Speaker 1I know what I'm building is a top three priority at Google and Apple like in the next 12 months. Not a top 10 priority, like a top three priority. I think talking about moats is a little bit of a luxury and you have to be more successful than Towness today for it to matter. Like I think the product in this category that will win will have a network effect at the agent level. I think you'll trust your agent to decide what data to share with other people without you intervening in five years. The speed of the market is insane, Harry. I've never seen anything like it. You can build now at the speed of machines, but you can only learn at the speed of humans. I don't think Instinct and Town are trying to do the same thing. We have passed the point where we will go back to a world where humans are looking at lines of code. I mean, the run rate's at least 75K per engineer.
Speaker 2This is 20VC with me, Harry Stebbings. Now, the hottest category in Silicon Valley is AI assistance, consumer and enterprise. On the consumer side, you've got Instinct, scaled to a $2.5 billion valuation, index and benchmark leading. On the enterprise side, you've got Town.com, founded by today's guest, Jean-Denis, or JD. Formerly, he was the CTO at Plaid, and this conversation today is probably one of the most pertinent discussions that there is on what is happening in the AI assistance market. Is it being truly commoditized? What's the business model behind it? Will consumer and enterprise converge? What does the future of AI assistance look like? This and so much more in the episode with JD today. But before we get into that, let's talk about Town.com. Before we dive into the show today, founders face a different set of challenges at every stage of growth. For Sid Shate, co-founder and CEO of D-Matrix, J.P. Morgan delivered the guidance and expertise to help navigate what came next. He credits J.P. Morgan's high-touch approach with supporting D-Matrix as it grew and expanded internationally. Whether you're in the early days or expanding into new markets, J.P. Morgan helps startups navigate complexity with real confidence, offering personalized guidance and deep sector expertise. Find out how J.P. Morgan helps founders at jpmorgan.com forward slash grow without limits. J.P. Morgan is the bank of the innovation economy. While J.P. Morgan powers your finances, Asana keeps the work moving. Most companies have tried AI. Most aren't seeing results. Not because AI doesn't work. It's because AI hasn't reached the workflows yet. That's the gap Asana is built to close. Asana is the opportunity. It's the operating system for human agent teams. Your easy button for AI productivity across every team. Ready-to-go AI teammates, pre-built for marketing, ops, and IT. No prompt engineering, no setup. They show up where the work is happening, already onboarded, in your workflows, ready to deliver. With Asana, your whole company can work on the same plan towards the same goal, whether you're a team of 10 or a team of 10,000. Asana, where humans and agents workflow together. Try it at asana.com. That's A-S-A-N-A dot com. While Asana aligns the roadmap, Base44 helps you build faster. You have the idea, but with most AI tools, you hit a wall. The setup, the config, the gap between what you pictured and what you actually ship. Well, Base44 is where that wall disappears. You describe it? Yeah, Base44 builds it. Apps, websites, AI agents, real working products, built in minutes, using nothing but plain language. And it's all batteries included. The backend, the database, the authentication. The hosting. The heavy lifting is handled, so you just really stay in the flow. This doesn't just take the busy work off your plate, but it gives you an advantage and pushes you past what you thought you could build alone. So in this market, fast is the baseline. To win, you just have to be first. Base44 is that edge, the move that skips the troubleshooting and gets you straight to the breakthrough. Build your next thing at Base44.com. That's Base44.com. You have now arrived at your destination. J.D., dude, I'm so excited for this because in all honesty, I have a lot of founders on the show where I kind of need to pretend to be excited by their product, and I'm not really. And I love town. I was saying the team use it here. I'm a DAU. And so I was so excited when we agreed to do this. So thank you so much for joining me today, man.
Speaker 1Yeah, thanks for having me. And honestly, I didn't know that you were a DAU until like three minutes ago. So I'm super happy. And send me all the feedback about the product because you're a DAU, but when you're a founder, you're always embarrassed about your product. For those that don't know. For those that don't know, what is town as specifically as possible? So we're an AI assistant that lives in your email and your calendar, and it tries to help you do work, right? So what it looks at, it looks at things that you do already, how you organize your day, emails you tend to send out. And it recommends AI automations that try to do some of the things that you would do normally yourself, just do them for you in the background. And yeah, we've been a product. We've been out in the market for about three months. Our ICP is just mainstream users, like mainstream people. People who, you know, use email, calendar, text messages to do work. We've been doing super well.
Speaker 2Can I be blunt, dude? We obviously did a show a couple of years ago. And I remember when you started your own thing, you were doing some boring shit in finance. And I remember the first round going down. I was like, I love JD, but it's pretty boring. What was the pivot?
Speaker 1So we've spent a year building an AI tax company, like business tax prep with AI. We got to some product market fit, but not enough. Where it was going. It's going to be a success. There's a thing like the truth is we failed at building a business that would be a great business. And yeah, after a year we were like, we got a reset and we spent three months kind of in the wilderness trying to figure out what we wanted to do. One of the areas we just looked at is why has no one built AI that operates out of email? Just there's so many people in the world who run their business and their life out of email and calendar. We were like, why is no one built a great product there? It was just about the time that Opus had gone fully agentic, right? This was like November, December last year. And so it was also the same time that models could start to actually do real work, not just do like a few steps, but really agentic work. And the prototype, we built a quick prototype in a couple of weeks and it had product market fit almost immediately. So that was the pivot. It was very lucky. You know, we didn't go talk to like a hundred customers and take their notes and, you know, we built for ourselves. I think it was one of those where the technology was changing so that what we wanted to do was possible at a time when, you know, people are very excited about trying AI products. You know, open call was happening. Like literally. As we were building the product, like open call was blowing up and we were like, oh shit, it's the same thing in many ways, right? They're trying to do the same thing. But it turns out there's a difference between something that's open source and it's amazing, but it's only a kind of tinker that can use. And that would be open call. I think with town, we've always been focused on how do you get just about anyone to be able to get value out of the product.
Speaker 2As an investor today, every company in some respects is questioned about how cannibalized could this be from any of the big providers. This is like right. In the sweet spot, just to be blunt, how do you want? And we've got Grogbot in the most recent times. How do we think about cannibalization by frontier model providers and Grogbot in recent weeks as a threat?
Speaker 1The truth is, I know what I'm building is a top three priority at Google and Apple, like in the next 12 months. Not a top 10 priority, like a top three priority. So I fall asleep very quickly. It's one of my superpowers. But I do wake up at like three in the morning. And immediately, usually when I wake up, like some dark. Thought goes into there and it's, you know, it's like roulette when you're a founder, like which dark thought will stop me from falling asleep again. And definitely the, this is going to be, you're like in the middle of the fairway and everyone's trying to, is going to try to get you. That's a hundred percent the fear, but I got to say, so you won't talk about moats, right? You're like, what's the mode? What's defensible. I think talking about moats is a little bit of a luxury and you have to be more successful than town is today for it to matter. Like my mindset right now is how do I get a hundred thousand or a million paying users? In a market that has a TAM of a billion potential, you know, paying users. Step one is like, you need, we have to have really deep product market fit. And I think none of the products today actually have really deep product market fit yet. Like grok bot's really cool. It's awesome, but it's a power user product. It's not a mainstream product. Town is great, but we have a lot of work to do to make it a true mainstream product. So even before I worry about defensibility, I'm still like, what is the right product experience? That's going to resonate with a mainstream. Cause the big players are not. They're not going to innovate their way there. They will copy their way there, but they have to copy someone who's been successful in the first place at building a mainstream product. Then number two, like, I think the product in this category that will win, we'll have a network effect at the agent level. And one of the features of town that users who figure out how to use it, they love it. The most is something called agent to agent. And that's where you ask your assistant, your townie come townies. You ask your townie a question and it realizes that it doesn't have the answer, but that the townie of one of your coworkers. Has the answer. And it just goes and asks it the question. And then that townie answers to find the feature. You have to go like in a sub part of the product to use it, but that is a network effect. Once you have your whole team on that, it's actually really difficult to imagine moving to a different product. And I think no one has figured out multi-user multiplayer AI today. Like, I think that's the thing that will be the moat in the meantime, we have lots of theories. Everyone in the market has lots of theories, right? So people will say like, well, you'll post train custom models, maybe custom models per company or per person. And that will allow. You to retain your users, right? People say, Hey, the context about a person that's the moat and people will be less and less willing to connect more data sources. So once you have product market fit with a user and they really love your experience and they've connected all the tools and all the connections, it's actually really hard for someone else to go in there because, you know, they won't have the connections. Other people think three, actually the moat, there's no new moat in this market and it'll be distribution. And so it's whoever already has the users who will win. So like, I think they're. For me, the competitor I would worry the most about would be would be probably for personal use cases, it would be meta and WhatsApp, right? They're going to have a personal assistant that's going to come in WhatsApp. I don't know if they're launching it in a day or in three months, but it's coming. They already have the distribution, right? Everyone's already using WhatsApp for messaging. If there's an agent in there that can do things for you, it's going to be extremely powerful, right? So every company, right? Some people think it's a device, like they think AI is changing the shape of software so that people will no longer ever go to websites. They will never use apps on their phone. The entry point for most digital interaction, much like the entry point today is either like a phone or a computer or a browser, the entry point will be an AI. And so whoever owns the devices is in the best place to put the AI in front of the user and they will win. If I think like, wait, if I think about all of these things, I'm like, oh my God, what do I do? Like, how do I, how do I win given all these
Speaker 2competitive forces? So what is the future interaction between humans and AI? And what I mean by that is, do we have like a consumer agent, an enterprise agent, and then a hardware agent that does maybe productivity and notes? How do we think about that multi-agent
Speaker 1versus single agent? Each human will have one, two, maybe three entry points into the digital space. Because I don't think you'll want to be like, oh, I'm doing sales. Let me use the Salesforce agent. Oh, I'm doing project management. Let me use the linear agent. Oh, I'm doing this other thing. You'll want one entry point. You won't want to ask for the same thing. You'll want to ask yourself the question. You just push a button and you start speaking. But there are real reasons why it may be more than one and it has to be. So one of them is just privacy and how your workplace is going to feel about their data being intermingled with your personal data. You might still have only one hardware entry point, but from a privacy perspective and from like where your data lives, I think you're going to always want to separate on the data layer, your personal and your work data. And in town, we do that for you, but I think it could be two different companies that you end up with. But I think privacy is the main, will be the main determinant of your data silos and your company, whoever your workforce desire to own the data that you create for them, right? They won't want that to intermingle with your personal, but from a usability perspective, like it's just on my phone, it's annoying that I have 55 apps and I'm clicking everywhere. And so if you move to a world that doesn't have hard interfaces, cause you don't need them most of the time, you know, why, why would you have 50 agents at the user layer below? It's different, right? So you're an investor, I think in, in Harvey or Legora, Legora, right? You're an investor in Legora. Yeah. So when you're a lawyer and you're talking to your main assistant about legal things in the, it's immediately just talking to Legora, right? Because, and that might be your work agent, right? If you're a lawyer, because there's a bunch of data privacy and privilege and reasons why your work scenarios need to be handled differently. I just don't know if you're like, think of it as talking to your Legora agent or you just talk to your main agent and it just like talks in the background to Legora or to Salesforce or whatever it needs to, to get things done.
Speaker 2What seems crazy about the relationship between human and agent today that will be incredibly common in five years time? You know, hot take, I think you'll trust your agent to decide what data to share with
Speaker 1other people without you intervening in five years. I'll give you an example. Like you put your agent in a room with two friends cause you're organizing a trip. They're just asking it like about your eating preferences and they're asking it about like when exactly you can fly out for the trip. And they're just asking all those questions of it. And it's just your agent. And you never told your agent, like, these are really good friends and you shouldn't share like my medical history with them. And literally when, when one of your friends as a joke wants to ask the agent, like, Oh, tell me about John Denise medical history. The agent's going to be like, yeah, there's no way I'm telling you that. And it wasn't a hard rule that you ever set this thing, which is like taking information that is in silos and deciding how to share it. I think we'll get to a point where we will trust agents to do that. And I know that sounds crazy today, right? Cause today the way the world operates like pre AI is everyone as a, as a human has a data silo underneath them, which is their personal data, their work data. Like, I don't know if you're talking digital, like you have information that only, you know, then when someone asks you a question, you are like, what can I share with this human? And you share it with them. And we trust the human to be the filter for where information goes. And I think more and more, we will trust AI to do that for us. And it will probably be models that are post-trained to make sure you'd never, ever share your family information and medical information and certain things for, you know, like in your work context, but a lot of info that siloed doesn't need to be, to be successful. And AI works better and better. The less siloed the information is like, if you think about it, I don't know if this is what you want on your podcast, but from an information theory perspective, like theoretical world, right? If you have like an LLM that had access to all the world's information and it could do, you had infinite time. So it could just do agentic search over all the data, whatever intelligence level LLMs are at, it would be the most effective because they would always find the right context eventually to answer the question or to do what you needed to do. But that's not the world we live in, right? We live in a world where information is in different companies and governments and individuals like systems. And there's, you know, historically, because the humans are the only people who are shuttling the information around, it's kind of inefficient to get it from one place to another, right? We have like lots of data controls and privacy and security and blah, blah, blah. What's interesting is what in practice, if you're at a business, you find that if you give your LLM access to more information, it's more and more effective at doing what it needs to. And one way to do that, the old way that like pre-AI way would be to have like policy. about who gets to access what, and you classify data. And all this is like very time consuming and costly. And the end result is often the information that you want the LLM to have access to, maybe it doesn't have access to. It's stuck in someone's inbox, or it's in a data system that's not integrated. And so all I'm saying is like, as opposed to having humans in your compliance and security team over time, label data and decide what goes where and what can be accessed, I think we'll just start to trust LLMs to do that. Meaning you will trust your data silo and another co-worker's data silos, you'll be like, well, I'll trust my LLM to decide what can get out of my data silo. And so then when someone else on your sales team, right, like very concrete example, someone on your sales team wants an intro to someone at a customer, and you're like at 1000 person company, and the person at the sales team knows that there must be someone at a 1000 person company that knows the right person at that vendor, right, right, that customer, it's somewhere. And normally, now they just go to Slack, and they're like, hey, who's working with client X, right? Who's working with them? Who knows? But really, what they could do is they're either going to be like, oh, I'm going to do this, could go talk to the agents of everyone else at the company. And all those agents have access to each person's inbox, and come back and say like, oh, well, look, like Liz has a personal relationship with the person that you want an intro to. It's not a work one. But you could ask her if she's willing to intro. Bob has a work relationship with the person you want an intro to, and they're due to have a meeting next week. Do you just want to see if Bob will invite you to the meeting? So you have the convo. And that's like a great business for the company. That's a great business outcome. That's what they want to happen. But to do that, right, the individuals have to trust that it's okay for some of the information that lives in your inbox to be made available to other people at the company. And right now, that seems insane. You asked me what I think in five years, I think we will be more okay with that. Because in practice, the LLMs will be really good at respecting privacy around things that you don't want to share. So you don't want your salary to be shared your coworkers, you don't want your medical history to be shared with friends. There's these things that are sacrosanct, and we get that. But you will be able to have an LLM that respects
Speaker 2these boundaries. How much wiggle room do you have on error? And what I mean by that is, do you have a mistake for whatever it is, you book the wrong thing, you do execute a wrong task, how much room for error do you have? And how much trust is lost?
Speaker 1My claim would be like the LLMs will be much more effective at this than humans. I'm going to tell you a story. I once worked at a place where there was a person, we just done an acquisition, okay, there was an email introducing the acquisition to the whole company. And this person had been against the acquisition. And so they meant to reply to a subset of folks, just tell them something like, I can't believe we hired these. Clowns, the words may have been different than that. And instead, they replied to everyone at the company. And this person's like an incredible person, they made a mistake, and it's totally fine. And everyone laughed about it. And everything was good forever after, right? But it's a human, very smart human, top point 1% who made a mistake, people make mistakes, like Bob from accounting makes a mistake. Liz from HR makes the spreadsheet with people salaries available to everyone by mistake. This happens all the time. I think the LLMs will make many fewer of these mistakes than humans pretty quickly.
Speaker 2My dearest friend is Jason. Who says that the biggest problem with agents is their goal seeking. And he talks about his agent going off and trying to buy six AP watches for him to increase culture in the company. Luckily, it was prevented because they needed engraving. And that was an extra step that the agent couldn't handle. But to what extent is this maniacal goal seeking tendency of agents a feature or a bug?
Speaker 1I don't have an answer for you. I think how much you should be willing to let your agent be goal seeking and for how long you let it run autonomously. Is that like a very, a very interesting question?
Speaker 2Is it your responsibility to usher people? To usher people? Guide them into like, what is best? Hey, we find best outcomes if you let them run for X.
Speaker 1One way to think about LLMs, right, is they're just, they turn energy into like GDP or into revenue. You know, like really you step way back, right? Power and silicon, and then you get intelligence and we're applying the intelligence towards business results. So, you know, if they get smart enough, it just creating GDP on the other end. And so like, you just say like, make money for me and then let it run for a long time and it can do whatever I want to. Or do we want to live in a universe where we think the human's role in this is actually to set the direction and make sure that the actions that are being taken align with some kind of human value system? So I live in that second universe where it is the human's responsibility to first allocate the resources. That means to say, how many tokens are we willing to spend to try to get a goal to set the goal, right, as well. So you set the goal and the budget and then to monitor, the overall shape of the actions that are taken to get the result. As the intelligence gets smarter, you might say, well, maybe, the allocating of resources, you're trusting an LLM to like analyze ahead of time what it thinks the ROI is on a pretend task and tell you how many tokens you should be willing to allocate before deciding to step away. And maybe on like monitoring the actions, it's also an agent that's doing that for you. I just don't think it's the same agent as the one that you put on the course to try to get to the result at the end of the day. You mentioned the different layers of kind
Speaker 2of the value stack there. What does the model infrastructure that you sit on top of look like? How do you think about model routing for different tasks? Are you locked into one?
Speaker 1I think it's because we're building an application for everyone. And we don't think most people care about understanding which model is better at what at a certain point in time. Our job is to, for what you're asking for, find a model that cost effectively gets you the result that you want. And so concrete examples, if we're generating images, we have opinions internally about when we might use like a Gemini model or an open AI model. When we're doing voice, we have opinions about 11 labs when we'd use 11 labs to do voice, right? I think it's our job to do that because as the technology changes every day, like right, literally every week or every two weeks, there's a fundamental change. It's our job to make sure you get the right ROI there. But it's, you know, it's tough. And there's things that are just like, is it the right result? That's easy for some contexts to know what the right result is. For others, it's very open-ended. You can't know ahead of time. So you have to kind of guess like how difficult do I think it is and how close to the frontier do I want to get? And then the other dimension for us is a voice. People don't like it when their townies sound very different. And one of the problems when you do model routing is there's some companies like Anthropic spends a lot of time. I know we make fun of them online, but they spend a lot of time actually making sure all of their model families roughly don't change too much in terms of their personality. They might be slightly more verbose or less and use different phrases, but they kind of sound similar enough over time. So if you use, if you use Anthropic to generate a model, you're going to have a lot of final output for your users. It's kind of hard to suddenly move to like Kimmy because it just sounds different. So people feel like their AI has been lobotomized. So that's like the way I think about it for coding. It matters less interestingly, because for coding, you're like, does it work? Does the code fulfill its purpose? It's like, yes, you might look at the code and decides the style that I like or not, but not really not anymore. Right. Before when you're talking or speaking to an assistant, if suddenly it's twice as verbose, like over text messages, it's writing you eight sentences as opposed to four. Like people don't like it. They're like, oh, I don't know. I don't like that. They will literally write tickets. They're like, despite my instructions that I gave it three weeks ago, like my text agent seems to capitalizing letters more stuff, like just literally you're like, okay. So, you know, the way I think about our stack is there is the part of the stack that deals with the user interface, like the feel and the personality. And there it is harder for me to just route wildly because I need consistency of the experience that is sometimes hard to get from other model families below that when it's just pure reasoning and intelligence. And especially when I don't have to show as many of the traces to the users, then there, yeah, I think it's very much a matter of finding, using the best model for the task. How do you think about how differing
Speaker 2model providers impact ultimate economics of a user? And what I mean by that is like 11 Labs is notoriously brilliant, but also notoriously about the cost of a Chanel handbag. And so my question is, how do you think about model selection balanced with cost? Well, Harry, the answer there for every
Speaker 1that I know, outside of a very, we don't is, yeah, it's well, we're hoping the price, we're hoping the cost curve makes it efficient in 18 to 24 months, right? In the meantime, you're subsidizing in part, right? Because that's what it takes to be a product market fit for these use cases, because you need to use frontier for too much of the work. Here's I think about it like we so one of the things that town does is we label emails, okay? Labeling emails does not in any way, shape or form require like opus level intelligence, it does not require like, you know, like, you know, you know, you know, it does not require sonnet level intelligence, right? And so for that, we're already below frontier, I think that will trend towards the cost of compute over time. And so I'm like, how do I use open weight models? How might I post train even my own, like smaller models, it's easy to say the price of that is going to be much smaller than it is today. And so for that part of my cogs, I don't think I stress out about it. And when I talk to other founders at AI companies, it is always it's also how the thinking goes. The question that no one quite knows is how much of the workload for any particular company stays close to the frontier, where, you know, it's very expensive, literally, nobody knows the answer to that. But for human level tasks, there's a decent amount of stuff like scheduling movies, working with one's calendar, answering emails that have been answered before doing research on competitors on a daily basis, like all these kinds of things, I think are trending pretty far from the frontier. And you can already use open weight models to do that really, really well. And so as soon as you know that, and you know, the price every nine to 12 months so you know where it's going to be so that you could price your product today at a point where you'll generate 20, 30% margins in 18 months. That's the way that we mostly think about it. But the open question is, at the end of the day, are you left with 10% of your tasks being frontier or 20% or 30%? And we don't know the answer to that. And that will change the economics of,
Speaker 2you know, these companies. What percent of tasks go through open versus frontier today? For us, it's mostly frontier. Why is that? With the greatest of respects, the task being asked I don't imagine you're actually that sophisticated. And this is where like, sorry, with the greatest of respects, we talked about instinct earlier, I give instinct hard problems. I want Odyssey tickets at the IMAX, continuously monitor it for days, by the minute that they're there. For you, in the greatest of respects, I ask like for email tagging and pre briefs, much easier.
Speaker 1Yeah, well, actually, a lot of people ask us to do hard things. So a lot of the custom workflows that people built will be quite complicated. And so that's where we will use most of our time. And we will use much cheaper models from one of the frontier providers. The main reason is because as a company, our focus, like imagine, I can improve our cogs by moving to open weight, but it doesn't give me much product advantage. It doesn't make my product work better. So if I have an engineer hour, what is the engineer hour best spent on? Is it taking my current AR making it more efficient? Or is it figuring out a way to grow the product faster? Or is it figuring out a way to grow the product faster by working on a better network effect feature, or making the model better and integrating with a new data source that makes its trajectories much better for a set of our users. And we're very focused on growing the pie faster, much more so than getting the ideal economics. And our economics are fine, right? They could be better. And I could move down the cost curve faster. But that's not the constraint to success for the business. So that's why we don't
Speaker 2do it. On the network effect side. Have there been any interesting lessons or observations? What have you learned on it?
Speaker 1From wall to wall. So first of all, interesting aspect for us of network effects is if the company has one person who is a tinkerer, and who starts to build things like team skills, team integrations, team routines, that is like building, those are all building blocks on town that everyone on the team gets for free, then we tend to see a lot more adoption faster. It's interesting, right? Because I'm we're trying to build a product that doesn't require the tinkerer. Because for the single player experience, we want you to be onboarded and get a ton of value. And if you're real estate agent, you have automations that are real estate agent specific. And if you're a salesperson, you get automations or salesperson specific, we're trying to give that that experience to you out of the box. But what happens is if there's a power user that's next to other users, they find ways to make those other users much more successful. And I think you'll find that with a lot of AI products. So one of the predictors actually is, is there a tinkerer on the team? So one of the questions we ask ourself a lot is, can we identify those folks? And can we make it easier for them to create virality for their other team members? And then the second observation is, there are a lot of functions that are underserved by AI within companies, even enterprises. So I'll give you a very simple one. If you're a sales team at an enterprise, you've been sold AI like in every direction now for three years. If you're a sales ops person, and you do not have 10 emails a day from like an AI company, you're you're something you've not on LinkedIn, like something's wrong. Totally. But there are other functions like executive assistants, chief of staff, HR team members, finance, some more junior finance team members that don't, there's don't have that much in their day to day, they like really don't. And a lot of their workflows still operate out of email or like recruiters. And so a product like town, I think often we find early adoption and growth. And I would say like some functions that seem from the outside, like less juicy, but actually that are really hungry for technology to help make their life easier. And so as soon as they adopted, there's an interesting effect, because they often work with like leaders or execs, or people in ops teams, and then we get penetration through the op teams. How important is time to wow or time to use a delight? Yeah, I think the only reason our product works today, honestly, is we have very low time to value for a single user. So the fact that with us, no configuration, you get out of the box, it gives a magic moment, then the user is like asking questions, what else could I do with this technology? That's for us all the like, our focus is on getting that right. Can we give you time to value there really quickly. And then we play the longer game on all the other integrations. For us, that's working really, really well. Since we've had town
Speaker 2and again, sorry, but instinct in the last two to three weeks breakout, seemingly. So I've been pitched four to five European towns or European instincts. I mean, literally four to five separate ones, some are we're enterprise, some we're consumer, some we're both. How should investors be thinking about this space? If you could advise us? We were talking about moats. So you know,
Speaker 1we're making like a town, we make some bets, right on why we think the product will last. I'll answer your question about Europeans, but we've made some bets. One of the bets that we made is you only get one AI system, it has a name, you give it an image, We call it a townie. And we have a whole brand around really building this relationship between the human and the AI. And for a lot of our users that resonates, like they want that they want something like that they trust that they shape and that they color that's in their image that they name. They like that a lot. It's silly, but actually, I think that is a form of defensibility, much like Snapchat structurally in the market is defensible, even though it's not nearly as good a business as TikTok or as Facebook or Instagram, because it's fundamentally different. It has a strong opinion about how it operates. There's some people that are drawn to that opinion. So we have a very strong opinion about the relationship between the user and their townie. And for a lot of our users that that really resonates. So we have that we have a bet on network effects, which we've talked a whole bunch about. And then the third bet that we make is we are big believers that through a lot of pre processing, you can get better outcomes for users. So we spend a lot of compute before you even ask a question to build a mental model of the user, like pre creating context in a way that allows us to be really effective at things like work networking, right, understanding the projects that you're working on understanding your company and those kinds of building blocks. So there's like three things that we think over time, make a difference for a product. So the question if I were an investor is like, why does someone deserve to win in Europe? And is it a distribution thing? Is it a GDPR and privacy thing? Is it a like town is not in fact doing any marketing in France. And so you could win in France if you're the town of France. And so you have to have some specific reason why some of these companies will win and game because these products are expensive to build. Like you said grokbot earlier, my R&D, a lot of my R&D is just keeping up with the Joneses, your agent has to be as capable at least forget your distribution strategy, forget all like the fact that you're good at work, forget like the network effect features. If Codex can do something that you cannot do, and that thing is something that matters to users, it's over, right? So you always have to be like, at least as good as a harness and capabilities as ever. It's expensive to do that. It's not like I have two engineers of the team trying to keep up with Codex, right? Codex is like 100 people making that thing better. And so we have to somehow be as effective on a lot of tasks as Codex. Otherwise, the user is going to be like, why would I pay $50 a month for town? It doesn't make sense. I'm going to pay $24.99 for OpenAI. That is if I were an investor, I would be like, is this local competitor have enough TAM? Are they going to be able to have a war chest that's big enough to just keep up with capabilities? And then do I believe they have a horizontal market like where they are? It'll be hard.
Speaker 2Is hiring in the Valley as insane as everyone says it is?
Speaker 1I don't find it crazy, honestly. Like I think when I was at Plaid and we were competing with talent for like Stripe, that felt no harder than what I'm doing now.
Speaker 2Can I ask what's been the hardest thing about the product build that you maybe didn't expect?
Speaker 1The speed of the market is insane, Harry. I've never seen anything like it. Before, in the past, you talk to customers, you build a feature, they would use it, and you would be like learning from that feature. And the learnings would go into the next feature and the next feature. And then eventually someone would copy your first feature. But then you had like your three learnings ahead. Do you know what I mean? You'd been able to like use your product market fit to generate more product market fit. You know, for startups, usually you're like milking these user insights for a really long time. Eventually you run out of user insights, but that would take like 10 years, right, to happen. So you have this entire period where you can just because you're number one or number two in a market, learn more and iterate. That's really good. The problem today is it is so much faster to build that as soon as something is working for somebody, everyone notices and is able to get there within like two weeks or four weeks, like copy really, really fast and learn. You can only learn at the speed of humans. Do you know what I mean? You can build now at the speed of machines, but you can only learn at the speed of humans. And so I'm not able to extract quite as many learnings as you know, that allows me to get to the next feature. So the way it feels right now is like speed is such a... It's so necessary, but everyone's moving fast. You know, when we started the company, like I would think first quarter and second quarter of this year, my mentality is like there's like 15 competitors in the startup universe that are competing with us, like maybe 15 companies that matter. And now I'm probably down to like two or three competitors. Like I feel like it's going to be like only a couple of companies are going to win this space, right? And we're like close from the startup starting gate. There's the like Apple, Google, Grogbot, Cursor, OpenAI and Anthropocene. And I feel like we're going to win this space. And we're going to win this space. You have to clear the clouds, right? For the startups. Usually also the startups would be really fast, but the established players wouldn't be that fast. But you got to be honest, like Claude, like Anthropic is very fast. Cursor, Grogbot, they're operating like at a speed that is, it's uncanny, you know, and one of my best friends runs engineering over there. So I'm always like, you know, whenever I talk to him, I'm sad that we're competing, you know, we're competing. And I'm like, that guy's good. That dude can get shit done. I got to beat one of the best people in the valley. And I'm like, I'm going to beat him. And I'm like, I'm going to beat him. And I'm at a company that has the DNA of a startup, but is operating with huge cost and scale advantages, right? You know, that's what's super stressful, because I think you can't rest for one minute, you don't have the feeling that the competitors, it'll take him six to 12 months to catch up. And I feel that like never, yeah, never before. You mentioned, you mentioned the two to three
Speaker 2that matter on the startup side. Who would you say those are? And why did you choose them? No, I'm not gonna say that. I'm not gonna give free marketing to competitors. You've got, you can't blame me for trying. I tried to do the Louis Theroux, you know, it's like, hey, how do you think about that? Like,
Speaker 1tell me. Yeah, look, it's a blue ocean market, right? You gotta understand, like, I never, when we go to most customers, they've not heard of anything. It's blue ocean, because people, people are using ChatGPT as a Google enhancer. Like that's the market. Competitors are great. They put pressure on you. They make you feel like you have to execute at a really high level. But what's important is you have a different strategy than a potential competitor. Like you mentioned instinct. I don't think instinct and town are trying to do the same thing. You don't? I don't think we're trying to monetize in the same way. I mean, we will see endgame, but we generate revenue from companies that are using us for work with network effects around multiple team members working on it. Their product doesn't do any of that. Maybe that is part of their strategy. I see a strategy that's more like customer acquisition, like with a free product that's fully subsidized right now. That might change, right? But if you look from the outside, the products have similar capabilities, but all harnesses have similar capabilities. But if you look at the ICPs, where the marketing is going, it just feels pretty different to me. I pay attention to like a grokbot more than I would to instinct because I think grokbot is going after a similar market to what we are. For me, that is more of the place where I'm like, how is our strategy differentiated from grokbot? How are we going to acquire a different customer? How are capabilities and our harness going to really stand out and feel really different to users? Like that's more like my mindset than.
Speaker 2To what extent is grokbot's integration into X a feature or a bug? To some corporates and to professional usage, it could be concerning, actually, the integration with the search.
Speaker 1I think brand for them. I think some people just won't want to touch it because of brand. That's just inevitable and it's, they're going to have to deal with forever. But from a distribution perspective, and I think for some segments, I think in early growth, it is probably quite useful. I think a person on X that uses these products is not actually product market fit, meaning that those are not the mainstream users. And you have to keep that in mind. And I, by the way, I think they think about that over and over again. So, I think that's, I think that's a really good point. So, I think that's a really good point. So, I think that's a really good point. So, I think that's a really good point. So, over at Grogbot all the time, but I don't think they think winning the power user slash influencer on X is where the market is. That is not where you win the market. That's the early adopter market.
Speaker 2But can you help me understand Apple's agent roadmap?
Speaker 1I think the problem for Apple is twofold. One, they're not a cloud company. They're just not, it's just not their DNA. They don't know how to do cloud. And the reason why that matters is because what we talked about earlier, agents are better, the more data they have. And the data, it's not all on the phone. So, the fact that they're not cloud is like one big issue. The second issue is they've like contorted themselves for competitive reasons around a privacy and on-device story that is like absolutely like puts them far away from the frontier. Local models on the phone, it's amazing, but they're just, it's just slower and dumber than what's at the frontier. And so, as long as they're committed to this like on-device, like privacy preserving stuff, it's not a good thing. So, I think that's a really good point. The privacy stance is good from a human perspective, but they've tied it too much to the on-device. So, not being good at cloud, then being on-device, and then from the privacy standpoint, making it difficult even on the phone to interoperate with all the data that they have, those are a lot of disadvantages to play with. Now, you know, at the same time, they do have the devices. So, the new Siri is going to be a much better personal assistant than the, I mean, that's rumored, but you know, people who've tried it. So, when you're in the valley, you've, you know, and so we all know it's going to be good. But I think, it's going to be good, but it's going to feel not nearly as powerful as Town or Grokbot. Like, it's not even going to be there in terms of its capabilities, but it'll be on your phone. It'll be convenient. You'll be able to like enable more data with it. It'll have a cloud component. It's going to be good, but it's going to be like nine months away, I think, capability-wise compared to everything that we've been talking about today. I don't know. I mean, they have a new CEO and we'll see how they take it, but I think they just need to hire somebody that has totally different DNA and be like, you guys, you don't understand. Like, the way people interface with digital data is changing and we either are figuring this out and we may have to throw a lot of our principles away or we're just not going to win this generation of the war. I think that is like a real risk for
Speaker 2them. Are you concerned by the data leakages that we're going to have in the kind of golden age of cyber threats that we're entering into? It seems like we've all kind of normalized cyber attacks and it's like, ah, Meta had one, ah, Anthropy had one. If you want to build AI that is used
Speaker 1for business use cases, you cannot get it wrong. We've passed the point where humans will read every line of code. That is never happening again. The most important lines of code around access controls and things like that for systems are still being read by humans. But overall, in the history of humanity, we have passed the point where we will go back to a world where humans are looking at lines of code. It's computers that are building code that is being shipped into production with various guardrails from testing to other models, like friendly models attacking you so that unfriendly models can't later find exploitations. That's the world we live in. Obviously, in the history of humanity, in this new world, there's going to be points where there's bad events that happen. You know, it's a little bit like chemicals. In like the 20th century, we started to do cool things with chemicals and then we would put the chemicals in rivers and then cities downstream, people got sick. And then we were like, oh, yeah, OK, let's pass like regulation like the EPA so that you can't just dump the chemicals in the river. You got to like clean them a little bit before you do. And then later you like label dangerous chemicals, not as dangerous where they can go, how you get rid of them. Like we learned along the way. a set of best practices, both like from regulatory perspective and just like best practices in industry. And right now what's happened is the like cost benefit of a tax is just thrown out of whack. And we're trying to figure out what the best practices look like. And you can't imagine we're going to get that right every step of the way. But I think we will have to because there's no way we're going back to a world where humans are looking at every line of code.
Speaker 2When we think about usage, how do you define a successful user?
Speaker 1Oh, I just define them as someone who pays me every month. If they keep paying me. No, I'm serious. I'm serious, right? If they keep paying me, I've done my job. I can't think of them as the most the more tokens they use. It's a dangerous way to think about it. Because if you think about it as like they use more tokens every month that's successful. What if they're using the tokens in a way where the ROI is less clear to them? Meaning they don't realize that they're using tokens to do things that they don't value as much. If you do too much of that, they wake up one day and they're just paying you too much and they get mad. And they churn off of the product. So I think you have to take a long term perspective. The problem with token maxing, there's two problems, in my opinion. One is like companies told in told people, hey, you can use as much money as you want on AI, which is bad. Like you want people to think is the ROI of using AI here worthwhile. So now, you know, people are not going against employees using AI. They're going they're like, no, no, we need incentives. So they use AI for good reason. One aspect, you need to think about ROI upfront. But the second problem is sometimes it's hard to know the ROI. Of something like, let me be more prepared for a meeting. How worth it is it to be more prepared for a meeting? For people who have back to back meetings all day, being able to in like one minute before the meeting feel prepared enough to not look like an idiot, it might be worth quite a lot for people who have meetings where they have someone else preparing and are presenting in the meeting, they don't have to present anything. And they're just sitting there. It's not worth anything, right? So I'm just using this example, like this workflow has totally different value for different folks, but it costs the exact same number of tokens, right? And I don't think that people think about it that way. So I think of success as paying me because if you're paying me every month, that means I'm mostly doing a job of delivering enough value. When you look at the $49, or the $15, or the $99, whichever plan you're on on town that you pay me, you're getting enough value. But I'm very concerned along the way with informing you about where you're spending money, because I think you need to feel like I am doing a good job of avoiding you spending too many tokens. So one of the most popular features for us for the last month is we started sending emails to people who were paying me for tokens. And I think that's a really good thing. We started sending emails when it looked like you had like rogue routines, like routines that were just costing a lot of tokens. And then people were like, Oh, thank you. I trust you. Now I feel that you're looking out for me using the product badly. That's the part for me. I don't know how to measure it. But I want people to success for me as you pay me and you trust that we are the right platform that helps you both use AI, but do so efficiently. And I think
Speaker 2if we can do that, we can we can have a pretty decent business. Yours is $14 a month, $49 a month and $199. Correct, which is the most profitable segment and which is the least profitable segment. And the reason I think about that is like my friend Jason Lemkin. He obviously pays for like Anthropic Pro or whatever it is to 99. And he spends about $15,000 of tokens. He is the worst customer for Anthropic, but he's on that like pro max individual plan. Yeah, we don't have a max
Speaker 1plan. And we have users who asked for it. And I've been asking myself, like, do we let the whales have a max plan? Because from a marketing perspective, it's useful. You know, they're just advocating, advocating for the product all the time. So I've thought about that. The $15 plan is a really good deal, I would say for users, we use it as a way for people to use the product enough that they realize they should pay 49 where the product is really powerful. So 15 has the worst the $15 plan has the worst unit economics is the most subsidized. And then I would say probably the $99 plan is the most profitable overall, because it's like a power user, but it's not a power user that's like trying to like, you know, spend unlimited numbers of spend. But we also have usage based pricing, right? So what happens for us is once you run into the plan limits, mostly you go to usage based. And so we try to adapt the spend to the user.
Speaker 2Can you choose one outcome for me? 100 million consumers paying 20 bucks per month, or a million customers paying 100 bucks a month?
Speaker 1The more, more users, more users paying less. Why is that? Well, because I'm going to go counterfactual. Because I think over time, in the work setting, AI will be used to do more and more and more for people. So I think the long term potential for like NR, driving more revenue per user is extremely large over the long term. So you want to acquire the users in a paying motion, because you want them to be for work use cases, because you will keep finding more ways for them to use AI to generate business value for themselves. Whereas in the personal sphere, it doesn't feel like that to me. You know, I only have so many like restaurant, restaurant dates, I need to book with my wife or trips, I need to organize with my friends, I only have, I only have so many, like, personal, like doctor's files, I need to send to a new doctor, like there's only so many of those things. And when I do those things, I save time and time is worth money. But on the business side, when I create something that generates value for the business, they make more money, and then they want more of that thing. So like, like a clear example for you, if you want to like a recruiting firm on the platform, they can take more clients because of town, they've used town to automate enough of the recruiting process that they literally take more clients and for them taking an incremental and without hiring anyone you this recruiting company, one more client is like an extra, $3,000 a month. And they pay they pay us like, you know, across all their users, like five, $600 a month. And so they're like, ROI is like super simple for them for a business use case, they're like, Oh, I pay $600 a month, and I get $3,000 of revenue. That makes sense. I like that they're making more money, everyone's happy. And I think for them, if I could show them the way that they could take another client, and even if it costs them another $500 on town, they would be willing to do that. And so I think the like the growth potential on the on the business side is much larger. So I'd rather have lots of users, but I think the growth potential on the on the business side is much larger. So I'd rather have lots of users, but I think the growth potential on the paying us less, because I think over time, I can show them that I can deliver more and more value, and it's worth it for them to spend more and more on town.
Speaker 2What is town not able to do because of model capability that you think will be incredible in two to three years?
Speaker 1Voice is so obvious. It's I mean, it's happening right now. It's not voice like you just speak to it. I just mean conversational.
Speaker 2Would you be an investor in 11 labs at a $22 billion price?
Speaker 1I think on 11 labs, like we're users of 11 labs, they sound the best. I'm not paid. I'm not an investor is very expensive. What I don't know is if it tops out. And that's I think the risk for something like 11 labs, meaning like, we just get voices good enough. And then you can get it I can put open weight models on base 10 and get it. But it just doesn't feel like that right now. I just don't know how much runway they have before it reaches that. And so that's why I'm not saying I'm bearish. I really like that company, but like 22 billions, a lot of money.
Speaker 2And how price sensitive are you in a year or two? With the greatest of respects right now, you can you can burn cash. It's about PMF and growth and beating others. In a two to three year, wait, you're bluntly trying to make economics work in a much more efficient manner. If we have two to 3 million users using voice, that's a hit to our margin
Speaker 1profile. For sure. I mean, I care a lot more about time. That's why I'm saying like, if the voice capability, it'd have to be like maybe twice as good as today, especially tone and expression and like the emotional read. Like once you solve that, I would want to go as cheap as possible. Because especially for like, once it feels good enough, it's almost there. Like I don't need much better. But you know, on the margin profile stuff, I don't ever think of it as burning money. I don't that's like my mom, my parents would not be okay with me saying words like that. So I think we're being thoughtful in our spend in order to optimize for growth in the short term and gross margin in the long term. The voice is not the where I'm really stressed out about it. Honestly, why are you really stressed out about it? It's just I think it's the percentage of tasks that are frontier because on everything else, I can imagine getting the prices down. But you know, the thesis that I just said before is over more ways to use AI to generate more revenue for a lot of companies. The implication there is it's like, there's like things at the frontier that generate more revenue. And the problem with the frontier is I have zero pricing power at the frontier. And I mean, I think this is what happened to cursor right at the end is like, you can have huge market share and be the customers love you and everything. If you're paying your suppliers and competing with your suppliers at 70% margin, eventually it gets like a little bit difficult. That that is the part that I'm worried about the end game. If we are but again, I have lots of ifs, I have to get to 10% margin. I have to have tens of millions of users, they have to be paying, I have to have a lot of scale. And then I'm like at a place where I'm still competing with my suppliers, I'm still competing with open AI and anthropic. And I'm just giving them money for the 20 or 30% of workloads that are at the frontier for me. And that's what makes the economics not work. That's the part where at the end of the game, I need some I need some solve for that by that I don't need it right now. The reason that's the only problem is that's the only part of my economics that's different from somebody else's. So then there's the macro question, which is is all AI, you know, is there not real product because it's all subsidized, right? That would be like the other take that some people could have. But otherwise, as long as you're not competing with your suppliers, you have the same economics as your competitors. And so then your ability to drive margin usually is driven by the competitive landscape more so than anything else right and so the fewer competitors you have the more margin you can have you are competing
Speaker 2with your suppliers i mean like astra i am you see you see as a direct competitor correct totally i
Speaker 1am today 100 i am today yeah but the percentage the percentage that was a face i am competing with astra but there's still the the it's the open box problem you would be shocked at how many people just don't know what i can do right it's like the problem is having a product experience that gets a normal person to get value out of ai is really hard that's why when people use town they like it and then they start paying for it the payment rate for us on acquisition is like more than 15 percent of users who try the product end up paying for it which is extremely high for plg because the value delivered relative to what they were getting out of chat gpt is just huge what did you crack that other people didn't to get that 15 the really inside behind the product was if you ask people up front to connect their email on their calendar you can know enough about them that you can suggest tasks that ai can do for them that's like the only insight and if you're working at opening i'm sorry this is where we joke before about me being more
Speaker 2mouthy and gobby do it is that that insightful my chat gpt is always like going here's all the things that we want from you and i'm like no way no way read write email abilities no i think their
Speaker 1suggestions are just like plain bad to be honest but they didn't even have suggestions until a few months ago the delta is this if you want to use chat gpt you don't have to connect your email they just don't force you to do it they ask you a bunch of times to do it now because they realize the value is helpful but the base experience they're trying to show someone normal hey you can have value in this product just because you have a chat box and that's how most people experience it our approach is more like listen you have to connect email calendar you cannot use our product if you don't do those things but if you do those things we can do all this magic for you here's what we know about you here's work that you normally do we'll recommend automations that automate that part of your work that's the part where
Speaker 2people are like oh that's really cool i think of it a bit like a hard paywall you know when you land and it's like hey pay your monthly subscription you're like hey connect your calendar and your email what percent churn at that moment 30 right off the bat yeah it's big huh you gotta be willing to take that hit what's the biggest internal product disagreement you guys have today oh good
Speaker 1one we have product market fit for like some purely personal use cases that we didn't expect like families like parents there's like tremendous product market fit for town there schools in america at least send a lot of emails and they have a lot of like portals where things have to happen for sports leagues and they're like this kids reports and there's a lot of scheduling for kids that has to happen for like haircuts and summer camps and all these things and our products because it's really good at email and it's really good at the scheduling stuff it it really has tremendous product market fit for families the question is like do we market to this group and do we spend time on it and do we spend time on it because it's a great group and it has a willingness to pay but it does not have the willingness to pay of a mid-market firm you know we can't do all the things so you know to your point it's like well we're a monetized platform and so our metrics are they are growing month over month revenue is what matters to the business and so it's why it's an argument because you're when you find product market fit somewhere you don't expect you have a few choices in life like one choice is you're like i love these users and i love parents i like love the users right i love the use cases use cases and the value super clear but it doesn't it's not fully aligned with how we've thought about the business growth but i think that's why we're having inter interesting thinking internally because we're like if we look around enough corners is this worth it or you know should we should we be more focused on our existing strategy which is fun when you build a company you learn things from users and you gotta make the right decisions what are you
Speaker 2guys at revenue wise today uh no sorry you gotta you gotta understand it's like it's like ping pong you give it a go you sometimes get hit back okay and it's like that and we're like fundraising or
Speaker 1like i will use those moments to create pr and growth for the business i'm not quite ready with
Speaker 2that one to do it yeah a hundred percent and you know what i would advise you to always separate moments too many times i see people like combine a fundraise with a revenue milestone do not do that those are two separate pr moments that can be made into two big moments not one why would you why would you amalgamate them and lose the ability for two hits the press i
Speaker 1think is more skeptical it used to be once upon a time raising at a certain valuation was so rare you could get publications to now the publications want more you're seeing a lot of skepticism on the
Speaker 2space itself you know instinct raised it two and a half billion dollars with no monetization do you think the skepticism around the space is warranted i mean for us we have the revenue
Speaker 1and the growth i don't know what you know competitors growth is if you can believe that some of these companies can get to tens of millions of people in the product in an area where the product will be the entry way for people to do digital things right that they're doing in apps on their phone right now if there is a winner there that comes out of the startup universe there is like a giant company to be built right the billion dollar price for the
Speaker 2new round did it start there or did it get ratcheted up and up and up yeah i can't i can't
Speaker 1deny or i can't confirm or deny i love i mean
Speaker 2ari i will not let you take any moments for me away from me you're not commenting anything and i'm not even prying but like the oh index doing it oh and that's not what's great for you is this all just pr like your name's just everywhere pretty good man you're you're amazing at trying
Speaker 1i'm old you know i'm like 47 years old i don't even know how old i am that's how old i am when you know you're old when you don't remember if you're turning a certain age so i'm 47 turning 48 in a few months and i've been around for a while and there are times in my life where publicly i've done things that i'm proud of and publicly i've done things that aren't that i'm not proud of so the reason i mentioned this is like i don't think it aligns with my value system on anything to like create pr just for the business i think i want the pr to be created by my users because they love the product so if you ever see any news about town that good or bad that is i'm not out there creating the pr that's just not i think that's a mistake that respectfully i know
Speaker 2i would push you to change and i yeah and i would say like look at a whisper flow as an alternative not a hugely dissimilar plg motion in all candor i think they've done a brilliant job at generating pr themselves through their own content through content that their users produce sure blade content is a hack to customer testimonials totally i i agree
Speaker 1i just don't think a fundraising story you know is part of the universe of things that i would want to like create a pr moment out of like not the kind that you're referring to from you know i don't want to make a point on the on the fundraise because this is as an angel it's not about not about us the fundraising in general is you you kind of mentioned there are these companies out there this goes back to the ethics and who i who i am like i have seen deals where it's like i invested like 200 and then the announcement is at 500 and then what you learn is that like you know they raise 65 million and like 5 millions at 500 and the other 60 is like 200 or 300 right there's a whole lot of that happening for sure in the valley personally i don't think it's ethical towards employees it's like the dilution wasn't that number one it's not where most of the demand was it's not how you should be pricing people's offers you can't i don't think you can look at someone in the eyes and say an investor that made 80 of their investment at like a 200 or 250 million dollar valuation but hey they put the last 20 at 500 and that's what i'm going to say like i just don't like that i don't feel like it is right i agree with you like that
Speaker 2uh dude we're going to do a quick fire round what is your best angel investment
Speaker 1oh it's either base 10 or modal right now those are the first two that come to mind
Speaker 2what's the bull case for town being a hundred billion dollar company what is needed to happen
Speaker 1in that world i think if we can get 10 million people paying for the product we we can get to that is that it yeah we make over 700 per year per user today how does that compare to dropbox
Speaker 2because dropbox must have way more than 10 million users you have to have the growth right i couldn't
Speaker 1i'd have to believe that i can keep getting a good rate of growth the problem i'm pretty far from dropbox it's been a long time since i worked there but there was a huge free huge pack of free users that were very costly on the cost side and then on the paying side i don't remember if the number was like 10 20 30 million but it did you know it did flatten out at some point and there was no way to generate more revenue or growth from the users i think what's different in the ai space is like i think you should be able to as you do more once you have a company on a platform using your platform as the core part of where ai works happen you can do a lot of things and you can do a lot of things and you can you you should be able to generate increasing revenue as as the token spend goes up who would you most like to add to your board who you do not have wow i think for the next board member i would love someone that's kind of cfo like late stage our business is going to be like the economics are going to have to be really really good if you have a board member with real operational experience on the on the finance side it's going to be very helpful as you scale this kind of company there's just a there's a bunch of stuff like we're going to have to buy compute at scale we're going to have to be like very very good about thinking about token spend it would be someone with that background i know you're making faces you're like
Speaker 2that was soon i thought though i get that need but i thought that would come in a couple time
Speaker 1maybe but i think i think we're in growth investor land for the for the next round so i think once we're in growth investor land they they will they will ask for me to have they will want for fundraise metrics that you know i can really defend and i think having someone with that
Speaker 2background will be helpful why didn't you subsidize completely you could raise another 200 million more and growth is everything yeah
Speaker 1you just go it burn the boats it's a good question i would be lying if i said there aren't mornings where i wake up and i think about it I believe that to prove value on the business side, you must make your customers pay. So I do think there might be a world where our PLG part is much more subsidized. But as soon as you get three to five team members, I really want to make money on that side. I really want to make sure I'm delivering value. I'll give you a story. When we launched the product initially, right, for the first five months, not launched, but we were like in private beta and then we opened the beta, we didn't have pricing. There were users who were spending a shit you not like $2,000 of compute a month, $4,000 of compute a month. There's someone on the platform who'd spent in five months was like $26,000 because there's no pushback on the token spend, right? There's no pushback at all. So I know, I know you're making faith. It was one that we would like call them. It would be like, look, like, let's figure it out. Like, you know, you're using the platform because you could just create like routines to automate more and more stuff. But is it really bringing value to them? So anyways, the reason I mentioned that is I'm a big. Lever getting pushback from the market about where you're delivering value and where you're not is really, really important. There would be ways to subsidize and do that. So for example, I could make the plans much cheaper. I could make them free. I could get free tokens to businesses. But what I've learned is like on the business side, they also don't like it if you don't charge them because they don't know how much it's going to cost one day. They want to know how much it's going to cost one day. You can't sell to a 500 person company and be like, yeah, just use my product for free internally until you get a bunch of usage. But then one day I'm like, I'm going to turn it off. And all your. Business processes are running on it. So the way we've approached it is on the growth side, we may or may not subsidize more and because we're emphasizing growth, but I really want a real business when a company is on this product and we have a real business when a company is on the product. And I'm very proud of that because I think that is the ultimate test of whether you're building something successful if you're not a pure consumer company. And I'm doubtful of pure consumer ad backed for AI for a couple of reasons. Like I think the tokens are way too expensive to do ad back now. And then number two, there's an incentive problem with ads. And I think people are going to want assistance that that are theirs, that are not being polluted by outside incentives like ads into the trajectories that they give you. Right. So if you ask to book like a flight and, you know, it uses an airline that is like paying for that flights to be recommended to you, that doesn't feel good. Right. So I'm a big believer that actually the economy around assistance will be paid for. And so I just want to pay for it as soon as possible.
Speaker 2What person if when you open. Twitter, would you be most thrilled to see love town? Elon Musk, because he has a competing product and it's Elon Musk.
Speaker 1How has your hiring process changed in an AI world? Well, you know, we have a weird hiring process. You don't know a fun thing about a hiring process. If someone on the team has worked very closely with someone else, we don't interview them. If it's a top person, because why would I just sell? I will just sell. It's very rare that it happens. But it has to be someone that they've worked extremely closely, like literally like next to. And they're like, this is one of the best people that I've ever worked with. And for like Eng, we're just like, let's go. Because I know it sounds odd and people are going to like comment like this guy's a total idiot. But a person that I trust, that's great on my team, great on my team. They tell me this other person is like one of the best people that I've ever worked with. And then I'm going to make that person like spent eight hours doing stupid whiteboard interview or like makes no sense. So either I don't trust my employee. So there's culture match. So we will be like, hey, come in, spend some time with us, you know, like you can code with us if you want to. We have to sell because we look, you know, if you don't interview somebody, they're also like, what kind of clowns are you? You're not interviewing anyone. So we will like allow them to get signal about us. But we are not evaluating whether they can do the core role.
Speaker 2Brian Singerman, who invests in funds and then invests in the companies beneath those funds, has a rule that if the manager is like balls to the wall, I am all in on this company. He'll automatically write the check. Kind of the same. You trust the person, you trust the layer beneath them. What percent of developer salary do you spend on tooling? At Salesforce, we spend $300 million on Anthropic. They spend $6 billion a year on Eng, 5%. I mean, the run rate's at least $75K per engineer. Wow. Split between core code and cursor?
Speaker 1Devon, Cloud, Codex, and then Town. We use Devon a lot. I'm like, I'm good advertising for Devon right now. For a lot of bugs that come in, for a lot of like simpler little things or little like visual tweaks, we are kicking off Devon because we just find the team experience. Slack's really, really good. We have a few people who use Cursor for like more visual, the front end. The model's really fast, right? Composer's really fast for front end. And then I would say it's probably 50-50 right now between Codex and Cloud. And that's obviously changed a lot. Like I think five months ago, I would have said it was mostly Cloud. But the new Codex is really good. The mobile experience is really good.
Speaker 2What is that in a year? Is that $75K, $150K, or is it $25K as costs come down?
Speaker 1You're just asking me an ROI question. People always ask me, are the teams bigger or smaller with AI? And I'm like, hmm. Okay. Imagine you're a normal company and you have a million dollars of revenue and $800K of costs. You make $200K profit. The $200K, you can hire one engineer with it. If the engineer can't make you more than $200K in revenue, you don't hire the engineer and you take the money in your pocket as the business owner. Now, AI happens. And AI means that suddenly that engineer can generate more than they could have before. So maybe before they could only generate $150K of revenue. Maybe now they can generate $250K of revenue. So suddenly AI makes you hire the incredible. One more person than you would have because there's an extra $50K of profit for you to make by hiring the engineer in the new world because they're more efficient. So we're at a stage of the business where I'm like, there's gold littered everywhere in front of me. I have customers that want integrations in order to sign the contract. I have people who want audit logs to sign the contract, who want SSO to work with phone numbers to sign the contract to be bigger. I'm like sitting in front of that. I have like a Dex product where people want more exports in order to like use the product more. It's all. It's all gold in front of me everywhere. And my limiters are my ability to hire, how much funding I have, and the growth rate of my revenue, right? Because I don't want to get too ahead of my revenue. So if you told me that there were better models and I could spend more, that's easier than hiring to do the high ROI stuff that I'm like the money that I'm leaving on the ground, I would do it immediately. So that's like the level I wish I think about it today. So I think about our global spend on like compute, you know, like whatever a million or whatever it is and on an annual basis. And I think like roughly like it's $400K. So I think about our global spend on like compute, you know, like whatever a million or whatever it is and on an annual basis. So with equity, maybe it's more like one and a half engineers. Am I getting one and a half engineers in Silicon Valley at our inflated rates from the yes, of course I am. So I'm like, it's not even close. I'm not even close to the place where I'm like, are we token maxing wrong? It's not even like ballpark there.
Speaker 2And then I think the other thing that we don't contemplate enough is like, do we see the tipping point in other categories that we've seen in coding, in legal, in sales, in marketing? Final one for you, JD. What are you most excited for in the next 10 years?
Speaker 1Well, it's definitely. My kids growing up with my kids and getting to spend time teaching them things like math and playing soccer with my son. That's 100% what I look forward to the most. But that's not what you meant. You meant what do I look forward to most in the universe? Well, I am a believer that even though people are very skeptical about AI and I understand why it may be scary and why any change is hard for humans or anyone to take on myself included. I do think we are getting closer to a world where. People have more of the things that they want and can do more of the things that they want to. So I just hope we come out of this with a better universe, more money for everybody, more ability for everyone to do the things that they want to. And I truly believe that like I wouldn't be doing what I'm doing to make money. I'm hoping that I'm I'm doing it because I hope we can remove a lot of the toil of people's day to day through this technology, not through just town. I think AI will help us make drugs and will help us build faster in the physical world. And while people be able to live further away from it. From cities because they can self drive in, which means they can have bigger houses with pools and be happy.
Speaker 2Like I'm a very, very much an optimist. Dude, I so appreciate you giving the time today. I know it's a very busy time. You've been amazing. And I can't thank you enough for putting up with my slightly pressing questions at points.
Speaker 1Every bit of skepticism, I would say something that does keep keep me up at night. But I think there are there are paths through the dark forest and there's a giant treasure with only one or two dragons at the end of it. So got to go for it.
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