His GPT Wrapper Has Half a Million Users—And Keeps Growing - Ep. 39 with Vicente Silveira

[00:00] We're nailing the use case of getting a collection of documents that you have and being able to do end-to-end workflows with those documents. Can you go to this URL and download the articles and then create a document with the main ideas in those articles and express those main ideas as sentences? Write the text of the site into the files and then, wow, interesting. Okay, so now we're getting some outputs. [00:30] you [00:34] Vicente, welcome to the show. Hey Dan, thank you for having me here. [00:38] Thanks for coming on. So for people who don't know, you are the CEO of AI PDF. It's one of the biggest AI PDF readers in the world. You have about 500,000 registered users. And it's only been live since the end of last year. You've done over 2 million conversations in the GPT store, and you just started monetizing and you have about almost 3000 paying subscribers. So it's a really, really cool business. And what I think is most interesting is [01:06] There's this narrative, especially in the earlier days of AI, but I think it's still happening now, which is like, oh, [01:13] yeah, all these like AI PDF companies are like, they're fucked and they're not going to do well and whatever. And I think you're actually building like a really interesting business. And you're also part of this like broader wave of, [01:25] people who are making things in the in this new ai economy with like really small lean teams you run the company break even without raising a ton of money i know you raised a friends and family around but like you haven't raised a ton of vc i think there's a lot of overlaps between the kinds of things that you're building and the kinds of things we're building internally at every and i just want to like um learn about how you're thinking about it we can swap stories we've done a lot of thinking too about fundraising and all that kind of stuff and how to build this because i think

[01:50] People assume, oh, you can't build a good business like this, but I think you can actually build like a sneaky, really, really great business this way. Small teams, especially with AI. So, yeah, tell us about that. Tell us about your business and how you're thinking about running it. [02:05] Yeah, it's funny you mentioned the whole rapper thing, right? We almost have like a... [02:09] a rapper death countdown, you know, clock, right? Which is like this many days since we died the last time that we keep track of. From time to time, we're pronounced dead, right? In reality, the business keeps growing. It's kind of interesting. At some point, it was when OpenAI, for the first time, they allowed to upload PDFs to ChatGPT, right? It was like, oh, all this stuff is dead, right? Including some of our competitors actually just gave up during that time. But we kept plugging at it. [02:39] At some level, you know, this is an interesting industry because everyone is pointing at each other saying you are a rapper, right? It's like, you know, I don't know if NVIDIA is a rapper around math. Everything goes from there. We started on this because we tried different things, actually. When we first started, when ChatGPT just was coming out and I was basically trying to do like property injection against ChatGPT and Sydney back then. And I found some stuff. [03:09] sent it to an email to Greg Brockman at OpenAI. And he replied, he's like, Oh, that's kind of interesting. You should talk to this guy here. Right. And at the time, I was actually still kind of like, I just, you know, got out of meta. And I was angling for a job at OpenAI. Right. I talked to this guy. He's like, Oh, you know, you have an interesting background. You know, right now, we're not hiring for this, maybe in a couple of months. So keep in mind. And I'm like, well, maybe you can give me like an API key or let me into this new developer program. And that's how we

[03:39] And we tried different ideas, but the PDF one really took off immediately. And in reality, it's because this is one of the... [03:48] first things that people are trying to figure out with AI. It's a lot of pain around dealing with lots of documents. And PDF is the main kind of document across platforms. So people just gravitated to that. [04:01] That's really interesting. And so people gravitated to that. And you've said people have declared your death multiple times. I mean, if a PDF reader is in ChatGPT, why are people using you? [04:14] Yeah, yeah, great question. So the thing is, when we looked at this, and we started building it, we actually at first, we didn't even have a place for people to upload PDFs, right? We just like, okay, you know, you can just give us a link, and we'll our server would go go there and fetch the content. And because we're growing so fast at the time, people were giving us like Google Drive links and Dropbox links. And Google, Google and Dropbox started rate limiting our IPs, right? [04:44] because they saw it as like as an aggressive bot. And this was a bad experience for our users because it would get an error. And we were like, well, you know, what we're gonna do? And we're like, well, I guess, [04:54] We could try to play the cat and mouse game with those guys, but we actually know how that works, and that would be kind of pretty bad. So we're like, well, what if we just let them upload? [05:04] their files. And we thought no one would do it. Right. And so the first version of our website, my co-founder, Kartik, he was like, it looks scary. And to our surprise, like in a week,

[05:17] That domain for our website became the number one domain for the links, you know, passing the Google domain and the Dropbox domain. So that was a lesson for us because it told us that the users that were gravitating to ChatGPT and going to all the trouble to enable plugins, that those people were actually risk takers and early adopters. Right. So we built that for them. And then kind of going back to your questions, like why do they kept using us even after this was created on ChatGPT proper? [05:47] they don't want to just upload one file. When it works, they basically want to upload their whole collection of files. And even to this date, I think on chat GPT, you're limited to like 20 files, right? And for us, it's like we have people with more than 150,000 files in one account. We have people with multi-level folders, right? No one else supports that kind of stuff, but it's important because people need that low friction and even respecting the intelligence [06:17] Right. So this part of the product experience. That's interesting, but I want to I want to push you a little more so like. [06:22] It sounds like, you know, one of the reasons people are still using you is because you're sort of staying one step ahead of what ChatGPT will do. [06:31] Do you have a theory about like [06:33] why they won't eventually do like a multi-folder upload like an example might be [06:38] Notebook LM where it's like [06:41] it does let you, it does let you open a lot of files. Like, are you worried about that at all? Or is there some strategic reason why, why you think, um,

[06:48] you're going to go deeper than a chat GPT or like a Gemini or whatever. [06:52] Yeah, I mean, this is interesting, right? Because I feel like that these guys, especially if you look at ChatGPT, their focus is to kind of restock AGI and create a product that's good enough to have enough [07:07] usability there so that lots of people use, they can collect the training data and then feed their machine. Right. I don't think they're going into one particular specific direction. They're mostly kind of like touching on kind of like what is the minimum for core use cases. Right. So I think, you know, is it is there a possibility that something like ChatGPT or Claude or something like that can actually compete with us and basically there's no need for this kind of platform. [07:37] Yes, but that is always the question for startups when things start, right? So when you think about a startup like Loom, why does Loom exist and was sold, I think, almost like a billion dollars to, I think, a classic. Loom is just recording video, right? But they did that use case so well. [07:55] that even though YouTube had all the technology to do it, they didn't do it. Vimeo had technology to do it, they didn't do it. All of the major providers, they had the technology, they didn't do it. But Loom actually nailed the use case. So for us, [08:07] we're nailing the use case of [08:09] getting a collection of documents that you have and being able to do end-to-end [08:14] workflows with those documents. [08:17] Hmm. That's, that's really interesting. I mean, I think it dovetails with some of the things that I think about, like one of the things that I have in my mind when we build things internally at every level.

[08:27] is that ChatGPT and Claude [08:30] I think, like you said, rightly, they're building for. [08:35] the most broad use cases possible. [08:38] Like they just want anyone to go on and be able to do whatever they want, basically. And it's sort of like. [08:43] In that way, it's a little bit like Excel where anyone can go to Excel and you have a blank [08:48] You know, like new sheet full of lots of cells and you can just like start typing numbers and anyone can do that. [08:54] And then people are going to discover as they're using chatty between Claude that. [08:59] um [09:01] So, [09:01] that they have specific, more specific, like they're going to discover use cases for themselves that they didn't know exist existed. So for example, we have a product called Spiral that lets you automate a lot of creative work. [09:14] Like it helps you do headlines and come up with tweets and all this kind of stuff. And that's a thing you can do with Claude. But Claude is not purpose built for it. [09:24] That's right. So our kind of like thesis is people will discover use cases for AI and discover problems to solve with AI by using these more general purpose tools. And then that will create demand for other players to sort of peel off some of those use cases for particular kinds of people, for particular kinds of workflows. Like for us, it's like marketers and creators who have like a very specific need for that kind of workflow. And having a product that's purpose built is going to serve those people better. [09:54] And I'm sort of curious for you because a key part of my thesis there is like,

[10:01] you need to have a particular persona in order to like be powerful enough for that kind of workflow. But it sounds like you're, you're kind of going a level up, which is like more general. Do you, [10:13] but do you have a particular persona in mind or like how do you think about it [10:17] Yeah, that's and we get this question quite a bit. And it's it is interesting because our [10:23] Our persona is [10:26] an early adopter of technology, a risk taker, that has an actual job to be done involving lots of documents. [10:35] Right. So that's like all of those things are important. Right. So one is like they're not just an early adopter. [10:42] Because an early adopter, and a lot of actually ChatGPT and Cloud use, is people that sign up to just see what's possible. They don't have an actual job they're going to do there, but they just want to get that familiar with technology, be able to talk to someone about it, those kinds of things. So a lot of that is that kind of use case. So that's one component of our user base. The other component of our user base is they have an actual job that they need to do. [11:08] right, today. And they have a lot of documents, right? So this is the combination of things that creates our, so like, who are these people, right? So we have like, there's a law firm, you know, the partner is an 80-year-old lawyer. [11:22] right and he found us he's like [11:26] I'm using this every day and we're going to have it within our firm. And I'm a decision maker. We're going to have it adopted here. And we have people that are like...

[11:38] researchers, we have accountants, we have writers. So there's a range of these different types of profiles. But what you find in common is that they bring a lot of documents to the platform. [11:50] And they are basically trying to get some job done today. And they want to do this in the new way, which is the AI first way. [12:00] So that's one thing on the persona. The other thing that I want to kind of highlight is... [12:05] We also differentiate from a platform like Cloud or ChatGPT in a way that I think [12:13] they're not really going there, which is we're building this from the perspective of, [12:19] It is kind of like we're giving a cloud drive to an AI agent. [12:24] Right? That's very different than you allow an AI agent to access some files. So what do I mean by that? And we can show you a little bit about that as well. But the agent that we have in AI Drive is capable of doing things like creating new files, updating metadata in files. [12:42] kind of going through the file structure, right? So it's really kind of driving that, effectively, that cloud drive to be able to accomplish a job for the user, right? Because a lot of the job involves manipulating a lot of documents, right? So that's like another very important point, which I think it's not really the focus for these other platforms. [13:02] Why do you think it's not the focus? [13:04] Because I think it's not... [13:06] It's not necessary to accomplish what they're trying to do. And it also introduces other types of considerations and risks that they may not be interested in dealing with, right? So, like, this opens up our platform for a Tinker-type user, right, to be able to do things like, so think about this. So you have, you know, chat history in something like ChatGPT, right? But that's something that, you know, you can go there, you can look at the chat history. In our product, chat history is actually made of files,

[13:35] that the AI can access those files on your behalf and use the same tools like a search tool to be able to go into those kinds of files right so these are all kind of like the same primitives that we're building on. So you're kind of like one of your core users you saw early adopters but it's like [13:52] I mean, I'm thinking of myself like, [13:55] information nerds. I was like, ooh, your chats are files. Like, that's amazing. Like, that's so cool. It's like feeding back into itself. But like, that's a certain kind of nerd that like cares about that. I was just going to illustrate what you're saying. So you see that we have this little system folder here. So basically, you're sharing AI PDF, and it's called the product is called in the actual product itself. It's called AI drive. And basically, you're showing me on the left, you have basically it looks like you have a list of files. So it's like, that's your drive. It's like a Google Drive. And then you have a chat window. [14:24] which I assume allows you to sort of chat with those files, sort of like a notebook LM. And then on the right, [14:30] It looks like there's a reader view where you can for any particular file that you're talking to. You can also like, see the PDF is that that's basically what I'm looking at. [14:38] Yes, that's right. And you can see like on the left side here, we have this kind of short shortcuts here. So if I click on this little, what looks like a clock, right? And you can see a history, right? So this is very similar that you have in ChatGPT. There's a history of the chats that you had today, right? And that's all you get in a typical platform that's not really open for people that are like tech enthusiasts like you and many others.

[15:08] under [15:09] the chat history folder. [15:10] right so we're building these as basically kind of like an open platform where everything from your chats uh to you know and we're going to be adding memory uh all of that will be basically just a file in the system that's interesting um i want to go back to what we were talking about earlier like so one persona is like information nerds like me and maybe those that like cuts across lots of different industries like there's probably information nerds who are lawyers or [15:40] specifically about that? Like your marketing is a lot more like general AI PDF stuff. How do you feel about that? [15:46] Yeah, and we're flipping that because our... [15:51] trajectory is going through this transformation right so if you go back when we built this and i think this is actually a good uh [15:59] I give this advice to people when they're thinking about what they're going to do. It's like what we did with the plugin. [16:05] That was the least... [16:07] effort thing that we could have done at the time, right? It's just like an API. At the time, I had a server running on Replit, right? Replit's amazing. And that was everything. [16:17] And with that, we discovered the market. [16:20] Right. But then at that point, we're just a plugin. So we couldn't operate independently. Right. What do you mean by it was just a plugin? There was no web app, no place for you to create an account. We had no direct relationship with the user. So you would go to ChatGPT, you would, you know, enable plugins, you would find us. I see. What was the ChatGPT plugin? Okay. I just forgot about that whole, that whole era.

[16:43] It wasn't that long ago, but it feels so, it feels like that. Um, [16:47] Yeah, so at that point, we were a product that only made sense as an add-on to ChatGPT. But what we realized is, one, is that people needed an actual environment where they could do the work end-to-end with the AI and the files, be able to verify the work of the files, which is another thing that we, as you can see on the right side of our AI drive screen, you have the actual files that are the source material. [17:17] realized and I think it will continue to be true for the foreseeable future is because of this arms race between the main providers. The ones that can play the game right now probably Google and Tropic [17:31] And OpenAI X, I guess, is coming up as well as a potential one. So you have maybe like four or five providers there that can actually... [17:41] provide unique capabilities when it comes to the models. And our users, because they are like you, right, and I heard you say in your podcast, you tried this on Claude or this on O1, right? They want to be able to have the latest and greatest. Now, if you're going to upload your files to ChatGPT and then tomorrow Claude has a better reasoning model, then now you get to upload your files to over there as well. So we bring that one place where you can use all the models combined. So that's the other aspect of that. [18:10] That makes a lot of sense. I mean, it's funny, like we do a lot of work with big companies where we help them figure out what to use and sometimes train their employees and that kind of thing.

[18:20] I think... [18:21] mostly they're not in this category. Like they just, [18:25] they're excited to see that Chachabiki had a new feature, but none of them have heard of Claude. They're like, what? Well, like, it's the best, like, it's whatever, you know? And so, yeah, I think there is, there is that sort of separate, a different, it's a different market. The early adoption market is a bit different for people who like really want to use the latest and greatest type thing. And I assume that, like I said earlier, like that sort of cuts across industries and there's, there is room for that kind of like, [18:51] nerd and what's [18:53] a nerd as a customer. And what's interesting to me about how you're doing this is, um, [18:58] Thank you. [19:00] you're kind of like, you're just taking the first step first. You're like, okay, cool. I'm going to make a plugin. Um, and then that starts working and then you're like, okay, like now our customers really need like a place that's like different from the chat to be form factor. So I'm going to go build this other thing. And then now you're adding more features onto it and that kind of thing, rather than I think there's this other approach, which is, [19:20] instead of getting to market super quick, [19:23] kind of, um, [19:25] come up with an idea, you make a deck, you raise money, you start to recruit a team and you take like a year to like put this thing that's like your vision into the world, you know, and that's an alternative thing. I think that works too. And there are always trade offs, like a trade off that I'm seeing here in the way that you've built this is, you know, [19:43] the product that you eventually ended up building is sufficiently different from where you started, that there's always this difference between what is public, what the public marketing is about and how you talk about it and where the product is now. And you need to constantly catch up. I've experienced that a lot before.

[20:06] So that's one trade off where someone who just like just put the vision out into the world, like singularly, like that's not their problem. Their problem is like, does anyone even want this? [20:17] I'm just curious how you came to that or why that's your methodology. [20:23] Yeah, I'm glad you asked this because [20:26] Thank you. [20:27] And you were right. There's different ways. And I have other friends, founders, and they took these other paths. Right. [20:35] I think one is, you know, what works for you, right? And for us, this whole thing started as a side project. [20:41] You know, with with me and CarTaker, we're just like packing over the weekend because we just love this stuff. Right. That's one. But, um, [20:48] But the other thing that I think about it now, and I think it's still a reality for us, is that we are, even though we may feel like, oh, you know, AI is kind of already sort of happened, right? And... [21:00] We are so early. [21:02] in this AI cycle, in the ground. And I'm sure you feel that way as well, Dennis. [21:10] the [21:11] the steady state [21:13] of this technology or the productivity state of this technology. [21:18] it's still not very clear what that's going to look like. Right. So just, just in this short time that we we've been doing this stuff, you start with like, okay, you have these, first you had these, these like specialized models, right. That people had a bunch of companies. And we talked to, I just recently talked to a, a,

[21:39] a company where they had a model for PDF extraction that they built on Watson and all of that. And then they tried it with, you know, GPT-4, and it just blew that thing that they worked on for years out of the water, right? So that was like a big phase shift there where a lot of people had like a rude awakening there. [22:09] model that was built over years with Microsoft resources, and they baked that against GPT-4, and it destroyed it, right? So that was the big first phase shift. But the reality is that that [22:26] We build this thing and, you know, at first chat barely worked. Now chat works pretty well, right? And we have multimodal chat and all those kinds of things. And now, you know, if you look at what Sam Altman did, [22:38] And a bunch of people building now are saying, building the foundation, they're pointing to like, [22:46] Oh, the AIs are going to become more and more capable so they can take more of the task and become these agents. Right. Well, everyone is talking about this kind of OK, we're going to move to agents. So the ground is shifting as we go along. Right. We have something like computer use now. So Claude did a little right, a little demo of that. So what I mean by that is. By. [23:07] building this for

[23:09] an early adopter crowd with an actual problem to solve, right? They have day jobs. That's very important, right? We are actually capable of tracking the evolution of this market, right? So that we don't get stuck on a kind of like a early internet type thing. Like, oh, there was ICQ. There was, you know, a bunch of different things that eventually became irrelevant once you got into the productivity state. So in a way, it's like for us doing this is a little bit also, [23:39] and also defensive as well. [23:42] Well, let me unpack a little bit of what you said, because the question I'd ask is sort of like that. [23:48] tinker mentality where you're going out and you're just building like the thing you're getting to market super quick. And it sounds like what you're saying is, [23:56] that by serving an early adopter market you'll be able to and you're incentivized to kind of like keep up with the latest and greatest so that you don't get left behind how do you bridge the two how do you bridge the gap between the two like what's the bridge from how you got to market to not being left behind because you're serving early adopters [24:14] Yeah, I think the early adopters, what they give to you is some leeway to experiment more. [24:21] right so for example we now introduced the agent into our product before if you look at our product right and it's still there so that's the main way you can see this menu right and basically i clicked on this menu in the chat prompt and you have the models from the main families here on tropical ai and uh and google gemini so you can just go into this and just do a regular chat which most users that's what they are uh they're used to but then we have our kind of like

[24:50] the users are pushing us forward, they want to go into the agent and be able to do more of that task. Right. So we're trying to basically work with the core of our early adopter users and also listen to the ones already moving forward to the next thing. [25:06] And what this early adopter users, what they give you is more tolerance for the experimentation. Because of course, and... [25:14] Something like an agent today still doesn't work. [25:17] great, right? You may have a moment where it's like amazing and the next run that you do, it may not, it may get stuck, right? But they actually want to see where that thing is going. So that's why these types of users, they help us kind of like bring the, both like the main use case and the leading use case at the same time forward. [25:39] I think this is like an important related point to kind of this competitive thing that we've been unpacking together. Like, how do you compete if you're like a two person team or I don't know how big you are, but we're eight people. So like, how do you compete? We're, you know, against OpenAI or Google or Notion, whatever. [25:57] And I think like the thing that comes to mind for me that I that I think you're saying, which I think is true for us as well. [26:04] is [26:05] People forget that [26:08] When you're a big company, you have to serve a lot of users. It's really hard to take risks. And [26:13] for a while there was this like, [26:15] feeling about a ai where it was like well the ai is going to be smart enough that it's never going to make mistakes so like uh big companies are going to do anything that startups can do and i was always just like no big companies like always find a way to things up um it's just like it just it's not because they're not smart it's just innovators dilemma stuff it's basic stuff that you just like can't take rent from it's like you know you're not going to be able to do it

[26:34] And I think like [26:37] I think that's why when you look at a lot of the AI stuff, like, for example, and I won't name names, because I don't, I don't like shitting on people directly. But for example, I got a fitness tracker app recently. And it's really great. Like, I love actually the way it works and the app and whatever, but like, they have an AI feature in it. And the AI is just like, it's so milquetoast, like, it just like doesn't say anything useful. Basically, it's like, [27:04] And the reason is they have to, they have to, [27:07] Thank you. [27:08] make it work for like the lowest common denominator user. They have to make it not confusing and they don't want to take any risks because they're like a big company and it would [27:15] It would be bad if it said something that was risky, which makes a lot of sense. But it means the experiences you're able to do as a bigger company are less good. [27:25] in a lot of ways than the experiences that you can do as like as a small company that you can just like decide, OK, yeah, we're going to serve these users that like don't care if there are rough edges and we're going to like explore on the boundaries of what's possible. And our users are going to understand if we return a result that's like not so great. And that allows us to experiment and they understand because they're like want the greater power and they understand that there's a trade off there, which I think I think that's really important. I think people miss that all the time. [27:51] Yeah, I think that's totally true. The only reason why startups have a shot at anything, it's because there's some... [28:01] I guess, core vulnerability to establish business. And usually that is their customers.

[28:07] Right. So the thing that makes them powerful is that they have lots of customers. It's the same thing that makes it hard for them to take risks with those customers. Right. So if you think about, you know, the mainstream products, you take a spreadsheet product. Right. [28:23] A product like that, there's a lot of investment that was done on a ton of features and training of users. I mean, they actually have certification courses for their users. Their user base... [28:34] goes there day in and day out expecting a certain experience. [28:38] They also want to know that there's some AI on the side, and you can put some AI on the side, that they do, sprinkle AI everywhere, but to radically change that experience. [28:48] to be something like oh it's going to be ai first you're not going to go you know click the buttons you're going to tell an agent to go do for that for you and that's going to be the core of the [29:00] anything. [29:01] That is a change that is just too radical. [29:04] you know, typically for the incumbents to be able to do. So that's why you have the opportunity for, for, you know, a startup like, like yours and ours to come in and introduce a new way of doing things, which I think is part of, [29:17] what we're trying to do, which is [29:19] What is going to be the new way of doing things? [29:22] Right. And we feel like it's a lot more. [29:26] Like we have this, this. [29:27] analogy here. So you just think like how a very wealthy person operates. [29:32] Right. They they operate through intelligent agents. Right. People that they hire. They're very smart. They learn everything about them and they operate all the complexity behind them.

[29:44] Right. And so that's kind of like where we think things are going with AI. Of course, right now it's it's too far from that, but it will approach and get very close to that. So this opportunity to build for the new experience, I think is is in leaning into that. Right. It's very important. [30:01] I totally agree. I think that that metaphor is so powerful. It's something that I've written about a lot and I thought about a lot is like, [30:09] if you want to know where things are going with agents, like, yeah, people have been hiring agents for a long time. Um, and they are, they're solving problems that, uh, with these agents that like, there's a lot of overlap with what AI is going to be able to do. And maybe you can do new things with AI that you couldn't do with like, [30:24] hiring a personal assistant or whatever, but like, [30:27] there's a lot there that you can just like kind of carry over. Like even for me, you know, I run a media company and [30:35] Um, [30:36] I employ a lot of people like, you know, editors and writers that help and designers that help me do things at a high level frequently all the time. You know, this YouTube video is going to be edited and it's going to have, you know, it's going to have an intro sequence and it's going to have a thumbnail and all that kind of stuff. [30:54] And [30:55] I can do that because the company is successful enough that I can hire those people. [31:00] But it took me a long time to get to a scale where I could do that. And I think if you want to understand, for example, where the future of media is going, it's not that teams of creatives are going away. It's just that an individual creator is going to be able to do a lot of the things I have to hire people to do, like

[31:18] on day one. And, you know, I'll still have lots of people doing stuff. I'll just be doing it at a higher scale because the people that are editing my videos can edit twice as many videos or whatever. Yeah, I think that that's I think that's a really good place to look for ideas. It's like, if you want to understand how people are going to run their calendars or their emails or whatever, just like look at [31:37] how people with assistants do it. [31:40] So, that's definitely a metaphor that we use a lot internally. I'm curious for you, like you raised only friends and family around. I assume with the kind of traction you have, you could have gone and raised a venture around. Why didn't you do it? [31:56] Yeah, I think first our experience raising was kind of very interesting, right? The beginning was like, oh, this is amazing. We're just going to be, you know, we're going to raise a ton of money now and maybe we should raise more. But [32:09] The beginning was fast and the process then later started dragging along. Right. And I felt like I was, you know, back working at Meta, doing PowerPoints and tweaking PowerPoints and prepping with a friend, you know, a friendly VC to talk to another one. And when we had users who were basically asking us to do stuff, and at the time it was just crazy. [32:30] Karthik and I, right? So I'm like, I'm hating this, right? And if we can just go and monetize, let's just do that, right? And then we'll come back. So I think that was the main reason. And it was a time as well where with AI, the whole kind of like hangover of the first wave of investments was what set in.

[32:50] So people were really worried about this. It was a time also for us product-wise that [32:56] you know, the product was very much dependent on OpenAI in ChatGPT specifically, right? Which is not the case now anymore. So for those reasons, we're like, yeah, let's just focus on the product, which I think was the right... [33:09] the right thing to do. [33:11] That's interesting. Do you think you might raise again in the future? Like, what's what's the path look like for you? [33:16] Yeah, I think so. I think the reason is we want to be [33:21] very careful in [33:26] I guess, diligent about when we do it, why we're doing it, right? And how we're going to use the capital that gets invested. And this is one of those things where kind of going back to [33:38] how many [33:39] how many people and how much softer that's the other question a startup is going to need moving forward [33:45] And [33:46] We [33:47] think it's actually less than [33:50] what has traditionally in the last five to 10 years been. Right. So we want to make sure we we do it right so that we were not like [34:00] Sometimes, you know, and I hear that, I think Bill Gurley famously said that, is companies raise money and that because they have that money, then they end up becoming more complacent, right? So that's one thing that for now... [34:12] we want to make sure we don't do. But yes, I think once we know, like, okay, this is going to allow us to grow in this, [34:18] this direction. [34:20] uh it will make sense right i think the other thing that i want to

[34:24] you know, maybe touch between this point and the previous one, [34:27] one. [34:29] how [34:29] both the experience is different, but also how you [34:33] end up saving. [34:35] money, right? If you just lean in is so we think about like, let's say a job like onboarding on our product. When you sign up for our product, our onboarding sucks. Your onboarding is beautiful. I did the spiral one. It's beautiful. [34:48] Thank you. We work really hard. Yeah, I love it. Our sucks. Right. And we're like, OK, we need to make our boarding better. [34:57] I do think that the way I want to do that is... [35:02] basically giving AI a job to do that onboarding of that user. [35:06] right so what does that mean to us is like instead of having sort of a [35:12] either a product that we attach to our app that will kind of be configured for that onboarding in some way, or build that ourselves, we are going to basically, [35:25] lend you an agent that knows about the product, knows where you came from. Oh, you know, we have a landing page for lawyers, right? You came from the landing page for lawyers, so you're likely a lawyer. Hey, you know, this is what the product does. Do you want to upload one of your files and I can show you what it can do for you? And it basically gives that AI a job. [35:46] And what we want to do is so we have a person that we just hired that is [35:51] going to be responsible for this area so that person's job

[35:55] the actual human that we hire is to basically [35:59] manage that little agent, right? And be responsible for the delivery that that little agent does, right? And of course, as the agent gets better, there will be more and more that it will be able to do. So it's kind of an interesting way to think about these things where we feel like that as we hire people, they will end up be responsible for certain agents on the product that have specific [36:29] I think that makes a lot of sense. Like I've been writing a lot about, um, you know, what I've been calling the allocation economy. And I think this is like, right on that. [36:37] on that train, which is like in an allocation economy, you're instead of doing a lot of the IC work, you're doing a lot of more management work where you're managing [36:48] the allocation of intelligence, managing agents, and [36:53] And in that world, like the skills of managers become more important than they are now, and they need to be more widely distributed. [37:00] So I think that makes a lot of sense. That's really interesting. I'm curious for you, [37:04] Do you have any sense, like quantifiable sense of [37:07] um you said earlier uh the things you can get done with a smaller team with less capital [37:13] You can get a lot more done now. Do you have any quantifiable sense of what that is versus like 10 or 15 years ago? [37:19] Oh, for sure. And you can see this. [37:22] everywhere right and i can give an example like working at meta right of course meta has a ton of money right and

[37:30] pre-Gen AI, and now they're deploying it very aggressively internally, as I hear from the outside. But pre-Gen AI, you would have things like, [37:38] a product manager, and I was a product manager there, they would want to know like, okay, you know, what's going on with this particular feature? What are the top issues that our customers are having in this particular, you know, area? And so you would, the PM would basically talk to you, [37:54] a person in the support area, which their job was only to go collate all this feedback and create this report. And it will take like maybe a day or, you know, if they're busy, maybe a little bit more, depending on how priority is your problem, how much of a priority. [38:12] So all of that now, right, it's like you can get done with with AI. That's that's just there. Right. You can you can get them directly with the eye. So that's just one example of how. [38:25] with the tools that we have now, we should be able to be [38:29] a lot more efficient and do things that would only be available to larger companies. [38:33] Yeah, I think that's true. I mean, we see some of the apps that we work on internally, like [38:39] I can see someone take something from zero to it's a fully finished product that would have taken a year and they can do it in like two or three months if they're good and they have a whole generalist skill set and we have a bunch of support for them and all that kind of stuff but it's it is kind of wild and I think I probably have a particular like anti-VC bias or I've been historically hesitant to raise we raised a little bit in 2020 we raised like 700k and we raised a

[39:06] amounts where it's like a VC would like laugh at it and be like, what are you going to do with that? And for me, I'm like, we've raised less than a million bucks. And if you look at [39:14] the number of products we've built. There's a lot of, there's a couple of different [39:19] products and companies that have come out of just that one raise. And I think in a year we'll have a bunch more. And I think about [39:26] raising capital and why you do that. It's, you know, [39:31] If you spend a couple months raising, [39:33] Theoretically. [39:35] you can hire more people and spend more on growth so that you can pull forward [39:39] the progress that you would have made over the next year and maybe make that progress in like three months or whatever. And I think that that equation [39:50] It's still there, but it's different. Like it's the amount that you can get done is you basically have gotten a lot of that pull forward effect from just like properly using AI. Yeah. [40:02] which is really interesting. And of course, everyone else has that. So, to some degree, like having extra capital can help. [40:09] Capital has always been pretty available for [40:12] technical teams and whatever. And I think [40:16] it's rarer to actually be using AI well than it is to like get capital in certain areas. Like some people have a very difficult time getting capital and that's a whole different problem. But if you're like a technical Silicon Valley-ish team, I think a lot of those people are [40:30] So, [40:30] just feel like they don't use AI that much in their daily work because they're like, oh, I'm better at it or I'm better than the AI or whatever. I think that's changing a lot. And people let it like.

[40:40] really gone headfirst into it are [40:42] quite a bit more productive. [40:44] Yeah. And for us, it's like productivity at all levels. Right. So I have a software engineering background. I coded like early in my career. [40:54] And then I stopped, pretty much went to the business side. I thought I was never going to be able to code again. And when this came back with Gen.ai, [41:03] He was like... [41:05] He was kind of, you know, you can't mountain bike anymore because you can't go up the mountain. Now you have an electric bike and, you know, up you go. Right. It was incredible. And and and then I say I have like two two mentors. One is, you know, AI. The other one is my co-founder. And I see that even for him, kind of world class engineer, you know, former Google AI. [41:27] Uh, [41:28] it makes him so much more productive. So it's like everyone goes up, like for whatever level you are at, you become a lot more capable. So that's that's absolutely true. And we think that there is a huge lack of awareness overall in the population. I think some of this is unlike your show, because it helps. [41:49] spread the word, which is [41:51] AI is actually for everyone. [41:53] Right. Like people are like if you just follow, I think the more mainstream media, you think that, well, one is going to kill you. But if it doesn't kill you, then it's going to, you know, take your jobs. And by the way, the rich will get richer. So so you end up like what kind of message is that? What does that do to the population? Right. Everyone gets demotivated. You take agency out of people on in reality, you have the opposite.

[42:18] You talked about like, [42:19] this is like learning to be a manager [42:22] Well, if you have access to an AI, [42:25] you are practicing being a manager. If you have a phone, you have access to AI. So you can start specifying a task. The AI doesn't do what you want. You're like, hmm, [42:35] Well, actually, my question wasn't good enough. That's a lot of what being a manager is. So you get your prompt, your question better. Now you have to look at the result, what the AI brought back. Is this quality good? Am I willing to put my name on this thing, right, that the AI came up with? So, yeah. [42:51] I think that makes a lot of sense. I mean, you know, obviously there's a lot of difficult social and economic issues to like, you know, broad AI rollouts. But I do think that point is totally right is people tend to miss how powerful this is as an immediate upskill for lots of people, like even people we have internally where [43:11] You know, for example, if English is their second language, like, [43:15] maybe they speak fluent English, but their writing of English was like not as good. And you could tell, and that limits their ability to like get hired or get promoted or do certain jobs. And like the minute ChatGPT came out, it was like a total [43:28] shift, you know, they could immediately write like fluent English and that [43:33] level of opportunity, like, it just opens up that they was, was not available before, and they didn't have to do anything. And I do think that that's, that's one of the things I would love to do with this show is, [43:45] show people the easy sort of ways that they can get started and also like where the what the most interesting or smartest or furthest ahead people are doing so that we can like bring forward

[43:56] like everyone else to use it because yeah i think um [44:00] Hopefully raising the floor like creates a lot more economic opportunities for people. And I love that. [44:07] you have this tutor in your pocket that I would have just talked to ChatGPT all day if I was 11 or whatever. And I think we're kind of lucky. [44:18] You know, there's a lot of concern about AI companies like racing ahead and releasing it to the public before it's ready and all that kind of stuff. I think on the other end of the scale. [44:28] We're kind of lucky that we live in a world where all these companies are trying to make it as cheap as possible for everyone to use. Like there's an alternative timeline where it's like IBM invented this and like [44:39] only like the dod gets access for the first like 15 years and um like that would suck [44:48] uh i don't know you know like i could just i could imagine a lot of things being written the other way where it's like well only like [44:55] Rich big companies get access to this like crazy intelligence and I'd rather just like have it have everyone if we have to pick I'd rather. [45:03] I'd rather this world, I think, to some degree. I don't know. At this point, I'm like, I'm totally off what we usually talk about in this show. But I think it's really interesting and important. [45:14] Yeah, I think you're totally right. And, uh, or you're in Europe, right? And there's still struggling with it. Yeah. I hope, uh, that a lot of people get to see this, right? Because, uh, uh, it's the first time that I see, uh, and I've seen these other, you know,

[45:29] tech revolutions where [45:31] most people are already equipped to be able to use it. [45:35] Right. So before it's like with the microcomputer was super expensive. You know, I actually grew up in Brazil. We couldn't get it. And we had to basically smuggle parts and kind of build Frankenstein computers there. [45:52] And, you know, even with cell phones, when they came out, right, it took time until people had access. The networks were not good. Right. And then now we get this thing that, you know, if you can get whatever social media on your phone, you can get AI. AI actually works better because, you know, most of it is lower bandwidth. So that works well. And I think, you know, for us is... [46:15] is what we see as we go into this direction of, [46:19] from just conversations with AIs, right? The I ask a question gives me an answer, right? Which is the initial like we chat to, you know, moving to agents, which is I give it a task. [46:30] and he executes that task for me, is [46:35] is this evolution of possibilities that you can have, right? So like one of the things that we spend a lot of time now is building tools, right? [46:44] for the agent right and it's kind of super interesting because you start to think about like [46:49] Is... [46:50] Is my tool good? [46:52] Is the tool self-explanatory? Does it do what it's expected to do? And so you have that second level there. And all of this is opportunity because...

[47:04] if you think about [47:06] Like people say, well, you know, when the AIs do everything, then there's nothing to be done. Right. But again, if you use the rich, you know, wealthy person metaphor, you can be very rich and you can hire a bunch of people to create a company to do something. But most of the time that doesn't work. [47:24] Right. Because it takes, you know, leadership and talent and some vision and some grit there to be able to organize these. [47:33] human agents, right, into a company that actually succeeds. And I think the same thing will play out with the agents, right? You're so right. Like, I'm literally, like, writing an article about this today, but, like, it's like there's this weird fallacy where people are, like, [47:48] well, agents are going to be doing it. So you won't have to think about it. And I'm like, have you ever managed a person who is literally a general intelligence? Like they are really, people are very smart, you know? It's hard. It's really hard. And it's, it for sure is like a [48:09] doing it yourself. [48:11] but even if someone else is doing it like the skill of delegating like the like going from [48:17] There's this thing that early managers, you have to figure out, it's like, okay, how much do I delegate and how much do I micromanage? Because if I micromanage, it'll get done the way I want it to get done. [48:28] But if I delegate, but then I have no leverage and I'm like just basically doing their job for them. So why did I hire them? [48:34] But if I...

[48:35] If I delegate, then I have more time to do other things or think at a higher level, but it comes back wrong. And that's like literally the problem that a lot of people are having with AI right now is like, oh, it sucks or whatever I have to do. I can just do it quicker myself. And I'm like, [48:49] That's exactly what managers face. [48:52] And and so a world where we have these cool, let's say it's AGI, like really cool agents that do all this stuff. I still think there's a lot of. [49:02] you're skipping a lot of invisible things that have to be done to like, [49:06] scope the task and pick the right resource and like have the taste and vision to be like, this is what I want done in the world. And I don't, I don't think like, okay, maybe we'll get to a point where even they're doing even that, but then there's, there's a lot of stuff that is sort of invisible in an AGI type scenario, or even before AGI, just like intelligent agent scenario where there's a, there's a lot of skill and talent that needs to be directing them. And I think you're pointing to that. [49:34] Yeah, we actually think a lot about delegation. I can show you something cool. [49:39] I would love to be cool because I think that there's a lot of talk about like agents and, and, and there's a lot of stuff happening, but I don't, I haven't seen anything like really compelling yet. Like we had, we had Yohei on last week and I think he's got a lot of cool stuff with, with, with baby AGI, but he's like. [49:55] I think a lot of that is still like pretty prototype and experimental. So yeah, I'm really curious to see what you guys are working on. [50:00] Yeah, let me show you some-- that's just kind of-- and this is a live demo here, so anything can help. So OK.

[50:07] I did download a few of your posts. I think the context window [50:12] Series of articles, right? So I just want to set the scene for people. So basically we're back. We're back in a drive. We've got the same kind of 3 column layout, which is like, we've got a, you know, a folder structure on the left. We've got a chat in the middle. And then we've got an open PDF on the right and you've downloaded some every articles, some from context window, which is our Sunday digest that goes out every Sunday. [50:35] and you're typing into the chat. And I'll read what you type after you're done typing it. [50:42] Yeah, so... [50:45] I'm just here in AI Drive, and I'm just typing this, you know, prompt. Hi, I'm talking to Dan Shipper, so I'm writing this to the AI, which is our agent in AI Drive, and he has a series of blogs, context window. [50:59] in the folder and then I'm pointing it to which folder I have downloaded it. Can you [51:06] read them all and suggest some interesting things. [51:14] talking point or conversation starters. [51:18] for us. And I'm going to use this thing here too as well, which is use the expert to plan, right? So this is one delegation tool. So the reason, and I'm going to fire this up as I explained, so now it's kind of processing. And if you click, you can see what it's actually doing. So first thing it's doing is getting a tool plan from the expert model. So the main agent you're talking to here is GPT-4-0, which is great, but it's actually not the smartest at planning.

[51:48] is we allow this agent to delegate to an expert in planning, which can be O1, which is great, or it can be the latest cloud, which is also very good. And you can see this here, right? It says, oh, you know, this is the task. I need to read all the blogs here. Give me a plan. And then you'll get a plan back. And yeah, so now it's outputting the conversation starters, right? So as you can see, let me just collapse here so we can look at all the tools that were used. So the first thing [52:18] plan it went about executing it which is okay let me take a look in drive at what's in you know in in in the folders and files so it went into the context window folder just to see which files were there okay great so now because and this is one of the things that you have to do with ai is that [52:36] Each AI has an amount of [52:39] context window or working memory that you can work with. And you have to work around that. So here-- [52:47] This particular model, GPT-4, that family doesn't have that big of a context window. So in this case here, we basically tell it like, okay, you're going to delegate the task of reading these files, right? So he went ahead and said, okay, [53:00] go into this folder and extract the key arguments, unique insights, personal anecdotes, blah, blah, blah, and organize this as conversation starters. And then you have the output here, which it brought back into the chat. And then we have here, I don't know, Apple integration to ChatGPT. So there's some interesting conversation starters here about the Apple integration there.

[53:30] with a smart agent, you can actually have two delegations that are happening to get that task done. [53:37] I get it. I think that's interesting. So which articles have you downloaded? [53:41] We can go take a look. So here, and I'm going to drive into the folder for context window. It's the Apple took the stage for WWDC, blue sky thinking, creator led businesses, generative thinking, spiraling out of control and your AI research assistant. [54:00] Because one of the tasks, and we can try it on this, I don't know if it'll work, but one of the tasks I really want to do, and I don't think these models are capable of it yet with just putting everything in the context window, is I want to create like a list of all of the like thesis statements or all of the ideas that were like, [54:18] Like, you know, for example, I have this allocation economy idea. That's like an ongoing thread that I write about. And I really want to create like a list of all those threads just to like have so I can [54:28] you know, [54:29] refer back to them. And this seems like a good [54:32] thing to do that with do you think we could try and see if it can pull out things like that let's give it a shot so where do you start from where is the information i would go into chain of thought which is my specific column on every um it would be every.to/chain-of-thought and download [54:50] let's let's try one thing real quick here uh can you go to every [54:56] .to [54:58] That's yeah, yeah. Yeah. Change of thought. You find a chain of thought.

[55:08] Thank you. [55:08] Just to make sure this is the website here. And chain of thought column. [55:14] URL. [55:16] And so we are starting to introduce... [55:19] Yeah, so we're going on the web, right? So we're using a tool that is a fetch URL content. So it's going at this every.to, so then it's, [55:30] website and let's see if it's going to find [55:33] the right URL. I apparently found something here. [55:36] Oh, it's actually... [55:38] going into AI Drive because we may have downloaded something. So this is one of the interesting things you see with the tool, right? It's a tool to go out to fetch URLs, but even files here on AI Drive are URLs itself. So... [55:53] He went there. Let's see what he did. [55:56] Oh, OK, there's some issue here. [55:59] Let me try another approach. That's one of the cool things about the agents, right, is that they kind of work around... [56:05] issues and the more tools you give if there's some overlap between the tools they can actually find workarounds so let's see what he did so he went for fetching orl again oh [56:14] You found it. [56:16] That's awesome. So what I wanted to do is there's a there's a place there's a way to sort it by newest. [56:24] Okay. [56:25] And so I would like it to just sort by newest and. [56:29] take the last, I don't know, all the articles that are on the first page of newest and [56:35] Tell me like, okay, what are the main ideas?

[56:40] um [56:41] of each article as bullet points. [56:45] Okay, we can give it a quick shot here. So you want the latest articles there? [56:49] yeah so basically there's a there's a it's a new url so i'll just send you the url just to make it a little bit it's just a parameter where you just sort my newest got it okay [57:00] So, [57:01] Basically, I wanted to go to that URL. [57:04] I wanted to go to each article in the URL. [57:06] And [57:07] Pull out the main idea of the article and express it as a sentence. And then give me a bulleted list of the main ideas. [57:18] Can you go to this URL, which is the chain of thought URL, and download the articles, and then create a document with the main ideas in those articles and express those main ideas as sentences? Use the expert to plan your task. So it's basically like, [57:32] Going to the expert model, which is, I guess, 01. Yeah, right now it's Claude. And Claude's basically like creating a plan. [57:38] So it's writing a plan for the other AI to follow. [57:43] And it's giving it some things like it's going to fetch the URL content and write to the file and all that kind of stuff. It's telling it, okay, here's some things to watch out for. [57:55] And now it's starting to take action. So we're starting to see it, I think, um, go to the, go to the site, um, and then write the files into into another file. So write this, write the text of the site into the files. [58:08] And then wow, interesting. Okay, so now we're getting some output. So the first one is how to figure out what people want is the article title. Understanding customer needs involves thinking in sequences. That's right. OpenAI launches a document and code editor.

[58:22] And the main idea is Canvas enhances human AI collaboration by allowing real-time document code editing. This is cool. Yeah, this is actually... [58:31] quite helpful. I think you may have converted me. [58:36] This is definitely different than I think the experience, for example, like one of my problems with [58:42] No, for example, is I have to like, go do all this manually, like uploading all this stuff manually and it's really nice to, uh. [58:51] uh to have just be able to fetch it for me and i think the other one the other thing that's nice is like [58:57] Yeah, there's some more complex workflows you can build here that is [59:02] helpful for text processing. I like this. This is cool. [59:06] Yeah, what we find is it's kind of interesting if you think about the evolution of these things, right? Something like if you do a Google search, you can't do something like this Google search because usually Google searches are optimized for like maybe two words. I think that the even perplexed, do you want to try this in perplexity? [59:23] Yeah, let's try that. Let's do a quick bake-off here. [59:28] Um, [59:29] I'm going to get the exact same prompt, to be fair. I love your confidence. Well, I mean, might as well, right? So we can go to perplexity here and-- [59:41] Oh, share this tab. OK, so can you see perplexity here? Yeah. [59:46] I guess eliminating any chance at Perfux that ever sponsors the show, but. No, no, no, no. They do great work. We like them a lot. You should do pro. Yeah. Make sure it's pro.

[59:58] Yeah, bro. All right, let's fire it. [1:00:01] Thank you. [1:00:02] Okay. Okay. So it's, it's doing some of the, um, [1:00:06] It's saying, okay, we're going to navigate to the URL. It did that. It's going to extract the main ideas from each article. And then it says, I apologize. I cannot directly download content from external websites. See, this is exactly what I'm talking about with the risk thing is perplexes a huge startup. Now there are all eyes on them. They have to make sure that it works for everyone and that they don't get sued. [1:00:28] And that means that they have to limit, like the edge of the product that they can build is not the edge of the technology. It's the edge of what they legally are allowed to do and like what they think can work for like the greatest number of people. And because you're, [1:00:48] You know, I don't know how many people are you now? [1:00:50] We are a very small team. So we have basically, it's Cardi and I and another three engineers. We have a few contractors for some front-end type work. You're five people. So what you can do, which is great, and what you can do with five people is just so... [1:01:04] much different and it's, it would not get easier if you had a hundred, it would get harder. I think that's a really important thing that people miss about this type of product. [1:01:13] Yeah, and that's also because I think perplexity's focus is really kind of like replacing Google, right? It's the answer engine versus the question, right? Which I think is a great angle. Our focus is not that. Our focus is like what you have, which is I have all these different kinds of documents here, and I need to manipulate and create new documents from it. And usually what that means is you have to be really good, like you have to have tools that are really good at fetching,

[1:01:41] Bar scene. [1:01:42] manipulating creating documents so it's a kind of like a different focus as well [1:01:46] Yeah, totally. Well, this is really, really cool. I'm really glad we got a chance to chat. Before we go, if people want to check out what you're working on and just check out your stuff personally, where can they find you on the internet? [1:02:00] Yeah, so our website is myaidrive.com. That's basically the app that we've been using here during the show. If you want to use, if you have ChatGPT Plus or Teams, you can see our GPT, which is in the productivity category. You can find me on X as well. And I think my handle is at Vicente S, so V-I-C-E. [1:02:23] in [1:02:24] T-E-S. That's my handle on X. And, you know, it's awesome to be able to discuss this kind of stuff with you, Dan. Thank you so much. A true pleasure. [1:02:33] Likewise. [1:03:03] that will leave you on the edge of your seat. [1:03:05] craving for more it's not just a show it's a journey into the future with dan shipper as the captain of the spaceship

[1:03:12] So do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life. [1:03:18] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.