Inflections as Asymmetric Warfare - Startups should leverage “inflections” to gain an unfair advantage. - Inflections create opportunities for asymmetric warfare against incumbents by offering radically different solutions. Transcript: Mike Maples Jr. 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. 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 mass computation and the personal computer. And, you know, computers used to be really expensive. 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 and you had a bunch of companies using the software licensing model, 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. 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. You know, all those companies got subsumed in Microsoft, because they could put it in the OS or outcompete them. So like, why do I think the AI sea change matters? What I see happen with these C 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. 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 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 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 wrongheaded. Of course, the incumbent has the advantage if you play by the rules of the incumbency. 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 can I as a startup exploit those new opportunities, not just insight in my product, but 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.

TextIQ’s Disruptive Approach - TextIQ offered AI-powered document discovery services, disrupting labor-intensive legal processes. - They competed on cost savings and accuracy, not software licensing. Transcript: Mike Maples Jr. 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. And so what TextIQ 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 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?

Counter-Positioning through Business Model Innovation - Startups should counter-position themselves against incumbents by adopting different business models. - This disorients incumbents, making it difficult for them to retaliate or copy. Transcript: Mike Maples Jr. Yeah, I think that this counter positioning thing is a really important thing to maybe double click on. 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, OK, 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. 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, 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. 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, right? It doesn’t solve the problem. It doesn’t impact their business at all. And so Google was counter-positioned to Microsoft from a business model perspective. And 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 model that’s facilitated. You know, 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. You know, there’s nobody in the company who knows how to react to your strategy because- The investors think that way. Dan Shipper Everybody does. You know, if you change your business model, everyone’s going to lose their mind. Yeah. Mike Maples Jr. 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.

AI’s Impact on Business Models - AI is shifting attractive business models, favoring outcome-based pricing. - This disrupts incumbent SaaS models, creating opportunities for startups. Transcript: Dan Shipper Yeah. So, um, 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. Sure. 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 as opposed to paying per month. Yes. Which I think is a really interesting one. Mike Maples Jr. Is that something you have your eye on? 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. Dan Shipper What is tailored services for long-term contracts? Mike Maples Jr. That could be like the defense subprimes. It could be a contract research organization for a pharma company. It’s somebody 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. Dan Shipper Like a law firm or something? Mike Maples Jr. 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. And so what TextIQ 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 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? Dan Shipper Well, that’s interesting because there’s also like there’s there’s cost per like task done. So 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. But even if they send the response and the response isn’t good, you still pay for it. And then there’s other companies that are sort of capturing part of the value that they generate. So if they increase your… Let 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

2min Snip Transcript: Mike Maples Jr. 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? Dan Shipper Well, that’s interesting because there’s also like there’s there’s cost per like task done. So 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. But even if they send the response and the response isn’t good, you still pay for it. And then there’s other companies that are sort of capturing part of the value that they generate. So if they increase your… Let 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 Mike Maples Jr. Do like the outcome based pricing models a lot. They both have their virtues, right? The thing about OpenAI is 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? Yeah. It’s sometimes hard to know if the job was done well or not. 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

Counter-Positioning: Startup Strategy - Counter-positioning is a powerful strategy for startups to compete against incumbents. - It involves adopting a business model that incumbents cannot easily replicate or counteract. - For example, Google’s ad-based revenue model counter-positioned them against Microsoft’s per-seat software licensing model, giving them an edge in the internet era. - An insight for a startup can be about what the product is or how it’s delivered, often relating to the business model itself. Transcript: Mike Maples Jr. Yeah, I think that this counter positioning thing is a really important thing to maybe double click on. 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, OK, 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. 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, 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. 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, right? It doesn’t solve the problem. It doesn’t impact their business at all. And so Google was counter-positioned to Microsoft from a business model perspective. And 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.

Applied Intuition’s Specialized AI - Applied Intuition provides simulation software for autonomous vehicles, requiring multi-disciplinary expertise. - This deep industry knowledge offers value beyond generic AI solutions. Transcript: Dan Shipper Well, let’s keep talking about counter-positioning. And I want to bring up, I think if I have to pick who the Microsoft is of the AI world, 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 is moving from being this API developer tool to a product company. They’re releasing all of these consumer-facing products. ChatGPT is sort of taking over. And so I think a lot of founders are thinking about, well, what if 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. Mike Maples Jr. 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 DeepSeq and things like that. But so, like, let’s just take an example. 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 simulation software for autonomous vehicles and also technology stacks for electric vehicles. 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 dye shops and everything else. 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. We can build the entire thing that you need to sort of update your strategy and roadmap to have the software-defined car, which is where the future is going. Now, 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 defined 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. 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. So like Kasser and Peter, they grew up in Detroit. Before they got in at Google and Waymo, they were in the car industry at GM. That’s cool. Yeah, so I like companies like that where, 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 company. And so, so I think that that’s, what I’m interested in is some of those. The AI wrapper thing

Wright Brothers vs. The Experts - Experts believed it would take a very long time to create a flying machine capable of carrying humans. - The New York Times even ran an ad titled “Flying Machines That Won’t Fly.” - Only 69 days later, the Wright brothers, a couple of bicycle mechanics, successfully flew their first plane at Kitty Hawk. - This anecdote highlights how tinkerers with permissionless innovation can often outperform experts attached to existing mental models. - Their practical success even forced a reevaluation and improvement of scientific understanding, like Bernoulli’s equation. Transcript: Mike Maples Jr. 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 it said that it, you know, it’s a waste of time to try. And they had a quote from like the head of engineering 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 what you see is that time and again, the experts are attached to their mental model, how the world works. And it’s the tinkerers. 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 planes. So the, 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. And yeah, that’s, that’s what I see happen more often in practice.

Design Win Model for B2B - Think of B2B sales as a “design win” where your product becomes integral to the customer’s overall strategy. - Aim to be a core component of their plans, visualized as a prominent element in their diagrams. - This works if you’re solving a problem they have little interest in addressing themselves or you’re solving an existential problem. Transcript: Mike Maples Jr. 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 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 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 okay. So that can work. The other way I think it can work is if you solve something existential for the customer. So like in the case

DeepSeek’s Constraint-Driven Innovation - DeepSeek’s approach, prioritizing efficiency over scale, mirrors the early internet’s limitations. - This constraint-driven innovation might challenge the resource-intensive approach of larger AI companies. Transcript: Dan Shipper Was so silly. I see less of that now, which is nice. But it was a very silly thing when it first started. Mike Maples Jr. 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 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, 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. None of these things looked all that interesting or impressive. But I was talking to Steve Sanofsky about this the other day. He was at Microsoft at the time when the internet took off. And he was at Cornell, and 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. Deep Seek 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. 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 where many people adopt that mindset. And that’s important because the early days of mass computation, the IBM PC had a 640K memory limit. And so like the Microsoft programmers had an advantage because they could write small fish in code. It wasn’t how many thousand lines of code anymore. It was how efficient is your code. And I think that we might see the same phenomenon here where 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, but it’ll be hard for them to respond to that because they’re 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. Dan 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 because 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, where in the beginning of AI and symbolic AI, in the 50s, neural networks were around then. But they were mostly ignored because the early AI people, particularly Marvin Minsky, 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 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, uh, and it 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.

Wright Brothers Analogy for AI - Mike Maples draws a parallel between AI’s current state and the Wright brothers’ invention of the airplane. - Experts dismissed the possibility of human flight, citing scientific reasons and publishing articles like “Flying Machines That Won’t Fly”. - Just 69 days later, the Wright brothers, a pair of bicycle mechanics, successfully flew their first plane at Kitty Hawk. - This anecdote highlights how tinkerers and permissionless innovators often disrupt established beliefs and drive progress, sometimes even forcing a revision of scientific understanding, as exemplified by subsequent refinements to Bernoulli’s equation. Transcript: Mike Maples Jr. 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 it said that it, you know, it’s a waste of time to try. And they had a quote from like the head of engineering 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 what you see is that time and again, the experts are attached to their mental model, how the world works. And

Wright Brothers vs. Experts - Experts claimed it would take a million years to create a flying machine capable of carrying humans. - The New York Times even ran an ad titled “Flying Machines That Won’t Fly.” - 69 days later, the Wright brothers, two bicycle mechanics, flew their first plane at Kitty Hawk. - This demonstrates how tinkerers with permissionless innovation can surpass experts attached to existing mental models. - Their engineering success even forced the evolution of scientific understanding, like Bernoulli’s equation, to explain the previously unexplainable. Transcript: Mike Maples Jr. 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 it said that it, you know, it’s a waste of time to try. And they had a quote from like the head of engineering 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 what you see is that time and again, the experts are attached to their mental model, how the world works. And it’s the tinkerers. 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?

Tinkerers Drive Innovation - “Tinkerers” with permissionless innovation often drive technological advancements. - Their practical experiments can even lead to revisions in scientific understanding. Transcript: Mike Maples Jr. 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 it said that it, you know, it’s a waste of time to try. And they had a quote from like the head of engineering 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 what you see is that time and again, the experts are attached to their mental model, how the world works. And it’s the tinkerers. 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 planes. So the, 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.

Design Win Model - Think of OEM-like businesses as “design win” models. - Your product needs to be integral to the customer’s product strategy. - You want to be viewed as solving a problem they have but don’t want to address themselves. Transcript: Mike Maples Jr. 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 provide. Sometimes like Twilio, you solve a problem that they really have, but that

Successful OEM Strategies for AI Startups - AI startups can succeed as OEMs by being integral to a customer’s product strategy. - They either solve a problem the customer has no interest in solving or address an existential need. Transcript: Dan Shipper 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 like when a large car manufacturer buys them, like it goes into a Ford vehicle and a customer is like maybe using it and maybe has no idea what it is, but like they’re using it. Is that sort of how it works? Mike Maples Jr. I think so. It is less of an end user type of thing, although that might change. I need to be careful what I’d say. 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? Dan 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 figured out a form factor that worked and then they have a that 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 and i’m curious how you think about oem type strategies and when they Work versus when they don’t yeah it’s it’s tricky um and you know what are some examples of where it’s worked um i’d say applied is working really well um intel has been great intel was, Mike Maples Jr. 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. 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 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 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 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 okay. So that can work. 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, 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 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, what their operating system is like and how many chips they have and 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 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 the new windshield wiper sensor thingy that I put in the Mercedes, right? And 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 somebody that can be your thought partner in how to build those things. And so that can be another kind of design when model that works.

Integrated Systems in Early Markets - In early markets, products often lack sufficient performance. - Vendors with integrated systems are rewarded because customers value incremental performance improvements. - Customers are willing to pay more for better performance in these early stages. Transcript: Mike Maples Jr. Well, and, and here’s how I internalize that, Dan. So, um, just to make sure that we’re on the same page with the same language, like what, what I understood from Clay, I 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 in early markets, the products are never quite good enough.

Tesla’s Integrated Approach - Tesla’s integrated approach was crucial in achieving “good enough” performance in the early EV market. - This contrasts with established car manufacturers’ modular approach, suitable for mature markets. Transcript: Dan Shipper 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 modular manufacturers that they swap in and out because 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 a easier OEM sell, like, cause applied can just, as long as they know that architecture, they can sell into it versus like a more Vertical, um, more, more integrated company. Yeah. Mike Maples Jr. Well, and, and here’s how I internalize that, Dan. So, um, just to make sure that we’re on the same page with the same language, like what, what I understood from Clay, I 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 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. But then what eventually happens is the performance gets mostly good enough. 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 get this modularity argument. 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 conservation of attractive profits, it goes to the modular suppliers rather than the integrated supplier,

Mike Maples Jr.’s 100-Bagger Startup Database - Mike Maples Jr. maintains a database of “100-bagger” startups (startups that returned 100x or more on their seed investment). - This database includes original pitch decks from companies like Airbnb, Dropbox, and Pinterest. - He uses the database to test investment frameworks like the inflection theory and gain insights into successful startups. - He analyzes what would have happened if someone had bought a share in the seed round of each company. Transcript: Mike Maples Jr. And so I’ll give you an example. I have this database of what I call 100 bagger startups. And I try to understand them all. I’ve got the original pitch deck for Airbed and Breakfast for what was Airbnb. And I’ve got for Dropbox and Pinterest all these companies, right? And I track, you know, if you’d bought a share in the seed round, what would have happened?

Mike Maples Jr. on AI and Startup Analysis - Mike Maples Jr. uses AI tools extensively, 3-5 hours daily, for tasks like analyzing his database of “100-bagger” startups (startups that returned 100x the initial investment). - He uses AI to research various aspects of these startups, such as which of Hamilton Helmer’s seven powers they harnessed, or which Clay Christensen jobs-to-be-done they fulfilled. - Maples also analyzes metrics like time to $10M and $100M in revenue, founder experience, and CEO changes. - This allows him to quickly process information and gain insights that would require a large team of human researchers to gather traditionally. - He finds this incredibly empowering and expresses a desire to be in his early twenties again to capitalize on these new possibilities. Transcript: Mike Maples Jr. And so I’ll give you an example. I have this database of what I call 100 bagger startups. And I try to understand them all. I’ve got the original pitch deck for Airbed and Breakfast for what was Airbnb. And I’ve got for Dropbox and Pinterest all these companies, right? And 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 try to understand if our frameworks would cause us to decide. Well, now that I have that list, I could do all kinds of things. Like, I can say, okay, please consider this list of 100 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 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 replaced their CEO? I mean, if you’re curious, it’s like having an unlimited supply of smart people to go do that research for you. It’s incredible. Dan Shipper 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. Mike Maples Jr. It’s just, it’s a miracle, right? Like I just wish I was in my early twenties again. I’d be, I’d be, I’d be dangerous. Me too. Dan Shipper Well, I

Analyzing 100-Bagger Startups with AI - Analyze successful startups (e.g., “100-baggers”) using frameworks like Hamilton Helmer’s Seven Powers or Clay Christensen’s Jobs to Be Done. - Use AI to backtest these frameworks against original pitch decks and other data. - Categorize startups based on criteria such as primary power, job to be done, time to revenue milestones, founder experience, and CEO changes. - Tools like ‘atomic eggs’ can automate this analysis by processing pitch decks and generating insights using various models, including pattern breaker insight stress tests and Sequoia’s Arc framework. Transcript: Dan Shipper Why not a hundred bagger founders, right? Like how much is really in the Airbnb deck that’s actually that useful? Yeah. Mike Maples Jr. So I’ve been, I’ve been working on that question a lot. And so, um, I’ve been, um, 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, um, 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 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 idea a hair on fire problem type? Is it a known problem type or is it a future vision problem type? You can run that against 100

Evaluating Startup Pitches - When evaluating startup pitches, focus on whether the idea is worth your time. - Look for something unique or ‘wacky and good’ that you might miss if you’re busy or tired. - Use various tests and frameworks to analyze the pitch and identify potential overlooked aspects. - Don’t necessarily try to predict the startup’s ultimate success, but rather determine if it warrants further investigation. Transcript: Mike Maples Jr. 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.

Analyzing Startup Pitches with AI - Use AI to analyze startup pitches by running different generative models against them. - An example is the “Pattern Breakers Insight Stress Test,” which can identify fundamental errors, non-consensus aspects, and harnessed inflections. - Another example is using the Sequoia Arc framework to categorize ideas as “hair on fire,” “known problem,” or “future vision” types. - Rate the AI’s confidence in its classifications to improve accuracy over time. - Use AI to analyze successful startups and identify commonalities and anomalies amongst their founders. Transcript: Mike Maples Jr. So I’ve been, I’ve been working on that question a lot. And so, um, I’ve been, um, 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, um, 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 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 idea a hair on fire problem type? Is it a known problem type or is it a future vision problem type? You can run that against 100

AI Natives - “AI natives” seamlessly integrate AI assistance into their workflows. - They represent a shift in how work gets done, similar to Zuckerberg’s intuitive understanding of social networking. Transcript: Dan Shipper 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, um, 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, um, other people maybe won’t see because they’re just, living in a different reality. 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. Mike Maples Jr. Yeah. You know, some of these AI companies you’ll go to, and there’ll be somebody who’s a couple years out of college, and they’ll be using Devon or Cursor or these other products, and 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 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? You want me to Google? Yeah. And so, so that I find interesting is, you know, and this is like how Zuckerberg was with social networking, right? Like, 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, 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 happens by the afternoon and decide whether we want to keep keep with this or not now now did Zuck say aha there’s a disruptive trend and i’m gonna get a leapfrog all these companies no like zuckerberg didn’t know anything about business at the time it’s 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.

AI-Accelerated Learning - Dan Shipper’s writer rapidly improved writing skills using AI feedback loops. - This demonstrates AI’s potential to accelerate learning and skill development. Transcript: Dan 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 he writes our Sunday email where we talk about all the new model releases. He’s such a nerd for new stuff that comes out, which is amazing. That’s the kind of person you want writing. And he also, when a new model comes out, I’ll often get early access. So, uh, we’ll get on the phone together. Uh, 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, 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, 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 Oh one and pull out what changed. And he did that. And we did that a couple of times. And we just covered the launch of DeepSeek together. And the first draft he did, it was like he made a year’s worth of progress in a month. I’ve worked with so many writers in my career at this point. And I’ve seen where people are 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.

Digital Darwinism - Traditional software development resembled waterfall methods, with pre-defined features and long development cycles. - The LAMP stack enabled lean startups and agile development. - A new model is emerging, possibly called “Darwinian engineering” or “digital Darwinism,” where AI tools facilitate a shift from agile to continuous development, mirroring natural evolution. Transcript: Mike Maples Jr. 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. What you have is a system designer, if you will. And then the system gets to operate

Product Building with AI - Building products with AI is like gardening; set the conditions and let them grow. - This differs from traditional product development, where every detail is manually controlled. Transcript: Dan Shipper And yeah, I think that’s such, it’s such a different way of, um, working. Such a different way of building products. 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 It’s so easy to build a feature. You can just build it in an hour. So sometimes you just build a lot of features and you’re like, oh, now the product’s kind of noisy. It’s kind of messy. And also, 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 it’s fully adaptive. But I do think you’re right. We’re kind of like, you can see that with, you know, like Chachamutu 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. Mike Maples Jr. And it’s just interesting, right? Like, because it kind of goes back to systems level thinking. 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 elements of the ecosystem operate under certain first principles. 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.

Sculptor vs Gardener - Working with traditional products is like being a sculptor, where you meticulously craft every detail with your own hands. - Conversely, working with AI models is like being a gardener, where you cultivate the environment and allow the product to grow organically. - This shift requires a different approach to product development, focusing on setting parameters and fostering conditions for growth rather than direct manipulation. - When ChatGPT responds to a prompt, no one at OpenAI decided precisely what it would say, contrasting with traditional media where content is deliberately chosen. - This exemplifies how AI is changing product creation from a direct, hands-on process to a more indirect and environmentally-focused one. Transcript: Dan 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 opening eye, like doesn’t, when it, when Chad Gbt responds to a prompt, like no one at open AI, like decided that it was going to say that, um, they, but which is Totally different from Facebook or whatever. Like someone decided what, what you were going to see in your, on Facebook. Um, or maybe if Facebook’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. Um, and, uh, and it’s, and it’s totally different and, and you’re right. Like, uh, 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, um, working. Such a different way of building products. 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 It’s so easy to build a feature. You can just build it in an hour. So sometimes you just build a lot of features and you’re like, oh, now the product’s kind of noisy. It’s kind of messy. And also, 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