How AI Startups Can Win With Better Strategy - Ep. 50 with Mike Maples

Our sponsor for this episode is Microsoft. Want seamless collaboration without the cost? Microsoft Teams offers a robust free plan for individuals that delivers unlimited chat, 60-minute video meetings, and file sharing—all within one intuitive workspace that keeps your projects moving forward. Head to https://aka.ms/every to use Teams for free, and experience effortless collaboration, today. Mike Maples knows how AI startups can beat incumbents with billions of dollars. Mike—who wrote early checks to Twitter, Twitch, Okta, and Lyft, and now invests through Floodgate, the fund he cofounded—told me it's not about the smartest model, or raising the most money. Startups can win in AI with better strategy. AI is changing the economics of startups—both how they’re started and how they’re funded. A new breed of companies is emerging, and I invited Mike on the show to talk about how they can best strategize. Last year, Mike co-authored a book called Pattern Breakers, which is essentially a guidebook to why there’s no guidebook to building companies. I really liked it, and my colleague Evan Armstrong reviewed it for Every, so I was glad to have him on. We talk about how shifts in technology create space for smaller players to compete—even with AI giants like OpenAI—and how to capitalize on them. If you found this episode interesting, please like, subscribe, comment, and share! Want even more? Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free. To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: Introduction: 00:02:20 Innovate the business model, not just the product: 00:06:02 How startups can compete against the likes of OpenAI: 00:15:49 Mike’s take on DeepSeek: 00:19:34 Why the future has always belonged to the tinkerers: 00:21:44 How small teams today can make big money: 00:24:03 Find niches that incumbents can’t or don’t want to enter: 00:28:55 The qualities of the truly AI-native: 00:47:08 How AI changes the funding model for software companies: 00:53:46 Knowledge work is moving toward systems-level thinking: 00:58:23 Links to resources mentioned in the episode: Mike Maple: @m2jr The fund Mike confounded, Floodgate: @floodgatefund Evan’s piece reviewing Pattern Breakers: "A New Book of the Startup Bible" Dan’s piece on the allocation economy: "The Knowledge Economy Is Over. Welcome to the Allocation Economy."

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Counter positioning is one of the most powerful ways a startup can have an insight. Most people think an insight is just about the product. The what is the product? The how is how you deliver the product? And the how can have an insight as well. When you're in a lawsuit, let's say you're Apple and you're in a lawsuit with Samsung. There's a ton of documents that have to get discovered for the core case. They hire these outsourcer firms of people to go pour through these documents and they charge them on a cost plus basis. So what Text IQ said is, well, we've got AI. Why don't you just send us all your documents and we'll send you back the ones that are discoverable and we'll have more accuracy. Now you're not competing for software license or proceed revenue or even a subscription price. You're saying, I'm a substitute for that labor spend. You used to spend $50 million a year on this.

• 0:45Speaker 00

  I can do it for a tenth of the price and much better. If I'm a SaaS vendor and I charge subscription by the seat and that's all I've ever done. Think about how embedded that must be in the culture, right? Every product manager thinks that way. The CFO thinks that way. There's nobody at the company who knows how to react to your strategy.

• 1:02Dan Shipper?

  If you change your business model, everyone's going to lose their mind. OpenAI is moving from being this API developer tool to like a product company. They're releasing all of these consumer facing product. A lot of founders are thinking about what if OpenAI includes this as part of ChatGPT or includes this in some new product that they release? And I'm curious how you would think about counter positioning that.

• 1:24Speaker 00

  I'm involved with a company called Applied Intuition and they create simulation software for autonomous vehicles. If you're GM or if you're Porsche or you're these big companies, that's pretty valuable. But you can't just get that when Sam Altman releases his next demo at a demo day event. To succeed as a company like that and to really ask for giant contracts from these companies, you have to have not only AI expertise and products, but you have to have multi-discipline expertise. Everybody says, kind of disses on these companies that are just an AI wrapper, right? And I'm like, well, if the thing that you're wrapping on top of involves a process that you really know about, that most people don't, that may be a path to a great company.

• 2:19Dan Shipper?

  Mike, welcome to the show.

• 2:20Speaker 00

  Thanks for having me. I've been looking forward to this.

• 2:23Dan Shipper?

  Yeah. So for people who don't know you, you are a legendary investor at Floodgate, which was one of the first seed firms. You were an early investor in Twitch, Lyft, Okta, and a bunch more. And you're also the author of the book Pattern Breakers, which is an excellent book that I've read. We've also reviewed it on every, which I guess I would summarize by, it's sort of like a guidebook about how there's no guidebook to building companies. So it's very, it's a little bit Taoist.

• 2:48Speaker 00

  A little bit Zen.

• 2:50Dan Shipper?

  Yeah, a little, a little Zen, which I love. I think that's so, I think that's so good and so important. You have a lot of emphasis on like founders winning by being extraordinarily different and, and breaking the established patterns of like how you're supposed to run a company. I loved it and I'm excited to chat with you about that and, and everything going on in AI on the show.

• 3:10Speaker 00

  Yeah, well, cool. Now, let's, let's get after it.

• 3:13Dan Shipper?

  Let's do it. So one of the things that I think is, I'm personally curious about is you started investing sort of when seed wasn't really a thing and helped to like invent this new way of capitalizing companies for an earlier era of startups, pre-AI startups, let's just say that, right? And I think that that is an example of the kind of thing that you, that you talk about in your book, Pattern Breakers, which is like taking a look at the landscape of maybe what companies need and how companies are funded and being like, well, there's this thing that it seems to make a lot of sense to me that there should be a seed stage funding mechanism and just going and doing it. And I'm kind of curious, like

• 3:58Dan Shipper?

  my, my feeling right now is that AI is sort of like radically changing the economics of starting a business. Software is like orders of magnitude cheaper to make today than it was 10 years ago. And I'm, I'm kind of curious, using that same sense of like, okay, I'm looking at the environment and looking at how things change and I'm kind of like maybe pushing away the established structures for a second. Like, how do you think that that might change investing and how companies raise money and all that kind of stuff?

• 4:28Speaker 00

  Yeah, I've been wondering about this a lot lately. So as you know, one of the, one of the things that I emphasize in startups is the power of harnessing inflections, right? So I like to say that, um, you know, business is never a fair fight and the startup has to have some unfair advantage way to win. And the way they do that is they harness inflections. Inflections allow the startup to wage asymmetric warfare on the present and show up with something radically different. Without inflections, they have to play in the incumbent sandbox. And so they're limited in their upside. So every now and then, though, you get something that I like to call a sea change. And when I was a kid, the sea change was a mass computation and the personal computer. And, you know, computers used to be really expensive. And

• 5:13Speaker 00

  then they became asymptotically free and ubiquitous and you had one on every desk in every home. And a whole new set of companies emerged. Software became a real business for the first time. Software used to be what you gave away because mainframes were expensive. You had to keep them running all the time. And so, uh, so the assumptions got inverted and you had a bunch of companies using the software licensing model, you know, Oracle, Microsoft, SAP, companies like that. Then you had, um, in the nineties, the era of mass connectivity, which I think was extended with the iPhone. And in mass connectivity, rather than processing power becoming free, communications bandwidth starts to become free. And you start to not just have computers everywhere, but you have everybody in the world and every device in

• 5:58Speaker 00

  the world connected in these networks. And, um, new business models came out of that. Subscription and SaaS and advertising. You know, it's interesting, there aren't any software licensing model companies started after 1990 that really mattered. You know, all those companies got sussumed in Microsoft because they could put it in the OS or out compete them. So like, why, why do I think the AI sea change matters? What I see happen with these sea changes is that some business models become relatively more attractive and some business models become relatively less attractive. And there's only nine business models that I know of in human history. And so the most recent business model I know of is 250 years old.

• 6:43Speaker 00

  It's the subscription model. And so, you know, what I, what I like to do is I like to say, okay, if there's nine business models so far in humanity, and every time there's a technology sea change, there's a migration of attractive business models from one set to the other. How might that migration occur this time? Because what, what you want when you're a startup is to be counter positioned to the incumbents. You know, this whole the incumbents have the advantage discussion is wrong headed. Of, of course, the incumbent has the advantage if you play by the rules of the incumbency. But, but like what you want to do is you want to say, how does AI make some business models relatively more attractive and less attractive? And how could I as a startup exploit those new opportunities?

• 7:25Speaker 00

  Not just insight in my product, but a, some type of an insight in my business mall go to market strategy that is, that disorients incumbents and, and where they have a disincentive to retaliate or to copy your strategy. So those are, that's mostly what I'm looking at these days from an AI point of view.

• 7:44Dan Shipper?

  Yeah. So I think like one of the things that I see a lot on from the business model perspective, and right now we're talking about, we're talking about business models for startups. I would also like to talk about business models for venture, like funding startups.

• 7:57Speaker 00

  Sure.

• 7:58Dan Shipper?

  But, but business models for startups, just to start there for a second. One of the things I'm seeing a lot of is paying per outcome as opposed to paying per month.

• 8:08Speaker 00

  Yes.

• 8:09Dan Shipper?

  Which I think is a really interesting one. Is that something you have your eye on?

• 8:12Speaker 00

  Oh, absolutely. So, you know, There's a business model called tailored services with long-term contracts. And right now, most people think that's unattractive.

• 8:23Dan Shipper?

  What is tailored services of long-term contracts?

• 8:26Speaker 00

  That could be like the defense subprimes. It could be a contract research organization for a pharma company. You know, it's it's somebody that you that offers services on a contract basis usually is labor intensive, usually is cost plus. And the conventional wisdom today is those are not attractive opportunities for software companies.

• 8:47Dan Shipper?

  Like a law firm or something?

• 8:49Speaker 00

  A lot like a law firm, perfect example. So like an example, like a law firm or legal services. A company I was involved with a few years ago was called Text IQ. And they would go to a big corporation and they would say, you know, when you're in a lawsuit, let's say you're Apple and you're in a lawsuit with Samsung. There's a ton of documents that have to get discovered for the court case. And so the way that happens in reality is they hire these outsourcer firms of people to go pour through these documents and they charge them on a cost plus basis.

• 9:21Dan Shipper?

  Yeah.

• 9:21Speaker 00

  And so what Text IQ said is, well, we've got AI. Why don't you just send us all your documents and we'll send you back the ones that are discoverable and we'll have more accuracy. Well, now you're not competing for software license, desktop revenue or proceed revenue or even a subscription price. You're saying, hey, look, I'm a substitute for that labor spend. You you used to spend $50 million a year on this contract outsourcer that sorts through these documents. I can do it for a tenth of the price and much better. And now you're competing over that labor cost bucket rather than the software spend bucket. And how many seats do I get?

• 9:57Dan Shipper?

  Well, that's interesting because there's also like there's there's cost per like task done. So it's so the so, you know, it's cost per document processed or whatever, which is sort of like what OpenAI does, you know, when you you send them a prompt, they send a response. But like even if they send the response and the response isn't good, you still pay for it, right? And then there's other companies that are sort of capturing the value, part of the value that they generate. So it's it's it's if they increase your, let's say, let's say it's a SDR bot. If they increase your sales by some amount, your close rate, they take a percent of that only when it's successful. Have you looked at those two?

• 10:37Speaker 00

  Yeah. And so I do like the outcome based pricing models a lot. You know, they both have their virtues, right? The thing about OpenAI is like you could use Dolly to generate some art that you don't think looks pretty enough. But OpenAI probably deserves to be compensated for the fact that you did that, right?

• 10:57Dan Shipper?

  Yeah. It's sometimes hard to know if the job was done well or not. It's like, it's not so clear.

• 11:03Speaker 00

  And sometimes it's the customer's fault that the job wasn't done well, right? It's tricky. You know, back in my ancient days when I was a founder, I used to have this expression when I would sell enterprise software. I called it, what does it take to ring the bell? And so like, if you go into the carnival, you know how there's that thing where you have this big mallet and you hit this thing? And hopefully it goes all the way up and rings the bell. But if it doesn't go all the way up, it makes no sound. It has to go all the way up and ring the bell. It's binary. And so what I used to say to the folks that I would work with is that the customer doesn't care that your software ran according to how the specification works. That's not what they're buying. They have a job to be done. They're paying you, they're hiring your product to do a job.

• 11:47Speaker 00

  And so we need to understand what's it going to take to ring the bell for doing that job? And if we, if we ring the bell, they're going to say, this is amazing. I want more of this. If it doesn't ring the bell, they're not going to care that the mechanism of our system works. They're not gonna, they're not going to be interested in that. And so like, for me, the outcome based models that we were just talking about a minute ago are kind of asking that, what is the job to be done in a Clay Christensen sort of lens? And then what does it mean to ring the bell? And can I get paid if I unambiguously succeed at that over and over again?

• 12:23Dan Shipper?

  And the thing that makes that, I think, interesting over like a SaaS model is that the incumbents are all going to be SaaS. And if you're guaranteed to get 20 bucks a seat or whatever it is, the idea of moving to like a pay for performance model is like very unappealing.

• 12:38Speaker 00

  That's right.

• 12:39Dan Shipper?

  So startups, to your counter positioning point, like that's a thing that startups can do that incumbents like, some incumbents already do this, like in the customer service world, this has been a thing for forever. But in general, this is not a thing. And so incumbents are not going to be able to do this very well.

• 12:54Speaker 00

  Yeah, I think that this counter positioning thing is a really important thing to maybe double click on. And so like, A great example is in the 90s, if you were a startup, the words that you dreaded to hear was "Microsoft has decided to compete in your market." Because you're just like, "Okay, I guess I'm out of business." Because even if they start losing, they're just going to bundle this thing in Windows, and I'm just hosed, right? And so that was happening to a lot of companies. Netscape just disappeared, basically, because Microsoft decides to bundle the browser in the operating system and go full ham against Netscape. Well, then, the internet happens, and then some people start to discover that you can

• 13:36Speaker 00

  monetize not by selling by the seat or by the desktop, but by selling ads. And that was Google. And Microsoft had no answer to that. You can't bundle something in your operating system and deal with the fact that Google is pricing by ads. It doesn't solve the problem. It doesn't impact their business at all. And so, Google was counter positioned to Microsoft from business model perspective. And like, counter positioning is one of the most powerful ways a startup can have an insight. Most people think an insight is just about the product, but it can also be about the what is the product, the how is how you deliver the product. And the how can have an insight as well. And quite often, the very best, most valuable companies have an insight around the business

• 14:20Speaker 00

  model that's facilitated. Google's business model couldn't work before the internet. The technology wouldn't have provided the empowerment necessary for Google to monetize with ads, but now, all of a sudden, it did. And so, that's what we look for with this counter positioning. And to your point, right? Like, now, it's sell the work, not the software. If I'm a company, if I'm a SaaS vendor, and I charge subscription by the seat, and that's all I've ever done, think about how embedded that must be in the culture, right? Every product manager thinks that way. The CFO thinks that way. There's nobody at the company who knows how to react to your strategy, because...

• 14:59Dan Shipper?

  Investors think that way.

• 15:01Speaker 00

  Everybody does.

• 15:02Dan Shipper?

  If you change your business model, everyone's going to lose their mind.

• 15:05Speaker 00

  Yeah. So how how would you even think about changing it midstream? You just it's just even if you knew to have the insight that perhaps you should consider it, you just you just wouldn't have the wherewithal to do it because it just it's so embedded in your culture. Your entire value delivery system is predicated on different model.

• 15:24Dan Shipper?

  Yeah. Well, let's keep talking about about counter positioning. And I want to bring up, I think, like, if I have to pick who the Microsoft is of the AI world. Like, huge, huge, huge tech companies like Microsoft and Google aside, I think the one right now to think about counter positioning, or at least a lot of startups are afraid of is OpenAI. OpenAI is moving from being this API, you know, developer tool to like a product company. They're releasing all of these consumer facing products. ChatGPT is sort of like taking over. And so I think a lot of founders are thinking about, well, what if what if, you know, ChatGPT includes this in their

• 16:03Dan Shipper?

  OpenAI includes this as part of ChatGPT or includes this in some new product that they release. And I'm curious how you would think about counter positioning that.

• 16:13Speaker 00

  Yeah. So there are a couple of ways. There are a couple of things I find really interesting about OpenAI from a counter positioning. So maybe we start with startups and then just there's some general stuff too, like with DeepSeek and things like that. But, um, so like, let's just take an example. I'm involved with a company called Applied Intuition, and they create simulation software.

• 16:35Dan Shipper?

  I love that name, by the way.

• 16:36Speaker 00

  Yeah, it's pretty good. Yeah, it creates simulation software for autonomous vehicles and also technology stacks for electric vehicles. And and these car companies, other than Tesla, don't really know how to do EVs, don't know how to do AVs. They don't really even know how to do software, right? Their entire business model is predicated on a supply chain that's 100 years old, where they get parts from Bosch and chips from all these people and, you know, parts from different tool and die shops and everything else. So so Applied Intuition says, okay, we've got a bunch of people from Google and Waymo, and now some people from Tesla and all the best autonomous vehicle, all the best EV companies in the world.

• 17:18Speaker 00

  We can build the entire thing that you need to sort of update your strategy and roadmap to have the software to find car, which is where the future is going. Now, if you're if you're GM, or if you're Porsche, or you're these big companies, that's pretty valuable, but you can't just get that when Sam Altman releases his next demo at a demo day event, right? Like, you know, if you're going to have a software to find car, there's a whole lot of things that you have to know intimately, the processes of how cars are made and manufactured and tested, the whole supply chain and, you know, how the delivery system works.

• 17:59Speaker 00

  And so you to succeed as a company like that, and to really ask for giant contracts from these companies, you have to have not only AI expertise and products, but you have to have multidiscipline expertise. You know, so like Casser and Peter, they grew up in Detroit, you know, before they got in at Google and Waymo, they were, you know, in the car industry at GM. And so, yeah, so I like companies like that where, you know, one way I like to think about is everybody says is kind of disses on these companies that are just an AI rapper, right? And I'm like, well, if the thing that you're wrapping on top of involves a process that you really know about, that most people don't, that may be that may be a path to a great

• 18:44Speaker 00

  company. And so, so I think that that's what I'm interested in is some of those.

• 18:49Dan Shipper?

  The AI rapper thing was so silly. I see less of that now, which is nice, but it was a very silly thing when it first started.

• 18:57Speaker 00

  So, so one, one, one other thing about this counter positioning and open AI that I think is interesting, I'd love to get your read on is, you know, one way I have internalized the deep seek stuff is that in the early days of the internet. All of the researchers from like Bell Labs and folks from like AT&T, Time Warner, the government said, "This internet thing's a toy. It's never going to be good enough. You know, we've tried this before. It doesn't work. These protocols are not going to be robust enough." And in the short term, you would have been right. You would, like, none of these things looked all that interesting or impressive. But, but I, you know, I was talking to Steve Sinofsky about this the other day, um, you know, he was at Microsoft

• 19:41Speaker 00

  at the time when the internet took off. And he was at Cornell and he saw CUCME and he, he goes to Gates, you know, "This is going to be a tidal wave. This is going to be a giant new phenomenon that we got to really pay attention to." Um, DeepSeek reminds me of that. So like the culture in AI, uh, the hyperscalers right now is you can solve all problems by throwing money at it. And, and the, the DeepSeek guys said, "Well, if we're limited with some fundamental constraints, what would we do?" I think that there's going to be a cultural shift in AI where many people adopt that mindset. And, and you know, that's important because the early days of mass computation, the, the IBM PC had a 640K

• 20:25Speaker 00

  memory limit. And so like the Microsoft programmers had an advantage because they could write small efficient code. It wasn't how many thousand lines of code anymore. It was how efficient is your code? And I think that, that we might see the same phenomenon here where, uh, people come from the bottoms up with very frugal, you know, sort of, uh, low cost by design solutions. And it'll be hard for the OpenAIs and the Anthropics and those guys. I mean, I have huge respect for what they're doing, but it'll be hard for them to respond to that because they're culturally embedded in their operating model is to, to solve everything by throwing money at it. You know, hire the best people, uh, throw money at it and just keep going, keep going faster.

• 21:10Dan Shipper?

  That's so interesting. You said so many things I want to talk about. So one is sort of like this, this toy thing where people and governments or like big companies like sort of ignore the internet at first, um, because it, they were like, "We tried it and it doesn't work. It doesn't scale" or whatever. You have the same history with neural networks, um, where like in the beginning of AI and symbolic AI, like in the 50s, um, neural networks were around then, uh, but they were mostly ignored because the early AI, AI people, particularly like Marvin Minsky.

• 21:40Dan Shipper?

  proved proved that single layer neural networks were not as powerful as as you know, other types of like Turing machines, basically, or couldn't do certain types of computations. And and I think academia sort of by and large felt like neural networks were not understandable enough. They didn't. There was no theory. And so it felt like a toy and and and it was basically ignored, except for a few kind of neural network researchers in the 80s and 90s. And then industry adopted it. And it blew up because they were like, well, it just works. Who cares? Who cares what the theory is? Which I think happens all the time. And I'll stop there. I'm curious. Curious if you have anything to add to that.

• 22:24Speaker 00

  Yeah. And it's funny, because I even like in when I was working on this book, you know, with pattern breaker stuff, one of the examples I used was the Wright brothers with the airplane. And so all the experts said it's going to take a million years for to create a flying contraption that can fly humans in it. You know, the New York Times ran an ad called Flying Machines That Won't Fly. And and it said that it, you know, it's a waste of time to try. And they had quote from like, the head of engineering of of the army and all this stuff. 69 days later, the Wright brothers at Kitty Hawk flew their first plane. And, you know, there were a couple of bicycle mechanics. And so

• 23:07Speaker 00

  what you see is that time and again, the experts are attached to their mental model how the world works. And it's it's the tinkerers. It's the it's the people who have permissionless innovation, who just tinker with stuff and make something work. And before you know it, they have to even change the science, right? People's understanding of, you know, Bernoulli's equation and all that stuff got modified and improved because of the success of the Wright brothers with their plane. So that, you know, people tend to think that the abstract science precedes the engineering, but quite often, the engineering and the tinkering causes the science to evolve to explain the unexplainable.

• 23:48Dan Shipper?

  Totally.

• 23:49Speaker 00

  And yeah, that's, that's what I see happen more often in practice.

• 23:53Dan Shipper?

  Hundred percent. I think the next point that you made is sort of like this big money versus small team thing, which I think happens all the time, too.

• 24:05Dan Shipper?

  Constraints breed creativity. And I think in general,

• 24:10Dan Shipper?

  being able to throw money at a problem means you don't have to spend time thinking about how to make it more efficient. And so I think your question about like, are the OpenAI's and anthropics of the world in trouble? I think that's an interesting one. I would bet not.

• 24:25Speaker 00

  Right, I would too.

• 24:26Dan Shipper?

  My, my feeling about that is, I mean, obviously the sort of cliche thing is like, okay, it's going to stimulate demand or whatever, which is fine. I think that's true. I think that they'll be able to integrate, most likely integrate this and having more efficient servers and that can, that can serve the, the demand that they currently have, I think. I think it's, I think will work. The thing that it seems like to me that this opens up is, I think we have like mass AI figured out, which is like, how do you scale these models up so that like a billion people can use ChatGPT? And how do you, how do you make that efficient and, and smart enough to work and all that kind of stuff?

• 25:08Dan Shipper?

  But I think one thing that people don't talk about nearly enough is that the capabilities of models today are in many ways not limited by the intelligence or the, the, the intelligence of the technology. They're limited by like the risk profiles of the companies that are serving them. And if you're a gigantic company, you're, you're OpenAI and you're literally like, you know, you have to go give government briefings before you launch anything, you're going to be pretty like careful about what you, what you put out. And I think the deep seek stuff is interesting because. It means that, and I mean risk in all sorts of different ways. There's lots of different ways to take risk. But it means that small teams can build little models for like

• 25:54Dan Shipper?

  problems that look like toys that, you know, an OpenAI would be like, well, we wouldn't do this. And I think that is the like, that's the big thing. I don't think that that takes away chat GPT, but it does mean that it, we, we have way more AI in different corners of the world than, than we would have otherwise, which I think is net good.

• 26:09Speaker 00

  Yeah. You know what, Dan? One of my favorite examples of this actually comes from the field of rocketry because it's, it's so visceral. So like, you know, Elon Musk, he'll launch a starship. If it blows up, he's like, okay, well, we, we instrumented it. We got telemetry. We'll, we'll, we'll make it better next time. NASA is not going to do that. Like NASA is not going to, like, if NASA launches a rocket, they don't sit there and say, easy come, easy go. It blew up. And so like the fact that Elon has a different risk profile and is not attached to whether it's successful with a capital S, it, it changes the calculus of what he can do. And it changes the speed with which he can move. And so like, I like to say that in many cases, the big company

• 26:55Speaker 00

  is not quote unquote dumb compared to the small company. It's to your point, they have a different risk profile. And, and they can't, they're, they're just certain things they can't do. Like when I was working with the guys at Justin TV, which became Twitch. If they launch something that's insecure, so what? Nobody knew who they were.

• 27:12Dan Shipper?

  Yeah.

• 27:12Speaker 00

  But Google can't do that, right? And, and Microsoft can't do that. And, you know, the big companies can't do that. Hollywood guys can't do that. Netflix can't do that. So, so, you know, not having to be burdened by what could go wrong is a big factor in trying things that could go right.

• 27:28Dan Shipper?

  That makes total sense. I want to go back to something that you were talking about earlier, talking about this company Applied Intuition, which, which you said sells into large car manufacturers. And I assume like, when a large car manufacturer buys them, like it goes into a Ford vehicle and, and a customer is like, maybe using it and maybe has no idea what it is, but like it, they're using it. Is that, that's sort of how it works?

• 27:49Speaker 00

  I, I think so. It, it's, it is less of an end user type of thing, although that might change. I need to be careful what I'd say, but, uh, but like, but the primary customer, right, is the car company that says, oh my God, the architecture of cars has changed. What do I do?

• 28:04Dan Shipper?

  Yeah. So the strategy question I want to ask you is like how you think about OEM relationships like that because I think that's going to be a common thing for a lot of AI companies, especially if you're working on more foundational model type things is you're going to be integrated into something else that has a consumer layer. And that's where OpenAI started and then they were like, actually, we want to own the UX layer because that's how everything took off is they they figured out a form factor that worked and then they have a data flywheel, there's all this stuff, right? And my last company was was an OEM and that is a difficult position when you're serving two customers, you know, there's like an end user and then there's a customer you need to sell to. It's hard to generate a lot of power or strategic advantage in that situation and it's hard to make a great product.

• 28:48Dan Shipper?

  And I'm curious how you think about OEM type strategies and when they work versus when they don't.

• 28:53Speaker 00

  Yeah, it's tricky. And, you know, what are some examples of where it's worked? I'd say Applied is working really well.

• 29:01Dan Shipper?

  Intel?

• 29:02Speaker 00

  It's been great. Intel was was a good one for the PCs. Another another good example would be Qualcomm back in the day, you know, with licensing their spread spectrum technologies and chips. And so so it can work. Broadcom would be another.

• 29:20Dan Shipper?

  Twilio, I guess?

• 29:21Speaker 00

  Twilio is an interesting one. I like that. And in fact, I like thinking of Twilio as a design win business more than a dev tool. I like that framing a lot, actually. And so the term I like to use to describe it is a design win model where, you know, you want to become viewed by the customer as integral to their product strategy. And so if they have like a slide that shows all these blocks and triangles and arrows and stuff, you need to be a big square in that slide, right? What you what you provide. Sometimes, like Twilio, you solve a problem that they really have, but that they just have no interest in solving on their own. So like if you're Uber, do you really want to have an entire team building a messaging,

• 30:06Speaker 00

  update, texting platform that's a substitute for Twilio? Probably none of your best developers want to do that inside of Uber, right? And so you're like, hey, you know, I'll just pay. I'll pay Twilio every time the earth turns a click or I send a message, I'll send them tiny fractions of a penny. That's, that's okay. So that can work. The other, the other way I think it can work is if you solve something existential for the customer. So like in the case of the car companies.

• 30:33Dan Shipper?

  The end customer or the customer, the, the customer you're selling to?

• 30:36Speaker 00

  For, for the OEM, actually. So like, like the, the problem that the car companies have is that the Tesla is just a fundamentally different architecture than ICE vehicles, right? And it's not just it's got a battery and they don't. It's, it has to do with how many, what, what their operating system is like and how many chips they have and how, how messages flow throughout their messaging bus and like Tesla is designed the way a car would be designed by Silicon Valley type of thinkers. Whereas the, you know, the ICE vehicles of today are mostly, you know, an amalgam of a bajillion part suppliers that they've done business with for a very long time. And it's kind

• 31:18Speaker 00

  of like whatever Bosch has this year is going to be the new windshield wiper sensor thingy that I put in the Mercedes, right? And that's, that's how they've, that's how they've operated. So they look at it and they're just like, look, you know, it's just a completely different paradigm of how you'd build a car. And so you need, you need somebody that can be your thought partner in how to build those things. And so that, that can be another kind of design when model that works.

• 31:45Dan Shipper?

  That's interesting.

• 31:46Speaker 00

  Yeah.

• 31:47Dan Shipper?

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• 32:01Dan Shipper?

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• 32:47Dan Shipper?

  So ditch the app overload and the subscription fatigue and use Teams to experience effortless collaboration today. Are you ready to streamline your workflow? Head to aka.ms/every to use Teams for free. Your productivity will thank you, and so will your wallet. And now, back to the episode. First of all, the thing that makes me think of is like, there's this like knife's edge, which is interesting of this strategy, which is you have to be critical to their business, but somehow they don't want to do it themselves, which is like, there's very few things that are like that.

• 33:20Speaker 00

  That's right.

• 33:21Dan Shipper?

  And that's really hard. Either you're critical and they're like, maybe we'll work with you, but then we'll buy you, or we'll just replace you, or you're not critical, and then it's horrible to like, try to sell that product. No one wants to do that.

• 33:33Speaker 00

  That's, and I love that framing of it. I haven't quite internalized it that way, but you're right. It's like, they either don't want to do it themselves because they just don't want to, or they don't want to do it themselves because they can't conceive of how they would.

• 33:47Dan Shipper?

  Yeah.

• 33:48Speaker 00

  And, you know, they're just like, even if I want to, it's kind of academic, I can't. But in both cases, it's something that they actively choose not to do themselves. And there's a persistent reason for that to continue.

• 34:00Dan Shipper?

  Yeah. And I guess the reason, you know, like, an applied intuition would work is, I'm just, I'm thinking back, you mentioned Clay Christensen, I'm sort of like, thinking about his conservation of attractive profits, where in the early days of new technologies, you want one company to integrate all the different steps of the, of the value chain, basically, because you can iterate much quicker. So like Tesla, they don't have this huge web of different suppliers, they probably have a few, but like, a lot of it, they're just doing themselves. Whereas it sounds like, you know, like GM or whatever, has like, thousands of different... Modular manufacturers that they swap in and out because like the architecture of the car has been around for so long that it's not changing. And so it doesn't have to be integrated.

• 34:45Dan Shipper?

  It can just be like, it can be very modular, which I guess is a easier OEM sell like, because Applied can just, as long as they know that architecture, they can sell into it versus like a more vertical, more, more integrated company.

• 34:59Speaker 00

  Yeah, well, and here's how I internalize that, Dan. So just to make sure that we're on the same page with the same language, like what, what I understood from Clay, I'm, I've kind of got a little bit of a crush, an intellectual crush on Clay Christensen. I think the guy was amazing and a great human being. So, so what I understood him to say is that in early markets, the products are never quite good enough. They don't perform well enough. And so what happens is vendors get rewarded for having the integrated system because the customers will pay incremental dollars for incrementally better performance because they value that enhanced performance, you know? And so, but then what eventually happens is the performance gets mostly good enough.

• 35:44Speaker 00

  And, and you know, what Clay Christensen would call it is overshot customers, you know? Now I'm trying to cram new features into my product to get customers to keep buying new things that I sell them, but now they don't want the new things as bad. And therefore you, you get this modularity argument that somebody else shows up and says, look, you're being overcharged. You don't have to have one guy be the system integrator anymore. In fact, you can just have a whole bunch of different components that you can mix and match and swap in and out. And so then the, the conservation of attractive profits, it goes to the, to the modular suppliers rather than the integrated supplier, which I think is happening.

• 36:21Dan Shipper?

  That was a much better summary of conservation of attractive profits than I gave you.

• 36:27Speaker 00

  Well, I don't know, but, but like, that's the brilliance of Tesla. Like, you know, Elon, everybody told him, you should act like a car company acts. You should have modular components and suppliers in the supply chain. Elon understood, no, nobody can make an electric car that's good enough. I have to control all the critical technologies because I have to have the ability to have something that rises to the level of good enough. Nobody's ever had that before. So like that, that's another reason, right? Architecturally, he's just totally different, right? His whole, his whole paradigm of how to build a car is just different from start to finish.

• 37:02Dan Shipper?

  So is that an argument for AI companies like owning the whole stack themselves right now as they're sort of innovating on what the products even look like and customers are willing to pay more for incremental value?

• 37:16Speaker 00

  Yeah, what I like about what Clay used to say was what Clay Christensen really had was a bunch of mental models for innovators. And you know, whenever I think of a mental model, I always like to ask, under what conditions? So under what conditions would I want to be the complete integrated solution? I believe that you want to be the complete integrated solution if the customers are desperate for more performance and will pay for that enhanced performance. Right? So like before Nvidia, there were silicon graphics. And like if you wanted to make dinosaurs in Jurassic Park, you had to buy the most expensive SGI machines, millions of dollars worth. And you know, if you could make the graphics run twice as fast, Industrial Light Magic

• 37:59Speaker 00

  would pay twice as much because you know, it was mission critical to render those dinosaurs overnight. But like now that there's chips commoditized, Nvidia has the better model because they say, hey, I'll just sell you off the shelf these GPUs. So I think that the question always becomes, under what conditions are you advantaged by being the integrated solution? And under what conditions are you advantaged by being a modular component of the solution?

• 38:25Dan Shipper?

  That's interesting. And I guess what's your best guess about where we are now in the AI landscape overall? Because I think that there's a lot of, there is this common thing, and I actually felt this too, like when O1 came out where people were like, I feel like my model is pretty much good enough. Like, I don't know what I would use O1 or O3 for, even in the demos, like they, I remember that one of the demos is like, list out the 13 Roman emperors, you know? And it's like, that's not really something that I care that much about generally. And like, I'm not doing, you know, most people are not doing PhD level research. That was my first feeling, but to be honest now, like I just use O1 all the time and I don't really use any other model or now I use O3. So I'm curious where, what you feel about where we are and how much performance improvements in terms of intelligence people are willing to pay for?

• 39:11Speaker 00

  Well, first of all, I'm

• 39:14Speaker 00

  I'm really excited, but like I just, I'm probably in these tools too much now. So I'm probably, I'm probably in these tools three, four, five hours a day. And, um, and, and there's a lot of things that I would benefit from in terms of enhanced performance. And if that's just me, I gotta believe there's a lot of other people like that, too.

• 39:38Speaker 00

  So I, the, the thing that I think is so interesting about AI is it really rewards the system thinker. And so, um, so I'll give you an example. Uh, you know, I have this database of what I call a hundred bagger startups. And I, I try to understand them all. I've got like, I've got the original pitch deck for Airbed and breakfast for it was Airbnb. And I've got it for Dropbox and Pinterest and all these companies, right? And I, I track, you know, if you'd bought a share in the seed round, what would have happened? I run the inflection theory against it. I run insights. I'd try to understand if our frameworks would have caused us to decide. Well, now that I have that list, I

• 40:20Speaker 00

  could do all kinds of things. Like I can say, okay, um, please consider this list of hundred bagger startups. Which of Hamilton Helmer's seven powers were harnessed by each of them as their primary power? Which Clay Christensen jobs to be done, uh, was the primary job that they did to get product market fit? How long did it take them to get 10 million in revenue? How long to take them to get a hundred million revenue? Which of them had the first time founder or CEO? Which of them replaced their CEO? You know, I mean, if you're curious, it's like, it's like having an unlimited supply of smart people to go do that research for you. It's incredible.

• 40:59Dan Shipper?

  I, I feel the same way. I can read and think about so many more things than I would have been able to previously. And it's, it makes it such a pleasure to get up every day. It's the best.

• 41:07Speaker 00

  It's unbelievable. It's just, it's a miracle, right? I, like, I just wish I was in my early twenties again. I'd be, I'd be, I'd be dangerous.

• 41:14Dan Shipper?

  Me too. Um, uh, well, I guess that, that just makes me think like, why hundred bagger startups? Why not hundred bagger founders? Right? Like, how much is really in the Airbnb deck that's actually that useful?

• 41:27Speaker 00

  Yeah, so I've been, I've been working on that question a lot. And so I've been applying our frameworks and backtesting them to prior startups. So I have these things that I call atomic eggs, and we'll probably launch them here pretty soon. But what an atomic egg lets you do is it lets you upload a pitch, and then it runs a whole bunch of different generative models against it. So an example would be pattern breakers insight stress test. So you could upload the Airbnb pitch deck, and it would spit out, this was the fundamental error insight with Airbnb, or this is like the part that was non consensus, or these

• 42:08Speaker 00

  are the inflections that Airbnb is harnessing. And the AI has gotten really good at that. And then the other thing that it can do, I like the Sequoia Arc framework, they talk about, is this, is this idea a hair on fire problem type? Is it a known problem type? Or is it a future vision problem type? You can, you can run that against 100 bagger startups, and, you know, and then I could say a scale of one to 10, how confident are you that that's the right way to classify it? And then back to your point about founders, you can start to say, okay, there's all these founders, what jumps out at you as anomalies about these founders? What, what jumps out at you as commonalities about these founders?

• 42:50Speaker 00

  Okay, now let's, let's group these startups in different clusters and run the same experiment again. And then, and then, once you get some patterns, you say, okay, how might those patterns shift in world of AI? How might they be the same in world of AI? Like, you could have just wondered about that as you walk down the street in the past, but like now you can act on that, right? You can act on that curiosity in real time. And that's just like, just such a game changer, right? If you're curious about this stuff.

• 43:17Dan Shipper?

  How much does it like, because I mean, you write a lot about pattern breakers, right? So like,

• 43:24Dan Shipper?

  I guess I'm thinking about business theories or strategy theories as pattern, patterns, right? There are always patterns that work under certain conditions. Sometimes they're like more general than others, but like they're usually not like infinitely general. I don't know what the right word is perfectly general. And like, I wonder, you know, for example, if you took the, let's say, let's say we like wound back the clock, we went back to like the 80s and we used all of the like frameworks they had in the 80s and put them into AI and like gave, gave them, you know, Cisco or whatever, pick whatever company

• 44:02Dan Shipper?

  you want, Google, like, would it have been able to tell the Google or the Airbnb pitch deck that it was a good company?

• 44:13Speaker 00

  I don't know that it could have predicted that it was going to have the success it had.

• 44:18Dan Shipper?

  Yeah.

• 44:18Speaker 00

  And I apply a slightly less stringent standard. What I really want to know is, should I spend time on this? Right? And so I needed, what I, what I need to know when I look at a pitch like Airbnb is, is there something that's wacky and good about this that I might overlook if I'm busy and tired that day? But like, if I can run a whole bunch of different tests against it. So like, you know, you talked earlier about these models, like Charlie Munger is somebody else who I've always respected. And, you know, he had this saying, the map is not the territory. And what he meant by that is that, you know, if you and I want to go from San Francisco to Cupertino and we use a flat map and we, let's say we use Google Maps or whatever, the odds that we will get there if

• 45:03Speaker 00

  we follow the directions are basically 100%, like 99.9%. In fact, I would argue that that map is a better representation of reality. Then all the complexities of all reality, you know, it's like you're trying to, you're trying to compress knowledge for the decision that matters, right? But like, if you want, if you and I want to go to Germany, the, the map is not going to be an accurate portrayal of the territory because straight line is not the shortest path on, on a flat map that represents a globe, right? It would look like a curved line. And so like what, what you learn is that it's like we talked about earlier, the question is, under what conditions is this

• 45:44Speaker 00

  model useful and under what conditions are the boundary conditions exceeded? And that's why you want to have a whole bunch of them, right? You want to, you want to have the right tool for the right situation. And then when it exceeds the scope of the boundaries, you want to not use that tool because you'll get bad, bad decision making.

• 46:00Dan Shipper?

  Are there any new things? Cause like, one of the things you talk about in your book a lot that I, I like, I like, cause this is sort of how I work. So it's maybe confirmation bias, but I like a lot is sort of the idea of living in the future, right? Like the best way to know what's coming is to just be like you're doing in these tools all day, every day. And you start to kind of like see things that you're, other people maybe won't see because they're just, they're living in a different reality. And, and your reality is going to sort of spread everywhere else eventually is the idea. I'm curious if there's anything like that, that you're feeling and seeing right now that you're kind of like sensitive to that is new and interesting to you.

• 46:41Speaker 00

  Yeah. You know, some of these AI companies you'll go to, and there'll be somebody who's a couple of years out of college and they'll be using Devon or cursors of these other products and they're, they're kind of creating these agentic oriented entities that go out and get a bunch of stuff for them and bring it back. And, and, and they just almost act like that's normal. So they're, they're, they're almost like programming these virtual employees to go out and do stuff for them. And you'll sit with them and you'll say, well, what motivated you to do that and to think about solving the problem that way? And they look at you funny like, well, how else would you do it?

• 47:19Dan Shipper?

  You want me to Google?

• 47:21Speaker 00

  Yeah. And so, so the thing that I find interesting is, um, you know, and this is like how Zuckerberg was with social networking, right? Like Zuck didn't have to unlearn anything. You know, he, he grew up at a time when the lamp stack was coming out and you could A B test things and the broadband was everywhere. Before Facebook, you know, like in the 90s, you had to have products that were well engineered because they just weren't scalable enough otherwise, right? You had to have experts that would architect and instrument the system so that it would be somewhat performant. Well, by the time Facebook comes around, Zuck's like, hey, well, we just try it and see what

• 48:00Speaker 00

  happens by the afternoon and decide whether we want to keep with this or not. Now, did Zuck say, aha, there's a disruptive trend and I'm going to get a leapfrog all these companies? No, like Zuckerberg didn't know anything about business at the time. It's almost like, it's like if you and I were raised in a world of Cartesian coordinates and now it's a world of polar coordinates and somebody's born in a world of polar coordinates and they don't even have to translate between the two. They're like, what else is there? That's the only thing there is. I think that some of these AI natives are like that. And so I really want to spend time with them. I want to spend time with anybody who says, my entire lived experience in business is a world where you're programming some form of AI assistance as a core function of the job.

• 48:45Dan Shipper?

  I love that. I mean, I see this all the time. Like, we have a writer who started working with us probably, I would say, two months ago. He's had a very successful career, not doing, not as a professional writer, just like working in AI at various tech companies and startups and has founded his own startups. And, but he's working for us mostly as a writer. And, and he's doing, he writes our Sunday email where we talk about all the new model releases. Like, he's such a nerd for new stuff that comes out, which is amazing. That's like the kind of person you want writing.

• 49:19Dan Shipper?

  And he also, like, when a new model comes out, like, I'll often get early access. So we'll get on the phone together. He'll write like a first take of like all the things that we saw and then I'll go through and like, put my own take on it and whatever. So we sort of, we co-write things together. And the first one that he did like that, I got the draft and I was like, oof. Like, he's, he's smart. He's excited about this stuff, but like, he's not a professional writer. I could tell, right? Like, it wasn't like something that I just punch up and like, I can just publish. It was like, I had to rewrite the whole thing. And what was crazy is, after we did that, I was just like, okay, I want you to take my draft and then your draft, and I want you to put it into O1 and pull out what changed.

• 50:04Dan Shipper?

  And he did that. And we did that a couple of times. And we just covered the launch of Deep Seek together. And the first draft he did, it was like he made a year's worth of progress in a month. Like, I've, I've seen, I've worked with so many writers in my career at this point. And I've seen where people are at, like, when I first started working with him, it takes them like, a thousand drafts to make the amount of progress that he made in a month. It's crazy.

• 50:31Speaker 00

  Yes. Yeah. I, it's so interesting, right? And I'm finding the same thing, Dan. So like, as I started working on these mental models for seed, and these generative models, I started to say to myself, what is a good mental model in the first place? Like, has anybody ever defined what one is? What should it contain? What makes it good versus bad? What, under what conditions is it good or bad? And, and there wasn't a whole lot about it. You know, there, there's a couple of books on mental models, but not a whole lot. And so, so I said, you know, before I start just saying, here's a mental model, jobs be done, I should create a foundational document that's the taxonomy of a good mental model, and the questions

• 51:13Speaker 00

  it should answer, and the flow that it should take. So I did that. Now, I can just say, I'm gonna, I'm just gonna write about jobs we done for what it is. And then I run it against this framework. And it says, you're missing A, B, and C. And I'm like, hey, well, can you elaborate on that? And it just, it just adds it, you know, and, and, you know, within 30 minutes, you have something that's just off the hook, right? It's just so good.

• 51:39Dan Shipper?

  That's great.

• 51:39Speaker 00

  And it's like, you just look at that, and you're just like, it just feels like magic. It feels like you put on some cape and just learned how to fly all of a sudden. You know, and I'm just like, but like, it goes back to reward system level thinking, right? You had to, you had to zoom out and say, wait, you know, if I'm gonna someday have 100 mental models, I ought to define a canonical baseline good one.

• 52:06Speaker 00

  And I ought to have a theory about what makes it good and I ought to apply that theory to everyone that I do because I'm going to get leverage if I do that. But now I'm going to make the AI do the work for me. And it teaches you stuff, right? Like now you say, oh, I thought I knew jobs we done was a mental model, but there are boundary conditions I hadn't thought about before that are kind of interesting. And so, yeah, it's, you know, it's just such a great time to be alive with this stuff.

• 52:30Dan Shipper?

  I agree. I want to go back to the question, the original question I asked you because I it's still on my mind, which is, software is getting so much cheaper to make. The VC model, even the seed model, which you pioneered, is predicated on a different world where it was expensive to make software at first, and then it was free to distribute. And I'm curious how you think that that might change the VC model, if at all. And I'll preface this by saying this is a selfish question, because I run every, we've got, I can't even, it's like, I don't even really have words for the kind of company we are. We have a newsletter with 100,000 subscribers, and then we have three different software products, and we're

• 53:16Dan Shipper?

  10 people. It's like a whole different, like, thing.

• 53:19Speaker 00

  Yeah. And I use that sparkle thing, by the way. It's cool.

• 53:23Dan Shipper?

  You do? Oh, I love that. That's great. Love to hear that. And, and like, I feel like I want a different funding model. And I'm, I'm working through different options, but I'm kind of curious how you think that that might change.

• 53:38Speaker 00

  Yeah, so I've been thinking about it a lot. So there's two different angles, and there's the, there's the angle that you're describing, and another person that I respect who thinks a lot about this is a guy named Greg Eisenberg, right, on, on Twitter. And so like, let me see if I can capture what I think it is. It's that. You have a situation where what it takes to build a product has collapsed yet again, just like it did with a Lampstack. And it's profound in a lot of ways. It's not just that it costs you less money to build a product, but like, you know, you had, you had the Chat PRD on a few episodes ago.

• 54:18Speaker 00

  Chat PRD lets one person have the entire idea premise of the product in their own mind and doesn't require them to therefore have a giant team of other people. You know, so it changes the dynamics of who can build software and what it takes to build it. And so you start to say, okay, well, are you going to have these tiny little companies that generate a ton of revenue and they don't even have to generate that much to be wildly efficient and profitable? Why would you need VC money at all? And I'm pretty sympathetic to that point of view, although I tend to go to the founders and say, look, I'm not under pressure to put a lot of money into you. I don't, you know, our funds are small and all things being equal, I'd rather have it

• 55:01Speaker 00

  be one and done and we try a few things. Here's the other thing, though, that I think is really interesting that I'm trying to find kindred spirits around. The Lampstack didn't just collapse the costs of startups. It created a new way of building. It created a new model of building, right? So you used to have waterfall development and you had to define everything that's in the release upfront and then you, you go on a death march for a year and you ship it and it either succeeds or it bombs. And that was just how products were. And then, then the Lampstack comes out and you have lean startups and agile. And what I'm seeing happen now, and I'm not sure what to call it.

• 55:44Speaker 00

  So right now I'm calling it Darwinian engineering or digital Darwinism. So like, if you think about it, like in an ecosystem, you don't have the individual elements and players in the ecosystem be programmed in a literal way.

• 56:04Speaker 00

  What you have is a system designer, if you will, and then the system gets to operate autonomously from the designer. And so I sit there and I think, man, you know, that kind of rhymes for me. So like, I think about it like, it's like natural evolution rather than traditional development. And that you're going to have AI tools that shift from agile to continuous adaptation. And you're gonna, you're gonna build software elements and components that are adaptive by design and that can sense and respond to the, to the inputs that they get in the real world, independent of the product. So rather than have a business model canvas, you have a business model dashboard

• 56:48Speaker 00

  that's live status of what's happening. And so, you know, if you're a gaming company, you're gonna, you're gonna shift from iterating games to creating living worlds, you know, and, you know, that kind of stuff. So I, I'm really interested in, like, what does that mean for what a product manager is? What does that mean for dashboards of the future? What does it mean for our QA happens? You know, all that stuff.

• 57:14Dan Shipper?

  I've thought about this too a lot because I think, I think we actually met originally because you read my article on the allocation economy.

• 57:20Speaker 00

  Yes.

• 57:21Dan Shipper?

  And so I sort of started to think a lot about like, what is the role of someone who's working in the allocation economy? And how is that different from someone in an analogy economy? And a way that I've been thinking about it is in, in a, in an analogy economy or just any previous economy,

• 57:38Dan Shipper?

  the work you're doing, especially as an IC, a little bit more, still, still a little bit as a middle manager or an executive, but like a lot of this is as an IC is you're, you're kind of like a sculptor. Like everything that happens, happens because you did it with your hands. You have your hands on every little piece of it.

• 57:58Dan Shipper?

  And I think working with AI models is a lot more like being a gardener. You're like setting the conditions for the thing to grow. And then it just sort of grows and the conditions are like hyper parameters. It's like the sun and the soil and the water and whatever. And that's going to change what comes out and, and you know, like OpenAI like doesn't. When it, when chat GPT responds to a prompt, like no one at OpenAI like decided that it was going to say that.

• 58:26Dan Shipper?

  They, which is totally different from Facebook or whatever, like someone decided what, what you were going to see in your, on Facebook. Or maybe if it's maybe a little bit, they have, they have AI too. But like, let's just say the New York Times, someone decided what's on the homepage. And, and it's, and it's totally different. And, and you're right, like you're, you can tune stuff, but it's like, it's much squishier because you're kind of tuning the like environmental conditions rather than the specific thing that happens. And yeah, I think that's such, it's such a different way of working. It's such a different way of building products.

• 59:08Dan Shipper?

  I don't think like, if I think about what we're building at every, like, I don't think we're quite there yet. What I see is like, I mean, obviously like building an organization, you are kind of like doing that. But like for individuals who are building products, like one of the things I see is like, it's so easy to build a feature. You can just build it in an hour. So it's like, sometimes you just build a lot of features and you're like, oh, there's, it's kind of, now the product's kind of noisy. It's kind of messy, you know? And, and also it's like, the hard thing is figuring out what to build, not actually building it, which is, which is a different thing. But we're not yet in a world where like, it's just, it's, it's fully adaptive. But I do think you're right. We're kind of like, you can see that with.

• 59:50Dan Shipper?

  You know, like chat GPT canvas or artifacts or whatever, where it's starting to like build its own UI and stuff. And I think that's where we're going.

• 59:57Speaker 00

  Yeah. And it's, it is just interesting, right? Like, cause it, and it kind of goes back to systems level thinking. It's, it's one thing to think of yourself as building components or building tools or building the end thing. It's another thing to say, I'm building an ecosystem. And the, the elements of the ecosystem operate under certain first principles. And, uh, but there, there's a lot of emergent properties that are going to occur in that ecosystem that are a, a function of the dynamism of the system and how it interacts with people. I think that that's just a fundamentally different worldview about how you architect products. And so I think that that's another, you know, there's the, what we said earlier, very low cost, uh, low end

• 1:00:40Speaker 00

  disruptive innovation ideas. But I think there's also this, hey, the way software ought to be built in the first place ideas, uh, is interesting as well.

• 1:00:50Dan Shipper?

  Yeah. It reminds me of like Notion, for example, you know, it's like Notion, um, it has a block system. It has these atomic elements that you can build anything with rather than like, they built a specific feature to do a specific job, which is, it's a different way of thinking about products. It's like making a language versus like making a hammer.

• 1:01:09Speaker 00

  That's right. That's right.

• 1:01:10Dan Shipper?

  Yeah.

• 1:01:11Speaker 00

  Yeah. And so I think that that's going to be really interesting. And I think that it, but it's like, you know, we used my example earlier. If, if I want to have mental models for investing, rather than just jumping straight to it, what I need to do is I need to like zoom out a little bit and say, okay, let me think about this in a systems level way. What makes a good mental model in the first place? Like what, how do I, how to make sure that I have a foundation built on something really powerful so that every subsequent piece of activity or thinking that I do is a multiplier effect on what's come before.

• 1:01:46Dan Shipper?

  Totally. Well, Mike, this is a pleasure. Um, I, I feel like I learned a lot.

• 1:01:54Speaker 00

  Me too.

• 1:01:54Dan Shipper?

  I'm really glad we got, got the chance to hang out. Thanks for coming on the show.

• 1:01:58Speaker 00

  Yeah. Thanks, Dan. It's great to see you.

• 1:02:07Speaker 02

  Oh my gosh, folks. You absolutely, positively have to smash that like 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 ChatGPT. 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.