approach because they actually needed to get a banking partner in order to even be like have the service launched.

• 11:02Garry Tan?

  I would say that Brian started off. Recognizing that a lot of the use cases may be way at the beginning part, you had cypherpunks, like people who were like radically into libertarianism and this idea that you should not have centralized banking. But they took it to an extreme where they wanted totally anonymous identities. I mean, it was more, Bitcoin really very early on was a lot more Silk Road than what it is today. So I actually think that Brian Armstrong early was like the exact opposite of, I mean, he was contrarian in another way. Like if you hung out with a lot of people who were really, really into Bitcoin in 2010, 2011, 2012,

• 11:45Garry Tan?

  the majority of people you ran across were cypherpunks who said, F the state, you know, F the laws and we're going to have sort of this radical freedom through Bitcoin.

• 11:57Jared Friedman?

  And there's Brian Armstrong like doing deals with banking companies and working with regulators.

• 12:04Harj Taggar?

  That's what he's contrarian bet was. It was that it's worth doing all of this like extra work at a, for a time where it wasn't clear the market even wanted it. Like it was very clear the cypherpunks and like Silk Road all wanted like crypto and anonymous payments. It wasn't clear that regular people would. And so why, it didn't seem like a valuable thing at the time to go and talk to a bank and do a partnership and go through all the like KYC and AML laws because like you'd only do that if you thought that regular people would want to trade crypto at some point.

• 12:33Jared Friedman?

  And those things actually make your product worse, right? Like, like, like forcing people to go through KYC like greatly increases the friction.

• 12:38Harj Taggar?

  And beyond that, they're like, they're the exact opposite of what the current market, what, what's like, current market could not have been more enraged about like Brian and Coinbase's approach to crypto at the time.

• 12:49Garry Tan?

  Yeah, I mean, you're just going to run across people who say like, it'll never work. But when markets are brand new and nascent, like, you know, often when you're that early, the thing that passes as You're obvious is clearly wrong, actually. So that might have been like a very profound version of that. Before anyone cancels us for saying that founders might get something out of doing illegal things. I actually don't think that people should by default go out and try to do illegal things. Like, I think the through line is not that you should do illegal things, or it's that you should think about from first principles, what are the things that markets and people need?

• 13:31Jared Friedman?

  Totally.

• 13:32Garry Tan?

  And then I think, basically, in the case of UberX and Lyft, it became very clear within the first few months of that coming out, especially in San Francisco, where literally we have some of the worst taxi cab infrastructure and really difficult transit. I mean, basically, that came out of necessity. And then the quality of life inside San Francisco went from here to here the second that you could actually just freely move around very easily without the uncertainty of like, call a taxi cab and they would just literally not show up.

• 14:08Jared Friedman?

  Not show up like half the time, I know.

• 14:09Garry Tan?

  And then it's like, how do you actually live in a city and get around? I think that these services literally made San Francisco 10x more livable in a lot of ways. So I think it's like, don't just run out and do illegal things. That's actually clearly net bad.

• 14:23Jared Friedman?

  No, of course.

• 14:24Garry Tan?

  It's more, what are things that people, users, want? And then thinking from first principles also does actually involve thinking about, well, what are the downsides, right? And how do we mitigate those things?

• 14:35Jared Friedman?

  I think the through line is that the laws were dumb and didn't make sense. And if you thought about it from first principles, you'd realize that these laws were actually holding, that these particular laws were actually holding back society rather than helping it because they were written in an era before the smartphone, when illegal taxi services were actually kind of a scourge of society because you'd have random people driving around. Like claiming to be a taxi, but then they'd like kidnap people. And like, there was no accountability and like tracking. But now that everyone is in like a smartphone based system, there actually is accountability and tracking. And Uber is like, is actually able to operate it safely. And the taxi medallion system that like ruled the country just didn't make any sense anymore in a smartphone era.

• 15:15Harj Taggar?

  Yeah, I think that's the key point. The key point is, yeah, don't go out and do things that the law explicitly says you cannot do. But finding laws that were written in a time before some big tech shift that changes everything and just don't reflect reality can be really valuable. I mean, again, going back to crypto, crypto is a prime example of this.

• 15:32Jared Friedman?

  Exactly that.

• 15:33Harj Taggar?

  You have lots of securities laws that were really well intentioned and designed to like protect consumers from scams and and buying things I didn't understand. And so like a big part of the SEC rule is like, you have to break things up into like brokers and intermediaries and clearinghouses. But a lot of the rules in the crypto world just don't make sense. Like, and so they almost have to be like rewritten while there's a lot of legislation going through now. But I think that's if you can find things where like the laws don't really cover the world as it exists today and you're brave enough to see what happens if you try, that can be

• 16:05Garry Tan?

  Yeah, the reality is like, actually, like, that's the role of government. Like, this is why we have a legislature. You know, one of the things we're trying to fight for right now is, for instance, open banking. So, you know, I think all of us sitting here and most of the people watching, I think, would take things like Plaid for granted, like, of course I want Plaid, like, you know, that's my data, that's my money. And so, you know, what do you mean, like, a giant bank has the right to charge exorbitant fees to get access to that data? But, you know, that's being adjudicated right now. Like, the Trump administration actually has to make some decisions and is an open comment period right now around whether or not, you know, a giant bank can prevent tiny startups like, you know, Y C startups

• 16:49Garry Tan?

  or the startups that people are going to start here. Can they charge a crazy amount or use their terms of service to actually block access to this stuff? And so my hope is that, you know, a functioning government will sort of think about all these things. You know, what the banks might be saying in order to get regulatory capture right now is That, oh, it's not safe. We're thinking about the safety of the consumer. But when you really dig into it, it's like they just don't want people to be able to switch off of their banks. Yeah. You know, they don't want the money to go to a lower fee place like this is their moat, right? Regulatory capture absolutely is one. And then the thing that is cool about a modern democracy. I think people would debate whether we have that on the one hand.

• 17:32Garry Tan?

  On the other hand, like, you know, I think on in individual cases, that might be, you know, the wrong thing happens here and there. But in aggregate, like over a long enough time period, if you can get your product and your service into the hands of enough people. You know, we do still live in a democratic society where, you know, representatives will vote and will like change the laws. And that's very much good news. So first principles plus democracy equals open markets and freedom, which like, well, that's what we're fighting for, but it does happen.

• 18:04Diana Hu?

  I guess one different question is, what are some of the current contrarian things like founders should be looking at right now? Since what we're saying is there's more competition now as opposed to a year ago. There hasn't been, to your point, harsh, a big model improvement. Oh, one preview has been all about a year or so. That was the big stepping function for model capabilities. And there hasn't been anything thus far. I mean, there's been incremental things with oh, three and all that, but nothing like that. That makes it harder for startups to find new, new ideas. What are the new things that are also in this gray area that founders can look into?

• 18:46Harj Taggar?

  Potentially one framework for finding them at least is not like a prescription for exactly what is to look at. What are like emerging playbooks for ways to build startups? That might be wrong, or it might be time to flip them. But to give you a concrete example, like DoorDash, I think was a prime example of this because when DoorDash started, there was actually another Y C company Spoon Rocket. Doing food delivery where they would actually like cook the food in these kitchens that were spread around the city.

• 19:13Garry Tan?

  Yeah, they operated ghost kitchens all over S F.

• 19:14Harj Taggar?

  Yeah, Sprig was another. Right. And actually, in that moment, those companies, I think, came out of this meme of like a full stack startup. There was this period where we're seen as just building software is not ambitious enough. And like the big opportunities are going to be in going full stack. Like, don't just build a food delivery app, actually have the kitchen and make the food. And that's where all the opportunities were. And I think that was becoming more established as like the playbook around 2014-ish era. And so in a way that like DoorDash is contrarian bet was actually say, we're just gonna do deliver. We're just actually gonna have like an app and a marketplace. And we're not gonna try to be a full stack startup, which was obviously the right bet in hindsight. And so what might be some of the the sort of consensus playbooks for building AI companies that have emerged over the last year?

• 19:58Harj Taggar?

  That like might be wrong, might be worth taking the other side of.

• 20:02Diana Hu?

  I guess what I can think of this has come up. There's the compound startup notion that Parker Conrad from Rippling really made it popular. I think it hasn't been as adopted. I think people try, but it's actually in practice really hard. I think it's one of those that for certain AI startups, it is actually possible to execute on it and not have to wait two years to ship the product, which is what I think Rippling roughly took for the first version. Maybe an example of a startup is Campfire. This is a Y C company that's building basically a AI native version for CFOs to compete versus NetSuite. And it turns out that NetSuite is a pretty big piece of software. It's very hard to compete.

• 20:46Diana Hu?

  You can't just do like a kind of a point solution in order to be adopted. So one thing to reevaluate, instead of doing the standard SaaS approach of let's just build the best point solution. This actually built the whole thing, which is not something typically done as an advice that we give for early stage startups, because you just end up not shipping for a long time and not getting customer feedback. Maybe in cases like this is the right answer. And Campfire, they've been closing a lot of now big accounts and it's taken a bit of time to get there, but it seems to be working.

• 21:20Garry Tan?

  Yeah, they're killing NetSuite, which is wild. Like, how can a startup that, you know, has a dozen people kill NetSuite? This is like hard to believe on the one hand. On the other hand, like this is the timeline we're on. So the other thing that's cool about Campfire that we're seeing in a lot of enterprise startups is. You know, with code gen increasingly, you can actually bring the switching cost closer to zero. And that was actually the, you know, that's how you can, how, you know, how can Campfire switch someone off of NetSuite on a timeframe that matters? Like even with the idea of a forward deployed engineer, like before code gen, like it'd be like six weeks of like writing custom like scripts to like convert one data schema to

• 21:59Garry Tan?

  another. And, you know, often very specific, like if, if it's dynamic schema on one end, that's a lot of custom work that's painstaking. And if you get something wrong, like the end result doesn't actually work and then you have a churned customer. So that's a very, like, that's some good news for people, especially doing very complex enterprise projects with like conversion. Like we're seeing more and more examples of enterprise sales that just wouldn't work, or it would be, you know, six months to actually get someone to say yes and sign, and then another six months to get a data, a data conversion or data integration done. If the demos are very good, like that six months could be like two weeks. And then

• 22:40Garry Tan?

  if you can code gen and get very, very good at a suite of tools to convert like data from the schema to yours, you could have like time to value in like less than a month when it used to take a year. So that's like very good news for startups out there that are doing enterprise.

• 22:57Harj Taggar?

  I think forward deployed engineers, we've spoken of a lot, but like that might be one that's worth wondering if that's going to shift back as well. Like if you think like Palantir, obviously was like invented it was an incredibly contrarian thing at the time that they did it. This was sort of like blur the line between consulting and, and software. It's now certainly become the adopted playbook. We talked about it with Bob Magru a few episodes ago.

• 23:16Jared Friedman?

  And he was actually fairly skeptical of it himself, even though he's one of the people who invented it, which is interesting.

• 23:22Harj Taggar?

  That reminds us how so.

• 23:24Jared Friedman?

  Well, he was saying essentially that like, he thinks it's like being greatly overused and he would like. Thinks it should be used very sparingly in very unusual situations as opposed to being like a default playbook, which is interesting.

• 23:35Harj Taggar?

  It has certainly become the default playbook.

• 23:37Jared Friedman?

  Yeah.

• 23:37Harj Taggar?

  It is actually working incredibly well. I think we're seeing like the companies are growing at like aggressive growth rates because they're employing the forward deployed engineer model. But yeah, if I had to, if I had to guess one thing, if I were trying to just purely pick what's like the, like, what's the one thing to be contrarian about, like, judged by just what's become the most entrenched default playbook, it might be the forward deployed engineer approach.

• 23:58Diana Hu?

  Actually, yeah, you have a good company that is flipping it on its head, Arch. There's GigaML.

• 24:05Harj Taggar?

  Yeah, I think Giga is taking the concept of the forward deployed engineer, which is everything you said, that you, what you want to basically transform sort of like your customer schema and business logic into sort of your schema and logic.

• 24:19Garry Tan?

  It's like consulting work, basically.

• 24:20Harj Taggar?

  Right. But instead of a human forward deployed engineer, they, they have, they're just using Cogen to do it, actually. They, they've built their own AI forward deployed engineer. And a big part of the reason they can win deals faster than their competitors is that the human deployed engineer still takes weeks to get the thing going, which is like really fast compared to like historic in enterprise consulting arrangements. But like the AI FDE can do it in minutes. And so I think that's like the kind of the flipping that plate, like, it's not really an FDE at all, really. It's actually just like product. Um, just like your customer inputting in specs and you like deliver them a product instantly. But I think that could be an example of a contrarian bet that will pay off really well.

• 24:59Garry Tan?

  So I'm hearing a request for startup, which is AI for AI FDE.

• 25:04Harj Taggar?

  Maybe.

• 25:07Garry Tan?

  It's just abstractions all the way down.

• 25:10Jared Friedman?

  Flock safety could be a really interesting one to talk about.

• 25:13Garry Tan?

  So the story of flock safety, I remember I was still initialized, we were looking at companies at demo day. And then funny enough, that morning, um, all the cars on my street in Noe Valley were broken into. And, you know, it was a professional crew, they came in and, uh, broke into every car on our street. They took all the bags out the back of the car. And then, you know, my house at the time had like a little alcove that was dark. And so they brought all the Bags into our driveway and then rummage through them all. And I had to cancel my meetings that morning to talk to the police. And I had a Nest camera that was like captured the whole thing. And it's like, you can't do anything like it was a pro crew. Like there were three people. It was like with mechanical like military style precision.

• 25:57Garry Tan?

  And the police basically said, sorry, unless you have a license plate, we can't do anything. And so that made the first principles investment very easy for me because Garrett Langley came in. He was founder from Atlanta. He had a successful exit previously, but this was hardware. So they were selling a camera about this. It basically had a Raspberry Pi with a camera in it and then a solar array. And computer vision had with ImageNet had been around long enough that you could run it at the edge in the device. And solar had gotten progressed just to a point where it was just good enough that you could run these like sort of in perpetuity.

• 26:39Garry Tan?

  And so that was his pitch, like, we're going to go to neighborhood groups, like neighborhood associations. And they already had started, I think, in Piedmont and in the Atlanta, larger, greater Atlanta area. There is a lot of things that from a VC standpoint, like VCs didn't like hardware. They don't like things that sell to potentially small markets. I mean, I think in the investment memo I put together, like I just called that out directly. It was like, well, if you multiply out the number of neighborhood groups and the ACV for those neighborhood groups. There's no way that, I mean, maybe the max was like 50 or 60 million dollars a year or something.

• 27:19Jared Friedman?

  Like the actual TAM of this thing was like only 50 million dollars a year.

• 27:23Garry Tan?

  I mean, speaking as a VC, like maybe the best exhortation I can give to the people listening is like, do not use that. You know, it's merely an indicator. It's like useful to know, but like neither investors nor founders should use that as like cross it off the list, right?

• 27:42Jared Friedman?

  And it was being run out of Atlanta, Georgia.

• 27:44Garry Tan?

  Right.

• 27:44Jared Friedman?

  Right. So it's like three things that make it made it like basically unfundable.

• 27:49Garry Tan?

  Yeah, I mean, that's why I like, I think I was hanging out with Brian Singerman at Founder's Fund for a little bit. And, you know, one of the things we always talk about is the more rules you have about investing, the more ways you can basically talk yourself out of making a lot of money in venture. So I don't know. I mean, to the extent that that is useful for founders, like, I think that that's true. Like, you know, we did a whole episode, last episode, about how, you know, you should be aware of the seven powers and moats, but it'd be the stupidest thing in the world to use that as your only criteria for whether or not you should work on a company. And likewise, you know, I think increasingly, even in our office hours, we find ourselves giving that advice with founders.

• 28:29Garry Tan?

  It's, you know, it is much better to go from first principles, from what does society need? What do users need? You know, what ideas could only happen now? And then the rest, like, you can kind of figure out, right? Like, the same calculus you could do for Coinbase very easily. Like, the, you know, total market for Bitcoin, you know, was not trillions of dollars. It was like, probably on the order of tens to hundreds of millions of dollars at the moment that Coinbase really started thinking and coalescing around that. Some of this is the good news. It's that, like, the people who actually change everything are the founders, actually. And so if you only want to work on things that are hot,

• 29:09Garry Tan?

  you're going to find yourself working on derivative ideas that end up being obvious, that end up having five, 10, 100 competitors. And then, you know, it's great for that number one, number two. But guess what? Like, number three through number 98 of all the people in that market are going to die. Like, their startups are going to die. So, yeah, this is just a little bit of a profound version of that story. I mean, flock safety today solves 10% of all reported crime in the United States.

• 29:39Jared Friedman?

  It's crazy. I feel like that's a generalizable lesson of flock safety, which is anytime if you have some startup idea and you go to talk to a bunch of VCs about it, a lot of them will give you feedback. And I could imagine that if Garrett had gone and talked to a bunch of VCs, they would have told him, like, oh, this isn't VC fundable, selling to local governments, hardware. Like, you should do B2B SaaS. It's cool that he didn't like he ended up in a market where like he had very little competition, perhaps because everybody else like thought it was too weird.

• 30:07Garry Tan?

  I think if you just really razor focused on your customer and the actual need like it just became so obvious. I mean, I literally got a flux safety in front of my house and even though it did not end up catching that crack band of thieves, I felt way safe when I had it in front of my house. And I think that that's what happened with their communities, you know, especially like I remember doing office hours with Garrett and he's like, this is insane. We actually caught, you know, someone who had kidnapped a kid.

• 30:38Jared Friedman?

  Wow.

• 30:38Garry Tan?

  Right. And so when you look at the actual human impact, I mean, going back to what we were saying earlier, it's like, don't run out and try to do illegal things. Run out and try to find things that humans really desperately want and need. And then you'll figure out the rest, honestly. Like if you're just really, really focused on people's problems and how severe those problems are, like, you know, you'll figure out the rest. It's like, you'll figure out the business model, you'll figure out the distribution, like, you know, flux safety later had to figure out that these crimes that they were solving, they would be on the evening news. And all it took was, you know, a media team that would reach out to the evening news anchor and say, hey, by the way, this was solved

• 31:20Garry Tan?

  by flux safety. Here's some video that you can run that's B-roll of like, you know, this, this crime was solved by this, you know, new technology. And then it started spreading virally, like one town would have a crime solved, often like sometimes like pretty intense, like violent crime. And then literally the neighbor, the, the city next door, like the police chief would say like, what is this? I need it right now. And so you can't learn that from like reading blog posts or like going on X and like sort of, or even just like asking chat GPT about it. Like you actually just have to try a bunch of stuff. And then everyone's story is very, very different. But I think Garrett and the flock team are really unique in that respect. It's like, just like first principles in

• 32:05Garry Tan?

  terms of what you're building, first principles and how you get more customers. Principles in terms of what your business model should be. I think that that's just a much better way to think about it. And then the the difference is that you can't sit in your chair, sitting sitting in front of a computer and divine these things per se. You actually have to get out of the house. You actually have to talk to customers. You know, that's where like, I think the goal setting at Y C helps a lot. Right? It wasn't like from a V C meeting. I think Garrett sat and figured out that he needed to change his go to market. You know, I think that he probably looked at basically, how do I work backwards from my growth goal? And then he realized that selling in neighborhood groups wasn't going to be good enough.

• 32:50Garry Tan?

  So one way or another, they really needed to get over to sell to city government, which sounded impossible, but they could definitely do it. And that's that's been like one of the big engines of their growth.

• 33:01Diana Hu?

  And just fast forward, what's their current valuation?

• 33:04Garry Tan?

  Yeah, it's worth seven and a half billion dollars now. And they're making way, way more than sixty million dollars a year. And it actually required a number of business model pivots. But you know, the core of what they built, like, you know, what they built on demo day and what they showed me was exactly, I mean, pretty similar. Like, like the tech itself is the same. What they did have to do is figure out that they couldn't just sell to neighborhood groups. They still sell to neighborhood groups. But what really cracked it for them was selling it to, you know, police departments and actually officially being used for it.

• 33:41Jared Friedman?

  Diana, do you have another example of places to look for contrarian bets?

• 33:44Diana Hu?

  I think one category is sort of the sci fi founder is really going after ideas that most people are scared to build because they're just so freaking hard. And sometimes science and physics are laws may need to be rediscovered to be possible. One example that really comes to mind is OpenAI. It wasn't clear whether AI was going to be a thing when it got when Sam started at a Y C. And it took many years. It seemed to be mostly a tinkering project for researchers that were publishing papers. They were doing kind of side quests. I mean, they had the. Rubik's Cube solver, they were solving Dota.

• 34:29Diana Hu?

  And it was unclear how all these would come together to what OpenAI became today.

• 34:34Jared Friedman?

  And also, something that people forget is that when OpenAI had launched, it got mostly negative press. There were some people, like a small like group of like techno-optimist people who thought it was really cool. But by and large, most people, especially the AI researcher establishment in academia and in other companies, mostly just like were extremely negative on the idea that a bunch of like

• 35:00Garry Tan?

  20 and 30 something.

• 35:01Jared Friedman?

  There's some things, yeah, could create AGI. They're like, you know, we're the experts in this. We've been doing this for like 50 years. If there was a way to do it, we would have already done it. These kids don't know anything. They haven't published any papers yet. There's no peer review here.

• 35:16Garry Tan?

  Yeah, not publishing papers was a really big critique. And, you know, especially around the scaling laws. Like, I think there was this aspect of they're spending how many millions of dollars on GPUs, on projects that will not cause more papers in the world to arise. Papers were like the thing that they were trying to, you know, sort of, you know, paperclip optimize for, which was totally the wrong thing. Like, the thing that really great builders optimize for is like outcomes for customers and users.

• 35:45Jared Friedman?

  It's the same thing when Elon started SpaceX. He wasn't the first billionaire to start a spaceflight company. I think he was like the fifth billionaire to like try to start a spaceflight company. And so all the press was like, oh, look, another billionaire like there to squander his fortune on like rockets.

• 35:58Diana Hu?

  And the whole idea of building reusable rockets was blasphemous, right?

• 36:02Jared Friedman?

  Yeah.

• 36:02Diana Hu?

  I think he went and talked to rocket scientists and they were like, this is not possible.

• 36:08Jared Friedman?

  Yeah.

• 36:09Diana Hu?

  And after many years and many launches, it didn't work out.

• 36:12Jared Friedman?

  Yeah, and then every time a rocket blew up, like there would be another huge wave of negative press. So like for both of those companies, like it required the founders to like stick to their guns in the face of most people telling them that they were like stupid or crazy for like a long time.

• 36:25Garry Tan?

  I mean, nine out of 10 people might tell you you're stupid or crazy, but then one out of 10 people might be exactly the person who believes what you believe. And then you're contrarian and you be Come right because it's necessary to actually attract and be a magnet in the world for all the people who agree with you. I hope you all take a moment to really think about how do you know what is real and correct in the world and reexamine all the sources of these things. And if it's coming from users, coming from your own personal experience, or the experience that of people who you directly talk to, great, like that's probably pretty good, verifiable stuff that you should use as a substrate of your reality.

• 37:06Garry Tan?

  But if you're doom scrolling on X, you're, you know, sort of listening to famous people, like, you know, to be frank, even including us, you know, we're all N equals one. The only people that matter are the people who you care about, who have certain problems, and your ability to solve it, and your ability to attract all the other people who want to solve those problems too. So with that, we'll see you next time.