The 7 Most Powerful Moats For AI Startups

In the early days, the only moat that startups have is speed. Once you make something people want, the question becomes what deeper moats can you build on to defend against the competition? Garry, Harj, Diana, and Jared dive into Hamilton Helmer’s Seven Powers framework to find out how these moats show up in practice today in AI startups.

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

• 0:00Garry Tan?

  This idea of moats is so pervasive and so important.

• 0:03Harj Taggar?

  It is interesting how moats have just become much more discussed by aspiring startup founders now than they were pre-AI.

• 0:11Garry Tan?

  What is going to prevent you from being basically subject to infinite competition?

• 0:16Harj Taggar?

  Like a moat is inherently a defensive thing and you have to have something to defend, otherwise like.

• 0:20Diana Hu?

  If you're nothing.

• 0:21Harj Taggar?

  Yeah, you have nothing to defend. Don't worry about your moat.

• 0:30Garry Tan?

  Welcome back to another episode of The Light Cone. Today, we're going to talk about moats. So in your head, you might be thinking about barbarians storming your gate. You've got this little startup and you've got every other company out there who wants to come and eat your lunch. And, you know, right outside your castle is a moat that keeps them away. Jared, when you were going to college campuses, this isn't sort of this trivial thing that people are thinking about. It's actually something that keeps them from starting companies right now.

• 1:01Jared Friedman?

  Yeah, this is a question that we got from a lot of very smart college students on our recent college trips. And basically their question is like, they don't see how these new AI agent companies, like a lot of the ones that we've talked about on this podcast, could have moats. It plays into this meme of like the chat GPT wrapper that like all of these companies could be easily cloned. And so they can see how you could build a business that makes some amount of revenue, but they don't really see how you can build a long enduring business. And so I think it's actually not true. I actually think these businesses do have quite deep and interesting moats, but they're not totally obvious what they would be. So I think this is an interesting topic for us to explore.

• 1:40Garry Tan?

  At our recent AI startup school backstage, I had this exchange with Sam Altman that I thought was kind of funny. You know, we spend a lot of time thinking about, you know, make something people want, very simple maxims that are sort of anti-business school. And yet, this idea of moats is so pervasive and so important. We sort of remarked how funny it is that one of the more important books to read these days is actually Business School Fodder. This book called The Seven. Powers. So today, we thought that we would actually go through those seven powers. What are they? What are some concrete examples? And ways that a startup founder who's just starting out

• 2:21Garry Tan?

  could or should be thinking about these things from real world examples that we've seen.

• 2:26Jared Friedman?

  So Diana, can you tell us a bit about this book?

• 2:29Diana Hu?

  This book was written by Hamilton Helmer, who taught at Stanford Economic School and was published in 2016. And the book title was The Seven Powers, The Foundations of Business Strategies. And a lot of the examples are more with the era of internet companies from the 2000s. So a lot of the examples are like Oracle, Facebook, Netflix, which is an older generation. So we want to do a bit of a reboot right now. How we applies now 2025 with AI.

• 3:01Jared Friedman?

  I think it's a little bit confusing the way he uses the terminology in the book. It's called the seven powers, but it would make a lot more sense if you just called the thing the seven moats. Because that's really what he's talking about. He's really talking about seven categories of moats that a business can have. And I, it's true that the examples are out of date, but I think the framework is actually pretty timeless. Like, it turns out, there's just only so many kinds of moats that a business can have, and they don't really change. And so like, even though the specific like versions of these moats are different in the AI agent world, like the categories haven't changed.

• 3:31Garry Tan?

  Thankfully, we live in a world where there's markets and there's free markets, where there's lots and lots of competition. And these moats, in a lot of ways, are the only way, if you're running a business, you can sort of fight against all of the other people who might want to do exactly what you're doing. And you know, famously, Peter Thiel talks about competition is for losers. And so the profound view there is that, given infinite competition, what is going to prevent you from being basically subject to infinite competition? And then as a result, you know, your margins, how much you can actually profit off of what you're selling, goes down to zero. And what that means is, like, actually, your business will die.

• 4:15Garry Tan?

  And so, you know, having a moat is relatively existential eventually.

• 4:20Jared Friedman?

  You made a great point earlier, Gary, that like, This is actually like, you kind of have to worry about this at the right time of a startup. Do you want to talk about like how like early stage founders should think about moats?

• 4:31Garry Tan?

  I mean, this is sort of why we generally tell people to go find a person with a real problem and then go solve that problem first. It's what's funny about the world that's a little surprising is that you can go almost anywhere and find some pain point, some problem that could be solved with software and especially with AI that frankly just isn't being solved. And if that and they're they're so numerous and so severe that if you find that thing and solve it, you literally can mint a billion dollar or 10 billion or even hundred or hundreds of billions of dollars market cap business. And it's just lying in plain sight. That's really the first thing that people should do.

• 5:15Garry Tan?

  Like you should just find a problem and go solve it. And then along the way, you will probably, as you work with customers, as you build the product itself and engineer it and figure out what data you need for it and all of these things, like you will stumble upon these seven powers.

• 5:32Jared Friedman?

  Yeah, the moats come later. Like it would be like pretty dumb for somebody to decide not to work on a startup idea because they can't see what the long term moats of that idea could be, right?

• 5:42Harj Taggar?

  It is interesting how moats have just become much more discussed by aspiring startup founders now than they were pre AI. Seems like the main reason for that presumably is just that big, the original chat GPT wrapper meme and that the moat that most people are worried about is moat against the big model companies and how like, are you not going to get crushed by one of the big labs when they decide the product you're working on is really valuable and they want to own it too?

• 6:09Diana Hu?

  And I think Varun from Windsurf, who we hosted some time ago, he said it himself. The early stages at the beginning, the only mile that startups have is really just speed.

• 6:24Diana Hu?

  Pass that and build something that people want. Then you figure out and go deeper into these type of modes that we're gonna discuss.

• 6:31Jared Friedman?

  I really like Varun's point that the only mode is speed. That is not one of the seven powers in the book, but I think it probably should be.

• 6:39Diana Hu?

  I think it also comes with a lot of the essays from PD. Because one of the tenants really at the beginning is, yes, you're a big company. Let's say OpenAI at this, OpenAI is the new Google. It's like, sure, OpenAI or Anthropic could build all these features. Let's say like Claude Code and then compete directly, let's say with Cursor or et cetera. And for a startup like Cursor to really win, even in the beginning, is they had relentless execution because a larger company like a Google or Anthropic, they just have a lot of more craft that they need to do in order to ship a product. They just have all these product managers, all these operations.

• 7:19Diana Hu?

  It needs to go through a PRD, some spec doc, and it takes a lot more time to ship a feature as opposed to Cursor. The incredible story about Cursor, when we hosted Michael Truell to come talk to the badge, he was sharing how his product development cycle for shipping features and sprint cycles were one day.

• 7:39Jared Friedman?

  One day, so one day sprint.

• 7:42Diana Hu?

  In the, at the beginning during a 2023, 2024 around era, they would start to, every day would restart the clock and try to ship things every day. I mean, that's like insane speed. There's no big company that could ship something at that speed. At most, weeks, couple of weeks, and maybe like larger companies, I don't know, if you're Google, maybe like multiple months or sometimes years. I mean, they had Google Bard or Gemini a long time ago. That took years to get out, right?

• 8:11Harj Taggar?

  I think Cursor and Windsor are great examples of when you should start thinking about the modes because for the first few years. I don't think it really matters that much. They just had to like, they proved out that hey, like Cogen is going to be a really valuable application of AI. The development environment is going to be very, very important to own. They like got rapid growth. And then it's only when they're at scale that, you know, like they have to start thinking about how are we going to defend against like Claude Code or Codex or all the other things coming in. And sort of like the mental model that's really stuck with me is when we spoke to Bob McGrew a couple of weeks ago and how, I think Jared, you brought this up actually, it was like, one way you could think about it is that sort of all of these startups are kind of forward deployed engineering teams for, for the labs, maybe. And so like early on,

• 8:56Harj Taggar?

  actually, because this is all green field, we don't actually know what the valuable verticals and products to build are. So in a sense, you don't, step one is just figure out what that is. And it wasn't actually even two years ago, it wasn't actually clear it was Cogen or the IDE. Once you figure that out and you find and you sort of struck gold and you keep digging, that's when you have to presumably at some point, you're going to get more competition because people are going to realize, oh, this is really valuable. There's lots of money to be made here. And then you have to start like defending like the treasure you found.

• 9:24Garry Tan?

  So I mean, all the things that we're about to cover, aside from speed, are sort of one to a billion, one to 10 billion, one to 100 billion, one to a trillion dollar sort of problems. And then the real stupid thing that people might do is watch this and look for this as a reason to not even get to one. So that would be probably the

• 9:47Jared Friedman?

  Or they try to use it to like pick between two different startup ideas because they're like trying to forecast five years in the future, which one will have a greater moat?

• 9:54Garry Tan?

  Which just isn't how it works. I mean, literally, you shouldn't do that.

• 9:58Harj Taggar?

  Like a moat is inherently a defensive thing and you have to have something to defend. Otherwise, like.

• 10:03Diana Hu?

  If you're nothing.

• 10:03Harj Taggar?

  Yeah, nothing to defend, don't worry about your moat.

• 10:07Jared Friedman?

  Otherwise, it's just like a puddle in a field.

• 10:09Garry Tan?

  Yeah.

• 10:10Harj Taggar?

  Exactly. Let's assume that someone has found something that's valuable that is worth defending. Should we talk through what some of the moats they could, they could think about are?

• 10:16Jared Friedman?

  Yeah. So process power. Again, like the terminology is kind of funky, but like basically it means you built something that's like, you built a very complicated business with a lot of stuff that's just hard for people to replicate. Just because you like built all this stuff. And so the example that he uses in his book is like the Toyota assembly line. And I think the AI version, the AI agent version of this is just a really complicated AI agent. That's been like finely honed over like multiple years to work really well under real world's conditions. We've, we've talked about a bunch of these on this podcast, like Jake Heller with Case Text is like the original example. A couple of other ones I was thinking about from more recent companies. We have like a couple of companies that sell AI agents to banks.

• 11:00Jared Friedman?

  We have Greenlight, who worked with Tom. They do KYC for banks and we have Casca, which like does loan origination for banks. So it essentially tells banks like which loans they should give. And I think these are interesting examples because for all of these AI agents, you could build a version of Greenlight or Casca or Case Text, like a, like a demo version in like a weekend hackathon. And I think when college students are thinking about these AI agents, I think what they have in their mind is like the weekend hackathon version of the product. And they're like, like, I could build that in a week. Like, how could that be defensible? And like, the reason is like, the, the version you build in a hackathon isn't useful to anyone. It's like, like, like, like if Casca or, or Greenlight fail, like the, the banks will lose millions of dollars. This is like mission critical infrastructure.

• 11:44Jared Friedman?

  It's, it's more like a self-driving car.

• 11:46Garry Tan?

  One way to look at it is way better engineering is actually, that's like the most profound form of process power. Like one example might be Plaid, which, you know, the surface area of the number of financial institutions that they have to support is so giant. It's, you know, probably on the order of thousands to tens of thousands of different, different websites, different crawlers. And then all of the different, you know, can you imagine like Plaid's C I C D structure? And then, you know, this is pure speculation, but if I were Zach running Plaid, like I, you know, know that I would want to be using code gen tool,

• 12:24Garry Tan?

  the latest code gen tools to be able to, you know, basically add every new financial institution on the planet quicker than anyone else. Like, that's sort of a very profound form of process power in the modern AI age.

• 12:38Jared Friedman?

  I think this is probably the main form of defensibility for the existing SaaS companies. Like if you look one generation before the AI agent companies, like why is Stripe or Rippling or Gusto defensible? I think it's mostly this, right? It's just like they've just built a lot of software and it'd be really expensive and hard to replicate all of it. And like you can't just copy it from their landing page. Like there's like, like the backend logic is like super deep.

• 12:59Harj Taggar?

  There's also, I feel like, kind of a Schlep blindness aspect to this going on too, where like the hackathon version of any AI tool is like quicker than ever to get to. But actually, the last like 10% of getting it to work reliably across like tens of thousands of KYC requests like per day is sort of like a particular type of painstaking

• 13:21Harj Taggar?

  drudgery work in a way that I think like lots of engineers are just not excited to do. And then that is also kind of like the teams at OpenAI are going to experience this too, right? Like if you're, if you're working in one of the big model labs and there's teams of people trying to invent AGI, it's going to be hard to get jazzed about nailing the like final 5% consistency on your like KYC tool.

• 13:42Jared Friedman?

  Yeah. And so I think this is especially true for like verticals like KYC that are, require specialized knowledge to even know what to, even to know what the edge cases are. Like if we had to pick from the seven powers, like I think speed and this, these are probably like the two dominant ones that come up the most often.

• 14:00Garry Tan?

  And those are most related to execution.

• 14:02Diana Hu?

  Is where the hardcore builders win. Having really good product taste and building the best product really matters. And I think it comes to a lot of the point, maybe the misconception is, I think a lot of these products, you can probably build the 80% solution with 20% of the effort. But for these solutions and products to work, you need the 99% accuracy one, which then takes like 10 times or even sometimes 100 times the amount of effort, right? It's sort of that Pareto principle type of thing. What about the, the other power for corner resources?

• 14:35Garry Tan?

  I think the classic view is they're just coveted assets or things that, you know, they're not arbitrageable. They must be independently valuable. And then sometimes they offer preferential access with, you know, Rates that are way lower. So the classic example that you could look at is you know, pharma companies have these patents that are very hard to get. They have to come up with them and then prove them and get through regulatory approval. And the sheer fact that they have a patent plus, you know, getting through FDA approval is something that can be very durable. And it's so powerful that patents have a limited lifespan because you don't want people to have that forever. A more modern example, I think, you know,

• 15:19Garry Tan?

  on the regulatory side might be, you know, Scale AI is doing a ton of work with the DOD, you know, Palantir as well. In order to even get there, it's, you know, a painstaking process. You've got to hire the right people. You've got to spend a lot of time in DC and Langley or wherever, you know, you're trying to sell to. And you've got to literally build SKIFs, like these, like sort of, you know, special data centers where, you know, it's at great pain and expense, you have to get embedded with the government. But then when you do, like, well, you've got it. You know, the corner of resource in some sense is even the brain space in people who work in the government. Like, you know, right now, if

• 16:04Garry Tan?

  you're working with AI, like, you've got to go through a Palantir or a Scale. And that's like literally written into their public documents around like how they're thinking about the nature of warfare and the nature of, you know, everything that they want to do, having to do with AI moving forward. So, you know, the corner of resource doesn't have to be a diamond mine. It could be the diamond mine in your customers' heads. Those examples are sort of closer to like being way up in the sky, having this like insane deck of corn, like worth hundreds of billions of dollars sort of situation. But what's relevant for startups that I think all of us sort of see every day is sort of what you were mentioning with this forward deployed engineer, you know, FDE forward

• 16:49Garry Tan?

  deployed engineer model, that that is what a lot of startups that are extremely successful today are literally doing. Like, they're going out and getting a corner Resource in the form of real data and real workflows. Literally sitting with a customer who normally would never get access to good software. And then spotting, okay, this is sort of the tailored time and motion. You know, first, you know, a request comes in by email. Then we take this and we enrich it in this way. Sometimes we have to have a call center, call this person like. You know, actually understanding what might be a very boring process. And then translating that into your own prompts, your own evals, eventually your own.

• 17:33Garry Tan?

  Data sets to tune your own models like those are all things that are incredibly valuable. And then clearly there are examples, you know, earlier. We're saying like character AI, for instance. You know, took L L M's, you have obviously built some of the first L L M's, then. Took many of them and then fine tune them in a way so that they could bring down the cost. Of serving those models by ten X. And so, you know, that itself is also a form of a cornered resource.

• 18:03Harj Taggar?

  The best cornered resource to have is your own model that can like do the specific work.

• 18:08Garry Tan?

  Yeah.

• 18:08Harj Taggar?

  Better, right?

• 18:09Jared Friedman?

  And for a while, people thought that was the only mode that you could have in this space. If you didn't have your own model, like you were totally hosed. Turns out that's not true. Turns out there's just one of the possible modes.

• 18:18Harj Taggar?

  Partly that is a threat people are worried about in in the big picture. The 10,000 foot scary thing is if the labs at some point decide treat their models as a cornered resource. And they restrict access.

• 18:29Garry Tan?

  I guess the interesting thing right now is like, it may well be true that. The, you know, platonic ideal, perfect manifestation of an AI system. Will require a lot of both, you know, maybe. Pre-training, post-training, R L H F, like just so many different things that you have to throw at it to get it to like. Chat G P T level, but we're also so early in the revolution that. You know, even if just context engineering gets you 80 or 90% of the way there, that's plenty.

• 19:02Garry Tan?

  That's actually all people need to do for like the first two years of their startup, almost always. You know, Cursor didn't start out by doing, you know, full parameter fine tunes of GPT-5, which they probably have access to now. They started just by making something people want. Earlier, we were saying like, don't use these frameworks to count yourself out prematurely. And this is a very profound version of that.

• 19:28Diana Hu?

  So the third power we're going to discuss is switching costs. That is the concept where you get a mode when your customers are kind of trapped because it becomes very expensive for them to find another solution, even if the other solution might be like a little bit better. It's just very painful for them to switch financially or in terms of the operations, times or effort, because they just have so much of it in the current solution. And examples that are given in the book are like databases like Oracle. When you have all of your system or record and all your data in Oracle, it becomes incredibly hard to migrate.

• 20:12Diana Hu?

  Like database migration is something that people don't do. Other example given is Salesforce. Because once you have all your customer records in Salesforce, you build all these workflows, the UI, and it's just a lot to retrain a lot of your sales team to use like a new software. You need to like migrate all the data. And then at that point, for the company to switch to a new CRM is probably going to take, I don't know, lose like a whole year of productivity or something, even if the new solution is a little bit better. I think how AI companies are building mode with this has to do with a version of what Gary mentioned with the forward deploy engineer. We've given examples of this with Happy Robot or Salient, where they start with

• 20:54Diana Hu?

  Specific workflows that are very customized per company. And they work with large enterprises. And part of it is actually with the forward deploy engineer, they may have actually very long pilot pilot periods, which might last like six months to a year. But if they succeed, these convert into seven figure contracts. And the reason why these pilots are so long is because they're very much building custom software for the specific operations in these companies. And the examples for Happy Robot, they got customers like DHL, where they went deep into integrating into a lot of the workflows for how all their logistic operations are done, which is very custom to the DHL operation.

• 21:39Diana Hu?

  Or the example for Salient, who's building AI voice agent for the financial industry. They integrate with banks and a lot of the banks have very different workflows on how they do a lot of the loan consolidation, how do you do the debt recovery, how they do a lot of the fraud monitoring and risk and compliance. And it's all a little bit different because all these companies have built kind of internal tools. And the whole part of bringing an AI company that builds these workflows, they build custom workflows then that work with them. But as a result, the trade off is you do have very long pilot cycles,

• 22:22Diana Hu?

  but the pot of gold is worth it because you end up with this big contract. And once you're in, you're kind of minted. And the big enterprise is not going to do another bake off because it's gonna, it's gonna be a huge waste of time for them to let's try the other whatever cool AI voice agent company. At that point is that we just wanna get the benefits. So that's how these AI companies are winning.

• 22:46Garry Tan?

  I think it's like at once a mote and it's also it's interesting in the age of AI that simultaneously you could see how AI brings down the cost of switching by a lot. And that's sort of another lever that a startup could use. Like if you can write, use code gen to basically extract data out of old ossified systems or your competitors, then you know, there are things that might have really relied on switching costs that you could potentially bring it down to zero.

• 23:18Jared Friedman?

  Yeah, there's actually two different flavors of switching costs, right? There's the the old school ones from the SaaS era, all the system of records like Salesforce, but also ATSs like Lever and Ashby where the switching cost was the painfulness of migrating data from one system to another. And I agree with Gary, LLMs might significantly reduce the switching costs because the LLMs can figure out how to like morph the data from the old schema into the new schema. You use browser automation on both sides to like solve issues where like people don't let you export the data. But then there's this new form of switching costs that I think is pretty native to the

• 23:58Jared Friedman?

  AI era, like you're talking about, to Tiana, which is like these lengthy onboarding processes that lead to like deep customizations of the logic of the agent, not just the data. That didn't really exist in the SaaS era. Like, I guess you'd like customize your Zendesk implementation a little bit, but like not that much.

• 24:16Garry Tan?

  Yeah, I mean, and then for AI companies on the consumer side, I mean, this is all very nascent, but like I think memory is already becoming a bit of a switching cost for me. Like, it actually blew me away that Claude was so behind on memory. And then, you know, my relationship with ChatGPT, I feel like, has evolved very significantly in the last year where I'm like, oh, I actually just generally, it seems to know, you know, what I'm into and what I care about. So, you know, that switching cost, I think, over time will only become greater and greater. And so personalization for consumer is actually a huge piece of that.

• 24:53Diana Hu?

  What about counter positioning, the other mote on the book?

• 24:56Jared Friedman?

  The definition of counter positioning is doing something that is difficult for the incumbent that you are competing with to copy because it would cannibalize their business. I think there's a couple of ways that this plays out. In every category, there is a Darwinian competition.

• 25:14Jared Friedman?

  Between the existing SaaS incumbents building their own AI agents and the new AI native companies building AI agents on top of the existing SaaS companies. So like for customer support, the existing SaaS incumbents like Zendesk and Intercom and Front are all building their own AI agents. But then we have like a new wave of companies that grew up in the last couple of years that are building AI agents that interface with with this system. I think it's like, I don't know, this could be a topic of a whole like light cone episode, which like who will win in in in each of these fights? I think is really interesting.

• 25:50Garry Tan?

  Unstoppable force meets immovable object.

• 25:53Jared Friedman?

  One way where this is playing out in the counter positioning is that all almost all of these SaaS incumbents, their pricing model is they charge per seat, i.e. per employee. And this is, I think, a very big Achilles heel that they have strategically, which is that if their AI agents do a good job and actually work, those companies will need fewer employees doing this work because they're like, the work will be automated by AI agents. And in a and in a simplistic way, that will just actually reduce, the more successful they are, the more they will reduce their revenue. My guess is like, some of them will be able to navigate this, like especially if they're still founder controlled. I think like Intercom, for example, like the, I think the founder controlled versions of these companies are smart enough to recognize that this is existential and they may be

• 26:38Jared Friedman?

  able to cannibalize themselves. I think the ones that are not founder controlled, I don't have a lot of hope for it. It's super hard to cannibalize your own revenue.

• 26:45Harj Taggar?

  The alternative, as we're seeing, is so much of the startups, um, pricing models are around sort of like work delivered or tasks completed. I think it's it's exactly what you said, but it's also that then switches the product towards having to actually be able to complete the work. And something I actually repeat at the last Y C batch, um, at the end as closing advice is that I wish the founders in a batch could just somehow go spend a month at some of the late stage companies. Um, uh, cause the top thing we hear from the founders running those companies is how hard a time they're having sort of resetting the engineering culture in their org to actually embrace AI, to use the tools to To do like context engineering and prompt engineering and and

• 27:27Harj Taggar?

  the the net result of these teams not actually being able to be AI native, one of a better term, is that they just can't deliver the products that work, right? And so like they both don't want to switch from per seat pricing because like that's what they're used to. And in a world of AI being able to do the work, there's going to be less seats to sell to. But they also just cannot deliver on products that can do the work. And so they they wouldn't that that pricing model is not going to make any sense for them either.

• 27:53Jared Friedman?

  Yeah, it's it's like the process engineering part. They're not good at the process engineering part for this new kind of engineering.

• 27:59Garry Tan?

  I mean, something sort of emerging that's very interesting in a bunch of Y C startups like Evoqua. For instance, they're doing customer support software, kind of like Service Titan, but for HVAC, so literally like people who help you with heating and air conditioning. And, you know, I think Service Titan has something like 1% wallet share, 1% of the gross transaction value of like a given HVAC company, which is very small, right? I mean, people don't spend that much money on software because these are relatively low margin service businesses. But the wild thing that Evoqua discovered is that, you know, they can come in as software. But then over time, they're actually getting a bigger and bigger chunk of the wallet share

• 28:43Garry Tan?

  because they can get the HVAC people to pay them actually for the customer support piece, which is not 1% of their spend, but 4 to 10% of their spend. So what you may well find is that this new breed of AI startup will actually have more growth and a higher wallet share. So, you know, actually, we may well be all undervaluing how powerful and how big the vertical SaaS AI companies will actually be. Because you're not like 1% of wallet share, you can get to 10.

• 29:20Diana Hu?

  That's what we talked in that episode where vertical AI SaaS agents will be 10 times, at least 10 times bigger than SaaS, because it's really, to your point, Gary, tapping into a whole different part of the spend of the companies is not the wallet of software where you're kind of, at this point, I suppose, is a bit of a. A finite budget, but it's really new space with things that were not then possible. And it was mostly workflows from people.

• 29:49Garry Tan?

  And I know that people are like pretty sensitive about workforce displacement. But you know, customer support for an HVAC services company is not a fun job. And you can tell because all of these customer support jobs actually have like 50, 80% annual attrition rates. Like they're just such torturous, not fun jobs that the companies themselves and the call centers themselves spend almost all of their time trying to vet and bring in more people to work on these terrible jobs. And so when you have better software, what's sort of happening is that instead of like, people aren't losing their jobs. These people are quitting their jobs anyway because it's terrible job.

• 30:29Garry Tan?

  And then if anything, what EvoCA has told me is that many of the people who were in those customer support sort of roles, now they're actually having more fun jobs because instead of like managing a whole set of people who don't want to be there. They're actually managing AI agents and then handling the interesting weird cases. The coolest part of it is like, they actually can go in and sometimes alter the prompts. And sometimes you actually have a direct impact on both the experience of the customer, but then also their own day to day. And that immediately is like a 10 times more interesting job, like wrangling a bunch of AI agents and making

• 31:13Garry Tan?

  the support process better and better over time. Like that's, you know, as knowledge work goes, like way more interesting than follow this script and read what the computer says.

• 31:22Jared Friedman?

  So Harj, you had a really interesting point about a second form of counter positioning.

• 31:28Harj Taggar?

  The space has moved so quickly that in every vertical, or many verticals, there's sort of early on emerged one company that's seen as the early winner in the space. And often it's actually like the second movers, at least in the YC context, we have seen over and over again that like there's advantage to being the second mover in a space. Like Stripe came after Braintree and authorized .NET, a bunch of things, and was able to like actually win by just building a better product. DoorDash came after Grubhub, Postmates, various other delivery services, and eventually went on to win. And so I think it's interesting to sort of just consider about if you're entering a vertical where it's already. Feels competitive or there are already, there's already seen to be like a early winner

• 32:12Harj Taggar?

  in the space. How do you counter position against them? One thing I think is really interesting here is Legora versus Harvey. Legora is obviously both in the legal AI space, Harvey is the early winner. The counter positioning that I see from Legora is Harvey came in early and maybe got early sales, but focused a lot on fine tuning, that's sort of like their product differentiation. When over time, it's seen that that was actually probably not the right move. You wanted to actually focus in on the application layer and actually just sort of building a better product. And Legora has focused on that. That's what their branding and positioning is. And it seems to be working really well for them as a second mover into the space. A company that I've worked more closely with, GigaML, entered the customer service space

• 32:54Harj Taggar?

  and they're competing with Sierra and DecaCon, like really well-known customer support companies. And from having seen their sales motion, how they've been able to sign up some big customers, their, I think their counter positioning is their product fundamentally just works better out of the box. And as a result, they can have a much faster sales and onboarding process. So it's like their counter positioning is if you want to sort of get your customer support working as quickly as possible, you should go through like the GigaML onboarding process versus like the DecaCon. And I think that's actually worked quite well for them.

• 33:26Jared Friedman?

  Yeah, GigaML is an interesting example of how, to your point about like hybrid displacement, it's clear that an AI agent can do this job, not just as well as a human, but actually much better than a human. Like the DoorDashers that the GigaML agents are talking to, a lot of them don't speak very good English. They speak all kinds of languages. You can't hire a customer support person who's fluent in 200 languages.

• 33:48Garry Tan?

  But LLMs are actually out of box.

• 33:51Jared Friedman?

  Out of the box. And they're infinitely patient if like there's a bad connection or so that's pretty interesting.

• 33:58Diana Hu?

  I think you have other example where, to your point of a superhuman abilities. Is where the AI version of the product actually works? I think, Harj, you had the example of a Duolingo versus Speak.

• 34:11Harj Taggar?

  Duolingo is obviously the biggest language learning app. I think most consumers know. The emerging criticism of it, I would say, is that it's actually just sort of like a gaming app versus a language learning app. That like the way that works is orthogonal to learning a true language. And then you have Speak, which is a, uses LLM, like uses voice to actually like help you practice and actually learn the language. And that counter positioning is working really well for them, right? And so Speak has got explosive growth and it's not trying to compete with Duolingo on the, we've got like lots of gamification and points and sort of like a great game mechanic. It's competing on, hey, we're actually just the place you should come if you want to learn the language by speaking it.

• 34:54Harj Taggar?

  I think the counter positioning mode is very, sort of close and overlaps with the branding mode idea. I think in the book he talks about, you know, like, brand is, it's essentially a mode when you become so well known that even if you have an equivalent product, consumers will still choose you because of the brand effects. And I think the example he uses is like Coca-Cola. In the AI context, I think it's probably harder to apply brand as a mode directly to startups. So it just takes time to acquire brand. But you can certainly see its effects. Like, the thing that still stuns me is OpenAI ChatGPT has more consumers using it per day than Google's Gemini.

• 35:33Harj Taggar?

  I think anyone who understands the models and uses them daily would say that Gemini Pro 2.5 and Gemini Flash 2.5 are like equivalent models.

• 35:41Jared Friedman?

  And Google also had all the users. Like, basically, everyone in the world is a user of Google. OpenAI had no users initially.

• 35:49Harj Taggar?

  Google was already one of the biggest consumer brands on the planet. It was almost certainly the biggest consumer brand on the internet. And yet, somebody else came along and built the brand as the consumer AI app and.

• 36:02Harj Taggar?

  Google is like playing catch up.

• 36:04Jared Friedman?

  If someone had tried, had told me in 2022 that that's how it would play out, I would have been fairly incredulous.

• 36:09Garry Tan?

  It's also a perfect example of counter positioning again. I mean, this is, Google had a, a business model that required it to continue to support ads and an organization that they shipped. And so you have the greatest cash cow in the history of man. So why would you disrupt it even at the cost of setting back human access to knowledge by a few years, even if that's like the core stated goal of Google itself, to organize the world's information?

• 36:41Diana Hu?

  There's also the untold story of how, the origin story of ChatGPT, how it came to be, which is really the original mode for startups with speed. It shipped very quickly in a matter of months with a very small team of a couple of engineers.

• 36:56Garry Tan?

  I mean, it required, you know, Sam Altman and Y C Research and Greg Brockman to go hire Ilya Sutskever out of DeepMind because he was there. And, you know, he, all the people, a lot of the people who went on to help create OpenAI, they came from DeepMind, like it was already in the right place. It's just that that place didn't nurture exactly the thing that society really needed.

• 37:22Diana Hu?

  For speed.

• 37:23Garry Tan?

  So there's that mode again, speed, number one.

• 37:26Jared Friedman?

  Do you want to talk about network economies, Diana?

• 37:28Diana Hu?

  Yeah, on the book, network economy is described as where the value of the product increases as more users or customer get and use the product and everyone derives more value as a effect of more people using it. And examples that we're given in the book are Facebook, where as you use it and your friends use it, it's more fun for me to use Facebook because all my friends are in there. As more users come in, then is the social network becomes more valuable. And this was very much the era of the internet where people talked about network effects that came to be. And the other example he gives is like Visa, the Visa network, where the more merchants are using Visa,

• 38:12Diana Hu?

  the more value the consumer gets because you can swipe the Visa card in more places. Then that becomes the the mode because it's harder to then acquire and amass this number and large number of users or merchants in order to to win. So that becomes very defensible. In the current era for AI, the shape of a network effects is different. It really comes into the shape of data. I think a lot of the data that a lot of AI companies get access to becomes the mode where the more data they get, the custom models they build become better. And the better models, it becomes a better product for users. And there's lots of examples of these. And besides like the big foundation

• 38:58Diana Hu?

  lab companies where they probably use some of the data, I don't know. I mean, they probably use some of the data from the users. They probably do.

• 39:04Garry Tan?

  ChatGPT almost certainly like feeds a lot of that back because you have a certain reward function for

• 39:10Diana Hu?

  Right.

• 39:11Garry Tan?

  each training run, right?

• 39:12Diana Hu?

  So all the history of every chat from ChatGPT one, two, three, four, five now goes fed into GPT six and then so on and so forth. It helps create the the next model version. And there's even smaller versions of this. For example, Cursor, they have probably one of the best tap tap autocomplete. Because one, the the free version of Cursor, they actually say it when you sign up that they they will use the data and they use that to train it. And the more users they get.

• 39:42Jared Friedman?

  I think it's like all the data. Like, I think it's like quite literally like every mouse click and every keystroke that you that you emit when you're using Cursor like is fed into a model, which is like kind of crazy.

• 39:55Diana Hu?

  Which then the more developers use Cursor, the better the product gets. And then they compound a lot of the a lot of the wins with that. And the version where this applies to AI startup is when they go work with enterprises and large companies, they get access to private data. I mentioned earlier, Salian or Happy Robot, when the employees of the companies where they become customers, as they use their product, they have a lot of that private data that makes a lot of the workflows better. And the way they improve that, which is the second. Way of having modes with networks is really evals. We talked a lot about evals being the key mode for AI startups. This evals is where you get a lot of the this workflow worked or didn't work.

• 40:40Diana Hu?

  And then take that back and iterate and improve your context engineering. And that is a flywheel that you can only achieve when you get more and more usage of your product, whether being a consumer or a or a AI vertical SaaS agent. So now the last mode in the book is scale economies. Jared, you want to tell us about it?

• 41:03Jared Friedman?

  Scale economies are economies of scale. You've invested a lot of money to build something that's really big. And as a result, you have economies of scale and you can offer the service cheaper than anybody else. So like the classic example would be like UPS or FedEx or the Amazon delivery network. They built like massive, like physical infrastructure. And as a result, they have like a lower cost per unit compared to a smaller competitor. I think the way this has played out in the AI world, I don't think it's actually played out that much at the application layer. It's really played out at the model layer, right? Like training a state of the art LLM is very capital intensive. Only a few companies can afford to do it.

• 41:44Jared Friedman?

  Once you've done it, you can afford to like let people do inference on that model very inexpensively. This is why the DeepSeek announcement was so was so earth shattering last year because it seemed like it might be a lot cheaper than people previously thought to train a frontier LLM, which would greatly diminish the power of this like economies of scale mode that people thought the the AI labs had.

• 42:07Diana Hu?

  The key thing about DeepSeek was they figure out and made public this new unlock for models, which is how to do RL. They still build on top of one of the large foundation models. So it's still expensive. The RL part is cheaper, but you still need the very expensive big foundation model. So that's one of the things that the media got wrong.

• 42:25Jared Friedman?

  There's a separate question that people talk about, which is like, how will the foundation model companies be defensible against each other? And like, this is certainly one way, right? It's just like, it's it's very hard to be a new entrant into that game now because of this economies of scale. And we were, we were thinking earlier about like how this had played out with startups. And there's not that many examples, but I think a couple of good ones. Well, one, one good one is a company of yours, Exa. Harge, do you want to explain what, what Exa does?

• 42:50Harj Taggar?

  Yeah, Exa is essentially search for AI agents. It provides an API for anyone building AI applications that wants to search the web.

• 42:58Jared Friedman?

  And the way I, I think this is playing out for Exa is in order to provide that service, they need to crawl the web, not the whole web like Google does, but a big chunk of it. And that's very expensive to do. It requires like a large, like fixed capital investment. But then, once, once you crawl a big chunk of the web, you can reuse that same crawl for, for many different customers.

• 43:17Harj Taggar?

  I think what's interesting about Exa, the, the parallel to the model companies is that they, they had invested in that, like sort of before agents had really taken off. Like they were fairly early to this. I think they were working this actually even pre chat GPT launching. So they made the investment early on, took a bet, same way that the lab companies took a bet on like transformers and, and scaling those.

• 43:38Jared Friedman?

  Yeah. And there are two companies in just the most recent batch, Channel Three and Orange Slice, that are both doing Exa.ai like plays where they crawl a big chunk of the web, have a big like static crawl on their own servers and then have agents that run on top of those, of that crawl. So I think we're going to see more and more of this, especially as the web agents work better.

• 43:57Garry Tan?

  You need to mainly focus on the first mote that isn't even in the book, which is speed. Like, you know, if you're really breaking your brain about like, oh, well, are we going to be a cornered resource or not? You're just thinking about it in the wrong way. Like, you should not start there. You should start with, do I have a specific person who has some sort of pain point? And it's pretty painful. It's not like a, oh, it'd be nice if I could do this. It's a, oh, I am not going to get promoted this year. Maybe I will get fired. Like, this is so painful that I don't want to go to work today. Like, that's sort of the type of pain that you're looking for. And if you can write software or build things that actually alleviate that pain, like existential

• 44:41Garry Tan?

  pain, like the business is going to go out of business, or, oh my God, we could totally take over everything next year. Like, that's sort of the feeling that you want in your customer. If you can find things like that, go, go, you know, go find that and go zero to one on that first. With that, see you guys next time.