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Every CEO Dan Shipper: Why no one on his team manually codes

Engineers ship specs and reviews while Claude Code, Cursor, and Gemini write the lines; a Head of AI Operations turns weekly workflows into prompts and agents.

Lenny RachitskyhostDan Shipperguest
Jul 17, 20251h 34mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 4:04

    Welcome and introduction

    1. LR

      (instrumental music) 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.

    2. DS

      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.

    3. LR

      What are some things that you believe about AI that most people don't?

    4. DS

      I hate the headlines that are like, "Entry level jobs are taken away by AI." Whenever I see a kid with ChatGPT, I'm like, "Holy shit. They're gonna 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." Like, he recorded all of it, put it into a prompt, and he never made the same mistake twice.

    5. LR

      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.

    6. DS

      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 gonna be doing.

    7. LR

      Today my guest is Dan Schipper. Dan is the co-founder and CEO of Every, 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 hand-write 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 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. 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, Granola and more. Check it out at lennysnewsletter.com and click bundle. With that, I bring you Dan Schipper. This episode is brought to you by CodeRabbit, the AI code review platform transforming how engineering teams ship faster with AI without sacrificing code quality. Code reviews are critical but time-consuming. CodeRabbit acts as your AI copilot, providing instant code review comments and potential impacts of every pull request. 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 one 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. 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, Vercel, and Webflow rely on Dx. Visit Dx's website at getdx.com/lenny.

  2. 4:04 – 7:06

    Hot takes on AI and job reshoring

    1. LR

      Dan, thank you so much for being here, and welcome to the podcast.

    2. DS

      Thank you for having me. I've obviously been a huge fan for a long time, and so it's an honor to be here.

    3. LR

      It's my honor, Dan. I feel like this is a podcast that was meant to be, uh, f- I'm so happy we're finally doing this. There's so damn much that I want to talk about. There's 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, uh, building with AI, using AI, evaluating AI than anyone else I know nearly. And so I really respect your insights and your, uh, 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, AI tools that most people don't believe?

    4. DS

      I'm gonna go with my hottest take, and this is the take that I have the least evidence for, so l- let's just start with that.

    5. LR

      Perfect.

    6. DS

      I have other more well-reasoned takes to give you, but this is my hottest one, which is I think that AI may be a, 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 skills needed to do the jobs that you're doing, but I think it may actually reshore a lot of jobs. And it'll do that in two ways. Um, one is there are a lot of expensive services that, uh, rich people and big companies, uh, are, pay for right now, so like a, you know, in-house counsel or, like, you know, a call center or whatever. Um, 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, um, who are in those jobs to serve more people cheaply. So if you're, uh, so it may not get rid of customer service, for example, but it may allow...... you know, 10 people in the Midwest who would normally be working at a call center to serve hundreds of thousands or millions of people. Maybe, maybe that's, maybe that's too much, but, like, a lot more people than they would ordinarily if they were the ones on the, on the phone all the time. And so it becomes much more cost effective for American companies to hire people in the US, and I think the people in the US are gonna be better in a l- in, in a lot of cases at using these AI tools to, um, do work. Uh, so I think it may actually make it more effective to have jo- those jobs in the US run by people sitting in the US who are 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, um, I think it's quite, uh, it's quite lost in the conversation over whether AI will get rid of jobs.

    7. LR

      I like optimistic takes about AI, so this is great. And like, to your point, well, I want TBD if this is good for other countries but good for the US.

  3. 7:06 – 14:35

    The power of Claude Code for non-coders

    1. LR

      What else, what else you got? What other-

    2. DS

      Okay.

    3. LR

      ... hot takes?

    4. DS

      Another, another big hot take, and this is, this is less, like, contrarian and more just, like, I think people are truly sleeping on it. I think people are truly sleeping on how good Claude Code is for non-coders. And I'll extend this to not just Claude Code, but Google just came out with the Gemini CLI com- command line interface, um, so things like that. And I'll tell you about, um, for people who are listening that don't know what Claude Code is, Claude Code is just a command line interface, so it's, you know, those black terminals that programmers use. Um, it's a command line interface that you can boot up, uh, 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 complete a task, like autonomously, agentically. It's a, uh, especially with Claude Opus 4 that just came out, it's like this gigantic leap forward in AI's ability to, um, work by itself. And, and Claude Code can even spawn multiple sub-agents 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 a terminal, 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 its notebook, go down its to-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, ChatGPT chat or a regular Claude chat. It's, like, actually processing every single file that you give it. And so I think it's incredibly powerful for, um, any kind of task that involves processing a lot of text.

    5. LR

      So as a simple way to think about this, you basically have an agent on your local computer that can read your local files and do your bidding?

    6. DS

      Yes, exactly. And, um, it can do that for long amounts of time without going off the rails.

    7. LR

      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.

    8. DS

      Exactly.

    9. LR

      So the hot take here is just Claude Code, which most people think is for engineers, is the most underrated, uh, tool for non-technical people.

    10. DS

      Yeah, exactly.

    11. LR

      What are some other ways you imagine people seeing this? This meeting note example is really cool, and I could see people do- using this. What else have you seen or taken note of?

    12. DS

      Um, something that I've done a lot, so I'm a writer for a lot of my job. And, um, for example, I love, um, and y- I know you're gonna ask me about books I love, so I'm gonna give you-

    13. LR

      (laughs)

    14. DS

      ... a sneak peek, which is I love War and Peace. I just read it for the third time.

    15. LR

      Wow.

    16. DS

      Um-

    17. LR

      That's a long book.

    18. DS

      It's so, 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, um, 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 in their voice and the expression in their eyes, like all that kind of stuff. Like, he's just like a, an incredible student of human behavior and psychology. And so I just downloaded War and Peace to my computer, which you can do 'cause it's public domain, and then I had Claude read, like, the first three chapters of War and Peace and pull out all of those descriptions and make, then make a guide for itself for, like, how to do descrip- like, character descriptions like Tolstoy. 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, I had it 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, like, you can get as deep and weird and nerdy for whatever subfield you care about as you want to. Same thing for, like, if you've got tons of customer interviews or, 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.

    19. LR

      You in- actually inspired me to use, this is not what you're describing, but it's also something that's very cool. This is gonna sound so nerdy. I'm reading Anna Karenina right now-

    20. DS

      Yes!

    21. LR

      ... based on (laughs) also Tolstoy, and this is, uh, recommended by a previous podcast guest, and so I was like, "All right, I gotta read this." Also very long. I'm on my Kindle, I'm just like, "All right, 13% in, I've been reading for months."

    22. DS

      Hot take, I think War and Peace is better than Anna Karenina, especially for, like, a tech person, but they're both good.

    23. LR

      Okay. (laughs)

    24. DS

      (laughs)

    25. LR

      There, there we go, there's

    26. NA

      (laughs)

    27. LR

      ... my ear. Um, (laughs) I saw you tweet this use case that I love that I've been using, which is just while I'm reading, having ChatGPT 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. Uh, An- Anthropic just shared this. I don't know if they shared it or someone found this in their legal briefings, that they actually bought-... tons of books and scan them themselves-

    28. DS

      Yeah.

    29. LR

      ... is how they, that's fair use. And so it has all this context. So just sitting there asking it, like, "What the heck is this thing in Russian society?" uh, is super fun.

    30. DS

      (laughs)

  4. 14:35 – 18:45

    The future of AI in business operations

    1. LR

      Before we do, uh, any other hot takes that you want to throw out there?

    2. DS

      I have one other hot take-

    3. LR

      Mm-hmm.

    4. DS

      ... um, which is, I have a definition for AGI. And so (laughs) AGI is, like, famously hard to define, like what i- what does it mean for it to be ar- artificially, artificial general intelligence. The Turing test was one, but, like, we've pretty much blown past the Turing test in a lot of ways, so w- we have no good one. Um, and so what I have noticed is that you can tell how much better AI is getting by how long, uh, a leash you can give it to do work. So with CoPilot, it was like a "you can tab complete," and that was like the beginning. Um, with ChatGPT, you ask it a question and it, it returns a response, and that's, like, maybe slightly better than a tab complete, and then now with, with Claude Opus 4.0 and Gemini and all that kind of stuff, like, it can go off and, 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, um, where you have to intervene. And I was thinking about this, and it reminded me of Winnicott, who was a child psychologist. He wrote this book called Playing in Reality, and his conceptualization for what it means to become an adult, uh, what it means to go from being an infant to a child to an adult, is when you're f- 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. Uh, maybe it's like you get left, left alone to cry it out. Like, who, who knows if that's, 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 gonna 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, uh, you get longer and longer periods of time where you can be on your own. So we're still in that kind of, like, 20 to 30 minutes is, like, maybe the to- maybe... I don't know. I guess you probably can't leave a toddler alone for 20 or 30 minutes, but like, it... you know, it's a little bit older than a toddler is.

    5. LR

      (laughs) Maybe 20, 30 seconds.

    6. DS

      But it's like... Yeah. (laughs) Um, y- you can... with a toddler, it's like, uh, you can be in the same room but not interacting with them tota- like, every single second-

    7. LR

      Yeah. Yeah, yeah, yeah.

    8. DS

      ... um, for, for 20, for 20 minutes sometimes. So it's ar- it's around there, and I think there's a similar... I think there... w- we have that s- 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 Claude 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. 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. And-

    9. LR

      Interesting.

    10. DS

      ... I think that's a good definition of AGI.

    11. LR

      And the profitable piece is also just the cost of running that thing and having it, like, you know-

    12. DS

      It's, it's partly the cost and partly the value.

    13. LR

      Right.

    14. DS

      Um, and obviously, you can, like, game this a little bit and be like, "Cool, I'm just gonna, like, tell Claude to, like, run in a loop forever," but, like, I'm talking about m- more than that, like, uh, more widespread, more widespread adoption of agents that, that work all the time. And, um, and I like the profitable thing, because if it costs a little bit of money and we're, we're... the bar is profitability, then there's like a... it has to actually be doing something useful for you to keep it on-

    15. LR

      It's interesting how that also is very, uh ... And the metaphor of, uh, 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, is also just directly correlated with how senior they are.

    16. DS

      Totally.

    17. LR

      Okay,

  5. 18:45 – 22:26

    AI’s role in enhancing human skills

    1. LR

      great. Uh, anything else along these lines?

    2. DS

      I mean, I have plenty of them. I think I'm generally ... Like, I hate the headlines that are like, "It's gonna replace jobs," um, or like, "It's gonna unemploy, like, two-thirds of the workforce." Like, I don't think that's true. I hate headlines that are like, "You don't use your brain when you use ChatGPT," or like, there ... Another, another good headline is like, um, uh, doctors alone, doctors plus AI or just AI, like, which one is better? "AI is better, therefore, like, doctors are, um, gonna be outmoded." Like all that stuff is, I think, pretty dumb. So for the doctors plus AI example, um, I think it's important to recognize that using AI is a skill. 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, it is gonna be better, but there's a lot... There's, like, so many contexts that doctors are, 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 will be totally and completely different. For the student example, um, or like the, you know, AI turns your brain off 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 is famously very skeptical of writing because he thought it would harm your memory, and it did. Uh, we don't remember things quite as well as they did back in the day 'cause they had to remember long e- 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, with, with AI where, yeah, you may, you may be slightly less engaged in certain tasks. But if you use it right, you're gonna be way more engaged in other tasks where you have much more power. And so you can construct a study 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 is wor- 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.

    3. LR

      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, I think it's really important this context you're sharing of 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.

    4. DS

      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 gonna change. And the example I always use is I live in Brooklyn and the tailor down the road, down the street from me, like, doesn't accept credit cards. Like, credit cards have been around for a long time. So, uh, 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- it- it's incredible. I love AI. It's so incredible, but it doesn't ... It doesn't actually give you an intuition for, um, 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,

  6. 22:26 – 25:40

    The evolution of AI tools and their applications

    1. DS

      um, maybe, like, a little bit of an intuition for it, um, is I built this thing over a weekend, like, a month ago, that was, um, can O3, can it predict what I'm going to say in a meeting? It's like we ... It's a benchmark. It's a s- it's the CEO benchmark. And the reason I did that is because OpenAI's ... The gold standard for OpenAI, um, for testing how powerful a model is, is they test, they, they test it on their internal code base. So they say, "How good is the, the new model at predicting what comes next in our internal code base?" 'Cause that's n- that's not anywhere el- out on the internet. So it's a really good, um, 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- internet, and some of the ... There's some overlap, but be kinda interesting. And so I ran a bunch of the frontier models on this, on just, like, my granola transcripts, and they're pretty bad. They are pretty bad. And it's not because they're not smart. Um, there's a real, there's this real push now. Um, Toby from Spotify, uh, coined this term called context engineering, which is like getting the context to the model, uh, 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. A- at the time, I called it knowledge orchestration. Um, I think context engineering is, is a better, probably a better term. But like, uh, it's totally true and, and, and it's ... 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 window and we're done. It's go- it's ... I think it's going to get better over time, but the minute w- it gets good at predicting what's, what's gonna s- what I'm gonna say next in a meeting, I'm just gonna use it as a tool, and that's gonna change the entire dynamic of what I say next in a meeting. So i- it's not as easy as it seems.

    2. LR

      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 he'll make the decisions.

    3. DS

      Yes, definitely. And I, I, I mean, we do this a little bit. It's not the same as-It's not the same as having... uh, 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, particular AI revolution, is that you don't have to repeat yourself. And so we had it, like, last quarter, 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 c- if I can help it. Um, so for us, um, 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... is this idea any good? Like, that kind of stuff. And we 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 case out to the edge, so that, um, writers who are not able to talk to me, like, by the time I see it, they've already talked to, like, some simulation of a simulation of me. And that's incredibly

  7. 25:40 – 29:50

    Building an AI-first company

    1. DS

      powerful.

    2. LR

      Let's follow this thread. This is exactly where I wanted to go.

    3. DS

      (laughs)

    4. LR

      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. Uh, it's... and it's super aligned with just so much of how you... of your writing. There's just, like, so much reason to study what you guys are doing.

    5. DS

      Oh, thank you.

    6. LR

      So let's... Yes. And this is benefiting all of us, so thank you.

    7. DS

      (laughs)

    8. LR

      So first of all, just tell people what the heck Every is, and then-

    9. DS

      (laughs)

    10. LR

      ... share a few insights into just how you operate. Uh, it's funny that you laugh (laughs) at, at whatever you say.

    11. DS

      Everyone asks that, 'cause it's just... it's like a... it's a very... it's just... it's a very weird shape of a company-

    12. LR

      It is, yeah.

    13. DS

      ... that you can actually see s- other companies that have this shape from earlier eras, but they're... it's a little bit... it's less common. It's... it doesn't make as much sense. And I think it's newly enabled by AI, and, and we can talk about why. Um, but w- the way, the way that I typically talk about, um, Every is, um, 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, um, anyone who's, who's basically interested in or working in AI at the frontier and wants to know what's going on, uh, reads us. We do a lot of, like, for example, whenever, um, whenever OpenAI or, 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, which is... it's, like, my ideal job. I've... I love it. It's the best.

    14. LR

      (laughs) That sounds awesome.

    15. DS

      I don't know if I can curse on this podcast, but it's the fucking best. (laughs)

    16. LR

      You can, you can.

    17. DS

      (laughs)

    18. LR

      (laughs) Perfect. Excellent use. Uh, and you call those vibe checks? Is that the-

    19. DS

      Yeah, we call them vibe checks.

    20. LR

      Vibe checks. Love those.

    21. DS

      Um, which I think is really important because... and this gets to the next part, the, the apps part of, of what we do. I think it's really important to do vibe checks and, 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, um, uh, uh, like, in your job or in your life? Because I think, um, that captures something that standard benchmarks just don't capture and really can't. And the best people to tell... 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, 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, um, in addition to our writing, and that, that helps us write great stuff. And that has turned into, um, a suite of apps that we run internally and the people who, uh, are... uh, 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 for people who are actually using it every day? And the suite of apps that we have, uh, one's called Quora. We just launched Quora publicly on the day that we're recording this, which is really awesome, um-

    22. LR

      Congratulations.

    23. DS

      Thank you. Uh, you can think of it like a chief of staff, an AI chief of staff for your email, helps manage your email with AI. It's very cool. We can go into more of it later. Uh, we have another one called Sparkle, uh, which is an AI file cleaner. We have another one called Spiral that does content automation with AI. We or- originally incubated, um, Lex, which is an AI document writer, which we spun out into its own company. And my Every co-founder, Nathan, runs that. Um, and, uh, basically, we bundle everything together. 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 we like to incubate and how do we like to incubate it, 'cause I think there's, there's a lot of... there's sh- some really interesting special things in there, but I- I've been blabbing for a while, so I'll stop there.

    24. LR

      There's also a consulting firm which I want to talk about, but let's hold off on that.

    25. DS

      Yeah, we have consulting. (laughs)

    26. LR

      (laughs) Yeah.

    27. DS

      We also do that. Um, and that, that is another... that's like the third leg of the stool in the business. It doesn't fit quite as n- nicely into my ideas and apps framing, but we, um, we spend a lot of time with big companies where we teach them how to... basically how to be AI first. We train all their people on how to use AI. And it's, it's very cool. It's, it's really, um... it's, it's really fun and, and a very, a very important part of what we do.

    28. LR

      That feels like a billion-dollar business right there. I want to come back to it.

    29. DS

      (laughs) I, I think so.

  8. 29:50 – 35:35

    Innovative AI operations and team dynamics

    1. DS

      (laughs)

    2. LR

      Because everybody wants to learn this.

    3. DS

      Yeah.

    4. LR

      Um, okay. So share a few ways that you guys operate. You mentioned that you... your team doesn't write any code. Uh, what are just some ways that allow you to operate this efficiently? I know your team's really small. You have a daily newsletter, you have three, four products, you have a consulting arm. How big is the team at Every?

    5. DS

      We have 15 people.

    6. LR

      15 people, okay. (laughs)

    7. DS

      Yeah.

    8. LR

      So just give us insight into some of the ways you operate that are kind of at the bleeding edge.

    9. DS

      Okay, so a couple things. Um, 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 is... are just automating as much as possible.... um, and I think that has been a big unlock. 'Cause 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, "Okay, am I gonna do this in the way that I know how? Or am I gonna do it in the new way that might not work?" Like, "I'm gonna spend a bunch of time in Zapier, like, building some no-code automation." Like, "I don't want to do that." And having an AI operations lead lets you basically identify those things and have them solved without people who are doing the work actually getting inv- uh, getting, like, 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 de- developing little applications internally. Um, but if you're good at making applications people use, it's great. Highly recommend having an AI, AI operations lead.

    10. LR

      I imagine you saw the CEO of Quora tweeted about this, wanting to hire exactly this sort of person.

    11. DS

      Mm-hmm. Yeah.

    12. LR

      So clearly this is a trend.

    13. DS

      Yeah.

    14. LR

      So the idea is this per- 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.

    15. DS

      Yeah. Yeah.

    16. LR

      And then, is this person mostly just you automating you, or can they help other people? How do you help-

    17. DS

      No, she helps, she helps everyone basically.

    18. LR

      Everyone? Okay.

    19. DS

      W- where we're starting right now is with the, uh, editorial operation. So there's so much stuff in the editorial operation where I, or, uh, our, our editor in t- 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, hours a day. Um, and so now Opus is at a point where you can give it a style guide and a prompt and it'll go through, uh, go through anything you're writing and copy edit it, which is amazing. Um, 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?" Um, anytime someone gives her something. So there's a little bit of, like, behavioral update too that has to happen, which I think is a really 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. Um, we don't have anyone really who's like, "I don't know, I don't really want to do this." And, and that's, 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.

    20. LR

      It is, uh, super cool. What is her background, this AI operations person?

    21. DS

      She, her name is Katie Parrot. Um, she does a lot, she actually does a lot of, um, ghostwriting for us. So she also, when, uh, when people inside of Every who are builders, um, often they just write themselves, but, like, sometimes they want help and she'll help, um, help them write about, like, whatever they're, whatever they're working on. So that's, that's how she started with us. She still does that, but she also spends a lot of time doing the AI operation stuff. Um, 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, the reason Katie is so good is because she's n- she's incredibly good at, at that kind of process stuff, or, like, thinking about that. Um, but she's also a great writer, and she's also, um, just incredibly, uh, 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, it's been really fantastic. So I think that's a... At minimum, you really just want someone who's just like, "I want to tinker, I want to build stuff." Um, there's also people who have a little bit more of that process orientation. I think that is important. Um, and to the extent they understand the craft of the thing that they're trying to build for, that also helps a lot.

    22. LR

      This is an amazing tip. I feel like everyone's gonna start hiring these people. Uh-

    23. DS

      I, I think so. There's, there's a couple other people who talk about this. Uh, I heard Rachel Woods, who's another, um, sort of... She thinks a lot about A- AI stuff. She, she's talking about it. I think it's becoming, like, it's becoming a thing and, and I think it's, I think it's really important and it, 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 Quora. And by the way, Quora is spelled C-O-R-A, so it's different from Q-U-O-R-A. Um, slightly confusing. There's a lot of copy that goes out in Quora 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. 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, um, Nitesh, who's one of the programmers, uh, engine- engineers on Quora built a Claude Code command that just uses that prompt and checks through the entire code base, um, for, for all the copy edits and then creates a pull request on GitHub and then sends the pull request to Kate. So she's just, like, looking at the pull requests and being like, "Does this make sense?" 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.

  9. 35:35 – 41:26

    Dan's AI stack

    1. DS

      I think that's so cool.

    2. LR

      That is extremely cool. Uh, I want to take... I'm gonna take this on a little tangent. You keep mentioning-

    3. DS

      Sure.

    4. LR

      ... Claude, and I'm a- I'm curious just what is kind of in the stack of tools that you find yourself using, your team ends up using? This, it seems like Claude is a core part of it.

    5. DS

      I do love Claude. I would say I'm generally... My first thing that I open is O3. I'm like a ChatGPT boy. Um, and I think O3 is super high quality. I think, um, it's great for writing, 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 ChatGPT 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, y- your average, your average ChatGPT user. And I also find, like, I, 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, I like that. I think that's, I think that's really great. So day-to-day, O3. That's my, that's my go-to. I think Claude Opus is... First of all, Claude Code-Everyone inside Every, that's basically what we use. Um, if you're building something, you're using Cloud Code. It- it's crazy, it's so good. Um, Gemini just came out with something, so I'm very excited to try that, um, because I think that that's the model that we use most for the apps that we build, like inside the apps. Uh, 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, um, which is OpenAI's coding tool and that's for, like, I want a one-off, self-contained, like, I want to pick off this little feature. What else do I use? Uh, going back to Claude. Claude Opus 4 can do something that no other model, except one other model that I can't talk about, um-

    6. LR

      Oh. (laughs)

    7. DS

      ... can do something that no-

    8. LR

      What is that?

    9. DS

      ... other model can do.

    10. LR

      Okay. We won't go there. We don't want to get you in trouble. Okay, go on.

    11. DS

      (laughs) But yeah, uh, n- 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?" Claude, for example, would always give it a B+. And then if you cha- if y- if you did another turn of the same conversation, you're like, "I updated this," it would always go to A-. And then if you give 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. Um, and there's various methods that you can use to, like, prompt e- prompt engineer around this, like give it a template or, like, whatever. And they sort of worked, but it just still doesn't, 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. Uh, it's really wild. And I think that's, 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, um, 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 Claude Code but for content, um, style product where, you know, you say, "I want, I want it to write a tweet." You give it all of the documents, it has a bunch of 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, um, it can judge things, part of its to-do list is, "Okay, I wrote three tweets. I'm gonna, 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 shotted it and we were like, "Great, this product works. Let's, like, let's start shipping it." (laughs) Um, so yeah, I love it for that.

    12. LR

      Are there any other AI tools that you just use regularly? You mentioned Granola even outside of the bottles. So what are, what are some that you think maybe people are sleeping on?

    13. DS

      I use Granola. So I used to use, uh, Super Whisper and Whisper Flow, which I think are fantastic. We have an internal version of that, uh, called Monologue that will be shipping in, like, a month or so that I, I use now. But y- 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. Um, I use, I use, we use Notion all the time and I specifically use their meeting recording. I think that's most, I think that's mostly the stack.

    14. LR

      Sweet. 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, error tracking, and more. Everything Posthog offers comes with a generous free tier that resets every month. More than 90% of customers use Posthog for free. You are gonna love working with a team this transparent and technical. You'll see engineers landing pull requests for your issues, and their support team provides code level assistance when things get tricky. Posthog lets you have all your data in one place beyond analytics events. Their data warehouse enables you to sync data from your PostgreSQL database, Stripe, HubSpot, S3, and many more sources. Finally, their new AI product, Analyst Max AI helps you get further faster. Get help building complex queries and setting up your account with an expert who's always standing by. Sign up today for free at posthog.com/lenny and make sure to tell them Lenny sent you. That's posthog.com/lenny.

  10. 41:26 – 48:29

    Compounding engineering

    1. LR

      Let's go back to ways that your team operates. You mentioned-

    2. DS

      Yeah.

    3. LR

      ... having Kate. Was that her name?

    4. DS

      Yeah.

    5. LR

      Okay. Uh, what else? What else do you do that you think other companies should be doing or will eventually start doing?

    6. DS

      So the Quora team, uh, which is Kieran and Natesh, basically-

    7. LR

      I love that that's the team, two people.

    8. DS

      That's the team, yeah. (laughs)

    9. LR

      (laughs)

    10. DS

      Well, it's Quora, uh, it's, it's Kieran, Natesh, and 15 Claude Code instances, so it's, you know, (laughs) it's more powerful than you think. Um-

    11. LR

      This is, I love that this is just, again, a glimpse into the future.

    12. DS

      (laughs) Um, 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, um, 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, um, 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, I need to do, right? Um, and so you could just be like, "Okay, cool, that's my job now. I'm gonna just, like, write PRDs." Um, 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 gonna 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, easier to write, 'cause you're writing le- less of them. And so finding those little speed ups where every time you're building something, you're doing... you're making it easier to do that, that same thing next time, I think gets you a lot more leverage in your engineering team. And so like, yeah, we have Kieran and Nitesh and, you know, Quora has... it just came out of... it just became public. It was in private beta. It has 2,500 active users and like, it's, 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, it's kind of crazy.

    13. LR

      (laughs) It is crazy. How do you do the speed up thing? Is it, um, prompts that they continue to refine? Is that-

    14. DS

      A lot of it is prompts and automations-

    15. LR

      Yeah.

    16. DS

      ... and stuff like that, yeah.

    17. LR

      Got it. For automations, what's the tool, what's the tool used for automating automations?

    18. DS

      What they're using a lot of is, is Claude Code. So you can do slash commands in Claude Code, which are like repeated prompts that you're, that you're doing.

    19. LR

      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 Claude Code-

    20. DS

      Yeah.

    21. LR

      ... more correct and more efficient.

    22. DS

      Exactly.

    23. LR

      Super interesting. And, and they just keep like a file or they put this into a project? Is that how they store their stuff?

    24. DS

      The... It's a GitHub, it's like a GitHub-

    25. LR

      GitHub.

    26. DS

      It's like in a GitHub where they-

    27. LR

      Okay.

    28. DS

      ... they can like share it with each other. Another thing that they do, which I think is very cool, is they, they use a bunch of Clauds 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.

    29. LR

      That's like a, that's a, that's an AI agent product called Friday?

    30. DS

      Yeah. Yeah.

  11. 48:29 – 50:10

    The impact of AI on learning and development

    1. DS

      company. Like, I think there's this huge question about, um, what happens when kids, uh, like entry level jobs are taken away by AI. And my take is like that, that's worth thinking about and it's, it's possible that that might be a problem at some point. But my take is whenever I see a kid with ChatGPT, I'm like, holy shit, they're gonna grow so, so much faster than any other person that I've worked with. Like, we have this guy, Alex Duffy, who works with us. Um, he writes for ContextWindow.And he, he just launched, um, we taught AIs how to, how to play Diplomacy with each other, um, which is really cool, and he did that whole thing and he's, I think he's really, really, really talented. And when he came to us, like, I guess almost a year ago now, it, it was one of those classic cases which I've seen, uh, 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, blah, 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 of the org. So, Natesh is another good example. And so, I think generally people are gonna figure out that, like, some 20-year-old with a ChatGPT subscription is, like, super powerful if you just, like, mentor them. (laughs) And

  12. 50:10 – 51:36

    Accelerating career growth with AI

    1. DS

      I think that's great.

    2. LR

      Man, there's so many threads that could follow here. Like, there's all this fear of entry-level people will never... Like, the roles are disappearing for entry-level people, 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 ChatGPT and these tools help you accelerate really quickly, so you don't really need to be at the bottom rung for a long time.

    3. DS

      Yeah. You're effectively, like, learning how to be one level above-

    4. LR

      Mm-hmm.

    5. DS

      ... um, 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 at what skills are gonna be valuable in the AI era, um, one big group of skills are the skills of managers. Today they're human managers, tomorrow everyone's a model manager. Right now, um, AI, um, is not... Like, right now, management skills are not broadly distributed 'cause it's very expensive, another expensive thing that, um... So 8% of the workforce is managers. It's now gonna be much cheaper to manage, um, so more people are gonna have to do it. And so that's the thing that, um, kids, 20-year-olds, whatever I see now are gonna start to have to learn in addition to, you know, th- their... It's not like you can just say, like, "Okay, go do it and then come back." Like, 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,

  13. 51:36 – 53:07

    Revolutionizing code review and workflow

    1. DS

      they're good at it.

    2. LR

      And the managing here is managing agents, right? Or-

    3. DS

      Yeah. You're managing AI, yeah.

    4. LR

      Okay. And so this is a good... Coming back to your point about how this, this core team and I guess you said everyone at Every doesn't write code. Zero code written. Now it's just managing agents that are writing code for you.

    5. DS

      Yeah.

    6. LR

      Okay. I don't... I've never heard of a company at this stage. So this is extremely cool. (laughs) So the workflow is they give it, here's what I want, I refine it using this cool prompt library that they've, that they build on, 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?

    7. DS

      Yeah.

    8. LR

      What does it feel like? And then continuing-

    9. DS

      Mm-hmm.

    10. LR

      ... to refine.

    11. DS

      Yeah.

    12. LR

      Wow. So you guys are at where Michael from Cursor said we will be. So we... I chatted with him a few months ago. He said in a year this is where he thinks things will be. We're, we're not looking at code anymore. You guys are already there. Although you were looking-

    13. DS

      Oh, yeah.

    14. LR

      ... at code. Okay. You're still looking at code?

    15. DS

      I think, uh, they're, they definitely are looking at code.

    16. LR

      Yeah.

    17. DS

      Um, so, you know, you're doing a code review before you merge anything.

    18. LR

      But not writing code.

    19. DS

      Um, and I do think, like, Danny who runs Spiral, which is the, uh, 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, um, 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 he understands it. He's just, like, off telling Claude code what to do. And I think that's, um, I think that's, that's really, that's

  14. 53:07 – 57:26

    The importance of coding knowledge

    1. DS

      really important.

    2. LR

      This is an insane milestone we're hitting here. 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 that... Like, you guys are there.

    3. DS

      Yeah.

    4. LR

      I think this is, like, so easy to overlook how wild this is. (laughs) You have a product team not writing code at all.

    5. DS

      It is really wild. I think it's really wild in particular just, like, having a small group of people that have... They're everyone's multi-dimensional, everyone, like, has all these different skills, everyone's a generalist, um, everyone's AI forward. So what you can do in an environment like that with a s- 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? All that kind of stuff. Um, and I think that's what makes the writing like that. That's why I like doing this 'cause the writing that we do from that I think is really good 'cause we can talk about it from a, from a sort of pre- position of experience. Um, 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-

    6. LR

      Yeah. This is what-

    7. DS

      ... which is-

    8. LR

      ... I was gonna ask.

    9. DS

      ... which is a, 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. Um, but this has been... This is, this is like a, a progression that is not a new progression. So for example, when I was in middle school learning to code, the, the new hot thing was scripting languages, which is like Python and JavaScript. And if you were a... But if you're a real programmer, you would understand the language underlying Python and JavaScript, which was, which was... It's written in C. Um, and scripting languages were just like, weren't, like, weren't totally real and in order to, like, really do anything interesting you had to be, be able to learn both parts of the stack. Same thing for C programmers. Um, when, I guess in the '70s C was invented, it was like, you gotta learn, you gotta be able to write assembly. (laughs) um, and English is just like a layer on top of scripting languages. So I think all those, all of those things were right in the sense that there's...... um, 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 are some times when, even if you're a JavaScript or a Python programmer, it's useful to know, like, how, how all that, how that stuff works, how it's written and see how it's, how it's implemented. It's, r- today, it's much less important than it used to be, but that took, like, 10 or 20 years. And I think that's, the same thing is gonna 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.

    10. LR

      Okay. 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?

    11. DS

      Like a real SaaS product? 'Cause it-

    12. LR

      Yeah. So like, "Hey, we have this idea, we wanna bring someone on to actually lead it."

    13. DS

      Very far. Like, not even, not within sight. But there's a lot of things that could be products that are a layer, a level down from that, that I think that you could do almost now. So like an example, um, we were talking about Dia, um, the browser, uh, from the, the new AI browser from The Browser Company. Dia has these things called skills, um, which are effectively like little, you know, AI apps that you can run in the browser. You can prompt them and, and they run on the webpage and do work for you. A non-technical person could build that. Same thing for, like, um, custom GPTs from ChatGPT, um, non-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 conventional SaaS app with zero programming knowledge, aside from just, like, a demo, there are going to be other forms of software. Um, one of my themes is, like, software is becoming content. There's gonna be other forms of software that don't look like the software of 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 if it's, I- 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.

    14. LR

      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.

    15. DS

      Yeah.

  15. 57:26 – 1:02:01

    Building AI-driven products

    1. LR

      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?

    2. DS

      I, I mean, I, I would love to talk about our, like, how we think about building products.

    3. LR

      Mm-hmm.

    4. DS

      Um, like, what products to build.

    5. LR

      Yeah.

    6. DS

      Like, what do we end up building?

    7. LR

      Yeah.

    8. DS

      Because I think that 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, this has 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, it's basically, goes back to something I said at the beginning, which is, there are these things that were historically really expensive, um, that only rich people or big companies could buy. So ge- a chief of staff for your, a chief of staff for your email, um, I think a therapist or, like, a lawyer is another interesting example. Um, uh, someone to, like, organize your closet or organize your, organize your computer is another example, someone to ghostwrite for you, um, that are, uh, becoming orders of magnitude cheaper so that everyone can use them, even if you're at a small startup. Um, and so basically, like, when you're running, like, we are, sort of this AI first company, you're running into these, all these little things where you're like, "I wish I had a ghostwriter 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 i- 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 Claude for that. I can use ChatGPT for that." Um, and so you're, uh, you're able to, you're able to use the ne- the demand that you have that, like, we can, we can afford a lawyer. We have ghostwriters, but, like, there's a lot more that we can't do because we can't afford it. So we still have our lawyer and we still have our ghostwriters, but we just do a lot more of that stuff. Um, and, um, so we notice that. We start to then use, like, ChatGPT and Claude 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, like, unbundle it into its own separate thing that, um, becomes an app. And, 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 gonna build another notes app? Like, we've been building notes apps for forever. Like, another B2B SaaS app?" Like, it's all the same stuff just in, like, slightly different packaging. And now it's like totally new territory. No one knows what's going on, no, like, everyone's inventing it as, as, 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 were the first thing on computers. Like, we were figuring out all these new workflows on spreadsheets, they got un- unbundled into B2B SaaS. Same thing for ChatGPT and Claude. Um, and what's really cool is you can be like, "Cool, I'm using Ch- I'm using ChatGPT for this. It's really useful for me." And you might be, like, one of the first people to, like, really notice that. Um, 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 and we're all kind of doing similar stuff. They become our first u- our first users. So we measure the success of the product by, like, is it a banger inside of Every? Um, like, Monologue, the, the, the app that I was talking to you about, like, everyone just started using it. We're like, "Okay, we've got something here." Um, and what's, 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 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 gonna be doing. So it may be kind of niche for now, but it will be a big deal in three years when everyone else has the same needs that we do.

    9. LR

      That is really cool. Uh, what I'm hearing is GPT wrappers are a good idea and are worth-

    10. DS

      (laughs)

    11. LR

      ... building.

    12. DS

      I, I 100% think GGT wrappers are amazing and they've been much maligned for absolutely no reason. And, um, people don't understand how

  16. 1:02:01 – 1:08:45

    Innovative fundraising strategies

    1. DS

      absolutely valuable they are.

    2. LR

      I think there's also just, uh, you guys are ... You raised a sip seed round. Uh, and when he ... So this is a good time to maybe talk about that just, like, these products don't have to become some mega billion dollar hit.

    3. DS

      Yeah.

    4. LR

      You kind of have this portfolio of companies, you have the content business. So I think there's-

    5. DS

      Yeah.

    6. LR

      ... a really interesting approach to that, how big these need to get to be successful. Maybe just talk about that.

    7. DS

      Yeah, yeah. I, I really want Every to be an institution, um, that teaches people, um, how to live a better, more human life with technology, particularly with AI. And both, like, teaches them how to do it with writing, um, 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 risks and do stuff and do weird stuff that, like, just doesn't make any sense. We can't justify it to anyone, but we just feel like it would be fun. Um, and so I think I'm always playing with that dynamic tension between institution serious, we want this to be, like, lasting and important, and it should just be fun, like, let's play around. And I think having that tension is, like, really 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, (laughs) more or less. Um, 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. (laughs) Um, but, you know, I think there's also s- is just ... But that's, that's kind of what I want. And so when we started Every, we raised, like, a very small 700K pre-seed round, and this was at the, the height of the creator economy. So we both, we both started our newsletters, you know, I started our newsletters around the same time. It was like the hippiest, craziest thing. People were throwing money around. It was, like, wild. Um, so but we raised 700K 'cause it was like, I want to raise enough for us to be able to experiment, have a little cash cushion, but not so much that it locks us into anything. And we, like, sent an email to all of our investors being like ... And you're one of our investors, so y- you've probably got this email being-

    8. LR

      Tiniest, tiny investor, but-

    9. DS

      Yeah.

    10. LR

      ... um, I'm in there. I'm in there.

    11. DS

      (laughs) Uh, we sent 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 s- sm- slightly modified safe that gave everyone the option to convert to equity in three years, even if we didn't raise more money. Um, so we, 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 the, do it the way we want to do it. Um, maybe it's not a huge business, but we love it. That's great. Um, and we did the same thing for this recent round where we raised up to two million from Reid Hoffman and, um, Starting Line VC. And, uh, we did it as what I've been calling a sip seed round, which is basically they've committed $2 million, but we can pull it down whenever we want. And it's, we just do it on a safe, at a set cap. Um, and for me, that was, 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. 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." Um, and also for our investors, like, I, I think Reid very much wants us to succeed, but, like, I don't think he ca- (laughs) he cares, like, what, what size of business this 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. Um, and I think that kind of alignment is what I was looking for, 'cause I think there's this core creative spirit to the thing that I want to maintain. And I really care about having, um, 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, um, 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, um, you, you don't necessarily ... Sometimes you do that by building a gigant- g- 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. Um, and so I really like creating this place where we can make a really good business, and I care a lot about that. But also the, the core or the soul of it is, um, changing about, changing how people see themselves in the world.

    12. LR

      I love that you've kind of, uh, innovated a new, like, a middle ground way of fundraising, not bootstrap and not just regular VC. It's a sip seed.

    13. DS

      Yeah.

    14. LR

      And I love that this two mil- ... Like, you know, if I raise 50 million, it'd be like, "Okay, I get it. Let's not put 50 million in our bank account." But you do that with two million, (laughs) it's too much for us. We can't-

    15. DS

      Yeah.

    16. LR

      We don't want to see that in our account.

    17. DS

      (laughs) That's another thing. And, you know, we'll see how this ages. Like, I might be back here in two years crying the blues 'cause like we didn't raise enough money or whatever. Who knows? Um, but that's the other thing is I do think we can get so much further with s- with very small amounts of money. Like, Quora, I think all in to build Quora, we've spent-... maybe 300K. Maybe. That's crazy because this, this product-

    18. LR

      And that includes salaries?

    19. DS

      Includes salaries, yeah.

    20. LR

      Wow.

    21. DS

      This product, um, 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 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, the amount done that would, would've taken a team of, like, 20 people. 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. Um, and I think there are other companies that are doing it. 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 a VC model. And I think, um, there's some differentiation in, in the stuff that, that we can do with founders, which is kind of cool. But, um, yeah. We're, we're... I'm just trying to figure out, like, a shape that works for me and that's different from other people, and we'll see how this goes. (laughs)

    22. LR

      We'll revisit in a couple years. (laughs)

    23. DS

      Yeah. (laughs)

    24. LR

      Seems like it's going great from the outside.

  17. 1:08:45 – 1:17:01

    Consulting and AI adoption in companies

    1. LR

      I'm gonna ask about a couple other things before we wrap up. One is-

    2. DS

      Yeah.

    3. LR

      ... 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, what the hell's everyone else figured out that we're not doing. Uh, 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 all... And I would just introduce them to each other. And it's cool 'cause you guys are basically solving that problem for a lot of companies. So, uh, one is just maybe share a bit about what that side of the business for folks. And then two, I feel like you, I imagine you've seen companies that have done this really well, have adopted AI things that 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?

    4. DS

      I love this question, um, and I have a very specific opinion about this. Um, 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, you know, pretty nascent. It's probably been over the last, like, six to nine months. But, like, it's a pretty big business now. Um, like, it's our, it's, it'll probably double this year. Like, last year we did about a million. Um, 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. Um-

    5. LR

      A billion I think. I predict a billion dollars-

    6. DS

      Yeah. (laughs)

    7. LR

      ... in a few years.

    8. DS

      But yeah, basically people are like, "Can you come help us learn how to do this?" So what we do is, um, 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 s- like, some of the stuff we were talking about earlier. Um, and then what we will do is, uh, first we present a little report that tells you that here's everything that we found, here's, um... Not only that, but you have a chatbot 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 Devon, like, a year ago. And then, um, Alex runs, like, m- 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, though, and we customize it 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, 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 gonna use, um, and here are the exact situations you're gonna 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. Um, it seems to be really cool. And then we'll often also, after this, go and build automations and do some of the AI operation stuff we were talking about earlier. Companies really like it. Um, I think the... And we work with a lot of, like, big hedge funds and PE firms and, um, big companies, all that kinda stuff. Um, to your other, to your s- your second question, which is like what separates the good companies from the bad, or the companies that end up adop- adopting this. I think the, the number one predictor is does the CEO use ChatGPT or insert your own chatbot. If the CEO's in it all the time being like, "This is the coolest thing," everybody else is gonna start doing it. If the CEO's like, "I don't know. This is for someone else," like, no one else is gonna be able to lead that charge. Um, and they're either going to have, uh, e- either they're gonna be negative on it and so definitely no one's gonna do it or they're going to have way unrealistic expectations because they have no intuition for what's possible and they're just gonna get really disappointed. But the CEOs that are using it all the time are able to, like, 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, um, we work with a hedge fund called Walley which I had the founder on my podcast, AI and I, um, a, a few weeks ago. They're a 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. First thing he did, which a lot of CEOs are doing, uh, is send the "We're an AI first company" email. Everyone's got the memo. You just gotta really do it. And one of the things he said in his memo, which I love, is...I wrote this... I wrote this e-mail with ChatGPT and you should too. So like-

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