Why Companies Are Becoming a Series of Loops | Anish Acharya on Lenny’s Podcast

15:36Lenny Rachitsky:Yeah, it's interesting. Like, I've been hearing more and more. I just saw a tweet that I think at OpenAI, the go-to-market team now is using Codex more. More of the go-to-market team is using Codex more often than even the engineering team. Yes.

15:50Anish Acharya:And think about how happy that makes them. Like, what does the go-to-market team want to do? I mean, this is a caricature, but I'm going to stand behind it, which is they want to hit the gym, they want to go to steak dinners, and they want to like, you know, like raise the trophy up for being salesperson of the quarter or of the year. so I actually think that this is a distillation of their job into the thing that they're the best in the world at that they're the most interested in and all the administration that goes around doing that core work is now handled for them so like that's where go-to-market is going and it's going to be awesome

16:21Lenny Rachitsky:this reminds me of a PM friend who has this had this really funny take that as a PM you're constantly having to say no to all these ideas that are coming at you and you have like I'll put on the roadmap we'll purchase it he's like okay I'm going to flip this I'm going to say yes to everything I'm going to build everything and then simulate every idea with like Simile or all these products that are launching where you could simulate how a user will react. And then that'll tell you, should we build this? What a hilarious way to rethink PM.

16:52Anish Acharya:And you know what's so beautiful about that? Actually, there's two things. And I'll tell you maybe the one that's less obvious to me, which is I feel like every PM at every company feels like they're a true zero to one thinker, but they're held back by the kind of, you know, the heavy hand of management and executives and founders and engineering capacity. And in a world where every story gets told, every product story gets told, every feature gets tried, I think a lot of PMs are going to realize they're actually not that good at zero to one. And it's much more fulfilling to work on someone else's good idea than your own bad idea.

17:23Anish Acharya:So I think even things like that are going to lead to a lot more organizational health than we've had in the past. You know, not to mention the fact that the idea that ends up winning doesn't have to be the one that's came up with by the person who can sell it best to an executive. It just gets tried and the best idea wins.

17:39Lenny Rachitsky:So going back to this loops idea, the way I'm thinking about it is how can every function start to think of their function as setting up an agent to be able to just go from input to some kind of impact? And what this makes me think about is something actually Claire tweeted recently, this point that people always used to joke that like soft skills are the least valuable and engineering skills the most important and valuable because they're so concrete. And it turns out that's what AI is the best at, the things that are verifiable and you know what success looks like. And so the question I think about now is just like, which skills can you not just turn into a loop because the output is so hard to verify?

18:20Anish Acharya:Yeah, I think that's right. And I think that those are gonna be the rate limiting factors because you can only do one steak dinner a night. I guess you could do a steak lunch, but to some extent, there's going to be these rate limiting factors in every system. I think a useful way to think about it is anytime the model's making a mistake or doing something you wouldn't do, what do you know that it doesn't know? And there's a really interesting example I heard from Ale at Kavak. He's probably the most sophisticated thinker on this stuff that I get to hang out with, where he said anytime their agent, they have an agent per customer, they sell used cars online.

18:53Anish Acharya:And when the agent gets stuck, it actually calls a human. And the human will coach the agent through. Now, the magic of that is not only does it unblock the agent, but of course the agent then captures all the traces and learns from it, like that's a really interesting mental model. It's either a knowledge gap or a data gap that you have to give the agent. And the next time it shouldn't have to call you.

19:13Lenny Rachitsky:I love that example. Basically, the takeaway here is you need to start thinking about every function as an agent loop. And the job is to figure out where it gets blocked, where it goes wrong and give it more context, more insight, more direction, basically.

19:28Anish Acharya:That's right. That's right. And then hopefully a lot of your day-to-day work, you know, when you come up with a new idea that works, the kind of implications of that idea. If it's a product, you know, there's marketing work, there's sales work, there's product marketing, there's communication, there's legal. All of that should be largely handled for you. And your idea, you know, your job is to go take a hike and dream the dreams and come up with the next hill to climb.

19:50Lenny Rachitsky:And this begs the question a bit of just what will separate the companies that win in this world where AI is kind of doing a lot of this. I imagine part of the answer is the human in this local maxima coming in with a better idea. Is there anything else that you think becomes like a differentiator in this world where AI is doing so much of the work that humans are currently doing?

20:09Anish Acharya:I mean, I think one thing that's under-discussed is that it's unclear that the sort of competitive equilibria that exists in a lot of industries will really change. You know, so let's say Pizza Hut and Domino's and Papa John's and Roundtable all get a data center of PhDs. And, you know, they're all going to either adopt it or have a CEO change and adopt it. So it'll be a rocky period and there'll be some relative shuffling. But I think that they're all going to embrace the new technology because most companies do. I don't know that one of them is going to have 99 % of the market. So I think kind of what's under discussed is that, yes, in the near term, I think there'll be winners and losers based on adoption of the technology and kind of how ambitiously you adopt it.

20:51Anish Acharya:But I do think there's a lot of industries that will sort of maintain their current competitive dynamics because they're not intelligence bound. I think the most useful question to ask yourself as a founder CEO is just, hey, if we assume these things are infinitely intelligent and astonishingly cheap, how would we reorganize the company? Because that's where we're going.

21:12Lenny Rachitsky:Kind of along those lines, you have this interesting take about this kind of split that might be coming within companies between these generalists and specialists and how AI plays into that. Talk about that.

21:22Anish Acharya:Yeah, well, I think that there's so many interesting things that this touches on. You know, one is the question of open weight versus frontier. So I'm sure, are you familiar with kind of Pareto efficiency? I'm sure you are. Please explain. Yeah, so Pareto efficiency is just, you know, for price performance, for example, what is the kind of, the efficient frontier is what's considered the optimal trade-off of, you know, a unit of performance for a unit of price. And, you know, are you sort of, if you're along that curve, you're always paying the rational amount for the performance. And what's interesting is that frontier models are actually irrationally priced in that, you know, first of all, mythos is infinite dollars a token.

22:02Anish Acharya:You can't use it. But if you look at even something like Fable 5, you know, for one IQ, conceptually one IQ of extra intelligence, you're paying 100x more than Opus 4.8. So it's not rational, but I think there's a lot of jobs in which you have unbounded upside, like drug discovery, you know? And if that one IQ point lets you discover the next, you know, statin, it's a trillion dollar outcome. So it's sort of rational to pay for the highest intelligence in these types of jobs and industries. Now, on the other hand, I'm going to pick on, you know, maybe legal. There's perhaps only so much upside to be had in legal or finance.

22:40Anish Acharya:And for those jobs, you actually do want to be very Pareto efficient and probably pay for, you know, good performance at a good price, not infinitely priced, infinite potential upside performance. So I think what we're going to see is a split between job functions that demand kind of mid-IQ intelligence, and those will often be open weight, sort of biased with reinforcement learning, you know, things that make the models even cheaper, more performant for a narrow job, along with, you know, incredibly, quote unquote, expensive, but performant frontier tokens for sales, support, research, engineering.

23:18Anish Acharya:So I think you're going to end up having both architectures. And, you know, we can touch on this in a little bit, but I do think there are these sort of comparative advantages amongst model families. And then also amongst, of course, individual models that we're seeing more and more of. It's not going to be one or the other.

23:31Lenny Rachitsky:Such an interesting insight. So just to make sure I understand what you're describing here, you're thinking there's going to be this split between kind of within an org of function and model where specific functions that have a lot more upside and potential leverage go for the frontier models and you describe these kind of product sales, engineering, research roles. And then there's like, and then for other functions, you don't need mythos. You don't need Astra, I think. Is that the latest one coming in? Yes, Astra. Yeah, yeah, yeah. And so it's both, you're saying the AI doesn't need to be the frontier model and the people don't have to be the smartest people in the world to do really well in that world.

24:07Anish Acharya:Yeah, and I don't want to be diminutive. Like there's extraordinary people in those job functions. I just think that they're bounded upside problems that they work on. You know, you can only kind of close the books correctly. You know, you can't close the books 100x better. So yeah, that's exactly what I'm saying.

24:24Lenny Rachitsky:I wonder if it's connected back to that discussion we had earlier about it's verifiable. If it's a lot more verifiable, you don't need the frontier model versus, I don't know, the potential upside.

24:35Anish Acharya:I'm not sure. I mean, I think for a verifiability is a good question because I think for like a drug development company, you have infinite upside. It is verifiable, but closing the books is also a verifiable problem, which has limited upside. I think the question is really how much upside is there and how hard is it to calculate it? Like, here's a nuance. Customer support, a customer may call in and report a bug. That bug may actually be the first breadcrumb to a thing that changes our entire organization. And if the CEO was on that call or the smartest person, they could follow the trail. But if we actually have this, quote unquote, mid IQ generalist, then not.

25:11Anish Acharya:That actually makes the case for as a basket of problems always use frontier intelligence. And I can, of course, make the case for the mid IQ basket as well, which is simply that we've crossed an intelligence threshold for almost every economically useful problem. And anything beyond that threshold is simply waste.

25:28Lenny Rachitsky:Yeah. And like I think I think people the point people forget is that the models that are not the frontier models, they were like that was what the frontier was. I don't know, six months ago. And we were so impressed and we loved it. I was like, holy shit, I can do all this. And now just because there's something better, we don't give those models as much credit.

25:43Anish Acharya:You're right, because it's sort of like, this thing is so crazy, you know, this is AGI, and then a day later, it's like the old thing that you throw in the dustbin.

25:52Lenny Rachitsky:So kind of along those lines, I hear people jokingly call you a model sommelier. Tell us, with your sommelier credentials, what's kind of like the current state of the model art? What is each model great at? what are models terrible at?

26:08Anish Acharya:Yes, well, as you know, the secret of every sommelier is 10 ,000 hours or maybe 10 ,000 bottles. So I think that the secret of being a model sommelier is just using them all. Drinking a lot. I push myself really hard to ship something with every new model that comes out. And I think you learn so much. You know, I think for people who believe the models are commodities or totally fungible, you just haven't actually used the models. And, you know, for example, in the last few weeks, I've been obsessed with Quen 3.8 Max. Quen is an awesome model. It's very good at long horizon tasks, and it's actually really creative.

26:43Anish Acharya:It's a great storyteller. So I've been using it to create these impossible documentaries, mostly of, you know, planets of the Star Wars universe. I did Tatooine, which turned out great. I did Bespin last night, and it can just work for four or five hours. It tells a great story. It generates all the video using the Minimax model via FAL, generates audio via 11. it actually like directs the movie, the five minute movie, and that it like cuts all of the clips in, it overlays it, it watches it. It's just extraordinary, you know? And that's just, it's got a totally different shape than GLM-52. GLM-53 just came out, which I used for a bunch of product work.

27:21Anish Acharya:It doesn't have a vision component. And it's sort of like this neurotic PhD you put in the corner. And they both have the role, right? This is a little bit of the kind of these tension of models. It's not that one is ahead of another, one is more intelligent. It's rather one is sort of has a mind that's shaped in one direction, perhaps creativity and openness for Quinn and others that are shaped in other directions like, you know, neuroticism and precision like GLM 5.3. So that I just make something with every model. And that's how I build my intuition.

27:52Lenny Rachitsky:How important is this habit, do you think, for people? Because I hear a lot that it's really important to be using these models. And kind of a secondary question is, How do you come up with what to do with these models? Because a lot of people want to try these things. They're like, okay, what do I do? I know. Any suggestions?

28:09Anish Acharya:Well, I think almost all of us have got, you know, the most insufferable thing for a long time was your app idea friend. You know, every time you went to have a beer, they're like, let me tell you my app idea. You're like, ah, here we go again. Like, we have got to be the app idea guys now. And all the silly ideas, actually, especially the silly ideas, because those are the ones that often have the most alpha, are the ones that we should all be building. So I've probably got, you know, two dozen apps that I've built. I've got one or two large apps that I iterate on. And I think that if you don't have a chassis on which to like, with which to use the models, it's really hard to come up with an idea from scratch every time.

28:45Anish Acharya:So I'd say like work on something. It's actually better if it's not important with a capital I and then keep finding new ways to invest in it and add to it with a model as a kind of tool rather than as a goal. Does that make sense?

28:57Lenny Rachitsky:Yeah. Like maybe even zooming out. I've been thinking more and more. One of the most important habits to build right now is to whenever you're about to do something, ask yourself, how can AI do this for me? Totally. I'm visualizing this like input to response, you know, like the whole idea of there's a space between input and response and meditation helps you think more deeply before you respond. And I feel like the trick now is insert in that moment. How can AI help me with this?

29:23Anish Acharya:Yeah, no, I think that's, that's, I mean, I'm also a long-time meditator. We should talk about a lot if you like. But yeah, I think that's right. I think in our day-to-day knowledge work, sometimes it's less obvious to me. I guess my mind is I've always been a consumer product person. I love products. So for me, it's easier to think of a new feature to add to my DJ streaming app or, you know, my sort of Google reader for X that I use than it is to kind of find a part of my life to automate. But you're right. There's some fun examples. You know, I posted about one a few weeks ago where I had my laptop transcribe everything that was happening in the kitchen.

29:56Anish Acharya:And then it would award or detract screen time from my son's iPad, depending on whether he was being good or bad. So it was like a fun little social experiment. It also had a fun outcome, which is that he recorded a video of himself saying, I love you, dad, over and over and put it next to the mic. To hack the metrics. To hack the metrics. So yes, anything becomes a measure. It's no longer useful. But it was just a cool little social experiment. I think those things are super fun.

30:25Lenny Rachitsky:That is hilarious. And so one of the measures was how often he said, I love you. And I was going to give him more screen time.

30:31Anish Acharya:Is he saying things that are positive and pro-social or negative and anti-social? And, you know, saying I love you is very pro-social.

30:38Lenny Rachitsky:That's so funny. I had a friend who built this little device that measured how often he and his kid laugh throughout the day. Oh, that's nice. Yeah. He hacked like one of those limitless pendants to do that. And then they just look at that metric every day.

30:51Anish Acharya:Isn't that so? And this is what I mean, you know, man. And like, I think we've spent a lot of the time on the show so far really like intriguing, talking about productivity, job loss, kind of all these like heady, important topics with a capital I. But what you just described, how often you laugh, like that's not a startup. That's probably not an economically consequential idea, but it is for like the quality of our life. And I think we tend to think that happiness is fixed. But what if it's not? You know, if you and I were doing jobs 100 years ago, like what would our jobs be? I promise they'd be less cool than they are right now.

31:22Anish Acharya:and maybe 100 years from now, they'll be that much cooler. So I think that the thing that gets often missed, and this is why I'm a fan of Claire and others, is just like, how can this thing add texture to our human lives, even if they don't have economic consequences?

31:36Lenny Rachitsky:Yeah. Along these lines, I saw somewhere you have this really interesting take that like the big opportunity, maybe just in consumer, but broadly is this idea of loop, make me happier. Yeah. Talk about that.

31:48Anish Acharya:Oh, yeah. Yeah. I mean, I think that we believe that people want to be more productive, but they don't. I think more people want to spend time than save time. There's a reason the biggest products in the world are kind of entertainment and social. So we get at the heart of how do we sort of deliver the value to the consumer? I think for most consumers, you know, sometimes I tease and I say it's like the Instagram AI user versus the XAI user. The XAI user is like, you know, fearful of being outside of the permanent underclass, is really opinionated on GLM 5.3 versus Kimmy K3. Like, they're just so pilled.

32:23Anish Acharya:And then the Instagram person is like, oh, this is like a better Google search, kind of. It's cool, you know, they don't get what all the hype is about. So I think that a lot of the, and it's really a product design failure. We have the capabilities to radically transform people's lives. I mean, Lenny, in many ways, we spent 40 years building a technology that enables better spreadsheets, right? We built this like technology that extends our intellect, but nothing to extend our soul. And I think that we have a little bit of a spiritual hunger, especially as a lot of these cultural institutions have gone away that fulfilled that, especially in the rest of America, you know, where you don't have as many hot yoga classes and Pilates and fasting and friends giving.

32:58Anish Acharya:So I think that the opportunity for this technology is like, hey, the basics of consumer need, how do we feel more connected, more loved? How do we make progress? How do we have fun? Like the things that we all aspire to, the basics, how do we apply the technology to those areas? That's what I want to see more of. And again, I don't think it's a model or a capability challenge. It's just a product design challenge.

33:20Lenny Rachitsky:I love this so much. We talked about this idea of loop, like grow my business, loop, find me more sales, loop, close support tickets. But the way you're describing here, I think you, the way you had, I have my notes here, just like loop, improve my health or loop, make me a better friend. And there's no reason the AI can't just think deeply and hard about all those things and figure out a way to actually do this.

33:41Anish Acharya:Yeah. And also, you know, look, there's a we have to dial this in. But I think there's a way that AI can sort of challenge you, can push you, can be disagreeable. This is also why I think startups are advantaged over incumbents. You know, the idea there's a thousand Google committees who would roll in their graves at the idea that they're going to release a model that's disagreeable or God forbid it should be like sexually suggestive. suggestive. But guess what? Those are all parts of human existence. So I think exploring the kind of uncomfortable parts of our social existence are things that startups are uniquely set up to do.

34:12Lenny Rachitsky:And I know you spend a lot of time investing in consumer companies. What I'm hearing here is this is a big opportunity for consumer businesses to basically build an app product that is exactly this loop around improving my connections with my friends and family.

34:26Anish Acharya:I think that's right. And, you know, I think the thing that's held back consumer so far a little bit, and it may be held back is just strong because if you if we kind of put this into iPhone terms, we're in iPhone 2010, right? What is iPhone 2010? I think it's pre Airbnb, pre WhatsApp, pre Uber, pre all the important kind of consumer companies. So it is early days. But what's held us back is I think three things. One is that the models have been expensive. So if you want to do a kind of free to use product, it's that hasn't been easy. The second is that we've kind of had an interface problem. Like chat makes sense if you're the highest agency person in the world, which is Elon and Sam.

35:02Anish Acharya:But for the average consumer, like their ideal interface is TikTok. So we need to find something between chat and TikTok. And then the fact that the technology has been so much more focused on productivity than things like, you know, human connection and entertainment. I think all those things are kind of up for grabs. Like the open weight models mean things are way cheaper. I think that we're starting to have conversations about things like Loop make me happier. And I think that founders like Eugenia, who you should have on the show, she's tremendous, are thinking really ambitiously about user interfaces.

35:35Anish Acharya:Brian Chesky from Airbnb, I think he started a foundation lab focused on next-gen user interfaces. So I think all those problems will get solved or they're at least in a better position to be solved than they were two years ago.

35:46Lenny Rachitsky:I want to come back to the whole space of consumer and AI and things like that. But I want to follow this thread a little bit more about just the optimism around where things might go. There's a lot of fear and worry about the future with AI and just generally. Mark Andreessen, when he came on the pod, had this really interesting take that AI came just in time to save us because population is declining, productivity is going down, there's all this war, climate change and all these things, and we would be in big trouble if AI wasn't here to fill that gap. Talk about just kind of your bigger picture perspective on why you think maybe people are underestimating the positive and the optimism around AI?

36:28Anish Acharya:One, I think it's a potential for it to be a sort of emotional, spiritual interface on which we can kind of get leverage and explore aspects of ourselves that have been really buried. If you look at the kind of effect of the Industrial Revolution, it's that there are these scale advantages which are insurmountable. And as much as, obviously, I'm very pro-capitalism and I love the economy that we live in, I think that centralization, it sort of discourages the individual in some ways and it maybe detracts from their identity. So I think one of the really magical things is this is a technology that really amplifies our identity, our agency.

37:02Anish Acharya:It kind of unbundles skill from desire, for example. If you want to make music, you can make music now. You don't have to know how to play the piano. If you want to be a programmer and make software, you can make software now. So it really amplifies our individuality. It allows us to explore aspects of our lives that we were never able to explore before. And, you know, just in terms of the nuts and bolts, like we have been in this sort of morass of 2 % GDP growth. Like who said that we have to be there? Why can't we be 10 or 15 or 20 %? And this is a technology with which not only can we dramatically drive productivity, we can dramatically drive ambition.

37:36Anish Acharya:Like think of the, maybe this is a caricature, but the 1950s and 1960s, we believed that we could do anything, right? We were coming out of World War II where the entire economy, the entire world mobilized in a way that we didn't think was possible. And one of my theories, Lenny, is that like when the stakes are high, we are awesome. When the stakes are low, we are at our absolute worst. And in a lot of ways, I think the world we lived in five years ago felt like a low stakes world, which is why we kind of collectively had a lot of these like side projects as a society, which weren't necessarily productive or making any of us happier.

38:09Anish Acharya:And now you've got, you know, it's not just Elon doing everything he's doing, but I think everybody feels like they're climbing the ambition ladder. You ship your bad ideas so you can discover your good ideas. And if you want to, you know, build software, great. If you want to build a bridge, you don't have to be a civil architect to know how to do that anymore. So I really think that we have the makings of a sort of dramatically happier, more fulfilled, more productive society. And, you know, and yet we're here talking about permanent underclass. I love all of that.

38:37Lenny Rachitsky:That all feels so right. It's so hard to really believe that. But if you actually look at how things have gone so far with the rise of AI, unemployment's down, people are making a lot of money. You know, obviously a lot of people are struggling. There's a lot of downsides. Data centers causing problems for people, things like that. A lot of issues. But it feels like as an economy and as a country, it feels like things are going well so far. Like they just almost like cured some kind of cancer the other day. So, yeah.

39:04Anish Acharya:Yeah, you're right. Moderna just did. I mean, the fact that Dario can write a blog post and say, what happens when we cure every disease? And then we debate it as a serious topic. Like what world are we living in here? I also think that we're collectively worried always, like these abstractions, the world, the average worker, the middle manager, the person who lives in the vicinity of a data center. These are abstractions, but our actual lives seem like they're getting more fulfilled. We're more capable. We're more empowered. So I think that's also something that the revealed preferences tend to show how people are experiencing it individually.

39:40Anish Acharya:And the stated preferences tend to show how people are sort of observing it or believe it's playing out societally. You know, if you ask people if they want a data center in their neighborhood, most will say no. But if you ask them if they use ChatGPT today, most people will say yes. So that's kind of the dissonance.

39:56Lenny Rachitsky:The obvious issue is just the PR of our NAI has not been great. There's a lot of fear mongering. Thoughts on that? What's going on there? or do you think that'll change?

40:05Anish Acharya:The most important thing that we can do with AI to change the kind of conversation around it is make important things cheap. There's two things that are extraordinarily important in America that have only gotten more expensive, right? Healthcare and education. If you look at healthcare, 45 % is administrative. So if you take a lot of that administrative burden out, you can actually see deflationary healthcare costs. Also things like curing every disease. That sounds awesome. You know, GLP-1s, also obviously not an AI thing, but I think is a reason to be optimistic about deflationary health costs.

40:38Anish Acharya:And then education as well. I think education now has the strongest form of competition, sort of traditional education that it's had in 200 years. And I think it's going to be very, very good to kind of unbundle learning from institutions. And also, by the way, like status from credentials. You know, like you don't need a Harvard degree, you just need a Git. And that's pretty cool.

41:00Lenny Rachitsky:I saw OpenAI recently slowed down their AI development. They paused their RL kind of phase on their latest model because of what they're seeing. So that's obviously a big concern for people, just how fast and smart these models get. Any thoughts on just that? That's like a big shift now. Instead of race ahead to the fastest, best model ever, okay, we actually have to slow these things down. That feels crazy.

41:22Anish Acharya:I mean, without commenting on OpenAI specifically, I think that maybe I'm a little skeptical on some of these things where I think the aura that Anthropic got from having a model that was too dangerous to release was extraordinary. And maybe they had a GPU shortage. Maybe the capabilities were more advanced than they actually wanted. Maybe they actually wanted to keep that proprietary model internal to extend their own lead. So I think there's a lot of sort of confounding factors that would cause you to pull back a little bit. Look, I do take the points about offensive cyber seriously, which is we should harden all of our systems before we make them trivial to penetrate.

42:03Anish Acharya:But I think the sort of concept of the model that's too dangerous to release, it kind of conflates marketing, inference capacity, and then also economic considerations like do you want to externalize your competitive advantage or use it to make yourself better?

42:19Lenny Rachitsky:Yeah, I always think about that when I have folks from Anthropoc and O 'Brien, the podcast, just like how an advantage they have when they have the best model. It's crazy, right? Just like that's a loop right there is just have the best model for longer and they can move so much faster.

42:32Anish Acharya:It's crazy though, you know, to take the other side for a moment, like it felt like Anthropica was unassailable and now opening eyes had an amazing six months and open weights are also ripping. So despite the like the sort of scary concept of this like, you know, supremely intelligent model that's totally proprietary to one company, so far the kind of industry trends haven't played that way at all.

42:53Lenny Rachitsky:Yeah, like GrokBot just came out of nowhere and is now like the most amazing AI kind of assistant tool. I'm just hooked on it.

43:01Anish Acharya:Oh, man. I mean, we should talk about the personal agents thing. Like, actually, the three big products that I love here, GrokBot's totally nailed it. And also the model underneath it is awesome, right? They came out of, you're right, out of left field. And that's, I know, a lot of the good work the cursor team did. I think ChatGPT work. It's kind of buried in the UI, but it's really, really good. It's one of the best products they've released. And then there's a startup that's really getting some buzz called Instinct, which has made some more aggressive and interesting trade-offs, but all in the same domain.

43:29Lenny Rachitsky:That's amazing. I don't know if it was a strategy for someone tweeting this vague tweet about how awesome it is. And everyone's like, what the hell are you talking about? That was really effective. Because I was like, how do I get that? Chat GPT work. The episode that will come out right before this is the PM in charge of that, Tara. Oh, really?

43:49Anish Acharya:Yeah. Oh, man, they did such a good job on it. It's really, I don't know how much you use it, but it's really well done.

43:55Lenny Rachitsky:What is better than Coork there? Is it that it runs in the cloud? Is that the big differentiator?

44:01Anish Acharya:It runs in the cloud. It does a good job of kind of caching browser credentials, though it's not as aggressive as Grok or Instinct. And it also, the remote feature, it's got the full duplex voice mode. So you can actually just call it. It can see all your threads and you can just talk to it and say, hey, what's happening across all my coding agents, across this, can you change that? So it's just because the full duplex voice is so good, you really feel like you're calling your assistant who knows everything that's happening in your world. Whereas with Grok, it's a sort of one-way transcription, right?

44:31Anish Acharya:Or with some of the other assistants, it's a text. So the voice plus the kind of model that can see all threads is really well done. Awesome, okay.

44:38Lenny Rachitsky:I want to come back to this AI assistant consumer stuff, but just something I wanted to kind of close a thread on. I feel like there's, in terms of jobs, and economy as a result of AI, I see it almost as the spectrum of, there's like the Dario end of the spectrum of 50 % of knowledge work will be disrupted and we're gonna have massive unemployment to like the Saks, David Saks spectrum of like, it's gonna be incredible. Jobs are gonna be fine. So far, everything is pointing in a good direction. Clearly you're closer to the Saks direction. Is there anything else just along the lines that you think might make people feel better about jobs in the future?

45:16Anish Acharya:I mean, I think that the entire trend of human existence has been that our desires grow faster than our ability to fulfill them. If you look at the things that are expectations today, there were unimaginable luxuries 500 years ago, even 50 years ago for things like therapy, right? Or things like antibiotics 100 years ago, right? I mean, it didn't matter how rich you were, you simply didn't have access to it. So I think we're underestimating human ambition, human desire. You know, people are going to be mad that they don't have a vacation home on Mars in 20 years. like really mad, like this really mad.

45:47Anish Acharya:They'll be seeing it on Insta and be like, come on, babe, we got to like work harder and make this happen. Every CEO is going to want to build a much bigger company. So I don't think already it feels like we're living a much larger form of sort of human existence than we could have imagined 100 years ago. And there's no reason that trend won't continue or accelerate.

46:05Lenny Rachitsky:This idea of ambition, I'm glad you brought that word up again. It's something that's coming up a lot on this podcast that not only is AI making it easy to be a lot more ambitious, It's almost making us have to be more ambitious because everybody else can just do all the easy stuff now. And now what separates us is just how big can you go? Okay, I'm going to make a personal website. Okay, it's cool. It'll be this white, simple background. No, okay, I'm going to make this 3D game where you have to like walk through a world and discover all the things that you, like everything is just getting more epic.

46:35Lenny Rachitsky:Thoughts on just this idea of ambition becoming a bigger, I don't know, skill and habit.

46:39Anish Acharya:A lot of things get conflated because when you and I say ambition on this pod, I think people have a very specific idea of what that ambition is, you know, ambition to be a founder, ambitious to build a beautiful software product. Like those are types of ambition, but there are other types of ambition. Let's talk about creative ambition. You know, when you're five, nobody says, Lenny, you're good at painting, but you're bad at drawing. You know, you just have an ambition or a desire to make something and you make it. Like that's something that's very unique and can now actually be encouraged. You know, think of very local ambition.

47:09Anish Acharya:So, you know, they have the NHS in the United Kingdom. I think it's this sort of treasured institution that's not working well. Maybe the way AI shows up in their society is making like the NHS as good as the iPhone, right? That's very specific and local to them. Or simply the ambition to be more connected to our family, to be more present parents. So the ambition doesn't have to be sort of ambition in the narrow economic sense. It can be really anything that we want to do more of. And who doesn't have that in their bones?

47:35Lenny Rachitsky:Yeah, that's such like my example is so dumb now that I think about it. It feels like the thing we have to unblock in our brain is, okay, AI, solve cancer. We're not like, that's where we can start to think now. And it's so unnatural to us, especially as product people that always have to think about the MVP and the constraints. Now we have to think big. Okay, what's the big for, what's the 10x, the thousandx version of this?

47:58Anish Acharya:Yes, I know, right? In a way, we have to think small because this thing that we've, our whole lives have been built around how precious software and intelligence is. And now it's totally not precious. like that probably will be a harder change for you and I.

48:11Lenny Rachitsky:Yeah. So like all these new little habits, I think a lot about on the Claude Co. team, they have a principle. You know what's better than me doing it? It's Claude doing it. And that just created this habit in everyone on the team. How do I help Claude do this thing for me? And I feel like that's a thing we all have to start to build in our head. And GrokBot's really good at that. Like, not an investor, no affiliation, but it's just like so simple and good at the stuff.

48:35Anish Acharya:Also think that there's so much learning that happens through doing. You know, it's funny, if you look at the number of people that are talking about Vibe Coding versus the number of people that are talking about their projects, people are a little embarrassed. I'm a little embarrassed to talk about a lot of my projects because they don't seem important or substantial enough. But so much of it is learning through execution or shipping or being fulfilled through execution and shipping. Like, I don't think we can underestimate that either.

48:57Lenny Rachitsky:I have a guest post coming together from someone at Google that works on a lot of their lab stuff. And I don't want to give away the goods, but just broadly her concept is that building is now the new reading where you build to learn and infuse and experience. And it's totally okay for most of it to go through and away because that's still building your muscle. Yeah.

49:20Anish Acharya:So well said. Yeah. It's a building as an activity rather than an outcome, right? Yeah.

49:25Lenny Rachitsky:Which I think a lot of people feel bad. I shipped all these things, but no one's using it. I never use it. And I think that the key here is like, that's actually okay. That's totally fine.

49:33Anish Acharya:Oh man. I mean, and it happens in every other domain, you know, I make a DJ set and three people listen to it and I listen to it 100 times. And it's very fulfilling. It doesn't matter.

49:43Lenny Rachitsky:We're going to talk about your DJ stuff later.

49:45Anish Acharya:Okay. Okay.

49:47Lenny Rachitsky:Okay, let's come back to consumer stuff. So you focus on consumer at A16Z. What's happening in consumer these days? What's the landscape? What are you excited about?

49:57Anish Acharya:So I think there's three big areas. We're very early, as we kind of discussed. The labs have done a good job, but there aren't as many sort of independent mass market consumer products. Coding agents are awesome. And I think a total lightning bolt. You know, I think it's easy to say most people don't want to make code. But the big change in my thinking is that coding agents are a way to interact with the world generally. And you've seen a lot of this on X, you know, people use cloud code to edit videos, you know, or codecs to create a game that they play with their kid on an airplane ride. Like there, it's sort of this general problem solving tool that consumers can use in very unique ways.

50:35Anish Acharya:And probably the best example of a company that we've invested in is Wabi, where it's sort of a platform for mini apps. People can create them, consume them, share them. So coding agents is one big area I think that's important. Personal agents, we had this incredible moment around OpenClaw, but guess what? OpenClaw is a dev thing. And Hermes, of course, and Moldbook, all of that is now getting distilled into mass market, consumer, and enterprise agents that people understand, which is what we discussed previously. And then the last is, I'm going to call it entertainment. That doesn't fully do it justice.

51:07Anish Acharya:I think that it's a lot of sort of creative tools. You know, Suna has done such an amazing job. It's companionship products, all that, that entire area is, you know, uncomfortable to talk about. So I think it's under discussed, but there's some huge, fast growing products there. I think those are the three big areas that we're watching right now.

51:25Lenny Rachitsky:that's really interesting just think of it these three buckets coding agents ai assistance kind of open club but much simpler and easier and more reliable uh sounds like basically the three described there that you're excited about are um instinct uh grok bot and chat gpt work that's right and then wabi i guess would fit in that first one it's like a personal coding agent that can build whatever you want for you

51:48Anish Acharya:yes yeah that's exactly right and then entertainment is the third bucket with like

51:52Lenny Rachitsky:like AI girlfriends and that kind of stuff. Yeah, those companies, it's like crazy. You guys put out these market maps and like five of them are these different companions.

51:59Anish Acharya:That's right. Though actually, to be accurate, it's more boyfriends than girlfriends, actually, you know? The majority of people using companion products are women that are in their 40s and 50s, actually.

52:10Lenny Rachitsky:Interesting. Okay, cool. So those are the three kind of areas you think the biggest opportunities will come from. And it's interesting that they connect to this idea of loop. I don't know, make me happier. Like, yeah, these are kind of, there's kind of like the jobs to be done for humans. Make me happy. Make me healthier. Make me live longer. That kind of stuff. Yes.

52:31Anish Acharya:Yes. Give me a channel for my ambition, a channel for my sort of fulfillment, and then a thing to do when I'm not doing everything else.

52:40Lenny Rachitsky:That makes sense. Okay. The other question I have for you along these lines is, there's so many companies and so many startups, so many products everyone's launching. the speed at which companies and products are shipped is like thousand X-ing. How do you think about durability in moats when you look at a startup? Because a lot of founders get that question. Everyone's getting that question. How am I not a rapper? What are signs that tell you this might be a durable thing?

53:06Anish Acharya:Well, I think there's two important ideas. One is something that Jesse from Decagon said, which I love, and that is that moats are most often discovered, not designed. I think it's really easy. I've done this as a founder to get in your own head about like, hey, I need a business plan that survives scrutiny from MBAs and BCs. I've got to have some really sophisticated, you know, idea of what my moat will be. And for that team, they just started shipping and it developed over time. Another great example of this is Cursor. You know, they were criticized a lot for not having a moat, but it turned out that initially being a high NPS DAU product was really good.

53:42Anish Acharya:And over time, they captured all the reasoning traces. They trained their own models, the Composer 1, 2 models and, you know, so on and so forth. We know how that story plays out. So moats can be discovered. They don't have to be designed is one. And I think the other is that we seem to have forgotten that the classic moats, none of the classic moats are based on how hard it is to make the software. You know, like we're not building self-driving cars. Most of us aren't. So it's network effects, it's scale advantages, it's brand effects, proprietary sort of data or what was historically called a cornered resource.

54:12Anish Acharya:Every moat from five years ago generally is still a good moat. We just need founders that have ambition in those directions. We need more multiplayer products. We need consumer social. We need products that get dramatically better the more you use them, like town, so on and so forth.

54:27Lenny Rachitsky:So still read Hamilton, Helmer, and all that stuff still applies. Yes, love his book. Yeah, he's been on the podcast.

54:37Anish Acharya:Yeah, I feel like there's only five real business books in the world. Every other one is in the business of selling business books and they're fake. And his is on my list of five.

54:46Lenny Rachitsky:Are there any other in this list that come to mind real quick around the topic?

54:49Anish Acharya:The two that are so obvious are High Output Management, which is like, that is as good as it ever was. And then Ben's book, you know, Hard Things is, it was the first emotionally honest book about business that was ever written. And that's why founders love it. That's why I love it. Because you read it and you're like, wow, I'm not the only one that's, you know, anxious and feels like a failure and can't tell anyone what I'm going through. Ben went through it too.

55:13Lenny Rachitsky:Two of the most mentioned books on this podcast. turns out. I'm not surprised. So on this moat idea, so say you're a founder and you're just like, you know, you're putting a pitch together, trying to pitch you or other VCs. What's the best way to talk about a moat? Is it like, can you just say we're going to discover it? We're not sure. Nobody really knows yet.

55:31Anish Acharya:Yeah, I think that we would happily take a bet on a product that doesn't have a quote unquote moat or durability story if it has, you know, a lot of momentum, a lot of craft, a lot of, you know, sort of growing engagement. It's actually the thing I've learned over the years. I used to be very worried about people stealing my idea, but I've learned that the big ideas are always supported by a dozen small ideas that are invisible. And even if somebody replicates your big idea, they never see the small ideas that make the big idea work. So I actually think that there's some just something special in the water with certain products that make them incredibly successful despite extraordinary competition.

56:08Anish Acharya:I mean, look at granola. You know, two years ago, it was really criticized and I don't know that they have a super strong durability story today, and yet it is like beloved and dominant. So I think, you know, you sort of, I listen to like what the customers are saying more than, you know, what the business books say.

56:23Lenny Rachitsky:I just saw Ram put out a report of the fastest growing companies, according to their data, and Granola is like number two or three. It's a tremendous product. The craft is really high. Yeah, and I think about that a lot these days because there's so much, like there's co-work, there's JGPT work, there's Cursor, there's Grogbot, and it's crazy how quickly one can switch from one to the other. And the underlying model is not that different. All it really is, most of it is the harness and slash UX of the product. And so to me, that tells you there's so much opportunity in the actual user experience being a moat or at least giving you a lot of time to find something that is durable.

57:03Anish Acharya:100%. And I think for the people that are at the edge, they pay for all of them because they all have their respective areas of specialization.

57:09Lenny Rachitsky:Yeah, I have many$200 a month plans right now.

57:12Anish Acharya:I know, I know, right.

57:14Lenny Rachitsky:It's a little painful, but yes, me too. And I think Cursor has a$300 a month plan now.

57:19Anish Acharya:So does Grok. Oh yeah, you're right. Yeah, that was the Grok plan. It's true with Grok Heavy.

57:23Lenny Rachitsky:RIP, yeah, RIP Cursor. I think they've transitioned away from that brand. Something I've been talking a lot about is the distribution. I call it distribution, this new moat, but it's like, it's always been a moat, but it feels like more and more that is actually a massive advantage because everybody is, there's like a thousand launch videos a day. Everyone's launching, launch, launch, launch. And really the ability to get your stuff into people's feed, get them continue to be reminded your product seeks feels like increasingly is powerful and important. Thoughts on the rising value of distribution, existing distribution being a big lever for growth and success?

58:04Anish Acharya:This is so important. And, you know, I asked Chris Dixon about this because he sort of authored the famous come for the tools, stay for the network. I think the issue is that our entire generation of founders and CEOs were trained on the theory of networks and network building. And as a result, every network that exists today is hyper trained to ensure no one else builds a network on their network. So I actually think that the sort of network effect has gone back to this grassroots, like true word of mouth. when somebody is getting a ton of mentions on X and on YouTube and on Instagram and all of these places organically, that is probably the best form of the sort of third-party network effect that you can hope for today.

58:44Anish Acharya:And actually, just like the web 2.0 era, unlike the mobile era where you had the app store and you had growth hacking and you had all of these sort of, you know, little cottage industries, we have to kind of build our own channels off of that word of mouth growth. So in a sense, it's a purer growth problem, but a harder one than we've had in a couple of product cycles.

59:04Lenny Rachitsky:And to get word of mouth, you need to build something. I always think about Seth Godin's line, build something remarkable, something that people, that is worth remarking about, which is basically, you know, build an amazing product that people want to talk about, which is, you know, a very hard to do. And it makes sense that because there's so much happening, people are just going to pay attention to what are my friends using and saying is worth paying attention to.

59:25Anish Acharya:Well, you know, here's the one thing I would say that here's the hopeful point, which is I always say that nobody has a growth problem these days. They have a product problem. And the reason for that is like you can build such a wildly ambitious product in any direction, you know, functional or emotional. You can charge a lot of money for it. So my challenge is like, hey, is it that you have a growth problem or is it a failure of our collective imagination? You know, if we imagined our product cost$1 ,000 a month,$10 ,000 a month, like what if our product was a software Birken bag? What would it have to do to justify that?

59:58Anish Acharya:Okay, let's figure out how we build that. And it comes back to the ambition question.

1:00:02Lenny Rachitsky:Yes. And still, though, you still need some advantage to get in front of people to get it out there, at least initially, because there's like a thousand things launching every day. Imagine that's still a big opportunity and I don't know, advantage, which to me makes me feel like incumbents have a huge advantage. They have the products, they can tell you, hey, go use Gemini. And every time you go search Google, I guess, do you feel like that? Do you feel like it's harder for startups now because of this distribution challenge? I don't know.

1:00:30Anish Acharya:I think it's easier because like Gemini, despite all the kind of heavy cross-selling Google has done, nobody would say they're winning. You know, startups can build in directions that incumbents are uncomfortable building in, like everything we talked around Companion, but there's many others. That's true. You know, prices can be pretty high. Like people are open to paying$200 a month or in the enterprise. They're open to signing million-dollar ACV contracts without really knowing what they're getting. So I think like the kind of, the floodgates are open. It feels like Christmas 2009 where everybody got their iPhone and want to download new apps.

1:01:02Anish Acharya:You know, that'll change at some point. People will feel like they're done and they're tired and they don't want to try any more apps. But the windows are open for now. I think it's easier for startups.

1:01:11Lenny Rachitsky:That's a really interesting insight. And you're lying about how it's not a distribution or growth problem. It's a product problem is such an important one. Because if your product was that good, people would talk about it and share it and use it.

1:01:23Anish Acharya:And it can be. I mean, what are the wild social experiments that we're going to see with this technology kind of intermediating them? You know, I think a lot about actually, here's a fun example from a few years ago. Have you heard of Mischief? Do you know Mischief? Yeah, yeah. They're awesome, right? They're sort of like this creative studio that uses technology as their medium. I'm very Web 2.0 in that way, actually. Many of the kind of, you know, the Ev Williams, the Kevin Roses, that's who they were, painters, except technology was their canvas. So they created this very funny product called Card Versus Card, where they shipped, I think, 100 ,000 people a debit card.

1:01:55Anish Acharya:And then every day they would text those people with a location that they had to spend the money at. And they would put$100 on the card and everyone would rush out to spend the money. And one or two would be able to spend it and everybody else would get declined. And it was just this hilarious social experiment that went hyper viral. And for me, it was always inspiring in the world of fintech because it was like, wow, why don't we build more products like that? You know, money is inherently social and yet all the financial products we have are so dry and personal and embarrassing. I think there's a sort of similar moment happening in AI right now where we can build these wildly ambitious products that touch on many of our social nerves.

1:02:29Anish Acharya:We just have to do it.

1:02:30Lenny Rachitsky:So let me follow that thread. You get to see tons of companies both pitching you and also companies you're working with that you're investing in. What are some counterintuitive lessons you've learned from watching the companies that operate well and have succeeded? Lessons that maybe go against typical wisdom, startup wisdom.

1:02:52Anish Acharya:I'll tell you the biggest one. And, you know, in the old days, which is three years ago, we would see a company. And if what they were doing, trying to do was too ambitious, we would, you know, not engage. Just too crazy, too complex. you know and implied by that is you wouldn't do a hundred million dollar seed because just it's too much money for almost any problem it's too much money for any person to actually manage it's too much money to build a this sort of talent to absorb like it doesn't make sense as an inception round i think today we're almost seeing the opposite problem where you know an idea that's too small is not something that we want to engage with and you can talk about how you put a hundred billion to work in the seed productively.

1:03:32Anish Acharya:Now, I'm not recommending you raise$100 billion, but I think that there's this sort of no ceiling on ambition is also showing up in how we're picking companies and maybe how they're picking us as well. Because when we invest, we tell every founder, like, we're here to help you build the strongest form of your vision. You know, Mark told this to me when him and I were talking about coming here. And he said, Anish, the way we sort of told our story when we were raising our first fund was, we were going to the moon or we were going to leave a moon-sized crater in the ground and there was no other option.

1:04:03Anish Acharya:That's sort of how we want to work with our founders as well.

1:04:06Lenny Rachitsky:I love that point. So can you get too ambitious? Is there a limit? Obviously, you look at the team and what they're, but kind of the key lesson here is be more ambitious. It's like the opposite of what used to be of like, here's our wedge, here's where we're going to go. What you're looking for is just how big is the idea?

1:04:23Anish Acharya:I mean, look at Adams. How crazy is Adams? I mean, what an incredible hero's journey for all of us collectively, but also what they're trying to do is something that five or seven or 10 years ago would have felt insurmountable. And now it's like, okay, it's challenging. Let's see. Interesting.

1:04:40Lenny Rachitsky:Is there anything else that has changed or I guess you've changed your mind about around what you think it takes to build a successful company these days?

1:04:47Anish Acharya:I mean, I think that the kind of old wisdom around consumer products have to be free. I'm almost taking the opposite take, which is let's think about consumer products that are extraordinarily expensive. I think every part of consumer discretionary spend is up for grabs right now. And I think a really useful, because price is a measure of product market fit, a really useful product exercise is what is the Birkenberg 10 ,000 a month, 1 ,000 a month version of our product. So I think expensive consumer software is something new and important that we wouldn't have thought about five years ago.

1:05:19Lenny Rachitsky:I love that framing because it just pushes you, again, to be more ambitious. Yeah. You mentioned Mark and you work closely with Mark Andreessen, Ben Horowitz. Yes. What's one thing you've learned from each of those guys?

1:05:33Anish Acharya:They're such extraordinary leaders, founders. I mean, the thing that I actually feel so grateful to be a part of that they really embody to their core, I think is a feeling of stewardship for the technology industry and for really the country and the sort of maybe the Western way of living and thinking. You know, and if you look at sort of a Ron Conway, you know, or Brooke Byers, Tom Perkins, there was this feeling, I think, of obligation to sort of leave it better than you found it from an industry perspective. and that sort of aspiration goes way beyond just being the best investor in the world, though we want to do that too.

1:06:17Anish Acharya:I see them both show up that way over and over again where they want to do hard, important things that don't directly benefit the firm or at least not singularly because they're just sort of important. You know, Ben has done a lot of that in the direction of the industry and Mark in sort of the direction of the country, though of course both work on both. Another really cool thing is just to see how I think Mark and Ben, but maybe the firm a little bit has shaped our collective ambition as a founder community. I think if you look at five or seven or 10 years ago, deep tech was deeply unpopular.

1:06:50Anish Acharya:You know, it wasn't a high status thing to be working on. It was very fringe. And now it's become very popular, high status and mainstream. And I think there's a lot of firms that have, of course, pulled in that direction. But I think that someone like Mark has been very full-throated in his support of working on sort of capital I important work in the national interest. and all of Silicon Valley has changed as a result.

1:07:11Lenny Rachitsky:And you guys have had some big wins in the past couple of weeks, investing wise too. Yeah, thank you. Congrats. Final question before we get to a very exciting lightning round. What's your advice to product people who are trying to think about what they might want to shift in how they work, how they think, how they operate to be more successful in the future, in their careers and with their companies?

1:07:37Anish Acharya:Just make more things. And I know it sounds silly. I know everybody says it, but just please like come up with a project. You don't have to tell anyone about it. It can be totally unimportant, but use it as a chassis to use all the new models, ship things, talk about them, build your own intuition. I promise you're one sort of slightly frustrating and then very fulfilling week away from being as pilled as anyone. So you just got to use the technology. And if not now, then when, right? This is all of us got in the game to build the products we saw in our mind's eye. And now we have a chance to do it.

1:08:14Anish Acharya:So just please, please use the models, you know, and tell me what you built. Text me, you know, tag me like I will reply and respond and engage with you. And so will everyone else because we the magic of Silicon Valley is that it's a very positive some mindset, you know, it's sort of everybody is building on each other and vulnerability is really rewarded. So I definitely would encourage people to use the models.

1:08:35Lenny Rachitsky:What's like a good heuristic if you're doing this enough? Is it like build something once a month? Is it like some number of hours per day sitting, talking, building? Anything that you think might help people be like, okay, you're doing a good job?

1:08:48Anish Acharya:I mean, just ship something once a week. And it doesn't have to be crazy. I mean, for example, when I was playing around with Codex, I had it build a slide deck for Mother's Day for my wife that pulled from my text messages. It looked at my photo gallery. It set some music to it. Created like a 20, you know, slide deck of our relationship and pulled some cool old texts from when I first asked her out. It was a really nice Mother's Day, you know? I mean, it wasn't important. It wasn't something that we might come back to, but it was shipping something. So it can be that small.

1:09:20Lenny Rachitsky:I know that you're our mutual friend, Nikhil Singal. You worked at Credit Karma with him. He had a really good way of thinking about this. he finds that people flip on AI and how they feel about it once they find some moment of joy that it had created for them. And this Mother's Day idea is such a good example. And so I think that's kind of a tip I always think about is just like, what's something that just will bring you joy if this works?

1:09:45Anish Acharya:What's something you can do for someone else? You know, maybe that's a good starting point as well.

1:09:49Lenny Rachitsky:I love that. Anish, before we get to a very exciting lighting round, is there anything else that you want to share? Anything else you want to double down on before we get into the lightning round? I don't think so. I've loved the conversation so far. Me too. And with that, we've reached our very exciting lightning round. I've got five questions for you. Are you ready? Okay. Here we go. What are two or three books that you find yourself recommending most to other people?

1:10:14Anish Acharya:Yeah. So, okay. Conquest and Cultures is my very favorite book. It's Thomas Sowell. and it just talks about how conquests have led to culture change in different societies around the world, sometimes positive, sometimes negative. I think to me, it's just the best historic view of sort of culture as the biggest driver of outcomes. And I've experienced a lot of that as, you know, somebody who was born in Canada and moved here to America, which has a very different culture of ambition. There's that word again. So Conquest and Cultures is great. Seven Powers, you mentioned, is actually just an awesome book.

1:10:47Anish Acharya:I think it's a very intellectual distillation about kind of, you know, moats and business theory and compounding advantages. I really like it. You know, maybe the third is the one that Mark, I actually thought that he was maybe punking me when he sent this to me. Before I started, I said, Mark, are there any books that I should read? And he sent me a couple. And one was a book, a textbook called Increasing Returns to Scale, which I think is Brian Arthur is the author. It's an awesome book. It's a slightly dense study of why things like software have such outlier economic effects, but it really helps put things in perspective or help me in terms of like, why does our industry work in the way that it does?

1:11:27Anish Acharya:Why does our culture work in the way that it does, right? Why are we so positive some? I'd love to say that we're better people, but I think it may be a sort of better system and structure we work with it.

1:11:35Lenny Rachitsky:Favorite recent movie or TV show you've really enjoyed?

1:11:38Anish Acharya:Oh man, I watch trashy movies and TV. I mean, we've watched House of Dragon. which is pretty cool yeah I don't know yeah maybe it's it's not highbrow it's not important with a capital I but it was awesome we loved that and then I I did see Odyssey I saw it in London I was there for a board meeting in you know an IMAX theater packed with people drinking pints and having fun and amazing it was cool because it was just a whole the movie was great but it was just like a theater experience like a weird social experience kind of alone together those are two recent ones

1:12:13Lenny Rachitsky:I have not been able to get tickets to the Odyssey at an IMAX. I actually have a GrokBot just watching the site constantly and finding me good seats. Perfect. Perfect. They've got the dumb capture, though, on the AMC website that hasn't been able to get there. Really? It's like a tricky one. It's a really tricky one. Is it the like select the fruits?

1:12:34Anish Acharya:Anyways.

1:12:34Lenny Rachitsky:Yeah, it's like you have to click three different matching shapes, which is like, come on, you can't do that. hopefully by the time this comes out, I've seen it. I think it's a few people I guess in a row. I'm like, I haven't seen it yet. Oh, man. Okay. Favorite new AI product. I don't know. Favorite AI product recently that you've given me joy.

1:12:55Anish Acharya:Oh, man. I've spent a lot of time with the personal agents. I think Grok's okay. I'm going to give it to GrokBots only because it's just so unhinged for it to be so ambitious about sort of caching credentials and getting work done on your behalf. Like I love it and I expect it from a startup, but they actually are doing things that I think no other sort of big company would do. It's really, really well done. It's a really thoughtful UI. It's got a really powerful foundation model. I think it also is like, okay, wait, maybe this is not a two horse race on the sort of model side. So I just think the product is like fun and ambitious and is sort of taking risks that other products like that wouldn't take.

1:13:33Lenny Rachitsky:Two more questions. Do your favorite life motto that you often come back to in work or in life? Oh, man.

1:13:39Anish Acharya:I've got one. It's, I learned this or I sort of, you know, this is from my founder days, but it also is something that's very true of parenting, which is, you know, don't discover things through a painful experience that somebody can just tell you. So, and I unfortunately have had a bad habit of sort of discovery versus learning from somebody who's just a few steps ahead of me. And I find my children have that habit too.

1:14:01Lenny Rachitsky:What's one example of something that you wish someone had told you?

1:14:04Anish Acharya:I mean, for my kids, it's don't touch the hot stove. For my startup, it was like, literally, my startup, it was don't build a product and a platform at the same time. You know, if you're going to be a platform company, build one. Our first company, we try to build a sort of social platform for mobile games and be a gaming studio. And, you know, somebody wise told me right away, like, look, being a studio is so hard, much less being a studio and a platform, pick one. And we did. And it was, it took us years to figure out we were wrong.

1:14:31Lenny Rachitsky:Final question. Ask Ben Horowitz what to ask you about. And he just said, ask him about his DJing. He's very good. and I went to your, I found your DJ site. I don't know if that you call it on SoundCloud slash IllScience. It's very good. I'm just listening to it while I work. Any tips for somebody that wants to get into DJing? Any tools you found useful? Any, I don't know, insights that might help someone become better at this?

1:14:57Anish Acharya:Totally. I mean, I think that this is why I love music models so much. DJing has always, I mean, I love DJing. I've been playing for 30 years now, since 95, actually 31, wow. and it's an awesome way to kind of express yourself musically if you're not a classically trained musician. You know, you select the music, you pick the records, you mix them together so it requires some technical skill. But now I think you can go a step further and just make music with the models. And the best part is when you can come up with music ideas and have the models do the kind of strong form of them. So I think music is just such a visceral, satisfying way to kind of, you know, experience and, you know, provide experiences in the world.

1:15:34Anish Acharya:So whether it's DJing or making music, I just suggest everyone do it.

1:15:38Lenny Rachitsky:I hadn't thought about how DJing has changed now that you have Suno and things like 11 Labs and all these things where you could just generate the music, not have to just splice together existing music.

1:15:47Anish Acharya:I mean, think of the history of it. You know, you went from, okay, you know, first you could only hear music if you were there with a person playing it on an instrument, you know, recorded music on the phonograph. The big change in music actually from a medium perspective was the cassette tape. Because the cassette tape was really the first time you could create, right? you could actually compose your own quote unquote album. I think a lot of why music struggled in the 2000s is the sort of that went away and we went back to broadcast. And now that people are making music again, I think the music industry is going to be bigger than it's ever been.

1:16:19Lenny Rachitsky:Wow, man, so much disruption. Anish, this was incredible. We covered so much ground. Final question, how can listeners be useful to you?

1:16:28Anish Acharya:I mean, show me what you're building. Please don't be despondent. Build something and then tag me and I'd love to see it. If you're interested in hearing more stuff like this, please follow me on X. I try to kind of engage and follow back. And otherwise, make sure you check out all the amazing folks in Lenny's network. Claire is a star. Alina is so, so good. And there's just so much compelling content here. Mikel, which we talked about. Mikel is the best man. That guy is everything.

1:16:56Lenny Rachitsky:Amazing. Anish, thank you so much for being here. Thank you, Lenny. Bye, everyone. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product.

1:17:35Lenny Rachitsky:This podcast has been produced by a third party and may include paid promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies, and individuals are not endorsed by AH Capital Management LLC, A16Z, or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy. Thank you.