Lenny's Podcast: Product | Career | Growth - The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every)

Starting point is 00:00:00 The business you're building, the team you're building, the way you're operating is the very bleeding edge of how companies are trying to operate in this AI era. We have a head of AI operations. She's just constantly like building prompts and building workflows so that I and everyone else on the team are just automating as much as possible. What are some things that you believe about AI that most people don't? I hate the headlines that are like entry-level jobs are taken away by AI. Whenever I see a kid with Chachibouti, I'm like, holy shit, they're going to go so much faster than any other person that I've worked with. We have this guy, he made like a year's worth of progress in like two months because every time I sat down with him and told him, okay, here's how you tell a story. Here's how you think about a headline.

Starting point is 00:00:36 Like he recorded all of it, put it into a prompt, and he never made the same mistake twice. There's this sense. We're getting to a place where you don't have to write any code. Like you have a product team, not writing code at all. No one is manually coding anymore. Organizations like ours, people who are playing at the edge, we're doing things that in like three years, everybody else is going to be doing. Today my guest is Dan Schipper. Dan is the co-founder and CEO of Ever.

Starting point is 00:00:59 which is a company that is at the very bleeding edge of what is possible with AI. Their team of just 15 employees has built and shipped four different products. They publish a daily newsletter, and they have a consulting arm that helps companies adopt the latest AI best practices. On their product team, their engineers don't handwrite a single line of code and instead use an arsenal of agents who help them craft requirements and build their products. Their editorial arm uses AI to publish better work faster, and they even have a person whose entire job is to help every employee at the company be

Starting point is 00:01:29 become more efficient using the latest AI workflows. In our conversation, Dan shares a bunch of tactics that they use internally to increase the leverage of their own employees, his personal AI tool stack, the one predictor that he's found for whether a company will successfully find huge productivity gains through AI, how he's building his company in a really unique way, a bunch of predictions for where AI is going, and so much more. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

Starting point is 00:01:58 And also, if you become an annual subscriber of my newsletter, you get a bunch of amazing products for free for one year, including Superhuman, Linear, Notion, Perplexity, Bolt, Gronola, and more. Check it out at Lenny's newsletter.com and click bundle. With that, I bring you Dan Shipper. This episode is brought to you by CodeRabbit, the AI Code Review platform, transforming how engineering teams shipped faster with AI without sacrificing code quality. Code reviews are critical, but time-consuming. CodeRabbit acts as your AI co-pilot, providing instant code review comments and potential impacts of every pull request.

Starting point is 00:02:35 Beyond just flagging issues, CodeRabbit provides one-click fix suggestions and lets you define custom code quality rules using AST grep patterns, catching subtle issues that traditional static analysis tools might miss. CodeRabbit also provides free AI code reviews directly in the IDE. It's available in VScode, cursor, and windsurf. CodeRabbit has so far reviewed more than 10 million PRs, installed on 1 million repositories, and is used by over 70,000 open source projects. Get CodeRabbit for free for an entire year at coderabbit.AI using code Lenny. That's coderabbit.ai. Today's episode is brought to you by DX. If you're an engineering leader or on a platform team, at some point, your CEO will inevitably ask you for productivity metrics.

Starting point is 00:03:23 But measuring engineering organizations is hard, and we can all agree that simple metrics, like the number of PRs or commits, doesn't tell the full story. That's where DX comes in. DX is an engineering intelligence solution designed by leading researchers, including those behind the Dora and Space frameworks. It combines quantitative data from developer tools with qualitative feedback from developers to give you a complete view of engineering productivity and the factors affecting it. Learn why some of the world's most iconic companies like Etsy, Dropbox, Twilio, Versel, and Webflow, rely on DX. Visit DX's website at getdX.com slash Lenny. Dan, thank you so much for being here and welcome to the podcast. Thank you for having me.

Starting point is 00:04:11 I've obviously been a huge fan for a long time and so it's an honor to be here. It's my honor, Dan. I feel like this is a podcast that was meant to be. I'm so happy for finally doing this. they're so damn much that I want to talk about. They're so damn much we can talk about. I thought it'd be fun to start with just some hot takes. And the reason I want to start here is I feel like you spend more time thinking about AI, building with AI, using AI, evaluating AI, than anyone else I know nearly.

Starting point is 00:04:40 And so I really respect your insights and your perspectives on where things are going. So let me just ask you this kind of question and see where this goes. what are some things that you believe about AI using AI tools that most people don't believe? I'm going to go with my hottest take. And this is the take that I have the least evidence for. So let's just start with that. I have other more well-reason takes to give you. But this is my hottest one, which is I think that AI may be one of the biggest force for reshoring American jobs. And so I think everyone is worried about it, unemploying people. And for sure, it will change the skill. needed to do the jobs that you're doing, but I think it may actually be sure a lot of jobs.

Starting point is 00:05:23 And it'll do that in two ways. One is there are a lot of expensive services that rich people and big companies are pay for right now. So like a in-house council or like, you know, call center or whatever. And what cheap intelligence does is it makes those kinds of things. affordable for small companies and individuals. So it stimulates demand. The other thing that it does is it allows people who are in those jobs to serve more people cheaply. So it may not get rid of customer service, for example, but it may allow, you know, 10 people in the Midwest who would

Starting point is 00:06:10 normally be working at a call center to serve hundreds of thousands or millions of people. maybe that's too much, but like a lot more people than they would ordinarily if they were the ones on the phone all the time. And so it becomes much more cost effective for American companies to hire people in the U.S. And I think the people in the U.S. are going to be better in a lot of cases at using these AI tools to do work. So I think it may actually make it more effective to have those jobs in the U.S. run by people sitting in the U.S. who are, you know, using it to get to get work done. And also the model companies are here too. So there's, there's a lot of American stuff happening. And you can, you can decide whether or not you think that's a good thing. But I think it's quite, it's quite lost in the conversation over whether AI will get rid of jobs. I like optimistic takes about AI. So this is great. And like to your point, I want TBD if this is good for other countries, but good for the US. What else? What else you got? What other hot takes? Another, another big hot take. And this is, this is less like contrarian and more

Starting point is 00:07:14 just like, I think people are truly sleeping on it. I think people are truly sleeping on how good Claude code is for non-coder's. And I'll extend this to not just cloud code, but Google just came out with the Gemini-C-Li command-light interface. So things like that. And I'll tell you about four people who are listening that don't know what cloud code is. Cloud code is just a command-line interface. So it's, you know, those black terminals that programmers use. It's a command-line interface that you can boot up. It has access to your file system. It knows how to use any kind of terminal command and it knows how to like browse the web, all that kind of stuff. You can give it something to do and it will go off and it will run for like 20 or 30 minutes and

Starting point is 00:07:52 complete a task like autonomously, agentically. It's a, especially with Claude Opus 4 that just came out, it's like this gigantic leap forward in AI's ability to work by itself. And Cloudcode can even spawn multiple subagents that do a bunch of tasks in parallel. And it's incredibly useful for programmers. Like everybody inside of every is using it all day. every day, like everyone's agent-pilled. They've got like 15 agents doing all this kind of stuff. It's crazy. But non-programmers don't use it because it's intimidating to use the terminal.

Starting point is 00:08:23 But you can like download, for example, you can download all your meeting notes and put it in a folder and just be like, okay, I want you to read every single one of my meeting notes and tell me something that I do, for example, is tell me all the time that I subtly avoided conflict. And it will, it writes a little to do list for itself. It can have like a little notebook. It can like go and read each little thing and then like write into his notebook, go down its do list and give you a summarized answer over multiple turns. So it's not just like stuffing everything into context, which is what you'd be doing with like a, you know, chat chit chat or a regular quad chat. It's like actually processing every single file that you give it. And so I think it's

Starting point is 00:09:00 incredibly powerful for any kind of task that involves processing a lot of text. So it's a simple way to think about this. You're basically having an agent on your local computer that can read your local files and do your bidding. Yes, exactly. And it can do that for long amounts of time without going off the rails. Interesting. And so there's like a small hurdle that non-technical people have to overcome, which is using their terminal and giving commands. But once they get it running, it's just you talk to it in English and ask it to do stuff. Exactly. So the hot take here is just Claude, which most people think is for engineers, is the most underrated tool for non-technical people.

Starting point is 00:09:42 Yeah, exactly. What are some other ways you imagine people seeing this? This meeting note example is really cool. And I could see people using this. What else have you seen or taken? Something that I've done a lot. So I'm a writer for a lot of my job. And for example, I love, and I know you're going to ask me about books I love.

Starting point is 00:09:59 So I'm going to give you a sneak peek, which is I love war and peace. I just read it for the third time. Wow. That's a long book. It's so long, but it's so good. I think Tolstoy is a brilliant writer. And one thing that I wanted to do is I was like, I want to inflect some of my writing with some of Tolstoy's style. And the way I did that is I think he's incredible at these little subtle sentences where he shows you what a character is thinking and feeling just by how they behave, like how they move their face or like the mismatch between the intonation and their voice and the expression in their eyes, like all that kind of stuff.

Starting point is 00:10:31 Like he's just like an incredible student of human behavior and psychology. and so I just downloaded War and Peace to my computer, which you can do because it's public domain. And then I had Claude read the first three chapters of War and Peace and pull out all of those descriptions and then make a guide for itself for like how to do a character descriptions like Pulse Toy. And you could totally do this with like a regular like opus command, but you couldn't put all of War and Peace into it. It would take a lot more handholding to get it to do this and it just sort of did this by itself, like without my like really intervening. It also ended up like downloading, I had to download a Russian version of War and Peace and the English version and then start comparing different scenes that I love to like tell me about things that I might have missed in the translations. So that you can get as deep and weird and nerdy for whatever subfield you care about as you want to.

Starting point is 00:11:20 Same thing for like if you've got tons of customer interviews or like tons of customer data you want to go through. It's like incredibly powerful for for going and figuring stuff out stuff out from big data sets like that. You actually inspired me to use. This is not what you're describing, but it's also something that's very cool. This is going to sound so nerdy. I'm reading Anna Karenina right now. Yes. Also, Tolstai, and this is recommended by a previous podcast guest.

Starting point is 00:11:45 And so I was like, all right, I got to read this. Also, very long. On my Kindle, I'm just like, all right, 13% in. I've been reading for months. Hot take, I think Wormpe's is better than Anna Karenina, especially for like a tech person, but they're both good. Okay. There we go.

Starting point is 00:11:59 There's my year. I saw you tweet this use case that I love that I've been using, which is just while I'm reading, having ChatTPT voice sitting around, and then just asking it questions, because you don't actually have to feed it the book. It knows the whole book. Anthropic just shared this. I don't know if they shared or someone found this in their legal briefings

Starting point is 00:12:17 that they actually bought tons of books and scanned them themselves. Yeah. It's how they did fair use. And so it has all its context. So just sitting there asking it, like, what the heck is this thing in Russian society is super fun. Okay, so this is awesome. So the tip here is just coming back to your hot tick.

Starting point is 00:12:35 The tip is you basically can have an agent using local files and doing all kinds of cool stuff on your computer versus having to upload it into projects or into your prompts and things like that. Yeah. Super cool. So I guess the bet here is that people are going to discover this and start using this just day to day. I think they absolutely will. And I also think probably the model companies are going to start making this more accessible. I think one of the things that will just come from ClaudeCode and other things like it into everything else you use, whether it's on the web or wherever, is all of the original AI apps were pasting a chat box into an existing UI.

Starting point is 00:13:17 So, you know, you've got co-pilot. It's got like the auto-complete in the IDE. You've got cursor. It's got a little sidebar with a little chat. And the difference with cloud code is you never look at the code. It's not meant for coding. It's not meant for coding by hand. It's meant for you to say, I want you to get something done, and it goes and does it.

Starting point is 00:13:37 And I think we're just getting to a point where for pretty much all of these, you know, all the usual applications, AI is going to be good enough that we can get rid of the interfaces more or less where you're like digging into all the things that it's actually doing. And you're sort of interleaved with its execution. and you're more just like, I'm delegating. It's going to go do it. Yeah, I had a cursor CEO, Michael Thorell on the podcast, and this is his big vision is what comes after code. And we don't need to be looking. Exactly, exactly.

Starting point is 00:14:08 And I also just had the founder of Base 44 on the podcast, who sold, you know, build his company, sold $80 million to Wix. And he shared that for the, so he's been around for six months, the company for the last three months. He hasn't touched a single line of front-end code, all Base 44 and, or sorry, all cursor and other tools.

Starting point is 00:14:25 he's using. So this is happening. Same thing for people inside it every. Like, no one is manually coding anymore. Okay. Definitely need to talk about that. Before we do, any other hot takes that you want to throw out there? I have one other hot take, which is I have a definition for AGI. And so AGI is like famously hard to define like what it, what does it mean for it to be artificial general intelligence? The Turing test was one, but like we pretty much

Starting point is 00:14:52 blown past the Turing test in a lot of ways. So we have no good one. And so what I have noticed is that you can tell how much better AI is getting by how long a leash you can give it to do work. So with co-pilot, it was like a you can tab complete. And that was like the beginning. With chat chadbt, you ask it a question and it returns response. And that's like maybe slightly better than a tab complete. and then now with Claude Opus 4 and Gemini and all that kind of stuff.

Starting point is 00:15:24 Like, I can go off and work for, also with deep research, it can go off and work for like 20 or 30 minutes. So that leash is getting longer where you have to intervene. And I was thinking about this, and it reminded me of Winnicott, who is a child psychologist. He wrote this book called Playing in Reality. And his conceptualization for what it means to become an adult, what it means to go from being an infant to a child to an adult

Starting point is 00:15:50 is when you're when you're first born, you're effectively fused with usually your mother, your caregiver. Like there's no difference between you and her or you and whoever your caregiver is. And growing up is this process of being gradually like let down in certain moments where you can handle being let down. So you learn that there's a separation between you and your caregiver. So for infants, it's like instead of being like fused at the hip for like every hour of every day, you get left alone. Maybe it's like you get left left on to cried out. Like who knows if that's

Starting point is 00:16:24 like the right thing to do with infants? A lot of consternation there. But like that's teaching you that there's a separation between you and your mom or you and your dad. Like there's there's not going to always be someone to pick you up. And raising a child is about knowing when they're ready to be let down a little bit and have to stand up on their own. So I think there's that same leash with human development. It's like you get longer and longer periods of time where you can be on your own. So we're still in the kind of like 20 to 30 minutes. It's like maybe maybe I don't know. I guess you probably can't leave a toddler alone for 20 or 30 minutes. But like, you know, it's a little bit older than a toddler. Maybe 20 seconds.

Starting point is 00:17:05 You can with a toddler, it's like you can be in the same room but not interacting with them totally like every single second for 20 minutes sometimes. So it's around there. And I think I think there's a similar, I think we have that similar leash with AGI. And so I think a good definition of AGI is when does it become economically profitable for people to run agents indefinitely? So it just never turns off. It's a cloud code that's always running. It's always doing something. You just never turn it off and you don't need to because like you know that it's worthwhile to keep it to keep it on.

Starting point is 00:17:40 It's never waiting for you to be like, okay, next thing. It'll always respond to you when you're like, okay, next thing. but it's off just essentially living its life like a teenager. And that is profitable for you. You'd rather have it do that than just wait for you to tell it what to do next. Interesting. I think that's the good definition of AGI. And the profitable piece is also just the cost of running that thing and having it.

Starting point is 00:18:02 It's partly the cost and partly the value. And obviously you can like game this a little bit and be like, cool, I'm just going to like tell Claude to like run in a loop forever. But like I'm talking about more than that, like more widespread. more widespread adoption of agents that work all the time. And I like the profitable thing because if it costs a little bit of money and the bar is profitability, then there's like a, it has to actually be doing something useful for you to keep it on. It's interesting how that also is very, in the metaphor of a senior employee and autonomy, and essentially the more autonomous they are, the less instruction you have to give, the less reviews you have to do

Starting point is 00:18:40 is also just directly correlated with how senior they are. Totally. Okay, great. Anything else along these lines? I mean, I have plenty of them. I think I'm generally, like, I hate the headlines that are like, it's going to replace jobs or, like, it's going to unemployed, like, two-thirds of the workforce. Like, I don't think that's true.

Starting point is 00:18:59 I hate headlines that are like, you don't use your brain when you use chat chitb-t. Or, like, another good headline is, like, doctors alone. doctors plus AI or just AI, like which one is better. AI is better. Therefore, like, doctors are going to be outmoded. Like, all that stuff is, I think, pretty dumb. So for the doctors plus AI example, I think it's important to recognize that using AI is a skill.

Starting point is 00:19:29 And so if you study doctors in a vacuum that, like, don't really have a lot of experience with AI, yeah, you could probably create a situation such that, like, it's better to just to just use an AI. And sometimes it is going to be better. But there's a lot, there's like so many contexts that doctors need to make decisions and do things that it's really hard to take one study and make any sort of conclusion about that. And it's especially hard when you're dealing with the technology that's developing so rapidly that doctors can't really be, like, expected to be experts at it yet. But I would guess in five or 10 years, that would be totally and completely different. For the student example, or like the, you know, AI turns your brain off

Starting point is 00:20:09 example. I think it's really important to understand that in the history of technology, it has always been the case that you give up certain skills in order to get other ones. So, for example, Plato was famously very skeptical of writing because he thought it would harm your memory. And it did. We don't remember things quite as well as they did back in the day because they had to remember long epic poems to entertain each other. But I think writing is a worthwhile trade for having a slightly worse memory. And I think something similar is going on with AI where, yeah, you may be slightly less engaged in certain tasks, but if you use it right, you're going to be way more engaged in other tasks where you have much more power. And so you can construct a study

Starting point is 00:20:56 that says brain connectivity goes down when you use AI in the same way that you could construct a study that says people's memory are worse when they have writing skills. But I don't think anyone would want to go back to a world where no one was literate. That is super interesting. There's all these studies that are showing the benefits of AI to students with these studies in Nigeria and just how fast people progress. So I think it's really important in this context. You're sharing up that you will lose some things, but the gain, the hope is the gain is much higher. And so far it seems like it will be. Yeah, I think people always, especially at the beginning of a tech hype cycle or a revolution paradigm shift, it's always easy to underestimate how quickly things are going to change. And the example I always

Starting point is 00:21:37 use is I live in Brooklyn and the tailor down the road down the street for me like doesn't accept credit cards. Like credit cards have been around for a long time. So it takes a long time for technology like this to be adopted even in the best case. And I think it's really easy to underestimate how complex specific contexts are that humans know how to like deal with. and just because you can get a really good score on a test, it's incredible. I love AI.

Starting point is 00:22:11 It's so incredible, but it doesn't actually give you an intuition for how difficult it is to actually be replacing specific parts of work or activities that you do. I think a really good thing to give you a maybe like a little bit of an intuition for it is I built this thing over a weekend like a month ago that was, Can O3, can it predict what I'm going to say in a meeting? It's like, let's a benchmark. It's the CEO benchmark. And the reason I did that is because Open AI is the gold standard for Open AI for testing how powerful a model is, is they test it on their internal code base.

Starting point is 00:22:58 So they say, how good is the new model at predicting what comes next in our internal code base? Because that's not anywhere out on the internet. So it's a really good, it's a really good benchmark for that. And so I was like, well, my meeting transcripts aren't anywhere on the internet. A lot of what I say is on the internet and some of the, there's some overlap, but be kind of interesting. And so I ran a bunch of the frontier models on this on just like my granola transcripts. And they're pretty bad.

Starting point is 00:23:25 They are pretty bad. And it's not because they're not smart. There's a real, there's this real push now. Toby from Spotify coined this term called context engineering. which is like getting the context to the model, the right context at the right time, like is at least half the performance. And I think that's 100% true. It's something that I've been writing about for like three years.

Starting point is 00:23:46 At the time I called it knowledge orchestration. I think context engineering is probably a better term. But like it's totally true. And and that's a very, very hard problem to solve. It's not just like a one shot problem where it's like, you know, gigantic context when doing and we're done. It's going, I think it's going to get better. over time, but the minute it gets good at predicting what I'm going to say next in a meeting,

Starting point is 00:24:11 I'm just going to use it as a tool, and that's going to change the entire dynamic of what I say next in a meeting. So it's not as easy as it seems. Interesting. I imagine you can build a GPT from that, and then instead of having a meeting with Dan, now just talk to this thing and help make decisions. Yes, definitely. And I mean, we do this a little bit. It's not the same as it's not the same as having, being able to predict exactly what I'm going to say in a meeting, but I think if you're a CEO or founder or manager, it's really stunning how much of your job is just repeating yourself. And that is one of the best things about this AI, particularly AI revolution, is that you don't have to repeat yourself. And so we had it like last quarter.

Starting point is 00:24:50 I tend to set like one or two quarterly goals. And like one of my big goals for us last quarter was don't repeat yourself. So I don't want to ever say the same thing in a meeting twice if I can help it. So for us at every like one of the big parts of every is we have a daily newsletter. And I'm spending a lot of time like giving feedback on headlines or giving feedback on how do you write an intro or like how is this idea any good like that kind of stuff. And we've started to codify all that into prompts that basically it's not the same as mimicking me. It can't exactly say exactly what I'm going to say in a meeting. but it pushes my taste out to the edge so that writers who are not able to talk to me,

Starting point is 00:25:32 by the time I see it, they've already talked to like some simulation of a simulation of me. And that's incredibly powerful. Let's follow this thread. This is exactly where I wanted to go. I feel like the business you're building, the team you're building, the way you're operating is the very bleeding edge of how companies will operate and are trying to operate in this AI era. You guys are trying to be super AI first. And it's super aligned with just so much of your writing.

Starting point is 00:26:00 There's just like so much reason to study what you guys are doing. Thank you. Yes. And this is benefiting all of us. So thank you. So first of all, just tell people what the heck every is. And then share a few insights into just how you operate.

Starting point is 00:26:17 It's funny that you laugh. Everyone asks that. Because it's just, it's like a very, it's just, it's a very weird shape of a, company that you can actually see other companies that have this shape from earlier eras, but it's a little bit, it's less common. It doesn't make as much sense. And I think it's newly

Starting point is 00:26:34 enabled by AI and we can talk about why. But the way, the way that I typically talk about every is we do ideas and apps at the edge of AI. So the core of the business is we have a daily newsletter. We've been doing it for about five years. We have about 100,000 subscribers. All the people from the top AI labs read us. Anyone who's basically interested in or working in AI at the frontier and wants to know what's going on reads us. We do a lot of like, for example, whenever, whatever Open AI or anthropic drop any model, like we get our hands on it early and then we get to play with it and write about it,

Starting point is 00:27:09 which is, it's like my ideal job. I love it. It's the best. I don't know if I can curse on this podcast, but it's the fucking mess. Perfect. Excellent use. And you call those vibe checks. Is that the...

Starting point is 00:27:21 Yeah, we call them vibe checks. Which I think it's really important because, and this gets to the next part, the apps part of what we do, I think it's really important to do vibe checks and to call them vibe checks because they're about, how does it feel to use this thing? And how does it feel to use it for work, for things that you would normally use it for, like in your job or in your life? Because I think that captures something that standard benchmarks just don't capture and really can't.

Starting point is 00:27:49 And the best people to write a vibe check are people that are actually at the edge using it for stuff. And so what we found over time is we have, we love, we think the best writing and content about technology is from people that are actually using it and building with it. And so we've always had this sort of function where we're always building little experiments in addition to our writing. And that helps us write great stuff. And that has turned into a suite of apps that we run internally. and the people who are building those apps are also writers, and they're contributing to things like vibe checks. So you get a really inside look into how is this stuff being built

Starting point is 00:28:30 from people who are actually using it every day? And the suite of apps that we have, one's called Cora. We just launched Cora publicly on the day that we're recording this, which is really awesome. Congratulations. Thank you. You can think of it like a chief of staff, an AI chief of staff for your email.

Starting point is 00:28:45 It helps you manage your email with AI. It's very cool. We can go into it more of it later. We have another one called Sparkle, which is an AI file cleaner. We have another one called Spiral that does content automation with AI. We originally incubated Lex, which is an AI document writer, which we spun out into its own company, and my every co-f underneath and runs that. And basically, we bundle everything together.

Starting point is 00:29:07 So you pay one price and you get access to all of the software that we make, and we're constantly putting new stuff in the bundle. And I can tell you more about, like, what kinds of things that we like to incubate and how do we like to incubate it? because I think there's a lot of, there's some really interesting special things in there, but I've been bladdering for a while, so I'll stop there. There's also consulting firm, which I want to talk about,

Starting point is 00:29:24 but let's talk about that. We have consulting. We also do that. And that is another, that's like the third leg of the stool in the business. It doesn't fit quite as nicely into my ideas and app streaming, but we spend a lot of time with big companies where we teach them how to, basically how to be AI first. We train all the people on how to use AI.

Starting point is 00:29:40 And it's very cool. It's really, it's really fun and a very, a very important part of what we do. That feels like a billion dollar, business right there. I want to come back to it. Because everybody wants to learn this. Okay, so share a few ways that you guys operate. You mentioned that your team doesn't write any code. What are just some ways that allow you to operate this efficiently? I know your team's really small. You have a daily newsletter.

Starting point is 00:30:06 You have three, four products. You have a consulting arm. How big is the team ever? We have 15 people. 15 people. Okay. So just give us insight into some of the ways you operate that are kind of at the bleeding edge. Okay, so a couple things. One, and I think everyone should do this, is we have an AI, a head of AI operations. I sit with her once a week, and every time I'm doing something repetitively, I'm like, we put it in a to-do list, and she's just constantly like building prompts and building workflows and stuff like that so that I and everyone else on the team are just automating as much as possible. And I think that has been a big unlock, because it's really hard to, if you're working in a job all day, you're fighting fires and like you're you're like,

Starting point is 00:30:47 okay, am I going to do this in the way that I know how or am I going to do it in the new way that might not work? Like, I'm going to spend a bunch of time in Zapier, like building some no code automation. Like, I don't want to do that. And having AI operations lead lets you basically identify those things and have them solved without people who are doing the work actually having to take time to do it, which I think makes it much more likely it happens. There's always a trick with that where it's like you have to make sure it gets used. So it's basically you're developing little applications internally. But if you're good at making applications people use, it's great. Highly recommend having an AI operations lead. I imagine you saw the CEO of CORA

Starting point is 00:31:25 tweeted about this wanting to hire exactly this sort of person. Yeah. So clearly this is a trend. So the idea is this, like your point that this needs to be somebody who's who's outside of the day-to-day work of the company and is specifically focused on helping the team. be more efficient with AI. Yeah. Yeah. And then is this person mostly just you automating you, or can they help other people? No, she helps. She helps everyone, basically. Where we're starting right now is with the editorial operation.

Starting point is 00:31:55 So there's so much stuff in the editorial operation where I are our editor-in-chief Kate, like Kate is constantly doing like little small copy edits to make sure everything is like in every style. and it takes like hours a day. And so now Opus is at a point where you can give it a style guide and a prompt and it will go through anything you're writing and copy edit it, which is amazing. The trick is it's not just building that. You also have to get Kate to be like, did you put this through the prompt yet anytime someone gives or something? So there's a little bit of like behavioral update too that has to happen, which I think is a really

Starting point is 00:32:36 interesting organizational challenge. And I think for us, it's a little easier because everybody inside the org is like very AI first and just like wants to go do it. We don't have anyone really who's like, I don't know, I don't really want to do this. And that's a whole, that's a whole different challenge, which I think a lot of organizations face. But there's always a problem of getting people to use it. That is a super cool. What is her background, this AI operations person? Her name is Katie Parrott. She does a lot. She actually does a lot of ghostwriting for us. So she also, when when people inside of every who are builders. Often they just write themselves, but like sometimes they want help, and she'll help them write about like whatever they're, whatever they're working on. So that's

Starting point is 00:33:15 how she started with us. She still does that, but she also spends a lot of time doing the AIA operation stuff. And then before that, she was, she worked at Animals, which is a content marketing agency, like one of the top content marketing agencies. And they're very process oriented. And I think the reason Katie is so good is because she's, she's incredibly good at that kind of process stuff or like thinking about that. But she's also a great writer and she's also just incredibly excited by AI. She just like wants to tinker and wants to use it and like that was the thing that got me to be like, okay, you should just come and do that instead of just ghostwriting. We should add this to your plate. And it's been really fantastic. So I think that's a at minimum. You really just want someone who's

Starting point is 00:34:00 just like, I want to tinker. I want to build stuff. There's also people who have a little bit more of that process orientation. I think that is important. And to the extent they understand the craft of the thing that they're trying to build for, that also helps a lot. This is an amazing tip. I feel like everyone's going to start hiring these people. I think so. There's a couple other people who talk about this. I heard Rachel Woods, who's another sort of, she thinks a lot about AI stuff. She's talking about it. I think it's becoming a thing. And I think it's really important. And it just like bleeds out into every other part of the org. So like, we're doing this inside of the editorial org, but there's a lot of copy that goes out on Cora. And by the way,

Starting point is 00:34:40 Cora is spelled C-O-R-A. So it's different from Q-U-O-R-A. Slightly confusing. There's a lot of copy that goes out in Cora or spiral or sparkle that we want to have that same every quality bar for. And so we have, you know, engineers sending Kate like, here's the Figma file. Like, can you go and, like, do copy edits? And that sucks for everybody. And Kate is one person. And it's just really hard to to do that. So one thing that we did, Natash, who's one of the programmers, engineers on Kora, built a cloud code command that just uses that prompt and checks through the entire code base for all the copy edits and then creates a pull request on GitHub and then sends the pull request to the gate. So she's just like looking at the pull request and being like, does this make sense?

Starting point is 00:35:26 And so you can translate that prompt into, for example, a format that engineers can use and suddenly your engineering team is writing marketing copy in the style you want. I think that's so cool. That is extremely cool. I want to take, I'm going to take us on a little tangent. You keep mentioning Claude. And I'm curious just what is kind of in the stack of tools that you find yourself using your team ends up using. This seems like Claude is a core part of it. I do love Claude. I would say I'm generally, my first thing that I open is O3. I'm like a Chachu-T boy. And I think O3 is super high quality. I think it's great for writing.

Starting point is 00:36:03 It's great for coding. It's great for all that stuff. And what it has that really makes a difference still from from Claude is it has memory. And I just love that. Like, I've spent so much time yelling at Chat Chb-T about like, I need my writing to be punchy and concise, you know? And it just knows that now. So I think when I ask it to write something for me, it's like actually better than yours or maybe not yours. But like, your average chat chit user.

Starting point is 00:36:29 And I also find like, I use it a lot for self-reflection and personal growth type stuff. So it knows me. So when I send it a meeting transcript and I'm like, how did I do? It's like, well, you did that thing that you normally do, but you're way better on this other thing. And I like that. I think that's really great. So day to day, oh three, that's my go-to. I think Claude Opus is, first of all, Claude code.

Starting point is 00:36:53 everyone inside every that's basically what we use. If you're building something, you're using cloud code. It's crazy. It's so good. Gemini just came out with something, so I'm very excited to try that because I think that's the model that we use most for the apps that we build, like inside the apps. It's incredibly powerful and it's incredibly cheap, which is great. So I want to try the CLI tool they came out with. We also use Codex a bit, which is OpenAI's coding tool, and that's for like, I want a one-off self-container. I want to pick off this little feature.

Starting point is 00:37:26 What else I use? Going back to Claude, Opus 4 can do something that no other model, except one other model that I can't talk about, can do something that no other model can do. We won't go there. We don't want to get you in trouble. Okay, go on. But yeah, no other model can do this, which is earlier versions of Claude and I think generally versions of other models, when you ask them, is this piece of writing any good?

Starting point is 00:37:53 Claude, for example, would always give it a B plus. And then if you did another turn of the same conversation, you're like, I updated this, it would always go to A minus. And then if you get it another turn, it would go to like A, you know? So it like doesn't have the same kind of gut. It's like it's sort of thinking about what you probably want to hear too much. And there's various methods that you can use to like prompt engineer around this, like give it a template or like whatever. And they sort of worked.

Starting point is 00:38:22 but it just still doesn't have that thing where it's like, can it tell if writing is interesting or any good? Does it have that gut sense? And Opus 4 has it. It's really wild. And I think that's super important because it opens up all these use cases where you might want to use a language model as a judge. So for us, for example, we're working on a new version of our product spiral, which does content automations. You've used that in the past. and we're doing a essentially clawed code but for content style product where you know you say

Starting point is 00:38:58 I wanted to write a tweet you give it all the documents it has much memories it creates a to do list for itself and then it goes and writes and one of the things that is so interesting is now because it can it can judge things part of its to do list is okay I wrote three tweets I'm going to like judge whether I think these are any good and then it can improve before it comes back to you. And that's just like a huge, huge unlock that we were struggling for like three months to like build this like crazy system to like try to get it to judge writing. And then Opus 4 just like one shot at it. And we're like, great, this product works. Let's like, let's start shipping it. So yeah, I love it for that. Are there any other AI tools that you just

Starting point is 00:39:39 use regularly? You mentioned granola, even outside of the bottles. So what are, were some that you think maybe people are sleeping on? I use granola. So I used to use Super Whisper. and Whisperflow, which I think are fantastic. We have an internal version of that called Monologue that will be shipping in like a month or so that I use now. But you can think of them as roughly equivalent. And I think like generally speech to text interfaces are the future. And more people should be using them and more people should be building them as affordances.

Starting point is 00:40:10 I use, we use an ocean all the time. And I specifically use their meeting recording. I think that's most I think that's mostly the stack. Okay. That was really helpful and super interesting. This episode is brought to you by PostHog, the product platform your engineers actually want to use. PostHog has all the tools that founders, developers, and product teams need, like product analytics, web analytics, session replays, heat maps, experimentation, surveys, LLM observability, air tracking, and more.

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Starting point is 00:41:11 Get help building complex queries and setting up your account with an expert who's always standing by. sign up today for free at post hog.com slash Lenny and make sure to tell them Lenny sent you. That's post-h-h-o-g.com slash Lenny. Let's go back to ways that your team operates. You mentioned having Kate, was that her name? Yeah. Okay. What else?

Starting point is 00:41:35 What else do you do that you think other companies should be doing or will eventually start doing? So the Quora team, which is Kieran and Natesh. Basically, that's the team. Two people. That's the team. Yeah. Well, it's Kora. It's Kieran, Natasha, and 15 Claude Code instances.

Starting point is 00:41:51 So it's, you know, it's more powerful than you think. I love that this is just, again, a glimpse into the future. One of the things that we do that I think is really cool. And they basically invented this. Like I had nothing to do with this is they invented the idea of compounding engineering. So basically for every unit of work, you should make the next unit of work easier to do. So an example is in a Claude code world where you're not coding a lot, you end up spending a lot of time essentially typing PRDs. Like here's a document with exactly the stuff that I need to do, right?

Starting point is 00:42:36 And so you could just be like, okay, cool, that's my job now. I'm going to just like write PRDs. And so each successive PRD, it's the same amount of work. or you could spend a little bit of time being like, there's a sort of platonic ideal of a PRD. And what I'm going to do is write a prompt that can take my rambling thoughts and then turn that into a PRD. And so you spend a little bit of work to make all of the next like PRDs that you're doing easier to write because you're writing less of them. And so finding those little speedups where every time you're building something, you're doing, you're making it easier to do that same thing next time,

Starting point is 00:43:16 I think gets you a lot more leverage in your engineering team. And so, like, yeah, we have Kieran and Natasha and, you know, Cora has, it just came out of, it just became public, it was in private beta, it has 2,500 active users and like, there's like millions of emails going through it. And like, that's one of the products that we do as a 15-person company. It's kind of crazy. It is crazy.

Starting point is 00:43:39 How do you do this speed up thing? Is it prompts that they continue to refine? A lot of it is prompts and automations and stuff like that. Yeah. Got it. For automations, what's the tool? What's the tool used for automating? Automations.

Starting point is 00:43:52 What they're using a lot of is is cloud code. So you can do slash commands and cloud code, which are like repeated prompts that you're doing. Got it. Okay. So basically they're building a library of prompts that make the process of here's what I want to build to a good solid PRD that you can feed into cloud code. Yeah. More correct and more efficient. Exactly. Super interesting. And they just keep like a file or they put this into a project. Is that how they

Starting point is 00:44:19 It's a GitHub. It's like a GitHub. It's like a GitHub where they can like share it with each other. Another thing that they do, which I think is very cool is they use a bunch of clods at once. But then they're also using like three other agents. So they love there's there's an agent called Friday that they love. That's like a that's an AI Asian product called Friday. Yeah. Yeah. I didn't heard of that. Okay. There's another one called Charlie that they really love. And in particular, I think, the thing they like about Charlie. We have a whole video about this, which I can send to you. Yeah, I'll point to it. They did like a S tier through F tier of AI agents, which I think is so funny. And one of the things I really like about Charlie is that it lives in GitHub. So you can, when you get a pull request, you can just be like at Charlie?

Starting point is 00:45:01 Like, can you check this out? And that seems to, seems to work really well to have like different agents that have like, maybe slightly different perspectives. It's like different people, you know, that have different perspectives and have different taste. Like you can, I think Kieran is, he's like a one of those like, like serious rails files who are just, they just love rails and they love the way that rails feels. And so I think he has a real sensitivity to, okay, this agent, you know, Chidibati, for example, it's very, it feels very terse and minimal and, and so, and it has a particular kind of style that maybe he likes versus,

Starting point is 00:45:37 I don't know, Claude is a slightly different style. And I think that's, I think all of that is so interesting that these things have personalities and that those that that changes what you might want to use it for or why you might want to use three of them at once that is so fascinating uh it makes me think about peter dange's conversation again where he talks about his hiring strategy and one of his key lessons and he ended up hiring like the current head of product for chat gpte the current head of marketing chat gpt the current head of engineering like because he hires these incredible people and his philosophies to hire a team of avengers where everyone is strong at certain things and together they are the perfect team versus everyone versus like the best at everything.

Starting point is 00:46:16 And it's interesting that you can almost do that with different product, different agents from different companies. You definitely can. And it makes me feel like there's a bigger market than people think, potentially, where people will want different companies agents, not just all Devons or not all code. I think there really is. It's definitely not like one, one agent to rule them all. So interesting.

Starting point is 00:46:36 Yeah. Oh my God. The two people on the Cora team are with their background. Are they both engineers or what are they? They're both engineers. Kieran's got this like crazy background where they both have really interesting backgrounds. Karen's got this crazy background where he was previously like VP and at a startup. So like was effectively like the CTO of a startup or maybe two startups and was one of the founders.

Starting point is 00:47:01 And then before that he was like a composer, like a professional composer. And before that he was a baker. So we did like a team retreat in France last year and he like taught us all how to make croissants. My croissant was horrible. His was like beautiful. Seems that. And that's what I really is. And generally, I think like that kind of multi-dimensional type of talent is the kind of person that I love having it every like, because we're all generalists. We all want to use AI for all these like weird, awesome creative things. And someone who has that background is going to have a good taste for not only agents, but

Starting point is 00:47:32 what should the landing page look like or whatever, which I think is increasingly important where you're trying to scale a team of generalists of 15 people to like. five products. So that's Kieran's background. Natasha's background is I'm jealous because he only started to learn to code when Chachapit came out. He'd wanted to learn to code forever and he's only known how to code in an AI era. And I keep telling him, dude, like, I learned a program in middle school from books. Like, I had to go to Barnes & Noble and, like, buy a book. And there was nothing, I couldn't Google anything about like how this, how this, why this function wasn't working. It was just like,

Starting point is 00:48:08 overflow even back then. Yeah, yeah, there wasn't tech overflow. There was like weird BDNet forums and stuff that like, I was like 12 and I probably shouldn't have been on there, whatever. So it's,

Starting point is 00:48:19 he has gone so much faster than any other engineer, I think, like in a pre-AI era. And I see the same thing in the rest of the company. Like, I think there's this huge question about, um, what happens when kids,

Starting point is 00:48:35 uh, like entry level jobs are taken away by AI. And my take is like that that's worth thinking about it. And it's possible that might be a problem at some point. But my take is whenever I see a kid with chatubit, I'm like, holy shit, they're going to go so much faster than any other person that I've worked with. Like we have this guy, Alex Duffy, who works with us. He writes for context window. And he just launched.

Starting point is 00:49:02 We taught AIs how to how to play diplomacy with each other, which is really cool. and he did that whole thing. And I think he's really, really, really talented. And when he came to us, like, I guess almost a year ago now, it was one of those classic cases which I've seen like over and over at every, which is you have great ideas, but you're not a good writer yet. And it's really hard for me to do anything with you until you're good enough at it. So I have to give you like small little things until you get better and blah,

Starting point is 00:49:30 blah, whatever. And what I noticed with him is he was just making a year, like he made like a year's worth of progress in like two months because every time I sat down with him and told him, okay, here's how you tell a story. Here's how you think about a headline. Like he recorded all of it, put it into a prompt, and like he never made the same mistake twice. And I think he's so much accelerated from where he would have been because of this stuff. And I see that in lots of other parts that it works.

Starting point is 00:49:58 So Natasha is another good example. And so I think generally people are going to figure out that like some 20 year old with chat TV subscription is like super powerful. if you just like mentor them. And I think that's great. Man, there's so many threads I could follow here. Like there's all this fear of entry level people will never, like the roles are disappearing for entry level people.

Starting point is 00:50:19 And so how will we ever have senior people if these people can't learn to do things as an entry level person? And what you're saying is chat GPT and these tools help you accelerate really quickly. So you don't really need to be at the bottom wrong for a long time. Yeah. You're effectively like learning how to be one level, above the entry level from the beginning. And you have to, and this is sort of my, my whole allocation economy thesis where when you look

Starting point is 00:50:45 at what skills are going to be valuable in the AI era, one big group of skills are the skills of managers. Today they're human managers, tomorrow, everyone's a model manager. Right now AI is not like right now management skills are not broadly distributed because it's very expensive. another expensive thing that, so 8% of the workforce as managers, it's now going to be much cheaper to manage. So more people are going to have to do it. And so that's the thing that kids, 20-year-olds, whatever I see is now are going to start to have to learn.

Starting point is 00:51:20 In addition to, you know, it's not like you can just say like, okay, go do it and then come back. You have to be able to go into the work that's being done and help make it better. But they're learning both at the same time. they're learning how to manage and how to do the actual work so that they're good at it. And the managing here is managing agents, right? Yeah, you're managing AI. Yeah. And so this is a good, coming back to your point about how this core team, and I guess you said everyone,

Starting point is 00:51:48 every doesn't write code, zero code written. Now it's just managing agents that are writing code for you. Yeah. Okay. I've never heard of a company at this stage. So this is extremely cool. So the workflow is they give it, here's what I want. I refine it using this cool prompts library that they build on.

Starting point is 00:52:09 And agents build code, write the code. Then basically the time is spent reviewing code and then reviewing the output. What does it look like? What does it look like? And then continuing to refine. Wow. So you guys are at where Michael from cursor said we will be. So I chatted with them a few months ago.

Starting point is 00:52:25 He said in a year, this is where he thinks we'll be. We're not looking at code anymore. You guys are already there. Although you're looking at code. Okay. You're still looking at code? They definitely are looking at code. So, you know, you're doing a code review before you know anything.

Starting point is 00:52:38 And I do think like Danny who runs Spiral, which is the Claude Code for Content Tool I was talking about that we're building. You know, he spent a couple of days like digging into the internals of some third party library that we were interested in. Just because it's like it's helpful to know. It's helpful to like understand those things. but then he's not actually like writing any code once you understand it. He's just like off telling cloud code what to do. And I think that's, I think that's really important. This is an insane milestone we're hitting here.

Starting point is 00:53:10 Like there's this, you know, sense. We're getting to a place where you don't need to really understand code. You don't have to write any code. Like we'll get there. And like you guys are there. I think this is like so easy to overlook how wild this is. You have a product team not writing code at all. It is really wild.

Starting point is 00:53:27 I think it's really wild in particular just like having a small group of people that have everyone's multi-dimensional. Everyone has all these different skills. Everyone's a generalist. Everyone's AI forward. So what you can do in an environment like that with just still a small team is crazy. And you're kind of inventing all these new principles for like, how do we work together? How do we do engineering?

Starting point is 00:53:46 All that kind of stuff. And I think that's what makes the right. Like that's why I like doing that is because the writing that we do from that. I think is really good because we can talk about it from a from a sort of position of experience. And but I do want to say something else, which is we're not at a point yet where the people that work at every could do what they do if they didn't know how to code. Yeah, this is what I was going to ask. Which is a different bar. And I think for a long time, it's going to be valuable to know how to code for a long time.

Starting point is 00:54:18 But this has been, this is, this is like a progression that is not a new progression. So, for example, when I was in middle school learning to code, the new hot thing was scripting languages, which is like Python and JavaScript. And if you were, but if you were a real programmer, you would understand the language underlying Python and JavaScript, which was, which is, that's written in C. And scripting language was like, weren't, like, weren't totally real. And in order to, like, really do anything interesting, you have to be able to learn both parts of the stack. Same thing for C programmers. When I guess in the 70s, C was invented, it was like, you got to learn, you got to be able to write assembly. And English is just like a layer on top of scripting languages.

Starting point is 00:55:03 So I think all of those things were right in the sense that there's, especially during transitions, there's a lot of reasons why it's important to be able to go down a layer in the stack. And it gets less and less frequent over time, but that still takes a long time. And there's sometimes when even if you're a JavaScript or Python programmer, it's useful to know like how all that, how that stuff works, how it's written and see how it's, how it's implemented. Today, it's much less important than it used to be, but that took like 10 or 20 years. And I think that the same thing is going to be true for programming. Like having that skill is super important and will accelerate you significantly. It will sort of start to get less important over time, but we're not close to that yet. Okay.

Starting point is 00:55:44 That's a really important point. I'm glad you went there. So do you have a sense of how far we might be from you hiring someone to build another product that isn't an engineer? Like a real SaaS product? Yeah. So like, hey, we have this idea. We want to bring someone on to actually lead it. Very far.

Starting point is 00:56:01 Like not even not within site. But there's a lot of things that could be products that are a level down from that, that I think that you could do almost now. So like an example, we were talking about DIA, the browser from the name. new AI browser from the browser company, Dia has these things called skills, which are effectively really like little AI apps that you can run in the browser. You can prompt them and they run on the web page and do work for you. A non-technal person could build that. Same thing for like custom Gpts from chat GBT. Um, the on technical person can definitely build that. So I think while I will, I will definitely maintain that we're not anywhere close to anybody being able to like build a

Starting point is 00:56:46 conventional SaaS app with zero programming knowledge, aside from just like a demo, there are going to be other forms of software. One of my things like software is becoming content, there's going to be other forms of software that don't look like the software today, but you can run, start and run as a business as a non-technical person, even if you don't know how to code and that'll happen very soon. I mean, it's already kind of happening. It's just, it doesn't look like the thing that you're asking about. It's like, it's sort of like the difference between a Hollywood movie and like a YouTube video. Okay, I think that's really reassuring to a lot of people. Basically, what you're seeing is AI just supercharges people who have a skill and allows them to do a lot more.

Starting point is 00:57:25 Yeah. Okay. Is there any other way that you guys operate that is really interesting that might be worth sharing that helps you operate really quickly, helps you do more with less? I mean, I would love to talk about our, like, how we think about building products, like what products to build. like what do we end up building? Because I think there's something sort of special about it that probably there's a playbook that is useful for people. So when I think about this is only sort of snapped into focus recently. So a lot of this was just like doing it intuitively without really a thought for it. But when I think about the kind of things that we have ended up incubating,

Starting point is 00:58:04 it's basically goes back to something I said at the beginning, which is there are these things that were historically really expensive that only rich people are big companies could buy. So a chief of staff for your email. I think a therapist or like a lawyer is another interesting example. Someone to like organize your closet or organize your computer is another example. Someone to go straight for you that are becoming orders of magnitude cheaper so that everyone can use them even if you're at a small startup.

Starting point is 00:58:38 And so basically like when you're running like we are sort of this AI first company, you're running into all these little things where you're like, I wish I had a ghost writer right now. But ghostwriters are really expensive. Or I wish I had a lawyer, but it wouldn't cost me like $25,000. Lawyers are really expensive. And there's a lot more demand for those services than can be fulfilled because they're so expensive. And what AI does is it allows you to be like, oh, I could just use cloud for that. I can use Chachapit for that. And so you're able to, you're able to you're able to use the demand that you have that like we can afford a lawyer we have ghost writers but like there's a lot more that we can't do because we can't afford it so we still have

Starting point is 00:59:22 our lawyer and we still have our ghost writers but we just do a lot more of that stuff um and um so we notice that we start to then use like chat jbtbt and clod first these general purpose tools to try it and see is this useful does this actually work all that kind of stuff and then if it does we will unbundle it into its own separate thing that becomes an app. And I think what's really special about this time is the entire game board has been like totally reset in terms of things you can build. Where, you know, five years ago, it was like, you're going to build another notes app. Like we've been building notes app for forever. Like another B2B SaaS app.

Starting point is 01:00:05 Like it's all the same stuff in like slightly different packaging. And now it's like totally new territory. No one knows what's going on. Everyone's inventing it as it happens, right? All these new workflows are being created in a very similar way to, I don't know, for example, when spreadsheets refers to the thing on computers, like we were figuring out all these new workflows on spreadsheets. They got on bundle on BDB SaaS.

Starting point is 01:00:26 Same thing for chatDBT and cloud. And what's really cool is you can be like, cool, I'm using, I'm using Chachievich for this. It's really useful for me. And you might be like one of the first people to like really notice that. And then because everybody that works at every is AI first and came to us because they reads every, they read every. So they all have the, we all have the same vibe where we're all kind of doing similar stuff. They become our first users. So we measure the success of the product by like, is it a banger inside of every?

Starting point is 01:00:58 Like monologue, the app that I was talking to you about, like everyone just started using it. We're like, okay, we've got something here. And what's really interesting then is if everyone inside of every uses it and people read every, they have a similar vibe to us too. So they become the next set of users. And that's a really, I think, interesting like pipeline for building applications or building apps. It's a totally new like green field so that all the stuff you're thinking about, like it's

Starting point is 01:01:26 probably new, which is really cool. And over time, what I think is organizations like ours, people who are playing at the edge, we're doing things that in like three years, everybody else is going to be doing. So it may be kind of niche for now, but it will be a big deal in three years when everyone else is the same needs that we do. That is really cool. What I'm hearing is GPT rappers are a good idea and are building. I 100% think GPT rappers are amazing and they've been much maligned for absolutely no reason and people don't understand how absolutely valuable they are. I think there's all also just, you guys are, you raised a sip seed round.

Starting point is 01:02:08 I want to, so there's a good time to maybe talk about that. Just like these products don't have to become some mega billion dollar hits. Yeah. You kind of have this portfolio of companies. You have the content business. So I think there's a really interesting approach to that, how big these need to get to be successful. Maybe you just talk about that. Yeah, I really want every to be an institution that teaches people how to live a better, more human life with technology.

Starting point is 01:02:33 particularly with AI and both like teaches them how to do it with writing and the content we make and then builds tools for them to do that. And um but I think fundamental to building an institution is at least for me the way I would like to do it is um I want internally it to feel like this creative playground where we have the opportunity to like take risk and do stuff and do weird stuff that like just doesn't make any sense we can't justify anyone but we just feel like it would be fun. Um and so I think I'm always playing with that dynamic tension between institutions serious. We want us to be lasting and important. And it should just be fun. Like, let's play around. And I think having that tension is like really

Starting point is 01:03:14 valuable. And so I've always been like sort of hesitant to raise a lot of money because I think it locks like locks you into like having to be that serious thing that's like totally going for it. And there's lots of companies that figure out that balance, but just for me like personally as a founder, I'm like, I want to keep the optionality alive. and I want to keep the kind of playful feeling alive. And I think part of that comes from, I know, like, I have the control to do what I want, more or less. There's probably also some, like, deeper psychological things going on there, which I'm happy to talk about if you want to get into it. But, you know, I think there's also just that's that's kind of what I want.

Starting point is 01:03:51 And so when we started every, we raised like a very small 700K precede round. And this is at the height of the creator economy. So we both, we both started our newsletters. You and I started our newsletters around the same time. It was like the hypeiest, craziest thing. People were throwing money around. It was like wild. But we raised 700K because it was like, I want to raise enough for us to be able to experiment,

Starting point is 01:04:12 have a little cash cushion, but not so much that it locks us into anything. And we like send an email to all of our investors being like, and you're one of our investors. So you've probably got this email. Tiny investor, but I'm in there. I'm in there. We send an email to everyone being like, this is probably not a venture business. So you should not expect us to raise again. And we even raised on this slightly modified safe that gave everyone the option to convert to equity in three years, even if we didn't raise more money.

Starting point is 01:04:40 So we did it in a way that allowed us the option to get really big and do the traditional thing and also the option to do it the way we want to do it. Maybe it's not a huge business, but we love it. That's great. And we did the same thing for this recent round where we raised up to $2 million from Reed Hoffman and starting line VC. and we did it as what I've been calling a SIPCed round, which is basically they've committed $2 million, but we can pull it down whenever we want. And we just do it on a safe, at a set cap. And for me, that's really helpful because it allows me psychologically to take a lot more risk. Like, I don't, if we go to zero on the bank account, I can get more money.

Starting point is 01:05:21 Great. I don't have to think about it. But what's also really helpful is I'm not and the rest of the team is not. staring at a gigantic number in the bank account being like, cool, like, we can burn this. Let's burn it. And also for our investors, like, I think Reed very much wants us to succeed, but like, I don't think he cares, like what size of businesses is. Like, I think he's more philosophically aligned with the thing that we're trying to do. And if it becomes a huge business, he's psyched for it. And I think that kind of alignment is what I was looking for, because I think

Starting point is 01:05:55 there's this core creative spirit to the thing that I want to maintain. And I really care about having a big impact, but I think there's a lot of ways to have an impact. And one of them is building a $10 billion business. I think another way is like really changing how people see the world, see themselves in the world. And I think that's what stories do. And you, you don't necessarily, sometimes you do that by building a gigantic company, but you don't necessarily always have to do that. like a lot of the stories that we care about most are from people who maybe they maybe they weren't rich at all. And so I really like creating this place where we can make a really good business. And I care a lot about that.

Starting point is 01:06:34 But also the core of the soul of it is changing about changing how people see themselves in the world. I love that you've kind of innovated a new like a middle ground way of fundraising, not bootstrap and not just regular VC. It's a sixth seed. And I love that this two million. Like, you know, if I raise 50 million, it'd be like, okay, I get it. not put 50 million in her bank account, but you do that with two million. It's too much for us. We can't.

Starting point is 01:06:59 You don't want to see that in her account. That's another thing. And we'll see how this ages. Like I might be back here in two years crying the blues because we didn't raise not money or whatever. Who knows? But that's the other thing is I do think we can get so much further with very small amounts of money. Like, Cora, I think all in to build Cora, we've spent maybe 300K. Maybe.

Starting point is 01:07:21 that's crazy because and that includes salaries includes salaries yeah this product was not even technically possible even if you had billions of dollars like three years ago not possible because you can't do email summarizing

Starting point is 01:07:37 and like automatic responses and all that kind of stuff without GPT so not only was it totally impossible but now we can get with two engineers like we can get you know the amount done that would have taken a team of like 20 people.

Starting point is 01:07:55 And I think that's, you know, that means that we need less money. And I don't think that VC has really caught up to that yet. And I think there are other companies that are doing, there's like a term called like seed strapping. So there are other companies that are like kind of starting to wake up to this too. And I'm curious about how it changes the VC model for sure for us. Like we have a specific like incubation model, which is a bit different from from VC model. and I think there's some differentiation in the stuff that we can do with founders, which is kind of cool. But yeah, I'm just trying to figure out like a shape that works for me, and that's different from other people.

Starting point is 01:08:38 And we'll see how this goes. We'll revisit in a couple years. Seems like it's going great from the outside. I want to ask about a couple other things before we wrap up. One is around this consulting arm that you have. I think it's really interesting because, like I said, I feel like this could be a billion dollar business. I feel like every company right now is trying to figure out what the hell is everyone else figured

Starting point is 01:09:00 out that we're not doing. I've had so many emails from chief product officers at companies being like, can you introduce me to some chief product officers that have done cool things with AI that we should learn from? Like so many people. And I just introduce them to each other. And it's cool because you guys are basically solving that problem for a lot of companies. So one is just maybe share a bit about. that side of the business for folks.

Starting point is 01:09:22 And then two, I feel like you, I imagine you've seen companies that have done this really well, have adopted AI, things have worked really well, they found really good productivity gains, and then you found companies that don't. What do you find is the difference between those two? I love this question,

Starting point is 01:09:36 and I have a very specific opinion about this. So one, yeah, the consulting arm, basically, like, we spend all of our time playing around with new models, writing about them, and building stuff with them, and we have a big audience. So naturally, like, we've gotten companies over time being like, can you just come and teach us how to do this? And so we started to do that. This is

Starting point is 01:09:54 pretty nascent. It's probably been over the last six to nine months. But like it's a pretty big business now. It's our it's it'll probably double this year like last year we did about a million. Maybe it'll be maybe it'll be more this year. We'll see. It depends on a couple we have a couple big contracts out. So it might be way more than that. I predict a billion dollars in a few years. But yeah, basically people are like, can you come help us learn how to do this? So what we, do is we spend some time going and researching your organization. So we go in and try to understand like what is what are all the different teams doing? What are the repetitive tasks? Some of the stuff we were talking about earlier. And then what we will do is first we present a little

Starting point is 01:10:37 report tells you like here's everything that we found. Here's not only that, but you have a chat bot where you can chat with all the interviews that we did and you can pull out your own insights. We have a whole dashboard where it shows you like here's here are the teams that are really into this here are the teams that are not. Here's like how much, um, uh, how much leverage you might be able to get on different teams based on the interviews and based on the AI analysis. It's pretty cool. Um, and this is like, that's an app that I like vibe coded like over a weekend with Devin like a year ago. And then um, Alex runs the part of the consulting like has helped upgrade it. Um, uh, then what we do is we have a training curriculum. So we go in and train each team that and we customize it

Starting point is 01:11:13 based on, um, the interviews that we do because one of the interesting things about AI is it's such a general purpose technology. And I think people who work inside companies, 10% of them are like, I'm super curious about this. 10% are like, I will never touch this. And 80% are like, if you tell me how to do it for my job, I'll do it. And so we customize the training to be like, here are the exact prompts you're going to use. And here's the exact situations you're going to use them. And that really, I think, helps drive the adoption. We spend four weeks with each team, an hour a week, that kind of thing. It seems to be really cool. And then we'll often also, after this go and build automations and do some of the AI operations stuff we were talking about earlier.

Starting point is 01:11:53 Companies really like it. I think we work with a lot of big hedge funds and PE firms and big companies, all that kind of stuff. To your other, to your second question, which is like what separates the good companies from the bad or the companies that end up adopting this? I think the number one predictor is does the CEO use chat chiptipT? Or insert your own chatbot. If the CEO is in it all the time being like, this is the coolest thing, everybody else is going to start doing it. If the CEO is like, I don't know, this is for someone else. Like no one else is going to be able to lead that charge.

Starting point is 01:12:32 And they're either going to have, either they're going to be negative on it. And so definitely no one's going to do it. Or they're going to have way unrealistic expectations because they have no intuition for what's possible. And they're just going to get really disappointed. But the CEOs that are using it all the time are able to both drive the excitement and set reasonable expectations for what can be achieved. And so those things end up working really well. And the people that do this really well, so for example, we work with a hedge fund called Walleye, which I had the founder on my podcast, AI and I a few weeks ago, they're gigantic $10 billion hedge fund. Like one of the things that they do, which I think is, I think they're basically the model for like how to do this.

Starting point is 01:13:11 first thing he did, which a lot of CEOs are doing, is send the, we're an AI first company email. Everyone's got the memo. You just got to really do it. And one of the things he said in his memo, which I love is, I wrote this email with chat chitpT and you should do. So like you got to like in the memo. You got to like lead from the front in that way. And then what he does and I think what a lot of other like really cool companies do is they're doing like weekly. meetings where people share prompts and share use cases. They're doing, they do like a weekly

Starting point is 01:13:46 email to their entire company being like, okay, here's our, here's our usage. Here are our usage stats for chat to BT. Here are the people that like, here are the people that came up with a new prompt and contributed to it. Like create this, this sort of like awareness and momentum because what's going back to the point I made earlier about, you know, 10% of people are early adopters. Those are the people inside of a company that you need to find and highlight because they're going to just go spend all this time figuring out what works. And then all you have to do is like translate what they learn into the rest of the organization. And so if you create forums for them to be rewarded, you're going to automatically transfer a lot of their learnings to everybody else

Starting point is 01:14:28 and encourage more of it. And I think that's kind of this, the secret. That is awesome. I love this advice. So just to reflect back what you just shared a few kind of tactics you find that you encourage within companies. One is just send this memo, the Toby memo. I don't know if that's the right way to describe it. Who I think was first along these lines, just were AI first. It's going to be part of your performance review. It's going to be asking, can you do it in AI before you could talk to anyone else? All these things. And then just note, I wrote this using chat chbtis. It's a great idea. This idea of a weekly meeting. So it's like a live or Zoom meeting where people share. Here's the thing I've learned about using AI. And then this weekly stats,

Starting point is 01:15:07 email of here's how much we're using chat, dbtu, across the org. Here's some people that did some awesome work. Yeah. Amazing. And I especially love this very simple heuristic of if your CEO uses chatup BT or Claude or whatever daily, it's going to work out. Yeah. That is super cool. I know it's early, but what kind of impact have you seen from a company kind of leaning into this and adopting AI widely?

Starting point is 01:15:31 Any anything you've seen either anecdotally or numbers-wise? It's early. It's really hard to say other than I think generally people who do this. well now feel like they can do way more work than they used to without having to hire more people. And so they're just they're just going further faster at the same budget. I actually don't see, you know, I don't see a lot of people being like, cool, we're going to like fire a bunch of people. Like also, I don't really want to do consulting around like that.

Starting point is 01:16:00 Like that sucks. But we've never had to say no. Mostly people are like, cool, I'm just going to go further with the people that I have. I think also back to kind of the first point I made about reshoring American jobs. I have seen some companies, not the ones that we work with, but I have seen some companies of people that I'm friends with where they're like, we have a call center somewhere, but I think I can get the same amount done with like two employees in the U.S.

Starting point is 01:16:28 that have that use like one of these, you know, customer service platforms. Like they're still not totally automatic. Like I think that Klarna CEO thing, that was bullshit. But yeah, you can have a couple people in the U.S. that maybe you pay a little bit less to than you would for like 100 people somewhere else. And obviously, you know, that's a calculus that everyone has to make for themselves. But I've definitely seen that happen. And yeah, I think that's the get more done with the same amount of people.

Starting point is 01:17:02 Maybe to close out our conversation, I want to come back to this idea. that you referenced, but I want to spend a little more time on this, which is this idea of the allocation economy. If I understand it correctly, we've been in this knowledge economy where people you pay to do a thing. And your thesis is that we're moving to this allocation economy where skills become, the manager skills become more important and we're going to be spending more over time managing. And I think what's amazing about this is it also tells you which skills will matter more in the future, which is something I think a lot of people are thinking about. So maybe just answer that question and share whatever you think is a

Starting point is 01:17:36 important to share it to give people sense of what you're thinking. Yeah. So this is based on our article I wrote like two, two and a half years ago. So this is back before like agents were even like thought of as viable. And I was like really trying to think about how do I express what in my experience using this every day. Like what what skills are useful for me? Because I think that'll be the case for for a lot of other people. And I think that's that's, that's the kind of the best method I think to do these sorts of predictions is you have to be doing it all the time yourself and then that informs your opinion about this stuff. So what I noticed using at the time like GBT3 or maybe GPD4 was that I was spending a lot of time, for example, thinking about how do I communicate the problem? How do I gather the right information for the problem? How do I put it in the right way? So, that the model that I'm working with gets it. How do I pick which model to give it to you? And how do I maybe divide up the task to be like, okay, this model does this, this model does this based on what I

Starting point is 01:18:47 know to be like what's good and what's bad? How do I give them feedback? How do I have like a vision for what I want and a set of criteria for whether it's good? All that stuff is exactly how I found myself using these tools. And I was like, oh, that's just managing. And And once that clicks for you, I think you'll start to see a lot of other things. So a really good example is there's a big complaint that it's like, well, how can I have an AI do this? Like, I can't trust that they're going to do it well. So I just do it myself. And I'm just like, yeah, that's exactly what every first time manager says.

Starting point is 01:19:25 You always have this problem where you're like, okay, if I delegate it, it's not done in the way that I want it to be done. If I do it myself, I get no leverage. And so that's how a manager has to learn how to be a manager is like, when do I lean in and maybe micromanage a little bit? And when can I delegate and how can I trust it and how do I divide out the task and all that kind of stuff? And so I think there's a lot of overlap in those skills. And it just those skills are not broadly distributed right now, but they will be in the future

Starting point is 01:19:55 because it will be so much cheaper to be a manager. And specifically, I was looking at the article you wrote, the skills that you highlight will be more valuable is evaluating talent, vision, taste, and to your point, when to get into the details, when it makes sense to dive in. Yeah. Awesome. And then there's also kind of a connected point you made that reference, which is that generalists will become more and more valuable in the future. You mentioned that everyone at every is a generalist. Share a little bit about that. Yeah, I find, I mean, maybe it's because I'm a generalist, so you should take this a grain of salt. Same, same. But I think that's one of the things that has made AI so awesome for me,

Starting point is 01:20:32 is like I love to dabble in different things. So it's like in one day I can be like coding an app and like making a video and like making images and writing and like all that kind of stuff. And chat GBT is right there with me. And I think what we've basically what has happened as civilization has progressed from like ancient Greece to now is what we've discovered is the more that we specialize, the better we can coordinate across many different people. And so it's sort of it's like the Adam Smith, you know, like there's a pin factory and someone's making a pin or whatever his thing is, is specialization in gains from trade. And there have been a lot of really good impacts of that. And I think you can like one of my favorite examples of this is back to like ancient Greece and ancient Athens. Athens was a civilization of generalists, at least for citizens. There's like they have some, you know, a bad history with women and people who are slaves. But like. like, let's just put that to the side for a second. If you're a citizen, generalist, you could, you could be expected to be a fighter, a judge, a juror, maybe a general. Like there's, you could expect it to have many different roles inside of your society in your lifetime.

Starting point is 01:21:54 That changed, though, because Athens became an empire. And as it became an empire, if you're going to send like a. general off to like go and invade Sicily or whatever, you, you want that person to be like pretty skilled. And so it started to break the general kind of thing into people start to have specific roles and they coordinate with each other and all that kind of stuff. And I think that that pattern has actually been really good for developing civilization, but it's also, in a lot of ways, like it's not as fun. It's actually really cool to be a well-rounded person. And I think the interesting thing about AI is that it's a little bit like you can think of it like having 10,000 PhDs in your pocket.

Starting point is 01:22:35 It's like it knows so much about every little branch of human knowledge and every art form and every way of making things or building things. And you just have access to that. So it's doing a lot of the, it's good for doing a lot of the specialized tasks that you might have had to spend like 10 years getting good at, you know, learning about this particular species of cicadas. You know exactly how they like, you know, reproduce. But now you've got this thing in your pocket that can tell you all about that in any given context at any given time. And so you're empowered to jump a lot more between all those different domains of skill. And you can get more done as, for example, like a founder where I think we can stay at 15 people much longer than we would be

Starting point is 01:23:23 able to. So the people inside of every can stay generalists for much longer. And I think that that may like sort of ripple out to the rest of the economy where instead of like gigantic massive corporations where like each person is doing like one little like button turning, you have many more smaller organizations with more generalists. And I think that would actually be a really good thing. This reminds me. I was talking to my personal trainer that I'm trying out for a little bit. And she said that she's a very big vision kind of high level person and not good at executing. getting, like, we're staying organized. And ChatTPET is such a godsend for her

Starting point is 01:23:57 because she's just like, here's what I want to do roughly, just help me get it done. That's great. I love it. Yeah, and it really made me think about just how much value all this stuff is going to unlock. This was amazing. It was everything I wanted to be.

Starting point is 01:24:10 But with that, we reached our very exciting lightning round. Dan, are you ready? I'm ready. Here we go. What are two or three books that you find yourself recommending most to other people? Well, I already recommended one, which is war in peace.

Starting point is 01:24:26 Definitely got to read that. If you want a Tulsaid primer, I would read the death of Ivan Iliitch. Another good one is a swim in a pond in the rain, which is by George Saunders, and that's a collection of Russian short stories that is also about writing. And in particular, I really like the Russians

Starting point is 01:24:43 because a lot of the Russian novelists are dealing with the effects of technology on a traditional Russian way of life. And they're very kind of in this really, interesting middle ground between a sort of romantic outlook on the world and a more rationalist like we're we're progress we're making progress and that's one of the things you'll find in Anna Karenina when um god what's the guys what Levin is out in the fields with the peasants like doing the scythe thing like that's that's Tolstoy like kind of like thinking about oh what would

Starting point is 01:25:15 it be like instead of being a nobleman who's like trying to make make farms way more efficient I was just like with my scythe that was really happy anyway so they're dealing with of similar stuff to, I think, AI. The master and his emissary is another really good one, and that's about basically how the different hemispheres of the brain view reality. It's really, really good. And I think it relates to a lot of AI stuff, too. I think, yeah, I think those are my three or four. Yeah. Excellent list. I think nobody's mentioned most, any of these. So that's always a good sign. Do you have a favorite recent movie or TV show? You've really enjoyed.

Starting point is 01:25:53 Yes. I really love Deadwood. Have you seen it? I absolutely love it. I remember when they stopped it for some reason. I think he had to go do something else at HBO. It was so sad. It's amazing. Yeah. David Mouch is incredible, National Treasure, incredible writer. But what I really think, what I really love about it, and I only recently watched it is he talks about Deadwood being about how or of forms out of chaos. So it's this like frontier town. People are going to it and like there's no law. There's no rules. And by like season three, there's like a mayor and like, you know, all the industry has come in and it's like a real proper town. And I just love that. And I think there's a lot of, there's a lot of parallels from the like the Western frontier to technology frontiers. And so I think that show is like a really interesting study in that kind of dynamic. I love how everything connects to how tech works and how AI came to be. I love this. Thank you. Do you have a favorite product you've recently discovered that you really love?

Starting point is 01:27:00 I don't have a good answer for that because I just spent a lot of time using our internal products. But my stock answer is granola. So I do really love granola. My one gripe with them and I hope they listen to this podcast is I really want to export all my notes. I want an API. But other than that, I think it's a fantastic product. That is definitely the most mentioned product in this segment for the past couple months. So catch up granola.

Starting point is 01:27:23 I can't help and mention you get a year free of granola if you become an annual subscriber of my newsletter. What a freaking deal. And not just you, but your whole company gets free granola for a year. What a deal? This is not a paid promotion by me. I just, you know, that's just how I feel. So I'm glad. I'm glad as part of the bundle.

Starting point is 01:27:40 Yeah, incredible. Okay. Do you have a favorite life motto that you often come back to find useful in work or in life? So basically, I use Chatsypee all the time and it has memory. So I was like, you know, I'm going on Lenny's podcast. What would my life motto be? And it said, your life motto is a witness deeply, build bravely. You prize slow attentive seeing, whether it's reading Tolstoy, tracking meditation themes, or x-raying in David Milch paragraph.

Starting point is 01:28:04 So, like, it's hitting all the stuff I just mentioned, which is really funny. And then build bravely, you turn those insights into concrete things like every and Cora and long-form essays and all that kind of stuff. So I think there's, I think there's something about that. Actually, this reminds me of the actual motto, which is, and I didn't come up with this. I think it's like Pliny the Younger said, do things worth writing about and write things worth reading? Seems like a pretty good summation. Do things worth writing about and read things worth reading. Write things worth reading.

Starting point is 01:28:37 That should be the motto of both of our newsletters. That is really good. Okay. And by the way, I love that you ask chat to BT. What's my life motto? And this is interesting. So it didn't give me the answer, but inspired the answer. And I think that's actually like exactly how I use it.

Starting point is 01:28:54 Wow. It's an extension of our brains already. Yeah. Last question. I was reading somewhere where you wrote that you stopped writing at one point. You were just like, I need to do other things. I need to build this company. And then you realize I need to get back to writing because things started going sideways.

Starting point is 01:29:12 And I feel like this is such an interesting corollary to a lot of this stuff. have you talked about, of you do things that make you happy, stay close to joy. Just share what happened there because I didn't know that. This is definitely not a lightning round thing. So I'm, I'll expound, but I'll try to do it as quickly as possible. Perfect. I think generally when you're building a company, even if you do it the way that I do it, or did it, which is, you know, you don't raise a lot of money and you try to, you try to stay in control. There is a big temptation to try to run the company in the way you think you should. And I have this weird thing where I'm like, I really love writing, but I also really

Starting point is 01:29:49 love business. And there just was, there were not a lot of models for me, um, of people who had successful businesses that, that were also writers. Turns out there are. Um, but I didn't know about that for a while. And so, you know, early on at every, like we were, it was growing really well, because I was writing a lot. Nathan was writing a lot. Um, and when I stopped writing, uh, the business didn't work as well because media businesses don't follow the same pattern as tech startups, because if you're a media business and you are a founder who then hires people to make the product, which is right, if you have product market fit before, you lose it. And maybe you hire people that are good writers, but that's hard. It's total opposite pattern for startups. You build the first

Starting point is 01:30:33 version of the product and then you hire people to build the rest of it. And, you know, so that's what I did. And I also really struggled with, okay, what are the implications for that? that and for my career. And I think it was hard for me to admit, like, I actually want to write because I just didn't have any examples of someone being the kind of writer that I wanted to be. And what's really interesting is, like, three years into the business, like, the business has been pretty flat. I was, like, pretty miserable because I was, like, not doing the thing that I really wanted to do. And I asked Chachy Ptia. I was like, is there, are there any examples of writers that have built businesses? And it was like, yeah, Joel Spolsky, who built Trello and Stack Overflow.

Starting point is 01:31:11 There's Jason Freed, who I've known for a long time and I have always always looked at to what I forgot about in this context. There is Sam Harris, who's got a great podcast and he's got a gigantic meditation app. There is Bill Simmons, who's like incredible podcaster and also Bill the Ringer, sold to Spotify or a couple hundred million bucks. Like there's a lot of these people. And there are patterns that they use to build companies that are pretty well understood. They're just not typical Silicon Valley patterns.

Starting point is 01:31:41 And so I was like, cool. Like, I just want to be a writer. I think it would be really fun. And so I sort of flipped. I still have the builder or entrepreneur, founder, part of my identity, but I sort of flipped it to be like, writing is at the center and I'm like unapologetic about it.

Starting point is 01:31:57 And that's actually good for the business. It's good for me and it's good for the business. And the more I've leaned into that doing the thing that, like, if you told anyone that you were starting a business where it's like, well, we're going to be a newsletter and we're going to incubate all these apps and we're going to do consulting. and whatever, they would be like, you're nuts.

Starting point is 01:32:14 Like, everyone wants to do that. Of course, every founder wants to do that. But, like, you have to focus. You have to, you can't write, like, whatever. But every time I've kind of just leaned into something that feels like the most, the ultimate luxury of like my hidden secret desire, it's actually worked a lot better. And I think you end up, what it really is is there's a huge tax to doing something every day that you're not quite, you don't quite like that much or you're not quite a fit for.

Starting point is 01:32:41 And by sort of giving into those secret desires, you end up finding a shape for the work that you do and the business that you build that is good for you. And that's always going to be a somewhat unique shape from other businesses that have been built. It's always going to rhyme with other things. But I think finding that unique shape instead of just kind of cargo culting, like what you think a company should look like is definitely a much better way to be successful. And it's also a much better way to live. I think this is going to hit hard with a lot of people who are listening, who are maybe founders or want to be founders. And this resonates with a lot of people that have been on this podcast during similar lessons. Dan, this was incredible.

Starting point is 01:33:19 Two final questions. Where can folks check out Every find you online, and how can listeners be useful to you? So you can find us at every.t.o. I'm also on Twitter at Dan Shipper. You can go there to check out our products, our newsletter, if you want to stay on top of AI, all that kind of stuff. I also have a podcast. It's called AI and I. You can find it on YouTube and on Spotify.

Starting point is 01:33:43 And how can people be useful? Honestly, I think the most useful thing for someone like me based on what I want to do is like, I want people to find interesting, cool ways to use AI that like actually helps make their lives better. So like, just go do that. And tell me about it. And I think that'll be great. What's the best way to tell you? Is it comments on your YouTube show?

Starting point is 01:34:04 Is it emailing you, DMU? I would say tweet me. If you subscribe to Every you can also reply to those emails and they eventually get forwarded to me so tweet me, reply to Every and if you want to comment on YouTube, great. I'm not in the YouTube comments as much as I should be there.

Starting point is 01:34:22 Don't do that. Maybe Dan do that. Okay, well, Dan, this was incredible. Thank you so much for sharing. Thanks for being here. Thanks for having me. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcast Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review

Starting point is 01:34:43 as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lenniespodcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every)

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