Transcript: 'How AI Startups Can Win With Better Strategy'

The transcript of AI & I with Mike Maples is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction: 00:02:20
Innovate the business model, not just the product: 00:06:02
How startups can compete against the likes of OpenAI: 00:15:49
Mike’s take on DeepSeek: 00:19:34
Why the future has always belonged to the tinkerers: 00:21:44
How small teams today can make big money: 00:24:03
Find niches that incumbents can’t or don’t want to enter: 00:28:55
The qualities of the truly AI-native: 00:45:50
How AI changes the funding model for software companies: 00:52:28
Knowledge work is moving toward systems-level thinking: 00:57:05

Transcript

Dan Shipper (00:02:20)

Mike, welcome to the show.

Mike Maples (00:02:21)

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

Dan Shipper (00:02:23)

Yeah. So for people who don't know, you are a legendary investor at Floodgate, which was one of the first seed firms. You were an early investor in Twitch, Lyft, Okta, and a bunch more. And you're also the author of the book Pattern Breakers, which is an excellent book that I've read.

We've also reviewed it on Every, which I guess I would summarize by: It's sort of a guidebook about how there's no guidebook to building companies. So, it's a little bit Taoist, a little bit Zen, which I love. I think that's so good and so important. But I think you have a lot of emphasis on founders winning by being extraordinarily different, and breaking the established patterns of how you're supposed to run a company. I loved it. And I'm excited to chat with you about that and everything going on in AI on the show.

So one of the things that I'm personally curious about is that you started investing when seed wasn't really a thing and helped to invent this new way of capitalizing companies for an earlier era of pre-AI startups. And I think that that is an example of the kind of thing that you talk about in your book Pattern Breakers, which is taking a look at the landscape of what companies need and how companies are funded and being like, 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 curious, my feeling right now is that AI is sort of radically changing the economics of starting a business. Software is orders of magnitude cheaper to make today than it was 10 years ago. And I'm curious, using that same sense of, okay, I'm looking at the environment and looking at how things change. And I’m 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 stuff?

Mike Maples (00:04:40)

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 business is never a fair fight and the startup has to have some unfair advantage to win. And the way they do that is they harness inflections. Inflections allow the startup to wage asymmetric warfare in 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 in the personal computer. And 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 that then you had, in the nineties, the era of mass connectivity, which I think was extended with the iPhone and in mass connectivity rather than processing power becoming free, communications bandwidth starts to become free and you start to not just have computers everywhere, but you have everybody in the world and every device in the world connected in these networks and new business models came out of that subscription and SaaS and advertising. It's interesting. There wasn't any software in 1990 that really mattered. All those companies got consumed by Microsoft because they could put it in the OS or outcompete them.

So why do I think the AI sea change matters? What I see happening with the sea changes is that some business models become relatively more attractive. And some business models become relatively less attractive. And there's only nine business models that I know of in human history. And so the most recent business model I know of is 250 years old. It's the subscription model. And so 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 position to the incumbents. The incumbents have the advantage that discussion is wrongheaded. Of course, the incumbent has the advantage if you play by the rules of the incumbency, but 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 inside in my product, but some type of an insight in my business model go-to-market strategy. It disorients incumbents and where they have a disincentive to retaliate or to copy your strategy. So those are mostly what I'm looking at these days from an AI point of view.

Dan Shipper (00:06:02)

So, I think one of the things that I see a lot of 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—funding startups. But business models for startups, just to start there for a second. One of the things I'm seeing a lot of is paying per outcome as opposed to paying per month, which I think is a really interesting one. Is that something you have your eye on?

Mike Maples (00:07:31)

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

Dan Shipper (00:07:36)

What are the tailored services of long-term contracts?

Mike Maples (00:07:43)

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

Dan Shipper (00:08:05)

Like a law firm or something?

Mike Maples (00:08:06)

A lot like a law firm. Perfect example. So an example like a law firm: A legal services AI 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, 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 be discovered for the court case. And so the way that happens in reality is they hire these outsourcer firms of people to go pore through these documents and they charge them on a cost-plus basis. And so what Text IQ said is 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 (00:09:15)

Well, that's interesting because there's also a cost per task done. So it's the cost per document processed or whatever, which is 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 capturing part of the value that they generate. So if they increase your— 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?

Mike Maples (00:10:59)

Yeah and so, I do like the outcome-based pricing models a lot. They both have their virtues, right? The thing about OpenAI is you could use DALL-E 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?

Dan Shipper (00:11:19)

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? And so it’s tricky.

Mike Maples (00:11:30)

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 if you go to the carnival, how there's that thing where you have this big mallet and you hit this thing and it hopefully goes all the way up and rings the bell. But if it doesn't go all the way up, it makes no sound. It has to go all the way up and ring the bell. It's binary. And so, what I used to say to the folks that I would work with is that the customer doesn't care that your software ran according to how the specification works. That's not what they're buying. They have a job to be done. They're paying you. They're hiring your product to do a job. And so we need to understand what it's 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, they're not going to care that the mechanism of our system works. They're not going to. be interested in that. And so for me, the outcome-based models that we were just talking about a minute ago are asking that, what is the job to be done, in a Clayton 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?

Dan Shipper (00:12:45)

And the thing that makes that interesting over a SaaS model is that the incumbents are all going to be SaaS. And if you're guaranteed to get $20 a seat or whatever it is, the idea of moving to a pay-for-performance model is very unappealing. So, to your counter positioning point, that's a thing that startups can do that incumbents. Some incumbents already do this in the customer service world. This has been a thing for forever, but in general this is not a thing and so incumbents are not going to be able to do this very well.

Mike Maples (00:13:19)

Yeah, I think that this counter positioning thing is a really important thing to maybe double click on and so, a great example is in the nineties, if you were a startup, the words that you dreaded to hear was Microsoft has decided to compete in your market. Because you're just like, okay, I guess I'm out of business because even if they start losing, they're just going to bundle this thing in Windows and I'm just hosed, right? And so that was happening to a lot of companies—Netscape just disappeared, basically, because Microsoft decided to bundle the browser in the operating system and go full ham against Netscape. Well, then the internet happens, and then some people start to discover that you can 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 ads. 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 what is the product and how you deliver the product. And the how can have an inside as well. And quite often the very best, most valuable companies have an inside around business model that's facilitated. Google's business model couldn't work before the internet. The technology wouldn't have provided the empowerment necessary for Google to monetize with ads. But now all of a sudden it did. And so that's what we look for with this counter positioning and to your point, right? Now it itself sells the work, not the software. If I'm a company, if I'm a SaaS vendor, and I charge a subscription by the seat, and that's all I've ever done, think about how embedded that must be in the culture, right? Every product manager thinks that way. The CFO thinks that way. There's nobody—

Dan Shipper (00:15:26)

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.

Mike Maples (00:15:32)

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

Dan Shipper (00:15:49)

Yeah. Well, let's keep talking about counter positioning. And I want to bring up— I think, if I have to pick who Microsoft is in 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. OpenAI is moving from Microsoft mechanics being this API developer tool to a product company. They're releasing all of these consumer-facing products. ChatGPT is sort of taking over there. And so I think a lot of founders are thinking about, well, what if OpenAI includes this as part of ChatGPT or includes this in some new product that they release? And I'm curious how you would think about counter positioning that.

Mike Maples (00:16:43)

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

Dan Shipper (00:17:05)

I love that name, by the way.

Mike Maples (00:17:07)

Yeah, it's pretty good. 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 parts from different tool and die 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 vehicles, 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? If you're gonna have a software-defined car, there's a whole lot of things that you have to know intimately about the processes of how cars are made and manufactured and tested, and the whole supply chain and how the delivery system works. And so to succeed as a company 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 multidisciplinary expertise. So Qasar and Peter, they grew up in Detroit, but before they got in at Google and Waymo, they were in the car industry at GM. And so, like companies, where one way I like to think about it is that everybody disses on these companies that are just an AI wrapper, right? And I'm like, if the thing that you're wrapping on top of involves a process that you really know about that most people don't, That may be a path to a great company. And so I think that that's what I'm interested in.

Dan Shipper (00:19:23)

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.