Full Transcript for What Happens When AI Agents Can Spend Your Money? | Jean-Denis Greze and Alex Rampell

Impact in the world is really, really important. We really wanted to build something that mattered and helped people. I get pinged by Ben Horowitz about, like, I found this amazing tool. It's called Town. It, like, changed my life. It's so great. We're trying to build a product that works with people to find more and more opportunities for them to deploy AI to be better at their job.

No one had built great AI around these very common knowledge worker workflows, like meetings, meeting prep, getting people to be prepared for their day. Why is Town better than OpenAI and Anthropic? It is risky always to be competing with your suppliers. You get to ask yourself, is that naturally the right state of the world? What if OpenAI is not the best model provider for the next three to five years? And so the first thing is we're just like, look, we're neutral.

We will use whatever supplier we think is the best for you in the moment. So that's like one reason. I think the second reason, which is like very important, is... AI is getting remarkably good at coding. So why are so many everyday business tests still done manually? In this episode, A16Z general partner Alex Rampell sits down with Town founder and CEO Jean -Denis Grez, the former CTO of Plaid, to discuss what it takes to make AI genuinely useful at work.

Jean -Denis shares why he walked away from a growing tax software business to build Town, an AI assistant that works across email, calendars, and everyday business workflows. They discuss why AI adoption is still concentrated among people who know how to experiment with the technology. How agents could make useful automation successful to everyone.

And why businesses may want AI assistants that aren't tied to a single model provider. They also get into what happens when agents start acting on our behalf. How that changes commerce and software. And why Jean -Denis believes the next big opportunity is helping people discover what AI can already do for them.

Hi everyone, I'm Alex Rampell, partner here at Andreessen Horowitz. And I'm here with Jean -Denis, the CEO and founder of Town. Thanks for having me. So what I find cool is that we worked together when you were the CTO of Plaid. And I had led our investment in Plaid here. Yes. And I heard you were leaving and going to start a company. And then I remember you told me you're working on an SMB tax tool.

Yes, we had coffee. We had coffee here in Menlo or Palo Alto. And I got a little not happy about it. I was like, ooh, ooh. Like I was so excited. And it's like SMB tax tool. That's rough. All cards on the table, right? You didn't tell me that at the moment. I know, I know. But I have to maintain a very happy face at all times. You never know.

And then this is what I find so funny about this is, you know, this whole story. I get pinged by Ben Horowitz about like I found this amazing tool. It's called Town. It changed my life. It's so great. And I'm like, that's good, right? It's like an app. So therefore, it falls under my fund. And I was like, wait, that's Jean -Denis' company.

But he does SMB tax. That doesn't make any sense. I'm so confused. And that's what I texted you. And then next thing, you know, a week later, we became partners, which I'm extremely excited about. But maybe tell us what Town does. And I think it's going to be incredibly interesting for everybody how you got to this point from the origination story.

It's like you left Plaid. Plaid, you started doing SMB tax. Kind of walk us through the founder's journey from there to here. I left Plaid. I took a few months off with family because I knew that if I was going to be a founder, I was going to have no more time off for a while. Yeah. And then started exploring in the very B2B way what would be interesting markets where AI could really change the product experience.

And for a variety of reasons, tax made a lot of sense. There's a lot of text in tax. And LLMs are very good at text. You deal with a lot of structured formats as inputs, like financials and lease documents. And it just felt like a medium where we could take this process of putting together a business tax return, which is pretty different from a personal tax return, and automate it.

And I did what engineers do, which is we did technical due diligence to prove to ourselves that it could be done. And the feeling was like, yes, within the next two years, the process for a small to medium sized business of going from your financials and a bunch of documents about your business and a finished tax return that could be automated with AI.

And we spent a year building that. And I think there's a nice day in it, but it worked from a product perspective, but not from a go -to -market perspective. Meaning even though I have no doubt that tax prep will be automated, how do you reach millions of businesses efficiently and sell that product? That had a lot more question marks than we anticipated.

And basically after one year, we had what I would describe as a good business pre -AI. That is, if I showed you the revenue number and the revenue growth in our first year, like a little bit below a million, and if I showed you what I thought we could do in year two, we thought we could get to like two and a half to three million in revenue, I think pre -AI you would have been like, that makes a ton of sense.

But post -AI, this was not going to be a venture scale company. And also, I think even more importantly, we realized that the incumbents were going to be able to use the technology to automate tax prep for their existing customers faster than we could grab market share. And that's like a key thing if you're a founder, you have to think about your advantage if you're a new entrant is you're faster moving.

You can build a different product. You can use different go -to -market approaches, but there's a ticking clock on the wall, which is how quickly can you get real word of mouth network effects and market penetration before the incumbents have other advantages. They have distribution advantages, right, for example, before they can catch up and copy what you're doing effectively enough.

And it felt for tax, like we wouldn't be able to get to hundreds of millions of revenue before our competitors, the incumbents could, because you can only sell tax once a year. Yeah. And that's like a, you know, They don't only sell it once a year. It's like the interaction. It's not like a, it just fundamentally cannot be a DAU tool. That's right.

And it also doesn't have a ton of word of mouth because it's rare for people to brag about their tax prep situation. And when they do again, they can only do it once a year after they've done the tax return. When you brag about how good your tax prep experience is, usually it's also not the time when people are purchasing or looking for a new tax preparer account.

And so they're going to forget in the next year. That's right. Those discontinuity make it really difficult to hyperscale. And so that's why it was not a great idea as a business. And it's funny, one of my best friends has built like a successful ed tech company. And one of the things you always complain about with ed tech, it's the same thing.

You can only sell in that tech twice a year. You can sell in the summer for the fall. And then you have a short window where you can sell at the end of the fall and winter season before the new year. Those are the only two times where you can really sell software to schools and to school districts. And he was like, that really limited my ability to hyperscale.

And I chose to forget that when we were doing tax. Yeah. So we built tax and after one year, after the first tax season, my co -founder and I, we went for a lot of walks with a lot of coffee for a week and really pondered the meaning of life and entrepreneurship and capitalism and decided we should do something else. We need to find a more fruitful market to play.

If we want to impact millions of people, there are better ways to get at that goal. That was a pretty brave thing to do. Most people probably wouldn't do that, especially because it wasn't not working. In the moment, it felt like failure, like we were failing. But I think, what are your core motivations for building a business? As a founder, there are these moments, they happen more often than you wish they did, but you really ask yourself the question, why am I doing this? And for us, it was like impact in the world.

It was really, really important. We really wanted to build something that mattered and help people. And so we stopped believing that we could have as much impact in the world as we ought to have because we didn't feel like we could scale the business quickly enough. And the second reason why I think you built a company for us was really enjoy working with our team.

But everyone on our team has an opportunity cost. They could be working at town or they could be working at a lot of other great companies in the valley. And so if you stop believing that you can have a great outcome, then it's really hard to come into the office every day and look at people in the eye. When they tell you like, hey, I'm having a hard time with this, or when you're trying to convince a candidate to come join your company and they have an offer from someone else that's liquid, and you're like, oh, well, who am I as a human being? Yeah, no, it's very hard.

I have to believe in that. I know your pain. I was there. So there's a few things that we looked at that and we were like, well, we got to make the decision. The counter on the other side is maybe we would have figured a way. And there's always this thing you never know for sure, right? And we're very resourceful humans, so you can always try something else.

But it just felt we didn't have the idea in our brain to make it a really breakthrough business.

And the hard part of it is admitting to other people that you failed. And to some of your employees, maybe like our tax team wasn't going to have a future at the company for I'm going to do tax. And you face that and you're like, first of all, who cares what other people think of you? My friends will still be my friends regardless of whether I succeed or fail.

For employees, you can help them find their next gig. We can treat them really well in terms of severance and other things and make it okay. And then you realize it's not as hard a decision as you might have emotionally reacted to initially. And it's the right decision. You start to get that conviction and then you just do it. One last little bit is because tax is annual, I think making the decision at that point in time, it was the only time we could make the decision.

You can't pivot like April 16th after you've told a bunch of people that you're going to do their tax return. You can't pivot in January after you've signed half of your partners, right? We had a window between, let's say, right around October 15th of probably a month where we could live in both universes. The universe where we kept doing tax and the universe where we couldn't.

And after that, the door was closing. And so I think that time pressure helped us make the decision more efficiently than otherwise. I think if you're not in a cyclical business, I think you can always tell yourself, well, let me try the next idea for a month. Let me try the next thing for the next three weeks. Maybe that'll work. Maybe this will work.

We had a short window, right, where we could change the direction potentially. And once the window was gone, it was like, hey, we have to decide right now. Otherwise, we're going to be here a year from now. And a year from now, we won't have the capital to make the change. So from there to, obviously, the name didn't change. It's still town.

It's a good domain. Town .com. It's a very good domain. But how did you get from there to where you are right now? We basically explored about four ideas pretty seriously. And it's not worth going all four. What led us to what became town was this question, which is why is no one built a great agent that works out of email and calendar? And we started asking that question, one, because when we're building the tax product, a lot of customers were telling us, like, hey, it's amazing that you're automating tax.

But I also spent a lot of time setting up meetings with my customers. When I go into the meetings, I'm trying to– or my clients– when I go into the meetings, I forget what the meeting was about. Clients always ask me the same tax questions. Wouldn't it be awesome if you could pre -draft answers for me? And so we realized from these needs that, like, these weren't very complicated things to do.

But no one had built great AI around these very common knowledge worker workflows, like meetings, meeting prep, meeting outcome, answering pretty simple emails that happen all the time, getting people to be prepared for their day. So we just started exploring that idea, and we did it at the time that Opus 4 .5 was happening, and agents, at least for coding, were able to do more and more autonomous work.

And I would say that very simple insight plus the model suddenly being able to be more agentic meant that we got product market fit very quickly. And project market fit is a– it's a magical thing. Once you have it, you kind of want to hang on for dear life and keep pulling at the string forever and ever. And so we didn't sit– we didn't actually sit back and ask ourselves, like, how big could the tan be? Or, like, will everyone want to do this? Or, you know, like, what does it mean to win? We were just like, hey, we're getting a ton of pull from users.

Let's go after this for a few months really hard and learn about the market and see what the opportunity could look like. And, you know, as we did it, we started– you know, we started to realize, well, most businesses run out of email on their calendar, right? Especially any role at a business that's external, that's like sales or business development or recruiting.

You know, the way companies work together is through email mostly. So we're like, oh, the TAM's got to be big. And it's not just in the U .S. It's, like, everywhere in the world. And then we start– like, when our customers start telling us what they liked about the product, we realize, oh, there's a depth to what they're asking us to solve for them.

It's not– this isn't as simple as just hooking up an LLM to your email. There's a lot more that goes into making this experience better and better. So we start feeling, like, on the product end, I'm not saying necessarily we had a line of sight to emote. But there was enough complexity that we thought, hey, we could really dig into this and build a great product experience.

And basically, we did that from December of last year until April, and we just kept going every day. Like, every day the product grew by a percent or a percent and a half. And our customers kept asking us for more, and the roadmap was very clear. And, you know, we got pulled until the point where we were like, hey, enough with this closed beta.

Like, we're ready to launch the product out into the world. So– and one of the things that I thought was very bold about what you did as well– so you have product market fit. But you're also– we talked about this a lot of do you go PLG, right? Or do you go– like, you have big companies that say, hey, give me your product. I want it.

But you kind of realized that if the customer is not the user– the customer is the person that writes the check. The user is the person that has to deal with the product. If the customer is not the user, you might end up building a bad product because you're making the CISO happy. You're making– you're not actually making the user happy.

You're making the customer happy. Yeah. We– my co -founder and I worked at Dropbox. And I think one of the big learnings for us from Dropbox is you can always take a world -class product experience and figure out how it works for the enterprise. It's really easy to go in that direction. I mean, let me rephrase that. It's tough to go in that direction, but it is possible.

It is almost impossible to go from an experience that starts from the compliance IT enterprise requirements and then figures out how to make the product experience incredible if you take that as the baseline. So, we felt– and I think until now, until maybe a couple months ago, we felt like there was so much alpha in just making the product better.

That should be our focus. Growth was just happening naturally. We're like, if we figure out the product experience and if our foundation from an enterprise perspective, from a data security and privacy perspective is good, we can figure out later how we shape the admin side of the product to be perfect for an enterprise. I think that was the right call for us.

I think, especially as consumers' expectations around what AI can do, those are going to be consumer -grade experiences. And so, our thesis is that CIOs, CEOs of companies are going to tell themselves, hey, this amazing personal assistant experience that I get on the personal side of the world, I want that for my company. And so, whoever has an enterprise experience that feels like a consumer experience, they're going to be set up to win.

And we were like, that's where we want to be as a business. And I think we've mostly actually succeeded in getting there. And now the question is, okay, first, we have large companies that are using the product. So, it's like, how do we go from just one enterprise deal to being able to do dozens effectively? How do we take the data, privacy, security requirements of not just a couple of customers, but how do we satisfy those for every enterprise company? That feels like a much more knowable problem now, and we can go after that with our heart out because the core product experience is good.

Yeah, I know. I agree. And I know you and I have talked about this a bit where I remember having a BlackBerry. Yes. And call it 2007. The iPhone launches, and then Steve Ballmer is like, nobody's going to buy an $800 phone. This is going to be a flop. It's such a joke. Blah, blah, blah. Eventually, Microsoft buys Nokia. But the BlackBerry died pretty quickly because my theory on this, at least, is that every CEO got an iPhone for Christmas that year or something, was taking pictures of their kids.

They're like, holy crap, this is really good. This thing's not that good. Hey, I'm the boss. You go figure out how to make this thing work here. Yeah. And we're probably, I talked to a lot of enterprise leaders, and it's funny, the ones that have seen the future are like, I need this. There are some that are like, oh, yeah, like, AI doesn't work that well.

I use Watson or something. Like, there are still plenty of those, but there are skeptics. But, like, it feels like the iPhone has emerged in terms of, like, people have been exposed to the wonders of AI in a personalized setting but not in a work setting. And it's kind of an open question as to why, and part of it is the permissioning part of it.

But I'd love for your thoughts on kind of if you agree with that framing and kind of what the next few years of, like, AI for work look like. Yeah. So, one, I agree with the framing. I think a little side note. There's this company, I believe it was called Good. Yeah, Good Technologies. Yeah, which was, it was an enterprise -ready email client for the iPhone.

Yeah, it was an app. It was, like, the ugliest thing in the history of the world. Yes, I used to use it. And I, the reason, because I think it's not just a BlackBerry story. Like, I don't want to be good, right? Because good eventually is not a good business because people are like, yeah, sure, you're enterprise on the iPhone, but you're not at the level of quality of every other iPhone app, right? And so, I think that's the risk for us.

It's not just being the BlackBerry. It's also, like, being good as opposed to, hey, people just using, like, Gmail, the Gmail app, right, on their iPhone because it's both the convenience and the enterprise features that a business is going to care about.

Yeah, so, I often think about things that happen at the speed of a human, right? And first of all, assistance as a category is used by a few million folks, right, worldwide today. It's still very early in the adoption curve. So, I don't even think all of the CEOs have gone there. And just like the iPhone, it will probably take 6, 12, 18 months for most people to experience this magic even in their personal life.

Once they do, then there's a question of how do you adopt that within the company? And I think it is more complicated than just the iPhone because the way agents work is they're more and more effective if they have access to the data and the APIs that run your business. If they have access to those things in a secure, privacy -preserving way, then they can do lots of things for you.

But if they don't, right, they're only really as smart as the data they have the access to. So, I think the way we think about the journey is, first, be able to go to any company and get a few people at that business simply by connecting their email and their calendar. So, not the core systems of record yet. To feel enough magic that they're like, oh, this is going to bring a lot of value in my work.

And as I said, for external facing roles, the calendar and email are real sources of insight about how you can automate part of someone's work. And so, then you can show up with the assistant. You can be like, I can help prepare you for these meetings. I can tweak the decks for that meeting to use the design language better and to be updated with the latest numbers that are in this PDF that was sent to you.

And just from that, people will start to feel the magic. And then you ask yourself, well, have I earned the right to ask you to connect your CRM? Have I earned the right for you to go to your IT team and ask all these other tools to be connected? And I think that journey will happen more at the speed of humans. And you have to have the other side of the coin.

You have to have the audit logs. You have to have the posture that allows, you know, privilege information not to be shared with the agent. You need all these. You need the cost control for the CFO so that they understand that if their whole team operates on town, they're understanding where money is being spent and that tokens are being used efficiently.

So that, right, it's all that side of things that will take more human time to adapt because you're still selling into, you know, a Fortune 100 company. That has to be run with a certain level of diligence. As you move down market, right, as you move towards SMBs and mid -market firms, I think you have fewer of those requirements, right, and the adoption will be much faster.

And so our current bet right now is we are really focusing on from a product experience and self -serve growth, the SMB market, the mid -market, and then from a what security compliance finance features do we need, focusing on the enterprise. And we think we can knock out that whole part of the space. Yeah. I mean, I think there's also a Javon's paradox to this whole assistant space if you think about it, right? So I have an executive assistant here.

But still, I don't– I mean, I trust her. I like her. I don't want her reading all of my email.

And actually, this just came up. I was in London. I thought I had a dinner to go to. So I could have sworn, like, I got an email about it. I texted Snap, my townie, and I was like, don't I have a dinner to go to? And it was like, yes, you do. You do. And it wasn't on your calendar? It was not on my calendar. It was my fault. Not my assistant's fault.

It was my fault because I didn't respond to an email. They didn't CC her. That's not their fault. That's my fault. And it caught it. But now I have it automatically going through. Like, it reads my email all the time. And that's something where– so I can– or my firm is nice enough to give me an executive assistant. There are plenty of people that don't have one.

And it turns out– you might just say, well, the market for executive assistants is rather small because most people don't have them. But it turns out if you could actually produce it via silicon, the market's quite large. And then you find things to do. Like, I'm going to have somebody read my email all the time, which I would never do, both from a privacy– it's actually– it's like a twofold– it's like a Javon's paradox of privacy.

Obviously, like for those that don't know, Javon's paradox is about how when the steam engine became more efficient. You know, the original– like James Watt made a more efficient steam engine. And then the assumption is that the demand for coal would go down because what used to take like a pound of coal now took like an ounce of coal. But actually, it turned out that it went up like a thousandfold because now you had something that could do it very efficiently.

So it feels like that probably exists for the assistant market. But I'm wondering, how do you define your market? Is it like assistant? Because that seems like it casts too narrow of a brush on it. I think of the market as– we're trying to build a product that works with people to find more and more opportunities for them to deploy AI to be better at their job.

And I think in some cases– so I think of our product as a token allocation machine. We're trying to give options to the human about where they could apply tokens. And then we choose– the human can choose whether they want the outcome of that. And I think as the tokens get cheaper, there are more and more places where you would apply tokens where you would never have done it with today's expensive tokens.

And you definitely would not have done it with humans before. So a very, very basic example is writing prep notes for every meeting that you walk into. Even if you have an EA, your EA is not spending for every meeting, every 30 minute that you go into writing perfect prep notes. It's just not a good use of time. But an LLM can do that for a penny, right? And so then suddenly for anybody feeling like you have prep notes for every meeting that does some research on who else is in that meeting.

Have they said anything in X in the last week that might be relevant for the meeting? Like all this long tail research is now worth it because it's so much cheaper. So our job is to find things that look like that more and more and to tell you as the user, hey, do you want us to do that for you? So I give you like another example that I'm recently pretty infatuated with.

It's like if you are a small business owner and you run a restaurant in America, your wholesalers, that is the people that you buy, the produce and other things that go into making food at your restaurant, they email you probably once a month, maybe once a quarter with an Excel spreadsheet or a PDF of the prices for all their inventory. So like thousands, if not 10 ,000s of items.

And the prices are always changing. And if you're a restaurant owner and you have a finance person part -time, what that person should do for you is once a quarter, look at how the prices have changed to see if you could order some things from different wholesalers to see if you're maybe getting ripped off or paying too much because the price of something has gone up 20 or 30%.

Mostly you don't do it because it's like a rare thing. And even though it could save you money, it's a pain in the ass. It's not part of your operations. You don't have a full -time person to do it. With a town, having a thing run once a month and do that analysis, like literally you can prompt your way to having that exist for you in five minutes.

And if it finds nothing, 11 out of 12 months that's worth doing, it's going to cost you like a dollar a month to run that thing. But the interesting thing is the one month out of a year where it finds something, it's going to find like hundreds if not thousands of dollars for you. Right? Right. And so now this thing that may or may not have been worth it, you should do it at least once a month.

You should do it whenever there's a change to the price. Maybe you should do it daily if you have a wholesaler that changes prices daily. It doesn't happen. But our job though is to show that opportunity to the restaurant owner because it's not that they aren't very smart or don't understand their business very well. They do. They're not going to think that that's valuable.

So what you kind of all tell them like, hey, listen,

you want me to always make sure that your wholesalers aren't changing prices in a way that's negative for your business? Sure, do that. You don't really care actually after how often it runs. What you care about is once in a while you get an email that tells you like, hey, you should probably move all these things to this other wholesaler.

They have a discount or like this one is really raising prices on these things and it's getting a little too expensive. Maybe you should remove those menu items. Like it's not rocket science to do this, right? But you have to have a product that tells you that this thing is possible that makes it easy. Well, the interesting point around that example is if you went to a restaurant owner and said, do you want an executive assistant? No, they would have no idea.

Whereas if you said, do you want to save– it's like Geico, right? It's like do you want to save 15% on your car? Like do you want to save money on iceberg lettuce and I can save you $500 a year and charge you $5 a year? The answer is yes. But how do you know– I mean this is kind of the product marketing question or challenge. Because I remember the two things that we talked about when we first talked about the new direction or the future direction of the business post -tax were PLG versus sales -led growth.

Where what you said really stuck with me. It's like if you want to build a product that is designed for the user, you have to build it for the user, not for the corporate overlords, all the lawyers at the company. And eventually you can work your way backwards into enterprise sales and sales -led growth. But the other thing is this kind of horizontal versus vertical.

And this is a question that a lot of companies struggle with a lot. And this kind of actually will bring us to a different point on like what do you think about the big guys, right? Like obviously like Anthropic, opening eye, opening eye, and everybody else who's kind of building things both for enterprise and for consumer. But how did you kind of think about horizontal versus vertical? Because what you said, hopefully I'm going to capture it correctly, was we're going to start off as horizontal as possible.

Because we don't want to be like, you know, we don't want to be siloed into like something that might be, we don't want to be siloed and branded as like the save you money on iceberg lettuce company. We want to do that and we're going to learn. This is my sales pitch to your candidates, by the way, now. So hopefully I'm getting this right.

We're going to learn from hopefully thousands, tens of thousands of businesses and people that use our product. And then eventually we can verticalize from there because we're going to let the market define that. And we're not going to presume that we know about the iceberg lettuce problem. We wouldn't know that until somebody actually downloads town, uses town, they learn from their townie.

And then we can actually recognize that that can be a vertical. But I guess how do you think about vertical versus horizontal? So the way I think about most AI products today is that the capabilities will be very similar. Meaning most harnesses in the work context will be able to do the same things. And so the differentiator, like I think what has been the main differentiator in Silicon Valley for a long time, which is you have different product features that are ahead of market.

It's going to be more and more difficult to sustain that kind of an advantage. And we'll put a pin in this one, but we can have an interesting conversation about I think the unit of value in software has changed from the app to the computer. But what that really means, it's gone from an app to anything that a human can do. And a human can do almost anything with a computer.

So we're abstracting away the app layer totally. And I think the value will drop in the app layer.

So if the capabilities aren't different, then how do you build a successful company? And I think our take on that is we're betting that as AI gets better, it can better customize the product for each user, like very specifically to their need. Meaning if you're an electrician that signs up for town and you're onboarding, the things we recommend to you that we can automate are very specific to electricians.

And if you're a venture capitalist, they're very specific to a venture capitalist. And if you're an EA, it's specific to an EA in that industry and so on and so forth. And so how do you do that? Well, the models aren't yet good enough where when you just ask the model, hey, recommend for Alex, who is a VC, things that he could automate with town.

I think you get pretty good surface level suggestions, but not really deep ones. So what we do, and because we don't look at user data, right? Like part of being enterprise ready is you can't look at user data. So what we do is we look at aggregate metrics of who are our power users on the platform today. And then we go talk to them. And when we talk to them, they tell us the kinds of things they've automated.

And so we might talk to 10 power users that run restaurants. And what we'll see when we talk to them, there's five things in common that they all do. And then we'll sit internally and we'll build prototype automations we call routines for restaurant owners that we think are really good, like the one about wholesalers. And we'll use that as the seed, right? And so when someone joins, we look at your email and inbox and we're like, you seem to be a restaurant owner.

And then we have templates of what we think really good problems and potential solutions are for restaurant owners that we've kind of hand carved from those interviews. And we put those in front of you. And you sign up and you start using the product. And then AI is getting better. And we're talking to more restaurant owners. And now we don't have just five.

We have like 15 things that we can do for restaurant owners. Still a little bit manual, but you can speed it up. You can schedule interviews automatically. You can ask the restaurant owners. You give them a free Amazon card. And you're doing an AI recorded interview of them and what they do with your piece of software. So you're no longer operating at the speed of humans.

You're operating at the speed of AI. And this library is getting better. And then the models get better. And so now the recommendations use the library less. And what we think is over time through this approach, we'll be able to offer 8020 very customized software for people. And people aren't just one role in the future. Right? Like, yes, you're a restaurant owner, but also you run finance for your restaurant too because you don't have a separate person doing that.

And also you have a vacation rental that you rent. And so really, you want your townie to be good at those three things. And also you have two kids at school and it can help you schedule their activities for the summer. So it's custom software all the way through. And our belief is that you can use AI to build that. I think the other strategy that you see in the market, which is I think what the labs do, is an FDE approach.

And this is very different. FDE is like, I'll have a human go into your business, understand how your business runs. Then they'll build custom software with AI that uses my labs AI in order to sort of view better. I think the FDE approach, you know, if you're an enterprise that's willing to spend like $5 million a year paying open AI for FDs, like it looks really good right now.

But I think over time, as the models get better, the FDE approach actually gets much worse. And our approach, which is much more around automation and using AI to build the custom software, will scale much better. And so the bet we're making is we start with PLG, SMB, mid -market enterprises that are very tech forward because their approach scales really, really well.

We'll let other people fight in the enterprise for this much more like handcrafted software. And the bet that we're making is the things that we learn in the flywheel that we can get will make us more successful in the long term than these more custom approaches. But maybe also define why is town better than OpenAI and Anthropic that now also have connectors like you can hook up your Slack, you can hook up your LinkedIn.

I mean, obviously, I believe I'm both an investor and a power user of town. So I have my answer, but I'd love to have you expound on why use town. And also just like business. I mean, so many people that are probably listening to this that might be entrepreneurs are also wondering like, uh -oh, are those guys going to eat my, not you, town.

Like you have this weird situation where you have the infrastructure layer and you have the application layer. That's always been the case. You know, like, you know, Cisco and Akamai were infrastructure companies in the 90s and then Amazon and eBay were application companies. But never in a million years did Cisco say, you know what, we're going to build a bookseller.

We're going to build an auction website. We're going to build a directory like Yahoo that competes with, no, they were just happy selling their switches and load balancers and everything. So I'd be curious, like, you know, maybe define how you think about town versus the people that you often buy the tokens from. And then how should others maybe think about this kind of weird time that we're living in where it's like infrastructure is becoming application? I mean, it is risky always to be competing with your suppliers, right? And we are competing in a way.

Because they have cheaper cocks, right? That's right. We're competing against your suppliers. So you got to ask yourself, is that naturally the right state of the world? So the small answer, which is not the good one, is we believe very different things about the experience of the user initially around email and the calendar that for whatever reason, none of the other players have really focused on.

And there's a lot of complexity to email and calendar, which makes our product much, much better there. But that's a small answer because obviously someone at OpenAI, Adentropic, at Google could wake up and decide to do that. So the better answer actually is to think about our customers and what they want. And when we talk to a mid -market firm, so that's a firm with like 500 to 1 ,000 employees about town, what we tell them is like, hey, listen, do you know which model provider is going to be the best model provider over the next three to five years? Like, what's your opinion on that? And they don't know.

And they don't know. And you're like, well, you know, if you buy the OpenAI product, well, then you'll always have OpenAI models in that product. And what if OpenAI is not the best model provider, doesn't have the best intelligence for you for the next three to five years? Then as AI does more and more for you, you may be at a competitive disadvantage.

Or what if they hike the rent? Yeah, all of that. And so the first thing is we're just like, look, we're neutral app. We will use whatever supplier we think is the best for you in the moment. And that's like a big part of it. It's also not just general intelligence, like which supplier is better at voice, is better at video, is better at slides at that time.

We feel like it is our job to determine from all the AI capabilities, frontier model, open weight, what is the right thing for you as a business? And I think you can save money there, but also you can de -risk ever having made the wrong partner. And there are companies that picked OpenAI like two years ago, and then Anthropic was better at coding for a 12 -month period.

And those companies had to use both because they realized to be competitive, they needed Claude, right? And they needed those models. And I think if you're a smart business, you really think about the neutrality of who's providing intelligence for you and who's routing. So that's like one reason. I think the second reason, which is like very important, is our stance is that you own and like truly own as a business, the data and memories and context that's being created on time.

And so, you know, in the old world, pre -computing, if you're a business, you had different assets. You had physical assets and you had maybe like human assets, right? And increasingly now, maybe people call it context, but there's the set of assets, which is all the things that AI knows about your business to help you run your business effectively.

And I think you should own that entire bucket. And I think you should own it so much that if there was ever a better solution than town, switching to that other solution would take, you know, a day. And that, you know, that's what I'm selling. I'm selling like you want to own that asset. And I think if you look at anyone who's deploying FDs to build software for you, they actually, what they're trying to do is they're trying to make it more difficult for you to switch at any point to someone else.

So I think that's the second. So first you want neutral intelligence, but I think you also want the app layer on top of the intelligence to be one that you feel comfortable you could move to something else. And in the new world, that's really like, you know, it's owning this context layer and having someone who makes it available to you. Now, that means town, the risk that I'm taking is that the switching cost for my customers is easier, right? But that's my selling point.

And look, if I can't have the best product in the market, then that's okay. Then that just means I'll lose. But that's like my competitive stance against the labs. And, you know, the third part for me is just on training. And I think, look, if you're a large enough company, the labs won't train on you. But it's the way you operate your business, if that ever makes it into the weight of someone that's reselling to someone else, that basically you're selling what makes your business successful to someone else.

And you really don't want to do that. So I think, you know, we are on the training layer. Where we are, like, you know, we don't train. Like, that's just not part of our philosophy as a business. And then the final one on competition is we believe different things about the world and about, I think, the relationship between a human and AI where we are probably more forward thinking than even the labs are.

So, you know, the team's product for town has a mode around basically building an internal CRM where we're trusting agents to decide what information at the company that is available to other employees at the company. Let me just give you an example. Like, you're in town and you say, does anyone know someone in the finance team at this potential customer? And town's able to look at, talk to the agents of other employees at your current company to find someone who's connected to that feed that you want.

That's like a really powerful product. But from a privacy perspective, you really have to design it really, really well to work. And the way I think about that is we are really thinking about how do you get AI to operate, humans to operate at the speed of AI and not at the speed of humans. So the speed of humans version of that question that just asked is you would go in Slack or Microsoft Teams and be like, does anyone know someone at that team? And then everyone at your company would have to read that message and think about it.

And you get an answer like a daily. Yeah, exactly. Humans are slow routers. That's right. And for us, you're going to get that answer immediately because we've decided that for that kind of a question, we will trust AI to mediate the security and privacy of that request within a company. And, you know, we believe things like that that can really speed up a company that I'm not seeing anyone else do.

And, you know, it's like when you're building a startup, you always have to think about like, what does the future look like? And I think the future looks like a lot less human in the loop than we see today. And I think our job is to get companies to be comfortable with that universe because the economic benefit for them far outstrips any risks that they would see in that experience today.

So those are the differentiators. I think that like data ownership and neutrality are like so important. And, you know, if you're, you know, look at what's happening to Rogo, what's happening to Harvey, right? If you look in OpenAI, they're building vertical companies that are competing with that. And from a competitive standpoint, like you're like, oh, man, those labs are really unstoppable.

But what's if you're a neutral business choosing who to work with that you got to think about like, well, how far up the stack are they going to go? Right. So I just think strategically, it shouldn't make sense for most large companies to really be beholden to any one lab. It seems like it's also like there's the Goldilocks zone of relevance here, which is if you had $1 trillion in revenue, I would obviously be very happy as a shareholder.

You'd be very happy as a CEO and co -founder. But you'd probably be the number one target for OpenAI and Anthropic. If you have $0 in revenue and nobody uses your product, they just wouldn't care. But if you're, and this is kind of like coding fell in the wrong side of the Goldilocks zone, arguably. Like the way that you, like if you were a coding agent right now, like what is, who's adding $10 billion a week or who knows what it is for, like Anthropic's adding so much.

It's so relevant for them. They cannot, to use a double negative, they cannot not ignore it. And, or sorry, they cannot ignore it, just single negative. Whereas for what, you know, you're doing where, again, this sounds like an insult. It's not, right? It's just like, this is probably not the biggest thing. I think it actually is the biggest thing, longer term, but short term, it's not coding, right? It's not like, oh my God, I have to do this or else it's game over for me.

Because like OpenAI, it seems like they learned that lesson from Anthropic where OpenAI had like 50 bets and then Anthropic went all in on code and that did really, really well. So, you know, there's also this element of just kind of like focus and relevance, which it seems like they're not going to focus. Like, you know, healthcare, really big.

Like law, really big. Coding, really big. These big bets, they almost have to make. Whereas these kind of horizontal ones that are spread out, it seems a little bit harder for them to commit to. I think for the SMB and MIG market, I would say I would agree with you. But the market is, the market for AI doing more for a business and helping the business be more successful is bigger than the coding market.

Oh, for sure. And so, you know, for me, it's a question of like when, not if, right? I think in the current environment, it's not going to move the needle at those companies, but I think in 12 months it will. And so it's going to move very, very quickly. Yeah, it's just right now you cannot not be. This is the one that I was looking for. You cannot not be in the coding market.

Correct. Right. Whereas this is something where it is like the iPhone, BlackBerry. There's a diffusion point here. And actually, this is another question I'd love to ask you. Like I have a theory. I'd love to see if you agree with it. Silicon Valley almost overhypes AI, if that's even possible. I don't think it is possible to overhype AI because every week it's better.

The rest of the world, I think, is almost underhyped AI. And I'm kind of curious, how do you think about, you know, people talk about crossing the chasm. But this is not a great example where it's not like this is a technology that only exists here. It's nowhere else in the rest of the world. No, people are using it throughout the world. Just not.

It's not evenly distributed. But I guess, how do you think about, like, where are we kind of short AI or what pockets of the economy, either geographically or by profession? Or like there are certain areas where, because this is the cool thing about having a very horizontal product and having a very horizontal PLG product where people are finding town from all over the world.

Like I remember you told me it's like, wow, we're big in Japan. Like that wasn't intentful. It wasn't like, let's go get big in Japan. It just became big in Japan. So you're finding this everywhere. Like, are there pockets that have surprised you? And there are pockets where you're like, wow, there's actually a lot more opportunity here than people have thought of? I'm thinking about individual anecdotes.

Like I'm thinking about this business that rents like surfboards on an island to people and then uses town to inspect like surfboards and little like beach buggies, whether they've been damaged to decide whether afterwards the person should get their deposit back. That's pretty cool. Yeah. But it's like, it's like, I'm like, that is not on my bingo card.

It's not on my 128 by 128 board bingo card. It's like just nowhere on there. Really what's happening is there is a tinker at that small business who there was something that was annoying to them. And then they figured out with town how they could automate comparing these pictures and deciding whether to release people's deposits. And what makes it work there is the tinker.

And so my answer to you is actually we're seeing use in all the weird spots of the economy. And the unlock is, is there someone over there that gets AI enough to want to tinker a solution to their problem? And the problem with that is that there aren't enough tinkers in the world or there aren't enough people that have the time to be tinkers to help their business.

And I think that's why AI is not diffusing. The reason why it's not diffusing is because even though in Claude or in ChatGPT, there has existed for the last 12 months features that at a lot of small businesses you could use to save you so much more time. But instead what you're doing is you're using them as a replacement, as like a better Google search.

And you're like, what's happening? And it's because the product experience can't just be a text box. And what we have to do is we have to, you can't teach someone. You have to just show the value prop to someone and do it for them. I think that's like the real unlock. And so whenever, you know, for our product, like the, what makes someone sick with town? Like it's this.

They've connected their email, their calendar, and ideally one really important system of record for their business. And then they've built one custom integration. That means they've built one thing that didn't come out of the box that was magical. And I'm trying to just get rid of the custom integration. I'm going to get each person, which is as soon as you connect your system of record, can I deliver value to you in a way that feels very you, that really helps you.

And that's the opportunity. And that's why you don't see it spread evenly. It's because in Silicon Valley, we're all working at companies next to engineers. And so we're much closer to this innate problem solving and tinkerer. Like we have tinkers everywhere in our firms. So yeah, our firms are using AI effectively. And most of the world just doesn't have that.

And so that's the real problem. And if you can fix that, if you can make a non -tinkerer into getting the same value as a tinkerer in the world of AI, you could be a ginormous business today because AI can solve so many business problems. You don't need more intelligence. The intelligence is there. It's just how do you deploy it effectively at scale? Yeah, it feels like it's an integration problem.

It's a product marketing problem. Yes. And it's also, I think this is really interesting to me as well. I met a professor at Harvard, a very, very smart woman. She's like the number two person there. And I'm talking to her about AI. She's like, yeah, I tried this a few years ago. It was okay, not great. And I was like, no, no, it's really good now.

So I had ChatGPT rewrite her dissertation that she wrote 20 -something years ago. But everything is now current. Like take everything today. And she was like, this is amazing. What AI is this? And I'm like, it's ChatGP. She's like, no, it's not. She's like, how is that? It's like, because this is moving so quickly. And a lot of people, they judge things on the present.

I think that's the other thing is like Silicon Valley tends to judge like where it could be. And that's where sometimes you oversell reality and reality distortion field this, that, and the other. But you see where this could go. And a lot of other people, it's like they see it as it is today. And it's like the slope of this stuff is also such where it's like if you follow this curve.

I mean, yes, where it is today, it feels like people aren't taking advantage of it partially because they were almost ironically early adopters. Yes. They tried something two years ago. They're like, yeah, this hallucinated. This did that. It didn't really work that well. And then they've kind of ossified that version of the reality. And if they try it again now, it's just I've done this experiment like 10 times.

So where did you get that AI? That's not the AI that I have. It's like, yes, it is. You just have to try it again. I mean, because the product space is moving so fast, this is going to keep happening, right? So I think the winning platform is one that continues to adapt the business to the best AI. Like that's the problem because the AI will just keep improving and improving and improving and improving forever.

And people's mental model of what's possible and not possible, what's difficult and not difficult. It's backwards facing. I mean, just with computer use and browser use today, like in the last two and a half months, the assistants can do things that were near impossible six months ago, right? And they can do them 90% of the time, whereas before they were doing it like 30% of the time.

Most people don't. They've not experienced that. And that's why when you do a demo with town of it's shopping for something for you or it's like using your software to do something that you're like, wait, don't you need a UI to do that? People are like, wow, I had no idea I could do this. But I don't think I don't know how you market that to people because I think if you told people like, oh, town now has computer use, it doesn't mean anything.

It needs to be much more concrete. Like, hey, I can make posters for this event that look just like the ones that you've been making in Canva. Hey, I can do it using your Canva subscription, right? Because you're already paying for it. And do you want me to just do that? And the person will be like, yeah, let's do a pass at that. And then five minutes later, when first 25 cents, it's showing you something that's super valuable that needs to go like, hey, do you want me to do that before every event like this that I noticed in your calendar? And then what happens is you separately change the design of your small business or your mid -market firm.

And then it should be like, you shouldn't even have to ask. But the next time it makes flyers, the flyers should look like the new design. And because it should have noticed that for you. And, you know, next when it can do video effectively, it should be like, hey, do you also want me to make video assets so you can advertise this on Instagram? It just needs to suggest this increasing, like, things that feel increasingly impossible in today's technology, but really it's just following the curve of what the models will be able to do for you.

Well, you've probably seen that agent traffic, and this is largely not assistant agent, but just kind of agent traffic writ large has vastly surpassed human traffic. So that's kind of point one. And then the other thing that's been in the news a lot is about this kind of adversarial versus call it maybe synergistic approach to agents, kind of this new generation of agents, not agents as in like, you know, chat GPT is, you know, just searching something on the Internet, but actually doing proactively things on your behalf.

So, like, Amazon has obviously adopted a somewhat adversarial approach, which makes sense because they're an advertising company. And, like, there was this covenant that existed on the Internet for a long time. I go to Yahoo Finance to look up a stock quote. Yahoo pays money to go get a feed of stock quotes from the NASDAQ or something, but my eyeballs then hit an E -Trade ad.

And the covenant is like, I, Yahoo, give you, Alex, free stock quote if you give me your eyeball. And that covenant has been broken. So that's so like people that have kind of hit that broken covenant are adversarial. But it feels like there's a big opportunity to be synergistic. It's like, wow, like it's like the new SEO. If I can be on the receiving end of agent traffic and not block it, not figure out how do I stop this, but like how do I welcome it? It seems like a massive opportunity on the other side there, too.

Yeah. I mean, you know, Amazon's loss should be Walmart's gain. Yeah. Right. I mean, Walmart, you know, they don't have the advertising business. And so, you know, their incentives are going to be different. And I think some, I mean, to your point, distribution has changed. I think the part that's interesting about the agent, by the way, is how does distribution change if you no longer trust your agent themselves not being interested in things? It's like a little bit of an interesting question mark, right? So say you use the Meta agent and Meta has large advertisers.

And then you ask it and say like United Airlines advertises a lot more on Meta than, you know, nobody airlines. And then you ask it to do a flight. You know, what happens if you feel like it's no longer giving you the neutral thing? Because this is the thing is there's always a there's what is there's still human incentives to promote products and marketing.

And so what does that look like in the world where you the human's eyeballs are no longer looking at things? It's a very open question. I'm a firm believer in the like you want a neutral agent that is yours, that you have no doubt. Like the only source of money for the agent is your wallet as the user. And then whoever is creating that agent, their incentive is to give you the best information for you as a human with no distortion in terms of the great forces of marketing.

And then you as a human, you have taste and you express your taste as you make the choices that you do. And you can ask your agent like, hey, what are people talking about an Instagram or like what's being advertised on it? You can ask that question because there you want to be flavored by the world of advertising and marketing. But again, you want your agent to be neutral vis -a -vis that.

The other you talk about adversarial versus not. I think the other thing that is interesting to me conceptually are like experiences like friction that you enjoy as a human without friction that you don't. So me walking around Soho in New York City shopping, it's friction that I enjoy. I like that I have to walk between stores. I like that there are other humans waiting in line because I can copy their styles.

I like the friction. It's a part of that shopping that is enjoyable. I like doing it. Then there's friction that I don't enjoy. I want to like order toilet paper at home. I don't, I'm not, you know what I mean? I just want the same toilet paper that I've been ordering for a while. Like just get it in there as quickly as possible. Like and as cheaply as possible.

And so I think sometimes when we think about agents too much, we think too much about reducing friction and toil and making things as efficient as possible. But humans don't always optimize, you know, for efficiency. And so, you know, the Amazon example for me on the ad is like, actually, I think there's times when I'm on Amazon, like when I'm buying Legos for my kids, where I do want ads of other construction like toys for my son.

Because I may realize when I, it's not Lego branded and I don't know about it and it's interesting. And then I want to ask my agent for some reviews of it. And I'm like, that's an interesting, is it popular with the kids? And maybe I want to buy it. And in that experience, I think Amazon can make a ton of money and there will still be value for places that are aggregates of supply with advertising and marketing.

But I think there's a lot of times where you're optimizing for friction where you don't want that stuff. So if I were Amazon and I had the $80 billion or whatever, $70 billion a year, like, advertising business, the question that I would ask is like, hey, I don't think you want to block agents. Because I think that is, I think there may be a bigger than Amazon company, like a headless Amazon could be a huge, huge business, right? But I think it puts more pressure on them to create a separate experience where you do want the ads and that's really good and making that worthwhile to go on on its own.

And I think I would still go to Amazon for those things at the end of the day. But, you know, the AI is moving so fast that it's going to change distribution and human habits at a rate where I don't think anyone can predict what the winning platforms will be in those spaces. And there's going to be huge losers, right? And Amazon might lose a huge chunk of their advertising business trying to fight the future because they can't predict what it would look like without.

Like the other one essentially is Muse for me. I don't know if you've seen the discussion around Muse. It's like free. Okay, cool. It's free personal agent. Cool. People are like, well, one day they're going to have to put ads in Muse. But actually, maybe they never have to put ads in Muse. Maybe Muse just gives them data that allows to target you better on Instagram.

And when you go to Instagram, you're on Instagram because you don't want efficiency if you're on Instagram. You want to be entertained, right? And if they can target better on Instagram because of the information from Muse, and Muse is still free. And on Instagram, now when you see the good that you like, they can also get you to pay for it using a wallet that they've accumulated on you from Muse.

Or Muse can look online for different variants of the thing. It's like a really nice flywheel. And what it means is that you've divorced the monetization from the agent experience for this personal use case.

And that's where a property like Amazon is at a disadvantage, I think. It might be with agents.

Having just been the aggregator of supply just may not be a good business model in the world of agents. It just may no longer be. And so there's really not much they can do about it. They're going to lose. And probably the counter move for them, honestly, is they should have a personal agent. Yeah. They should have a Muse. That's actually the counter move for them.

It's not to block Muse. It's to have a Muse. Have a better Muse. A better Muse and have the exclusive on shopping for that. Right. And hope that's enough of an incentive and put it part of Prime. But I don't know if they're able to do that. Well, the fun thing is with Amazon, there's a website called Camel, Camel, Camel. It's a price engine for Amazon.

So you basically say, I want to know when Dr. Pepper is, right now it's $8. Tell me when it's $6. And then buy it, basically. The second it's $6. No, it doesn't do the buying. It just emails you and then you have to do the buying. Yeah. 20 million people use this website. 20 million people? Yeah. 20 million people in America. And this is just a...

And it's an affiliate website. And this has been around for 18 years.

So, Jesse and I wrote a post about this where the best way to predict the future is to observe the present. Again, going back to like it's not evenly distributed. And like this thing is the future. Because like the other way of thinking about agents is there are people that have more time than money. Yeah. And there are people that have more money than time.

Yep. And people that have more money than time, they're not going to use camel, camel, camel. People that have more time than money, they are. But wait a minute. What if you now can give everybody who has more money than time, give them all of the tools and the agent does it? Like, of course they're going to use that. It's an IQ test. So, these things are definitely going to be a lot more pervasive.

And again, sometimes you don't even have to like picture what might happen. You just have to look at like, you know, coupons. Yeah. Coupons. Like, you shouldn't use a coupon if you're wealthy. Because it takes too much time. You're going to spend like two hours like hunting for the right coupon. But guess what? Like, so, and that's another covenant that's being broken because it was a very neat way of dividing the world.

Yeah. I mean, MX premium membership, it's the same way. Now, it'll just optimize all of your, all of your benefits. The breakage model is dead. Yeah. And so, we're all going to abuse all of our Chase and MX benefits. And then it's not cost effective. Because then it's going to be adversarial. This is kind of the adversarial versus synergistic thing.

So, by the way, the, the through line, all of those is, I just think supply aggregation is a lot less valuable than it used to be very valuable because human attention was limited. And now supply aggregation is a lot less valuable because human attention can be automated away. Yeah. This has been an awesome conversation. Thank you again for coming in today and go town.

Yeah. Thanks so much, Alex. It's always great to chat. Thank you.

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