Transcription
This episode is with Dan Chipper, who's incubating a bunch of AI startup ideas, and he gives a few away for free to us. He also showed me a really cool tool about data sets and how you can build startup ideas around it. I can't stop thinking about that part. Uh, enjoy the episode. Uh, I know you're going to come up with a bunch of startup ideas just from listening.
Start [Music]
B. We got Dan Chipper; he's incubating some of the most interesting AI startups right now, and he's doing it in a really interesting way. So I brought him here so that he could, he can share some of his startup ideas with everyone listening, um, because no one likes to be greedy with their startup ideas; we don't like that. So welcome.
Thanks for having me. I'm really excited.
All right, shall we dig in?
Let's Do It.
Um, so the, the, the first idea is advanced voice notes. So ideally, I assume you're, you're aware of advanced voice mode from chat GPT, um, really, really cool way to like chat back and forth with, with AI. And what I, what I notice, um, when I'm using that is I'll often like go take a walk and I'll like think about an issue, like let's say it's a business issue or a strategy issue, and I'm like having it help me like think through all the different ways I could address it and whatever, and it's really good at like helping me come to like a conclusion. And what I really want is then, like there are people I work with, like, uh, Brandon who runs our studio or Kate who's our editor-in-chief, there are people who I want to like send that to, and I want them to like get a little bit of the summary, like the high-level, this is what this is what Dan thinks about this, um, but I know they're going to follow up with like a bunch of questions. And what I want is for them to get a like text summary of it and then be able to talk to the transcript of our conversation, um, to like get like maybe the, the summary is just sort of the tip of the iceberg, but like get all the reasoning behind what I'm, what I'm, you know, why, why I decided what I decided or why I'm thinking the way I'm thinking. And I think a, that'll just like help me like avoid like answering lots and lots and lots of questions, but B, it just gives everyone else like much richer access to much richer context. And it's sort of, it's basically like a voice note, but like it's a voice note that you can like talk to and have a conversation with. That's like just an interesting idea, which is like if you think about media, like what, where, you know, is the future of media, media that you talk to it, like what are, what's dumb media, and how do you make, you know, is that, is that like a theme that you, you're thinking a lot about?
We are actually. Um, so one of the internal incubations we have is, um, and I think we'll probably release it, I don't know, like in the next week or two, so it might be before this podcast is out, um, but it's called TL;DR, um, and what it does is, um, it takes all of the meetings that we have and then turns them into podcasts that are kind of like the Notebook LM podcast. So for me, like if I miss a meeting, and we're at a scale now where I can't go to all the meetings, like while I'm listening, while I'm, like, while I'm doing dishes, I can like listen to a podcast that like gives me all the key takeaways of the meeting in like three minutes, um, and I think that's really cool. And like, to, to sort of add on to that, one thing we've, we've prototyped is this same interaction of like you're listening to a podcast, the host is like saying something, and then, um, you can interrupt it and be like, wait, I didn't get that, and then it will just resp, like have a conversation with you, um, and it's sort of like, you know, you know that, like, you know that meme of like, uh, someone who's like talking to the a wall and like it's a print, like print out of like their friends on the wall, and they're like, this is that's, that's what podcasting is. So it's sort of like that meme, but like they come out of the wall and they become real people for a second. It's very cool.
I think it's human nature to want that, right? Like people listening to this podcast are probably like, wait, Dan, I want you to explain XYZ thing. So, um, I think that there's probably a hundred startup ideas just around that sort of concept. Um, so, you know, people listening, one of the things I would do is just think about the media you consume and then think about how can I turn that media into two-way with AI. Um, I would also think about how do you prioritize, uh, the things that people are going to pay for. You know, a lot of people listening to this, Bootstrap Founders, Indie Hackers, um, yeah, like there's obviously some consumer media that they, they can do, but maybe they're interested in more B2B stuff, stuff that they can sell to businesses, prosumers. So with your idea, you know, that you're, you're sort of giving, you're talking about right now, like how do you think about monetization of it, um, and how big could it, could it be?
That's interesting. I have to think about that. I, I think, I think like the, the way that I think about it is new technologies create the opportunity for new media formats. Um, every new technology, like YouTube videos, new media format from the just because the internet, right, um, killing Hollywood, um, I think, uh, the, and the, the thing that is true about this wave of AI is it's taking storytelling and it's making it way cheaper than it has ever been before, um, and I don't think that that replaces professional storytellers, but what it does is it allows you to tell stories in places that was way too expensive previously. So like, for example, I would never hire a really good storyteller to like go summarize our meetings for me, um, but I'll have the AI do it because it doesn't cost me any money, um, and so looking for places where, um, uh, there are stories to tell that were previously too expensive is like the, that I think that's the total addressable market of this sort of idea. And then in terms of charging for it, we've been, we've been thinking about this because like, uh, like our strategy is like everything we incubate, we bundle it into the every subscription, um, and but we've been thinking to ourselves like, okay, for something like this, like would people pay more for it? And if so, like how do we charge for it?
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I think like the thing that, that, that, that I think would, would sort of work for, I mean, for the, for the podcast idea, maybe for the voice mode idea or voice note idea is like on number of downloads or plays or listens, because like that's the, that's the amount of value that's being gotten out of this thing, and, uh, and so, and it scales nicely with the scale of the organization, um, so I think that's a cool way to do it, but I don't know. Yeah, I mean, I think, yeah, people are tired of, uh, subscriptions, you know, you know what I mean? Like, I, I don't know about you, I see like a 9.99 subscription and I'm like, or even if it's like, yeah, $10, $5, $3, I'm kind of just like, ah, I know I'm going to forget to cancel this, you know what I mean? So I think we're all kind of collectively got to this point where we're, we're, we're getting sick of it, um, so I do love, and, and you know this, I do love kind of more novel ways of monetizing.
Yeah, I think, and I think that's a new sort of trend in the AI pricing realm is, um, not charging per month but charging like per successfully completed task or something like that.
Yeah.
Um, and I think, I think that's going to work really well.
Cool. I mean, it's an interesting, this is an, you know, this is an interesting prompt, I would say, this, this idea, um, curious what people like in the comment section come up with. So drop it in. What's your, what's your next idea?
My next idea, um, is called N of One. The, the basic gist is, it is, you know, are you familiar with Kaggle?
No.
So Kaggle is this like, um, uh, it's this, uh, basically like market for data scientists where you can post a data set and a bounty and then have data scientists like fulfill the bounty, um, and like sort of win the prize with your data set. So like an example is like the, you know, like the Netflix, the, the Netflix challenge where they were like, we'll give a million dollars to anyone who can like improve our, our recommendation algorithm.
Of course, that was OG.
Yes, I remember. I remember that.
Yeah, so that's really cool. Well, I think the really interesting thing now is a, there's way more interesting data sets that are like all personal ones, and B, everyone's a data scientist because like everyone, anyone can just like prompt tune, like GPT-4 to like find some, like do something interesting with the data set. So it's a platform to, um, post your own data sets, uh, whether that's like a personal one or a business one, and then, uh, and post bounties for anyone to make predictions that are useful for you about the data set. So the place that it came from is for me, um, I OCD, um, and what I think is really interesting is I think we're getting to a place where if I, uh, take Whoop data and probably some amount of like voice and face, facial movement data, um, I, I'm pretty sure I've been working on this, but I'm pretty sure I can get, um, I can get it to label whether or not I'm having symptoms and at any, on any given day. And if it can accurately label that, my next question is like, can something predict when I'm going to be symptomatic or not, um, and so what I'm going to do is, or what I want to do is basically like post a big data set of, you know, all, like all of my biometrics and whether or not I was having OCD on that particular day, and, uh, and then post a bounty and have, you know, it's like 10, 10K for anyone who can predict my OCD, um, and, um, and then I think if that works, and I think it will, then I can get people like you or like anyone else who has like an interesting problem like that to like post their own bounties and share it with their audience and like sort of start to bootstrap a little bit of like a, a market there. And the reason I think it's interesting is, uh, one, OCD sucks, and B, really fun if someone could predict it for me, but two is, um, it's a new way of doing science actually, um, because the, the way that like science proceeds right now is like you're trying to find like underlying, underlying explanations for things by like doing high-end studies on like lots and lots and lots of people and like doing like a linear regression to like understand like how, like how things work at a group level. And I think instead what we should be doing is, um, uh, making predictive models for in individuals, for N of One, um, that just like solve the problem and just help us like basically predict the problem and, and worry about the generalized scientific explanation later, um, and I think this, this is like a platform to do that.
All right, let's get into this. First of, I, I just pulled up, uh, Kaggle. I realized I've actually been on this website. This is a gift because there's probably, there's probably tons of startup ideas that you can come up with just by hanging out on Kaggle, right?
Yeah, yeah, definitely. Um, like just pulling it up right now, like looking at the different data sets, it says 406,000 high-quality public data sets, everything from avocado prices to video game sales, and then you can, there's all these like ready-to-deploy ML models, uh, on top of it, um, it just feels like this is a gold mine for coming up with startup ideas.
It definitely is. And I think the, the, the important thing is that Kaggle was built for a time when the people who could do this stuff were people who like had, had like PhDs in data science. Y, and it's like totally changed now where like you, anyone can enter one of these competitions and like do better than a PhD could five years ago, um, just with GPT-4 probably, um, and so I think that opens up a ton of opportunities. I'm looking at, check this out. So I'm going to share my screen real quick. We're going to, we're derailing a bit, need to show what's happening here. There's literally, uh, a data set called open-source data for Hacker Noon Startup of the Year votes, over 623,000 total votes, 30,000 startups, 4,000 cities. Think about what you can do with that data if you're trying to come up with startup ideas, trying to sell to startups, right?
Totally.
Um, yeah, I guess, yeah, what, what would you do with it?
I would be interested in, well, I think, I think people who are building startups, like probably people listening to the show would be interested to know like what, what, what is this community, you know, what are people in, call it Sydney, Australia, what, what do they find interesting from a startup perspective, and then contrast that from Austin, Texas, and be like, maybe there's some interesting startups coming out of Sydney, um, and in Austin everyone's building AI startups, but in Sydney they're building marketplaces, and there's actually like a, a lot of opportunity to build some marketplace idea for, for Austin that's, that's just like one example. Or maybe there's like a, like some sort of like real estate or community-building play for, uh, a city that has a surprisingly high density of a certain kind of company, right?
Yes. Yeah, yes.
Um, so I, I love just, I think people should just go on Kaggle and just look at some of their data sets and just ask them, okay, here's a random data set, what are some startup ideas I can come up with it? And I think the easiest startup ideas with some of these data sets is database as a service, um, so I've seen that trend pop up more and more, DaaS, database as a service, MH, which basically, what does that mean? It's a fancy way of saying you're taking some data, you're curating the data, you're putting a paywall against it, you're putting a Stripe link, and you're selling it, often to, um, to social audiences. So either organic, you're building Twitter account, TikTok, you're paying creators to post about it, um, or, or you're throwing some paid ads behind it.
Yeah, I like that.
Um, cool. You got, you got, you got more [Music] ideas?
I got plenty of ideas. Let's keep going. Um, one, one like one idea that's sort of like on this like new media format train is, um, there are, I think there's this like social aspiration around the great books. It's like Marcus Aurelius or like Plato or like Moby Dick or whatever. It's like, um, books that form the like Western Canon. People feel like they should have read them, but like they're kind of a drag to read in a lot of cases, um, and, and I also think they're actually like really useful, like they're really, they're really great if you actually can get into them, but like you have to kind of be in like an English class or something to like really get something out of it. And even then, a lot of English classes are like not that great, um, and what's really interesting about AI is it can make those, um, those stories available in new formats and in new translations that are like tailored for specific types of people or specific situations, um, so a really like easy example is, you know, if you try to read Plato, like most of the, most of the Plato translations are like really trying to like get the historical, like what, what would be like the right word, like, like the historical context, right? And they're not really like trying to make it like engaging and interesting for a specific, you know, for like someone who's in, in their 30s, speaks English, like is into tech, right? Like they're just, that's just not on their mind, um, and I think you can basically create new versions of great books, um, that are like translated specifically for particular types of people, are super easy to read, are super engaging, and you can even do it in like a multimodal type way where you do like, like you can have a, a Play-Doh movie where you watch a like a Platonic dialogue, and it's like, it's probably mostly AI generated, but like probably you need like an editor to like put it together. But I think you can like create a new great books library where you can consume these out-of-copyright books in, um, really digestible translations in audio and video formats, and even maybe like you can add some, you know, advanced voice mode interactivity type stuff. And I think one, there's tons and tons and tons and tons of students that like need to like read this for school, and there are all these like sites like CliffsNotes or whatever or SparkNotes that like are just so last generation, um, and I think that market is like very capturable. And then two, uh, I just think that there's like a lot of people who are kind of in the like business, like in intellectual space that I would, I would probably like put myself in, the stuff that we make it every, in that, that place, um, who aspire to like be familiar with that Canon who would like pay for this. So okay, question, question before I get into this, would you sell like, sell these books, like, how do you, how do you monetize it?
I would, um, I would probably sell either like one-off access or a subscription, and or, um, I would create a like beautiful physical version. This is more, less for the students and more for the like business thing. I would create like a really beautiful physical version that I would charge a lot for and then have a companion like digital version that like can help you kind of like get into it and like, you know, gives you commentary or, or, you know, you can listen to it or you can watch it or whatever, as like a, um, yeah, a digital component that makes it easier to consume. But I think, I think books, I think like books started out as luxury goods, and then because of the printing press they became like, there was this push to make them cheaper because you needed ways to get more information into people's hands, and they're no longer necessary as, um, as cheap forms of like information dispersal because we have the internet, um, and I think that they need to go back to being really well, be well-made, beautiful, like luxury goods and that you charge a lot for, and I think that's maybe, that's a slightly different idea, but it's like, I think that's a great business.
Yeah, I mean, it's a great business. CU, it's repeatable, it's a good customer base that you can charge, you know, you, the lifetime value is high.
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And, and, and Cliff Notes. I was just, I was just perplexing, uh, them to see like what their revenue is. It's it's private; they they they don't say what it is. But both, I think they they sold—think I was—that one of them, uh, got acquired by Cliff Notes, got acquired by IDG Books for $14.2 million in 1998. So you got to think that these businesses are—I mean, 1998 was like hundreds of years ago—that's a lot of money. It in 224.
Yeah, exactly. So you got to think that, you know, these businesses are making millions of dollars a year. Um, so I I I I, you know, I tend to think, think that the more interesting idea is like the luxury piece of it. Yeah, and the prompt being like, "What's something that's not luxury today that could be luxury," um, is really interesting. And then if you can use AI to go and create that thing, then you're looking, you're looking good. Yeah, I think, and I think the the the the place to look or the way to ask that question is, um, I think there's this, there's an interesting effect where as technology makes things cheaper, the things that they end up making really cheap, um, end up becoming luxury status goods. So like, um, an example would be like Broadway, right? Like plays were like the—and musicals and stuff are like the way that people would like see live, see enter—like see entertainment in a lot of ways; they were they were very cheap a long time ago. Everyone would go. Um, and now it's like, because we have TV and phones and whatever, like you go to Broadway and it's like a special, special, special night out that's very expensive. Um, so it doesn't go away; it just gets less popular and becomes more of a, more of a luxury good. And I think, um, I think that trend will continue with AI stuff. So I look at what is AI making cheaper, and then look at what—like that thing will become more expensive just for a smaller percent of the population. Yeah, and it becomes more like a status symbol. It's like, "Oh, I'm going to Broadway; I'm going to a play," right? Yeah, yeah. Or, "I have a handmade shirt," artisanal, you know, like that—people, you know, this shirt is super cheap, but like people pay a lot of money for like something that's handmade. Yeah. Also, you know, limited edition things—there's only 100 versions of that t-shirt.
Um, so if you're listening to this, like this is—we gave you, we gave you a gold mine before, but this is a gold mine because I think like luxury, limited edition, AI-generated stuff, um, you know, it's it's in our nature to always, to to think about, "Let's build things for the masses." It's like, you know, I'm wearing a Kirkland t-shirt under here, you know, um, the the epitome of, you know, to the masses, um, and I love it, but you know, I don't feel special when I wear it, you know what I mean? So feeling special, status, luxury—a lot of opportunity. I would, I would revise that slightly, which is like, all I, I think all that is true except I think in this specific space, um, you need to like key on like human-made and handmade rather than AI-made, and that's the thing that that gives it status. Um, like a good example is like I like this watch, right? Like this watch isn't that expensive, but like watches are get really expensive, and this is like more expensive probably than it should be, um, and it's because it's like a handmade thing. And what is it, what kind of watch is it? Oh, it's it's a Zen. This is this is like this is not like super ball or anything like that, but it's I—it was I think it's a Zen; it is a Zen, but it's not a Zen like—not not what you're thinking—S I N N. Um, it's a, it's a German brand. Got it. Um, I had to say someone else is, you know, gonna be like, "You let him say Zin, but you didn't question him on it," you know. Um, the the nerds will know what I'm talking about. Um, yeah, uh, so but like, you know, I could tell the time for a lot cheaper than this, um, or like it's on my, it's on my phone, you know, like I don't need to have this; I just think it's cool. Um, and that's because it's like, uh, it's it's crafted; it's like it's a thing; it's diff—it's different from like the sort of like mass-produced, like everyone has it thing. Um, yeah, there you go. Um, and so I I think there's always room for that, and it's it's easy to overlook it in the kind of like AI technology race, but like it it creates more opportunities for those kinds of things.
Yeah, I agree. I think, uh, so there's a really interesting trend that's happening right now, which is a lot of college students are anti-AI. Have you been seeing this? No? Yeah, really, really. It was surprising at first, um, because I was like thinking like, "Oh, come on, like early adopters," right? Yeah. Um, but turns out that a lot of them, uh, for for a variety of reasons—if if you actually do like do some market research—like they're just, they they tried ChatGPT a year and a half ago; it wasn't as good as it is now. Um, they are are are entering a job market that's highly competitive, and they're worried that the junior jobs are going to AI, so they kind of just see it as like, you know, "Oh, I don't like this thing because like it might—I'm competing against it." All that to say, there's a huge opportunity to build anti-AI products just in general. Yeah, or like I have this, I have this—I'm not being paid for this—I have this thing called the, called the Brick, um, which you've probably seen on the Instagram ads, and I really like it; like it just locks me out of my phone. I tap it; it locks me out of my phone. I leave it somewhere, and then I can't get in my phone. Um, and I'm an investor in Light Phone, um, it's sort of like a, you know, weekend phone that doesn't doesn't have all the smart stuff in it. And, um, yeah, I think I think that's a huge thing. I did not know about the anti-AI thing; I thought I thought it was really big for like doing your homework. Um, so I'm surprised to hear that. Yeah, it is surprising. Yeah, what the, the what I've heard recently, um, I was at a dinner last night, and it wasn't about college students, but it was about kids and voice mode, and, um, that voice is becoming this like really, really natural interface for that generation of like, let's say like three to like 12-year-olds, um, specifically because even before ChatGPT, because, um, parents didn't want to give their kids phones for obvious reasons, um, and so the compromise position was, uh, an Apple Watch. Um, and the thing that an Apple Watch has is like, obviously like the screen is like—it's really hard to do anything on the screen, so kids just figured out how to like get them like the absolute max out of Siri; like they're just like Siri Masters. And, um, and, and now with advanced voice mode, like even kids who like can't really read yet, they can interact with advanced voice mode, and they can just spend hours and hours and hours asking question after question after question, um, and advanced voice mode will like answer in a way that like an adult like would probably just get tired, you know. Um, and so there's a lot of like—there was just a lot of speculation last night about, um, uh, whether we're kind of like entering a new like oral culture where you're going to do a lot more interacting with computers and everything around you in, in, in voice instead of in text. Um, I think that's a really interesting thing to think about, and I think in particular if you think about starting businesses, thinking about voice-first interfaces is a really interesting place to explore. We've already kind of talked about that a little bit, but, but like just as a, as a broader theme, I feel like that'll be way more prevalent soon.
So yes, I agree, and I also think that like if all these kids grow up and they're and they're, you know, they're used to voice, they're going to want to continue using voice even, you know, right—they're just going to be used to it; it's gonna be natural. So look at—I would say like, "What's the opportunity? What's the startup opportunity looking at discovery engines, like content discovery engines?" Like, for example, maybe that, maybe the voice-first version of Expedia is a huge opportunity, you know? Expedia—let's just check their market cap: $24 billion company, and it's like, yeah, could you get 1%, 2%, 5% of Expedia just focusing on voice-first and making it for voice-first? Like, not crazy. Yeah, I wonder like what the, what the features of a particular kind of product are that make it more likely to be good, like better voice-first, right? Like, um, for example, like I always get like annoyed at like IVRs, like those phone systems, because like they list like 15 options, and then I can't remember all the options, um, whereas like if I'm having a conversation with someone, I can like pretty much keep the conversation in my head. So there's something like that for like that th—that kind of dynamic—a more conversational dynamic is better for voice versus like, I don't know, like logistics or something like that maybe is like not as good, you know, because you have to, you have to hold all the like stuff in your head. I don't know. Yeah, uh, it's a good, it's a good question. You know, I I got to think about it. I think, um, you're right though, like voice, voice does, you know, lend itself to certain spaces better than others. Um, I also think that one thing I was thinking about is like, how do you integrate with Siri? Like, you can integrate with Siri, right? Um, I don't know; I think that you can, you can like make it available as an action on Siri or something like that, but I don't really, honestly, I don't really know.
Yeah, well, what I think what matters is as a lot of these platforms grow—Siri, Alexa, ChatGPT—a lot of them are adding voice; there's going to be ways for developers to to embed themselves into that workflow. And if you could be known as the voice version of XYZ, assuming that that audience of people really want to use voice and it and it lends itself well to it, like there's a lot of opportunity there. Um, and you know, someone's going to be listening to this and they're going to say, "Oh, but Expedia could add voice at any time," right? And it's like, yeah, you could have said the same thing about mobile, you know? There was a whole generation of mobile-first companies that came out, created literally trillions of dollars of of market cap and value, and it happened because there was an opportunity from a UX perspective to create something that was just really, really dialed in for that use case and for that UX. The same is true with with voice. Yeah, I mean, I I just think that whole thing—like there was that whole thing of like for the last couple years of like, "Oh, yeah, every big company's just gonna like integrate AI and like no startup's going to be able to compete," and it's just like pretty much all of those integrations suck; like maybe, maybe a couple of them are okay, you know. Um, but really it's a totally new paradigm and like way of using computers, and the, uh, like big companies are really limited in the risk of the amount of risk that they can take to like make their actual interface good for that specific thing because they can't risk like pissing off their regular users. And that's a tremendous advantage for startups to like actually figure out what the future of this is like and start with the early adopters of people who really want to use it that way and then slowly just get into the mass market after that.
Totally okay. I want to end with one last thing, which I I don't I don't prep you with anything, so, um, you're you're out there, you're building AI apps, um, you're getting traction on them—how do you think about distribution for those apps? How should people, how should someone who's building an AI app think about, uh, you know, getting their first thousand, 10,000, 25,000 users? Uh, well, this is a, this is quite a layup because, uh, I just send it out to my newsletter, um, basically like I spent 5 years building a media business, um, of people that like are really, like really engaged and hopefully like really, really love the brand and really love the the writing and the videos and all that kind of stuff that we that we put out. And so when we have an idea like this, um, we can just push it out to those people, and we pretty much immediately get like, you know, hundreds, two thousands of people try it on the first day, um, which is really amazing. And I I think, um, the things that I've learned about that that have been really useful is—I and everyone else at every—like we basically write about things that we care about, um, that we genuinely like, that are about things that are sort of like in our lives or things that we think about, and that has it attracted an audience of people who are like us and sort of have a lot of the same characteristics. Um, and that allows us to—I think one of the interesting things about the AI wave right now is it sort of resets the playing field where there's a lot of low-hanging fruit of things to build, and so it becomes, uh, that that's a real opportunity to build things for yourself that like was not true a couple years ago because all the low-hanging fruit had been picked, and everyone had built their, like, "I built this for myself" app. Um, and so what's been really nice for us is like we build things for ourselves, and then we have an audience of people who are like us, so they probably want it, um, and that becomes the like first, you know, thousand, 5,000, 10,000 user. And then, um, obviously like that that runs out, and you have to like scale from there, but like that's that's how we think about it, uh, is actually distribution first and then build something second. Yeah, and then, you know, once you have your first users, then you can do some product-led growth stuff, like it could be like, "Okay, I've got a thousand DAUs; how do I get these people to share this?" And maybe it's like incentivizing them for like a free, you know, free three months or six months, or maybe it's you get access to these early features to share, or maybe it's, you know, "Come to this event in New York" for, you know—so I think, uh—every—which you guys, you guys cover like AI and startups right and stuff like that, so I think like everyone should be thinking about creating their own version of Every, right? So what is the media, what is the media distribution arm, um, for whatever it is you're creating?
Um, I actually tend to think that like—and I'm curious your thoughts on this—like I I don't know if I would, you know, in 2025 start with like essentially a blog and a newsletter as as the way to get traffic. Like, why, why, why do you recommend starting there? That's a good question. I actually, I was not thinking about that from like, uh, what I would do in 2025; it's a really interesting one. Um, definitely like we got lucky 'cause when we started Every, it was like right before the big Substack wave, and so we got a lot of subscribers, and like that was the whole thing. And I do think that like email is just really powerful to have, um, but it's much harder to grow a newsletter now than it was five years ago. I'd probably start with YouTube actually, um, I think, and funnel people into—from YouTube into a newsletter, um, that feels like the right move. Um, and we have a YouTube channel, and it's growing really fast, and it's it's been really great; it's like it's definitely a little bit less effective to like get a YouTube subscriber to like read an email—like that that conversion is not, not nearly as good as it was like back in the day in like old Twitter, you know, the heyday of Substack. Um, but, uh, yeah, I'd probably start with YouTube. Yeah, I'm honestly, I'm I gotta say I'm subscrib—I'm I'm surprised that how many people subscribe to my newsletter via the YouTube description—like I put like a little lead magnet, greenberg.com, which is where, yeah, people sign up, and I literally get hundreds of people per day, wow, coming and signing up to the newsletter from that. And the cool thing—what you can do, uh, on kit.com, formerly known as ConvertKit, is you can segment those people, so you can say, "Okay, here's all the people that came from YouTube," and and you can even do it so, "Here's all the people"—there's ways to do it—so, "Here's all the people that came from this video," so, um, there's really interesting ways to segment your audience so that you send out, uh, invitations to the app to the right people, right time, because you know it's not just about getting users to your app; it's about getting the right users initially because you like the insights, the feedback that you're going to get from them, um, the fanbase, the community that you're going to build, like, is important. And, uh, so anyways, I like I like I like your style, Dan; I like your style. Thank you. Great minds think alike. Yeah.
Dan, thanks for coming on. Um, besides Every, uh, is there any anything else you want to plug? Definitely. I mean, obviously subscribe to Every, e.to, um, if you're interested in some of the products we build, uh, you should check out Spiral, uh, which helps automate, uh, a lot of your, uh, repetitive creative work—like doing tweets and LinkedIn posts and YouTube descriptions and all that kind of stuff—that's spiral.computer. And you should also check out Sparkle, which is another product that we built that, um, automatically, uh, organizes your file system with AI for you, um, and that's makeitsparkle.co. Cool. And yeah, I encourage people to check it out, have fun, and just see, see like how Dan's thinking about shipping these these products, you know, and and and how he's building it, uh, it'll give you inspiration and and, uh, it's just cool to see. Um, if you like this episode—you didn't like it until you click like on YouTube, of course—uh, that's how I know, um, to keep making videos, keep making podcasts. Uh, I'll see you in the comment section. Dan, thanks again for coming on. I'll see you around. See you later. [Music] Downy