Meet the Slowest Startup Incubator in the World—Pumping Out Billion-dollar Companies

[00:00] I love this. Oh, thanks. I feel like that's actually really meaningful. I gotta say Sam and I, [00:06] this morning lifted each other and we were like [00:09] Are we sure we should be on this podcast? [00:12] There's two good companies to start now. There's the AI native company that pushes the ball forward inside of some category. [00:19] or there's the AI durable, [00:20] company that [00:21] effectively uses AI where the core of the machine is not going to change. [00:40] Dan here, and I want to take a second away from the episode to tell you about Granola. Granola is an AI note taker for your meetings, and I use it pretty much every day. That may sound a little bit weird or a little bit creepy, like transcribe all your meetings. Well, for me, it's actually kind of indispensable as a leader. Every is about 20 people now, and it's really important to me that I understand how decisions get made, how I'm showing up in meetings, and how I can help my team the best way I can. Granola acts a little bit like a leadership log for me so I can see how I've done in meetings, [01:10] Sam and Dan, welcome to the show. [01:25] Glad to be here. Thanks, Dan. Thanks for having us. You guys are both good friends of mine. You run the incubator Bolton and Watt, which I think is one of the most interesting incubators that I've ever run into. And you're coincidentally, maybe or maybe not so coincidentally, you run it down the street from us. So I'm in the every office in Borum Hill. And I believe you guys, your office is like a few blocks away, right? Yeah, it is. Yeah. And Dan, we went on a jhana meditation retreat together a few months ago.

[01:55] ago. So there's just a lot of like interesting overlaps. Um, and I, I just really respect you guys. I think, um, it's very easy to say, Oh, I'm running a startup studio and it's very hard to actually do it well. And you're one of the few people that do it well. And I was thinking before this, um, I met both of you like 13 years ago in the New York tech scene. It was like 2012, 2013. And so we're sort of like in the same scene for a long time. And then Dan, uh, Dan Freeman and I's [02:25] party you hosted maybe 10 years ago. I didn't realize it was at my party. It was at your party. And so you are like, uh, you know, interwoven part of, of the story in these funny ways too. I take all credit. I don't know why I don't get Carrie. Like, uh, uh, that's amazing. That's, that's really fun. Um, yeah, I remember you were at like Imgur and A16Z and yeah, very, uh, yeah. [02:55] what you guys do and what it means to be, I think you call yourselves the slowest incubator in the world, what that actually means and how your model works. [03:03] Yeah, so we try to start a new company every couple of years, often in like a really niche vertical that somehow combines software services and some real world component. And the idea is we come up with idea. We run ourselves through five or 10 million revenue. [03:21] And then we go find a CEO who's better than us to take it from this sort of the next to next. And we remain involved as a partner to the company for its life, really involve board members who have spent thousands of hours thinking about the whole competitive landscape, the company, competitors, all this stuff. So it's a really different relationship than a traditional incubator, which may say, OK, here's a million bucks. Here's an idea. I'll help coach from the sidelines. But we're actually like.

[03:46] in the seats and that means we're really concentrated. We've started two companies to date and we're about to start our third and just to give you an example of the types of things we like to do our first company is called Moxie and it helps nurses open their own med spas. So these are nurses who are doing aesthetic medical aesthetics like Botox, filler, lasers etc. [04:10] And we're sort of her back office that helps her stay compliant, grow her business, and really everything you need to do with med spa. So we now have hundreds and hundreds of these med spa clinics across the U.S., all in partnership with nurses. So that was our first business we started about three and a half years ago. And then our second one is a contemporary funeral home. And what that means is we have no physical real estate whatsoever. We do arrange everything online over the phone. [04:40] of in-person funerals. They're generally at wedding venues that we booked out a year in advance, Saturday night, but totally open Tuesday morning at nine. And we're now the largest provider of funeral services in California and just about to launch a bunch of new states. So we have a taste for these sort of like weird businesses that are like not YC, that have really real world implications and the sort of reimagining these types of bundles. [05:06] I love it. Wait, tell us where is Moxie stage wise? [05:10] Sure. So Moxie is a Series C company, you know, into the, into the,

[05:19] tens of millions in revenue and 600 plus customers, [05:25] Team globally is like 200. So it's like a comfortably mid-stage company. [05:32] and vis-a-vis AI, we launched it. [05:36] you know, some number of months before the release of ChatGPT. And so it is hilariously a sort of like [05:43] just before AI company that [05:46] had none of that in its conception and, you know, it has had to, [05:53] almost as much as a company [05:54] that was started in like... [05:56] 2015 has had to sort of adapt as opposed to being much more native to that way of thinking. [06:02] And so your model is like, okay, I'm going to do all the really, at least for me, the really fun stuff, which is like, I'm going to come up with the idea. And then I'm going to do all the like hardest stuff of just like rolling the boulder up the hill for years until it starts rolling by itself. And you're like, I'm out. Like, how does, how did you come up with that? Why do you do it that way? How is the whole thing structured to make this work? It's very, it's a very like weird, different thing. [06:27] Yeah. I mean, I think to some degree it's like an intersection of where we thought would be the most fun. And also we thought like the most value would be created. I think like, [06:37] we looked at a bunch of other incubators and I'll put every aside for a second, but I think there was sort of this, like, someone who wanted to have a bunch of ideas and like let those go, [06:47] and sourcy and

[06:49] we realized that I think to make, [06:51] To maximize the success of every shot, we realized that we needed to actually go, you know, eat the glass, figure it out, you know, push the boulder up the hill, figure out if the boulder wants to go uphill, as Dan often will say about that sort of early product market fit journey. [07:07] And so we thought that was the place where, okay, if you could ask someone else to do that, you could, like, pay McKenzie to come up with startup ideas. But the hard piece is, like, figuring out, like, okay, what's actually market signal? How do you make changes when no data quite tells what to do? And if we could get really good at that, we get permission to do all kinds of other things as well. And so that was sort of, like, the origin of it. And I think Dan and I both feel really lucky that it worked so well the first time, [07:37] doesn't make sense. And now we can be like, oh, actually it has. We've done this two times, and we're gonna keep doing it in that way. - The only thing I'll add is, and maybe the years is a little bit of a gross heuristic, but my experience, and I think a lot of my founder friends' experience is, [07:54] Before this, I had built one company over 10 years, and years four through 10, [08:00] there was a little bit of like a constant existential question of, am I doing the thing that is like most interesting and most useful? And am I spending my time the right way? And I've sort of like learned the physics of the business. And now I feel like I'm in purely execution mode. And, um, that, um, [08:19] feeling of like a little bit of existential dread. One way to avoid it is simply to not be doing, not being directly responsible for years four through 10 or to vice versa, be responsible for years four through 10 across five companies, you know, by the time we get there. And, uh, I think

[08:35] I'm sure it will have some other challenges. [08:38] that, you know, some other psychological challenges, but, you know, we are, we are, I think, [08:44] for me at least, I feel that we're here optimizing for what's most [08:48] enjoyable and exciting for us. We also sort of like pretend on the found journey. That's like the, is the same skillset all the way. Um, and I think like what you do and how you do it, [08:58] really depends on the stage of the business. And so there's actually a lot of value specialization, too. I'm like, okay, we know what this looks like going from like zero to one million revenue over and over again. And here's what it takes from one to ten or the systems or the people, how you build intuition. And so we're also just like getting a lot of reps in in that early stage that very few people actually get to do that and see what success looks like on the other side. [09:22] You guys also have an interesting model for how you break up the work between yourselves. How does that work? [09:28] And it's changed a little bit over time that like when we started, we both started Moxie together and then Dan stayed on CEO. I went to start the second business. And at least for now, we're sort of like tag team. And so I'll run one business. Then starts next. Then I'll sort of start the one after. Yeah. [09:47] We'll see how long that lasts. I think one of the things I've really appreciated about this working model is, [09:54] Dan sits next to me. We each have full context of the thing the other person's working on. We can be really great thought partners and push other person and emotionally supportive when things aren't working. And also we have.

[10:08] our own space to try things our own way at the same time. And so I found that really a useful way to build companies. We have all the upside from co-founders, but also like just a much larger scope and span too. And then we have other partners inside Bolton and Watt, some of whom work with us within the company. And then we have one who has been with us for four or five months who specializes in the concept development phase. [10:38] and the thesis there is [10:41] historically when samurai rolled off one company we rolled into a cold start on the search for the next one and going forward we want to be rolling into we've just validated our next idea and we're we're pressing go um and so we're starting to like [10:57] bit by bit, not [10:58] in a like overly accelerated way. Um, [11:02] institutionalize and specialize in the different components that we have. [11:07] want to be best in the world at. [11:09] And I think we realize like the two constraints to us are speed. Like we want to be the world's slowest, but we also could be a little faster right now. We're like one every two years. And so like the question we asked ourselves last year was like, could we take it to 18 months and what would that look like? And maybe take it to a year. And the two constraints, like the first point that Dan said, like do we have a great idea and do we have a great one once a year? And I think, [11:34] We are extremely big believers in execution and also believe the idea matters a lot. And so do we have a good idea? And then second, our other biggest constraint is just talent. We need to find great early stage employees, great executives, great CEOs. And those are sort of the two things that are holding us up back and sort of limit our speed.

[11:55] Okay, so now it seems like, and you tell me if I'm getting this wrong, but it seems like a lot of the kinds of opportunities you guys gravitate toward are like real world, like unsexy business, like funeral homes and med spas. And at least with Moxie, it's sort of a business in a box type thing where… [12:16] you give someone the tools that they need to start one of these businesses and you partner with them and maybe take a cut of the revenue or something like that. I don't know exactly how it works, something like that. And I'm curious how that – [12:31] model or how you're thinking about that model in an AI world. You started Moxie right as Chatwipki came out for the first time. How are you thinking about how the model evolves now that AI is a thing? [12:45] Yeah, I think we were first attracted to those businesses because we thought they would be really hard to build against. [12:52] And there's a huge existing opportunity. And we said, OK, we're pretty good at this sort of operational complexity where you might combine. You almost have to build like a services company and a software company at the same time. [13:03] this sort of [13:04] Competition looks a lot different. There aren't, you know, 10 YC companies starting each of these. We sort of like that space of play. [13:13] And then I think one... [13:15] As sort of like Chachabee came out and the world started to change, I think one of the things we realized in a kind of accidental way is the types of businesses we're spending time on are maybe more resilient to AI trends. And we should think about AI as like an accelerant of the sort of speed in which you could build these businesses. But fundamentally, like the business model of a funeral home or a med spa doesn't change because AI is out there.

[13:45] after a company that was formed to commercialize this team engine in 1775. And so we're like, [13:50] hyper aware of like how technology changes businesses. And also the same time, I think we've chosen maybe a sort of counter position to say, okay, a lot of things are going to change. What's continue to be valuable and how do we sit with the current on those? [14:05] And how does that work in your mind, Dan? Because we've had a few more existential conversations about where AI may go. And at least at certain times in our conversations, you were like, nothing's going to say the same. It's all totally going to be different. And I don't actually, I think you're maybe a little bit different. You're feeling a little bit differently now. But where are you currently? And how does that filter into the strategy? [14:35] them and trying to remember which branch has what. The bottleneck isn't writing code anymore, it's coordinating agents. [14:41] Intent is a developer workspace built for orchestrating agents, not just running them side by side. It starts with a living spec that updates as agents make progress, so every task stays aligned with no manual coordination. Intent works best with Augment's Augie and their context engine, but you can also bring Cloud Code, Codex, or OpenCode. Intent is what comes after your IDE. Try it yourself at augmentcode.com slash intent. That's augmentcode.com slash intent. Build with intent. And now, back to the episode. [15:11] There's the AI native company that pushes the ball forward inside of some category.

[15:16] or there's the AI durable company that, [15:19] effectively uses AI where the core of the machine is not going to change. And if you look in our first two [15:26] you know, like [15:27] There's no such thing as an AI native crematory. [15:30] you know it's just like dramatically changed yeah exactly we'll put on the blockchain and use AI and then and you know [15:40] Like we're not expecting a robotic injector anytime in the next seven to 10 years. And and so that the like core work of a med spa. [15:52] will be, you know, the Mets Plus themselves are actually like, [15:56] of the latest technology, the latest medical technology, you know, [16:01] when GLP-1s come out, like MedSpots are one of the early adopters of actually spreading it to their communities. [16:10] At the end of the day, what happens inside the walls of a med spa is not deeply impacted by AI. However, there are around the edges spots where it can really matter in terms of reaching the right customers, serving them and communicating with them effectively at all hours of the day at an affordable price for the business. And so we want to be... [16:30] great deployers of AI inside of our operation. We want to help. [16:34] are [16:35] uh, partners, you know, deploy it to the maximum effective degree. You know, we want them to be on the early, early edge, not the bleeding edge necessarily. There's like no, no need for them to be there. Um, they can always just be a few months later and, you know, not take risk with their customer relationships. Um,

[16:52] And so I think we've kind of said for now, [16:58] between the AI forward and the AI durable, we really like being great users of it inside that AI durable category. And maybe we'll end up changing our minds and finding the other category is really fun. But right now it's like, [17:12] Yeah. [17:13] Every idea we've had in that category has had multiple formidable looking competitors doing something different. [17:20] almost exactly like our idea there. And we tend to want to look at a category and say, we've got something interesting in our idea to say it's like worth our time and feels exciting to us. [17:31] All the smart people are listening to Dan and every, and we'd rather compete against the less smart people. I would rather not compete against you. So I love that. [17:41] So if you're if you're thinking about, OK, how do I make this? Well, how am I concentrating on the things that are not going to change and how am I using it to get to get more operationally efficient rather than being truly like totally AI native in like, you know, a way that a YC company would be or whatever? [18:01] you thought it would. Maybe I could speak to the company discovery process where I think we've actually seen... [18:07] probably the greatest transformation, which roughly maps to... [18:12] the more green fields. [18:15] the better AI can be, basically. And whenever I talk to my founder friends that are seed stage, they're like, "Oh my God, our engineering is 10x faster." And then I talk to the like,

[18:24] you know, Series D friends, and they're like, "We're like 10% faster. What is everyone talking about?" Which is the sort of classic dichotomy right now. In the new company discovery process, [18:36] we are... [18:38] Roughly speaking, every stage has been rethought. So the first step is like, let's find some verticals to go start to poke around in. And this used to be like a week of Googling and maybe calling some friends to just get the basic facts. And this is now like... [18:54] a mega prompt to generate a list of categories and a mega prompt to assess what we think is a good business and our particular point of view of what we're looking for and start to narrow in on a couple of different ones to... [19:10] go talk to real people in. And we did a little bit of an exercise this time around where [19:16] We both did the like [19:18] AI curation and then [19:20] I did like a human point of view and I actually felt in that moment, like, do you remember the children's story of the, it's like a myth of the guy who was competing against the like automated tunnel builder, machine to build a tunnel? That's a real thing. John Henry. John Henry. Yeah. Yeah. Well, it's a real thing. It's not like a guy fucking... [19:40] put a hammer through a mountain. [19:42] Well, he, he, I think did actually try to compete against the, the automated thing and then died. Yeah, I'm pretty sure. Oh, okay. Yeah.

[19:54] *laughs* [19:55] Anyways, I was the John Henry in that story and fortunately came out the other end. And we did as a group. [20:03] select three categories. One of the three was like my human point of view. Two of the three were like, the AI was like, no, no, no, these are screaming matches. And then more interestingly, we built a... [20:18] insight with this. [20:19] AI ultimately forced us to move from Google Docs to Notion, which I was like fighting for years. And we built an agent identity that we call Matthew Bolton, which is like a horrible name because Matthew Bolton. [20:30] was the bull dinner lot. And, uh, [20:33] Matthew Bolton is our... [20:36] assistant in... [20:38] being really good in the customer discovery process [20:41] So he helps us. [20:42] prepare for every call and looks at the persona we're talking to, [20:47] looks at our current hypotheses and what our like validation focus is and basically says like, here's the areas to dive into. And of course we review and make sure we like talk about the right things, but he, he makes the prep more efficient and then he, [21:01] Afterwards, I'm going to go. [21:03] The like, IAD transcript goes directly into a notion table. We run Matthew Bolt on and it regenerates a point of view on, for each of our core hypotheses on the idea. [21:13] what's totally validated, what's totally invalidated, where do we need it? [21:17] dive in more, it pulls out the relevant quotes from the people. And so it's been a huge hit in that. [21:22] way. [21:23] where it's totally missed for us, which is also kind of interesting. We've talked to other people who do these like synthetic customer calls, effectively. They like make AI into the customer.

[21:33] And that's [21:34] we just can't make that. We haven't yet been able to make that work at all. The, the, [21:39] Basically, anything that strikes us as a good idea, so like passes some basic sniff test, [21:45] the AI is like, I'd love to buy this from you. Like I'm so, no matter what we do, it's like, it's like, you know, [21:53] it just expresses a 10 out of 10 customer pull. And, um, and so like, um, [21:58] We tried that. [22:00] and then flew to meet a real prospective customer for a category and just like fell on her face completely. [22:07] And [22:07] you know, have iterated our way past that, but like, we just kind of don't think it's actually useful for that. It doesn't seem to... [22:15] Um, [22:16] It doesn't know the nuances of the psychology of the, like, I've worked in this industry for 15 years and I'm deciding what to buy right now. And or it's like, just it knows that you want it to say yes or thinks you wanted to say yes. So it does kind of. We've desperately tried to have it be like in sycophancy mode and. [22:33] we can't get it out of that. And so maybe someone else can. There's like a... [22:37] a way to make that useful, but [22:39] I would say at the moment, [22:41] our point of view is like it can help us talk to people effectively but it cannot [22:45] actually reduce the number of people we need to talk to to get to confidence. [22:49] Got it. Okay, this is really interesting. Are you able to show us Matthew Bolton? [22:53] Like, show us the goods. And I'm also curious about this prompt, the prompts that you're using to do, like, okay – [23:01] research business ideas and also like filter them that sounds really interesting too

[23:07] Okay, so we have different Matthew Bolt-on flavors. I think there is an underlying flavor. [23:15] like agent identity doc, [23:17] This was for a specific category dive we were doing into P&C insurance, and we kind of break down... [23:26] our own thinking across a few notes, across a few docs. So one is we have what we call a PO view, a point of view on, um, [23:35] what we think the opportunity is, what's the problem we're solving, who are we serving. What's a PNC? [23:42] PNC is property and casualty insurance. - Okay, got it. So that's the area you're looking at. - That's the area we're looking at, exactly. So this is the vertical we're exploring. So we have our point of view. [23:55] We have a hypothesis tracker. [23:57] with a list of effectively what are the core things we believe about the category, what has to ultimately prove true, what are we looking to discover, [24:07] through [24:08] potential customer calls. And then we have transcripts and our own notes from every call we've had. And so when we call... [24:19] Matthew Bulton, we ask it to reread our latest point of view. We ask it to read the hypothesis tracker. [24:26] read the most recent 10 calls, and then for every hypothesis, [24:32] Just read, just re update effectively. What's the evidence for that hypothesis? What's the evidence against what's the strength of it? And, you know, basically point us in the right direction, tell us like where to spend more time.

[24:46] where to say, great, we're good to go on this. This is kind of sick. I love this. Oh, thanks. I feel like that's actually really meaningful. I got to say, Sam and I, [24:57] this morning with each other and we were like, [24:59] are we sure we should be on this podcast? [25:02] I literally said to Dan, I was like, let's make a list of all the places we've tried to use AI and it hasn't worked. Cause I think, uh, um, so it's nice to hear that from you. Exactly. Exactly. We're like from the guy himself. Um, the, uh, so you, you know, ultimately you just say like run PNC analysis and it runs through all these different steps and, um, [25:23] um, [25:25] one of [25:27] What feels like personally meaningful about this is... [25:31] We try to... [25:33] be intellectually honest with ourselves. It's like something we hold ourselves to. And, [25:37] I think you probably know this because you're building new products all the time, but the nature of starting something new is it requires a manic energy and a little bit of a suspension of disbelief because there's just no reason any new company should succeed or any new product should succeed. [25:53] And this keeps us really rigorous and fact-based, which is what we aspire to. And we can balance that with our own business. [26:01] You know, we can be sycophants to ourselves and ask it to remain fact based and balance these two opinions. But it like totally helps. [26:11] Do you find that it's it works with the like, because I find if you're if you ask it for reasons against or for it's going to come up with it can come up with anything. So like, do you find that it is actually good at weighing evidence for you? Or are you you're just surfacing it and then like, making your own conclusion based on the evidence that it surfaces for and against what you what you believe?

[26:31] If we try to ask it an opinion on a high-level question, I have not... [26:37] I have not given, I've not trusted it with that. Um, or I, [26:41] I tend to take those results with skepticism, but I think it's really good at finding the quotes. At the end of the day, to support a hypothesis, in a perfect world, what I want to bring to Sam is... [26:53] Here's the three key things that must be true in this idea. And I've got three quotes from different people. [27:00] that like directly speak to each. And this will just like [27:04] Thank you. [27:04] much, much more efficiently help you get there. Um, [27:07] And, you know, maybe or maybe not, you know, we ultimately meet that bar. Maybe we lower the bar because we just find there's something core and exciting in an area. [27:16] the... [27:17] This. [27:18] Keeps us honest. [27:21] on the detailed things we ask it to do. [27:25] In some degree, that's the role like Dan and I play for each other, like is keep each other honest. And there's a way to sort of like get the short version of that with AI2 where like, OK, make the best counter argument. And and then sort of just can like sharpen your thinking. [27:39] Even if you're not looking for it to like tell you're wrong, but you can sort of help it have a tease out like what in what ways could you be wrong, which is a really useful tool. [27:49] Sometimes I'll say that and then we'll make some good counter argument and I'll be like, oh, fuck off. What do you say?

[28:19] and our team use it all the time to ship website updates, and the results honestly are beautiful. [28:24] It's really easy to use. There's real-time collaboration, which means your team can design together, see changes instantly, and ship faster. [28:31] A robust CMS gives you everything you need for great SEO and content management. [28:35] It also has advanced analytics with integrated A-B testing that lets you optimize and improve continuously. Then it's just one click to publish. Your changes go live in seconds so you can iterate at the speed of your ideas. And it's not just us. Companies like Perplexity, Miro, and Mixpanel trust Framer for their websites because the tool is enterprise-grade. They offer premium hosting, enterprise security, and 99.99% uptime SLAs. Whether you're launching a new website, testing landing pages, or building out your full site, Framer gives you the tools and support to move fast and build something great. [29:05] Everyone on your team can use it. [29:06] Learn how you can get more out of your dot-com from a Framer specialist or get started building today at Framer.com slash Dan for 30% off a Framer Pro annual plan. That's Framer.com slash Dan for 30% off. Rules and restrictions may apply. Seriously, we use this tool all the time. You should really check it out. And now, back to the episode. [29:26] And when you're talking about like feeling outmoded on the research front, is that, [29:33] At what part of the research process? Because I also feel like, [29:38] If I was going to send you off versus Claude off to do some research, I'd be like, I'm [29:43] I am sure that Claude would cover a wider breadth, but I feel pretty confident that [29:49] If you gave me a research report on a sector, like I would like it better than the one that Claude made.

[29:55] it would just take you a lot longer, but I'm... [29:59] Curious. [30:01] I'm not that confident in your job. Yeah. I think it will do a quite good job on basic market industry reports. [30:09] I mean, it certainly beats [30:13] for the one-week desk research up front, [30:17] Um, [30:18] It beats the, like... [30:20] pretty low and medium quality reporting that was out there. [30:24] There's kind of good stuff in, we really like public company filings. [30:28] whenever there's public company, [30:30] as in the category that we're looking at. [30:34] and [30:35] sometimes we're not just asking it to go do something. We're like, [30:38] uploading a bunch of [30:39] thick PDFs too. And it's quite a good parser of that stuff. [30:46] That makes sense. I think maybe what I'm saying is I would trust it to give me the consensus opinion about how people think about a particular space. [30:54] And... [30:55] I would trust you to, even if you were using AI to do it, to come back with something that felt [31:01] new and interesting. [31:03] in a way that I don't think that Claude can get to on its own. [31:05] That I probably agree with. I don't think we've gotten good ideas from it. I think we've [31:13] I think we've uncovered facts and then we have our own earned point of view. Like we learned a lot through building Moxie and for two years have been looking for another category where a Moxie style business might be a good idea. Um, and we have, um,

[31:28] a relatively differentiated point of view on [31:32] actually what Moxie is and what makes it successful. And we have talked to [31:37] between Sam and I probably [31:39] 60 people starting quote unquote business in a box in different categories, most of which don't have the properties that we think are essential for that. [31:47] And so there's a combination of the like, [31:52] earned point of view through years of billing. [31:56] with [31:57] its ability to, of course, consume massive amounts of information and then fit that to our point of view. [32:03] Yeah, and I think if you ask Claude, for example, to give you a bunch of business-in-the-box ideas, they probably wouldn't be ones that felt good to us. [32:11] because we have like certain analogies and ways of thinking and looking at it that [32:16] You can ask it to do the research and figure out the underlying category properties, but the default properties. [32:24] of that isn't the answer we want. [32:27] Hmm. [32:28] And you guys have done a lot of, aside from using AI in your sort of company discovery process, you've been doing a lot of thinking about how to, [32:37] bolt on AI to your existing businesses that are not necessarily like AI native and doing the transformation process there. What have you learned from that? [32:47] Yeah, I think it's really interesting. Like Inception Company, Series A Company, Series C. And I think they're like... [32:53] two parts of this of like one, how do you, [32:56] actually get people to start using AI, which we talked about. And then I think the second piece is like what's actually worked and what hasn't.

[33:04] Dan and I were talking about this the other day of like, [33:06] should you have an initiative, like an AI initiative? Is that a good idea or a bad idea? And I think my perspective was like, [33:14] It's a bad idea because you don't want to sort of lead with the hammer. And, you know, we all remember the time, like, NoSQL, everyone was putting everything in NoSQL, whether it belonged there or not. Everyone was building a Slack bot, you know, 10 years ago, whether one needed it or not. [33:30] But you do need something to kind of like shock the system. You need something to be like, okay, great, there's like a new tool set. [33:37] And I think like, [33:38] the point of view I've come to is [33:41] you shouldn't give anyone credit for using AI. [33:44] But you should make sure that the expectation is that they use AI or sorry, the expectation is they'll deliver the best product and output knowing that AI exists. [33:56] And so to do that effectively, you both need to like sort of seed [33:59] what are the tools you can use and give a lot of good examples. [34:02] And, you know, you start like demanding that when you see the results from someone on your team, [34:07] that they've actually used the best possibilities, but you don't get any points for [34:11] generating a bunch of copy that was clearly written by AI and is bad to read, right? Like, you have to best the copy, but, like, if you use a prompt to get – [34:20] 70% of the way there or even a hundred percent that that's great. Um, and so like, [34:26] That's where you found it. Like you sort of have to like, at least I found it. You sort of have to figure out who are the people on your team that are [34:33] to seed these ideas so other people get examples and then actually make it successful for those folks.

[34:39] I think like, [34:40] One of the places that and I think this goes to Dan's point earlier of like where we're seeing the most action inception. [34:47] is really like on the experimental, experimental edge of things. So, um, a few examples of this, like, [34:54] really good at generating landing pages and like pushing our thinking there and coming with stuff. [34:59] a ton of work to like integrate that back into Webflow and how it fit with our system and have a consistent header and footer. And so there's almost like two phases. There's like research developments and then there's like production. [35:11] We've had [35:12] a number of people on the team build sort of like throwaway apps that have been really useful. So, for example, one of the challenges we have is for the funeral business, people call in and they might mention some town name and we know whether we service that. [35:26] designers spun up an app where anyone type in a hospital name, a town name and it resolves whether we can find it or not. Instead of like integrating that and spending engineering time on like figuring out how to deploy that safely integrated, all that stuff. It's just a separate app. That's like a link from our main one. And so we found that like, [35:45] Enabling those types of things has worked really well. [35:48] And [35:49] and or engineers, [35:51] have gotten more productive, but a lot of the core things engineer does is still the same, right? Like they're faster coding, but deployment still takes work. Maintenance still takes work. And so, um, [36:01] It's almost like we have sort of like two different pieces that are being enabled by AI, but like in parallel paths today.

[36:10] It sounds like you have like the Greenfield things are quite fast. And then anytime it has to touch something that already exists, it's like a speed up, but it's not. [36:19] totally changing everything, if that makes sense. Yeah, I think that's exactly right. And there are some places where it's like made a huge difference. Our talent team was reminded me the other day that they've made something called SamGBT, which they trained on all my blog posts, and they use to reach out on my behalf to potential candidates on LinkedIn. And so it's like, train my voice, I forgot it existed. And that's worked really well for them. And so there's sort [36:49] testing system. [36:50] I also think it's interesting kind of the approach of you don't get credit for using it, but you do. [36:55] do get credit if you basically you have to just do the best possible thing given that ai exists the thing that that seems to solve is what you don't want is people just doing potemkin villages of like i used ai for all this stuff and it's just like it's all just for show basically um i'm curious though because my experience we do a lot of like ai transformation type stuff with big companies and my experience is if you don't [37:21] Mm-hmm. [37:21] If you just say that, then people will just continue doing it the way they already know how to do because they're like, well, if I want to get the best quality, I have to do the thing I already know. So how do you deal with that? [37:32] I think it's like finding a few people to set the example and then start comparing the work. [37:38] And so like, [37:39] we don't count like, Oh, how many PR, like how much of your PR was AI generated? Because like, if you,

[37:44] commit and push a bad PR because AI wrote it and you're like, blame AI, like that's a terrible outcome. [37:49] Instead, we can be like, oh, look at this engineer who's like done all this great work. Ask them how you do it in a public forum. [37:56] And then sort of like continue to like, [37:58] raise the floor. And so, [38:01] That's what we found to be much more accessible. It takes a lot of active work to say, OK, let's find these examples across the company. Let's potentially see those examples and then continue to elevate those. [38:13] Have you guys seen any change since Opus 4.5 came out? I feel like there's been a big change, at least for us and just on the broader X sphere. Has that filtered into any of your businesses since they sort of exist in a slightly probably less tech forward part of the economy and part of the world? Or are you still kind of in the regular touchy PT type? [38:37] Wave. [38:38] The early word out of the Max Engineering is like, [38:43] Yeah, this is better, but not this is a... [38:46] step function in different [38:48] experience. And we are doing a lot of work there to retool [38:52] in order to experience more benefit. Because we're seeing exactly the newer engineer working on more greenfield projects. [39:00] moving much faster than, you know, [39:02] working on something that touches multiple parts of the system. [39:07] And that's like, you know, 40 person product engineering team. [39:10] more mature code base and so on. Um, [39:13] So we have not seen the like night and day transformation that,

[39:17] The X-Sphere is reporting. [39:19] I just, I, we saw a story like similar from not on, like, it feels like over the last year where everyone's talking about like geo instead of SEO and like everything's going to change and agente commerce. [39:29] And I think like that's one place also where we've seen people, [39:33] much more incremental change. We're getting more traffic from ChatGBT, but [39:39] It's almost like that's another channel for us. And we have to think about the same way we do like a paid search. There's like a cat mouse game to figure out how to get free results. [39:49] There's going to be a paid version of it. [39:51] but fundamentally like one, we don't think people are going to buy a funeral via chat. And two, um, [39:59] That may not be true for a lot of products, right? Like when I do a flight search, I still prefer to do that myself versus like ask a travel agency to do that. And so for us, it wasn't really... [40:09] a shift in the way we thought about marketing. It was like, okay, great. We have to pay attention to like these five channels. Here's another one that we have to, [40:17] make sure we're ahead of, but also [40:19] doesn't actually change our business fundamentally. [40:22] Can you guys give me a preview of what your next business might be or what areas you're interested in? [40:29] Thank you. [40:30] I think we cannot. And I don't know if Sam knew I would say that, but... [40:36] I think we can not, not because we're like... [40:39] hiding a secret so much as [40:41] The level of embarrassment and like literal day zero is so extreme that I do not think I can tolerate it.

[40:50] I think we just made, we're not like, we're not like 30 days from launch an announcement. Um, we're, we're, [40:57] a few months out. We, we do know now what it is. Um, [41:01] But that's literally as of the last, like, five days. [41:06] And so we're in that like, [41:07] we just have to build more before we feel a little bit more comfortable. Um, [41:13] Talk to me about it publicly. [41:15] I think we can... [41:17] maybe go back to the original theme. I think like we, I think it's like a useful distinction to be like, okay, what are like things that are like purely enable? The unit of work has changed. Um, [41:27] And then there's sort of this other category of like, [41:30] it's some combination of like hard bundle in the real world. [41:35] And, [41:36] Those types of things we're really interested in. So like the PNC insurance one that Dan listed, there's some transformation aspects to it. And there's some things that are like, I'm going to work the same. And so I think we're looking for. [41:50] some sort of like [41:51] secular change in the world, like the world's changing. And it could be, you know, the death rates going up. It could be that more people want to do Botox. AI is sort of this mega trend, but we're sort of like looking for the intersection of AI in some other trends rather than AI being the primary trend. And so that sort of like helps guide the types of things we're interested in. When do you start to talk about your new products, Dan? I mean, I've been like, all my products start as basically blog posts of like,

[42:21] Like, you know, so pretty much immediately. I think of new product, new products as content first and then in businesses second, if they seem like they have legs. [42:33] Hmm. Let's go. [42:35] Yeah, and it's been, like, so fun to watch you share all these, like, little toys and experiments because it feels like this sort of, like, [42:44] edge of what's fun and cool and like potentially useful. Like you almost like don't fully ask the last question, which actually I think I really appreciate because it takes you, [42:54] into like all these new places and, and our business are like, well, will someone pay for this? Uh, you know, like it sort of like is the first question we ask. Um, and so, um, [43:03] we're both doing something kind of weird, but like in really different directions. Totally. And what I think is also fun is, I don't know if you remember this, but when I started Every, you were also thinking about newsletters and you were thinking about newsletters in a almost similar kind of way, but it was very, it was very you. It was like, I think you were doing cars at first. You were running like vertical specific newsletters that like people would pay [43:33] There's just, I don't know, we're always kind of like on parallel tracks, but with very different personalities that come out in the way that the businesses actually get built. [43:42] Totally. Yeah. Yeah. I think we are running a automotive professional publication. And the idea was like doing a vertical stack version of information, which ended up not working for, or maybe execute a different way of works. But like, it's like, yeah, we're working on professional newsletter. And then,

[44:01] They're two completely different things. [44:03] It's very hard. This was pre-built in a lot. I just want to put that out there. That's pre-built in a lot. Yeah, Dan doesn't want to be associated with that. You wouldn't have done cars, car newsletters. [44:16] Cool, guys. This is awesome. I love having you. Whenever your new thing launches, I would love to have you back to talk about how AI is involved in that. And yeah, just love being on our parallel paths in Brooklyn together. [44:34] Next time in person. [44:35] Sounds good. [44:36] Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI&I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure unadulterated knowledge bombs about chat GPT. [45:06] on the edge of your seat. [45:08] craving for more it's not just a show it's a journey into the future with dan shipper as the captain of the spaceship [45:15] So do yourself a favor. Hit like, smash subscribe, and strap in for the ride of your life. [45:21] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.