Show full transcript 0:27 In the old days, three years ago, 0:28 we would see a company and if what they were trying to do was too ambitious we would you know not engage today 0:34 We're almost in the opposite problem an idea 0:36 That's too small is not something that we want to engage with you have this interesting take that company building more and more 0:42 It's gonna become this kind of series of creating loops 0:44 We're gonna see this sort of cascading set of everything from a loop per person to loops that can run large parts of the company with that said 0:52 I think humans are a critical ingredient the loop will help you climb to the local maxima 0:57 But then at Plateaus, you need human intuition. 0:59 You need somebody to actually help you land 1:01 at the base of the next hill. 1:02 They have this take that the big opportunity is this idea of, 1:05 loop, make me happier. 1:07 We believe that people want to be more productive, 1:10 but they don't. 1:10 I think more people want to spend time than save time. 1:13 So I think that the opportunity for this technology 1:15 is the basics of consumer need. 1:18 How do we feel more connected, more loved? 1:20 Do we make progress? 1:21 How do we have fun? 1:22 I don't think it's a model or a capability challenge. 1:25 It's just a product design challenge. 1:28 Today, my guest is Anish Acharya, Anish's general partner at A16Z, where he focuses on consumer 1:34 investing. He is one of the most insightful, thought-provoking, mind-expanding, in-the-weeds 1:40 product investors I've met. He's been at A16Z for over seven years now, and unlike a lot of 1:46 ECs, and why I love having Anish on the podcast is that he is a longtime product builder and 1:51 founder. He founded a company called Social Deck, which he sold to Google and then ended 1:55 up leading a number of efforts within Google. Then he started a new company called Snowball, 1:59 which he then again sold, this time to Credit Karma, where he moved to VPR product and then GM 2:05 of the broader consumer product and the entire credit card business. This conversation will get 2:10 your mind buzzing. Before we get into it, don't forget to check out Lenny'sProductPass.com 2:15 for a free year of the hottest and most beautifully crafted AI products 2:19 in the world, available exclusively to Lenny's newsletter subscribers. 2:22 With that, I bring you Anish Acharya. 2:29 Anish, thank you so much for being here and welcome to the podcast. 2:33 Thank you, Lani. I'm so excited to be here, thrilled. 2:35 I want to start with a very light topic. 2:38 I want to talk about this meme of the permanent underclass. 2:43 It's kind of this joke that people have joke about, 2:46 this idea that if you kind of fall behind 2:49 and aren't just like on top of all the latest AI tools, 2:52 aren't becoming the most productive person ever, 2:54 you're gonna become part of this permanent underclass 2:56 and fall behind and have a really hard time. 2:59 And some people are joke about it. 3:01 I think a lot of people take this really seriously 3:03 and stress a lot of people out. 3:05 How real of a concern do you think this really is? 3:07 How seriously do you think people should take this? 3:09 Not very seriously. 3:10 And it's a funny dark fantasy that we seem to have 3:12 as you know, Silicon Valley collectively. 3:14 Like things have never been better 3:16 really by almost every measure 3:18 by how sort of distributed all the opportunities are 3:21 by the kind of technology we have access 3:23 access to the types of ambition we're allowed to have and to the number of companies that 3:28 are sort of independently working on things that are winning. And yet, there's this sort 3:32 of discussion of permanent underclass, being outside of the light cone, I've heard it. 3:36 And it's not just something for deep insiders or outsiders, it feels like there's a real 3:40 fear kind of from, you know, researchers at Foundation Model Labs, all the way through 3:46 to the Silicon Valley layman. I mean, here's like a couple of points that I 3:50 think are really important. So first, I think the last era of tech was a lot more centralized. 3:55 If you look at network effects, that's sort of the gold standard you worked on a network 3:58 effects product. That's the gold standard of businesses from the mobile era. And those 4:04 things led to dramatic centralization, right? Of course, all of them are definitely sort 4:09 of the end of one networks. If you look at what's happening now, it's like every 4:12 part of the stack, there's not even two relevant players, there's like 20, you 4:20 Both, if you look at coding agents, 4:22 we were talking about it, like your mental model 4:24 for two years ago should have been, 4:26 would have been, I think, it should be winner take all. 4:28 And yet, Claude Code, Codex, Lovable, Replet, Wobby, 4:33 like they're all sort of working. 4:35 So it's really, really encouraging to see that. 4:37 You know, the second thing, 4:38 you've heard all the kind of economic data, 4:40 everything from radiologists 4:41 who are supposed to be cooked every year 4:43 for, I think, about 20 years now. 4:45 And of course, job postings are higher 4:47 than they've ever been, as well as programmers, you know? 4:49 So, I don't know that the empirical data bears it out. 4:52 I think the final thing is that there's this sort of discussion about RSI, and I know RSI 4:58 is like recursive self-improvement is a fun term to throw around, but if you ask the most 5:02 sophisticated individuals at the labs, it's not actually RSI that's occurring, which 5:07 could lead to some sort of runaway winner because they were an Epsilon ahead of the 5:11 others. 5:11 It's autocatalytic effects, which just means you're using the technology to 5:16 improve your process, but it's not truly recursive. 5:18 So I think everything from the most empirical to the most technical view points in the other direction and yet 5:23 We can't seem to let go of this fantasy 5:25 Something I've been thinking about recently seeing all these like even seeing these crazy 5:31 Stories about open AI is models hacking hugging face. Yes all these stories to me feels like 5:36 There's always been this question of are we on the fast takeoff or the slow takeoff scenario 5:40 And it feels very much so that we are on the slow takeoff scenario because every one of these milestones 5:46 It's like, holy shit, it hacked. 5:48 We had no idea it was doing this, but like we're catching it, we're watching it, we're 5:51 observing it, we're iterating, evolving. 5:53 There's always this fear, okay, but tomorrow it's going to take off. 5:56 What I'm hearing from you is that's probably not the case, which I think is the source 6:00 of a lot of people's fears, this idea that all of a sudden it's going to become super-super-intelligent 6:03 and then we're in big trouble. 6:05 That's right. 6:05 Like the line of reasoning for that case is always everything up until now, then 6:10 something happens that no one can quite articulate and then fast takeoff. 6:14 So I don't believe that that's going to happen. 6:17 I do think that model progress is happening faster 6:19 than ever before, but if you look at something 6:21 like economic diffusion, you know, I grew up in a small town, 6:25 I went back home last summer, like people's lives 6:27 haven't changed that much. 6:28 So if nothing else, the sort of slow rate 6:31 of economic diffusion will catch it. 6:33 I think the other thing that's under discussed Lenny 6:35 is, you know, how many problems 6:36 are truly intelligence bound? 6:38 Like if you had a, you know, a data center 6:40 of PhDs working at FedEx or Domino's Pizza, 6:43 Are they going to be like exponentially dominating supply chain and pizzas? 6:47 Like I don't think so. 6:48 So I think we might be overestimating how many problems are intelligence bound versus 6:52 bound by other things. 6:53 This episode is brought to you by our season's presenting sponsor WorkOS. 6:58 What do OpenAI, Anthropic, Cursor, Replit, Sierra, Clay, and hundreds of other winning 7:04 companies all have in common? 7:06 They are all powered by WorkOS. 7:08 If you're building a product for the enterprise, you've felt the pain of integrating 7:11 single sign-on, skim, r-back, audit logs, and other features required by large companies. 7:17 WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built 7:23 specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand 7:29 up market ends up working with WorkOS. And that's because they are the best, whether you are a 7:34 seed-stage startup trying to land your first enterprise customer or a unicorn expanding 7:38 globally. WorkOS is the fastest path to becoming enterprise ready and unblocking world. It's 7:43 essentially striped for enterprise features. Visit workOS.com to get started or just hit up 7:49 their slack where they have actual engineers waiting to answer your questions. WorkOS allows 7:53 you to build faster with delightful APIs, comprehensive docs, and a smooth developer 7:58 experience. Go to workOS.com to make your app enterprise ready today. 8:03 What are you seeing inside of companies in terms of, is there more of a divide happening and do 8:10 you think there will be more of a divide between the people that are becoming really good and 8:13 embracing versus like, I don't have time for this. I hate all this stuff. My job's already so 8:18 stressful. What are you seeing happening and where do you think things will go inside of 8:21 companies in terms of maybe a divide? I mean, I have so many thoughts on this. I don't think 8:25 we give the average employee enough credit. I think we have this abstraction of a white 8:29 caller employee. The white caller manager, the abstraction is like some Dilbert Esk 8:34 manager who's just shuffling paper all day long. It is abstraction of consumers that 8:39 they're sort of these low agency NBCs. Of course, that would never apply to us or our 8:42 friends. We have this abstraction that everybody else's job is super-automatable by AI, but 8:48 of course ours is not. 8:49 I think when you actually get into the details, a lot of people are actually excited 8:53 to better themselves, get more leverage. You see this with, of course, sophisticated 8:59 companies like Google, but even a company like Kevok where they sell, you know, used cars in Mexico, 9:04 they've got this concept of a Jedi Academy where they're teaching everybody at the company, 9:08 including the mechanics, how to use the new tools and technologies. And kind of at the end of the 9:12 six week course, they ship a cutting edge in production agent. So I actually think that 9:17 more people are embracing the technology than we sort of like to discuss. I think a 9:21 big change is going to be using AI versus reorganizing your entire company around AI. 9:27 And a great example of this is if you look at the diffusion of electricity as a technology, 9:32 it took 40 years for us to get from the inception of electricity to reorganizing factories. 9:38 And that means like burning the buildings down and starting from scratch versus taking 9:43 what was previously coal and simply swapping it with electricity. 9:46 So I do think that like the most ambitious companies are rethinking everything around 9:50 the models and those that are a little less ambitious or perhaps a little earlier 9:55 are thinking more about how do we give people an existing orgs, existing job 9:59 functions access to the technology. So kind of a theme I'm hearing so far is we 10:03 can be a little less stressed about where things are going and and the future 10:08 of your job, your careers. I think so man. I mean I think if you even just 10:12 think of the kind of incentives for the CEO and executives, you know, Sundar 10:16 running Google, he doesn't want to run a more efficient $4 trillion company. 10:19 He wants to build a $40 trillion company. So anytime you have an 10:23 economically productive unit. It's rational to kind of, especially if it gets more productive, 10:28 to maintain that sort of presence in your organization. And then I don't know what you 10:32 hear, but anecdotally, I talked to a good friend who's an executive at Google. And I said, 10:36 Hey, have you laid anyone off? And he said, No, we didn't. What we instead do is now rip 10:40 through our roadmap. So two years of roadmap happens in three months. And we're actually 10:45 our hardest problem is knowing what to add to the roadmap, which by the way is like 10:48 Every p.m.'s fantasy, you know how much emotion has been drained on prioritization conversations between you and I 10:55 So yeah, I actually think that people shouldn't be as stressed 10:58 And I think they should feel really empowered and by the way the best way to do it is just to ship stuff 11:02 I mean Claire is my muse. She's so awesome because she's always shipping. Yes, Claire. Oh, she's 11:09 Shipping she's trying things. She's not afraid to be a little embarrassed by it 11:14 And if you just see her whole kind of affect, 11:16 she feels like the best version of herself 11:18 that she's ever been. 11:19 And I think we all have an opportunity to be that. 11:22 I love that. 11:23 I want to be clairvoyant when I grow up. 11:25 Totally. 11:26 Kind of along those lines, you have this interesting take 11:29 that company building more and more 11:30 is going to become this kind of series of creating loops 11:34 and creating series of loops. 11:35 Talk about that. 11:36 Yeah, yeah. 11:37 Well, I think the broad concept and the loops concept 11:41 kind of gets teased a little on X 11:42 because sometimes it feels like maybe we're big-braining it. 11:45 I know there is a big meme around graphs, 11:48 like the next stage of Loops's graphs. 11:50 But let me make the kind of steelman for it, 11:51 which is we had prompts, and then we invented agents. 11:56 Agents, of course, are just models in a loop 11:58 with tools and memory and skill files. 12:01 And then we had sort of loops, 12:03 which are sets of agents that are doing tasks. 12:06 If you look at a lot of work in coding, 12:08 coding is such a great domain 12:09 because you have the best models 12:11 and you have the most sort of technically-apped customer, 12:13 plus you have these established loops, 12:16 like bug fix or sort of bug report comes in, 12:19 repro gets generated, bug fix gets created, 12:22 bug fix gets reviewed. 12:24 If high-risk, then human should confirm 12:26 that it's okay to ship to prod, 12:28 and if low-risk, it just gets shipped. 12:30 And maybe you even email the customer 12:31 and say, hey, we fixed the bug. 12:32 You reported that happens in five minutes. 12:34 There are many loops like that in engineering, 12:36 everything from bug fixes, the customer feedback, 12:39 to sales demos, to new feature development. 12:44 So coding sets us up well, itself up well. 12:46 So you have a coding loop 12:47 and the kind of change it makes is to the code base. 12:51 My question is what are the business loops, right? 12:53 So if you're the GM of a business, 12:55 you're looking across many job functions 12:57 and you've got loops running and coding and marketing 12:59 and sales and support and legal, 13:02 the output of all of those loops 13:03 is something that is itself a loop 13:05 that you should be able to optimize for. 13:07 And I think the strong form of this 13:08 is that it sends a message to the CEO saying, 13:11 hey, we need to actually make a change 13:13 to one of the physical aspects of the business 13:15 or to our business model or to our strategy. 13:18 So I think we're gonna see this sort of cascading set 13:20 of everything from a loop per person, 13:22 loop per job function, 13:24 loop across entire business units to loops 13:27 that can run large parts of the company. 13:30 With that said, I think humans are a critical ingredient. 13:33 I just don't think that most work in the organization 13:35 can be done fully autonomously. 13:37 when you think of what a human will do 13:39 in this like AI native company, 13:41 sales, support, strategy, and exceptions, right? 13:45 And all those things are super critical. 13:47 We've seen one thing Lenny, 13:48 it's that the ability for models to do new thinking 13:51 out of distribution thinking is still really limited. 13:53 And I don't actually take the point 13:55 that some of the new thinking in math 13:56 is actually representative of new thinking 13:58 in domains like business. 13:59 So you're still gonna need a person to say, 14:01 hey, here's the thing I think we should make 14:04 and have them be right about it. 14:05 Let me just kind of make sure this point is really clear because it's so interesting. 14:09 What you're saying here is engineering and building more and more is becoming this loop 14:13 of input, feedback, support, ticket, whatever input of just like what to build and then AI 14:20 more and more is taking that deciding here's a PR here is this ready and then shipping it. 14:25 And you're saying that you expect that to spread to like say go to market legal 14:31 growth support. So maybe describe what that loop looks like or may look like within a company. 14:38 I mean, a great example is a growth team. You worked out the growth team at Airbnb, right? 14:41 Yeah, supply growth. Yeah, awesome. Right. So you remember those like war, I don't know how 14:45 you ran your team, but I'm guessing it was something like you got everyone together, 14:48 you built a list of possible experiments, you prioritize them, you built them, 14:52 you ship them, you measure them. A lot of brainstorming, a lot of spreadsheets. 14:55 So, the loop version of that should be that every variant gets generated, every variant 15:01 gets measured. 15:02 Once you get to stat-sig with a high enough p-value, you converge and ship that variant. 15:07 You then have a long-term holdout and you start working on the next experiment. 15:11 And then you're going to hit some local maxima. 15:13 And I think this is really important. 15:15 The loop will help you climb to the local maxima, but then a plateaus. 15:19 And you need some sort of out-of-distribution thinking. 15:21 You need human intuition. 15:22 You need somebody to actually help you land at the base of the next hill. 15:26 Yeah. 15:26 You have this chart. 15:27 I don't know. 15:28 Maybe we'll show it over as we talk about this, which is such an interesting way of thinking 15:32 about it. 15:32 This idea that agents will help you hill climb and reach some new plateau and then you need 15:39 a human there to think about a bigger idea, kind of unlock it. 15:43 And then it keeps going and going and there's kind of this like agent to human kind of 15:46 back and forth. 15:47 Yes. 15:48 Yeah. 15:48 And you know what really illustrates that? 15:49 if you've ever tried to have an agent come up with a business idea for you, like, you 15:53 know, hey, Claude, make me a million dollars, make no mistakes. Like, why doesn't that work? 15:58 You know, and it's because you sort of need to set it in the right direction and nothing 16:03 in the technology has shown us that that is not needed. 16:06 Yeah, it's interesting. Like, I've been hearing more and more. I just saw a tweet 16:09 that I think at OpenAI, the go-to-market team now is using Codex more, more of 16:14 the go-to-market team is using Codex more often than even the engineering team. 16:18 Yes. And then think about how happy that makes them. Like, what does the go-to-market team want to do? 16:23 I mean, this is a caricature, but I'm gonna stand behind it, which is they want to hit the gym, 16:28 they want to go to steak dinners, and they want to like, you know, 16:32 like raise the trophy up for being salesperson of the quarter of the year. 16:37 So I actually think that this is a distillation of their job into the thing that they're the best in the world at, 16:42 that the most interested in, 16:43 And all the administration that goes around doing that core work is now handled for them 16:48 So like that's where go to market is going and it's gonna be awesome 16:51 This reminds me of a PM friend who has this had this really funny take that as a PM 16:57 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 road map 17:02 We'll purchase it. He's like, okay. I'm gonna flip this. I'm gonna say just everything 17:05 I'm gonna build everything and then simulate every of every idea with like simile or all these products that are launching where you 17:12 could simulate how a user will react. 17:15 And then that'll tell you, should we build this? 17:19 What a hilarious way to rethink the app. 17:21 And you know what's so beautiful about that? 17:23 Actually, there's two things. 17:24 And I'll tell you maybe the one that's less obvious to me, 17:26 which is I feel like every PM at every company 17:28 feels like they're a true zero to one thinker, 17:31 but they're held back by the kind of, 17:33 the heavy hand of management and executives 17:35 and founders and engineering capacity. 17:39 And in a world where every story gets told, 17:42 Every product story gets told every feature gets tried 17:44 I think a lot of pms are gonna realize they're actually not that good at zero to one and it's much more fulfilling to work on 17:49 Someone else's good idea than your own bad idea 17:52 So I think even things like that are going to lead you a lot more 17:55 Organizational health than we've had in the past 17:58 You know not to mention the fact that the idea that ends up winning doesn't have to be the one 18:03 That's came up with by the person who can sell it best to an executive 18:06 It just gets tried and the best idea wins so coming back to this loops idea the the way 18:11 I'm thinking about it is how can every function start to think of their function as setting up 18:16 an agent to be able to just go from input to some kind of impact. And what this makes me think 18:23 about is something actually Claire tweeted recently, this point that people always used to joke that 18:28 like soft skills are the least valuable and engineering skills the most important valuable 18:32 because they're so concrete. And it turns out that's what AI is the best at, the things 18:36 that are verifiable and, you know, you know what success looks like. 18:41 And so the question I think about now is just like, which skills can you not just turn into 18:46 a loop because the output is so hard to verify? 18:49 Yeah, I think that's right. 18:50 And I think that those are going to be the rate limiting factors because you can only 18:54 do one steak dinner a night. 18:55 I guess you could do a steak lunch. 18:56 But you know, to some extent, there's going to be these rate limiting factors 19:00 in every system. 19:02 I think a useful way to think about it is anytime the model's making a mistake 19:05 or doing something you wouldn't do, 19:08 what do you know that it doesn't know? 19:10 And there's a really interesting example I heard 19:12 from Ale at Kevok. 19:14 He's probably the most sophisticated thinker 19:16 on this stuff that I get to hang out with, 19:18 where he said anytime they have an agent per customer, 19:21 they sell used cars online, 19:22 and when the agent gets stuck, 19:24 it actually calls a human, 19:25 and the human will coach the agent through. 19:28 Now, the magic of that is not only does it 19:30 unblock the agent, 19:30 but of course the agent then captures all the traces 19:33 and learns from it. 19:34 Like that's a really interesting mental model. 19:36 It's either a knowledge gap or a data gap 19:38 that you have to give the agent. 19:40 And the next time it shouldn't have to call you. 19:42 I love that example. 19:44 Basically the takeaway here is 19:46 you need to start thinking about every function 19:48 as an agent loop. 19:50 And the job is to figure out where it gets blocked, 19:53 where it goes wrong and give it more context, 19:55 more insight, more direction basically. 19:58 That's right, that's right. 19:59 And then hopefully a lot of your day to day work, 20:01 you know, when you come up with a new idea 20:03 that works, the kind of implications of that idea. If it's a product, you know, there's 20:07 marketing work, there's sales work, there's product marketing, there's communication, 20:10 there's legal, all of that should be largely handled for you. And your idea, you know, 20:15 your job is to go take a hike and dream the dreams and come up with the next hill 20:19 to climb. 20:19 And this begs the question a bit of just what will separate the companies that win 20:24 in this world where AI is kind of doing a lot of this? I imagine part of the 20:28 answer is the human in this local maxima coming in with a better idea. So anything 20:32 else that you think becomes like a differentiator in this world where AI is doing so much of 20:37 the work that humans are currently doing? I mean, I think one thing that's under discussed is that 20:42 it's unclear that the sort of competitive equilibria that exists in a lot of industries 20:47 will really change. So let's say Pizza Hut and Domino's and Papa John's and Roundtable all get 20:55 at a data center of PhDs. 20:57 And, you know, they're all gonna either adopt it 21:00 or have a CEO change and adopt it. 21:02 So it'll be a rocky period 21:03 and there'll be some, you know, relative shuffling. 21:05 But I think that they're all gonna embrace 21:07 the new technology because most companies do. 21:09 I don't know that one of them 21:10 is gonna have 99% of the market. 21:12 So I think kind of what's under discussed 21:14 is that yes, in the near term, 21:15 I think there'll be winners and losers 21:16 based on adoption of the technology 21:18 and kind of how ambitiously you adopt it. 21:21 But I do think there's a lot of industries 21:22 that will sort of maintain their current competitive dynamics because they're not intelligence bound. 21:28 I think the most useful question to ask yourself as a founder CEO is just, 21:33 hey, if we assume these things are infinitely intelligent and astonishingly cheap, 21:39 how would we reorganize the company? Because that's where we're going. 21:42 Kind of along those lines, you have this interesting take about this kind of split 21:45 that might be coming within companies between these generalists and specialists 21:49 and how AI plays into that. Talk about that. 21:51 Yeah, well, I think that there's so many interesting things that this touches on you know 21:55 One is the question of open weight versus frontier. So I'm sure are you familiar with kind of Pareto efficiency? 22:01 I'm sure you are please explain. Yeah, so Pareto efficiency is just you know for price performance for example 22:08 What is the kind of the efficient frontier is what's considered the optimal trade-off of you know 22:14 a unit of performance for a unit of price and 22:16 And, you know, are you sort of, if you're along that curve, you're always paying the 22:20 rational amount for the performance. 22:23 And what's interesting is that frontier models are actually irrationally priced in that, 22:28 you know, first of all, mythos is infinite dollars a token. 22:31 You can't use it. 22:33 But if you look at even something like Fable 5, you know, for one IQ, conceptually 22:38 one IQ of extra intelligence, you're paying 100x more than Opus 4.8. 22:42 So it's not rational, but I think there's a lot of jobs in which you have unbounded upside like drug discovery 22:49 You know, and if that one IQ point lets you discover the next, you know statin 22:54 It's a trillion dollar outcome 22:56 So it's sort of rational to pay for the highest intelligence in these types of jobs and industries now on the other hand 23:02 I'm gonna pick on, you know, maybe legal 23:05 There's perhaps only so much upside to be had in legal or finance and for those jobs 23:10 you actually do want to be very Pareto-efficient and probably pay for good performance at a good 23:16 price, not infinitely priced infinite potential upside performance. So I think what we're going to 23:21 see is a split between job functions that demand kind of mid-IQ intelligence and those will often 23:30 be open-weight sort of biased with reinforcement learning, things that make the models even 23:36 cheaper, more performant for a narrow job, along with incredibly quote-unquote expensive, 23:42 but performant frontier tokens for sales, support, research, engineering. I think you're going to 23:48 end up having both architectures. We can touch on this in a little bit, but I do think there are 23:52 these comparative advantages amongst model families, and then also amongst, of course, 23:57 individual models that we're seeing more and more of. It's not going to be one or the other. 24:01 Such an interesting insight. Just to make sure I understand what you're describing here, 24:04 you're thinking there's going to be this split between kind of within an org of 24:09 function and model where specific functions that have a lot more upside and 24:14 potential leverage go for the frontier models and you describe these kind of product sales 24:20 engineering research roles and then there's like and then for other functions, you don't need you don't need mythos 24:26 You don't need astra. I think is there's that the latest one and so it's both 24:30 you're saying the AI doesn't need to be the frontier model and the people don't have to be the 24:34 smartest people in the world to do really well in that world. Yeah, and I don't, I don't want to be 24:38 diminutive. Like there's extraordinary people in those job functions. I just think that they're 24:42 bounded upside problems that they work on. You know, you could only kind of close the books 24:47 correctly. You know, you can't close the books 100x better. So yeah, that's exactly what 24:53 I'm saying. I wonder if it's connected back to that discussion we had earlier about it's 24:56 verifiable. If it's a lot more verifiable, you don't need the frontier model versus, 25:02 I don't know, the potential upside. 25:04 I'm not sure. I mean, I think for a verifiability is a good question because I think for a drug 25:10 development company, you have infinite upside. It is verifiable. But closing the books is 25:15 also a verifiable problem which has limited upside. I think the question is really how 25:21 much upside is there and how hard is it to calculate it? Like here's a nuance. 25:24 customer support, a customer may call in and report a bug. 25:28 That bug may actually be the first breadcrumb 25:30 to a thing that changes our entire organization. 25:33 And if the CEO was on that call or the smartest person, 25:35 they could follow the trail. 25:37 But if we actually have this quote unquote 25:38 mid IQ generalist, then not. 25:40 That actually makes the case for as a basket of problems 25:43 always use frontier intelligence. 25:45 And I can of course make the case 25:47 for the mid IQ basket as well, 25:49 which is simply that we've crossed an intelligence 25:51 threshold for almost every economically useful problem. And anything beyond that threshold is 25:56 simply waste. Yeah. And like I think people, the point people forget is that the models that are 26:01 not the frontier models, they were, like that was what the frontier was, I don't know, six months 26:05 ago. And we were so impressed and we loved it. It was like, holy shit, I can do all this. 26:09 And not just because there's something better, we don't give those models as much credit. 26:12 You're right. Because it's sort of like, this thing is so crazy, you know, this is, 26:17 This is AGI, and then a day later it's like the old thing that you throw in the dustbin. 26:22 So kind of along those lines, I hear people jokingly call you a model sommelier. 26:28 Tell us, with your sommelier credentials, what's kind of like the current state of 26:32 the model arts? 26:34 What is each model grade at? 26:36 What model is terrible at? 26:38 Yes. 26:38 Well, as you know, the secret of every sommelier is 10,000 hours or maybe 10,000 26:43 bottles. 26:43 So I think that the secret of being a model Somalia is just using them all. 26:47 I push myself really hard to ship something with every new model that comes out. 26:52 And I think you learn so much. 26:53 You know, I think for people who believe the models are commodities or totally fungible, 26:58 you just haven't actually used the models. 27:00 And, you know, for example, in the last few weeks, I've been obsessed with Quinn 3.8 Max. 27:05 Quinn is an awesome model. 27:07 It's very good at long horizon tasks. 27:11 And it's actually really creative. 27:12 it's a great storyteller. So I've been using it to create these impossible documentaries mostly of 27:19 you know planets of the Star Wars universe. I did Tatooine which turned out great. I did 27:24 Bespin last night and it can just work for four or five hours. It tells a great story. It generates 27:28 all the video using the mini max model via foul generates audio via 11. It actually like directs 27:35 the movie the five minute movie and that it like cuts all of the clips in it overlays it 27:40 it watches it. It's just extraordinary, you know. And that's just, it's got a totally 27:44 different shape than GLM 52, GLM 53 just came out, which I used for a bunch of product work. 27:50 It doesn't have a vision component. And it's sort of like this neurotic PhD you put in 27:55 the corner. And they both have the role, right? This is a little bit of the kind 27:59 of these tension of models. It's not that one is ahead of another, one is more intelligent. 28:03 It's rather, one is sort of has a mind that's shaped in one direction, perhaps 28:07 creativity and openness for Quen and others that are shaped in other directions like neuroticism 28:14 and precision like GLM53. So that I just make something with every model and that's how I 28:20 build my intuition. How important is this habit do you think for people? Because I hear a lot that 28:25 it's really important to be using these models. And kind of a secondary question is how do you 28:31 come up with what to do with these models? Because a lot of people want to try these 28:35 things are like, okay, what do I do? I know any suggestions? Well, I think almost all of us have 28:40 got, you know, the most insufferable thing for a long time was your app idea friend. You know, 28:45 every time you went to have a beer, they're like, let me tell you my app idea, you're like, 28:48 ah, here we go again. Like, we have got to be the app idea guys. Now, and all the silly ideas 28:54 actually, especially the silly ideas, because those are the ones that often have the most 28:58 alpha are the ones that we should all be building. So I probably got, you know, two 29:02 dozen apps that I built, I've got one or two large apps that I iterate on. 29:07 And I think that if you don't have a chassis on which to like, with 29:10 which to use the models, it's really hard to come up with an idea from scratch 29:13 every time. 29:15 So I'd say like, work on something. 29:17 It's actually better if it's not important with a capital I, and then 29:20 keep finding new ways to invest in it and add to it with the model as a 29:24 kind of tool rather than as a goal. 29:26 Does that make sense? 29:27 Yeah. 29:28 Like maybe even zooming out, I've been thinking more and more, one 29:31 One of the most important habits to build right now is to whenever you're about to do 29:35 something, ask yourself, how can AI do this for me? 29:38 Totally. 29:38 And I'm visualizing this input to response, like the whole idea of there's a space between 29:43 input and response. 29:44 And meditation helps you think more deeply before you respond. 29:48 And I feel like the trick now is insert in that moment. 29:51 How can AI help me with this? 29:53 Yeah, no. 29:53 I mean, I'm also a long time meditator, we should talk about a lot if you like. 29:58 But yeah, I think that's right. 29:59 I think in our day to day knowledge work, sometimes it's less obvious to me. I guess my mind is I've 30:04 always been a consumer product person. I love products. So for me, it's easier to think of a new 30:08 feature to add to my DJ streaming app or, you know, my sort of Google Reader for X that I use 30:14 than it is to kind of find a part of my life to automate. But you're right. There's some fun 30:18 examples. You know, I posted about one, a few weeks ago where I had my laptop transcribe 30:23 everything that was happening in the kitchen and then it would award or detract 30:29 screen time from my son's iPad depending on whether he was being good or bad. So 30:34 it's like a fun little social experiment. It also had a fun outcome which is that 30:37 he recorded a video of himself saying I love you dad over and over and put it 30:40 next to the mic. To hack the metrics. To hack the metrics. So yes, so yes the 30:46 kind of you know anything becomes a measure it's no longer useful but 30:51 it was just a cool little social experiment. I think those things are 30:53 super fun. 30:54 That is hilarious. 30:56 And so one of the measures was how often he said, I love you, and I was going to give him 30:59 a screen time. 31:00 Is he saying things that are positive and pro-social or negative and anti-social? 31:05 And saying I love you is very pro-social. 31:07 That's so funny. 31:08 I had a friend who built this little device that measured how often he and his kid 31:12 laugh throughout the day. 31:14 Oh, that's nice. 31:15 Yeah. 31:15 He hacked one of those limitless pendants to do that, and then they just look 31:19 at that metric every day. 31:20 Isn't that so? 31:21 And this is what I mean. 31:22 You know man, like I think we've spent a lot of the time on the show so far really like intriguing talking about 31:27 Productivity job loss kind of all these like heady important topics with a capital I 31:31 But what you just described how often you laugh like that's not a startup 31:36 That's probably not an economically consequential idea 31:39 But it is for like the quality of our life 31:42 And I think we tend to think that happiness is fixed 31:45 But what if it's not, you know, if you and I were doing jobs a hundred years ago 31:48 Like what would our jobs be I promise they'd be less cool than they are right now 31:51 and maybe a hundred years from now they'll be that much cooler. So I think that the thing that gets 31:56 often missed, and this is why I'm a fan of Claire and others, is just like how can this thing add 32:00 texture to our human lives even if they don't have economic consequences. 32:05 Yeah. Along these lines I saw somewhere you have this really interesting take that 32:09 like the big opportunity maybe just in consumer but broadly is this idea of 32:13 loop make me happier. Yeah. Talk about that. Oh yeah. I mean I think that we believe that 32:21 that people want to be more productive, but they don't. 32:23 I think more people want to spend time than save time. 32:26 There's a reason the biggest products in the world 32:29 are kind of entertainment and social. 32:31 So we get at the heart of how do we sort of 32:33 deliver the value to the consumer? 32:35 I think for most consumers, you know, 32:37 sometimes I tease and I say it's like 32:39 the Instagram AI user versus the XAI user. 32:42 The XAI user is like, you know, 32:44 fearful of being outside of the permanent underclass 32:46 is really opinionated on GLM53 versus Kimmy K3. 32:50 Like they're just so piled and then the Instagram person is like, oh, this is like a better Google search 32:55 Kind of it's cool. You know, they don't get what all the hype is about 32:58 So I think that a lot of the and it's really a product design failure 33:01 We have the capabilities to radically transform people's lives 33:04 I mean Lenny in many ways we spend 40 years building a technology that enables better spreadsheets, right? 33:10 We built this like technology that extends our intellect but nothing to extend our soul 33:14 And I think that we have a little bit of a spiritual hunger 33:17 especially as a lot of these cultural institutions have gone away that fulfilled that, especially in 33:21 the rest of America, where you don't have as many hot yoga classes and Pilates and fasting and friends 33:27 giving. So I think that the opportunity for this technology is like, hey, the basics of consumer 33:32 need. How do we feel more connected, more loved? Do we make progress? How do we have fun? 33:38 Like the things that we all aspire to, the basics, how do we apply this technology to 33:42 those areas? That's what I want to see more of. And again, I don't think it's a model or 33:46 capability challenge, it's just a product design challenge. 33:50 I love this so much. 33:51 We talked about this idea of loop, like grow my business, loop, find me more sales, loop, 33:56 close support tickets. 33:57 But the way you're describing here, I think you, the way you had to have my notes here 34:01 just like loop, improve my health or loop, make me a better friend. 34:05 And there's no reason that I can't just think deeply and hard about all those things 34:09 and figure out a way to actually do this. 34:10 Yeah. 34:11 And also, you know, look, there's a, we have to dial this in, but I think there's 34:14 way that AI can sort of challenge you, can push you, can be disagreeable. This is also 34:19 why I think startups are advantaged over incumbents. The idea those 1,000 Google committees who 34:25 would roll in their graves at the idea that they're going to release a model that's disagreeable 34:29 or God forbid it should be sexually suggestive or, but guess what, those are all parts 34:33 of human existence. So I think exploring the kind of uncomfortable parts of our social 34:38 existence are things that startups are uniquely set up to do. 34:41 And I know you spent a lot of time investing in consumer companies. 34:45 What I'm hearing here is this is a big opportunity for consumer businesses to basically build 34:49 a app product that is exactly this loop around and improve my connections with my friends 34:55 and family. 34:55 I think that's right. 34:56 And I think the thing that's held back consumer so far a little bit and maybe held 34:59 back is just strong because if we kind of put this into iPhone terms, we're in 35:03 iPhone 2010, right? 35:05 What is iPhone 2010? 35:06 I think it's pre Airbnb, pre WhatsApp, pre Uber, pre all the important kind of consumer companies. 35:13 So it is early days. But what's held us back is I think three things. One is that 35:17 the models have been expensive. So if you want to do a kind of free to use product, 35:21 it's that hasn't been easy. The second is that we've kind of had an interface problem. 35:27 Like chat makes sense if you're the highest agency person in the world, which is Elon and 35:31 Sam. But for the average consumer, like their ideal interface is TikTok. So we need to find 35:36 something between chat and TikTok. And then the fact that the technology has been so much 35:43 more focused on productivity than things like, you know, human connection and entertainment. 35:48 I think all those things are kind of up for grabs. Like the open weight models mean things 35:51 are way cheaper. I think that we're starting to have conversations about things like 35:55 loop make me happier. And I think that founders like Eugenia, who you should have on the 36:00 show she's tremendous are thinking really ambitiously about user interfaces. Brian Chesky 36:05 from Airbnb, I think he started a foundation lab focused on next-gen user interfaces. So 36:09 I think all those problems will get solved or they're at least in a better position to 36:13 be solved than they were two years ago. 36:15 I want to come back to this, the whole space of consumer and AI and things like that. 36:19 But I want to follow this thread a little bit more about just the optimism around 36:24 where things might go. There's a lot of fear and worry about the future with 36:30 AI and just generally, Mark Andreessen when he came on the pod had this really interesting 36:34 take that AI came just in time to save us because population is declining, productivity 36:40 is going down, there's all this war, climate change and all these things and we would 36:45 be in big trouble if I wasn't here to fill that gap. 36:49 Talk about just kind of your bigger picture perspective on why you think maybe people 36:53 are underestimating the positive and optimism around AI. 36:57 One, I think it's a potential for it to be sort of emotional, spiritual interface on 37:03 which we can kind of get leverage and explore aspects of ourselves that have been really 37:06 buried. 37:07 If you look at the kind of effect of the Industrial Revolution, it's that there are 37:10 these scale advantages, which are insurmountable. 37:13 And as much as, obviously, I'm very pro-capitalism and I love the economy that we live in, 37:18 I think that centralization, it sort of discourages the individual in some ways 37:23 and it maybe detracts from their identity. 37:25 So I think one of the really magical things is this is a technology that really amplifies our identity our agency 37:31 It kind of unbundles skill from desire for example if you want to make music you can make music now 37:36 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 37:41 So it really amplifies our individuality 37:44 It allows us to explore aspects of our lives that we were never able to explore before and you know 37:50 just in terms of the nuts and bolts, like we have been in this sort of morass of 2% GDP growth. 37:56 Like who said that we have to be there? Why can't we be 10 or 15 or 20%? And this is a technology 38:01 with which not only can we dramatically drive productivity, we can dramatically drive ambition. 38:06 Like think of the, maybe this is a caricature, but the 1950s and 1960s, we believe that we 38:10 can do anything, right? We were coming out of World War II where the entire economy, 38:15 the entire world mobilized in a way that we didn't think was possible. 38:18 And one of my theories Lenny is that like when the stakes are high, we are awesome when the stakes are low 38:24 We are at our absolute worst and in a lot of ways 38:26 I think the world we lived in five years ago felt like a low stakes world 38:30 Which is why we kind of collectively had a lot of these like side projects as a society 38:35 Which weren't necessarily productive or making any of us happier and now you've got you know 38:39 It's not just Elon doing everything he's doing 38:41 But I think everybody feels like they're climbing the ambition ladder you ship your bad ideas 38:46 so you can discover your good ideas. And if you want to, you know, build software, great. If you 38:50 want to build a bridge, you don't have to be a civil architect to know how to do that anymore. 38:54 So I really think that we have the makings of a sort of dramatically happier, more fulfilled, 39:00 more productive society. And, you know, and yet we're here talking about permanent underclass. 39:05 I love all of that. That all feels so right. It's so hard to really believe that. 39:10 But if you actually look at how things have gone so far with the rise of AI, 39:15 unemployment is down. People are making a lot of money. Obviously, a lot of people are struggling. 39:19 There's a lot of downsides, data centers causing problems for people, things like that. A lot of 39:23 issues. But it feels like as an economy and as a country, it feels like things are going well 39:28 so far. They just almost cured some kind of cancer the other day. So yeah. 39:33 Yeah, you're right. Moderna just did. I mean, the fact that Dario can write a blog post and 39:38 say what happens when we cure every disease and then we debate it as a serious topic, 39:42 like, what world are we living in here, you know? 39:45 I also think that we're collectively worried always, 39:49 like, you know, the kind of these abstractions, 39:52 the world, the average worker, the middle manager, 39:55 the person who lives in the vicinity of a data center, 39:57 these are abstractions, but our actual lives 40:00 seem like they're getting more fulfilled, 40:02 we're more capable, we're more empowered. 40:04 So I think that's also something 40:05 and the revealed preferences tend to show 40:08 how people are experiencing it individually 40:09 and the stated preferences tend to show 40:11 how people are sort of observing it or believe it's playing out societally. 40:16 If you ask people if they want a data center in their neighborhood, most will say no, but 40:20 if you ask them if they use chatGPT today, most people will say yes, so that's kind of 40:24 the dissonance. 40:26 The obvious issue is that just the PR over NAI has not been great. 40:29 There's a lot of zero-mongering thoughts on that. 40:32 What's going on there? 40:33 Do you think that'll change? 40:34 The most important thing that we can do with AI to change the kind of conversation 40:38 around it is make important things cheap. 40:40 There's two things that are extraordinarily important in America that have only gotten 40:44 more expensive, right? 40:46 Healthcare and education. 40:47 If you look at healthcare, 45% is administrative. 40:51 So if you take a lot of that administrative burden out, you can actually see deflationary 40:55 healthcare costs, also things like curing every disease. 40:58 That sounds awesome. 40:59 You know, GLP-1s also obviously not an AI thing, but I think is a reason to be 41:04 optimistic about deflationary health costs. 41:08 And then education as well. 41:09 I think education now has the strongest form of competition, sort of traditional education 41:14 that it's had in 200 years. And I think it's going to be very, very good to kind of unbundle 41:19 learning from institutions. And also, by the way, like status from credentials, you know, 41:25 like you don't need a Harvard degree, you just need to get. And that's pretty cool. 41:30 This episode is brought to you by Mercury, radically different banking now with spend. 41:34 I've been a mercury customer for so many years now, I switched all my business banking 41:39 to mercury, and honestly, I could not be happier. 41:42 It's what online banking feels like when it's built by product people, not by bankers. 41:47 And now with spend, you can give your team individual cards, set spending limits per 41:52 person or per team, and have expense receipts automatically pulled in from Gmail or over 41:57 text. 41:58 You can even give your AI agents their own cards with their own limits and policies. 42:02 Most founders start out the same way. 42:04 One card used by everybody at the company. 42:07 It works until it stops working. 42:09 Someone goes over, a receipt disappears, you spend two days trying to figure out who spent 42:13 what and why. 42:14 Spend is expense management built directly into Mercury. 42:18 All your team's cards, budgets, and reimbursements all live in the same place as your business 42:23 thinking. 42:24 No chasing, no manual reviews, no end of month scramble. 42:27 The result is a team that can move fast in a founder who is no longer the 42:31 bottleneck. Learn more and get signed up at mercury.com. Mercury is a fintech company, 42:36 not an FDIC-insured bank. Banking services provided the choice of financial group and 42:40 column in a member's FDIC. The IO card is issued by Patriot Bank and a member FDIC 42:45 pursuant to a license for MasterCard International Incorporated. I saw open AI recently slow 42:50 down their AI development. They paused their RL kind of phase on their latest model 42:55 because of what they're seeing. So that's obviously a big concern for people just 43:00 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 43:07 Okay, we actually have to slow these things down. That feels crazy. I mean without commenting on open AI specifically 43:12 I think that maybe I'm a little skeptical on some of these things where I think the kind of the aura 43:18 That Anthropa got from having a model that was too dangerous to release was extraordinary 43:23 And maybe they had a GPU shortage. 43:26 Now, maybe it was, you know, the capabilities were more advanced than they actually wanted. 43:31 You know, maybe they actually wanted to keep that proprietary model internal to extend their own lead. 43:36 So I think there's a lot of sort of confounding factors that would cause you to pull back a little bit. 43:41 Look, I do take the points about offensive cyber seriously, 43:46 which is we should harden all of our systems before we make them trivial to penetrate. 43:50 But I think the sort of concept of the model that's too dangerous to release it, it kind 43:55 of conflates marketing, inference capacity. 43:59 And then also economic considerations, like do you want to externalize your competitive 44:04 advantage or use it to make yourself better? 44:06 Yeah, I always think about that when I have folks from Anthropica and Ovan and the podcast, 44:11 just like how an advantage they have when they have the best model. 44:14 It's crazy, right? 44:15 Just like that's a loop right there is just have the best model for longer and 44:18 they can move so much faster. 44:19 It's crazy, though, you know, to take the other side for a moment, like, it felt like 44:24 Anthropica was unassailable, and now OpenAI has had an amazing six months, and open weights 44:28 are also ripping. 44:30 So despite the, like, the sort of scary concept of this, like, you know, supremely intelligent 44:34 model that's totally proprietary to one company, the so far the kind of industry trends 44:39 haven't played that way at all. 44:41 Yeah, like, Grockbott just came out of nowhere and is now, like, the most amazing 44:45 AI kind of assistant tool, and I'm just hooked on it. 44:48 Oh man, I mean, we should talk about the personal agent thing like actually the three big products that I love here 44:54 Grockbot's totally nailed it. And also the model underneath it is awesome, right? They came out of your right out of left field 45:00 And that's I know a lot of the good work the cursor team did. I think chat you PT work 45:04 It's kind of buried in the UI, but it's really really good 45:07 It's one of the best products they've released and then there's a startup 45:10 That's really getting some buzz called instinct which has made some more aggressive and interesting trade-offs 45:15 But all in the same domain. That's amazing. Hey, I don't know if it was a strategy for some rant someone tweeting this vague 45:22 Yeah, about how awesome it is and everyone's like what the hell are you talking about? 45:25 That was really effective because there's nothing to get that 45:29 Chatcha PT work the episode that will come out right before this is the 45:33 PM in charge of that Tara. Yeah, really? Yeah. Oh man. They did such a good job on it 45:40 It's really I don't know how much you use it, but it's really well done. What is the what is it better? 45:45 then co-work there. Is it that that runs in the cloud? Is that the big differentiator? It runs in the 45:49 cloud. It does a good job of kind of caching browser credentials, though it's not as aggressive as 45:54 grok or instinct. And it also the remote feature, it's got the full duplex voice mode. So you 46:02 can actually just call it and can see all your threads and you can just talk to it and say, 46:05 hey, what's happening across all my coding agents across this, can you change that? So 46:09 it's just because the full duplex voice is so good, you really feel like you're calling 46:13 your assistant who knows everything that's happening in your world. Whereas with GROC, 46:16 it's a sort of one-way transcription, right? Or with some of the other assistants, it's a text. 46:21 So the voice plus the kind of model of it can see all threads is really well done. 46:25 Awesome. Okay. I want to come back to this AI assistant consumer stuff, but 46:29 just something I wanted to kind of close the thread on. I feel like there's, in terms of 46:34 jobs and the economy as a result of AI, I see it almost as the spectrum of, 46:40 there's like the Dario under the spectrum of 50% of knowledge work will be disrupted 46:45 and we're going to have massive unemployment to like the sacks, David sacks spectrum of 46:49 like it's going to be incredible. Jobs are going to be fine. So far, everything is pointing 46:54 in a good direction. Clearly, you're closer to the sacks direction. Is there anything 46:59 else just along the lines that you think might make people feel better about jobs in 47:03 the future? 47:04 I mean, I think that the entire trend of human existence has been that our 47:07 desires grow faster than our ability to fulfill them. If you look at the things that are expectations 47:13 today, there were unimaginable luxuries 500 years ago, even 50 years ago for things like therapy, 47:19 right? Or things like antibiotics 100 years ago, right? I mean, didn't matter how rich you were, 47:23 you simply didn't have access to it. So I think we're underestimating human ambition, human desire. 47:30 People are going to be mad that they don't have a vacation home in Mars in 20 years, 47:33 like really mad, like this really mad. They'll be seeing it on Insta and be like, 47:36 Come on, babe. We got to work harder and make this happen. Every CEO is going to want to build 47:41 a much bigger company. Already, it feels like we're living a much larger form of human existence 47:47 than we could have imagined a hundred years ago. There's no reason that trend won't continue 47:52 or accelerate. This idea of ambition, I'm glad you brought that word up again. It's something 47:56 that's coming up a lot on this podcast that not only is AI making it easy to be a lot more 48:02 ambitious. It's almost making us have to be more ambitious because everybody else can just do all 48:07 the easy stuff now. And now it separates us. It's just how big can you go? Okay, I'm going to make 48:12 a personal website. Okay, it's cool. It'll be this white simple background. No, okay, I'm going to 48:16 make this 3D game where you have to like walk through a world and discover all the things 48:20 that like everything is just getting more epic. Thoughts on just this idea of ambition 48:24 becoming a bigger, I don't know, skill and habit. A lot of things get conflated because 48:29 because when you and I say ambition on this pod, I think people have a very specific idea 48:32 of what that ambition is. 48:34 Admission to be a founder, ambitious to build a beautiful software product, those are types 48:39 of ambition. 48:40 But there are other types of ambition, let's talk about creative ambition. 48:43 When you're five, nobody says Lenny, you're good at painting, but you're bad at drawing. 48:47 You just have an ambition or a desire to make something and you make it. 48:51 That's something that's very unique and can now actually be encouraged. 48:54 you know, think of very local ambition. So, you know, they have the NHS in the United Kingdom. 49:00 I think it's this sort of treasured institution that's not working well. Maybe the way AI shows 49:04 up in their society is making like the NHS as good as the iPhone, right? That's very specific 49:09 and local to them, or simply the ambition to be more connected to our family, to be more 49:13 present parents. So the ambition doesn't have to be sort of ambition in the narrow economic 49:18 sense. It can be really anything that we want to do more of. And who doesn't have that 49:22 in their bones. Yeah, that's such like my example is so dumb now that I think about it like the 49:26 it feels like the thing we have to unblock in our brain is okay AI solve cancer we're not we're 49:33 not like like that's where we can start to think now and it's so unnatural to us especially as product 49:38 people that always have to think about MVP and the constraints now we have to think big okay what 49:43 can what's the big for what's the 10x the 1000x version of this. Yes I know right in a way 49:47 we have to think small because this thing that we've our whole lives been built around how 49:51 of precious software and intelligences. 49:54 And now it's totally not precious. 49:55 Like that probably will be a harder change for you and I. 49:58 Yeah. 49:59 So like all these new little habits, 50:01 I think a lot about on the Claude Co. team, 50:04 they have a principle. 50:06 You know what's better than me doing it? 50:07 It's Claude doing it. 50:09 And that just created this habit in everyone on the team. 50:12 How do I help Claude do this thing for me? 50:13 And I feel like that's a thing 50:14 we all have to start to build in our head. 50:16 And Grockbot's really good at that. 50:18 Like, and not investor, not no affiliation, 50:20 but it's just like so simple and good at the stuff. 50:22 I also think that there's so much learning that happens through doing. 50:26 You know, it's funny if you look at the number of people that are talking about 50:28 vibe coding versus the number of people that are talking about their projects. 50:31 People are a little embarrassed. 50:33 I'm a little embarrassed to talk about a lot of my projects because they don't seem 50:35 important or substantial enough. 50:37 But so much of it is learning through execution or shipping or being 50:41 fulfilled through execution and shipping. 50:43 Like, I don't think we can underestimate that either. 50:45 I have a guest post coming together from someone at Google that works on a lot of 50:49 their lab stuff. And I don't want to give away the the goods, but just broadly her concept 50:54 is that building is now the new reading where you build to learn and infuse and experience. 51:00 And it's totally okay for most of it to go through in a way because that's still building 51:05 your your muscle. Yeah. 51:07 So also, yeah, it's building as an activity rather than an outcome, which I think a lot 51:14 of people feel bad. I shipped all these things, but no one's using I never use it. 51:17 And I think that the key here is like that's actually okay, and that's totally fine 51:20 Oh, man 51:21 I mean and it happens in every other domain 51:23 You know like I make a DJ set and like three people listen to it and I listen to it a hundred times and it's like it's very fulfilling 51:29 It doesn't matter. Hmm. We're gonna talk about your DJ stuff later. Okay. Okay 51:34 Okay, let's come back to consumer stuff. So you focus on consumer at a 16 z what's kind of like what's happening in consumer these days 51:42 What's kind of like the landscape what you excited about? So I think there's three big areas 51:46 We're very early as we kind of discussed, you know 51:48 The labs have done a good job, but there aren't as many sort of independent mass market consumer products 51:54 Coding agents are awesome. And I think a total lightning bolt 51:57 You know, I think it's easy to say most people don't want to make code 52:00 But the the big change in my thinking is that coding agents are a way to interact with the world generally 52:05 And you've seen a lot of this on X, you know 52:07 people use cloud code to edit videos, you know, or codex to create a game that they play with 52:14 their kid on an airplane ride. Like, it's sort of this general problem-solving tool that 52:20 consumers can use in very unique ways, you know, and probably the best example of a company 52:25 that we've invested in is Wabi, where it's sort of a platform for many apps. People 52:28 can create them, consume them, share them. So, Codi agents is one big area I think 52:32 that's important. Personal agents, we have this credible moment around open cloud, 52:36 but guess what? Open Claw is a dev thing, you know? And Hermes, of course, and Moltbook, 52:42 like all of that is now getting distilled into mass market consumer and enterprise agents that 52:47 people understand, which is what we discussed previously. And then the last is, I'm going to 52:52 call it entertainment, that that doesn't fully do it justice. I think that it's a lot of sort 52:56 of creative tools, you know, Suna has done such an amazing job. It's companionship products, 53:01 products, all that entire areas, you know, uncomfortable to talk about. So I think it's 53:07 under discussed, but there's some huge fast growing products there. I think those are the 53:11 three big areas that we're watching right now. 53:13 That's really interesting. Just thinking of these three buckets. Coding agents, AI assistance, 53:18 kind of open cloud, but much simpler and easier and more reliable. Sounds like basically 53:23 the three described there that you're excited about are Instinct, Grockbot, and Chat 53:29 GPT work. That's right. And then Wabi, I guess, would fit in that first one. It's like a personal 53:33 coding agent that can bill whatever you want for you. Yes, that's exactly right. And then entertainment 53:38 is their bucket with like, like our girlfriends and that kind of stuff for it. Yeah, those companies, 53:43 it's like crazy. You look, you guys put out these market maps and like five of them are these 53:46 different like. Right. Though, though actually to be accurate is more boyfriends than girlfriends 53:51 actually, you know, the majority of people using companion products are women that are in 53:56 there are 40s and 50s, actually. Interesting. Okay, cool. So, so those are the three kind 54:02 of areas you think the biggest opportunities will come from. And it's interesting that 54:07 they connect to this idea of loop. I don't know, make me happier. Like, like they, yeah, 54:11 these are kind of, there's kind of like the jobs to be done for humans. Make me happy. 54:16 Make me healthier. Make me live longer, that kind of stuff. Yes. Yes, give me a 54:20 channel for my ambition, a channel for my sort of fulfillment, and then a thing 54:25 to do when I'm not doing everything else. 54:27 Hmm. 54:27 That makes sense. 54:29 Okay. 54:30 The other question I have for you along these lines is, there's so many companies and so 54:35 many startups, so many products, everyone's launching. 54:38 The speed at which companies and products are shipped is like 1,000xing. 54:43 How do you think about durability and moats when you look at a startup? 54:47 Because a lot of founders get that question. 54:48 Everyone's getting that question. 54:49 How am I not a rapper? 54:51 What are signs that tell you this might be a durable thing? 54:53 Well, I think there's two important ideas. 54:56 One is something that Jesse from Decagon said, which I love, 55:00 and that is that moats are most often discovered, not designed. 55:04 I think it's really easy. 55:05 I've done this as a founder to get in your own head about, 55:07 like, hey, I need a business plan that 55:10 survives scrutiny from MBAs and BCs. 55:12 I've got to have some really sophisticated idea 55:15 of what my moat will be. 55:16 And for that team, they just started shipping, 55:18 and it developed over time. 55:20 Another great example of this is Cursor. 55:22 You know they were criticized a lot for not having a moat 55:25 But it turned out that initially being a high-end PSDA you product was really good and over time 55:31 They captured all the reasoning traces they train their own models the composer one two models and you know so on and so forth 55:36 We know how that story plays out so motes can be discovered 55:40 They don't have to be designed is one and I think the other is that we seem to have forgotten that the classic motes 55:45 None of the classic motes are based on how hard it is to make the software 55:48 You know like we're not building self-driving cars 55:51 most of us aren't. So it's network effects, it's scaled advantages, it's brand effects, 55:56 proprietary sort of data, what was historically called a cornered resource. Every moat from 56:01 five years ago generally is still a good moat. We just need founders that have ambition in those 56:06 directions. We need more multiplayer products. We need consumer social. We need products that 56:10 get dramatically better the more you use them like town and so on and so forth. 56:15 So still read Hamilton Helpmer and all that stuff still applies. 56:19 Yes. Love his book. 56:22 Yeah, he's been on the podcast. 56:24 Yeah. 56:25 I feel like there's only five real business books in the world. 56:28 Every other one is in the business of selling business books, 56:30 and they're fake, and his is on my list of five. 56:34 Are there any other in this list that come to mind real quickly on the topic? 56:37 The two that are so obvious are high output management, 56:40 which is like that is as good as it ever was. 56:43 And then Ben's book, you know, hard things is it was the first 56:47 emotionally honest book about business that was ever written and that's why 56:51 founders love it. That's why I love it because you read it and you're like, 56:54 wow, I'm not the only one that's, you know, anxious and feels like a failure and 56:58 can't tell anyone what I'm going through. Ben went through it too. 57:01 Two of the most mentioned books on this podcast turns out. 57:04 I'm not surprised. 57:05 So, so on this mode idea, so say you're a founder and you're just like, 57:08 you know, you're putting a pitch together trying to pitch you or other VCs. 57:12 What's the best way to talk about a mode? Is it like, can you just say, 57:16 we're going to discover it. We're not sure. Nobody really knows yet. 57:19 Yeah, I think that we would happily take a bet on a product that doesn't have a quote-unquote 57:23 moat or durability story if it has a lot of momentum, a lot of craft, a lot of sort of 57:29 growing engagement. It's actually the thing I've learned over the years. I used to be very 57:33 worried about people stealing my idea. But I've learned that the big ideas are always supported 57:38 by a dozen small ideas that are invisible. And even if somebody replicates your big idea, 57:42 they never see the small ideas that make the big idea work. 57:45 So I actually think that there's some just something special in the water with certain 57:50 products that make them incredibly successful despite extraordinary competition. 57:55 I mean, look at granola. 57:57 Two years ago, it was really criticized. 57:59 And I don't know that they have a super strong durability story today, and yet it is beloved 58:04 and dominant. 58:05 So I listen to what the customers are saying more than what the business books say. 58:11 You get a free year of granola, become a subscriber to Lenny's newsletter. 58:14 I'm going to, I sort of do this more often. 58:16 When it's part of this product pass, lennysproductpass.com, you get a free air of all these amazing products 58:20 including granola. 58:21 I just saw Ram put out a report of the fastest growing companies according to their date 58:26 and granola is like number two or three. 58:27 It's a tremendous product is that craft is really high. 58:31 Yeah. 58:31 And that, I think about that a lot these days because there's so much, like there's 58:37 co-work, there's chat, GPT work, there's cursor, there's grokwap, and it's crazy 58:41 see how quickly one can switch from one to the other. And the underlying model is not 58:46 that different. All it really is, most of it is the harness and slash UX of the product. 58:52 And so to me, that tells you there's so much opportunity in the actual user experience 58:57 being a moat, or at least giving you a lot of time to find something that is durable. 59:01 100%. And I think for the people that are at the edge, they pay for all of them 59:05 because they all have their respective areas of specialization. Yeah. I have many 59:09 $2,200 a month plans right now. I know. I know, right. It's a little painful, but yes, me too. 59:14 And I think Curster has a $300 a month plan now. So does GROC. Oh, yeah, you're right. Yeah, 59:19 that was the GROC plan. It's pretty much GROC heavy. RIP. Yeah, RIP, Curster. I think they've 59:24 transitioned away from that brand. Something I've been talking a lot about is the distribution. 59:31 I call the distribution as new moat, but it's like it's always been a moat. But it feels 59:34 like more and more, that is actually a massive advantage because there's like a thousand launch 59:42 videos a day, everyone's launching, launch launch launch, and really the ability to get your stuff 59:47 into people's feed, get them continue to be reminded of your products. It feels like 59:54 increasingly is powerful and important. Thoughts on the rising value of distribution, 59:59 existing distribution being a big lever for growth and success. 60:02 This is so important and, you know, I asked Chris Dixon about this because he sort of 60:07 authored the famous Come for the Toolstay for the Network. I think the issue is that 60:11 our entire generation of founders and CEOs were trained on the theory of networks and 60:15 network building. And as a result, every network that exists today is hyper trained 60:20 to ensure no one else builds a network on their network. So I actually think that 60:24 the sort of network effect has gone back to this grassroots like true word of mouth 60:29 When somebody is getting a ton of mentions on X and on YouTube and on Instagram and all 60:35 of these places organically, that is probably the best form of the sort of third party network 60:41 effect that you can hope for today. 60:43 And actually just like the web 2.0 era, unlike the mobile era where you had the app store 60:47 and you had growth hacking and you had all of these sort of little cottage industries, 60:51 we have to kind of build our own channels off of that word of mouth growth. 60:55 So, in a sense, it's a pure growth problem, but a harder one than we've had in a couple 61:01 of product cycles. 61:02 To get word of mouth, you need to build something. 61:05 I always think about Seth Godin's line, build something remarkable, something that is worth 61:10 remarking about, which is basically build an amazing product that people want to talk 61:14 about, which is a very hard to do. 61:18 It makes sense that because there's so much happening, people are just going to pay 61:21 attention to what are my friends using and saying it's worth paying attention to. 61:24 Well, you know, here's the one thing I would say that the here's the hopeful point, which is I always say that nobody has a growth problem these days 61:30 They have a product problem 61:32 I mean the reason for that is like you can build such a wildly ambitious product in any direction, you know functional or emotional 61:39 You can charge a lot of money for it. So my challenge is like, hey 61:43 Is it that you have a growth problem or is it a failure of our collective? 61:47 Imagination, you know, if we imagined our product cost a thousand dollars a month ten thousand dollars a month 61:52 Like what if our product was a software Birkenbeck? 61:55 What would it have to do to justify that? 61:57 Okay, let's figure out how we build that. 61:58 And it comes back to the ambition question. 62:01 Yes. 62:02 And still though, you still need some advantage 62:05 to get in front of people, to get it out there, 62:08 at least initially, 62:09 because there's like a thousand things launching every day. 62:12 Imagine that's still a big opportunity 62:13 and I don't know, advantage, 62:14 which to me makes me feel like incumbents 62:16 have a huge advantage. 62:18 They have the products, they can tell you, 62:19 Hey, go use Gemini and every time you go search Google. 62:23 I guess, do you feel like that, 62:24 do you feel like it's harder for startups now 62:26 because of this distribution challenge? 62:28 I don't know. 62:29 I think it's easier because like Gemini, 62:31 despite all the kind of heavy cross-selling Google has done, 62:34 nobody would say they're winning. 62:36 You know, startups can build in directions 62:38 that incumbents are uncomfortable building in, 62:40 like everything we talked around companion, 62:42 but there's many others. 62:44 That's true. 62:45 You know, prices can be pretty high. 62:47 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 62:54 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 you apps 63:00 You know, that'll change at some point people will feel like they've you know 63:04 They're done and they're tired and they don't want to try any more apps, but the windows are open for now 63:08 I think it's easier for startups. That's a really interesting insight and you're lying about how it's not a 63:13 distribution or growth problem, it's a product problem is such an important one. Because if your 63:17 product was that good, people would talk about it and share it and use it. And it can be. I mean, 63:22 what are the wild social experiments that we're going to see with this technology kind of intermediating 63:27 them? You know, I think a lot about actually, here's a fun example from a few years ago. 63:32 Have you heard of mischief? Do you know mischief? Yeah. Yeah. Yeah. Right. They're 63:35 awesome, right? They're sort of like this creative studio that uses technology as their 63:39 medium, a very web 2.0 in that way, actually, many of the kind of, you know, the 63:42 Eve Williams, the Kevin Roses, that's who they were painters, except technology was 63:45 their canvas. So they created this very funny product called card versus card where 63:51 they shipped, I think 100,000 people a debit card. And then every day they would 63:54 text those people with a location that they had to spend the money at and 63:59 they would put $100 on the card and everyone would rush out to spend the 64:02 money and one or two would be able to spend it and everybody else would 64:06 get declined. It was just this hilarious social experiment that went hyper viral. And for me, 64:11 it was always inspiring in the world of fintech because it was like, wow, why don't we build 64:14 more products like that? Money is inherently social, and yet all the financial products we 64:18 have are so dry and personal and embarrassing. I think there's a similar moment happening in 64:23 AI right now where we can build these wildly ambitious products that touch on many of our 64:27 social nerves. We just have to do it. So let me follow that thread. You get to see tons 64:32 of companies both pitching you and also companies you're working with that you're 64:36 investing in. What are some counterintuitive lessons you've learned from watching the companies 64:44 that operate well and have succeeded? Lessons that maybe go against typical wisdom, start 64:50 up wisdom. 64:51 I'll tell you the biggest one. In the old days, which is three years ago, we would 64:55 see a company and if what they were doing, trying to do was too ambitious, we would 64:59 and not engage. Just too crazy, too complex. And implied by that is you wouldn't do a hundred 65:06 million dollar seed because it's too much money for almost any problem. It's too much 65:11 money for any person to actually manage. It's too much money to build a certain talent 65:15 to absorb. It doesn't make sense as an inception round. I think today we're almost seeing 65:20 the opposite problem where an idea that's too small is not something that we want 65:25 to engage with and you can talk about how you put $100 million to work in the seed productively. 65:30 Now, I'm not recommending you raise $100 million, but I think that there's this sort of no ceiling 65:35 on ambition is also showing up in how we're picking companies and maybe how they're picking 65:40 us as well because when we invest, we tell every founder, like, we're here to help you 65:45 build the strongest form of your vision. Mark told this to me when him and I were 65:50 talking about coming here and he said, Anish, the way we sort of told our story 65:54 when we were raising our first fund was, you know, we were going to the moon or we were 65:57 going to leave a moon size crater in the ground. And there was no other option. That's sort 66:02 of how, how we want to work with our founders as well. 66:05 I love that point. So it's like, is, can you get too ambitious? Is there a limit? Obviously 66:10 you look at the, you know, the team and what they're, but kind of the key lesson here 66:13 is be more ambitious. It's like the opposite of what used to be like, here's, here's 66:18 our wedge, here's where we're going to go. What you're looking for is just 66:21 how big is the idea? 66:22 I mean, look at Adams. How crazy is Adams? I mean, what an incredible hero's journey for 66:27 all of us collectively, but also what they're trying to do is something that five or seven 66:31 or 10 years ago would have felt insurmountable. And now it's like, okay, it's challenging. 66:37 Let's see. 66:38 Interesting. Is there anything else that has changed or I guess you've changed your 66:41 mind about around what you think it takes to build a successful company these days? 66:46 I mean, I think that the kind of old wisdom around consumer products have to be free. 66:51 I'm almost taking the opposite take, which is let's think about consumer products that 66:54 are extraordinarily expensive. 66:56 I think every part of consumer discretionary spend is up for grabs right now. 67:02 And I think a really useful, because price is a measure of product market fit, a really 67:06 useful product exercise is what is the broken bag 10,000 a month, 1,000 a month version 67:10 of our product. 67:11 So I think expensive consumer software is something like you and important that we wouldn't have thought about five years ago 67:17 I love that framing because it just pushes you again to be more ambitious 67:21 Yeah, you mentioned Mark and you work closely with Mark Andreessen Ben Horowitz. Yes, what's what's one thing you've learned from each of those guys? 67:31 There's such extraordinary 67:34 Leaders founders. I mean the thing that I actually feel so grateful to be a part of that they really embody to their core 67:41 or I think is a feeling of stewardship for the technology industry and for really the 67:48 country and the sort of maybe the Western way of living and thinking, you know, and if you 67:53 look at sort of a Ron Conway, you know, or Brook Byers, Tom Perkins, there was this 68:01 feeling I think of obligation to sort of leave it better than you found it from an 68:07 industry perspective. And that aspiration goes way beyond just being the best investor in the 68:13 world, though we want to do that too. I see them both show up that way over and over again, 68:18 where they want to do hard important things that don't directly benefit the firm, or at least not 68:23 singularly, because they're just important. Ben has done a lot of that in the direction of the 68:27 industry and Mark and the direction of the country, though, of course, both work on both. 68:33 Another really cool thing is just to see how I think Mark and Ben, but maybe the firm a little bit, 68:38 has shaped our collective ambition as a founder community. And if you look at five or seven or 68:45 ten years ago, Deep Tech was deeply unpopular. It wasn't a high status thing to be working on. 68:50 It was very fringe. And now it's become very popular, high status and mainstream. And I think 68:56 there's a lot of firms that have, of course, pulled in that direction. But I think that 69:00 And someone like Mark has been very full-throated in his support of working on sort of capital 69:05 I important work in the national interest. 69:07 And all of Silicon Valley has changed as a result. 69:10 And you guys have had some big wins in the past couple of weeks investing wise too. 69:15 Yeah. 69:15 Thank you. 69:16 Congrats. 69:18 Final question before we get to a very exciting lightning round. 69:22 What's your advice to product people who are trying to think about what they might 69:28 want to shift in how they work, how they think, how they operate to be more successful in the future 69:33 in their careers and with their companies. Just make more things. And I know it sounds silly. 69:38 I know everybody says it, but just please come up with a project. You don't have to tell anyone 69:43 about it. It can be totally unimportant, but use it as a chassis to use all the new models, 69:49 ship things, talk about them, build your own intuition. I promise you're one sort of 69:54 slightly frustrating and then very fulfilling week away from being as 69:59 pilled as anyone. So you just got to use the technology. And if not now then 70:04 when, right? This is all of us got in the game to build the products we saw in our 70:09 mind's eye. And now you have a chance to do it. So just please, please use the 70:13 models, you know, and tell me what you built text me, you know, tag me, like 70:18 I will reply and respond and engage with you and so will everyone else 70:21 because the magic of Silicon Valley is that it's a very positive, some mindset, you know, 70:26 it's sort of everybody is building on each other and vulnerability is really rewarded. 70:31 So I definitely would encourage people to use the models. 70:33 What's like a good heuristic if you're doing this enough? Is it like build something once a 70:38 month? Is it like some number of hours per day sitting, talking, building? Anything that you 70:44 think might help people be like, okay, you're doing a good job? I mean, just ship something 70:47 once a week and it doesn't have to be crazy. For example, when I was playing around with Codex, 70:53 I had it build a slide deck for Mother's Day for my wife that pulled from my text messages. 71:01 It looked at my photo gallery. It sets a music to it. It created a 20 slide deck of our relationship 71:08 and pulled some cool old texts from when I first asked her out. It was a really nice 71:12 Mother's Day. It wasn't important. It wasn't something that we might come back to, 71:16 but it was shipping something. So it can be that small. 71:18 I know that you're our mutual friend, Nick Helsingahl. You worked at Credit Karma with him. 71:24 He had a really good way of thinking about this. He finds that people flip on AI and how they 71:30 feel about it once they find some moment of joy that it had created for them. And this 71:35 Mother's Day idea is such a good example. And so I think that's kind of a tip I always 71:40 think about is just like, what's something that just will bring you joy if this works? 71:43 What's something you can do for someone else, you know, maybe that's a good starting point as well. I love that 71:49 Anish before we get to a very exciting lighting round 71:51 Is there anything else that you want to share anything else you want to double down on before we get into the lightning round? 71:58 I don't think so. I've loved the conversation so far me too and with that we reached our very exciting lightning round 72:04 I've got five questions for you. Are you ready? Okay? Okay. Here we go 72:07 What are two or three books that you find yourself recommending most to other people? 72:12 Yeah, so okay Conquest and Cultures is my very favorite book. It's Thomas Sowell and it just talks about how 72:20 Conquest have led to culture change in different societies around the world. Sometimes positive, sometimes negative. I think to me 72:27 it's just the best historic view of sort of culture as the biggest driver of outcomes 72:32 and I've experienced a lot of that as you know, somebody who's born in Canada and moved here to America 72:37 Which has a very different culture of ambition. There's that word again 72:41 So, Conquest and Cultures is great, Seven Powers, you mentioned, is actually just an 72:45 awesome book. 72:46 I think it's a very intellectual distillation about kind of moats and business theory and 72:51 compounding advantages. 72:53 I really like it. 72:54 Maybe the third is the one that Mark, I actually thought that he was maybe punking me when 72:59 he sent this to me. 73:00 Before I started, I said, Mark, are there any books that I should read? 73:02 And he sent me a couple. 73:04 And one was a book, a textbook, called Increasing Returns to Scale, which I think 73:09 as Brian Arthur is the author. It's an awesome book. It's a slightly dense study of why things 73:16 like software have such outlier economic effects, but it really helps put things in perspective 73:21 or help me in terms of why does our industry work in the way that it does? Why does our 73:25 culture work in the way that it does? Why are we so positive some? I'd love to say 73:29 that we're better people, but I think it may be a better system and structure we 73:32 work with it. 73:33 Favorite recent movie or TV show you've really enjoyed? 73:36 Oh, man, I watched trashy movies and TV. I mean, we've watched House of Dragon 73:42 Which is pretty cool. Yeah, I don't know. Yeah, maybe it's it's not high-brow. It's not important with the capital 73:48 I but it was awesome. We love that and 73:51 Then I I did see Odyssey 73:55 I saw it in London I was there for a board meeting in you know 73:58 an IMAX theater packed with people drinking pints and having fun and amazing 74:02 It was cool because it's just a whole the movie was great 74:04 But it was just like a theater experience like a weird social experience kind of alone together 74:09 Those are two recent ones. I have I have not been able to get tickets to the Odyssey at an IMAX 74:16 I actually have a grokba just watching the site constantly and finding me good seats 74:21 Perfect perfect then they've got them dumb capture though an AMC website that hasn't been able to get there really 74:28 It's like a tricky one. It's a really tricky one 74:30 Did they like select the fruits anyways? Yeah, it's like you have to click three different matching shapes, which like come on. You can't do that 74:39 Hopefully by the time this comes out, I've seen it and I think it's a few by guests in a room like I haven't seen it yet 74:45 No, man, okay 74:48 Favorite new AI product. I don't know favorite AI product recently that you've given me joy 74:53 Oh, man, I've spent a lot of time with the personal agents 74:56 I think grok's okay. I'm gonna give it to a grok bots only because it's just so unhinged for it to be so ambitious about 75:03 Sort of caching credentials and getting work done on your behalf like I love it and I expect it from a startup 75:10 But they actually are doing things that I think no other sort of big company would do. It's a really really well done 75:16 It's a really thoughtful UI. It's got a really powerful foundation model 75:19 I think it also was like, okay, wait, maybe this is not a two-horse race on the sort of model side 75:24 So, I just think the product is like fun and ambitious and is sort of taking risks that 75:30 other products like that wouldn't take. 75:31 Two more questions. 75:32 Do your favorite life motto that you often come back to in work or in life? 75:36 Oh, man. 75:37 I've got one. 75:38 I learned this or I sort of, you know, this is from my founder days, but it also is 75:43 something that's very true of parenting, which is, you know, don't discover things 75:47 through painful experience that somebody can just tell you. 75:51 So, and I unfortunately have had a bad habit of sort of discovery versus learning from 75:55 somebody who's just a few steps ahead of me, and I find my children have that habit, too. 75:59 What's one example of something that you wish someone had told you? 76:02 I mean, for my kids, it's don't touch the hot stove. 76:06 For my startup, it was like, literally, my startup, it was don't build a product in 76:11 a platform at the same time. 76:12 You know, if you're going to be a platform company, build one, our first company, 76:14 we try to build a sort of a social platform for mobile games and via gaming studio. 76:19 And you know somebody wise told me right away like look you can't being a studio is so hard much less being a studio and a platform 76:25 Pick one and and we did and then it was it took us years to figure out what we were wrong 76:29 Final question asked Ben Horowitz. What to ask you about and he just said asking about his DJing 76:35 He's very good and I went here. I found your DJ site 76:39 I don't know if that you call it on SoundCloud slash ill science. That's very good 76:43 I'm just like listening to it while I work 76:47 Any tips for somebody that wants to get into DJing, any tools you found useful, any, I don't know, 76:53 insights that might help someone become better at this? 76:56 Totally. I mean, I thought that this is why I love music models so much. So DJing has always, 77:01 I mean, I love DJ. I've been playing for 30 years now since 95, actually 31. Wow. And it's 77:07 an awesome way to kind of express yourself musically if you're not a classically trained 77:11 musician. You know, you select the music, you pick the records, you mix them together so 77:15 that requires some technical skill. 77:16 But now I think you can go a step further 77:18 and just make music with the models. 77:20 And the best part is when you can come up with music ideas 77:22 and have the models do the kind of strong form of them. 77:25 So I think music is just such a visceral, satisfying way 77:28 to kind of, you know, experience 77:30 and, you know, provide experiences in the world. 77:33 So whether it's DJing or making music, 77:35 I just suggest everyone do it. 77:37 I hadn't thought about how DJing has changed 77:38 now that you have Suno and things like 11 Labs 77:40 and all these things where you could just generate 77:42 the music not have to just splice together existing music. I mean think of the history of it you know 77:47 you went from okay you know first you could only hear music if you were there with a person playing 77:52 on an instrument you know recorded music on the phonograph the big change in music actually 77:57 from a medium perspective was the cassette tape because the cassette tape was really the first 78:01 time you could create right you could actually compose your own quote-unquote album I think 78:07 a lot of why music struggled in the 2000s is the sort of that went away and we went back 78:11 to broadcast. And now that people are making music again, I think the music industry is going 78:16 to be bigger than it's ever been. Wow. Man. So much disruption. I mean, this was incredible. 78:23 We covered so much ground. Final question. How can listeners be useful to you? I mean, 78:27 show me what you're building. Please don't be despondent. Build something and then 78:31 tag me. And I'd love to see it. If you're interested in hearing more stuff like this, 78:36 please follow me on X. I'd try to kind of engage and follow back. And otherwise, 78:40 make sure you check out all the amazing folks in Lenny's network. Claire is a 78:45 star, Lena is so so good, and there's just so much compelling content here. 78:49 Nicole, what you we talked about? 78:50 Nicole is the best man. That guy is everything. 78:54 Amazing. Anish, thank you so much for being here. 78:58 Thank you, Lenny. 78:59 Bye, everyone. Thank you so much for listening. If you found this valuable, 79:04 you can subscribe to the show on Apple Podcasts, Spotify, or your favorite 79:07 podcast app. Also please consider giving us a rating or a leaving review as that really helps 79:13 other listeners find the podcast. You can find all past episodes or learn more about the show 79:18 at LenniesPodcast.com. See you in the next episode!
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