Why Medium is HIDING from AI | E2179

Today's show: On a special edition of TWiST, Alex presents a trio of TWiST 500 interviews. First up, Medium CEO Tony Stubblebine on the company's new Really Simple Licensing (or RSL) initiative, and how it's helping to compensate writers for their content. THEN, Human Native CEO Dr. James Smith joins for an even deeper dive on AI licensing, why a writer's content isn't just DATA to them, and why the company is pivoting away from its old marketplace model. FINALLY, Jason Marks of TELO Trucks stops by to show off his EV mini-truck, walk us through why people need a powerful but tiny truck, and explain why they're building trucks right here in the US of A. Timestamps: (0:00) Intro: It's a trio of TWiST 500 interviews today! (02:57) Alex chats with Medium CEO Tony Stubblebine (04:54) Why Medium's more worried about HIDING from AI than anything else (10:24) Nexos.ai. Stop Shadow AI in its tracks with the unified platform for secure AI adoption and productivity. Try it with a free 14-day trial at nexos.ai/twist. (11:27) Show Continues… (15:02) What is RSL and why aren't AI companies offering publishers a good value? (19:58) Gusto. Check out the online payroll and benefits experts with software built specifically for small business and startups. Try Gusto today and get three months FREE at Gusto.com/twist. (20:59) Show Continues… (22:03) How Medium calculates its revenue split with writers (26:57) Alex welcomes Human Native CEO/founder Dr. James Smith. (27:34) Writers see their content as their life's work, but tech co's just see DATA (30:01) DevStats translates complex engineering metrics into a shared language everyone at your company can understand. Get 20% off by going to DevStats.com/twist (31:06) Show Continues… (33:01) Why H. N. is pivoting away from licensing and the marketplace model (47:23) TELO Trucks CEO Jason Marks joins the show (48:25) What is a "mini-truck" and why they're TELO's focus (52:28) Why TELO is building their EV trucks right here in the US (54:42) Why towing capacity still matters, even on a mini-truck Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank you to our partners: Nexos.ai. Stop Shadow AI in its tracks with the unified platform for secure AI adoption and productivity. Try it with a free 14-day trial at nexos.ai/twist. Gusto. Check out the online payroll and benefits experts with software built specifically for small business and startups. Try Gusto today and get three months FREE at Gusto.com/twist. DevStats translates complex engineering metrics into a shared language everyone at your company can understand. Get 20% off by going to DevStats.com/twist Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com Subscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

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

• 0:00Speaker 00

  We are not going to make any change unless an AI company comes to the table with money. Right? Like, I didn't want to do this, not in a way that wasn't transparent to our community. And but now that it's out, we can literally start having these conversations. And the conversations kind of don't make sense until you have a number, right? Are we saying we're going to pay every story one penny? No one's great care, right? Is it $5? Well, some people are going to start to care. Is it the best stories that show up in AI results over and over again? Are they going to make $100, $1,000? Oh, that that's going to catch people's attention. And so, you know, we have to start the negotiation to where to see where it goes.

• 0:44Speaker 01

  This Week in Startups is brought to you by DevStats. Check out DevStats today and get 20% off plus access to their dedicated Slack channel. Just go to devstats.com/twist. Gusto. Check out the online payroll and benefits experts with software built specifically for small businesses and startups. Try Gusto today and get three months free at gusto.com/twist. And nexos.ai. Stop shadow AI in its tracks with the unified platform for secure AI adoption and productivity. Try it with a free 14-day trial at nexos.ai/twist. That's N E X O S dot A I slash twist.

• 1:28Speaker 01

  Hey, everybody. Welcome back to This Week in Startups. This is Alex, and today on the show, I have three amazing Twist 500 companies coming your way. First up, we are going to talk to Medium CEO Tony Stubblebine. Now, if you're a long-term listener on the show, you know, we've had Tony on before, so why now? Well, just last week, Medium, along with other companies like Yahoo and Reddit, all joined together under a new initiative called RSL, or Really Simple Licensing. Think of it as the RSS for the monetization of media in the generative AI era, if you will. I'm curious about why he picked this project back and why now? Then, after that, we're going to talk to human native. Now, this is a Twist 500 startup that I added originally to the list. Because they were building an awesome AI content marketplace.

• 2:13Speaker 01

  Essentially, publishers and AI companies would meet in the middle, under the startups' auspices, and find a way to make a deal. Well, turns out they're pivoting a little bit. I learned a ton in this chat about the pitfalls, you might say, and the potential in monetizing IP in the generative AI moment. Then, to wrap up our show today, we're going to talk to the CEO of Telo Trucks. Now, if you recall Slate, the Jeff Bezos-backed, small EV truck company, this is like that, but cute, and also pretty far down the road. So if you care about EVs, you care about transport. Well, Telo Trucks is an absolute treat, and I want one. But no more from me right now. Let's dive into it, and here's Medium's CEO.

• 2:58Speaker 01

  All right, now, we're all big fans of using AI search tools. I'm a big fan of ChatGPT's GPT-5, but you might be a perplexity guy. You might be a Claude fanatic. You might love what Google has cooked up. One thing, though, that people don't really understand is how many queries these search engines send out. They might read 10, 20, 30 pages. And what that has led to is a change in how the internet functions. Now, a lot of websites are seeing an enormous number of pings and crawls from AI search engines, AI search agents, and often the compensation structure for those is zero. So companies are working to find a way forward to ensure that there's good attribution and good economics on the side of people who write content. Cloudflare's paper crawl model was one such effort, and we have a couple startups out there, things like Tolbit and Human Native, and created by humans, that are building their own marketplaces.

• 3:42Speaker 01

  Now, this week, there was a new product that came out called Really Simple Licensing, or RSL. If you know RSS, well, it's a term that you're probably pretty familiar with, and it's a fun little play on that old-school framework. One of the companies that's signed on to RSL is Medium, along with Reddit and Yahoo and others. I'm fascinated by this new approach to ensure attribution and compensation for the internet and the world of people who scribble online. So please join me in welcoming back to the show Medium CEO, Tony Stubblebine. Tony, how are you? Hey, I'm good. Thanks for having me back. Dude, my absolute pleasure. So catching people up a little bit. Last year, Medium hit 1 million paid subs in April. April, you announced cash flow positivity in May of last year.

• 4:23Speaker 01

  Big steps for the company. And then since then, Tony, AI has only become more popular. And we've seen the rise of generative AI search and, as I mentioned, a lot more queries. So just to set some context here, how much more now is Medium getting hit up by AI search engines than it was last year when you reached those financial milestones?

• 4:43Speaker 00

  Yeah, I mean, the rise of AI sort of impact is everywhere. Like, we see it in the slop that gets posted to Medium, which we do our best to either delete or to hide. You know, I think we're more focused often on hiding than anything. We see it in the comments and we're like constantly fighting in authentic comment bots. And we see it just in the the traffic to the site through all of the crawlers. Because it's not just the AI companies you've heard of. It's all the up and coming wannabe AI companies that are trying to get new training data. And I think what we found is, you know, one of our jobs is we have to represent the writers on Medium. Like, we're like, without them, we're nothing.

• 5:28Speaker 00

  And and in particular, we're fighting this issue with the AI companies, which is not technological. It's human, right? I think they, the way that they have operated to date breaks the social contract. That they, you know, essentially, society is based on an exchange of value. And they've taken value from the writers and creators of the internet and really offered nothing back. And that's not good. It's not fair. I think a lot of our writers would call it theft and like literally do.

• 6:04Speaker 00

  But more, it's like, I mean, we could just think of this as an urgent internet need. If that exchange of value goes away, the public internet goes away. This is what people are calling the dead internet theory. It's just going to be only AI slop and all of us creators and writers will kind of return. We'll to private spaces and to paywalls, which is like, I mean, there's some something to that. But it's not the end of the world. But I think, you know, if we want to protect a public information superhighway, we have to fix this thing. And the core thing we're trying to fix is

• 6:40Speaker 00

  the behavior of the AI execs and to get them to come to the table and find some workable model that works for all parties.

• 6:48Speaker 01

  Yeah, and that's why the really simple licensing idea resonates with me because it's not one company going out there and saying, this is what we're going to do. It's a collective of very highly trafficked websites and companies. So I guess, take us back to the beginning. When did this idea come to Medium's shores? And how did you decide that it was the right approach amongst others for Medium to take?

• 7:09Speaker 00

  We decided it was the right approach before we knew of this particular approach. Right away, when ChatGPT launched and we started to see what was happening with like, kind of the, the lack of exchange of value, like the theft essentially of people's content, that we thought like, there's no way to resolve this except by force. Like, if they're going to be antisocial, we have to be antisocial. And the way to do that is to get a coalition going. And I tried to get a coalition going. And, but we're not really quite, you know, we're not Facebook or Metas that call themselves now, right? We don't quite have that heft.

• 7:50Speaker 00

  And I think what the bigger companies tried to do is just cut individual deals instead. And so if there were individual deals to cut, those have all been cut. And now we're at the point where what's left is the coalition. So it feels a little bit late, but overall, I'm happy to have, have people going in the direction of an internet standard. Like, we can't fight what's going on with vendor lock-in. We can't fight it one-on-one. If we, if we get together, we have enough clout to say, look, you're not going to be able to train on new data again unless you come to the table. Or worse, you're going to be, you're going to be the individual company that's not able

• 8:31Speaker 00

  to train on new data while these other companies, while your competitors do, because they pay for it. And so that standard, when we saw it, we saw it pretty early. I think we were one of the first companies to sign on. And I bet you, we were the fastest. Like, between seeing it and saying yes, was like, probably 30 minutes.

• 8:51Speaker 01

  Which is instantaneous in business time.

• 8:55Speaker 00

  Yes. And it's because we'd already thought it all the way through. Like, we knew what we were looking for and, and we had been vocal. And I think that's also why they came to us early. And said, look, you know, the, one of the creators, Eckhart Walter, is also one of the creators of RSS. So like, I understood, he was a credible creator of internet protocols.

• 9:16Speaker 00

  I worry for your listeners that here we are talking about internet protocols, but it's there's something exciting in it for me. And, and so we signed on and really the only, the only thing we changed is that we knew that our intention was if we negotiated with AI companies that we would pass all of the money back to the writers. I think we're the only

• 9:41Speaker 01

  All of it.

• 9:42Speaker 00

  All of, okay. My CFO is like, Tony, what about the legal fees? Stop saying all of it. All right. Caveat, maybe if the legal fees are really high, we're going to pay like, get like at costs, let's say, we're going to pass it all through to the creators. We already like, Medium is just not in the sell your data business. We never have, this is again, an easy decision, doesn't even matter how much money it is. It's a pass through to, to the writers. And as far as I know, we're the only platform that is thinking that way. And I mean, I was like, along with shaming the AI companies, I'd like to shame the other platforms that they also should be doing that.

• 10:24Jason Calacanis?

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• 11:09Jason Calacanis?

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• 11:27Speaker 01

  I love having you on. Also, I think people are interested in internet protocols because they're the framework on which the internet sits. And most people that watch the show are building internet-based companies. Some are doing hardware, absolutely, but like mostly it's online. So I think this is pretty down, down the pike. Now, you are talking about AI training, and I had framed this mentally more as an AI inference point because RAG queries go out there and ping, as I said, dozens of sites. It's fun to watch Google's AI go ping 70 sites. I'm like, sweet. But that puts a big load on things. So I had it more framed from the inference side. You're talking more about training. Is that a better way to think of things in this case? That we're talking about defending training data?

• 12:04Speaker 00

  This is, I mean, this is where you've like fallen into the complexities of it. I mean, certainly, training is one because there is no exchange of value, not even sending traffic back. The, the RAG side of it that you're talking about, where they make a summary, essentially, the AI-generated summary world often has citations and often sends traffic back. So at least they're creeping back into the world of an exchange of value. Problem is the exchange of value is very weak. Like, you know, I would say it's like, to the degree that Google is trade, is trading their prior search traffic with like a citation and a

• 12:44Speaker 00

  generated result, it's probably we're giving up like 100 clicks to one. And so it's just, it's miniscule. And it's not nothing. I mean, I will say that the traffic that comes from ChatGPT now converts to a paying member on Medium four times higher than normal traffic. It's higher intent, as you would expect. It's like, a person says, hey, this summary is not enough. I want to read deeper. Oh, that's our dream Medium reader. Like, we like people that think deeply and read deeply and care about being smarter and understanding all the complexities of something.

• 13:18Speaker 01

  But higher, higher willingness to pay doesn't imply more total paid if the amount of people coming back to Medium is lower. So does it net out to be even? Or is it still dramatically? Okay.

• 13:29Speaker 00

  Not even close. And, and so, and that's the fear, right? Is that what we have is really like a temporary moment where Where our business is shaky, like, you know, the sort of the social media platform business is shaky, and also the businesses they're building is shaky. Like, what's going to go happen to the world of rag results when there is no more, you know, rag to retrieve, right?

• 13:55Speaker 01

  Right, right. No, that's what I've been thinking about. The best thing possible here is that if there's a way to bring monetary value from the AI companies to the creators in a way that works for everybody, then we'll have healthier creators, short term and long term, and also healthier AI companies long term. Like, it feels like we're shouting for all the systems to work at once, which feels a little bit surreal.

• 14:18Speaker 00

  Right. And so what's weird, right, is these are smart people on the AI leadership side. Why didn't they see this coming? Why didn't they start with a collaborative approach? I mean, like I said, they started with an antisocial approach. It's like, grab it and then come fight with us. And this is where I think Matt Prince at CloudFlare actually like had it right. It's like, they have not voluntarily come to the table. So the first step is to force them. And that is a mass blocking of crawlers everywhere.

• 14:50Speaker 01

  Has that had an impact, do you think? Because CloudFlare's been a little bit quiet about PaperCrawl since its kind of Thunderclap announcement. And I don't have a good feel for how impactful that's been. And also, Tony, I'm curious, did you guys consider that before going the RSL route?

• 15:07Speaker 00

  Yeah, we have two problems with it. But I say this with a lot of respect for CloudFlare. Like, we needed people to move early, and they did. And they moved in an articulate way, and they moved in the way that they were most capable of doing. But at the end of the day, you can't protect the internet through vendor lock-in. You know, we can't say, oh, let's all sign up for CloudFlare in order to solve the problems of the internet. And so at the end of the day, we're going to need CloudFlare to support RSL.

• 15:35Speaker 00

  And then this is this particular thing for us, which is we made an addition to the RSL standard that would allow us to do the block on a per-page basis and Cloudflare does it on a per-site basis. And that's because most companies are viewing this as we want our company to get paid. And we're viewing this as we want individual people to get paid. And so we need to then give those people their own access controls because a lot of them are not going to opt in under any conditions because they're so morally distraught with the AI companies.

• 16:09Speaker 01

  Yeah, there's a lot of folks out there who are, and there's a lot of folks out there who are not. And I think there's a lot of folks who are in the middle who are probably your target here because some people are like, screw it, crawl me, I don't care. And some people are, you know, very much on the other side of things. Okay, so we both agree that having a unified front here, a united front is super important. And I think with the other sides that you mentioned or that I mentioned, there is enough heft there to make this stick. So what's been the response to your knowledge from the AI side of things? Because we can talk about the publisher side until the cows come home, but if the AI companies don't engage, I wonder if this will this will work. So any encouraging signs on that side of the fence?

• 16:47Speaker 00

  Yeah, I think we're going to find out. It just launched. I happen to have already something scheduled the next day. But they were not anywhere near prepared to speak to it. But I think this idea that first of all, we have to show credible force, I just, I don't like, this is not how I like to do business, but this is kind of, you know, like this is the prisoner's dilemma. Like if they like, how they show up is how we have to show up. And so credible force is to have enough content kind of under this umbrella. And I think legal threats have not really worked. Can I tell you a lesson that Medium learned early on?

• 17:30Speaker 03

  I would love to hear it.

• 17:31Speaker 00

  So it really early on, we realized that even though like that we're a big enough data set that we're able to poison any language model. And the reason we learned this is because Medium, like by design, is just filled with M dashes. So you know this theory right now that you know it's AI generated because it's filled with M dashes. Well, you know where that came from? Medium, because our founder loved M dashes, popularized a feature, like an automatic feature in the editor that would convert various dashes and whatnot to M dashes. And so then it's became culturally a way to write on Medium. And it did spread beyond Medium itself.

• 18:13Speaker 00

  I think most modern text editors on the internet right now do this. Like if you do a double dash, it'll automatically connect. But you know, essentially, we created a, you know, we created a trend which now shows up in the language models. And so like having seen that, whenever someone comes to us and says, you know, like, look, you know, you don't have legal standing. How are you going to block them? They're just going to get around you like the crawlers will get around you somehow. It's pointless and whatnot. I said, well, you know, like at the end of the day, we could go back to just poisoning our results. Like, you know, I don't know, like the silly way is like, oh, you know, it really increased Medium's riz if we got involved in like slang maxing or, you know, the results.

• 18:58Speaker 00

  Like we can rewrite like whatever gets returned to the crawler with whatever crazy modern slang like we want, or we could get really, you know, like, we could get a lot harsher than that. Like, you know, we can essentially put slander into the results. Like anytime you hear the word OpenAI or see the word OpenAI in a Medium text, when a crawler is reading it, we can just say, comma, filled with hallucinations, comma, and just move on, right? And so now like that's every, like every language model is filled with the slander. And I was like, hey, that's your fault. Follow our terms of service, right? So I think, you know, we'd like to avoid that level of warfare and instead just like,

• 19:42Speaker 00

  like, deal with reality. Like, like these companies don't get this for free. And if they don't do something, they're like the whole, the whole foundation they're built on will disappear over time.

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• 20:39Jason Calacanis?

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• 21:00Speaker 01

  So you said in your post on Medium's implementation of RSL that you're doing, quote, "the simplest version of this new RSL standard, which prohibits AI companies from using your stories to train their AI models, but allows them to summarize and link back to your writing in AI-generated search results, effectively closing the training door and demanding fair attribution on the, what I would call the inference or RAGs type of thing." Seems like a good, a good starting point. When do you think you're going to have the either demand from the AI side or the confidence to roll out more of RSL and essentially start executing on paper inference?

• 21:38Speaker 00

  Yeah, we are not going to make any change unless an AI company comes to the table with money. So in a way, the announcement from us is a start of a discussion with our own community about how we're planning to negotiate on their behalf, right? Like I didn't...

• 21:59Speaker 00

  Want to do this not in a way that wasn't transparent to our community. And but now that it's out, we can literally start having these conversations. And the conversations kind of don't make sense until you have a number, right? Are we saying we're going to pay every story one penny? No one's going to care, right? Is it $5? Well, some people are going to start to care. Is it the best stories that show up in AI results over and over again? Are they going to make $100, $1,000? No, that that's going to catch people's attention. And so, you know, we have to start the negotiation to where to see where it goes.

• 22:34Speaker 01

  You you kind of trod on my next question there a little bit, which is, what do you think the economics of this could become in time? And I don't know what sort I would ask this, Tony, because the answer is going to be it depends. But let's say that I'm someone who has written 1,000 pieces of of of content on on Medium over the years. So I'm probably one of your power writers, if you will. And my material gets reasonable out of search traffic. I have some dedicated readers in the app. I have some paid readers. And there's also this AI component. So do you think this replaces a material percentage of subscription fees in terms of like how much money a writer might get? Or is this more of a dust on top? I'm trying to figure out the scale of possible return for your more active writers.

• 23:14Speaker 00

  Yeah, I think that the standard internet pay rate is probably where we should end up. So, you know, you could look at this through the ad model, right? If we're if Google sends you 1,000 clicks and you have like run of the mill ads on it, you're going to make around $5, right? So the $5 CPM, $5 RPM, like that is roughly the standard pay rate. And some people do quite a bit better than that. And that is kind of the business of media these days is to surpass that. But your average person is is going to be in that in that world. So. You've got an apples to oranges thing where they're not going to send a thousand clicks because literally the summary is stealing your clicks.

• 23:57Speaker 00

  But they are going to have to give something in that realm to even be on par. And if they go below, you have to factor in they're also not sending you the clicks. This like this thing that got you to write online in the first place, this validation of having people read you and this validation of having your ideas spread, that's also going away. So I would say like in order for this to actually work, to actually create a healthy ecosystem, they're actually probably going to have to come up from that that level.

• 24:32Speaker 01

  I want to close with just a question about AI and writing in general. You joked about the N dashes and how you changed the way people think about AI writing. By the way, screw you because I'm a big N dash guy. And now people are like, oh, AI wrote this. I'm like, no, these are artisanal hand grown words, my friends.

• 24:48Speaker 00

  AI trained on the best writers on the internet and now it uses a lot of N dashes. That is.

• 24:53Speaker 01

  You're welcome, everybody. You're freaking welcome. No, my concern is that people now use AIs to generate summaries of writing and they often use AI to create words. And there does seem to be people slowly backing away from the process of writing things down and reading them. And of course, technology changes, people's habits, I'm not here to be a lot of it. But I'm curious if that represents a material challenge to the ethos of medium, which is writer first, writers writing and people reading those writers. Like, is the culture shifting away from what medium does?

• 25:27Speaker 00

  I'm always shocked. Any sort like this, we get so many questions along the lines of what you're saying of like culture trend, whatever. And I'm like, and I just come back to look, writing is thinking and reading is learning, right? That like, like for thousands of years, humans learned through story for a reason. Like the way our brains work require all that context. And the idea that summary is very replace story is kind of ridiculous and not even a new concept, right? Like the Cliff Notes didn't replace the books. And so I like, I just, I always come back just to the first principle. Like, is it, does the world still reward people who want to be smarter? Yes. Does your life get better if you're smarter?

• 26:08Speaker 00

  Yes. So 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.

• 26:23Speaker 01

  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, come 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 medium writers out there because the evil of writing, it's, it's, it's, it's too important to let die.

• 26:49Speaker 00

  We're both fans of writing. Thank you.

• 26:51Speaker 01

  All right, Tony. Bye.

• 26:57Speaker 01

  So 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?

• 27:28Speaker 04

  Hey, Alex. I'm good. Thanks. Thanks for having me.

• 27:30Speaker 01

  Yeah, my pleasure. So I had put your company amongst several others, your toll bits. 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 going to be ample supply, that publishers, authors, people who own data sets 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?

• 27:59Speaker 04

  I think there was a mismatch, but I'm not necessarily sure it was always just about the demand itself as to like, it 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 and 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 holders would come and talk about, you know,

• 28:40Speaker 04

  the respect for their work and how and having things like editorial control over headlines that were reproduced. And AI companies would come and say like, okay, that's great, but we want this really specific requirement. We want exactly this, 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.

• 29:08Speaker 01

  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. So is this a, a, 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?

• 29:25Speaker 04

  I think a lot of it to do 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. And, you know, we worked with a rights holder who'd been approached by an AI company asking for Arabic language content with transcription. It's never like, yeah, we were 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, you know, you can help license it. That's a real challenge for them.

• 30:01Jason Calacanis?

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• 30:45Jason Calacanis?

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• 31:06Speaker 04

  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. 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, like 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. And 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,

• 31:51Speaker 04

  we attempted to translate and be the buffer between those two.

• 31:55Speaker 01

  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.

• 32:07Speaker 04

  Exactly.

• 32:08Speaker 01

  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?

• 32:20Speaker 04

  So 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 parts. Um. We then tried to do a few different things. One of the things that we tried 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.

• 32:59Speaker 04

  But in order to do that, you need a lot of precedence, you need a lot of standardizations and you need the ability for, you know, 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. And a couple of things, I'm, you know, been a product manager at Google and Deep Minds, who built lots of technology as a team. I'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.

• 33:35Speaker 01

  Those are different skills? Who would have thought?

• 33:37Speaker 04

  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.

• 33:43Speaker 01

  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, 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 it to form the large in their large language models. Lately, though, talking to both publishers and AI entrepreneurs, it seems that the RAG use case, using data real time to help server query has become much more important. So a, a, 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?

• 34:26Speaker 04

  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? Right? The thing that's changing about these generative AI platforms, people are not leaving the walled garden systems. And you know, 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

• 35:08Speaker 04

  free internet and on the open internet? And I think that's a really difficult one. The 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.

• 35:42Speaker 01

  Yeah. So i- 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?

• 35:57Speaker 04

  I, well, I think the media organizations and large tech companies have had a storied history to this point. You know, 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, like quite frankly, right? And so I think there is a lot of distrust, that even if we can solve all of the technical issues, there's still some fundamental barriers to overcome.

• 36:19Speaker 01

  This does bring up one of my favorite quotes. In 2024, coverage of your seed round and tech run, 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."

• 36:32Speaker 04

  Yeah.

• 36:33Speaker 01

  Opens a lot of doors. Okay, so let's, 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? Because it sounds like you're, you're leading me towards a, there's a technical solution to part of these problems and we're going to go build it.

• 37:01Speaker 04

  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. When we, we, 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.

• 37:17Speaker 01

  You can say, you can say pounds, it's fine.

• 37:19Speaker 04

  I'm just being respectful of your, you know, the majority of your audience. Also, having worked for Google, uh, sometimes I'm kind of this mid-Atlantic, half-English and half, uh, American in the way that I speak.

• 37:29Speaker 01

  Just don't say kilometer or France and we'll be fine.

• 37:31Speaker 04

  I'll just do it all with a Scottish accent and then you, nobody will understand what I'm saying anyway. Um, so.

• 37:36Speaker 01

  Actually, a, as a fan of Still Game, the Scottish television show, I think I'll be okay.

• 37:40Speaker 04

  You've seen Still Game? That's great. I always recommend that. That's a great way.

• 37:43Speaker 01

  Every single, every single episode several times, including the more recent seasons, which were good, but.

• 37:46Speaker 04

  Yeah.

• 37:47Speaker 01

  Oh, and I've seen the stage shows and the original, uh, stage production. So, yeah.

• 37:50Speaker 04

  You're, you're a mega fan. Wow. Wow. Okay. We should talk about that later.

• 37:55Speaker 01

  We will. Uh, back to you. Yeah.

• 37:57Speaker 04

  Back to, uh, dollars. And so the, 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 do, how does a content company that doesn't have any particular expertise in building AI models answer those questions?

• 38:30Speaker 01

  Well, it, 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. So that sounds to me almost like an internal search engine and taxonomy generator.

• 38:43Speaker 04

  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.

• 39:02Speaker 01

  Snowflakes, Databricks.

• 39:03Speaker 04

  Yeah, calendars, 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? I don't think there are category defining companies yet in that space, which help you extract in 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.

• 39:43Speaker 01

  Does 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 length, so it's both words and data points.

• 39:56Speaker 04

  And audio.

• 39:57Speaker 01

  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.

• 40:13Speaker 04

  I think it's a bit of both, quite frankly. I think there's a lot of great software engineering and data engineering of, you know, traditional means, but now that is 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.

• 40:32Speaker 01

  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, you know, 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, can look into it. So I guess what's the, the product that you're pushing towards here? And what does it look like when it reaches the market?

• 40:55Speaker 04

  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.

• 41:22Speaker 01

  I see. Okay. Now. Uh, one of my favorite companies, Box, uh, because I've talked to the CEO, Aaron, a bunch of times and he's just charming. Um, has been talking ad nauseam for the last couple of quarters, maybe years at this point, about helping people who have their data stored inside of Box, which is a traditional enterprise. Um, oh man, it's been a while. Enterprise sync and share files, EFSS, I forget the acronym, whatever it is.

• 41:46Speaker 04

  Yeah.

• 41:46Speaker 01

  They, 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? Would your system work best for companies that have a lot of on-prem storage? Or do you think this is a, a multi-cloud affair?

• 42:05Speaker 04

  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 taking 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.

• 42:33Speaker 01

  Okay. And then on the do something useful with that data, is that a thing that you think your company is going to, to focus on? Or more like help people get to that point? And then from there, it's choose your own adventure?

• 42:44Speaker 04

  I think it's honestly, we're still early enough that we get to figure this out and that's the fun bit. Um, 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 India's.

• 42:56Speaker 01

  No, no, you're fine.

• 42:57Speaker 04

  This company.

• 42:58Speaker 01

  That could be one of like a hundred thousand people.

• 43:00Speaker 04

  Yeah, exactly. This was like a C-level person at this company. They have 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.

• 43:12Speaker 01

  Oh, interesting. So you're facilitating these companies that would have come to the marketplace for a, a cut of those, you know, trillion rag calls at a fraction of a penny each.

• 43:21Speaker 04

  Yeah.

• 43:21Speaker 01

  And instead allowing them to build something on their own.

• 43:24Speaker 04

  Exactly. Cause 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. And I was like, cool. Well, that's where we can help.

• 43:32Speaker 01

  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 Anthrobius, your OpenAI, your XAI, et cetera, your Mistral's. 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. Is that a broad enough dataset? Or is perhaps a narrow enough dataset to build something useful on top of it?

• 44:13Speaker 01

  Or will most of that value, do you think, come to fruition when other companies can combine multiple data sets 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?

• 44:28Speaker 04

  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. If your articles, if 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.

• 45:04Speaker 01

  So The Guardian, to pick a paper that you and I both read, 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.

• 45:14Speaker 04

  Yep.

• 45:15Speaker 01

  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. Okay, I can see that.

• 45:27Speaker 04

  Exactly.

• 45:28Speaker 01

  But that's going to require a lot of. Technical leadership at companies that have famously not been so technically leading. And I'm Mike making fun of my own industry here, journalism, just to pick one. Do they have enough chops to do that, James? Or they, they're bad at websites. Okay, like, how can they build AI systems?

• 45:48Speaker 04

  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 chat GPT. I don't know if you saw, there was a great podcast, sorry to talk about other podcasts on this podcast.

• 46:06Speaker 01

  Please, no.

• 46:06Speaker 04

  There's a great podcast then that the leader of chat PRD did with Tom Tungis from.

• 46:12Speaker 01

  Oh, Tamaz.

• 46:13Speaker 04

  Yeah, 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 like 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. What 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?

• 46:44Speaker 01

  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?

• 46:51Speaker 04

  Human Native AI, that would be nice.

• 46:53Speaker 01

  So James, just before you go, drop your URL and a job you're currently hiring for.

• 46:58Speaker 04

  We are human native dot AI, that's one word, human native dot AI. And what we're hiring for, well, actually surprise us. If 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.

• 47:17Speaker 01

  All right, James, thank you very much.

• 47:18Speaker 04

  Thank you, Alex. Cheers.

• 47:23Speaker 01

  Hey, welcome back to Twist. Now, I learned to drive in an F250 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 could install 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? Telo is doing

• 48:04Speaker 01

  just that. And to tell us more about that, please welcome to the show Jason Marks, co-founder and CEO of Telo Trucks. Jason, hey, how you doing?

• 48:12Speaker 03

  Hey, thanks for having me.

• 48:14Speaker 01

  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?

• 48:29Speaker 03

  Yep. So we build mini trucks. That's what the MT and 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. If our vehicle was available today, it would actually be the smallest vehicle on US roads, despite the fact that it has a five-foot bed and seats five people.

• 48:56Speaker 01

  It's even smaller than than one of those like tiny little two-door Chevys that I see at times?

• 49:00Speaker 03

  Absolutely. It's feet shorter than that. It's shorter than a two-door Mini Cooper.

• 49:04Speaker 01

  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 why this came to mind for you?

• 49:14Speaker 03

  Well, first off, I'm a truck guy. I've driven a Toyota Tacoma 230,000 miles, I think, so far. I 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.

• 49:52Speaker 01

  So tell me about that unique position. Why are you the right guy to build Telo?

• 49:56Speaker 03

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

• 50:41Speaker 03

  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-

• 50:57Speaker 01

  Let's go ahead and, actually, Jason, hold there, because I feel like we should just show people what we're talking about now. So 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?

• 51:15Speaker 03

  That's right.

• 51:16Speaker 01

  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 1,000 pounds?

• 51:40Speaker 03

  Yeah, that's exactly, 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.

• 51:57Speaker 01

  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. So 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 you're building it here. 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?

• 52:38Speaker 03

  Well, first off, I think one of the things to 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. They started with 2,400 Roadsters that were contract manufactured from Lotus. Then they grew into 10,000 or so Model Ss. 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

• 53:22Speaker 03

  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. What you need to start with is a product that people love, are 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. So we do that in-house. We own that.

• 54:07Speaker 03

  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 Eight Mile and seeing Eminem, you know, stamping those big steel sheets and those big presses. Like, that is what America's been amazing at doing in the last 120 years, and they will continue doing it for us.

• 54:31Speaker 01

  That is a, that is a callback to a movie I have not seen in a while and did not see coming. But yes, Eminem does, and that movie, working at Detroit stamping facility, and it looks about as interesting as you would think to have that as your career. But I'm glad that we're good addicts and makes for companies like yours possible. Okay, so this thing is gonna have a 152-inch length. It's gonna be able to tow, according to your website, 2,000 pounds, 6,600 pounds.

• 54:54Speaker 03

  6,600 pounds towing.

• 54:56Speaker 01

  Oh, I'm sorry. A payload of 2,000 pounds, yes, and 6,600 pounds towing. 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?

• 55:10Speaker 03

  For the mid-sized truck market, so like the Tacomas, the Rangers, the Colorados, that would be a fairly substantial amount.

• 55:23Speaker 01

  Okay.

• 55:23Speaker 03

  It's right on par with the lower end of like the F-150s, 1500 vehicles. It certainly doesn't compete at the scale of the Class 3 or Class 4 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.

• 56:04Speaker 01

  Okay, 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.

• 56:26Speaker 03

  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, like that's the use case that we feel really strongly about. That's an application we can absolutely address. We do not see this as an application where you're doing towing cross-country with our vehicle.

• 56:47Speaker 01

  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?

• 56:59Speaker 03

  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.

• 57:11Speaker 01

  Ah, so you need to have in your back pocket, even if it's not something you're going to use every day?

• 57:15Speaker 03

  Correct. 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.

• 57:30Speaker 01

  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 Tucker 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?

• 57:50Speaker 03

  Yeah, it starts at like the mid, the low 40s and it works its way up to there depending on how you accessorize the vehicle, the 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 the vehicle. We don't think we're going to be the cheapest option available on the market. We think there'll be, I mean, we just heard the announcements that 40 Making for a new EV platform starting at 30,000. You've seen other companies like Slate come in with 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.

• 58:34Speaker 03

  So that is the focus we want. And if that is has a emotional and financial burden for you, then this is an option that may make a lot of sense.

• 58:42Speaker 01

  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?

• 58:54Speaker 03

  You know, I think that the market's really the difference between us. I think they're going after, you know, the being trying to be a lower cost approachable entry point for a vehicle is fantastic for a collection of people that really might use that. People that want a secondary vehicle, new drivers, older people that don't have things, they get families that they're driving around very frequently. That's an excellent option for for them. Our specific application is for people that need to do truck stuff in downtown cities.

• 59:24Speaker 01

  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 the city, you're using it for transit as well as transport. Okay, I'll take that. How how big is that market? I have no idea because I don't know how 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.

• 59:47Speaker 03

  Well, 3 million trucks are sold in downtown cities every year.

• 59:52Speaker 03

  Yep.

• 59:52Speaker 01

  3 million?

• 59:53Speaker 03

  3 million. So, all right. I mean, we're, we're in a pretty big market. Now, there doesn't, part of the challenge is when you look backwards and say, how big is that market? Well, there hasn't really, it doesn't exist in 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. So.

• 1:00:10Speaker 01

  No, no, no, wrong, sir. Some of my friends around town have imported Japanese K truck. Yeah, exactly.

• 1:00:16Speaker 03

  Should've space, zero wheel button, US-based.

• 1:00:19Speaker 01

  Oh, okay.

• 1:00:21Speaker 03

  10,000 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 US. 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.

• 1:00:47Speaker 01

  That's actually one of the reasons why my thesis about your company being successful exists, because I know how much people want the little K trucks. And I know that because there's one a couple of streets from me up, and I'm often walking the dogs and the kids. And you should see how people react to it. They do it like this, "Oh, what's that?" They get totally enraptured in the idea of having a small city car that has a cargo carrying capacity. So, Jason, I agree with your thesis, and I love what you're building. Why 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.

• 1:01:25Speaker 03

  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. So, 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.

• 1:02:13Speaker 01

  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?

• 1:02:26Speaker 03

  And 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.

• 1:02:58Speaker 01

  You founded the company in 2022, right? It's been 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?

• 1:03:18Speaker 03

  So there's a coin that was, there was a term that was coined by an automaker, I won't call them out by name, but that was virtual validation. And then meaning that they were going to build and validate their vehicles entirely in software before they built any hardware. They 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 to software. It's just, 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.

• 1:04:00Speaker 03

  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. What 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 in AI tools. For example, crash testing in the just 20 years ago, used to be build something of a portion of the vehicle or 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 to slam it into the, that's why things take so long.

• 1:04:41Speaker 03

  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. And 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.

• 1:05:08Speaker 01

  Okay. And by the way, it was Portia who said virtual validation, right?

• 1:05:11Speaker 03

  It was not Portia, but I won't, I won't say it again.

• 1:05:14Speaker 01

  Well, it's the proudest what I can Google while I'm also paying attention at the same time. 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. I know you guys said in 2023, you had 500 pre-orders that have scaled to, I think, 2,000 in 2024. How many pre-orders are you guys at today? And what does a first production run look like in terms of scale for Tella?

• 1:05:42Speaker 03

  I think we're just under 12,000 pre-orders right now.

• 1:05:46Speaker 01

  Okay.

• 1:05:47Speaker 03

  And so our, our goal has been to get our first vehicle into customers' hands in 2026. That has always been our kind of milestone. It's, it's probably looking towards the end of that year that we'll kind of do those deliveries. We 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 it's, that those, 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.

• 1:06:33Speaker 01

  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? Is that 10,000 or is it something more like?

• 1:06:43Speaker 03

  Yeah, so I financially modeled this to the nth degree, but it's between 10,000 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.

• 1:07:07Speaker 01

  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, yeah?

• 1:07:16Speaker 03

  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.

• 1:07:23Speaker 01

  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 have they raised, gives you an idea for burn and so forth. I was only able to find a couple of relatively small funding rounds for Telo.

• 1:07:38Speaker 03

  Yeah.

• 1:07:39Speaker 01

  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. So 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?

• 1:07:57Speaker 03

  So both the things you said are true, by the way.

• 1:08:00Speaker 01

  Okay, there we go.

• 1:08:01Speaker 03

  With spending only $6 million in the last 18 or so months, we've built two on-road vehicles that are registered through 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.

• 1:08:34Speaker 01

  Okay, 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 its, I'll call it impressive capital consumption as it works towards functional scale.

• 1:08:55Speaker 03

  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, we will need to raise before unit profitability at least another $100 million.

• 1:09:13Speaker 01

  That's not that much for what you're doing because car companies scale in revenue terms very quickly because you're selling $50,000, just big number, a round number, $50,000 units. So the revenue scale is pretty neatly along with that. That still feels cheap, frankly.

• 1:09:29Speaker 03

  Yeah. Again, to be, to be clear, like, we had 11 people in the company up until recently. Only 11, and that got us to where we are today.

• 1:09:39Speaker 01

  All right, well, if people want to learn more, it's telotrucks.com, T E L O trucks.com, and I think they're just fantastic. Jason, all the best, and when you start manufacturing, I'd love to have you back on so we can see what the factory floor looks like.

• 1:09:51Speaker 03

  Yeah, wonderful.

• 1:09:53Speaker 01

  Awesome, thank you.