From Blood Transfusions to Burritos, How Zipline is Automating Delivery | E2238

23:12In the Texas area where you guys have built up the most, what does it cost me to have a delivery executed if I'm inside of a normal service area and I'm doing a normal sized and weighted package? Yeah, right now it's very similar. In the long run, it probably shouldn't seem that surprising that these kinds of technologies are gonna be far less expensive. I mean, using a 4 ,000 pound gas combustion vehicle driven by a human to deliver something to your house that weighs on average five pounds, you do not have to be a physicist to understand like this is actually a really odd way of solving that problem.

23:43And by the way, we do that five and a half billion times every year just for instant deliveries. So just where we're delivering something, using a car, delivering a dinner or delivering a grocery bag to you, again, you do not have to be a genius to understand And actually, the way you want to do that is with if you're delivering something that weighs five pounds and you want to do it fast, you want to do that with a vehicle that weighs about 50 pounds and is electric and is a robot. I think as soon as you understand that, you understand actually something secret and exciting about the future, where the future is going.

24:12And, you know, the reality is those kinds of systems are going to be far more cost effective for customers. so many customers that we talk to who use food delivery or grocery delivery a lot, there is this feeling of like, man, it is really expensive to have a private taxi for your burrito. Yes. I love that meme. Yeah. You've seen the Donnie Darko. I think it's like a Donnie Darko meme. Exactly. There's a guy who's like, inflation is terrible. And the mom's like, inflation is terrible. Or did you order a private taxi for your burrito? It's not that surprising that these kinds of systems wind up being really expensive.

24:44We're having to like put all kinds of different crazy fees, whether it's a regulatory response fee, extended range fee, rush delivery fee, you know, taxes, tip, service fee. Like, you know, at the end of the day, there's a lot, I would say, of like fee exhaustion. And at the end of the day, we want to design technology that can actually price much more transparently, where it's just like actually a good deal for customers, where you get a really high quality experience. So instead of food showing up like 30 minutes later, 45 minutes later, and it's kind of cold and gross and french fries are ice cold and the milkshake is melted or whatever you know something should be able to be delivered in three minutes so the food actually tastes like you're having an in-restaurant experience at home where you're getting something delivered instantaneously if your kid isn't feeling well late at night whatever like fast is much better and you know we ought to be able to price transparently in a way that is much better for our customers so again you know the technology is already comparable to using a car and in the long run, it will be much less expensive.

25:40Is this something that gets like 30 % cheaper each year, or should I have a longer time horizon? No, I think that's about how you should think about it. I think that's about how you should think about it. And by the way, the cost of using humans driving cars is getting more expensive every year because the cost of gasoline, the cost of labor go up. Cost of repairing used cars, cost of purchasing vehicles, et cetera, et cetera, et cetera. Humans want to be paid more money every year, and they should be. I think, you know, in general, it's not great if we have like major parts of the economy that are designed around forcing humans to accept lower and lower wages every year.

26:10Well, I have a lot of thoughts about that, but we only have so much time. I want to stay on money though. I want to talk about what your company needs. There's this really fun picture you guys put up of some of your drones going out and about. And this is a time-lapse photo that shows different lights and different paths that drones are taking. Is this P1 or P2 that I'm looking at here? This is Platform 2. This is P2. So clearly you have to build up physical infra to support the drones. And this is, I presume, charging and maintenance and so forth. So not a capital light business to a degree. I know you guys raised, I think it was$330 million in April of 23.

26:46Do you guys need more money? And as you mentioned earlier, investors and certain business models, how attractive are the capital markets for Zipline today? I mean, Zipline's lucky. We've been able to work with most of the best investors in the world, both on the venture and growth side, but now also even more on the public and crossover side. These investors really believed in us. I mean, you know, some of the investors that participate in that round, you mentioned like Fidelity and Bailey Gifford and Capital and Sequoia, Andreessen Horowitz, many others like these investors have believed in us for a decade.

27:16And I think that I would say, you know, I and ZipLine's board and all of these investors are suddenly looking at like these growth numbers and these, you know, the product market fit. And it's like it is suddenly very clear to us that this is going to be one of the biggest markets on earth and that it is all going to be automated in the next three to five years. And there's just a humongous opportunity to like, it is going to create so much wealth and health in the world to connect humans together more efficiently in the way that Zipline is doing. And so, yeah, it is definitely a very capital intensive business.

27:47We are building this infrastructure out across every part of the US and also across eight plus different countries all over the world. You got to go build ground infrastructure. You got to build service centers. You got to build the hardware. You got to build the factories. But we're really lucky to have a lot of the biggest investors in the world helping us build this infrastructure out. And at the end of the day, I do think it's very obvious that someone's going to build an automated logistic system for Earth. And we think that that is going to be one of the most impactful things for humanity to build over the next five to seven years.

28:16And clearly it's working out. June 2022, Zipline Cited had done 325 ,000 deliveries by August of last year, 1.6 million plus. The most recent number is 2 million plus. Last question. When does it arrive in Providence, Rhode Island? Because dear God, I never want to drive again. So not announcing Providence, but actually we do have a pretty cool thing to announce today, which is that we are just basically in the first Q1 of 2026. So in the coming months, we're going to launch in Houston and in Phoenix. So those will be our second and third major metros in the US. Maybe Providence will be number four, Alex.

28:51That's going the other direction. Don't hold your breath. But look, we do hear, Every single person on Twitter, like in my comments, is always like, when is it going to United City? When is it going to United City? I mean, ZipLine is now accelerating. And so we are going to be launching new metros at an accelerating pace through this year. We'll be announcing more metros probably sometime in Q2. And we will get to the point where we're launching a lot of metros every quarter as quickly as we possibly can. The goal is to get to national scale in the coming couple of years. So how long until we hit 3 million deliveries then?

29:23What's the next marginal million going to take you in terms of time? It's a really good question. I mean, it'll happen, you know, it probably happened in the first half of this year, I would guess. The system is really accelerating. It's crazy. I mean, if you look at Platform 2 deliveries, which is the extraordinarily high growth part of ZipLine today, I think that we've done more than half of all deliveries in the last 30 days. It really kind of gives you a sense for like, you know, that is what happens when you're on an exponential curve. We expect Platform 2, I mean, just to give a sense, I mean, ZipLine is now doing more flights per day than United Airlines.

29:56globally. And by the end of this year, we will be doing more flights than all other airlines in the United States combined. So we are, you know, the system is growing really, really, really fast. And we are going to double flight volumes this quarter. And then we're expecting to double flight volumes next quarter. And then we're going to double flight volumes in Q3. And we're going to double flight volumes in Q4. So yes, it is a crazy time. And for hardware companies, sometimes software companies can scale like that. But for hardware companies, it's really hard to really hard to scale that fast.

30:25Well, now I know why you have the enormous new quadruple factory, and I'm looking forward to you quadrupling that yet again. For folks who want to keep track of the company, where can they find you online? And is there a job you want to shout out into the audience that you want to fill and you are looking for candidates? Zipline.com. You know, the company is growing really, really fast. We're hiring across engineering and operations. And, you know, if you want to work on something that is an insane adventure and it's building global infrastructure that's going to save lives and save money for people and save time, you know, I do think that AI and robotics are going to remake the world over the next five years.

30:55And we have an opportunity to build universal high income and abundance for humanity if we get it right. And so it feels like an amazing time to be able to be building. Keller, thank you so much. We'll have you back on when you hit 3 million and then we'll have you back on when you hit 4 million and soon you'll be on once a week. Thanks for coming on. Awesome. Thanks, Alex.

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32:34But while there's infinite attention paid to new LLM launches and they're changing leaderboards, some startups are working on something a little bit different, video models. And amongst the companies building video models, there are startups that are taking that work even further and are building world models. One such company is Runway, and they announced their first general world model at the end of 2025. So to help us understand what world models are, why they matter, and what they mean for both the AI industry as a whole and his startup, please welcome to the show, it's Anastasis, the co-founder and CTO of Runway.

33:07Good to be here. So glad you're here, man. So let's start with some basics for folks out there who are not as familiar with what we're talking about. What is a world model and perhaps how does it differ from the models that people are probably a little bit more familiar with? Yeah, so a world model is essentially it's a system that builds representation of environment and simulates different actions in that environment. So, you know, video models, I think most people are familiar with. You type in a prompt, you wait for a bit, and then you get back and output a video like, you know, 10 second video or whatnot.

33:37World models, the main difference is essentially that you're providing continuous interactive input to the model. and in the form of actions. So a model might be, you know, you might have be having a conversation with an agent. You might be navigating a space. You might be controlling a robot in real time. So the main difference is essentially that the model is generating frames continuously based on your input. It's not that you're just providing one input at the beginning, then you get back an output. And that's a very important property because it just allows to build, essentially simulate what happens when you take specific actions and simulate what happens if you do one thing or another.

34:16That's kind of a nutshell what the world model is. When you say a world model could involve talking to an agent, when I think about that example, to me, that's very much a text-based experience. So can you put that into the kind of world model context and how that might be different from how people currently interact with what they consider to be AI agents today? Yeah, so a world model is, we're building multi-model systems so they can generate video and audio. So the idea is that, you know, like a lot of communication, a lot of interaction is not verbal. You want to simulate parts of, you know, simulate aspects of like interaction that are not captured by text alone.

34:49Just getting the transcript of a conversation is going to, you're going to miss a lot of like the, you know, the vibes of the conversation, like the, yeah, how people are feeling, how people are behaving. And so that's the idea of like, that's something that a world model will capture in a way that just simply a language agent will not. But it's a bit more broader than that. I think there is, you know, so much about our physical world that's just not captured and cannot be described by text. And this is a lot of like our focus and kind of our mission to show that like models that are just trained directly on physical reality are able to do things that language models cannot do.

35:26So you guys have discussed how your recent video model, Gen 4.5, was kind of a prerequisite to build GWM1, your first general world model. Can you talk us through how you built 4.5 and then how that became kind of the foundation for this first general world model? Yeah, so for some context, Gen 4.5 is our latest base video model. So that's more like a traditional video model where you have text to video or you're providing an image and you generate a video. So we've been training those models for quite some time. I think at this point, we're probably dinosaurs of generative AI. We've been around for seven years, and we've been really focused on pushing the frontier of what image and video models can do, and going from super low resolution or very experimental use to those models, to them being used in production, to being used for the final frame of a film or a campaign.

36:16And Gen 4.5 is kind of our latest video model that we pre-trained. And all our world model research basically starts from our base video model and kind of post trains that model for doing this kind of continuous generation. So it's very important that the base model is able to understand physics, understand some like getting knowledge about the real world, because that's, you know, it's going to set a bound of like how far the world model can go, like how realistic will be. That's what I wanted to get at, because to me, using a video model as the foundation to build a world model makes good sense, but it seems like it would only be as intelligent as the initial learning.

36:53So if you've had only Looney Tunes cartoons, for example, it's going to think that physics works kind of one way when in reality it doesn't. So how hard is it to ensure that the video model learns an accurate representation of reality so that way you can kind of model that in the world model itself? Yeah, so it's very important and it's a big part of how we're pre-training the models to make sure that we measure how well they're at physics. It's not, you know, by giving them exams. It's more by just testing how they perform in different scenarios, how they do in like solid mechanics. Can you tell how good they are just by testing them?

37:27Or do you have to do sort of like in-world model testing to say like, okay, this is simulating gravity at, you know, 1.1x, what reality is? Yeah, so you can do that with regular video models. You can basically, you know, take a, basically shoot different videos in the real world of like different interactions. You might have like, you know, you drop a ball from a ceiling. And so you have this ground truth. You have a video of like the actual physical phenomenon happening. And then you put the first frame and you basically generate a video with that, with a video model. And then you compare it, like how much does the actual interaction like match, like what was the generated interaction?

38:05And you can you can get some metrics that basically show you the understanding of physics of this model is kind of, you know, you have some kind of quantitative measure for it. And what we found is basically very predictably just putting more compute and more data to these models makes them better at physics. And the better they are at physics, the more they can simulate those interactions, which is, you know, very critical for robotics for like all those different domains. This is the bitter lesson argument that we can just put more computed things and then they get better. Have I had that right?

38:34We're a very bitter, bitter lesson-pilled lab. Well, I was going to say it sounds like it because you're working on some of like the, it seems like the most compute intensive things you can. because when you guys announced GWM1, you said that it's like 4.5, but auto-aggressive. So it kind of looks back and then projects. That just sounds incredibly compute intensive to me to the point to which it almost feels, oh, I don't know, cost prohibitive. But I presume that I'm wrong about that. But what is the cost ratio compared to say a traditional text LLN? Yeah, so it depends on a few factors. I would say a big focus for us is making the training more data efficient.

39:09Like we obviously don't have the resources that Google or like another big lab has. So we need to make sure that we can do more with less. So there is a lot of choices that actually matter a lot in how you pre-train those models or like how they're on the architecture, on the data to make them more efficient so they can learn from less data or from less compute. And so that's a really big focus for us. We generally expect that world models will need more compute than language models. They, you know, like video is much more high dimensional, especially if you're, as we're starting to train on long video and long context, you know, you're training on an entire, you know, like an hour of video of like performing a complex task instead of just training on a single shot.

39:50Like you're going to be able, the compute requirements are going to be pretty massive. And so we're very, very paranoid about making sure we can, we can train the next models. We'll talk about getting more compute online in just a second, but I want to talk about the response to GWM1, your first world model announced a little bit ago. What's been market response to it and how close do you think other people are to catching up to where Runway is today? So the response has been great. I think one of the nice things was that like we like one of the things we try very intentionally to do is show that it's not just one specific use case, but you have this kind of the same world model foundation powering very different domains like robotics or, you know, more game like interactive experiences.

40:31And there's not that much different actually in how we train those different models. So we got a lot of response from very different audiences. It's actually been very surprising how quickly the robotics and autonomy folks have become interested in world models. When we started going into that earlier this year, I was expecting it to be a lot of convincing to make them want to try world models. But it hasn't really been the case. Can you explain to people out there who aren't familiar with the why as to why robotics and autonomy are good places to use world models? So building models for autonomy, like there is a few different really big challenges involved.

41:12So training those models, like the policies, which is that the models that are controlling the robot are controlling the car, you know, it's very data intensive and collecting that data is very slow. And collecting the right data is very slow as well. So it's even harder. So like if you take an autonomous driving scenario, it's very difficult to collect data of accidents, right? So most of the data you might collect is actually things that are very easy to simulate that you have a ton of, you have like, you know, the car moving forward. Getting the data for the right edge cases is very, very critical.

41:44And that's something that, you know, if you're able to generate that data, you're able to make those models much more robust and be able to handle edge cases much better. That's one area. And then the other is evaluation. So, you know, assuming you've got a very good model, how do you test that it's actually good, that it can actually do all the different tasks that you want it to do, that it can actually handle different environments? Like there is a lot of impressive demos of like robots doing, you know, backflips and crazy things, but those are very sensitive to the environment. So like if you change one, like if you move the table a bit, then the robot might not be able to do the task with a lot of those demos.

42:23So making sure that you can evaluate those models well before you deploy them is really important. And that's something that you can do inside the simulation instead of doing that in the real world. Right. So you can create scenarios that are rare in the real world in your physics intelligent world model. Or in the case of robotics, in my mind, you could spin up, I don't know, a thousand digital twins of your robot you're building, throw it at, you know, 55 ,000 different scenarios and learn more in a single day than you could in an actual physical environment in what feels like several years to me.

42:54Do I have this right? Exactly, yeah. And it's beyond being slow, it's very expensive and it's unsafe. Like in my break, you know, robots break all the time when trying to, you know, test new tasks. So exactly. So you can, you know, simulate a million examples of the robot trying to do laundry in a day. As I have more children, my interest in humanoid robotic help at the home just keeps going up. So I'm like, please God, someone get this right. Okay, so the thing that really blew my mind about your first general world model is the fact that you do want to use it across worlds, which is kind of the video game example, if you will.

43:28Avatars, which is kind of replication of humans and also robotics, which feel like three pretty different use cases. And so I don't do what you do. So I'm curious, would it have been easier to build a world model that was good at one of those things? Or if you build an intelligent enough world model, it just can do such a wide variety of different tasks. So we really, I guess our biggest bet is in generalization in that if you train a model on all of those things, then it will become, you know, like the skills that it learns to do one thing will transfer to the other. So if we take worlds as an example, what is the, you know, if you want to build the ideal video game or like the most, you know, the most realistic kind of life simulator, you don't want just to navigate and move around the world.

44:13You want to, you know, talk with people and characters in the world. you want to go and like do interactions or like kind of manipulate things or perform actions. So the avatar's data can help to basically build a more kind of human interaction within that world. The robotics data can help with like more of the kind of actions that you can take in the world. So it kind of all fits to each other. And it's kind of similar to how, you know, we think about pre-training those models. We're not training video models for just animation or just more narrative content or just talking head video. we're training it all at once and then it helps kind of there is a cross kind of improvement from doing that kind of co-training.

44:54So how quickly should we expect to see world models like the one you've built improve? I think we all got pretty excited about the progress of LLM's in 2024. I think last year some people realized it was going to be, you know, a bit of a challenge to keep the same pace of improvement but world models feel a little bit more green feel to me. So how fast should you guys improve this technology over the next, I don't know, six or 12 months? Very quickly. I think, I guess one other thing about it is, you know, image models have an offset of improvement compared to language models and video models are an offset over image models.

45:25So what happened with language models and what happened with image models will happen with video models and world models. So it goes text, image, video, world. So what we see improvements in the first one will take a couple of stops to reach world models, essentially. Exactly. Yeah. So those models are very rapidly improving. And, you know, like when, you know, if you look at the videos of Gen 1 and Gen 2, that was, you know, in two years ago, like at that point, we had to have a watermark to basically tell people this is generated, this is not real. But now that looks a bit ridiculous in retrospect, like it's, you could obviously tell it's generated.

46:00So it's not just that the models improve so rapidly, but people are becoming also very used to like, you know, this is a new reality. Like generated content is hard to tell apart from real content. Yeah. So last year you guys raised a couple hundred million dollars, new shiny valuation, reported to be about three billion. There's a lot of competitors out there with a lot of compute. We talked about this a little bit earlier. I know you guys are working with CoreWeave and NVIDIA on building out more compute, but how constrained are you on the compute front today? And do you think you have enough capital to get fully unlocked this year?

46:31Again, we're very paranoid and want to make sure that we can train the next model. So we have great relationships with both the hyperscalers. We have a great relationship with NVIDIA. We actually just collaborated with them on running our model on Rubin. So we're going to do what we need in order to be able to get access to make sure we can scale our models. Ruben is the recently announced NVIDIA chipset that replaces Blackwell, which replaces the Hopper set. So if you're curious what that is, that's the most recent NVIDIA announcement that was put out in their CES keynote in early January. Interesting that you bring up NVIDIA, and I don't mean to poke here, but NVIDIA did announce in the same set of news updates to their Cosmos World Foundation model.

47:14And I don't want to say it's a direct competitor because I'm not technical enough, but is it? And also, is it a little bit weird to be competing partially with one of your own backers? So, yeah, I think the Cosmos is a series of world models and it tackles some of the physical AI use case as well. I think it's like all big labs are going to be working on world models. And if we were to not collaborate with folks that are working on similar research, then it would really not leave a space for anyone to collaborate with. I think there is a lot of like we see it's more as a partnership. Like NVIDIA has a big platform of doing for simulation.

47:54Like they have IsaacSIM, which is a very widely used robotics platform. So I see it more as kind of complementary and like kind of focusing on a few different kind of few separate use cases around world models. So not really competitors directly to what you're building, but it's impossible to avoid some overlap in the AI world because people are kind of running in the same direction. So you can't. OK, that makes sense to me. I'm also glad to hear it because when I was just prepping for our chat, I was like, wait a minute, is NVIDIA trying to kill them? That's so ridiculous because I knew they were an investor in one of your earlier rounds.

48:23So slightly not a thing. There's another company called Descartes out there that's building world models. Good to see other startups pursuing this as well. How do you view the competitive landscape? Is it as, I don't know, high risk as the LLM world or is it a smaller number of players with kind of diverging approaches to the problem? So I think one important thing about world models, which we discussed earlier, is in order to build a great world model, you need a great video model. And so it really helps to have that advantage of actually having been building those models for a long time. Some of the other players in the space that are entering world models are basically leveraging open source video models and then post-training them for an autoregressive kind of world modeling.

49:03and that tends to be very limiting because if you don't have control over the pre-training, there is only like so few things you can do to basically improve performance. So I think that's a very important point in terms of like building a team that can kind of execute on world models. At the same time, again, this is like, for me, I think this is the, you know, I'm very confident there's going to be the same level of interest and competition that's going to be, that's been happening on LLMs and so there's going to be a lot more players in the space. Looking at robotics use of world models and also kind of the what we might call the video game context for them, which do you think is going to be the bigger driver of revenue and demand for a runway in the next couple of years?

49:43So I would treat it as essentially two pillars. So there is the kind of physical AI pillar and then the real-time video pillar. Physically, I think there is a huge potential of world models being used in autonomy, both for like training those models, but actually we believe that video models will end up controlling, becoming the policies themselves, like controlling the robot, controlling the car directly with the video model. And so there is a huge potential to build a big platform there. And then the real-time video, We think that this is essentially maybe the next step beyond kind of the, there's a lot of talk about generative UI.

50:19You can kind of vibe code an app and have it in a few minutes. But actually, I think even that will start to seem too slow of a way to build interfaces. You should be able to kind of generate, use a video model to essentially generate on the fly the exact experience that you want to have. and avatars is like the maybe the the most easy place to start but we we expect that a lot of software will actually be driven by generative video models yes so the way that i see that coming to the market is essentially i'm going to have a a vr headset at some point in time in the future i'm going to be able to enter into my own world model that is defined by me based on how i want to approach it and i think it'll be like a combination of like play and work and then i'll have to define workspaces inside of that that i can design myself to your point about software but it seems like there's still a lot of pieces that need to come together to make that fun because I don't think that would be as great of an experience on like a desktop computer or a laptop.

51:16So I wonder when we're going to get a different hardware form factor to take what we have today and kind of blend it together and also just make it much more immersive and interesting. I always feel like Apple missed the mark with their Vision Pro headset because it's such an AR traditional desktop style approach to work. I don't know if any of that made sense, Anastasis, but that's kind of where my mind goes when I think about trying to take, you know the productive side of ai and building it into a personal world model if that makes sense yeah i think it's a really great point i think the software problem in vr is maybe underlooked like it's very difficult to make great experiences for vr and that's the lack of great experiences is part can explain you know there's a lot of hardware reasons but it can explain part of like why the adoption has been slow i do think there's a lot of use cases for real-time video that are just on your desktop computer as well or your phone.

52:06Something like avatars easily works on those form factors. It doesn't necessarily need to be a full-blown kind of world simulation. But VR would be maybe the ultimate kind of destination for those models for sure. Well, I expect you guys to do something with VR because I have high hopes for this. I don't know. Do you ever look at your phone and realize it's the same interface you've had for like 10 years and you look at your desktop and it's the exact same thing you've been using for 20 years, 30 years? It feels like we're due to take these new technologies and really redefine what it means to compute and be productive.

52:38So that's what I'm hoping for. Before I let you go, I have to ask. I talked to a lot of founders, a lot of people working in the AI realm. Everyone's hiring brilliant engineers. But when I was researching Runway, you guys have a lot of people that have an arts background. And that's just different. So I'm curious what impact that has on the company's approach to building products and company culture. It's just a unique way to approach building an AI company today. So tell me about it. Yeah. So I would say, yeah, art school is not the place where Frontier AI labs tend to start. I think we've been unusual in that respect.

53:11Just for context, Runway started in art school here at NYU. And since then, I would say we learned two main things that are kind of coming from this art school program. So all three of us co-founders, we were kind of students of that program. And then Runway kind of started as an offshoot of that. One thing is a big focus on kind of a strong demo culture and cultural prototyping, making sure we can very quickly make something to show and tell the story of like what we're building has been really important. And the other part is not having like having a healthy skepticism of like credentials and healthy skepticism of like you need to be from a specific background to contribute to AI or whatever kind of deep technical field.

53:55like some of our best people you know we've hired folks from the biggest AI labs and we've hired folks from that had not done research in any kind of industry capacity and might have had like some amazing open source projects or like some demonstration that they've done something great and they've been very competitive with the folks that came from the big labs so I think that's probably the biggest thing has come from from this kind of less traditional background is basically being more open-minded about how to build the culture, how to build a team to make a great kind of AI company. Also, you guys are not based in the same four block radius of San Francisco that everyone else seems to be based in.

54:35Does that also change up who you can hire and how you run the company? Because there has been recently this meme that everyone has to be in San Francisco. And even though I used to live there, I don't think that's the case, personally. Yeah, I also used to live there and I strongly don't think that's the case. So we have offices in SF and we have offices in London. But yeah, our main office is in New York. I think talent can come from anywhere. We're a very distributed team. I think there is definitely value in being outsiders and thinking a bit differently than that kind of small radius of companies in San Francisco.

55:08Yeah, it's good to spend some time there, but not too much time because then it's harder to come up with new ideas or different approaches. Yeah. If everyone's thinking the same thing, everyone's going to build the same thing. All right, Anastas, where can people find Runway Online? And do you want to shout a job you're having a hard time filling out into the ether in case someone listening is the perfect candidate for you? Yeah, so RunwayMail.com is the website where you can, you know, try out our tools and our models. And then, yeah, we have a ton of open roles. But I would say our research scientists and go-to-market roles are probably top of mind right now.

55:40All right, man, I appreciate it. And when you do build VR, call me up. I want to test it out. We'll talk to you then. Of course, first in line. Thank you. So from our dear friends over at the Startups subreddit, Jason, there was a great founder asking a question. He has some friends. They're working on a company. They spent$100 ,000 so far on fees, costs, expenses, building their prototype. They're getting close to an MVP and they've made very little progress, he thinks. No customers, no real traction. And so he took his question to the community and said, hey guys, when do you know when to either pivot or give up?

56:11Doesn't have a lot of venture backing behind him. He's bootstrapping. So Jason, for founders out there who are doing it the hard way with their own scratch, when do you know that you've reached a terminal point and need to take your plan in a new direction? You know, pivoting is so hard because sometimes if you keep trying and knocking on that door, eventually it opens, right? And other times you could be at it for five or 10 years and you're just never able to crack it. So there's no perfect answer here. A good way to know if this is worth continuing to bang your head against the wall is to talk to the customer base there.

56:53The other thing is your intuition. And sometimes, you know, something like building an electric vehicle seems like an insurmountable task. And so if you were to look at a company like Tesla building an electric vehicle, man, there were so many near-death moments for that company. but through sheer force of will and not pivoting, they figured it out. But there were moments to pivot. Tesla had early on a deal to make a smart car with Mercedes, and then they did the RAV4. You can pull up images of those for sale on Get a Trailer, as an example, or Bring a Trailer. And just do a search. Use RAV4 EV.

57:34This car, you can probably get it for$5 or$10K right now, or the Mercedes smart car. And I think we may have brought this up on a previous episode, but they could have pivoted to just providing the drivetrain to Mercedes. But instead they said, we're going to start with technologists who want$150 ,000 roadster and eventually get there. So here's your 2002 Toyota RAV4 EV. It's a looker. No, it's not. I mean, it's the ugliest car ever with the ugly logo on the side for EV. What did that go for? Is that cars in bids or is that Bring a Trailer? This is Bring a Trailer. I love Bring a Trailer, by the way.

58:11It's one of my favorite websites. Also, there's Bring a Trailer and then there's Cars and Bids by my pal Doug. Oh, it sold for$3 ,500. Right. So you can put this in your EV museum. This was powered by Tesla. And there's a smart car version which should have worked. But, you know, they persevered. But there were people who wanted them to stop making their own cars and just provide for other people. Why? It was perceivably a safer bet. Maybe you have like a 90 % chance of making a business work, whereas making an electric car company was a 20 % chance, a 10 % chance. So the fortitude of the founder is critically important.

58:53SpaceX, the other one. He blew up the first two or three rockets. It looked like it was going to go out of business. So I'm a big fan of persevering if you believe you're going to be right, but you also have to be able to raise the funding. You have to be able to explain this to investors. And even Elon had a hard time with that. There were investors who were going to bail him out who were not going to participate because he wouldn't pivot. And he had his own bankroll and he was willing to put it on the line. So this is one of the hardest, hardest questions any founder can face. And you got to go with your gut and your instincts based upon the data and what you perceive is the best path forward.

59:34I want to do a follow-up on that because your two examples are very good, but they're hard. Thinking about Tesla, thinking about SpaceX, building cars, building rockets. Okay. If you're a founder in a software category, right? And you've spent 100K and you've answered on this Reddit forum thread that you haven't shipped an MVP yet. Is that an indication that your current team is just not up to snuff? Because I feel like Jason, in the era of lovable, in the era of vibe coding, in the era of AI tooling, you should be able to get something out the door with that amount of money. Am I being overly censorious here?

1:00:07Yeah. I mean, if your team can't get the job done, fire your team and start over. And that's a hard thing to do. If a team can ship, they will have shipped. If they can't ship, they won't ship. And you'll know that within 90 days. So you got to be cutthroat about it. And this is why having founders who actually write the code is critical. On the Y Combinator application, somebody will find the screenshot for us and we'll get the exact wording. They say something to the effect of who's writing the code, their name, who's the name of the person writing the code. And I think that question was hard fought by Paul Graham because it might have started like, who's the developer on your team?

1:00:49And then they must have refined it at some point to say who's actually writing the code, put their name in this box, because when they do the interview, they want to make sure it's a founder, not an outsourced tech company. So when you outsource your technology, like that's fine in the early days, maybe it's, but it's suboptimal. What you really want to do is what I did. I had Brian Alvey as my co-founder for Weblogs Inc. He wrote the CMS. There were no good blogging CMSs. There was like movable type. This kid, Matt Mullenweg was trying to convince me to use this open source WordPress that didn't work at the time.

1:01:23And I told him like, hey kid, you know, I'm not going to use your broken CMS. I got Brian Alvey. He's going to write our custom CMS. That'll be our advantage. And I was right because Brian Alvey was going faster than him and was building it specifically for Engadget, Autoblog, Joystick, and all these great blogs we were doing. So that gave us a competitive advantage against Nick Denton, who was doing movable type at the time and was trying to push them to ship the features he needed, but that was a negotiation. So I made the right decision then. Literally, the right decision would be to not do content and just go all in on BlogSmith, which was what Brian was building.

1:01:58And we should have just built that. It would have been a billion dollar company or a hundred, probably could have been a hundred million dollar company instead of a 30 million. I think the question that you were alluding to, the correct phrasing is, who writes code or does other technical work on your product? Was any of it done by a non-founder? Which I think is a brilliant way to phrase that. A brilliant way to phrase it. Hard for a question. Just cut everybody. Find a co-founder who writes the code that you can mind meld with if you're the go-to market person, if you're the hacker. But that's just from first principles.

1:02:28If you have the idea in your head, you're talking to the customer and you can build the product, man, you don't need to have phone calls and you don't need to have standups. You just ship code, ship code, ship code. That has been the success of Y Combinator. That was Paul Graham's, I think, his great innovation. If you were to rank all his great innovations, that would be number one, two, and three. Only fund or accept into Y Combinator teams of three with two of them writing the code. And then the chances of them actually shipping product and learning will be greater. We basically cribbed that innovation.

1:03:02And actually, Roloff, both of us, was saying, Like you had a lot of idea people. You got a lot of designers in your incubator, but not a lot of developers. You need to get more developers. And so that became our focus. And as we got more applications, we were able to do that. So yeah, start over. Sure. If you have a founder question, a startup question that you want to bring before Jason and have it discussed on the show, shoot us an email to, I don't know, askjason at thisweekinstartups.com. We'll take a look. We'll put the best ones on the show and we'll even give you and your company a shout out if you are particularly deserving.

1:03:31Or go to our This Week in Startups subreddit.