Why Medium is HIDING from AI | E2179

26:00Like, does the world still reward people who want to be smarter? Yes. Does your life get better if you're smarter? Yes. So in In the world of smart people, writing makes you smarter and reading stories from other people makes you smarter. And so the Medium business is built on that premise. Reading and writing is for smart people and there's always going to be people that value being smarter. Well, if people want to learn more about RSL and what it is, they can go to rslstandard.org and of course, medium.com for all things Medium. Tony, as always, an absolute treat. Once you corral a couple of AI companies by the neck and drag them to the negotiating table and get them to slap their checkbooks down the table.

26:38Come back on and tell me how that goes. And I'm going to be watching very closely to see how the economics work out for the media and writers out there because they're writing. It's it's it's it's too important to let die. We're both fans of writing. Thank you. All right. Bye.

26:57So continuing our conversation about intellectual property in the era of generative AI, We're next speaking with a Twist 500 startup based out of the UK that was building an AI content marketplace, a system in which IP holders could license their content relatively easily and quickly to AI companies that wanted to pay for it. However, the company is pivoting a bit. They have new plans for the future. So we're going to talk about why they ran into a dead end and what they're going to do next. Please welcome to the show. It's Human Native co-founder and CEO, Dr. James Smith. James, how are you? Hey, Alex.

27:29I'm good. Thanks. Thanks for having me. Yeah, my pleasure. So I had put your company amongst several others, your Tolbits, you're created by humans as a company working to connect IP holders and AI companies. And my view of this was always that there was gonna be ample supply, that publishers, authors, people who own datasets would love to license their data and get paid for it. You just told me the company is pivoting a bit. So I presume that on the demand side, there was a bit of a mismatch. Is that fair? I think there was a mismatch, but I'm not necessarily sure it was always just about the demand itself.

28:04It felt like a communication breakdown a lot of times, quite frankly. So you're right. I mean, rights holders want to get paid. They're facing immense pressure. Their traditional business models on the internet are collapsing. We need a new economic model for the internet around AI. But rights holders think about content as content, as their life's work. It's very emotional. They put a huge amount of effort into it. and AI companies think about it like data. It's numbers that help them improve their models. And that is a real fundamental mismatch. And so what we were finding is that rights orders would come and talk about the respect for their work and having things like editorial control over headlines that were reproduced.

28:46And AI companies would come in and say like, okay, that's great, but we want this really specific requirement. We want exactly this and nothing else. And we're going to pay for just this. And what's the ROI? and can we prove this is going to improve our model performance? And so it became this quite difficult conversation between two sets of people who didn't speak the same language. We attempted to play translator and that was really hard. So James, I'm really curious about the difference between data and content. To me, it's tomato, tomato, but you're making it sound like a really big difference.

29:15So is this a structure difference, how the information is presented? Is this a type of data question or is this more of a philosophical difference in just how people think about the information in question? I think a lot of it is to do with structure. A lot of it's to do with rights holders have been creating content over decades, perhaps. And for the last maybe 10 or 15 years, they've been throwing it into a cloud storage system. And they might not have a good understanding of what they have. We worked with a rights holder who'd been approached by an AI company asking for Arabic language content with transcriptionists.

29:45And they were like, yeah, we're pretty sure we created some stuff like that, but we have no idea where it is or what's in there. and they're like, if you can find it in our archive and here's access, then good luck. You can help license it. That's a real challenge for them.

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30:37Armed with this info, they were able to revise their roadmap to focus on high impact work. And now they're shipping new features twice as fast, sometimes within just a couple of weeks. So stop shipping late. Check out DevStats today and get 20 % off, plus access to their dedicated Slack channel. That's a really good idea for a startup. Have that Slack channel so you can talk directly to your customers. Go to devstats.com slash twist for your 20 % discount. But the philosophical argument is also huge. You've probably seen there's a lot of debate happening both in the United States and especially in the UK around this idea of fair use or we don't have that in the UK but the ability to use this content for copyright I've been lucky enough to be asked to give evidence at the House of Lords and the House of Commons select committees on these topics and met rights holders and for them it is a really emotional issue that philosophy part of it of this is my life's work how dare you copy it and change it and then use it for your own purposes to make money that is a really fundamental challenge for them.

31:40And so the way that manifested themselves is sometimes in the business aspects, they would come in with very different ideas about price and turn and level of control. And as I said, we attempted to translate and be the buffer between those two. So publishers wanted high price, low shared control. And I presume on the AI side, they wanted low price, high levels of control. And that would probably sound like a different language at the negotiation table. Exactly. So how long did you guys try to bridge that gap? Like how long did you sit there and be like, okay, we can make this work. We can get Bob and Jane to talk it out and reach a common understanding before you were like, okay, this is not going to be the path forward.

32:20So we did this for about 15 months. We started our company in April of last year and we went on until about the end of Q2 this year with a marketplace model. We attempted to prove that we could connect these two parties. We then tried to do a few different things. One of the things that we try to do is I have a very clear idea of what human native the marketplace would look like if the company was a billion dollar company. It would be we'd have to solve the ability for trillions of transactions happening effectively in real time for fractions of a cent each time. And our company would look like infrastructure and we would be that low friction barrier to enable these two parties to transact in close to real time.

32:59But in order to do that, you need a lot of precedence, you need a lot of standardizations, and you need the ability for supply and demand to meet in that way. In the end, it felt like we'd started out being this buffer between the two. We tried to let them speak to each other and then provide the infrastructure and the tools to do that. And eventually, it just felt like being involved in licensing was not the right path forward for us as a company and frankly, wasn't a good use of our talents. A couple of things, I've been a product manager at Google and DeepMind. We built lots of technology as a team.

33:29I'm not entirely sure I'm the world's best salesperson or at least the best middleman between two organizations that are trying to get the best contract. Those are different skills. Who would have thought? Yeah. And so I think it's time for us to focus on what we're good at, which is building product and building technology. So we'll get to that in just a second. But I want to go back to your point about trillions of transactions for a fraction of a penny, because there's two main ways that data is used by AI companies. And a year ago, we were talking mostly about training data. AI companies going out there, hoovering up a large chunk of the internet, and then using that to form the large and their large language models.

34:02Lately, though, talking to both publishers and AI entrepreneurs, it seems that the RAG use case, using data real time to help serve a query has become much more important. So as things go from kind of the training use case to the RAG use case, do you think that's going to help to resolve any of the issues that you've mentioned? Or is this just the same problem as before, slightly different application, but no change to the fundamental disconnect that we just discussed? I think it's still the same fundamental disconnect, Alex. I think the challenge is going to be, do publishers want to participate in a RAG-type system where they don't get any control over the user experience and they lose their direct relationship with the customer?

34:41The thing that's changing about these generative AI platforms, people are not leaving them. They're walled garden systems. And you've seen traffic referrals drop off a cliff. And so if you're a media publisher, you don't have any incentive to work in one of these systems other than perhaps get a little bit of revenue because you're losing your direct relationship with the customer and your ability to control how your message lands. For the AI companies, their situation is going to be, well, why should we pay for this information if it's available on the free internet and the open internet? And I think that's a really difficult one.

35:13The other fundamental thing and the thing that's dissuaded us from building our own RAG solution because we had a working version of this was simply that AI companies have that, a lot of the time, it's not invented here syndrome. They want to control critical parts of their infrastructure and that's really understandable. And so if RAG system and working with publishers is going to be a key part of how they deliver their user experience, they're going to want to own the technical infrastructure that makes that happen. And they're going to want to go out and talk to publishers and bring them into that platform independently.

35:42Yeah. So if the data structure and discoverability problem hadn't existed, do you think there would have been a way to translate between content holders and AI companies? Or would the other issues that you've mentioned be enough to still scuttle that as a possibility? Well, I think the media organizations and large tech companies have had a storied history to this point. There's 20 years of history in some of these conversations. And so getting into the middle of that as a startup was also a challenge, quite frankly. And so I think there's a lot of distrust that even if we can solve all the technical issues, there's still some fundamental barriers to overcome.

36:19This does bring up one of my favorite quotes. In 2024 coverage of your seed round and tech runs, you said, I'm the CEO of a two-month-old company and have been able to get meetings with CEOs of 160-year-old publishing companies. And I was like, well, yep, that's the power of tech. Yeah. Opens a lot of doors. Okay. So let's talk about solutions then, because it sounds like right now, the idea of building a shared place for demand and supply to come and meet and reach an agreement and then execute it is a bit rough. So what do companies need that you can bring to them soon to help them take their information and either use it internally for their own AI usage or make it available for sharing?

36:56Because it sounds like you're leading me towards a, there's a technical solution to part of these problems and we're going to go build it. Well, that's what we're hoping. Yeah. I mean, obviously I'm an entrepreneur, so we're trying to sell a vision about what we believe in, but definitely this is what we're seeing. Some of the partners we work with, and we have worked with some very large publishing partners are doing their own deals and have been successful and have made tens of millions of dollars in AI licensing deals. You can say pounds. It's fine. I'm just being respectful of your, you know, majority of your audience.

37:22Also, having worked for Google, sometimes I'm kind of just made Atlantic, half English and half American in the way that I speak. Just don't say kilometer or France and we'll be fine. I'll just do it all with a Scottish accent and then nobody will understand what I'm saying anyway. Actually, as a fan of Still Game, the Scottish television show, I think I'll be okay. You've seen Still Game? That's great. I always recommend that. That's a great way. Every single episode several times, including the more recent seasons, which was good. Oh, and I've seen the stage shows and the original stage production.

37:50So yeah. You're a mega fun. Wow. Wow. Okay. We should talk about that later. We will. Back to you. Yeah. Back to dollars. And so the challenge I think is going to be helping these companies answer the more sophisticated AI buyer requests. If an AI content company, AI licensing company comes along and says, hey, we want to license only videos of sunscreen bottles with busy backgrounds, not clear backgrounds. So Alex, not James in this scenario, how does a content company that doesn't have any particular expertise in building AI models answer those questions? Well, it sounds like what they would need to buy is a third party AI service that would go through all their information and crawl to find it.

38:36So that sounds to me almost like an internal search engine and taxonomy generator. Something along those lines, and that's the kind of types of pilot programs that we're doing at the moment. We're helping companies make their data assets useful. Because if you think about the last 10 or 15 years, there's been a huge amount of progress in big data. There's lots of category defining companies which have helped organizations make use of data. Snowflakes, Databricks. Yeah, palenters, which help people basically make use of data and do things with it. What happens if your data in that question is images, video, audio?

39:14I don't think there are category defining companies yet in that space, which help you extract meaning and useful value out of that content. That might be that value might be external. It might be for licensing opportunities, but it might be internal. One of the companies we're speaking to has a lot of call centers and they have a lot of audio recordings from their call centers. They would like to be able to analyze those and extract meaning. What are their customers saying to them through those call recordings? I think that is a really interesting space. And there's a lot of possibilities there.

39:43Does the system that you're envisioning to help people find, you know, videos of sunscreen with busy backgrounds versus non-busy backgrounds and to dig through call center data, which I presume is transcripts and call links. So it's both words and data points. And audio. Oh, yeah. And audio. Do you use traditional non-AI technologies to parse that information in this vision? Or do you use anything predicated on generative AI? I want to ask about vector search, if it's the latter, and if it's the former, I don't. So just guide me with how you think the tool is built. I think it's a bit of both, quite frankly.

40:15I think there's a lot of great software engineering and data engineering of traditional means, but now that it's more possible at large scale. And then I think, yes, I do think generative AI has unlocked a lot of ability to understand content in particular. And so there's some really interesting techniques that we're exploring there. So is this a system by which companies can prepare or is it a system by which they can explore? Because to me, we talked about the lack of data being searchable and findable. So you could help people find it or help them kind of structure it in a way that other people can look into it.

40:50So I guess, what's the product that you're pushing towards here? And what does it look like when it reaches the market? Oh, you're truly pushing me at this point, Alex. This is what we're trying to experiment with right now. I think the two are fundamentally linked. I think preparation of content enables exploration. And the goal might be exploration or use, but you can't do it without the preparation. And so we are, for example, working with a company today where we're going to be showing them the latest version of what we built, which is an interface for understanding and searching their content, but it's all built on the work we did to prepare their content.

41:22I see. Okay. Now, one of my favorite companies, Box, because I've talked to the CEO, Aaron, bunch of times and he's charming um has been talking ad nauseum for the last couple of quarters maybe years at this point about helping people who have their data stored inside a box which is a traditional uh enterprise um oh man it's been a while enterprise sync and share files efss i forget the acronym whatever it is yeah they have a bunch of data in the cloud for their customers and so they're building tools that i think are kind of aligned to what you're describing because they already have the data so why not help people kind of figure it out what it is and use it But would your system work best for companies that have a lot of on-prem storage?

42:01Or do you think this is a multi-cloud affair? We are trying to build this solution where it doesn't matter where the data is. So the data could be on multi-cloud. The data could be on-prem. It's probably for people who, sorry, Aaron, the data is not already in box. Because there's a lot of people who have been using lots of different systems to store their data. It could be they've been syncing it raw into S3. It could be Dropbox. It could be Box. It could be Microsoft SharePoint. It doesn't really matter where the data is, but people need a unified view and then they need to be able to actually do something useful with that data.

42:33Okay. And then on the do something useful with that data, is that a thing that you think your company is going to focus on or more like help people get to that point? And then from there, it's choose your own adventure. I think it's honestly, we're still early enough that we get to figure this out. And that's the fun bit. I was talking to a very senior person at a multinational global company. Sorry, I don't like using names because we're under a lot of NDAs. No, no, you're fine. This company - That could be one of like 100 ,000 people. Yeah, exactly. This was like a C-level person at this company.

43:03They have a huge archive and they're like, well, yeah, we could license our content for 10 million a year. And then somebody will build a billion dollar product on that content. But we'd like to have a shot at building that billion dollar product. Oh, interesting. So you're facilitating these companies that would have come into the marketplace for a cut of those trillion rag calls at a fraction of a penny each. And instead allowing them to build something on their own. Exactly. Because what I said to him was, well, have you figured out how to unlock your archive yet? And they're like, oh, no, no, we haven't.

43:30I was like, cool, that's where we can help. So a question about that, because the way that I've always seen this is data in aggregate is powerful. Data in smaller chunks is less powerful. But I'm also aware of what you said earlier about the sunscreen example, which sometimes an incredibly narrow slice of data is very, very powerful. But that's all from the perspective of these large AI companies, your Anthropics, your OpenAI, your XIS, et cetera, your Mistrales. For companies that would want to build something on top of their own data, they only have their own box at that point, right? So in this case, the publisher has their list of content through time.

44:08Is that a broad enough data set or is perhaps a narrow enough data set to build something useful on top of it? Or will most of that value, do you think, come to fruition when other companies can combine multiple data that's from similar companies to build something? Because to me, it's cool that they want to build a billion dollar company on top of it, but do they have enough grist for that mill? It's a really great question. And I think there's so many possibilities now with these advances in the models that we're seeing. If you were to take a great third-party model or open source model and then apply your archive to it and create an AI product, which deep dives into your article and gives you insight into that.

44:46If your archive is rich enough, that's a really cool product. but equally you can also take your open source model your archive and then as we just talked about use a rag system to bring in the content you don't have and then there's also an equally compelling product this is a choose your own adventure story and i'm really excited to see where it goes so the guardian to pick a paper that you and i both read uh could take an open source llm bring its own data after working with your company to get it all suited up and booted up to go. And then they could also rag out to, I don't know, the times, pick a times from either of the side of our ocean, and then have an expanded guardian AI model that can help explain news even.

45:26Okay, I can see that. That's going to require a lot of technical leadership at companies that have famously not been so technically leading. And I'm making fun of my own industry here, journalism, just to pick one. Do they have enough chops to do that, James? Or are they? because they're bad at websites, okay? Like how can they build AI systems? Well, I think we're going to find out because I think the barrier to entry to these systems is getting lower and lower, right? Today, it's still quite hard for a non-technical person to use some of these AI systems in a way that's not just using ChatGPT.

46:01I don't know if you saw, there was a great podcast. Sorry to talk about other podcasts on this podcast. Oh, please, no. There was a great podcast that the leader of chat PRD did with Tom Tungas from - Oh, Tomaz. Yeah, Tomaz, sorry, from Theory Ventures. And he talks about how he wrote a script to take his 36 podcasts that he subscribes to because he doesn't have 36 hours in the week to listen to them all, to then extract insights from them so that he can get a quick digest of what's happening in tech this week. And I think it's a really cool example, but because Tom is incredibly technical, he was able to write that script and do it.

46:34What if there was a system that enabled many more people to do those types of things? And so we talk about technical leadership at these companies. What if it's not technical leadership? What if it's just business and operational leadership? Ah, and so the technical leadership would then pool at a company, perhaps one based out of the UK, perhaps one called, I don't know, human native? Human native AI. That would be nice. So James, just before you go, drop your URL and a job you're currently hiring for. We are human native.ai. That's one word, human native.ai. And what we're hiring for will actually surprise us.

47:05If you think you can provide value to what we're doing, I think we're really interested to speak to you. We're looking for high agency people who can help us figure out what's next. This is a really interesting market. There's a lot of good to be done. I'm excited to see where it goes. All right, James. Thank you very much. Thank you, Alex. Cheers.

47:23Hey, welcome back to Twist. Now, I learned to drive in an F-250 stretch bed with an enormous gear shift coming out of the floor and more torque than a tank. It was a great car to learn to drive in because you basically couldn't stall it thanks to how grunty it was. But since my youth, trucks have gotten bigger and bigger, often without any increase or even a decrease in their cargo area. In short, I think that most American pickups today look and operate more like a minivan with a wheelbarrow attached. But there are a couple of companies out there who are thinking differently. So what if we made smaller trucks with actual functional beds and maybe batteries instead of the two huge fuel tanks my dad's truck still has?

48:04Tello is doing just that. And to tell us more about that, please welcome to the show, Jason Marks, co-founder and CEO of Tello Trucks. Jason, hey, how you doing? Hey, thanks for having me. My absolute pleasure. So your company is building something called the MT1, which I have to say two things about. One, it's very small as far as trucks go. And two, it's absolutely adorable. But why don't you tell us about the truck and why you picked this particular form factor to start with? Yep. So we build mini trucks. That's what the MT1 stands for, mini trucks. We build crew cab pickup trucks with the same capabilities as a midsize work truck, like a Tacoma, maybe a smaller F-150, but packaged into the length, actually smaller in length, than this year's two-door Mini Cooper.

48:47If our vehicle was available today, it would actually be the smallest vehicle on U.S. roads, despite the fact that it has a five-foot bed and seats five people. It's even smaller than one of those tiny little two-door Chevys that I see at times? Absolutely. It's feet shorter than that. It's shorter than a two-door Mini Cooper. That's absolutely awesome. So what was the original inspiration for making such a small truck? And we'll show some pictures here in a second, but I'm curious why this came to mind for you. Well, first off, I'm a truck guy. I've driven a Toyota Tacoma 230 ,000 miles, I think, so far.

49:21I have a 180-pound dog. I live in downtown San Francisco, and I do truck stuff. I go mountain biking. I go snowboarding. I carry a lot of the stuff we use for the shop on a day-to-day basis. So having a truck, it rocks. It's actually a really useful thing to have. But I can't ever navigate downtown San Francisco. I can't park it anywhere. My wife wants to go out to dinner. It's just such a headache. It has both a financial and like an emotional burden trying to navigate a downtown city. And we felt like we were uniquely positioned to kind of fix this. So tell me about that unique positioning. Why are you the right guy to build Telo?

49:56Yeah, so my background, so I grew up in the Seattle area. I built motorcycles and vehicles from scratch when I was a kid, crashed them in blazes of glory in my high school parking lot. But I was studying mechanical engineering, and when I graduated, I went into automotive safety. So I worked on some of the very first autonomous driving vehicles on the sensor side, then on the software side, then on the hardware side. I ended up doing the safety systems for some of the very first electric pickup trucks that came to market. So I had a really big background in automotive safety and had a good understanding of why vehicles are designed in the way they were designed.

50:31And when you understand stuff like that, you might understand why, well, with this transition to this new energy kind of domain, we can do things uniquely capable in these vehicle platforms that have never been possible before. If you remove the 1 ,000 pound giant engine block from the front of a vehicle, repackage the front of the vehicle in a way to just support the crash safety side of things, you can rethink that entire front structural design and really shrink the footprint of the vehicle. And where that matters... Let's go ahead and actually, Jason, hold there, because I feel like we should just show people what we're talking about now.

51:01So if you're listening to the audio version of this, we're on telotrucks.com looking at their comparison tool. If you're watching the video, look at this. So here is your truck and it is superimposed right next to a Toyota. This is a Tacoma, yeah? That's right. And it's much, much, much smaller. And just for folks who are curious, this is the Telo truck in comparison to a Mini Cooper. Basically the exact same length, a little bit taller and designed to carry quite a lot of cargo. And Jason, just to be clear though, in this example, we're once again seeing a car designed here to hold an enormous engine in front and you essentially cut off the nose and save, what's that, a couple of feet and a thousand pounds.

51:40Yeah, that's exactly right. And you make it up in battery weight, of course. So it's not like we're necessarily coming in lighter weight than a Mini Cooper. But what we are doing is actually packaging a truck that can do truck stuff for specific areas where truck stuff hasn't been possible before. I think it's especially pertinent to me. We were talking before the show, but I live in Rhode Island in Providence, which city designed, I swear to you, for horses. And whenever someone drives one of these larger standard today, modern American trucks, it takes up essentially three lanes and everyone hates them.

52:10So I think this would be absolutely ideal for me. But sticking to the geographic theme, one reason why I wanted to talk to you is you guys are planning to both design and build these inside the United States. I think in a facility in Irvine, California, when I think about EV manufacturing, I think about a global footprint and the rise of China and all this. I was a little surprised to see your ability to hear. So I'm curious why that choice and how hard is it to make something like this here in the States if you don't have Tesla scale? Well, first off, I think one of the things I understand about what Tesla came about, and my co-founder was early in the days of Tesla, both founders of Tesla invested in our company, were the only other EV company they've invested in, is they didn't start at scale.

52:51They started with 2 ,400 Roadsters that were contract manufactured from Lotus. Then they grew into 10 ,000 or so Model S's. So they did not come out of the gates trying to build 100 ,000 to a million vehicles per year. And a lot of other startups that came before us in the last 10 years felt they needed to compete at that scale and made huge financial investments without actually getting vehicles in customers' hands. And that just burned capital quicker than ever, as soon as the markets changed, they ran into huge headwinds and they were unable to actually substantiate their company. So we think it's really pertinent to look at what actually was successful in the history of automotive and say, you can't start out of the gates at high volume.

53:30What you need to start with is a product that people love or willing to pay for and find ways to get to unit profitability at moderate volumes that you can actually sustain in the way that you're actually developing. So we contract manufacture a lot of our vehicle. What we do in house is we do all the engineering in house. So we don't have to pay any non-recurring engineering costs to suppliers that would otherwise charge hundreds of millions of dollars for it. We build all of our battery packs in house, which is the number one cost driver of the EV. It's about 33 % of the direct material costs of the EV go into the battery pack.

54:05So we do that in house. We own that manufacturing in-house. And the things that automakers that have been doing for 120 years, we let them keep doing it, stamping steel structures in the vehicle. Detroit is extremely good at that. You may remember watching 8 Mile and seeing Eminem stamping those big steel sheets and those big presses. That is what America's been amazing at doing in the last 120 years, and they will continue doing it for us. That is a callback to a movie I have not seen in a while and did not see coming. But yes, Eminem does in that movie work in a Detroit stamping facility and it looks about as interesting as you would think to have that as your career.

54:42But I'm glad that we're good at it because it makes for companies like yours possible. Okay, so this thing is going to have a 152 inch length. It's going to be able to tow, according to your website, 2 ,000 pounds, 6.6 thousand pounds. 6 ,600 pounds towing. Oh, I'm sorry. A payload of 2 ,000 pounds. Yes. And 6.6 thousand pounds towing. uh for someone like myself who has not driven a truck in a while i'm not familiar with just how competitive that is are those big numbers or are those relatively small numbers for the mid-sized truck market so like the tacomas the um the rangers the uh colorados that would be a fairly substantial amount okay so right on par with the lower end of like the f-150s 1500 uh vehicles.

55:28It certainly doesn't compete at the scale of the class three or class four trucks. But at the same time, EVs have a really interesting kind of characteristic about them from an engineering perspective where payload doesn't have a substantial impact on range. It has somewhat of an impact, but not very substantial one. Towing, on the other hand, has a dramatic impact on range. So EV trucks in particular are not the best vehicles for long haul towing when you worry about the time to get there because you have to stop for charging frequently, more frequently than you would stop to refill for a gas or diesel vehicle.

56:04Okay, but I'm thinking about your truck, the MT1, and I'm thinking about myself as someone who wants to show it to his spouse because I would like to buy one. And I don't think I would ever care about towing. Now, capacity, sure, because I might move, you know, concrete bags or maybe soil or gravel, whatever. But is towing a key use case for the MT1? Because to me, it feels a little bit to the side. if that makes sense. Yeah, no, I think it's a fair assessment. I think there's applications where if you're using a work truck in a city and you want to go pick up a trailer from Home Depot or you want to go pull your boat out of the water, that's the use case that we feel really strongly about, that's an application we can absolutely address.

56:42We do not see this as an application where you're doing towing cross-country with our vehicle. Okay. Now, in a couple of your posts, you guys mentioned that fleets have shown a lot of interest in this vehicle. Do they have any towing requirements or are they mostly just looking for the same cargo carrying capacity that we're describing? Absolutely. I mean, the majority of the applications of trucks, even in downtown cities, are not towing applications. But there are certain instances where if you did not have it, it's a non-starter. Ah, so you need to have in your back pocket, even if it's not something you're going to use every day.

57:15Correct. People don't, even commercial customers don't buy their vehicles for the 95 or 98 % use case. They buy it for the 0.1 % use case and making sure it can satisfy at least the 98 % use case. Okay, that makes good sense to me. Now, I want to talk about cost because I went back through a lot of media coverage of the company and the only thing that I could find was an old Tuck Lynch article saying that before incentives, this is back in 2023, so things may have changed. You guys were thinking about 50K for this truck. Is that still the right price range for what the MT1 is going to cost? Yeah, it starts at the low 40s and it works its way up there depending on how you accessorize the vehicle.

57:55the range and the motor options. And again, the average cost of a vehicle in the US is$49 ,000 right now. So we want to be on par with what you'd expect from a cost of a vehicle. We don't think we're going to be the cheapest option available on the market. We just heard the announcements that Ford's making for a new EV platform starting at$30 ,000. You've seen other companies like Slate come in with lower dollar amounts. We think that what we're trying to address is a specific, unique capability that doesn't exist in the market today that nobody is solving. The fact that you can't have a fully capable crew cab work truck that works for a downtown city.

58:33So that is the focus we want. And if that has an emotional and financial burden for you, then this is an option that may make a lot of sense. So is the main difference between you guys and Slate that their truck holds two and your truck holds four, so yours is just more capable as also a kind of a vehicle for going around the city, even if you're not using the bed? You know, I think that the market's really the difference between us. I think they're going after, you know, trying to be a lower cost approachable entry point for a vehicle is fantastic for a collection of people that really might use that.

59:08People that want a secondary vehicle, new drivers, older people that don't have families that they're driving around very frequently. That's an excellent option for them. Our specific application is for people that need to do truck stuff in downtown cities. Okay. So they might have a dog they want to keep in the cab. They might have a child they want to keep in the cab. So it has to double as a car as well, effectively, because if you're driving around a city, you're using it for transit as well as transport. Okay, I'll take that. How big is that market? I have no idea because I don't know how common I am because you're really talking to me here and this resonates, but I don't know if I'm one of 10 or one of 10 million.

59:47Well, 3 million trucks are sold in downtown cities every year. What? And yeah. Three million? Three million. All right. I mean, we're in a pretty big market. Now, part of the challenge is when you look backwards and say, how big is that market? Well, it doesn't exist anything in this market. So it's hard to say, how many people are going to buy a mini truck? Well, zero people bought a mini truck in the last 10 years. No, no, no. Wrong, sir. Some of my friends around town have imported Japanese K-trucks. Yeah, exactly. I should have said it. Zero people bought in US space. So 10 ,000. Oh, okay.

1:00:2110 ,000 K trucks were imported all of last year. They're the number one most imported vehicle from Japan per the 25-year rule in the U.S. And that's actually a pretty good sign to show that there is latent demand. Because if a consumer is going to go out and spend the money and effort and energy to try to import something from Japan that's not even legal, that's got 100 ,000 to 200 ,000 miles on it, it doesn't meet crash safety requirements, that means that there's some latent demand for this product. that that's actually one of the reasons why my thesis about your company being successful is exists because i know people want the little k trucks and i know that because there's one a couple streets for me up and i'm often walking the dogs and the kids and you should see how people react to it they go like this oh what's that they get totally enraptured in the idea of having this small city car that has a cargo carrying capacity so jason i agree with your thesis, and I love what you're building.

1:01:13Why hasn't the American major car manufacturers gotten to the same conclusion? I have a thesis about this, but I'm curious why you think they're not already doing what you're doing. Well, there's a couple of interesting just phenomenon that's occurred in the last 15 years. One of them is in 2010, the Environmental Protection Agency changed their rules and regulations where they, for the longest time, light-duty trucks were exempt from emissions regulations, and they just brought them back into the fold, but they based their emissions requirements on the size of the vehicle. Namely, the bigger the vehicle you drive, the less stringent they are on the MPG your vehicle had to get.

1:01:53So a lot of automakers went, oh man, I have to invest a billion dollars to make a new motor, or I just make my wheelbase a couple inches longer and I meet all requirements. And so if you look at something like the Ford Ranger over the last 15 years, it's gotten 50 % larger, but 0 % different in fuel economy. Well, that's just depressing, but people respond to incentives. Okay, so people were incentivized to make larger trucks versus smaller trucks. But even with all that, if you're making an EV, we're not talking about emission standards or cafe or whatever. So why haven't they tried to do this?

1:02:26And that's the challenge here is a lot of automakers are condensing their vehicle platforms. They're trying to build three different global vehicle platforms for every single one of their vehicle designs. So if your truck platform is your big diesel or gas-based vehicle, well, swapping it into a battery electric platform without changing much of the other infrastructure just leaves you with that same giant platform. This would have to be a ground-up redesign. And in traditional automotive, a ground-up redesign takes at least eight years to accomplish. you founded the company in 2022 right it's been uh three years since then how have you been able to go so much faster just less red tape less historical baggage or did you actually change some fundamental thinking to get this truck to where it is today so quickly so there's a coin that there's a term that was coined by an automaker i won't call them out by name but that was virtual validation, meaning that they were going to build and validate their vehicles entirely in software before they built any hardware.

1:03:34They were going to use the state-of-the-art tools to do that. But when you have an automaker that employs 100 ,000 validation engineers, like trying to say, hey, we're going to remove all of you validation engineers, we're going to move all of the software, it's an impossible thing to do. So you can't, the fact that automotive design cycles take three to five years and then the engineering cycles take another three to five years, now you've got an eight-year product cycle from inception to actually deliveries, it's largely because of the way that we've moved from hardware design, hardware validation, and implementation of manufacturing protocols.

1:04:12What we're doing is we're building the entire company from the ground up using software to build and validate our company and also to use like kind of some of the cream of the crop and AI tools. Like, for example, crash testing in the just 20 years ago used to be build something, a portion of the vehicle or a scale model vehicle, slam it across as many times as you can. Every time you have to rebuild something, that's a ton of engineering design cycles to rebuild that and just slam it into the. That's why things take so long. Well, recently we've gotten so good at physics-based simulations that we can now crash a vehicle, but that still takes like 10 ,000 compute nodes and 24 to 48 hours to compute.

1:04:52And so even those at scale still don't, like they're much faster than the months-long process, but they're days-long process. So the question we've posed is how do you make that a minutes-long process? And that's a lot of how we implement, how we do all of our build and design and validation. okay and by the way it was porsche who said virtual validation right it was not porsche but i won't i won't play it well it proves what i can google while i'm also paying attention at the same time uh okay so we talked about scale earlier and how you're not going to go after the day one mass manufacturing approach you're going to start smaller and then build from there uh i know you guys said in 2023 you had 500 pre-orders that have scaled to i think 2000 in 2024 how many pre-orders are you guys at today and what does a first uh production run look like in terms of scale for tello uh i think we're just under 12 000 pre-orders right now okay and so our our goal has been to get our first vehicle in a customer's hands in 2026 that has always been our kind of milestone it's probably looking towards the end of that year that we're going to do those deliveries.

1:06:00We will deliver a small batch of vehicles to first early access customers that will probably be somewhat incomplete from a software perspective. So these won't be a mass run of vehicles. We'll have our engineers stationed with them and making sure that those get to a state where we're very happy with. We'll then build 500 right after that. But if we feel happy with that, then we'll build 5 ,000. And 5 ,000 is really the break-even point for the company, where if we can build 5 ,000, we can be unit profitable on the vehicles. And then just because I'm an accounting dork, how many vehicles would you need to have enough gross margin on them to actually pay for the operating side of the business as well?

1:06:41Is that 10 ,000 or is it something more like 50? Yeah. So I've financially modeled this to the nth degree, but it's between 10 and 20 ,000 to actually get corporate profitability. But you have, there's a lot of options open to us once we hit those numbers. Like we will probably start bringing more and more in-house after we hit unit profitability, which has its own set of operating costs that are associated with the work we're doing. So it's not going to all happen at once. But once you bring stuff in-house, you don't pay someone else's gross margin and you can over time lower your bill of materials, labor costs and get more efficient.

1:07:15Yeah, of course. But there's a capital expenditure with doing each of those things. So it would have to coincide with the financing as well. Which is actually one thing I wanted to talk about. So whenever I talk to a founder, especially if it's a company that I haven't talked to before, I always go through their fundraising history to figure out who's backing them, how much are they raised, gives the idea for burn and so forth. I was only able to find a couple of relatively small funding rounds for Telo. And either I'm missing some numbers and you guys have raised money I don't know about, or you're the single most capital efficient company of all time.

1:07:47So how much money have you guys raised so far and how much more are you going to need to get to that 5 ,000 vehicle break-even economics point. So both the things you said are true, by the way. Oh, okay. There we go. With spending only$6 million in the last 18 or so months, we've built two on-road vehicles that are registered for the state of California. We've built a battery MPI line. We've engineered an entire set of vehicles for a beta and gamma build of our vehicles. That said, we have not, and I unfortunately can't tell you too much until a few weeks from now, but we've definitely been in the fundraising process for quite some time.

1:08:34Okay. You can't get specific, but you could probably give me a guidepost here. Do you need to raise tens or hundreds of millions of dollars? And I'm asking because I saw Rivian go through its life, go through its IPO, and I've been tracking, it's, I'll call it impressive capital consumption as it works towards functional scale. So the fact that you even asked it in the tens of hundreds is actually, I appreciate that because that is how we're thinking about those numbers. It's in the tens. It's like, we will need to raise before unit profitability, at least another a hundred million dollars. That's not that much for what you're doing.

1:09:16Cause car companies scale in revenue terms, very quickly because you're selling$50 ,000, just big number, round number,$50 ,000 units. So the revenue scale is pretty neatly along with that. That still feels cheap, frankly. Yeah. Again, to be clear, we had 11 people in the company up until recently, only 11, and that got us to where we are today. All right. Well, if people want to learn more, it's telotrucks.com, T-E-L-O-Trucks.com. And I I think they're just fantastic. Jason, all the best. And when you start manufacturing, love to have you back on so we can see what the factory floor looks like.

1:09:51Yeah, wonderful. Awesome, thank you.