Competing with OpenAI and Meta for your inbox | with Jean-Denis Greze (Town Co-founder & CEO)

0:00You've got competition, they're well-funded, and they've got distribution. So the question then is, how do you win? The incumbents look much less scary than they would have before. Hey everyone, it's Todd Jackson, partner here at First Round. In just a few months, AI assistants have exploded into one of the most crowded races in tech, with Meta, OpenAI, and XAI all launching their own. My guest today was building in this space before just about all of them, and he thinks the stakes are much bigger than another app. We're building the window pane through which humans will interact with all content.

0:29That's Jean-Denis Grez, or JDG, co-founder and CEO of Town. Town gives everyone an AI assistant for work, called a Townie, that learns how you work and proactively offers to take things off your plate. The company just announced its Series B at a billion-dollar valuation. But being in a hot market means when something works, competitors are on your heels in months, not years. Even if you're ahead of the market, the ability of your competitors to take the good things out of your product and dissect them and launch their own version is weeks to months. whereas it used to be like years to decades.

1:01In our conversation, JDG explains why now is the moment for AI assistance, what it's like when competitors can copy your best feature in weeks, and why he believes the smartest move for a business is to never bet on a single AI lab. Who's going to have the best frontier model in a year, in two years, in five years? So why would you commit to using a product by someone who will only support it with their models? Let's dive in.

1:29JDG, welcome to the show. Thanks for having me, Todd. I'm really excited for this conversation because there's so much happening right now in the world of agents. I feel like three months ago it was niche. But since then, Instinct, GrokBot, Muse, Dots. But Town's actually been around longer than all of them. And so I'm wondering what it's like to suddenly be in the hottest category in tech. I think it's better to be in the hottest category in tech than the coldest one. So you look at it, you're trying to build a product that changes the world. and sometimes you're building ahead of what people are ready for.

2:03And sometimes you're in the middle of the moment. And right now, I think when we started building the product, there was still an open question, like, how good can we make an assistant in the work context? Like, would people's behavior change away from apps on their phone, websites on the desktop, towards interfacing more and more with an agent as the beginning and the end of their journey with digital content? And it felt a little more crazy than it does today. Right now, it feels like it's the zeitgeist. And I think everyone's recognized, you know, we're not building a new app. We're building a browser or a device.

2:36Like, we're building the window pane through which humans will interact with, you know, with all content or with a lot of content. And that's, I feel lucky to be in that position. Now, the negative of it, right, is like you've got competition and the competition are the biggest boys and the biggest girls in the playground and they're well-funded and they've got distribution. But like we always knew that would happen. I don't think there was ever a world for us where we were like, if it really starts working, is there any doubt that we'll be a top three priority at Microsoft, a top three priority at Google?

3:06Like we knew we would be, right? So the question then is like, how do you win? How do you position yourself from a product perspective, a marketing perspective? Is there something really emotional, something intrinsic to the human being on the other side that you could get more right than any other than any other experience? And I feel like we get a good shot there. Right. Well, I mean, it's obviously an enormous market. It's the kind of technology that's applicable to almost everyone in the world. Yes. Do you think most people will have like one of these things or multiple of these things? I don't love predicting all human behavior.

3:37I think from a human perspective, you always want the simplest interface. So you usually want fewer things. Like there's not many of us carrying like four phones around, right? And to the extent that this technology is more like a device or a browser, I don't think you're going to have a hundred of them. That being said, I think personal life and work life have different constraints around them, different requirements. So the way people feel about privacy is different in both realms. If from a company's perspective, they want to own the data that is generated and that their employees like work with.

4:09If you're a company, you don't want to be monetized. You don't want your data to be monetized through ads by someone else, right? Or through transaction volume. On the personal side, you might be okay with the trade-off of like, yes, I get this convenient experience and it's free. But the trade-off of that is someone else can train on my data or someone else can present ads in another surface or someone can make, you know, every time I purchase something, they can get a cut. You might be okay with that experience. You're okay with that for your credit card. You're okay with it when you go to Instagram, right?

4:37You're okay when you use Gmail for free. But I think there's a difference in the work context and the personal. And yeah, privacy, data ownership, all those things will mean, in my opinion, that people have two interfaces on their phones, one for their personal life and one for their work. That generally life bifurcates into personal and work. And the product features that you need, probably the business models of these companies will be very different. And you guys were pretty insistent on choosing work from the beginning. Yeah, so it's interesting because I think the bifurcation from a user experience is less than people believe.

5:08So, for example, I think even in the work context, you will want to connect your personal calendar to your agent. And I think in the personal context, you will want your work calendar. Because you want to make sure that you're not scheduling over a date night. But I think from a data ownership perspective and the kind of things you're doing, it's different, right? So, in the work context, if you're preparing for a sales meeting, you don't want the notes or any of the prep that goes into it or the data. You don't want that to go in the person's personal bubble of knowledge. because what if they leave the company, then they would have all this information with them.

5:40The business absolutely doesn't want them. So you have some things like calendar and time use where I think you will have like a softer membrane. But in terms of the kind of things that you do and where the data resides, it's different. Other examples, people are like, oh, in both personal and work, people travel. Yeah, that is true. But in the personal context, when you travel, you have a different set of requirements in the work. In the work, if you're a company, You may have like a business travel agent that provides you white glove service if anything goes wrong, if things have to be rebooked.

6:11So you're willing to pay a different amount for the ticket because you want the flexibility of rebooking or rescheduling. And you want all your employees that travel to benefit from that. Right. And so the way travel booking happens in the work context is not the same as in the personal context. Right. So even a use case that's similar, it's like different APIs, it's different integrations, it's different end user requirements. And then there are things that are very, very different. In the work context, you might have an agent that's really good at optimizing ad spend on Google and Meta. An agent that's really good at analyzing how competitors are evolving their positioning for your marketing team.

6:44You have an agent that deeply knows about internal metrics so they can give you advice. In the personal context, it's going to be different. You're going to be booking a restaurant. You're going to be booking travel. You're going to be figuring out a date for a bunch of friends to hang out to go watch a basketball game or something. It's just like a different set of things that you're optimizing for. Interesting because I think you always told me that like you need to do, town needs to do a little bit of the personal stuff, but you really, really wanted to focus on productivity and work. Why were you, how was that so clear to you early on?

7:11I just think the business is going to be much larger on the business side because I think there's much more elasticity in that market. Like step way, way back. What is AI? AI is a way to use power to create intelligence. And so in your personal life, where can you apply intelligence to be a more fulfilled human being? it's things like, oh, getting rid of some of the things that are annoying around paying my bills or making sure I always have a great restaurant set up for date night with my wife. So there's a limit actually to how much you're going to do in your personal life. And there's a limit to how much you're going to spend.

7:45On the work side, it's very different. If you can spend dollars to apply AI into things that generate more revenue for your business, you'll keep wanting to do more and more of that. Yeah, it's almost endless. Yeah, so you'll keep, I mean, I don't think it's endless, But as we find more opportunities to make your business more successful, you will find more ways to leverage AI to do so. And so the willingness to spend the number of tokens that are going to be spent there are much, much higher. The second reason is I think the business side is actually more interesting also from a product perspective because it's more complicated.

8:16Because in your work life, you're always working with other people. And so there's all these interesting problems around how do you get AIs that work well among many people. You have also more willingness to share data within organizations. That is the data silos that exist today. AI is in a really good place to break them down in the business context to help you move faster as a company. So this whole set of things to me just is more, it's a more complex product with more willingness to pay. It benefits more from AI getting smarter, right? Because in the personal life, booking a restaurant kind of solved with AI today, right?

8:49Can it get better? Sure. I can get better the next 12 months. But after that, it's like, you don't really, you won't need more frontier. And the task is completed. In the work context, we're like nowhere near the ceiling. And so I just view it as like the market is bigger. The willingness to spend is much larger. And actually the product space is more interesting. And so for us, it was like a no brainer desk where we would focus. I want to go deeper into some of the work stuff in a bit, but just like backing up a sec, why has so much changed in like the past six months to a year that has made these always on assistants possible?

9:19Like why is now the moment this is like blowing up? the first thing that had to happen is the models had to be good enough to autonomously take on some white color work. And Anthropic in November and December of last year released models that started to be able to do that kind of work. And that's why you had OpenClaw kind of start to blow up right after that. Because suddenly your agent could look at your inbox and label emails. It could start to do some basic scheduling. it could on the fly generate code that was good enough that it could automate a workflow for you that started to be possible last you know november december and so that's exactly at the time that we started to look at ai plus email ai plus calendaring but at the same time you have right you have open cluster to take off and that was the first wave of innovation and obviously the models kept getting better but then in july i was in june july of this year the second thing that happened is the models got really good at browser use and computer use.

10:15And so that meant that now the agent can operate not just with their APIs and where you've given like very well-shaped access, but it can also operate with a long tail of tasks in a way that's much more like a human. You know, it can run like your Figma for you. It can generate videos. It started to be able to do just a lot more things, not because there was an LLM that could generate videos, but because it could use a video editing tool. And so that's the second big change from the models being able to do that, those kind of tasks. And then there's a few other things underneath that are important.

10:49Like I think even in the first quarter of this year on kind of knowledge work, it would be correct maybe 70, 80 % of the time. And then when it was wrong, it was like wrong in a more unhinged way. I think now we're much more in the like, it works like 90, 95 % of the time. And when it's wrong, it's wrong in ways that are much less annoying. and where the work product is still mostly useful to you. So that's the combination of things from a pure tech perspective. The last thing I would say feature-wise that's been a differentiator is you can see definitely in town and a little bit in Muse, you're seeing the agents be more proactive, right?

11:28And they're starting not just to wait for you to tell them what to do, but they're starting to be like, hey, I can do this thing end-to-end for you. I've noticed you've done this before. Or do you want me to do this every time it happens? And I think that's kind of the ultimate form of these products where you don't have as a user. And don't think of a Silicon Valley user. Think of like a mainstream user. Like you don't have to learn how this thing works. It starts to just show you how it could help you. And that's, I think, really, really magical for folks. So given just the amount of activity in this space and stuff happening from all these different companies, including some very big ones, how does it feel to be kind of in the middle of this as a product builder i mean day to day like when i just when i look at my computer and what my team is working on it's the most exhilarating experience that i could be in i mean we're building so fast things that work so well for users you know it's an awesome feeling because you want to as a product engineer and product builder you just want to be delivering value to others and now you can do it faster than ever and you can get feedback really quickly.

12:33So that's, that's incredible. The other part that's exhilarating is, you know, that you're going to be given better tools any minute. Do you know, it's like you're, you, you know, we talked about the tech shift, right? Around the models being, getting good at knowledge work, and then being able to manipulate a browser or computer effectively. It's just, we're just going to get, you know, more of those coming up. The thing that's worrisome in this is you know the what it takes to build true defensibility and the ability even if you're ahead of the market the ability of your competitors to catch up and take the good things out of your product and dissect them and launch their own version is like it's so fast you know weeks to months whereas it used to be like years to decades right it would just take a really really really long time.

13:26And that's stressful because you're trying to, you're trying to grow fast. You're trying to impact as many people as possible. And you, you, you get a feature that's magical and you have clean air and you're like, you know, you're on top of the world. That's where we were. We launched in June. Right. And I think all of June, all of July, we were like, oh, we're untouchable. Like this thing is just, you know, totally untouchable. And then it's grown amazingly since then, but now there's clearly other people left and right. They're in the race. You thought they weren't in the race, but they're, you know, they're like next to you.

13:55Within a matter of months, basically. That's right. Exactly. And now, you know, for us, we just shipped a whole set of new features around team and collaboration, like agents working together with people. And we feel the experience is like really amazing. We still need to tune it to make it perfect, but we're like, hey, do I have one month, two months, three months of clean air with these kinds of interactions before everyone else has it? And so now I have, as opposed to having like two years to develop network effects and like real moats i have a quarter maybe if i'm lucky two quarters right that's the part about this which which at times can feel overwhelming because on a from a pure product perspective i think you have to be right not just once you probably have to be right four five times over and over and over again over 18 18 months to to really really truly win so that that's the thing that you might that you might think is you know that's the thing that maybe makes it a little more difficult to sleep at night.

14:48But at the end of the day, for any of these businesses, end game, you always needed a moat that was more than product differentiation, right? But the interesting thing as a startup is this doesn't just affect you. It actually affects everybody in the market, right? And there are some of the big players out there that we're competing with. They also don't really have network effects. They may have distribution advantage, but it's not a network effect. They just have some existing distribution that they can go into. And so you actually look at a startup and you're like, actually, yes, it does mean other competitors can also catch up with me, but I can catch up that with companies that seemed impossible to compete with, right?

15:21And you can see that there are startups that are taking very ambitious mandates for things that before would have taken five years to build. And now you know they can build it in 12 months, right? And so it means whole markets that felt safe are available for competition. So I always remind myself of that. It's like, hey, it's like, first of all, I'm operating in probably the largest market that's ever existed, right? So the price to be had is just ginormous. Number two, we've been ahead of the market a number of times, and I still think we are today. So like, let's keep pressing our advantage.

15:50And number three, the incumbents look much less scary than they would have before. Yeah. On the incumbents, there's this interesting sort of point where you are building on top of Anthropic and OpenAI and others. And at the same time now, they have very competitive products to yours in a way. So what is this like when you're sort of building on top of companies that are also trying to compete with you. I think we're seeing this a lot, right? Well, to be fair, when I talk to OpenAI or Anthropic, they're very like, oh, no, no, the API, you're like a huge user for the API product. We love you. We're there.

16:24And then they're shipping, they're shipping competing products. There's a couple of things. First of all, vertical integration is not, it's not necessarily the place where markets go. So I have this conversation with business customers a lot. I asked them this very simple question. I'm like, who's going to have the best foundation model? Who's going to have the best frontier model in a year? Do you know? It's hard to predict. In two years. Because I would have been wrong multiple times if you had asked me six months ago. In five years. Yeah. It's impossible. Yeah. See, I'm like, so why would you commit to using a product by someone who will only support it with their models?

17:00And I don't think you should, I think if you're a smart business, you should look, that product layer should be someone that is neutral, that will always use the best models for the tasks at hand at the right price, right? Because it's not just about intelligence, it's also price, right? The reality is the frontier models compared to the open weight models, the price difference can be, you know, 20 cents on the dollar, 10 cents on the dollar, right? It's huge. And so I think if you have that discussion, and I think customers are more discerning because some of them bet on one of the frontier labs and it was the wrong one to bet on for about 12 to 15 months, right?

17:36And they feel burned because they made huge token, you know, they had huge like token spend requirements there. So my feeling is you really shouldn't go with someone who wants to vertically integrate the intelligence because the intelligence layer is so important for the success of your business that you want a provider that's neutral. You talked about pricing and ROI a little bit. And this also seems like another thing that everybody's doing differently. Like Muse is free. What is your way of thinking about pricing and ROI for your end customer? You know, whenever Brett Taylor at Sierra talks, he wants like outcome-based pricing, right?

18:14And every time I listen to it, I'm like, I love it. And I want to hang out with Brett Taylor. And I want to do outcome-based pricing. But I think for us, it's not the right mental model for our users. Well, the outcomes are way less clear. Yeah, there's way too many outcomes. It's like if the ticket is closed, the outcome is clear. Exactly. But the question I've asked myself is maybe for some outcomes that are common, right? Like scheduling a meeting, I could just price that at like, oh, that's like 10 cents every time you want to schedule a meeting on town. But I view it, and maybe here I'm too philosophical about it, but I view it as like, look, I have tokens that are super efficient.

18:48You buy a bundle of them as a user, and then you choose what's the right ROI of using those tokens for you. And I don't set a pricing outside of like what is the price of the token from an inference perspective, plus a little bit of a platform kind of fee applied on top. But then as a user, you decide. So, you know, if you are in your calendar all day and you want to have crazy rules about managing your calendar automatically, and that's worth for you to spend$10 a month of your town tokens on that, go for it, right? But if you're a recruiter and you think actually the most important thing for you to automate is market mapping and searching for candidates using a bunch of APIs and third-party websites to find people that you should try to recruit and you want to spend$100 a month on that, which is like our second most expensive plan is$99 a month, go.

19:35And you can do that all day and that's totally okay. And the way we view it is I should just give you efficient tokens and let our users decide what's the optimal way to spend them. But what's interesting is, you know, when we were designing the pricing initially, we talked to a bunch of customers were like, oh, what if we make usage based across the board? Meaning you can have as many seats as you want, whatever. It's nothing. And we just charge you for usage. And I would think most users were like, well, I want to start with something that's more predictable. So can you start with seat pricing?

20:05And then above that, maybe I'd start to do usage for people who use it a little bit more. If I have a use case, that's really high ROI. And that's what we're seeing in practice. Like people want the predictability of something that's like, hey, give me enough tokens for each of my employees that's included in a kind of more seed based pricing. And then after that, I'll see what the usage is and I may decide to go usage based for things on top of that. But I want to make that decision after I understand how everyone on my team is using the product and where they're seeing ROI. And I think a really interesting product opportunity for us and for a lot of other folks is to actually give great visibility to businesses about how tokens are being spent so that they can have conversations internally about the ROI of different ways of spending them.

20:49For us on the business side, like the big opportunities for us are like data privacy and security being so good that businesses are like, yes, this is the obvious choice. The model routing being incredible so that people are like, yes, I don't want to do a hard commit with any of the frontier labs. This is a like much better way to do it. Number three is like owning your data. So feeling like you can move off of town whenever you want and it's okay, right? Because the operating system of all of your contacts that makes your business successful, like that is yours. It is a business asset that you have in town enables you to feel like you really own it.

21:23And the final one is helping you realize that there are opportunities to use tokens in an ROI positive way, your org, but also to notice ways in which it might not be such a good ROI so that you can tell those users, hey, do a little bit less of that. And I think those are four enterprise features that I think if we nail, even if the core product isn't that differentiated, which it will be, are really meaningful reasons for a business to work with us as opposed to someone else. We met back originally in 2015 at Dropbox, where I was working on product, you were working on engineering. So if you think back to that time, it was over 10 years ago, are there still similarities in the way that we built products back then to the way that you're doing it now?

22:04What is same, what is different? The other thing that's interesting about the agent is it doesn't often have that much product surface area. It's like you can think about it as like you can make a button appear and disappear without there being a button that appears and disappears. You just basically you can change the agent behavior behind the scenes in a way that the user doesn't really, they might perceive it as better or worse, but they don't, it's not like the UI is changing around though. That's right. Yeah, so you can experiment all the time. Once you have that observation, I think there's a lot of arguments that would have happened in the past on a product and a design team about what to build, how to build it and blah, blah, blah.

22:34And now it should just be like, guys, what are we talking about? We can just test. And in two weeks, we will have a pretty good first pass answer at this question that can then inform the direction much better than whatever philosophy, rational arguments we're doing now. So that's like one thing that's the same that we can do a lot more of. The other thing that I'm reminded of this all the time, but like, you still have to talk to your users and you still have to synthesize what do the users want out of the product. So I'll give you a town example right now. I'm in a conversation with a user over Slack.

23:08So we have a shared Slack with some users and this one, they're like, hey, I'm still spending some time in Gmail to handle my email and I want to be doing everything in town. And it's interesting because we do not think of ourselves as an email client. We're trying to automate things out of email and make your email inbox much more manageable so that when you look at it at the end of the day, there's just seven emails and they're actually the only seven emails that you need to spend time on. And there's 30 or 50 other ones that we handled for you or archived for you or did the tasks automatically or, you know, whatever it may be.

23:41That's the way I think about it. But the way what this user wants is they're like, no, no, that's OK. But why can't I just still do those seven emails from town? I'm already spending a lot of my time in town. I'd love to be able to do the emails. So the user told us that. And I think for me from that conversation, the immediate thing I thought about is I wonder how many other of our users feel that way. because this is not how we intend for the product to be used. This is not how we use the product. It is not where our product direction is going. But if I found out tomorrow that 75 % of our users would love that experience, and if we had that experience, their willingness to pay and not sure on the product would be much higher, then I sure as heck should build that, right?

24:21And this is a kind of observation where you really need to understand what do the majority of users want? What might they want? And the only way to do that still is to like talk to them. Is to talk to them. And to create an environment where they're willing to tell you the feedback and to be direct with you and to like, you know, help you make the product better for them. And it's really easy to forget. It's really easy with AI to just like build new features all the time. And it's really easy to like add more capabilities. But it's true. I've heard about this a lot actually with different companies because all of the product engineering is happening so fast.

24:55And this is for more enterprise companies or like, you know, sales led enterprise companies. Where they're like, yeah, our sales team doesn't know what's happening. Everything in the product is new in the past two weeks, and we have to retrain the sales team, it seems like, weekly. When I talk about town, I say town is a, you know, we're like a work assistant. And then people are like, oh, what's different about it? And I'm like, well, it's built for companies. And, you know, you're the four reasons why that's meaningful. It's very proactive, meaning you don't have to know what it could do for you.

25:21It starts to suggest what it could do for you. And it's multiplayer. That's what I say. But there's an alternative explanation of what the town product is. the town product is an onboarding platform to help humans use ai in their life and in their work we're an onboarding platform the actual product of town is to help a mainstream person understand what they can do with ai and also what they can't do with ai and to be really clear about those things because i i think the challenge the core challenge right now with these very horizontal assistance and ai in general is how do you get a human to get the most out of it and that is an education problem and you can educate in the product you can educate via content you can you can you can educate in a million ways but you you got to educate that is the product one of the features we shipped was this the agents can work together and so like 10 of our users discovered that like early, like months ago.

26:20They called agent to agent and they loved it. And I was like, well, how do we make people realize they can do agent to agent? And then someone on the team was like, well, we're already in email and people are already using us for calendaring. We're like, well, what if instead of doing that, it was just your agent that added itself to the thread and that just managed the back and forth for calendaring. So we did that. And we actually didn't ship it for old users. This is an experience that only new users get. Oh, that's interesting. So new users use this this thing called CC or Townie, they use it a lot more because we just showed them how it could be magical for calendaring.

26:51And then they start to think about a bunch of other uses, uses for CC or Townie. So part of it, you know, for me, it's just asking like, how many places in my product can I take the new features and can I just deliver value to the user without them having to actively discover it? Cause that's the whole, cause if you can do that, you're kind of training the user, they see the thing and then they start to, you know, they start to do it on their own. Tony Vincent, your co-founder, is an incredible designer. What does having a design-oriented co-founder mean and why does it matter so much for this product category that you are in?

27:24What is design? Design is understanding a problem and figuring out a really clever way to solve the problem for a human being. Whereas I think product is more understanding all the problems that your users could be having and helping decide which problems are worth solving for the business because they're going to bring the most value to the user. but then you solve the problem and you get to figure out how to solve it in a way where that user is going to be like oh my god yes like this solves it so perfectly so tony is both a world-class pm and a world-class designer and so it it's pretty amazing because i mean he's he can look at the entire landscape and be like actually these are the two things we need to solve and yes they're not what the users are telling us but they're like the abstraction above what they're telling us that really if we solve it, they'll be really happy.

28:18Oh, and then by the way, I think there's three ways that we could solve that that are clever. And let me build a couple of prototypes and let's put them in front of users and see what happens. And you can do all that in his head and you know, like a day. So given that you, I agree, are also a very good product thinker and Tony is both a product thinker and a designer. How do you guys, like, how does it work inside town? I think most of the time we agree. And when we don't, it's great because that means like there's something really worth like going at it. When we agree, often for me, it's like, look, my job as CEO is not really to do that much product is he and I, we work together on setting the direction.

28:51What are the most prompts to solve? And then I know I can just be like, Hey, you can go and solve it. When we disagree on something, then that means we've put our finger on something important and it's worth the disagreement being seen through. And we bring other people from the team in because it means, it means there's like something about the direction of the company or something about what we're learning from the market, that's not simple. It's not easy to solve. Early on, we had a disagreement about whether it was like one agent versus many per user. And we disagreed for, I think there's a bunch of people for which multiple agents is the best because it's in some ways more powerful.

29:27But actually the mental model for a human one-on-one is really easy. I just have one, it has a name, it can do everything, but it knows me well. And I think it's one where when you step back, the decision wasn't who's right. The decision is like, what company do we want to build? And which is the right building block for something that could be differentiated for the market? And at the time in the market, you know, it was like, the products were like Claude, but everyone had a Claude, or everyone had a ChatGPT. Everyone even had like a poke. It was just a poke. And we're like, no, it's a townie and it has a name.

29:59Mine could be Ivy. Someone else's could be Brock, or Cleon, or whatever you want. And it has a It has an email address. It has one consistent personality across the board. And it learns about me and it learns about itself. Like just that. But we disagreed about it a bunch. And I think the place where we could agree was once we realized like we were building a relationship and an emotional product. And if you're going to build a relationship, it just needs to be one. And that's what we want to do. I think another one we disagreed on is it's more recent, but like on whether browser use was ready.

30:31I think Tony was more in there like, we got to go now and do it. And I was like, it still doesn't feel like it's quite there. And I was wrong on that one. And the reason is my priors were based on data that was a couple months old. And the models had just gotten better so quickly. Yeah. Right? Like the disagreement was again on like, is the feel of these browser use scenarios good enough for the mainstream user? Right? I just didn't realize how quickly they were changing at the time. So on this, you know, the co-founder relationship and with Tony, one of the things I think is really interesting, and you've told this story before, so we don't have to rehash the whole thing, but about you pivoting away from the original idea of town, which was tax preparation for small businesses.

31:13And I think that's like a really hard decision for a lot of founders to go through. And so how did you guys do it? For me personally, I think it came from a realization that it wouldn't be the right outcome for the team. Like it wasn't the right problem for this incredible team to tackle for five years if I didn't think it could be like a huge business, given the opportunity costs that all of the folks had to, you know, to work at town versus something else. The problem is then the hard things are like, oh my God, we're going to have to let people go. And we're going to have to tell our investors and we're going to have to tell our customers.

31:43We're going to have to tell our partners that are banking on us to offer tax with them next year. Because you're a human, you feel responsibility towards others, right? So there's that. And then you just know it's going to suck. You know, it's going to be like a month and a half of suck. You know? I want to come back to this idea that you've talked about, which is the idea of designing an agent that people inherently can trust and why that is so important for a personal agent, why that is so important in a work context. How do you do that? What are sort of the ingredients of that, of building an agent that people can trust?

32:18I'm laughing because this is one of the things that's changed since we started the company. And that's something we've been talking about for the last month. So when we built the very early product, there's actually no agent. You just wrote prompts and then the prompts would run on every email that came in or you could write a prompt that would run over time. From a pure security perspective, we were like, okay, let's make any tool that can do a right action based on an incoming email that must, must, must, must, must, must have a human in the loop. And then likewise, then we started, we added an assistant later, like an agent that you could just talk to.

32:50And there we were like, again, if that agent accesses data that's not trusted and then wants to do a right action, it's gotta be human in the loop. So the principle there was like one way to earn trust, It's just simply disallow the system from doing things that couldn't be trusted by telling the user about the thing. That was like a core, core principle. Then number two, we start to get users and we start to hear what would make them trust the product. And the things that make them trust the product is like, hey, we are deleting your data as quickly as possible after we're done using it. Hey, we don't re-index like all of your information.

33:24we only index the things that are absolutely necessary to deliver you a good product experience hey like our privacy policy says we do not train right um unless you opt into it like we had a number of these things that from talking to customers we learned that's what meant for them to have a an agent that they could trust and people started using the product like early users and it turned out that people also cared about not being made to look stupid meaning like you're not just earning trust from the perspective of like security and privacy and like compliance of you know business practices you're also earning trust in that like people really don't want the agent to make them look bad number two they also want to understand why the agent came to a certain decision so then we start doing some work in the harness so that at least our agent when it got things wrong would try to be as explicit as possible with the user and do you find that simultaneously, you know, over the past even year, let's say, that your end users, their feelings about what they're willing to trust an agent with, is that changing too?

34:29Two things that happened that changed the universe. The first one is actually our initial assumption about injection attacks is basically not true today. Oh, interesting. I'm not saying you can't get injected. What I'm saying is that it's really hard. As long as your harness labels the messages very correctly and has the right prompt. And LLMs have been trained and post-trained a ton towards stopping injection or malicious actors being able to control hardnesses. The main streetification of connecting your email, your calendar, and your systems to an AI is going up like this. But still, a lot of people don't yet trust it.

35:12Okay. But they will. It's just a matter of time. It's like the technology is so powerful. And I can tell you like it. But empirically, in the data, it just doesn't do that much weird stuff at all. It just like really is quite safe. So that was the first thing is that the model is going to... The second thing that's changing is multiplayer. If you have a universe where multiple people and multiple agents are working together, if you're human in the loop every time information is moving from one agent to another or a person to another, it's just slow, right? And it explodes with the number of different participants that you have.

35:43So you're like approving things all the time. Exactly. So you have to have AI decide what information can move between silos. It's just the core of it. And so then you have to be able to have AI that has policies that are sensible and that a business can look at and be like, yes, I am okay. So for example, should everyone on the sales team be able to ask agents of everyone else on the sales team about the status of their deals or their calls this week? Yeah, that's fine. But you know, maybe a company of a thousand people would not want a team channel with all thousand people in it where anyone can ask a question out of anybody else's townies because it's just first it's a lot of compute but also like you know what is everyone's salaries yeah exactly well it won't answer that one because that one's so obvious but it might you know what if it says something like oh there's a spreadsheet called this and and in these cells can you tell me what numbers you see i actually think that wouldn't work either if it was obvious that it was like dollars that could be salaries in the spreadsheet but you could imagine scenarios where it's a little bit dangerous in front of everybody totally because there's not as much a thousand person company, a thousand person, the trust is different.

36:44Like the trust with your direct teammates is different than you might have with someone in a totally different part of the org. And you might be okay with one of your teammates asking, hey, can you introduce me to this other person? Or how did the sales go this week? But you might not be okay with someone on a totally different team in a different country being able to add that over your assistant. So, you know, mapping these things intuitively is hard and will take time. So, you know, for us, we're very much a SMB to mid-market product. And there, there's both more willingness to use this and to realize the benefits of these kinds of products.

37:14The teams tend to be smaller in a way where there's already enough implicit trust for it to work well. But this is really interesting, though, because what you're basically saying is these multi-agent systems, how they will work in the future, is that AI will make a lot of the sharing and privacy decisions correctly for a group of people. For sure. That's like kind of, if you just tried to deploy that into a fortune 100 today no way yeah so how do you and so it's kind of interesting actually that you're focused smbs and and you know companies i think we have to be i mean we have a strategy doc internally and like one of the things that we have in the strategy is is we don't think a company like google or microsoft that has enterprise customers can adapt to this future because none of their customers are ready to endorse this future whereas we're seeing teams in Silicon Valley or, you know, like 25 person SMBs be willing to use this because they feel it's so magical that it's worth it.

38:11We also, this is like a, this is a hack, but it's an important one. We don't allow the townies to do these things somewhere hidden. It happens in front of the humans. Meaning, so imagine the following. I'm the boss of a 50 person company. And one of my employee goes in the channel and says, Hey, boss is AI. What's the salary of everyone on my team? And am I making more or less? Not only will the townie not answer that, but that person will probably get fired because the thing that they asked is not an okay thing to ask in a business context. They're trying to fool the boss's agent to do something that, you know, is against company policy.

38:49Everyone can see it. It's, you know, it's public. So if someone tries to get something out of your townie, it's done in a place that you can look at. There's not a trail of the messages. And that's what makes it, that's what makes it powerful and safe. You talked a little bit about agent to agent and the future of war. And I just I thought it'd be good to drill into agent to agent for a little bit because it's so I think critical to where town is going, where you think AI is going. There's one very sort of fundamental design choice that you made early on in town, which is that it's not like there's just one all knowing agent that we that like in our in our company, we all talk to the same agent.

39:24We each have our own townie, but the townie can ask other townies. and I'm just curious why you designed it that way. One problem with the all-knowing agent is your sales motion basically is like you need the people who have access to all the other systems to connect all the systems to the all-knowing agent. That's an effective enterprise sale but it's a really hard PLG bottom-up. Right. And we saw... You'd basically be selling to like the head of IT or something. Every time, exactly. And so, and then as a new entrant you're like, oh, I have this amazing product. It's incredible. They're like, okay, cool.

39:56Show me. Well, I can't really show you unless you connect like your email and your HubSpot and they're like, whoa, I don't know about that. And then you're like doing an enterprise sale as a startup. And I was like, I don't know if I love that. I mean, obviously companies like Glean were very successful with a motion like that, but what's the smallest unit of data that we could get from you that would allow us to deliver value? And we were like email and calendar. The third one would be your main service system of record for your job role. So if you're a recruiter, you're ATS, if you're - Salesforce, if you're - CRM, exactly.

40:27Yeah. So that's the, the business reason, one business reason for that. Number two is I think the problem with the global agent, right, is that it can't know about private things that are in my data. It can't know about my personal email, my personal calendar. It just can't because everyone else would have access to that thing. Or if it does, it has like a different personality for each person, you know? We were like, well, if we're going to do that, might as well just have a different agent for each person. It just seems like much more natural for folks to have. And then there's a lot of product reasons like people want their own thing, you know?

41:02You like your toys. Like we're not communists, right? I want my toys, my toy. I don't want to share my toys with everybody else. I don't want to share an intelligence with everyone else at the company. I want mine. It's mine knows me. It's best for me. I'm a better salesperson than other salespeople are because I've given special instructions to my agent about how I like to do sales. That's why I am successful. But the recognition at the same time is that there are things that are company-wide. So what we do is you can have a company integration that all the agents have access to, and they all have access to the same data.

41:30Because as a company, we have agreed to have a company integration. You just don't need to start with that. And then the last principle for us is like, whether it's one or many, fine, you can disagree. I'm not saying ours is the best solution. It's the one that was opinionated that we had. But whether you're in email, in text message, in Slack, in Teams, you can add town or you can add your townie and you can bring them there. right and that's no different than what you can do with some of these like general agents you can call on them wherever wherever you want our Taiwanese also have they have their own knowledge about you but then they have shared knowledge about the company they have shared skills and shared routines are shared with everyone at the company that everyone can work on and edit so we still have that feeling that there is like a shared set of primitives of intelligence that we can all work on as team members together to make better within a normal company a normal organization which people or teams do you think are most kind of underserved by AI today?

42:26And why do you think that's the case? My hot take on this is that, well, definitely not engineering. So we know that. So I think in most large businesses, they kind of know what are, if you look at enterprise, they're like, okay, what are my teams that have the most operational process that doesn't involve the physical world, right? What are my teams that are driving revenue? what are the teams that are driving business opportunity those teams get ai real fast you know so like sales teams support teams engineering teams very fast very fast then there's lots of places in a large org where it's difficult to measure and define the roi of people's time right and there i think there's much less ai so an example would be like a secretary it's like well, how do I measure the efficiency of that person, right?

43:19And it's the wrong mindset, but I think there, there's no FD at OpenAI that's going at a large corp with like hundreds of secretaries and be like, we will make your secretary-like team like much, much more effective, just not happening. Even though in practice, the secretaries could be much more effective and could support many more employees, making them much more effective at anything that involves like scheduling and use of their email. They just don't. It just does. It's just not thought about. So the main part of this answer is like, I think different large companies have a different weakness for where they're not applying AI based on what their business looks like, right?

43:57So large companies that have lots of divisions that do things very differently have no standardized ops. And so then none of the ops teams anywhere are using AI. But really all the ops teams should be using flexible AI like town or another one because a lot of their workflows could be automated. But they just aren't. What we see in the mid-market, it's fascinating. It's like salespeople really love town. And I'm like, I don't get it. There's so much AI for sales. Like literally, you know, you can't walk down San Francisco being hit over the head for like an AI for sales product. What do you believe about AI agents that most people today would disagree with you about?

44:37I am much more bullish on AI, more and more deciding how information gets shared. A long time ago, there was a company called Facebook. You work there. You'll know this. Facebook did not have a feed. you would go to Facebook and you would click on different friends' names and you would see their wall and you would see what they'd done recently. And then one day Facebook was like, there shall be a feed and it shall be algorithmic. And people went nuts. Like, I don't know if people now realize people went nuts. People were like, AI is going to feed us content. It's the end of the world. Old school AI.

45:11Like Facebook will decide what people see, right? Now, obviously in hindsight, some of that stuff did be, was a little bit problematic, but just, it was like revolting to people that what comes to you would be decided by AI. And the claim that I would make today is that AI will decide what gets shared from you increasingly, not to everyone in the world, but to people that you're closely associated with, your family, your coworkers. I don't mean it'll just like push information willy nilly. What I mean is when those folks need something, the answer of which lives somewhere in your systems, AI will decide what can be shared proactively without you being there.

45:49And I think today, when you tell that to folks, it feels crazy. And their reaction is a little bit like people were about the feeds. And I think fundamentally, it's not that different of a concept. And I think for a bunch of scenarios, this will happen and it will unlock a ton of value and will make people's lives much easier. And it'll be wonderful. A ton of value, especially inside companies, because you just think about in companies, the number one bottleneck is interpersonal human communication and knowledge sharing. That's what I think, but you know. Going to get, the world's got to get this.

46:22So Town's going to solve this? We're hoping to. I mean, I think we're actively solving it. It's just, you know, talking about product is like you have to build a great product experience. And this is not a, you can't tell people about this. We can do the podcast and tell people about this, but this doesn't exist in the world. Like people are not using this. So people have no expectation that this can happen. And so the real challenge on this is not, can you do A to A? Can agents talk to agents? That's not the hard part, right? It's not even the privacy. You got to get the privacy right or you're out of business.

46:53You got to get those things right. It's, can you deliver something of value that they wouldn't have had without this? Where they're like, oh my God, this is so valuable. Obviously, I'm going to keep doing more of this. And that way they learn that this thing is possible. And then they do more and more and more of it. that's the challenge with 8a it's not building it it's delivering something so valuable from this experience that it it avoids seven loops between humans where everybody feels like they got hours back that week and a better better business have come and i think if we can do that then we'll be very happy as a business amazing jdg thanks for coming thanks for having me todd appreciate it