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

[00:00] 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 court 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 TextIQ said is, well, we've got AI – [00:28] 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 per seat 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. 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. [00:58] thinks that way. There's nobody in the company who knows how to react to your strategy. If you change your business model, everyone's going to lose their mind. OpenAI is moving from being this API developer tool to a product company. They're releasing all these consumer-facing products. 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. I'm curious how you would think about counter-positioning that. I'm involved with a company called Applied Intuition, [01:28] 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

[01:58] 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. [02:19] Mike, welcome to the show. Thanks for having me. I've been looking forward to this. [02:49] Zen. Yeah. 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, um, uh, founders winning by being extraordinarily different, um, and, and breaking the established patterns of like how you're supposed to run a company. Um, I loved it and I'm, I'm excited to chat with you about that and, and, uh, everything going on in AI on the show. [03:10] Yeah, well, cool. Let's get after it. 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.

[03:40] 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 my feeling right now is that AI is sort of like radically changing the economics of starting a business. [04:05] Software is like orders of magnitude cheaper to make today than it was 10 years ago. [04:10] And 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. How do you think that that might change investing and how companies raise money and all that kind of stuff? [04:27] Yeah, I've been wondering about this a lot lately. So as you know, one of the things that I emphasize in startups is the power of harnessing inflections, right? So I like to say that, you know, business is never a fair fight. [04:41] And the startup has to have some unfair advantage way to win. [04:45] 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. [05:04] And when I was a kid, the sea change was mass computation and the personal computer. And, you know, computers used to be really expensive.

[05:12] And 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 the assumptions got inverted. [05:33] And you had a bunch of companies using the software licensing model, you know, Oracle, Microsoft, SAP, companies like that. Then you had in the 90s the era of mass connectivity, which I think was extended with the iPhone. [05:45] 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 the world connected in these networks. And 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. [06:15] because they can put it in the OS or... [06:18] outcompete them. So like, why, why do I think the AIC change matters? Why, [06:23] What I see happen with these sea changes is that some business models become relatively more attractive. [06:30] And some business models become relatively less attractive. [06:34] 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.

[06:42] It's the subscription model. And so, you know, what I like to do is I like to say, okay, if there's nine business models that [06:49] so far in humanity, [06:51] And every time there's a technology sea change, there's a migration of attractive business models from one set to the other. [06:58] How might that migration occur this time? Because 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, but [07:10] Of course the incumbent has the advantage if you play by the rules of the incumbency. [07:14] But what you want to do is you want to say, how does AI make some business models relatively more attractive and less attractive? [07:22] And how could I, as a startup, exploit those new opportunities, not just insight in my product, but a [07:27] some type of an insight in my business model, go-to-market strategy that disorients incumbents and where they have a disincentive to retaliate or to copy your strategy. So that's mostly what I'm looking at these days from an AI point of view. Yeah. So I think one of the things that I see a lot from the business model perspective, and right now we're talking about business models for startups. I would also like to talk about business models for venture, like funding startups. [07:57] Um, but, um, but business models for startups, just to start there for a second. Um, one of the things I'm seeing a lot of is paying per outcome. [08:06] as opposed to paying per month. [08:08] Yes. Which I think is a really interesting one. Is that something you have your eye on? [08:12] 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. What is tailored services of long term contracts? That could be like the defense subprimes. It could be.

[08:30] a contract research organization for a pharma company. You know, it's somebody that offers services, right? [08:37] 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. Like a law firm or something? 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. [09:07] There's a ton of documents that have to get discovered for the court case. [09:12] And so the way that happens in reality is they hire these outsourcer [09:16] firms of people to go pour through these documents and they charge them on a cost plus basis. Yeah. 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. [09:31] Well, now you're not competing for software license desktop revenue or per seat revenue or even a subscription price. You're saying, hey, look, I'm a substitute for that labor spend. 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.

[10:01] per task done. [10:04] It's cost per document processed or whatever, which is sort of like what OpenAI does. When you send them a prompt, they send a response. Even if they send the response and the response isn't good, you still pay for it. [10:16] 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? Yeah. And so I do like the outcome based pricing models a lot. [10:46] The thing about OpenAI is you could use Dolly to generate some art that you don't think looks pretty enough. [10:53] But OpenAI probably deserves to be compensated for the fact that you did that, right? Yeah. It's sometimes hard to know if the job was done well or not. It's like it's not so clear. 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. [11:23] 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,

[11:36] The customer doesn't care that your software ran according to how the specification works. [11:41] That's not what they're buying. They have a job to be done. They're hiring your product to do a job. [11:47] And so we need to understand what's it going to take to ring the bell for doing that job. And 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 – [11:58] they're not going to care that the mechanism of our system works. 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, [12:10] 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? 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. [12:40] 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, um, this is not a thing. And so incumbents are not going to be able to do this very well. Yeah. I think that this counter positioning thing is a really important thing to maybe double click on. And so like, um, [13:00] A great example is in the 90s, if you were a startup, you're going to be a little bit [13:04] The words that you dreaded to hear was Microsoft has decided to compete in your market because you're just like, OK, I guess I'm out of business because.

[13:12] Even if they start losing, they're just going to bundle this thing in Windows and [13:16] And I'm just hosed, right? And so that was happening to a lot of companies. You know, Netscape just disappeared, basically, because Microsoft decides to bundle the browser, you know, in the operating system and go full ham, right, against Netscape. Well, [13:31] Then the Internet happens, and then some people start to discover that you can monetize not by selling by the seat or by the desktop, but by selling ads. [13:41] And that was Google. [13:42] And Microsoft had no answer to that. [13:45] You can't bundle something in your operating system and deal with the fact that Google is pricing by ads. Right. It doesn't it doesn't solve the problem. It doesn't impact their business at all. And so Google was counterpositioned to Microsoft from business model perspective. And like counterpositioning is one of the most powerful ways a startup can have an insight. Most people think an insight is just about product, but it can also be about the what is the product. [14:15] can have an insight as well. And quite often, the very best, most valuable companies have an insight around the business model. [14:21] that's facilitated, you know, Google's business model couldn't work before the Internet. You know, the technology wouldn't have provided the empowerment necessary for Google to monetize with ads. But now all of a sudden it did. [14:33] And so that's what that what we look for with this counter positioning. And to your point, right, like now it's sell sell the work, not the software. [14:41] 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.

[14:53] The CFO thinks that way. There's nobody in the company who knows how to react to your strategy because... The investors think that way. Everybody does. If you change your business model, everyone's going to lose their mind. [15:05] Yeah, so how would you even think about changing it midstream? You just – even if you knew to have the insight that perhaps you should consider it, you just – [15:15] You just wouldn't have the wherewithal to do it because it's so embedded in your culture. Your entire value delivery system is predicated on a different model. [15:24] Yeah. Well, let's keep talking about counterpositioning. 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 counterpositioning, or at least a lot of startups are afraid of, is OpenAI. [15:54] 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, 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. Yeah. So there, there are a couple of ways, there are a couple of things I find really interesting about OpenAI from a counter positioning. So maybe, maybe we start with startups and then just there's some general

[16:24] with DeepSeq and things like that. But, but, um, so like, let's just take an example. Um, I'm involved with a company called Applied Intuition and they create simulation software. I love that name by the way. Yeah, it's pretty good. Yeah. It creates a simulation software for autonomous vehicles and also technology stacks for, um, 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 [16:54] entire business models 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 dye shops and everything else. So so applied intuition says, OK, we've got a bunch of people from Google and Waymo and now some people from Tesla and all the best products. [17:14] autonomous vehicle, all the best EV companies in the world, [17:18] We can build the entire thing that you need, [17:21] to sort of update your strategy and roadmap to have [17:25] the software to find car, which is where the future is going. Now, if you're, if you're GM, you [17:32] 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 defined car. [17:46] there's a whole lot of things that you have to know intimately about, [17:50] the processes of how cars are made and manufactured and tested, the whole supply chain and, you know, how the delivery system works. And so you to succeed as a company like that and to really ask for giant contracts from these companies,

[18:05] You have to have not only AI expertise and products, but you have to have multidiscipline expertise. You know, so like Kasser 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. [18:20] That's cool. Yeah, so I like companies like that where – one way I like to think about it is everybody says – 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 – [18:36] involves a process that you really know about. [18:40] that most people don't, that may be a path to a great company. And so I think that that's what I'm interested in is some of those. The AI wrapper thing was so silly. I see less of that now, which is nice. But it was a very silly thing when it first started. So one other thing about this counter-positioning in open AI that I think is interesting, and I'd love to get your read on, is – [19:05] 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 [19:15] And folks from like AT&T, Time Warner, [19:18] The government said, this Internet thing's a toy. [19:22] It's never going to be good enough. [19:25] We've tried this before. It doesn't work. These protocols are not going to be robust enough. [19:30] And in the short term, you would have been right. Like none of these things looked all that interesting or impressive. But, you know, I was talking to Steve Sanofsky about this the other day.

[19:40] You know, is it Microsoft at the time when the Internet took off and he was at Cornell? He saw see you see me and 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. [19:53] So DeepSeek reminds me of that. So like the culture in AI, the hyperscalers right now, is you can solve all problems by throwing money at it. [20:03] And 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 now. [20:14] where many people adopt that mindset. And that's important because the early days of mass computation [20:22] The IBM PC had a 640K memory limit. And so, like, the Microsoft programmers had an advantage because they could write small fission code. It wasn't how many thousand lines of code anymore. It was how fission is your code. And I think that we might see the same phenomenon here where – [20:39] People come from the bottoms up with very frugal, you know, sort of low cost by design solutions. And it'll be hard for the open AIs and the Anthropics and those guys. I mean, I have huge respect for what they're doing. [20:53] But it'll be hard for them to respond to that because there are [20:56] culturally embedded in their operating model is to solve everything by throwing money at it. You know, hire the best people, throw money at it and just keep going, keep going faster. 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, because they're like, we tried it and it doesn't work. It doesn't scale or

[21:26] where like in the beginning of AI and symbolic AI, like in the fifties, um, neural networks were around then. Uh, but they were mostly ignored because the early AI, AI people, particularly like Marvin [21:39] uh, proved, proved that single layer neural networks were not as powerful as, um, as, you know, other types of like Turing machines basically, or current couldn't do certain types of computations. Um, and, and I think academia sort of, [21:55] And by and large felt like neural networks were not understandable enough. There was no theory. And so it felt like a toy and was basically ignored, except for a few kind of neural network researchers in the 80s and 90s. And then industry adopted it. And [22:13] 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. 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. [22:41] that can fly humans in it. You know, the, the, the New York times ran an ad called, um, [22:46] flying machines that won't fly. [22:49] And and it said that, you know, it's a waste of time to try. And they had a quote from like the head of engineering of of the army and all this stuff. Sixty nine days later, the Wright brothers at Kitty Hawk flew their first plane.

[23:03] And, you know, there are a couple of bicycle mechanics. And so what you see is that time and again, the experts... [23:10] are attached to their mental model of how the world works. And it's the tinkerers. It's the people who have permissionless innovation that, [23:21] 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 matters. [23:29] of Bernoulli's equation and all that stuff got modified and improved because of the success of the Wright brothers with their planes. So people tend to think that the abstract science precedes the engineering, but quite often – [23:43] the engineering and the tinkering causes the science to evolve, to explain the unexplainable. Totally. And that's what I see happen more often in practice. [23:53] A 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. Constraints breed creativity. And I think in general, being able to throw money at a problem means you don't have to spend time thinking about how to make it more efficient. [24:23] thing one. I would bet not. Right. I would too. My, my feeling about that is, uh, 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, uh, the demand that they currently have. I think, uh,

[24:47] I think it's, I think will work. Um, 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 chat GPT? Um, and how do you, how do you make that, [25:04] efficient and smart enough to work and all that kind of stuff. But I think one thing that [25:10] 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 intelligence of the technology. They're limited by the risk profiles of the companies that are serving them. And if you're a gigantic company, you're open AI, and you're literally like, you have to go give government briefings before you launch anything, [25:40] what you put out. And I think the deep seek stuff is interesting because, um, [25:44] Uh, it means that, and I, 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 problems that look like toys that, you know, an open AI would be like, well, we wouldn't do this. Um, 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 we would have otherwise, which I think is net good. Yeah. You know what, Dan, one of my favorite examples of this actually [26:14] mocketry because it's so visceral. So like, you know, Elon Musk, he'll launch a starship, and if it blows up,

[26:21] He's like, okay, well, we instrumented it. We got telemetry. We'll make it better next time. [26:28] NASA's not going to do that. [26:29] Like, NASA's 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... [26:41] and is not attached to whether it's successful or capital S – [26:45] It changes the calculus of what he can do. [26:49] And it changes the speed with which he can move. And so, like, I like to say that in many cases, the big company is not, quote, unquote, dumb compared to the small company. It's to your point, they have a different risk profile. [27:00] And they can't – there are just certain things they can't do. Like when I was working with the guys at Justin TV, which became Twitch – [27:07] If they launch something and it's insecure, so what? Nobody knew who they were. But Google can't do that, right? 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, you know, not having to be burdened by what could go wrong [27:25] is a big factor in trying things that could go right. That makes total sense. I want to go back to something that you were talking about earlier, talking about this company Applied Intuition, which you said sells into large car manufacturers. And I assume when a large car manufacturer buys them, it goes into a Ford vehicle, and a customer is maybe using it and maybe has no idea what it is, but they're using it. Is that sort of how it works? I think so. It's less of an end-user type of thing, although that might change. I need to be careful what I'd say.

[27:55] uh, [27:56] But like the primary customer, right, is the car company that says, oh, my God, the architecture of cars has changed. What do I do? [28:04] 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. [28:21] 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 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 an OEM. And that is a difficult position when you're serving two customers. There's 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. [28:51] strategies and when they work versus when they don't. Yeah, it's tricky. And, you know, what are some examples of where it's worked? I'd say Applied is working really well. Intel has been great. Intel was a good one for the PCs. Another good example would be Qualcomm back in the day, you [29:13] technologies and chips. And so it can work. Broadcom would be another. Twilio, I guess. Twilio is an interesting one. I like that. 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.

[29:36] where 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 provide. Sometimes, like Twilio, you solve a problem that they really have, [29:59] 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 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, I'll just 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 okay. So that can work. [30:29] potential. [30:29] for the customer. So like in the case of the car companies, the end customer or the customer, the customer you're selling to for the OEM actually. So like, like the problem that the car companies have, [30:41] 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 has to do with how many vehicles. [30:53] what their operating system is like and how many chips they have and how messages flow throughout their messaging bus. Tesla is designed the way a car would be designed by Silicon Valley type of thinkers, whereas the ICE vehicles of today are mostly an amalgam of a bajillion parts suppliers that they've done business with for a very long time. And it's kind of like whatever Bosch has this year is going to be –

[31:21] the new windshield wiper sensor thingy that I put in the Mercedes, right? And that's how they've operated. [31:28] So they look at it and they're just like, look, you know, [31:31] It's just a completely different paradigm of how you'd build a car. And so you need somebody that can be your thought partner in how to build those things. [31:40] And so that can be another kind of design when model that works. That's interesting. Yeah. Hey there, Dan here. I wanted to take a one minute break from the episode to tell you about our latest sponsor. All right, let's play a game. What powerhouse productivity tool is also free for individuals? [32:01] Nope, not that one. Try again. [32:07] You may not expect this, but it's Microsoft Teams. [32:10] The same teams that big enterprises swear by [32:14] also has a free plan for individuals. [32:17] Whether you're jamming on a side project or bootstrapping a startup or building a community, [32:21] Teams has all of the features that other platforms nickel and dynum for using. [32:26] You can get unlimited chat, 60 minute video meetings, file sharing and collaborative workspaces all for free. And the real magic is that everything is integrated in one seamless collaborative workspace. That means there's no more hopping between different applications for messages, meetings and file sharing. [32:43] Teams puts it all at your fingertips to save you time and money. So ditch the app overload and the subscription fatigue. [32:50] and use Teams to experience effortless collaboration today.

[32:54] Are you ready to streamline your workflow? Head to aka.ms slash every to use Teams for free. Your productivity will thank you, and so will your wallet. [33:03] And now? [33:04] Back to the episode. First of all, the thing that makes me think of is there's this 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 there's very few things that are like that. That's right. 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... [33:31] try to sell that product. No one wants to do that. That's, and I love that framing of it. I haven't quite, I haven't quite, [33:37] internalize it that way, but you're right. They either don't want to do it themselves because they just don't want to [33:43] Or they don't want to do it themselves because they can't conceive of how they would. [33:48] 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. [33:57] And there's a persistent reason for that to continue. [34:00] Yeah. And, and I guess the reason, um, you know, like an applied intuition would work is I'm just, I'm thinking back to, you mentioned Clay Christensen. I'm sort of like thinking about his, um, conservation of attractive profits. [34:12] where in the early days of new technologies, you want one company to integrate all the different steps of the value chain, basically, because you can iterate much quicker. So Tesla, they don't have this huge web of different suppliers. They probably have a few, but a lot of it, they're just doing themselves. Whereas it sounds like GM or whatever has thousands of different

[34:37] 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. It can just be like it can be very modular, which I guess is an easier OEM cell. [34:51] As long as they know that architecture, they can sell into it versus a more vertical, more integrated company. Yeah. Well, and here's how I internalize that, Dan, just to make sure that we're on the same page with the same language. What I understood from Clay, 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 what I understood him to say is that, [35:20] In early markets, [35:22] the products are never quite good enough. [35:24] 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. [35:37] You know, and so but then what eventually happens is the performance gets mostly good enough. And, you know, what Clay Christians 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. [35:53] that I sell them, but now they don't want the new things as bad. And therefore, you get this modularity. [36:00] argument. Somebody else shows up and says, look, you're being overcharged. [36:04] 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.

[36:12] And so then the conservation of attractive profits, it goes to the modular suppliers rather than the integrated supplier. [36:19] which I think is happening. That was a much better summary of conservation of attractive profits than I gave you. Well, I don't know. But that's the brilliance of Tesla. 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 [36:49] 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. So is that an argument for, um, AI companies, like owning the whole stack themselves right now as they're, uh, uh, as they're sort of innovating on what the products even look like and customers are willing to pay more for incremental value? [37:16] Yeah, what I like about what Clay used to say was that what Clay Christensen really had was a bunch of mental models for – [37:23] 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? Or, [37:34] I believe that you want to be the complete integrated solution if the customers are, [37:39] are desperate for more performance. [37:42] And we'll 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.

[37:55] And, you know, if you could make the graphics run twice as fast, Industrial Light and Magic 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... [38:08] But NVIDIA has the better model because they say, hey, I'll just sell you off the shelf these GPUs. [38:13] 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? [38:24] 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, [38:38] I feel like my model is pretty much good enough. Like, I don't know what I would use 01 or 03 for. Even in the demos, like, I remember one of the demos was like, [38:46] list out the 13 Roman emperors, you know, and it's like, that's not really something that I, 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. Um, so I'm curious where, what you feel about where we are and how much performance improvements in terms of intelligent people are willing to pay for. Um, well, first of all, um, I'm, [39:14] Yeah. [39:14] 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,

[39:26] And there's a lot of things that I would benefit from in terms of enhanced performance. And if that's just me, I've got to believe there's a lot of other people like that, too. [39:36] Um, [39:38] So the thing that I think is so interesting about AI is, [39:43] is it really rewards the system thinker. And so I'll give you an example. You know, I have this database of... [39:52] what I call a hundred bagger startups. [39:54] And I try to understand them all. I've got the original pitch deck for Airbed and Breakfast for Airbnb, and I've got it for Dropbox and Pinterest and all these companies, right? [40:05] And I track, you know, if you'd bought a share in the seed round, what would have happened? [40:11] I run the inflection theory against it. I run insights. I try to understand if our frameworks would cause us to decide. [40:18] Well, now that I have that list, [40:20] I could do all kinds of things. Like I can say, okay, um, I, [40:24] Please consider this list of 100-bagger startups. [40:27] Which of Hamilton Helmer's seven powers were harnessed by each of them as their primary power? Which Clay Christensen jobs to be done? [40:36] 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 did it take them to get $100 million in revenue? Which of them had a first-time founder or CEO? Which of them... [40:49] replace their CEO. You know, I mean, if you're curious, you know, [40:53] It's like having an unlimited supply of smart people to go do that research for you. It's incredible.

[40:59] 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 makes it such a pleasure to get up every day. It's the best. It's unbelievable. It's just it's a miracle. Right. Like, I just wish I was in my early 20s again. I'd be I'd be I'd be dangerous. Me, too. 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? [41:27] Yeah, so I've been working on that question a lot. And so I've been applying our frameworks and backtesting them. [41:37] 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. [41:48] And then it runs a whole bunch of different generative models against it. So an example would be Patternbreaker's Insight Stress Test. So you could upload the Airbnb pitch deck. [41:59] And it would spit out, this was the fundamental insight with Airbnb. Or this is like the part that was non-consensus. Or these are the inflections that Airbnb is harnessing. [42:11] And, um, [42:13] 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 idea a hair on fire problem type? Is it a known problem type or is it a future vision problem type? [42:29] You can run that against 100-bagger startups, and then I could say a scale of 1 to 10, how confident are you that that's the right way to classify it? And then back to your point about founders –

[42:40] you can start to say, okay, there's all these founders – [42:43] What jumps out at you as anomalies about these founders? What jumps out at you as commonalities about these founders? Okay, now let's group these startups in different clusters and run the same experiment again. And then once you get some patterns, you say, okay, how might those patterns shift in a world of AI? How might they be the same in a world of AI? [43:02] 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. [43:11] And that's just like... [43:13] Just such a game changer, right, if you're curious about this stuff. [43:17] How much does it like, because I mean, you, you write a lot about pattern breakers, right? So like, yeah. [43:23] Um, [43:24] I guess I'm thinking about business theories or strategy theories as patterns, right? There are always patterns that work under certain conditions. Sometimes they're more general than others, but they're usually not. [43:38] Bye. [43:39] infinitely general. I don't know what the right word is. Perfectly general. [43:45] I wonder, for example, if you took the... Let's say we wound back the clock. We went back to the 80s and we used all of the frameworks they had in the 80s and put them into AI and gave them Cisco or whatever. Pick whatever company you want, Google. Would it have... [44:06] been able to tell the Google or the Airbnb pitch deck that it was a good company.

[44:12] I don't know that it could have predicted that it was going to have the success it had. Yeah. And I... [44:19] apply a slightly less stringent standard. What I really want to know is, should I spend time on this? [44:25] Right. And so I needed what I what I need to know when I look at a pitch like Airbnb is. [44:30] Um, [44:31] 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 – [44:43] These models, like... [44:45] And 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, [44:56] San Francisco to Cupertino and we use a flat map. [44:59] and we, let's say we use Google Maps or whatever, the odds that we will get there if we follow the directions are basically 100%, like 99.9%. In fact, I would argue that that map is a better representation of reality [45:13] than 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 to if you and I want to go to Germany, [45:25] The map is not going to be an accurate portrayal of the territory because straight line is not the shortest path 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. [45:42] The question is, under what conditions is this 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 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 decision making. Are there any new things? Because one of the things you talk about in your book a lot that I like, because 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.

[46:12] 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. [46:41] Yeah, you know, some of these AI companies you'll go to, and there will be somebody who's a couple years out of college. [46:49] And they'll be using Devon or Cursors, these other products, and they're kind of creating these agentic-oriented entities that, [46:57] that go out and get a bunch of stuff for them and bring it back. And they just almost act like that's normal. So 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? You want me to Google? Yeah. And so the thing that I find interesting is – [47:25] You know, and this is like how Zuckerberg was with social networking, right? Like, [47:30] Zuck didn't have to unlearn anything. You know, 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.

[47:44] 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, it's like, hey, well, we just try it and see what happens by the afternoon. [48:02] and decide whether we want to keep with this or not. [48:05] Now, did Zuck say, aha, there's a disruptive trend and I'm going to 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. [48:21] 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 – [48:36] 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. [48:45] 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. [48:53] He's had a very successful career not as a professional writer, just like working in AI at various tech companies and startups and has founded his own startups. But he's working for us mostly as a writer. [49:05] And, uh, and he's doing, he writes our Sunday email, uh, 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. Um, yeah.

[49:19] 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... [49:30] put my own take on it and, and, and whatever. So we sort of, we co-write things together. And the first one that he did it like that, um, I got the draft and I was like, Ooh, um, like he's, he's smart. He's excited about this stuff, but like, he's not a professional writer. I can 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. Um, [49:52] 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 01 and pull out what changed. [50:03] And he did that. And we did that a couple of times. And [50:07] We just covered the launch of DeepSeek together. And the first draft he did... [50:13] 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, 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. [50:31] Yes. Yeah. I, I've, it's so interesting. Right. And I'm finding the same thing, Dan. So like, I, [50:37] As I started working on these mental models for seed and these generative models, I started to say to myself, [50:43] What is a good mental model in the first place? Like, has anybody ever defined what one is?

[50:49] What should it contain? What makes it good versus bad? What, under what conditions is it good or bad? And, and, [50:56] There wasn't a whole lot about it. You know, there's a couple books on mental models, but not a whole lot. So I said, you know, before I start just saying, here's a mental model, jobs will be done, [51:06] I should create a foundational foundation. [51:09] a document that's the taxonomy of a good mental model and the questions it should answer and the flow that it should take. So I did that. [51:16] Now I can just say I'm just going to write about jobs we've done for what it is. [51:23] And then I run it against this framework. [51:25] and it says you're missing A, B, and C, [51:28] And I'm like, hey, well, can you elaborate on that? And it just adds it. [51:32] And within 30 minutes, you have something that's just off the hook, right? It's just so good. That's great. And it's like you just look at that, and you're just like, [51:42] 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 going to someday have 100 mental models. [52:01] I ought to define a canonical baseline good one. [52:05] 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. Now I'm going to make the AI do the work for me. [52:16] And it teaches you stuff, right? Like now you say, oh, I thought I knew Jobs Be Done was a mental model, but there are

[52:22] 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. I agree. I want to go back to the question, the original question I asked you, because it's still on my mind, which is software is getting so much cheaper to make. [52:40] Um, the VC model, even the seed model, which you pioneered, um, is, um, is, uh, [52:46] predicated on a different world where it was expensive to make software at first, and then it was free to distribute. And, 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, 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 [53:11] A newsletter with 100,000 subscribers, and then we have three different software products, and we're... [53:15] 10 people. It's like a whole different, like, [53:18] thing. [53:19] Yeah. And I use that sparkle thing, by the way. It's cool. You do? Oh, I love that. That's great. I love to hear that. And I feel like I want a different funding model. And I'm working through different options, but I'm kind of curious how you think that that might change. [53:49] Berg right on on Twitter and so like let me see if I can capture what I think it is it's that I'm

[53:56] um, [53:57] You have a situation where what it takes to build a product is, [54:01] has collapsed yet again, just like it did with a lamp stack. 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 had the Chat PRD on a few episodes ago. Chat PRD lets one person have the entire idea premise of the product in their own mind, [54:24] and doesn't require them to therefore have a giant team of other people, [54:28] 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 companies? [54:45] 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. [54:58] and all things being equal, I'd rather have it be one and done [55:02] 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. [55:12] The... [55:13] The LAMP stack didn't just collapse the costs of startups. [55:17] it created a new way of building. It created a new model of building, right? So you used to have waterfall development [55:24] And you had to define everything that's in the release up front. And then you go on a death march for a year and you ship it and it either succeeds or it bombs.

[55:33] And that was just how products were. And then the LAMP stack comes out, and you have Lean Startups and Agile. [55:39] And what I'm seeing happen now is, [55:41] And I'm not sure what to call it. [55:44] So right now I'm calling it Darwinian. [55:47] engineering or digital Darwinism. So like, if you think about it, like in a, in an ecosystem, [55:55] you don't have the individual elements and players in the ecosystem be – [56:02] programmed in a literal way. [56:04] What you have is a system designer, if you will. [56:07] 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. [56:17] So like, [56:18] I think about it like, [56:20] It's like natural evolution rather than traditional development. [56:24] and that you're going to have AI tools [56:28] that shift from agile to continuous adaptation. And you're going to build software elements and components that, [56:36] that are adaptive by design [56:39] and that can sense and respond to the inputs that they get in the real world, [56:43] independent of the program. So rather than have a business model canvas, you have a business model dashboard that's live status of what's, [56:50] And so, you know, if you're a gaming company, you're going to shift from iterating games to creating living worlds. [56:58] you know, that kind of stuff. So I'm really interested in [57:03] Like, what does that mean for what a product manager is?

[57:06] What does that mean for dashboards of the future? What does it mean for how QA happens? [57:12] You know, all that stuff. [57:14] I've thought about this too a lot because I think, I think we actually met originally cause you read my article on the allocation economy. Yes. And I sort of, 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 a knowledge economy? And, [57:31] A way that I've been thinking about it is in a knowledge economy or just any previous economy, [57:37] The work you're doing, especially as an IC, a little bit more, still a little bit as a middle manager or an executive, but a lot of this is as an IC is you're kind of like a sculptor. [57:50] Like everything that happens happens because you did it with your hands. [57:54] you have your hands on every little piece of it. [57:57] Yeah. [57:58] And I think working with AI models is a lot more like being a gardener. [58:03] You're like setting the conditions for the thing to grow. [58:07] And then it just sort of grows. And the conditions are like hyperparameters. It's like the sun and the soil and the water and whatever. And that's going to change what comes out. And, you know, like, OpenAI, like, doesn't, [58:19] When ChatGPT responds to a prompt, no one at OpenAI decided that it was going to say that. [58:24] Um, [58:25] Which is totally different from Facebook or whatever. Someone decided what you were going to see on Facebook. If Facebook's maybe a little bit, they have AI too. But let's just say the New York Times. Someone decided what's on the homepage.

[58:41] And it's totally different. And you're right. [58:47] You can tune stuff, but it's like... [58:50] 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. [59:04] working. It's such a different way of building products. Um, 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, um, [59:15] I mean, obviously, like building an organization, you are kind of like doing that. But like for individuals who are building products, like... [59:23] 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? [59:35] And also it's like, the hard thing is figuring out what to build, not actually building it, which is a different thing. But we're not yet in a world where like, it's just, it's fully adaptive. But I do think you're right. We're kind of like, you can see that with, [59:50] you know, like Chachupi 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. Yeah. And it's just interesting, right? Like, because it kind of goes back to, [1:00:01] systems-level thinking, [1:00:03] 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, hey,

[1:00:11] I'm building an ecosystem that, [1:00:14] And the elements of the ecosystem operate under certain first principles, and [1:00:18] And but there's a lot of emergent properties that are going to occur in that ecosystem that are 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. [1:00:34] And so I think that that's another, you know, there's the what we said earlier, very low cost. [1:00:40] low-end disruptive innovation ideas. But I think there's also this, hey, the way software ought to be built in the first place ideas, right? [1:00:48] is interesting as well. Yeah. It reminds me of like notion, for example, you know, it's like notion, um, [1:00:55] 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. [1:01:09] That's right. That's right. Yeah. 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 use my example earlier. If I want to have mental models for investing. Yeah. [1:01:21] Rather than just jumping straight to it, what I need to do is I need to, like, zoom out a little bit and say – [1:01:27] Okay, let me think about this in a systems-level way. [1:01:30] what makes a good mental model in the first place? Like, how do I, how to make sure that I have a foundation built on something really powerful so that every subsequent... [1:01:39] piece of activity or thinking that I do is a multiplier effect on what's come before. Totally. Well, Mike, I,

[1:01:50] This is a pleasure. I feel like I learned a lot. Me too. I'm really glad we got the chance to hang out. Thanks for coming on the show. Yeah, thanks, Dan. It's great to see you. [1:02:00] 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 chat GPT. [1:02:30] on the edge of your seat. [1:02:31] 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. [1:02:39] So do yourself a favor. Hit like, smash subscribe and strap in for the ride of your life. [1:02:44] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.