The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code. | Dan Shipper (Every)

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 ChatGPT, I'm like, "Holy cow, 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." 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 Shipper. 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 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 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.

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With that, I bring you Dan Shipper.

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

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.

It's my honor, Dan. I feel like this is a podcast that was meant to be. 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, building with AI, using AI, evaluating AI than anyone else I know nearly. 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 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-reasoned takes to give you, but this is my hottest one, which is I think that AI may be one of the biggest forces 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. One is there are a lot of expensive services that rich people and big companies pay for right now. So like an in-house counsel or like a 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 if you're so it may not get rid of customer service, for example, but it may allow 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 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 going to be better in a lot of cases at using these AI tools to do work. I so I think it may actually make it more effective to have those jobs in the US run by people sitting in the US who are using it to get work done and also the model companies are here too. So there's a lot of American stuff happening and 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, one TBD if this was good for other countries, but good for the US. What else? What else you got? What other hot takes?

Another big hot take and 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 command line interface. Um, so things like that. And I'll tell you about 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. 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 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 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 the 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 a 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 chat GPT chat or a regular quad chat. It's like actually processing every single file that you give it. And so I think it's incredibly powerful for any kind of task that involves processing a lot of text.

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.

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 code, which most people think is for engineers, is the most underrated tool for non-technical people.

Yeah, exactly. What are some other ways you imagine people seeing this? This meeting note example is really cool and I could see people doing using this. What else have you seen or think?

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, 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 It's so long, but it's so good. I think Tolstoy is a brilliant writer. And one thing that I wanted to do was 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 in their voice and the expression in their eyes. Like all that kind of stuff. 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 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 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 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 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 going to sound so nerdy. I'm reading Anna Karenina right now based on also Tolstoy. And this is recommended by a previous podcast guest and so I was like, "All right, I got to read this." Also very long. I'm on my Kindle, I'm just like, "All right, 13% in. I've been reading for months." Hot take. I think War and Peace is better than Anna Karenina, especially for like a tech person. But they're both good. Okay, there there we go. 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 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. And Anthropic just shared this. I don't know if they shared or someone found this in their legal briefings that they actually bought tons of books and scanned them themselves. Yeah. Is how they did 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?" is super fun.

Okay. So this is awesome. So the tip here is just coming back to your hot take, 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 the 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. Like I think one of the things that will just come from Claude code and other things like it into the everything else you use whether it's on the web or wherever is the original all the original AI apps were pasting a chat box into an existing UI. So you know you've got co-pilot, it's got a little it's got like the autocomplete in the IDE. You've got Cursor, it's got a little sidebar with a little chat. And the difference with Claude 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. 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 and it's you're sort of interled with its execution and you're more just like I'm delegating. It's gonna go do it.

Yeah. I had Cursor CEO Michael Terrell on the podcast and this is his big vision is what comes after code. And we don't be like exactly exactly. And I also just had the founder of Baseform on the podcast who sold, you know, built this company, sold 80 million bucks 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 Baseform and or sorry, all Cursor and other tools he's using. So this is happening. Same thing for people inside of 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 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 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 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 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 you have to intervene. And I was thinking about this and it reminded me of Winnicott, who's 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 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 alone to cry it out. Like who knows if that's like the right thing to do with infants. There's 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 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 is like maybe maybe I don't know. I guess you probably can't leave a toddler alone for 20 to 30 minutes, but like, you know, it's a little bit older than a toddler. Maybe 20 30 seconds. You you can with a toddler, it's like you can be in the same room but not interacting with them total like every single second for 20 for 20 minutes sometimes. So, it's it's around there. And I think there's a similar I think that we have that a 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 I think that's a good definition of AGI. And the profitable piece is also just the cost of running that thing and having it. 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 a more widespread more widespread adoption of agents that that work all the time. And and I like the profitable thing because if it costs a little bit of money and 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.

It's interesting how that also is very 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 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 unemploy 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's another 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. 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 it's better 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 a technology that's developing so rapidly that doctors can't really be 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, 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 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 with AI where yeah, you may 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 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 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 I think it's really important this context you're sharing of 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. 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 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 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's incredible. I love AI. It's so incredible, but it doesn't 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 would 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 we it's a benchmark. It's the CEO benchmark. And the reason I did that is because OpenAI's the gold standard for OpenAI for testing how powerful a model is, they test they test it on their internal codebase. So they say how good is the new model at predicting what comes next in our internal codebase because that's not anywhere out on the internet. So 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 it'd 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. 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. At the time I called it knowledge orchestration. I think context engineering is a better probably a better term, but like it's totally true and and and it's that's a very very hard problem to solve. It's not just like a oneshot problem where it's like, you know, gigantic context bundle and we're done. It's going it's I think it's going to get better over time, but the minute it gets good at predicting what's what's going to what I'm going to say next in a meeting, 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 he'll make decisions.

Definitely. And I 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 particular AI revolution is that you don't have to repeat yourself. And so we had it like last quarter. I 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 what 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, like 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. It's and it's super aligned with just so much of how of your writing. There's just like so much reason to study what you guys are doing.

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.

It's funny that you laugh, but Every is. Everyone asks that because it's just it's like a it's 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 they're it's a little bit it's less common. It doesn't make as much sense and I think it's newly enabled by AI and 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 whenever OpenAI or or Anthropic drop a new 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 love it. It's the best. I don't know if I can curse on this podcast, but it's the perfect excellent use.

And you call those vibe checks.

Yeah, we call them vibe checks. Which I think is really important because and this gets to the next part, the apps part of 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. 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 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 that helps us write great stuff. And that has turned into a suite of apps that we run internally. And the people who are 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 from people who are actually using it every day and the suite of apps that we have. One's called Kora. We just launched Kora 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. It helps you manage your email with AI. It's very cool. We can go into 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-founder Nathan runs that. And 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 because I think there's there's some really interesting special things in there, but I've been blabbing for a while, so I'll stop there.

There's also a consulting firm which I want to talk about, but let's hold off on that. We have consulting. We also do that. And that 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 and it's very cool. It's it's really fun and 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.

I think so because everybody wants to learn this.

Okay. So, share a few ways that you guys operate. You mentioned that you 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. You have three, four products. You have a consulting arm. How big is the team of Every?

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, "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. 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 in 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 AI operations lead.

I imagine you saw the CF Kora tweeted about this wanting to hire exactly this sort of person.

Yeah. So clearly this is a trend. So the idea is this person, 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. Okay. Where we're starting right now is with the editorial operation. So, there's so much stuff in the editorial operation where I or our 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 hours a day. And so now Opus is at a point where you can give it a style guide and a prompt and it'll go through 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 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. 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.

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

She her name is Katie Parrot. Um, she does a lot she actually does a lot of ghost writing 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 help them write about like whatever 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 operations 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 at that kind of process stuff or like thinking about that. But she's also a great writer and she's also incredibly excited about AI. She just like wants to tinker and wants to use it and like that was a thing that got me to be like, okay, you should just come and do that instead of just ghost writing. 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. 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 I think so. There's there's a couple other people who talk about this. So I heard Rachel Woods, who's another sort of she thinks a lot about AI stuff. She she's talking about I think it's becoming like it's becoming a thing and and I think it's I think it's really important and 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 Kora. And by the way, Kora is spelled C O R A, so it's different from Q O, slightly confusing. There's a lot of copy that goes out in Kora 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, Nish, who's one of the programmers, engineers on Kora, built a Claude code command that just uses that prompt and checks through the entire codebase 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 request 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.

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 I'm curious just what is kind of in the stack of tools that you find yourself using that your team ends up using. It 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 ChatGPT boy. And I think O3 is super high quality. I think 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 you your average your average ChatGPT user. 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, 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. If you're building something, you're using Claude code. It 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 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-contained, like I want to pick off this little feature. What else do I use? Going back to Claude, Claude Opus 4 can do something that no other model except one other model that I can't talk about. It 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? Claude, for example, would always give it a B+ and then if you change if 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 give it another turn, it would go to like A, you know? So it's like it 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 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. 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, 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 style product where, you know, you say I want

I wanted to write a tweet. You give it all 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 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.

Um, so yeah, I love it for that. 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? 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 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.

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Let's go back to ways that your team operates. You mentioned having Kate, was that her name? Yeah. Okay. Uh, what else? What else do you do that you think other companies should be doing or will eventually start doing? So the Kora team, uh, which is Kieran and Nateesh, basically, that's the team. Two people. That's the team. Yeah. Well, it's Kora. It's, it's Kieran, Nateesh, and 15 Claude code instances. So it's, you know, it's more powerful than you think. This is, I love that this is just again, a glimpse into the future.

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 going to 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 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, 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, 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 Nesh, and you know, Kora has, it just came out of, it just became public. It was in private beta, 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, it's kind of crazy.

It is crazy. How do you do this speed-up thing? Is it, um, 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? 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. 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. Yeah. More correct and more efficient. Exactly. Super interesting. And, and they just keep like a file, or they put this into a project. Is that how they store? It's a GitHub. It's like a GitHub. It's like in their GitHub where they, 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 Claudes 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 a, that's an AI agent product called Friday. Yeah. Heard of that. Okay. Um, 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, um, I can send to you. Yeah, I'll point to it. They did like a, you know, S tier through F tier of AI agents, which I think is so funny. Um, and, um, one of the things I really like about, about Charlie is that it lives in GitHub. So you can, when you get a, when you get a pull request, you can just be like, @Charlie, like, can you, can you check this out? Um, 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 tastes. Like, you can, Kieran is, he's like a, one of those like ra, like serious Rails folks 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, Chukg, for example, it's very, it feels very churn and minimal and and professional. And so, and it has a particular kind of style that maybe he likes versus, I don't know, Claude is a slightly different style. And I think that's, I think all of that is so interesting that 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 Deng'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 GPT, the current head of marketing at GPT, the current head of engineering, like because he hires the, incredible people. And his philosophy is 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. And it's interesting that you can always 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 Codex. I think there really is. It's definitely not like one, one agent to rule them all. So interesting. Yeah.

Oh my god. The two people on the Kora team, what's 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 Eng at, uh, at a startup. So like was effectively like the CTO of a, of, of a startup, or maybe two startups. Um, and, uh, and was, was one of the founders. And then, but 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. Um, seems that, and generally, I think like that kind of multi-dimensional type of talent is the kind of person that I love having at 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 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 learning to code when ChatGPT came out. Um, he had 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 to 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. Stack Overflow even back then? Yeah. Yeah. There wasn't Stack Overflow. There's like weird BBNet forums and stuff that like, I was like 12, and I probably shouldn't have been on there or whatever. So, uh, it's, 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, uh, like entry-level jobs are taken away by AI. And my take is like, 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, they're going to go 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 Context Window, and he, he just launched, 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, 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, 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, Natasha is another good example. And so I think generally people are going to figure out that like, some 20-year-old with a ChatGPT 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, 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. Yeah. You're effectively like learning how to be one level above, 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 going to 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 because it's very expensive. Another expensive thing that, um, so 8% of the workforce is managers. It's now going to be much cheaper to manage, um, so more people are going to have to do it. And so that's the thing that, um, kids, 20-year-olds, whatever, I see now are going to start to have to learn. In addition to, you know, they're, 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, 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, this core team, and I guess you said everyone, every doesn't write code, zero code written. Now it's just managing agents that are writing code for you. Yeah. Okay. I don't, 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'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? What does it feel like? And then continuing to refine. Yeah. 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're looking at code. Okay. You're still looking at code. They, they definitely are looking at code. Um, so, you know, you're doing a code review before you anything. 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, 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 really important.

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 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. I think it's really wild in particular, just like having a small group of people that have, everyone's multi-dimensional, everyone like has all these different skills, everyone's a generalist, everyone's AI forward. So what you can do in an environment like that with a 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'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. 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. Yeah. This is what I was going to ask. 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, but if you were a real programmer, you would understand the language underlying Python and JavaScript, which was, that's written in C. Um, and scripting languages 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 got to learn, you got to be able to write assembly. 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's sometimes when even if you're a JavaScript or 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 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 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, 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? Because, Yeah. So, like, hey, we have this idea. We want to bring someone on to actually lead it. 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, 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 web page and do work for you. A non-technical person could build that. Same thing for like, um, custom GPTs from ChatGPT. The 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 things is like software is becoming content. There's going to be other forms of software that don't look like the software of today, but you can run and 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 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. 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, I mean, I would love to talk about our, like how we think about building products. Um, like what products to build, like what do we end up building, 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, 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, it 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 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, 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, 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, the demand that you have that, like, 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 going to 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, 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 it happens, right? All these new workflows are being created in a very similar, similar way to, I don't know, for example, when spreadsheets were first a thing on computers, like we were figuring out all these new workflows on spreadsheets, they got 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, I'm using ChatGPT for this, it's really useful for me. And you might be 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 read 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, 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 app that I was talking to you about, like everyone just started using it, and 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 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 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 has the same needs that we do.

That is really cool. Uh, what I'm hearing is GPT wrappers are a good idea and are worth building. I, I 100% think GPT wrappers are amazing, and they've been much maligned for absolutely no reason, and, um, people don't understand how absolutely valuable they are. I think there's also just, you guys are, you raised the SIP seed round. Uh, I want to, 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. Yeah, you kind of have this portfolio of companies, you have the content business. So I think there's a really interesting approach to the, how big these need to get to be successful. Maybe just talk about that.

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 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, 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. Um, but, you know, I think there's also just that, 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 and I started our newsletters around the same time. It was like the hypiest, craziest thing. People were throwing money around. It was like wild. Um, so, but we raised $700k because 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 you've probably got this email. Tiniest, tiny investor, but I'm, I'm in there. I'm in there. 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 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 $2 million from Reid Hoffman and, um, Starting Line VC. And 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 Reed very much wants us to succeed, but like, I don't think he, 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, because 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 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 core of the soul of it is, um, changing about changing how people see themselves in the world.

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. And I love that this two mill, 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 $2 million. It's too much for us. We can't, we don't want to see that in our account. 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 because 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 with very small amounts of money. Like Kora, I think all in to build Kora, we've spent maybe $300k, maybe that's crazy because it includes salaries. Yeah. Wow. 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 have 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, 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 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. We'll revisit in a couple years. Yeah, 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, 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 just introduce them to each other, and it's cool because 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 is 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 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, 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, billion. I, 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, 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 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, tells you like, 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 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 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, um, 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. 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 operations stuff we were talking about earlier. Companies really like it. Um, I think the, we work with a lot of like big hedge funds and PE firms and, um, big companies, all that kind of stuff. Um, to your other, to your, 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, the number one predictor is, does the CEO use ChatGPT 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. Um, and they're either going to have, uh, 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 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 Walleye, which I had the founder on my podcast, AI and I, um, 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. 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, I wrote this email with ChatGPT, and you should too. 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, uh, meetings where people share prompts and share use cases. They're doing, uh, they do like a weekly email to their entire company being like, "Okay, here's our, here's our usage. Here are our usage stats for ChatGPT. Here are the, here are the people that like, um, uh, 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 like 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 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, uh, tactics you find that you encourage within companies. One is just send, 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 we're 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 you talk to anyone else? All these things. And then just note, I help, I wrote this using ChatGPT. It's a great idea. Uh, 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. Uh, and then this weekly stats email of, here's how much we're using ChatGPT 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 ChatGPT or Claude or whatever daily, that you, it's going to work out. Yeah, that is super cool. Uh, 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? Any anything you've seen either anecdotally or numbers wise?

It's early. It's really hard to say other than, um, 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. Um, and so they're, they're just, they're just going further faster, um, 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 like that. Like, that sucks. Um, but we've never had to say no. Um, mostly people are like, "Cool, I'm just going to go further with the, with the people that I, um, that I have." I think also back to kind of the first point I made about reassuring American jobs. Um, I have seen some companies, not the ones that we worked with, but I have seen some companies of people that I'm friends with where they're like, "We have a call center somewhere." Um, but I think I can get the same amount done with like two employees in the US that have that use like one of these, you know, customer service platforms. Like they're still not totally automatic. Like I think that Clara CEO thing that was, um, uh, but yeah, you can have a couple people in the US that maybe, uh, maybe you pay a little bit less to than you would for like 100 people somewhere else. And obviously, you know, those are, that's a calculus that everyone has to make for themselves. But I've definitely seen that happen. And, um, yeah, I think I think that's, that's the, you get more done with the same amount of people.

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. Uh, if I understand it correctly, we've been in this knowledge economy where people get paid 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 of our 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, so maybe just answer that question and share whatever you think is important to share to give people a sense of what you're thinking.

Yeah.

So, this is uh based on an 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. Um, and I was like, really trying to think about how do I express, um, what in my experience using this every day, like, what, what skills are useful for me. Um, because I think that'll be the case for, for a lot of other people, and I think 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, um, what I noticed using at the time, like GBD3 or maybe GBD4, um, was that I was spending a lot of time, uh, 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? And how do I maybe divide up the task to be like, "Okay, this model does this, this model does this." Um, based on what I know to be like what's good and what's bad? How do I give them feedback? Um, how do I have like a, um, 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, like, 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 AI do this? Like, I can't trust that they're going to do it well, so I just I should just do it myself." And I'm just like, "Yeah, that's exactly what every first-time manager says." 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 and maybe micromanage a little bit, and when, when can I delegate, and how can I trust it, and how do I divide up the task, and all that kind of stuff? And so I think there's a lot of overlap in those skills and, um, it just, those skills are not broadly distributed right now, um, but they will be in the future 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: 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 you referenced, which is that generalists will become more and more valuable in the future. You mentioned that everyone at Every is a generalist. Yeah. Uh, share a little bit about that. Yeah. I find, I mean, maybe it's because I'm a generalist, so you should take this with a grain of salt. Same. But I think that's one of the things that has made AI so awesome for me 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 is right there with me. Um, and I think what we've basically, what has happened as civilization has progressed from like ancient Greece to now is, uh, what we've discovered is the more that we specialize, the 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, um, specialization and trade. And, uh, 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 is back to like ancient Greece, ancient Athens. Um, Athens is was a civilization of generalists, at least for citizens. It was, there's like, they have some, you know, a bad history with women and people who are slaves. But like, let's just put that to the side for a second. If you were a citizen generalist, you could, you could be expected to be, um, a, uh, a fighter, a judge, a juror, um, uh, maybe a general. Like, there's, you could expect it to have, um, many different roles inside of your society, um, in your lifetime. 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, um, 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. Um, 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. It's like, it knows so much about every little branch of human knowledge, and every art form, and every, you know, 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 cicada, so you know exactly how they like, you know, reproduce. Um, 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, um, you're empowered to jump a lot more between all those different domains of skill. And, uh, and you can get more done as, for example, like a founder, where, um, I think we can stay at 15 people much longer, um, than we would be 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 into 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 uh reminds me, I was uh 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, executing, getting like, or staying organized. And GPT is such a godsend for her because she's just like, here's what I want to do roughly, just help me get it done. That's great. And so, 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 it to be. 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 and Peace. Um, definitely got to read that. Uh, if you want like a Tolstoy primer, I would read The Death, The Death of Ivan Ilyich. Um, 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. Um, and I, I in particular, I really like the Russians because they're a lot of the Russian novelists are dealing with the effects of technology on traditional Russian way of life. And they're very kind of in this really interesting, um, 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 guy's, what Levins 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 it be like instead of being a nobleman who's like trying to make farms way more efficient, I was just like with my scythe, that was like really happy. Anyway. So they're dealing with a lot of similar stuff to, I think, AI. Um, uh, The Master and His Emissary is another really good one, and that's about, um, basically how the different hemispheres of the brain, uh, view reality. It's really, really good, and I think it, um, I think it relates to a lot of AI stuff too. I think, yeah, I think I think those are my, those are my three or four. Yeah.

Excellent list. I think nobody's mentioned most, uh, either any of these, so this is, that's always a good sign. Uh, do you have a favorite recent movie or TV show you really enjoyed? Yes. Um, I really love Deadwood. Um, have you seen it? I absolutely love it. Uh, 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 was, it's amazing. Uh, yeah, David Milch is incredible. National treasure, incredible writer. But what I, what I really think, what I really love about it, and I only recently watched it, is, um, he talks about Deadwood being about how order forms out of chaos. Um, 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, there, 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, um, 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, I love this. Thank you.

Uh, do you have a favorite product you've recently discovered that you really love? I don't have a good answer for that because I just spent a lot of time using our internal products. Um, but I like my, my stock answer is Granola. Um, so I do, I do really love Granola. I, 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. Um, 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, yeah, Ketchup Granola. I can't help but 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 the, what a deal. This is not a paid promotion, uh, by me. I just, you know, that's just what how I feel. So, I'm glad, uh, I'm glad it's part of the bundle. 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, like I used to be all, and 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." Um, you, you prize slow, attentive seeing, whether it's reading Tolstoy, tracking meditation themes, or X-raying a David Milch paragraph. So, like, we're, we're, it's hitting all the stuff I just mentioned, which is really funny. Um, and then build bravely, you turn those insights into concrete things like Every, and Kora, and long-form essays, and, and all that kind of stuff. So, I think there's, I think there's something about that. Actually, this reminds me, this actually reminds me of the actual motto, which is, and I didn't come up with this, I think it's like, ply the younger, um, uh, said, um, "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. Write things worth reading. That's, that's should be the motto of both of our newsletters. That is really good.

Okay. And by the way, I love that you asked ChatGPT, "What's my life motto?" And wait, this is interesting. So, it didn't give me the answer, but inspired the answer. Yeah. And I think that's actually, like, exactly how I use it. Wow. It's an extension of our brains already. Yeah.

Uh, last question. I was, uh, 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 realized, I need to get back to writing because things started going sideways. And I feel like this, this is such an interesting corollary to a lot of the stuff you talked about of, do things that make you happy, stay close to joy. Uh, just share what happened there because, because I didn't know that. This is definitely not a lightning round thing. So, I'm, uh, I'll expound, but I'll try to do it as quickly as possible. Perfect. Um, 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's 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 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 writing, if you have product market fit before, you lose it. Um, and maybe you hire people that are good writers, but that's hard. Um, it's a total opposite pattern for startups. You build the first version of the product, and then you hire people to build the rest of it. And, you know, so that's what I did. Um, and I also really struggled with, okay, what are the implications for that and for my career? And, um, 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 Chat, I was like, "Are there any examples of writers that have built businesses?" And it was like, "Yeah, uh, Joel Spolsky, who built Trello and Stack Overflow. Um, there's, uh, Jason Friedberg, uh, who I've known for a long time and I have always, always looked up to, but I forgot about in this context. There is, um, Sam Harris, who's got a great podcast and he's got a gigantic meditation app." Um, there is, um, Bill Simmons, who's like an incredible podcaster and also built The Ringer, sold Spotify for 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. 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, 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. Um, 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. Like, everyone wants to do that. Of course, every founder wants to do that, but like, you, you have to focus. You have to, like, you can't write, like, whatever. But every time I've kind of just leaned into, um, something that feels like the most, the ultimate luxury of like, my my hidden secret desire, it's actually worked a lot better. And, um, I think you end up, what, 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. And by sort of giving into those 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. There's, 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 sharing similar lessons. Dan, this was incredible. 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.to. Uh, I'm also on Twitter at DanShipper. Um, you can, uh, go there to check out our, uh, our, uh, 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 Die. You can find it on YouTube and on Spotify. Um, and how can people be useful? Honestly, I think the, the most useful thing, um, 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 their, make their lives better. So, like, just go do that and tell me about it. Um, and I think that'll be great. Um, what's the best way to tell you? Is it, uh, comments on your YouTube show? Is it emailing you, DMing you? Uh, I would say, uh, tweet me. Um, uh, you, if you subscribe to Every, you can also reply to those emails, and they, they eventually get forwarded to me. Um, so tweet me, reply to Every. Um, and if you want to comment on YouTube, great. Um, I'm not in the YouTube comments as much as I should be. Don't do that. Maybe don't do that. Um, 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 Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennispodcast.com. See you in the next episode.