[Anish Acharya]: I think that when the product you can deliver cannot be a 100x better than everything else, of course distribution is what matters. And I think the lie that we have sometimes told ourselves as founders is that something that's incrementally better is a 100x better. You know, I think of this as silver bullets versus lead bullets. You know, one silver bullet is a dramatic improvement. Many lead bullets are many small incremental improvements. 10 or 50 or even 100 small improvements, 10 or 50 or 100 lead bullets never equal a silver bullet. You really need that 100x value leap. Now, with the models that we have access to, we are awash in silver bullets, right? There are silver bullets everywhere. So, I actually do think in this day and age with the technologies we have access to, you can win by betting by having a better product.

[Anish Acharya]: I'm Aneesh. I'm a general partner at Andreessen Horowitz. I invest out of our AI apps fund. That means consumer and enterprise. For consumer, we love to invest in companies that are weird and working. For enterprise, we love to invest in companies that are working, maybe less weird. Could be weird. Uh, no judgement. Um, I personally am an engineer product person. I write a lot of code in my free time and, you know, I feel like we're living in the age of miracles. So, if you're building, I want to hear from you.

[Anish Acharya]: Yeah, I've been thinking about this for some time. You know, if you look at just the broad trend in AI in terms of how many people are first trying new AI products without being paid to. Because I think of customer acquisition cost as a form of subsidy. You know, the customer is not motivated enough to do it on their own. So, the company really has to push them to try the new product. And the magic of organic product adoption is that the customer is excited enough to just try it with no further incentive. So, the first thing that we really saw was the uptake of ChatGPT and Midjourney and a number of other very early AI products. All of the traffic was organic, which was different from what we had seen in consumer product adoption for maybe 10 years.

[Anish Acharya]: Looking at the early data around willingness to pay, what we saw was two interesting things. One was that a lot of people were willing to pay, so high number of people that were willing to pay, and the second that the AI companies quickly blew through what we thought were the ceilings on ability to pay or the sort of amount that a customer would pay for a subscription. There's actually interesting reason for that. I'd love to give our AI companies credit and say it was foresight or experimentation, but the truth is the COGS for AI companies is non-trivial, right? It can actually be very, very high, especially for products like video generation. And because you had real costs in these businesses, they had to charge customers real money. And to deliver the very best product experiences in generations, they had to charge a lot of money. And what many of these AI companies found is that even as they raised prices, customers were willing to pay and in fact wanted to pay more. So that really got me thinking about, "Hey, what is the extreme version of this?" And I love exploring ideas in their extreme. I just think it's a very useful way to extract the kind of core of your thinking. In the extreme of, you know, people being willing to pay high prices, there's two actual applications.

[Anish Acharya]: The first is that you can build a software company with real revenue scale with very few customers on a relative basis, right? 41,000 for the $100 million run rate at $200 a month. And the second is that software should subsume almost every part of a consumer spend over time. And increasingly those dollars are going to be captured by AI and by software products. So I think it's actually a very, very optimistic prediction, one that we've seen come true, which is that more individuals will be able to build large-scale AI companies and consumers will have more of their needs met through software.

[Anish Acharya]: I would say in the world that we're living in, there are no marketing problems, there are only product problems. I don't think products should have CAC today. And if you need significant customer acquisition costs, that means you haven't sufficiently delivered on the product. The truth is that founders and companies and products were never able to deliver with the kind of ambition that they can deliver today. You can just go insanely deep.

[Anish Acharya]: And part of that is because the models can do things that they could never do before. You know, we had 40 years of building models that enabled or extended the intellectual parts of our brain and the intellectual parts of our society, right? But that was only a single sort of aspect of the human experience and really of our civilization society that was addressable by technology. Now, with these new subjective creative computers, we can address the entire non-deterministic part of human society and the human experience. That is our emotions, our relationships, our desire for self-expression, the creative work that we do. So, this entire part, arguably a larger part of the human experience, is now addressable through technology, and that simply wasn't the case before. So, I think that's one important point. The second is that thanks to AI code and the like collapsing costs and difficulty of making software, it just way easier for a small number of people to build a lot more. So, this simply was not possible prior, and now you have founders that can do more things at an order of magnitude lower cost. And as a result, you can go deep or go home instead of go and bigger go home.

[Ad Narrator]: What if your CRM kept up with the conversations that matter for you? Meet Attio, the AI CRM for modern businesses. Connect your email, calls, and product data, and Attio instantly builds your CRM with complete context. Then, ask Attio anything. Spot a deal at risk, draft outreach, uncover new prospects, spend less time in your CRM and more time building relationships. Whether you're running product-led growth or enterprise sales, Attio adapts to how your team sells and grows. And if you're building with AI agents, plug any MCP client straight into your CRM, so your agents always have the context they need to act. 9,000 companies build with Attio, including Granola, Modal, and WhisperFlow. Try Attio free at attio.com/eo.

[Anish Acharya]: So narrow startups are companies that build incredibly opinionated deep products, charge very high prices for a relatively small number of people. You know, the simple math is that charging 41,000 people $200 a month is a $100 million run rate business, and there's a lot of precedent for this already occurring. You know, we see Google Ultra's top SKU is 250 a month, Grok is 300 a month, OpenAI's is 200 a month, I believe Anthropic's is 200 a month. Consumers are flocking to these products organically. They're paying high prices for them, and over and over again we're seeing them delivered the value that they expected. So the whole idea behind narrow startups is build small, go deep, and charge a lot.

[Anish Acharya]: I think specialization is a new moat. I think that you have the ability to go so much deeper with the new technology and the collapsing cost of software creation for an individual customer that you can just be so much more specialized for that customer that it's hard to compete with. You know, somebody's going to have to build 3 years of road map to have a competitive product. So it's simply differentiation taken to an extreme degree. I think that's an interesting and important form of a moat, which is particularly relevant to narrow startup.

[Anish Acharya]: I think the second is if you look at ChatGPT, they're trying to do a lot of things, and if you think about areas in which there's a really rich software ecosystem that has to be built to really capture the value, I don't know where that's going to fall on their priority list. A great example is meeting recorders. There's many products that now take notes for you by transcribing speech to text. That is great, but to fully capture the value, you probably need to build a whole office suite. You need spreadsheets, you need word processors, you need a diary app and a notes app, and you need all kinds of software. It's just not obvious to me that the labs are going to actually get to that. So, I do think that building a rich software ecosystem, a rich product ecosystem is another way to compete.

[Anish Acharya]: Look, I think the third thing is that there are many product categories like AI code where you benefit from using many models. Right, it's better to be able to use Anthropic and OpenAI and Google's models. And if you're at OpenAI, you're never going to be able to build a product that also uses Google's models. So, being multi-model is a way to compete with the labs and fintech.

[Anish Acharya]: The other important point is that when these products over deliver for their customers, and they can. If you ask Cursor to help you generate a feature with a model, sometimes it's like, "Wow, this was even better than what I had hoped for or what I had imagined." So, one, the fact that these products can actually have those attributes and can over deliver on the customer's expectations, but the second is that they can charge for it. You know, sometimes the model has to think really hard to deliver that extraordinary outcome, and guess what? When it does, it's expensive. And that is the way that it should be. I think that when the product you deliver cannot be 100x better than everything else, of course distribution is what matters. And I think the lie that we have sometimes told ourselves as founders is that something that's incrementally better is 100x better. You know, I think of this as silver bullets versus lead bullets. One silver bullet is a dramatic improvement. Many lead bullets are many small incremental improvements like 10 or 50 or even 100 small improvements, 10 or 50 or 100 lead bullets never equal a silver bullet. You really need that 100x value leap. Now, with the models that we have access to, we are awash in silver bullets, right? There are silver bullets everywhere. So, I actually do think in this day and age with the technologies we have access to, you can win by betting by having a better product.

[Anish Acharya]: Predicting total addressable market is a fool's errand. It's just impossible. It's very, very difficult and it's a common source of failure for investors certainly, but even for founders. When I was a first-time founder, I had this big brain way thinking of, you know, products, which is, "Hey, we need a big market. It has a needs to have a big TAM." I wasn't even quite sure what TAM meant, but it seemed important and I know you needed a big one. A big one is better than a small one. And that's why a lot of my early thinking was in markets like healthcare and, you know, disease management and I just didn't know anything about those markets nor did I have energy for those markets. You know, how I built a successful product was building something that I wanted to see exist and I was personally passionate about, which was sort of social graphs and mobile games and that's what me and my founder built.

[Anish Acharya]: Look, when the iPhone App Store was released, there were 6 million iPhones in the world. Like, that's not much of a TAM, but we built there because it felt like it was growing quickly and we had a lot of energy for the market and we bet on, you know, perhaps not even thinking about the TAM and we were right. So, I don't think about TAM very much at all. I do think about value delivered to the customer and the, you know, price they're willing to pay. So, I think the most useful prompt for a founder right now is what is the $1,000 a month SKU of our product. Right? That is the direction we need to be thinking about. Like, what is the extraordinarily expensive? What would the product need to do? Does it do it today? Would people be willing to pay? Have we tested it? So, I think if you find customers that are willing to pay dramatic prices for your product, you're probably on the right track. You know, conversely, if you have a free product that you have to pay customers to try, you're probably on the wrong track.

[Anish Acharya]: It's a much more useful signal for builders than thinking about concepts like TAM. If people are paying for it, they're getting value typically. Of course, what is like upstream of that, things like retention, things like customer acquisition cost. So these things can be measured, but this is why it's so useful to build in an area in which you have great intuition cuz you just you feel the feelings. You know it. You talk to the customer. You perhaps you're the customer yourself or you've got great intuition around their pain points.

[Anish Acharya]: The customer has more ideas for your road map than you have. Like there are a lot of qualitative signals. The most overriding signal is you simply can't keep up with everything that is happening as a result. Like that's how you know your product market fit as Mark famously said, the market is pulling the product out of you often violently. That is the experience of it.

[Anish Acharya]: I mean, I think there are many psychological traps from being a founder. I can tell you a few of the ones that I fell prey to and experiences as a founder. So one is trying to talk yourself into having product market fit. Like if you have to talk yourself into it, you don't have it. I think that's incredibly important. I think the second is and perhaps a related point, you know, you're looking for metrics that will justify the fact that you have market fit and you go crazy looking to calibrate on what's good retention, what's good CAC. Those are often not productive. Ultimately, a business has physics. And if you're losing 90% of your customers at the end of year one, like even if that's best in class for the category, it's very difficult to build something that's working. So I think thinking about the sort of business health from first principles rather than frameworks is often more productive.

[Anish Acharya]: I think the final trap that you can often fall into is the power user trap. Power users are power users and that's great that they're getting that much value out of the product, but if you're not able to capture the value that they're getting, you know, they still only count as one dot on your growth chart. So you really do have to either build for power users and capture the value you're creating, which is the narrow startups idea, or you need to build for a mass market and not, you know, tell yourself that having some really happy power users is a substitute for having broad market fit.

[Anish Acharya]: The most important piece of advice is that there is no marketing problems, there are only product problems. Be insanely ambitious on product, raise prices, adjust based on what you hear from the customer, and don't worry so much about business books, frameworks, just build for a small number of people, charge a lot, go insanely deep, and you know, more likely than not you'll find your way to success. Like this is not a 20, 30, 50-year idea, this is like a 3, 5, 7-year idea. That's what the abundance agenda means, and it's coming now. Because there's both abundant capital and dramatic consumer interest in these new products. You know, if you were ever going to start a company, start it now. Like if there are better and worse times, and this is the best time I've seen in my entire career by a long shot.