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- [00:00]
A
I love this.
- [00:01]
B
Oh, thanks. I feel like that's actually really meaningful. You know, I gotta say, Sam and I this morning looked at each other and we were like, are we sure we should be on this podcast? There's two good companies to start now. There's the AI native company that pushes the ball forward inside of some category, or there's the AI durable company that effectively uses AI where the core of the machine is not going to change.
- [00:41]
A
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 meanings. 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, 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, what situations came up in a particular week, and how I can do better next time. If you're trying to improve as a leader and scale your company, try Granola as your AI powered notepad for meetings. Head to Granola AI every code, every to get three months free. And now back to the episode. Sam and Dan, welcome to the show.
- [01:25]
C
Glad to be here. Thanks, Dan.
- [01:27]
B
Thanks for having us.
- [01:28]
A
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 Boreham Hill, and I believe you guys, your office is like a few blocks away, right?
- [01:47]
C
Yeah, it is. Yeah.
- [01:49]
A
And Dan, we went on a Jhana meditation retreat together a few. A few months ago. So there's just a lot of like, interesting overl. And I just really respect you guys. I think 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.
- [02:07]
C
And it's funny, I was thinking before this, 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. Dan Freeman and I's business partner, Emma, I reconnected with at a party you hosted maybe 10 years ago.
- [02:27]
A
I didn't realize it was at my party.
- [02:29]
C
It was at your party. And so you are like, you know, interwoven part of the story in these funny ways too.
- [02:35]
A
I take all credit. I don't know why I don't get Carrie. Like, that's amazing. That's really fun. Yeah, I remember you were at like imgur and a16z and. Yeah, yeah, very. Yeah. Okay, Well, I guess maybe let's start with. For people who don't know 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]
C
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 5 or 10 million revenue and then we go find a CEO who's better than us to take it from the sort of the next 10x. And we remain involved as a partner, 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, okay, here's a million bucks, here's an idea, I'll help coach from the sidelines. But we're actually like in the seats. And that means we're really concentrated. Um, we've started two companies to date and we're about to start our third. Um, and just to give you an example of the types of things we like to do, um, 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, et cetera. And we're sort of her back office. That helps her stay compliant, grow her business, and really everything you need to do at med spa. So we now have hundreds and hundreds of these med spot clinics across the US all in partnership with these 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. When we have sort of in person funerals, they're generally at wedding venues that we booked out year in advance Saturday night. But Totally open Tuesday morning at 9 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 weird businesses that are not YC Zeitgeist, that have really real world implications and this sort of reimagining these types of bundles.
- [05:07]
A
I love it. Wait, tell us where is Moxie stage wise?
- [05:11]
B
Sure. So Moxie is a series C company, you know, into the, into the tens of millions in revenue and 600 plus customers team globally is like 200. So it's like a comfortably mid stage company and vis a vis AI. We launched it, you know, some number of months before the release of ChatGPT. And so it is hilariously a sort of like just before AI company that had none of that in, in its conception. And you know, it has had to almost as much as a company that was started in like 2015 has had to sort of adapt as opposed to being much more native to that way of thinking.
- [06:03]
A
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.
- [06:28]
C
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 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 and serve.
- [06:48]
A
See.
- [06:49]
C
And we realized that I think to make, to maximize assess 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. And so we thought that was the place where, okay, if you can, if you could ask someone else to do that, you could like pay mackenzie 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 says 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, I both feel really lucky that it worked so well the first time because I think if it didn't work, people would be like, oh, your model doesn't make sense. And now we can like, oh, actually it has. We've done this two times and we'll keep doing it in that way.
- [07:45]
B
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, uh, before this I built one company over 10 years. And years four through 10, 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 that feeling of like a little bit of existential dread. One way to avoid it is simply to not be doing, not be 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 I think that I'm sure it will have some other challenges that, you know, some other psychological challenges. But you know, we are, we are, I think for me at least I feel that we're, we're here optimizing for like what's most enjoyable and exciting for us.
- [08:50]
C
We also sort of like pretend on the found journey that is the same skill set all the way. And I think like what you do and how you do it really depends on the stage of the business. And so there's actually a lot of value specialization too of like, okay, we know what this looks like, going from like 0 to 1 million revenue over and over again. And here's what it takes from 1 to 10, or the systems or the people. How do 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.
- [09:23]
A
You guys also have an interesting model for how you break up the work between yourselves. How does that work?
- [09:29]
C
It's changed a little bit over time. Like when, so 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. So I'll run one business, then starts the next, and then I'll sort of start the one after. We'll see how long that lasts. I think one of the things I've really appreciate about this working model is 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 the other person and emotionally supportive when things aren't working. And also we have 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 where you have all the upside from co founders, but also like just a much larger scope and span too.
- [10:22]
B
And then we have other partners inside BoltonWatt, 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. So let's generate ideas, validate ideas. And the thesis there is 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. And so we're starting to like, bit by bit, not in a like overly accelerated way, institutionalize and specialize in the different components that we want to be best in the world at.
- [11:10]
C
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 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 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 aback and sort of limit our speed.
- [11:56]
A
And 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, unsexy. Business like funeral homes and med spas and at least with Moxie is sort of a business in a box type thing where 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, or something like that. I don't know exactly how it works, something like that. And I'm curious how that model or how you're thinking about that model in an, in an AI world. You started Moxie right as ChatGPT came out for the first time. Yeah. How does that, how are you thinking about how the model evolves now that AI is a thing?
- [12:45]
C
Yeah. I think we were first attracted to those businesses because we thought they would be really hard to build against and there's a huge existing opportunity. And we said, okay, we're pretty good at this operational complexity where you might combine. You almost have to build like a services company and a software company at the same time. The sort of competition looks a lot different. There aren't 10 YC companies starting each of these. We sort of like that space to play. And then I think one as sort of like ChatGPT 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 spot doesn't change because AI is out there. And the other day I was thinking back, so we named Bolton Watt after a company that was formed to commercialize a steam engine in 1775. And so we're like hyper aware of 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
- [14:04]
A
those 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, 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, where are you currently and how does that filter into the strategy if you're using AI to code, you know the pain. Too many terminal panes. Multiple agents running at once, copy pasting context between them and trying to remember which branch has what. The bottleneck isn't writing code anymore. It's coordinating agents. 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 Claude code Codex or open code. Intent is what comes after your IDE. Try it yourself@AugmentCode.com intent that's AugmentCode.com intent. Build with intent. And now back to the episode.
- [15:09]
B
I think our view is like, there's two good companies to start now. There's the AI native company that pushes the ball forward inside of some category, or there's the AI durable company that effectively uses AI where the core of the machine is not going to change. And if you look in our first two, you know, like, there's no such thing as an AI native crematory, you know, it's just like it's going dramatically. Yeah, exactly.
- [15:35]
C
Like we'll put on the blockchain and
- [15:36]
B
use AI and then the, and you know, like we're not expecting it a robotic injector anytime in the next seven to 10 years. And, and so the, the like core work of a med spa will be, you know, med spots themselves are actually like conveyors of the latest technology, the latest medical technology. You know, when GLP1s come out. Like medspas are one of the early adopters of actually spreading it to their communities. And, but at the end of the day, like, what happens inside the walls of events is not deeply impacted by AI. However, there are like, 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 great deployers of AI inside of our operation. We want to help our 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 and they can always just be a few months later and, you know, not take risk with their customer relationships. And, and so I think we've, we've kind of said for now, between the AI forward and the AI durable, we really like being like 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 every idea we've had in that category has had multiple formidable looking competitors doing something 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]
C
All the smart people are listening to Dan and every and we'd rather compete against the less smart people who.
- [17:37]
A
I would rather not compete against you. So I love that. So if you're, if you're thinking about, okay, how do I make this, how am I concentrating on the things that are not going to change and how am I using it to get AI to get more operationally efficient rather than being like truly like totally AI native in a way that a YC company would be or whatever. What are the actual real operational efficiencies you have found? What's worked and like what has not worked as well as you thought it would.
- [18:03]
B
Maybe I could speak to the like company discovery process where I think we've actually seen probably the greatest transformation which, which roughly like maps to the, the more green fields, the better AI can be. Basically like 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, you know, series D friends and they're like, we're like 10% faster. Like what is everyone talking about? Which is the sort of like classic dichotomy. The, in the, in the new company discovery process, we are roughly speaking like every stage has been rethought so that 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 like calling some friends to just get the basic facts. And this is now like a mega prompt to like generate a list of categories, then a mega prompt to assess like what we think is a good business and like our particular point of view of what we're looking for and start to narrow in on a couple different, a couple different ones to go talk to real people in. And we did a, we did a little bit of an exercise this time around where we both did the like AI curation. And then 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, the guy who was competing against the like automated tunnel builder machine to build a tunnel.
- [19:35]
A
That's a real thing, like John Henry or something.
- [19:37]
B
John Henry, yes. Yeah, well, it's a real face. Like a guy fucking put a hammer through a mountain.
- [19:43]
A
Well, he, he, I think did actually try to compete against the, the automated thing and then. Yeah, I'm pretty sure.
- [19:49]
B
Okay, yeah, well, RIP Jock Henry. Anyways, I was the John Henry in that story and fortunately it came out the other end and we did as a group 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 insight. This 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 our horrible name because Matthew Bolton was the Boltoner Watt. And Matthew Bolton is our assistant in being really good in the customer discovery process. So he helps us prepare for every call and like, looks at the Persona of who we're talking to, looks at our current hypotheses and what our like validation focus is and basically says here's the areas to dive into. And of course we review and make sure we talk about the right things, but he makes the prep more efficient. And then afterwards the AI transcript goes directly into a notion table. We run Matthew Bolton and it regenerates a point of view for each of our core hypotheses on the idea. What's totally validated, what's totally invalidated, where do we need to dive in more? It pulls out the relevant quotes from the people. And so it's been a huge hit in that way 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 and we just can't make that. We haven't yet been able to make that work at all. The basically anything that strikes us as a good idea so like passes some basic sniff test. 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, it just expresses a 10 out of 10 customer poll. And, and so like we, we tried that and then flew to meet a real prospective customer for a category and just like fell on our face completely and 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. 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.
- [22:23]
A
Or it's like just. It knows that you want it to say yes or it thinks you want it to say yes. So it does kind of.
- [22:29]
B
We've desperately tried to have it be like in sycophancy mode and we can't get it out of that. I mean, so maybe someone else can. There's like a way to make that useful. But I would say at the moment, our point of view is like, it can help us talk to people effectively, but it cannot actually reduce the number of people we need to talk to to get to confidence.
- [22:49]
A
Got it. Okay, this is really interesting. Are you able to show us Matthew Bolton, like, show us the. Show us the goods. And I'm also curious about this prompt. The prompts that you're using to do like, okay, research business ideas and also filter them. That sounds really interesting too.
- [23:07]
B
Okay, so we have different Matthew Bolton flavors. I think there is an underlying, like, agent identity doc. This is for. This was for a specific category dive we were doing into PNC insurance. And we kind of break down our own thinking across a few notes, across a few docs. So one is we have what we call a pov, a point of view on what we think the opportunity is, what's the problem we're solving, who are we serving?
- [23:41]
A
What's a pnc?
- [23:43]
B
PNC is Property and Casualty Insurance.
- [23:46]
A
Okay, got it. So that's the area you're looking at.
- [23:49]
B
That's the area we're looking on. Exactly. So this is the vertical we're exploring. So we have our point of view. We have a hypothesis tracker 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 through potential customer calls? And then we have transcripts and our own notes from every call we've had. And so when we call Matthew Bolton, we ask it to reread our latest point of view. We ask it to read the hypothesis tracker, read the most recent 10 calls, and then for every hypothesis, 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, where to say, great, we're good to go on this.
- [24:49]
A
This is kind of sick. I love this.
- [24:52]
B
Oh, thanks. I feel like that's actually really meaningful. You know, I got to say, Sam and I this morning looked at each other and we were like, are we sure we should be on this podcast?
- [25:03]
C
I literally said to Dan, I was like, let's make a list of all the places we've tried to use AI and worked. Because I think so. It's nice to hear that from you.
- [25:12]
B
Exactly, exactly. Or like from the guy himself. The. So, you know, ultimately you just say like, run PNC analysis and it runs through all these different steps. And one of what, what feels like personally meaningful about this is we try to be intellectually honest with ourselves. This is like something we, we hold ourselves to. And I think you probably know this because you're building new products all the time. But like, the nature of starting something new is it requires like a manic energy and a little bit of a suspension of disbelief because there's just like no reason any new company should succeed or any new product should succeed. And this keeps us like, really rigorous and fact based, which is what we aspire to. And we can balance that with like our own. You know, we can be sycophants to ourselves and ask it to remain fact based and balance these two opinions. But it, it like totally helps.
- [26:12]
A
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.
- [26:32]
B
If we try to ask it an opinion on a high level question, I've not trusted it with that or 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 here's the three key things that must be true in this idea. And I've got three quotes from different people that like, directly speak to each. And this will just like, much, much more efficiently help me get there. And, you know, maybe or maybe not. I'm, 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. But the, this keeps us honest on the detailed things we ask it to do and somebody.
- [27:27]
C
That's the role Dan and I play. For each other is keep each other honest. And there's a way to sort of get the short version of that with AI too. We're like okay, make the best counter argument and then sort of just can like sharpen your thinking. Even if you're not looking for it to like tell you wrong, but you can sort of help it have it tease out like what in what ways could you be wrong?
- [27:47]
B
Which is really useful tool sometimes I'll say that and and then it, you know, we'll make some good counter argument and I'll be like ah, fuck off. Like what are you saying?
- [27:59]
A
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- [30:02]
B
I'm not that confident. Your job. Yeah, I think it will do like a quite good job on basic market industry reports. I mean it's, it certainly beats
- [30:14]
A
for
- [30:14]
B
the one week desk research up front. It beats the like pretty low and medium quality reporting that was out there. There's, there's kind of good stuff in we, we really like public company filings. Whenever there's public company as in the, in the category that we're looking at. And sometimes we're not just asking it to go do something, we're like uploading a bunch of thick PDFs too. And it's quite a good parser of that stuff.
- [30:46]
A
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. And I would trust you to, even if you were using AI to do it, to come back with something that felt new and interesting in a way that I don't think that Claude can get to on its own.
- [31:07]
B
That I probably agree with. I don't think we've gotten good ideas from it. I think we've uncovered facts and then we have our own earned point of view. 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. And we have a relatively differentiated point of view on actually what Moxie is and what makes it successful. And we have talked to, between Sam And I, probably 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. And so there's a combination of the earned point of view through years of building with its ability to of course consume massive amounts of information and then fit that to our point of view.
- [32:03]
C
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 because we have certain analogies and ways of thinking and looking at it that you can ask it to do the research and figure out the underlying like category properties. But like the default of that isn't the answer we want.
- [32:29]
A
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 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?
- [32:48]
C
Yeah, I think it's really interesting. Inception Company series A, company series C. And I think there are two parts of this. Of one, how do you actually get people to start using AI, which we shot about. And then I think the second piece is what's actually worked and what hasn't. Dan and I were talking about this the other day of should you have an AI initiative? Is that a good idea or a bad idea? And I think my perspective was is a bad idea because you don't want to sort of lead with the hammer. And we all remember the time like, no SQL. Everyone was putting everything into SQL, whether it belonged or not. Everyone was building a Slack bot 10 years ago, whether one needed or not. But you do need something to kind of shock the system. You need something to be like, okay, great, there's a new tool set. And I think the point of view I've come to is you shouldn't give anyone credit for using AI, 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. And so to do that effectively, you both need to sort of seed what are the tools you can use and give a lot of good examples. And you need to start demanding that when you see the results from someone on your team that they've actually used the best possibilities. But you don't get any points for training. Generating a bunch of copy that's 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 70% of the way there, even 100%, that that's great. And so like, that's where we 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 to seed these ideas so other people get examples and then actually make it successful for those folks. I think one of the places that, and I think this goes to Dan's point earlier of where we're seeing the most action. Inception is really on the experimental edge of things. So a few examples of this, really good at generating landing pages and pushing our thinking there and stuff. A ton of work to integrate that back into webflow and have it fit with our system and have a Consistent header and footer. And so there's almost like two phases. There's like research, development and then there's like production. We've had 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 home business, people call in and they might mention some town name and we know whether we service that. So a designer spun up an app where anyone can type in a hospital name, a town name, and it resolves whether we can find it or not. Instead of integrating that and spending engineering time on figuring out how to deploy that safely integrated, all that stuff, it's just a separate app that's a link from our main one. And so we found that enabling those types of things has worked really well and our engineers 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 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]
A
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 totally changing everything, if that makes sense.
- [36:22]
C
Yeah, I think that's exactly right. And there are some places where it's made a huge difference. Our talent team was reminding 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 existed. And that's worked really well for them. And so there's sort of these like places where it's kind of, kind of unlock enable into an existing system.
- [36:51]
A
I also think it's interesting, kind of the approach of you don't get credit for using it, but you 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. 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, if you just say that, then people will just continue doing it the way they already know how to how to do because they're like, well that's if I want to get the best quality, I have to do the thing I already know. So how do you deal with that?
- [37:33]
C
I think it's like finding a few people to set the example and then start comparing the work. And so we don't count how much your PR was AI generated. Because if you commit and push a bad PR because AI wrote it and you're like, blame AI, that's a terrible outcome. Instead we can be like, oh, look at this engineer who's done all this great work. Ask them how you do it in a public forum and then sort of continue to raise the floor. And so that's what we found to be much more successful. It takes a lot of active work to say, okay, let's find these examples across the company. Let's potentially see those examples and then continue to elevate those.
- [38:14]
A
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 is it. Are you still kind of in the regular ChatGPT type wave?
- [38:39]
B
The early word out of the max engineering is like, yeah, this is better but not this is a step function, different experience. And we are doing a lot of work there to like retool in order to experience more benefit because we're seeing exactly the like newer engineer working on more greenfield project moving much faster than you know, working on something that like touches multiple parts of the system and that's like you know, 40 person product engineering team, more mature code base and so on. So we have not seen the like night and day transformation that the X sphere is reporting.
- [39:20]
C
We saw sort of like similar phenomenon. Like it feels like over the last year where everyone's talking about like GEO instead of SEO and like everything things going to change in gente commerce. And I think like that's one place also where we've seen much more incremental change. We're getting more traffic from ChatGPT, but it's almost like just another channel for us and we have to think about the same way we do like paid search. There's like a cat mouse game to figure out how to get free results. There's going to be a paid version of it. But fundamentally like one, we don't think people buy a funeral via chat and two, 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 agent to do it. And so for us, it wasn't really a shift in the way we thought about marketing. It was just like, okay, great, we have to pay attention to, like, these five channels. Here's another one that we have to make sure we're ahead of, but also doesn't actually change our business fundamentally.
- [40:23]
A
Can you guys give me a preview of what your next business might be or what areas you're interested in?
- [40:31]
B
I think we cannot. And, and I don't know if Sam knew, I would say that, but I think we cannot not because we're like, hiding a secret so much as the level of embarrassment on, like, literal day zero is so extreme that I do not think I can tolerate it. I think we just need. We're not like, we're not like 30 days from launch and announcement. We're. We're a few months out. We. We do know now what it is, but that's literally as of the last, like, five days. And so we're in that, like, we. We just have to build more before we feel a little bit more comfortable talking about it publicly.
- [41:16]
C
I think we can maybe go back to the original theme. I think it's a useful distinction to be like, okay, what are things that are purely AI enabled? The unit of work has changed. And then there's this other category of. It's some combination of hard bundle in the real world and those types of things we're really interested in. So the PNC insurance one that Dan listed, there's some transformation aspects to it, and there's some things that are kind of going to work the same. And so I think we're looking for some sort of secular change in the world. The world's changing, and it could be the death rates going up. It could be that more people want to do Botox. AI is sort of this megatrend, but we're sort of like looking for the intersection of AI in some other trend, rather than AI being the primary trend. Um, and so that sort of like, helps guide the types of things we're interested in.
- [42:12]
B
When do you start to talk about new. Your new products, Dan?
- [42:15]
A
I mean, I've been like, my. All my products start as basically blog posts of like, I built this little thing over the weekend. Like, you know, so pretty much immediately I think of new product, new products as content first and then. And businesses second. If they seem like they have legs,
- [42:35]
B
that's Cool. Yeah.
- [42:36]
C
And it's been like so fun to watch you share all these little toys and experiments because it feels like this sort of edge of what's fun and cool and potentially useful. But you almost don't fully ask the last question, which actually I think I really appreciate because it takes you into all these new places and our business are like, well, will someone pay for this? Is the first question we ask. Um, and so we're both doing something kind of weird, but like in really different directions.
- [43:07]
A
Totally, 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, it was very. It was very you. It was like, I think you. You were doing cars at first. You were wanted to do like vertical specific newsletters that like people would pay for. So like cars or trucks or whatever. And. Yeah, so 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.
- [43:43]
C
Totally. Yeah. Yeah. I think we were running a automotive professional publication and the idea was like, do like a vertical stack version of information which ended up not working for. Or maybe execute a different way would have worked, but like, it's like, yeah, we're working professional newsletter. And then they're two completely different things.
- [44:03]
A
It's. Yeah, it's very hard.
- [44:05]
B
This is pre built in a lot. I just want to put that out there.
- [44:08]
C
That's pre built in.
- [44:09]
B
Yeah.
- [44:09]
C
Dan doesn't want to be associated with that.
- [44:12]
A
You didn't. You wouldn't have done cars car newsletters. Yeah. 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. Is. Is involved in that. And yeah, just. Just love being on our. On our parallel paths in. In. In Brooklyn together next time in person. Sounds good.
- [44:44]
B
Oh my gosh, folks, you absolutely, positively have to smash that like button, button and subscribe to AI and 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 Every episode is a roller coaster of emotions, insights and laughter that will leave you on the edge of your seat 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. So do yourself a favor, hit like Smash. Subscribe and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely, hopelessly in love with you.