AI Agents, VC Insights on AI, and Building in Public, with Yohei Nakajima, Creator of BabyAGI

People are just doing what ChatGPT is telling them to do. Could you just remove the person from that equation? Was the question I asked myself. And the challenge I posed myself was, all right, let's prototype an autonomous startup founder this weekend.

Which is an insane sentence, by the way, but yes, carry on.

And then I was like, oh, make the world a better place. I was like, and then just started building a list and like started thinking about ways to make the better place. And I was like, oh, okay, I guess this could be more than a startup founder. I think the next one I did was Build as many paperclips as possible.

Canonical.

And it started by saying, this is actually kind of dangerous. Let's first create safety protocols, was the first thing it did. And so if you give companies the scaled ability to control what a chatbot might nudge a person to think, there's a lot of responsibility on that to nudge humans in the right direction or not take advantage of it.

Hello and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Lubenz, joined by my co-host Eric Thornberg.

Hello and welcome back to The Cognitive Revolution. My guest today is Yohei Nakajima.

Yohei is a venture capitalist, founder of Untapped Capital, who's become best known over the last year for what he has personally built. In public, originally mostly for himself, but ultimately inspiring tens of thousands with the potential of AI automation and agents. Yohei unreasonably modestly describes himself as lazy, but after this conversation and after watching all of his project releases, I think his defining qualities are really curiosity, playfulness, and a love of exploration. Over the course of 70 public releases, he's helped the entire AI community get a glimpse of the sort of highly personalized AI-powered conveniences that seem destined to raise living standards for all. And as much and perhaps more than any other creator or builder that I follow, it's always been very clear that he is having a ton of fun along the way. We cover a lot of ground in this conversation, starting with his recent TED AI talk, Then running through the many apps that he's built and how he goes about it before finally digging into how he thinks about the AI space as an investor. The first half of this conversation is a great study for any aspiring AI operations specialists, especially considering what a key role no-code proficiency has played in Yohei's journey. And I personally found a lot of value in his answers to my rapid-fire investment framework questions in the second half. Overall though, if there's one thing that everyone should take away from Yohei, it's the way that he's delighted in the process of incorporating AI into his life in a way that serves his own idiosyncratic and ever-evolving purposes. If you find value in the show and want to help it grow, I'd suggest sending this episode to a person you know who tried ChatGPT and still says they couldn't figure out what they'd actually use it for. An Apple or Spotify review would be helpful too. And of course, you can always reach out to us for any reason at tcr@turpentine.co or by DMing me on your favorite social media platform. Now, here's my conversation with Yohei Nakajima, venture capitalist and prolific in-public AI builder. Yohei Nakajima, welcome to The Cognitive Revolution.

Thank you.

Very excited to have you here. You have been one of my favorite Twitter follows for the last year or more. And I think you probably really don't need much of an introduction to our AI-obsessed audience, but it has been fun to follow your progress as you've really gone down the rabbit hole of building all sorts of AI projects while also continuing to run your VC firm. And increasingly, I understand those are kind of bleeding together as you're automating more and more of the processes that the firm does. So I want to kind of get into all of that and understand your perspective on building with AI, investing in the AI arena right now. But for starters, I understand that you just are pretty fresh off of a TED Talk at the TED AI event in San Francisco.

Yeah, that was quite an experience.

So tell me about it. And, you know, I don't think we'll have access to the video for a while, but perhaps you can kind of share your message.

It should be up soon, so I wouldn't be surprised if it's in the next few days. But yeah, it was a pretty cool, wild experience. I mean, I remember when the TED AI organizers reached out asking if I was interested in talking, and I'd been such a big TED fan forever, so I was so excited.

It's an easy yes?

Yeah, I was like, immediately yes. And I'm pretty sure I like sat down like a day later and just like drafted out like a version 1 of my speech as like just a thought exercise. And I actually really liked that. So I just kind of— I used like 70% of that directly, um, in my talk. But, uh, what was wild for me, it was really more the speaking experience. Like, I don't know when the last time I memorized a 9-minute monologue was. So, so there was a lot of practice compared to other public speaking. But, um, the, the experience was fun. I think all the speakers too. I mean, we don't get that opportunity to like prep And like practice a short talk. So all the speakers were kind of all, you know, in their like zone practicing. And, and it was really cool to kind of all be in the back speaker room, like pacing around, like trying to remember a talk, like all a little bit nervous and kind of bonding through that experience. And I was completely fanboying over like, I mean, it was like Andrew Ng and Stephen Wolfram and, you know, Jim from the Voyager paper, June from Stanford Smallville, like all my favorite, you know, AI follows were all kind of there. Harrison Chase, Amjad from Replit, Sarah Guo from Conviction. So it was a really, really fun, just brainy, nerdy few days.

Yeah, it sounds awesome. Was the audience an AI-obsessed audience? I mean, typically at TED, it's super diverse and then people kind of get exposed to all these, I understand it as often pretty new ideas, where part of the value is that people are hearing from people they might not always hear from. And obviously for the speakers, it's a high-value audience. Was this still that kind of audience or was it like a more intensive AI audience?

It was, I'd say, like somewhere in between, right? Compared to like a TED audience, there was a lot more AI enthusiasts, but compared to like an AI event, there was a lot more kind of TED enthusiasts. Like there are some people who just love TED, who've been going to TED. Not surprisingly, San Francisco has a pretty large group of those folks, and this was the first TED in San Francisco, so it definitely did attract a pretty diverse crowd. And I mean, right now, if you're in SF and you're interested in TED, the likelihood that you're interested in AI is, I think, pretty high. So yeah, it was different from a lot of my other AI events to some extent, for sure.

It's an interesting thought experiment for me because I basically only speak to an AI-obsessed audience. How did you think about the challenge of trying to say something substantive enough to be of value to those that are deep in it, but also accessible enough to those that are maybe newer.

Yeah, TED is interesting in that their main audience is almost more online than in person. Obviously, the event is huge, but they get so many viewerships on, so they really optimize for kind of production quality. And then, when I was thinking about what to talk about, I was thinking, okay, it's probably one of, you know, going to be one of my more watched videos. I want to appeal to a wide audience. So I ended up going for a very like broad theme of identity and then kind of tied my work from Baby AGI into identity, into kind of collective conscious and the opportunity for AI to increase understanding amongst people. So that was the direction I went.

Yeah, that's interesting. Definitely see a lot of potential for that, actually. Going back to my experience as a GPT-4 red teamer, one of the more interesting experiments that I look back on now, and of course now we can just continue building this sort of stuff, but was mediation between neighbors. I played the role of 2 hostile neighbors and asked GPT-4 to play the role of mediator between us and also just tried a bunch of these little simulations.

One was like a group workout thread where GPT-4 was basically supposed to facilitate and kind of encourage us. And another one was tech support for my 90-year-old grandmother who has an iPhone and calls me up when she can't find the email she wants to find or whatever.

And I do find it's just so striking how empathetic it can be and kind of understanding, and it can pick up on And it's great, it's great at translating ideas into a context that someone else might understand from a different background. You know, I mean, even translating language, but not just language, but it can almost translate culture to some extent too. Um, so if used correctly, I think it can definitely help people better understand each other. Both in the usage of AI, right? Or you can use AI to help 2 people communicate with each other, or just as like an individual talking, using ChatGPT, you can just like decide to try to better understand somebody and use ChatGPT to help you do that. But also in the development too, right? I mean, kind of tying it back to Baby AGI, right? A lot of Baby AGI, I'm trying to get it to work better. And my general ideation is like looking at Baby AGI do something, and then if it's not doing something the way I would do it, think about what do I need to adjust so it would act more like me. So it's a very, a lot of self-reflection as I'm trying to build Baby AGI. It's like just trying to close the gap between what I'm watching and what I would do. So I think both in the usage and development. we better understand ourselves and our cognitive process.

I do think that's becoming more and more striking all the time. I definitely want to unpack and get into all the good baby AGI stuff in just a second, but it's been a striking theme in recent research that a lot of the patterns that the latest projects are demonstrating are looking more and more human-like.

And I'm someone who's pretty resistant, borderline hostile to AI-human analogies because I think it's too easy to mislead ourselves and confuse ourselves with them. But some of these recent things, I'm like, boy, that's starting to look pretty similar to what I understand our own architecture to be.

If you look at the word AI, artificial intelligence, and assuming when we talk about intelligence, we're talking about human intelligence, then it is natural for advancement of AI to start looking more like human. I think A large part of AI is trying to map our understanding of our cognitive processes onto a software stack to some extent.

Do we have to wait for the video or can you give us a little bit more about the vision for the improved understanding future that you outlined at TED?

I went more for a narrative that weaved together ideas. I talked about the development process of baby AGI being self-reflective. I talked about identities being less internal, but more our many roles amongst the many different groups of people that we're part of, right? Whether, you know, couple, country, family, all that kind of stuff. So, all the different roles make up who we are, which kind of led into the idea of how our identities are actually to some extent overlapping because we have shared identities that we both feel like we're part of. And you have that. So, we are complicated is kind of where I think we're on the middle point. Yeah, touched on language and how it's linear compared to our parallel world and how it's limiting and just understanding that, kind of setting that context. And then jumping to anthills and how they act like neural networks and how the anthill has a better understanding of the colony's environment and intent than a single ant. And perhaps suggesting that that might imply that I too am part of something that's just really complicated that I would never fully understand. Which led to talking about the journey of self-discovery as a continuous process as opposed to a destination, because you're never going to fully understand yourself because we're just too complicated. It's what I was trying to get to. And then, um, and because our identities are overlapping, the journey to self-discovery is a shared journey. And then I talk about the opportunity for AI to help strengthen understanding, collaboration, uh, and then end with a hurrah, hurrah, let's, let's Let's collaborate. Let's build cool stuff. Let's embrace the fusion of AI and our shared spirit for a better future. It was a fun talk. It was very much more narrative, slightly confusing on purpose, and inspirational at the end. That's what I was going for.

Well, there's nothing like the modern AI moment to create some confusion. So I think in some sense, you'd be doing the audience a disservice to suggest that you or anyone else has it all figured out.

I think accepting confusion is an important part.

Yeah, certainly. So maybe we'll circle back to some of these kind of super big picture questions a little bit later. But for the moment, I'd love to get a little bit more into some of your projects. Obviously, the project that you're best known for is this Baby AGI project. And I've been following you for some time when that dropped and certainly know that you'd probably put out 20 different things before—

I think I'm at like 80. I was at AI project number 70, which was BBAGI.

Wow, that's amazing. So maybe even start earlier than that. I guess you kind of just catch the bug and this is what you do, right?

It'll be helpful to even go back before AI. I've always been a tinkerer. My dad was an engineer and he bought me books, so I did start playing with code in high school. I never became a developer, but I always enjoyed it. And so I always found myself just like hearing about something and just spending a really short, tight amount of time just digging in. Like, I remember when Rails, Ruby on Rails picked up, you know, whenever that was, it was like really hot. I would just, I spent a Christmas break learning Ruby on Rails and that was just my like hobby project. And then at a couple of other places I've worked at like Techstars and Scrum Ventures, I kept building, you know, internal tools almost for myself. So I'd have like a Google Sheet script our Google Sheet with a ton of scripts in there, just kind of automating like data entry and like connecting a Google Sheet to a Crunchbase API, an email search API, all that kind of stuff. I was already building at Techstars and I kind of rebuilt it at Scrum. So I've always been just a build stuff to be more efficient guy. So when I started our fund, I knew from day one that I wanted a custom CRM because I had all these workflows in mind that I've built at other firms. And I wanted a CRM that was just like that, all that was baked in. Interestingly, I actually initially tried to code it myself, and I was using PHP and MySQL because that's kind of what I knew. And I quickly realized that is not where a general partner should be spending time. This was in 2020 when like Webflow and Airtable, they were all kind of like hitting that kind of growth, they're all hitting growth level companies. So, so I decided I want to learn about no-code and just scrap my code and just decided to build what I was trying to build with code on Airtable. And so that was my first kind of foray into building in public. It was pandemic hit. I was starting a fund. It was the first time I wasn't working for anybody. So as I started building, I just started tweeting about it. It was mostly just refreshing that I could, because I always thought I was building cool stuff, but I wasn't allowed to share it. And also pandemic, right? I wasn't seeing anyone. So I was just lonely building at home. And the result of that was I got a lot of no-code deal flow. A whole bunch of no-code founders reached out, a whole bunch of VCs that knew me, saw me as the no-code guy and sent me all their no-code deals. This is an awesome hack. And then kind of organically, the same thing happened in Web3 where I just found myself interested in NFTs, launched an NFT project and got a whole bunch of Web3 deal flow, met a whole bunch of Web3 VCs. And I guess to some extent, AI was the same. It was more organic. I came across the OpenAI API in August. And then as soon as I started playing with it, I just was obsessed. And that's when I started building one or two little hacks a week. Initially, it was no-code. And then eventually when I realized that I could get ChatGPT to write code, I switched to kind of coding code. And then one of the coding projects ended up being BabyAGI.

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You've got your own little recursive self-improvement, uh, you know, exponential takeoff, uh, phenomenon here.

It's fun to think about starting with code, realizing it wasn't worth my time, switching to no-code, and then like now it's like code is faster.

Yeah, man, it's That has changed dramatically. This is an area actually where I kind of— I don't know if I struggle or maybe I just don't even have the right mindset to even begin to struggle. But I'm pretty savvy with all these technology things, certainly all about the AI these days, but even before that too. Certainly I can make things work in a no-code environment, but I just don't find myself applying it to my own life very much. I don't build, I don't automate workflows for myself. I'll do that for projects or if the customer success team needs something, I can step up and make it happen. But is there, do you have a tip or a perspective or something along these lines that would help me or others that struggle in the same way to identify and develop this as a personal practice? Because I've never caught— I've never managed to catch that wave for myself.

I think, and I'll caveat that, like, to some extent, part of me building automations for myself is fun and exploratory. And so the return on investment isn't just efficiency gains, but also enjoyment and learning. If I was just looking at efficiency gain versus the amount of time I'm spending on building, especially right now, I don't think it would be a great ROI. But then when you layer on enjoyment, learning, deal flow, other stuff, then it does end up—

Getting AI invites.

Yeah, exactly. So the return on investment comes from other parts. But I think that the tip where I started was really just like being lazy. I hate data entry. I hate doing the same thing twice. If I caught myself doing something and I thought, I feel like I shouldn't be doing this, like, can I automate this? It's like kind of where my first thought went. So even like, you know, even using a Google Sheet, a lot of people might, you know, there's a lot of email hunting tools. I use Norbert. So you have a list of names, you need to get all their email addresses. You could sit there and copy paste names from a Google Sheet into a web form and then copy paste output back into a Google Sheet. But like, as soon as I get to number 5, I'm like, ah, I don't want to do this anymore. And like, even if it takes me a minute, like depending on how long the list is, I'm like, how long would it take me to Uh, you know, build a script that would automate this. And then maybe I'll spend 5 minutes building a script and then just like pressing go and then just watching the script do all the work for me. And like, I remember the first time I did something like that, I was just like, oh my God, this time I'm gonna do everything. And then once you start building those, then you have these little sheets, you know, like little code that you can reuse. So it becomes more and more efficient the more you do it because I'm like, oh, okay. I need to scrape, you know, 1,000 websites. I already did that before. Let me just go find that project, pull that script, and adjust it so I can use it for this project. And so they do stack on top of each other. And at this point, I feel like most of my idea, you know, a lot of my ideas do end up basically pulling like small bits from past projects. So it just becomes more and more efficient, I guess.

Yeah, that's interesting. I guess I have to develop that practice. Maybe I in some ways have like a little too high of a tolerance for pain and just Power through more of that stuff than I should.

And then especially now, I think now it's a little bit different. Now there's a part of me that has a whole bunch of curiosity-based projects. When I ask myself, oh, I wonder if I could get DALL-E to do this. Last night I was wondering if DALL-E could do 360 images. I asked ChatGPT, I just asked it if it could do it. When I have a question about whether or not GPT-4 or DALL-E can do something, till I try it and post about it is usually like 12 hours. If I think about it in the morning, I'll do it during my lunch break. If I think about it in the afternoon, I'll do it at night after I get my kids down. And so I just have a really fast curiosity to— when I think about it, I'll just open up ChatGPT and ask the question and then I'll get to it and fix it when I have the time, but I'll start the project immediately.

Yeah, the modern language models are An unbelievable boon for that.

They're so fun. Yeah.

So you had 70 projects leading up to and perhaps including Baby AGI. Are they all just kind of retired at this point? How many of them would you say continue to play a role in your life? I think that's also kind of an important calibration for people who may not understand just how much of this stuff is kind of spun up, but also eventually left behind. But I don't know how it is for you.

Yeah, I mean, I'd say it's hard to say because some of the projects do stack. When I say 70 projects, some of them might be like improvements on an existing project to some extent, right? So like my investment memo, like my AI analysts that are doing a lot of research for me, there's probably, you know, there are probably 15 projects that have stacked on top of each other where the first version was no-code, but it was slower. And then like I rebuilt it on code and then I added kind of API integrations, right? But I would use, you know, So they stack on top. I mean, I think probably 20 to 30% of the projects I actively use. So probably I think like 70 to 80% get to some extent retired. But of the retired percent, of the 70, 80%, maybe 20 to 40% are kind of just ghost mode active. So like people can still use it. I still sometimes come back and play with it and maybe I'll come back to improve on it later. But you see, 50% to 60% get retired.

Yeah.

You mentioned Amjad from the Today I event. He was an early guest on our podcast. And I also think about Flow from Lindy in this context, just the notion of single use or— single use would be even more extreme, but kind of disposable software or single use software. is definitely something I'm increasingly playing with these days. It sounds like you build a little bit more to last than that.

I definitely do single use though. Like, I remember even like, I mean, going back to the just trying to automate everything when we saw that our home was assessed really high one year and I was like, okay, I want to contest this. I just went to ChatGPT and said, okay, I want to contest my home assessment price, walk me through it. And it just led me out step by step and I was like, okay, go find some comps. I was like, I'll go find some comps. And then eventually it even wrote software, like wrote code to calculate adjusted price of all my comps. And then eventually just wrote my whole assessment or the report that I ended up sending in and it actually totally worked. But that was one-time use, right?

Take us then to the baby AGI moment. I mean, there's a lot of interesting angles to this. It was one of the first, if not the first, of a small Cambrian explosion of its own of projects. I guess I would describe them as putting the language model in the loop.

Yeah, basically.

Tell us how you started that project and obviously it's continued to evolve.

It was actually in March. It technically wasn't the first, but it was the first popular one and definitely kicked off the whole trend. I was looking at HustleGPT. I don't know if you remember that, but people were using ChatGPT as their co-founder. And there was like a whole community and Discord of people like starting with 100 and growing it. And I was just fascinated by that idea. And I wanted to do it myself, but I thought I'm too busy. I should not do that. But when I was, but as I watched people do it on Twitter, they were just doing what ChatGPT told them to do. So I was like, I feel like if that's all, you know, people are just doing what ChatGPT is telling them to do, could you just remove the person from that equation? Was the question I asked myself. And the challenge I posed myself was, all right, let's prototype an autonomous startup founder this weekend.

Which is an insane sentence, by the way. But yes, carry on.

Well, and then I thought, okay, how do I work? I wake up in the morning, I look at my task list, I start at the top and I go through them one by one. And then at the end of the day, you know, and as I finish tasks, as I get emails, I add new tasks to my task list. And then every once in a while, I reprioritize my tasks. And that was pretty much like, okay, that's basically what a founder probably does too. So let's just start there. So it was the start with an objective, it would create a task list, and then it would just start executing the task list. Anytime a task was finished, you know, send it to OpenAI to see if it wants to create any more tasks. Anytime there was a new task, uh, send it to a prior reprioritization agent that would just like look at all the tasks. Reorder them and then send it back into the execution queue. So it was really just 3 prompts looped around in a while true loop. And when I asked it to like, you know, build a mobile health company, it would just start kind of start thinking through. And then I think somewhere in there, I also did a stored at least the task names as in Pinecone embedded. And then when it was creating new tasks, it would look at previous tasks that, you know, the most similar tasks it had created. So it created that kind of randomness and kept it at least initially from like just repeating the same tasks over and over again. But yeah, when I asked it to start a startup, it just like started thinking through business model, fundraising strategy, hiring strategy, like, you know, let's research some competitors. And it was mostly language. I mean, it was all language because it was just OpenAI, but it was thinking through all the different aspects of building a startup. And then I posted that video online kind of casually, like, whoa, if I press go, it just keeps going. And that's when that video went wild. And I think it quickly went to like a million impressions, people asking, well, can this do more than be a startup founder? And then I was like, oh, make the world a better place. And I just started building a list and started thinking about ways to make the world a better place. And I was like, oh, okay, I guess this could be more than a startup founder. I think the next one I did was build as many paperclips as possible.

Canonical.

And it started by saying, this is actually kind of dangerous. Let's first create safety protocols, was the first thing it did. And then somebody tagged Yud, who pointed out that, oh, it started with safety protocols. This is better than most big AI companies. And he retweeted that project. Of course, all the doomers saw what I was working on, which Which turned into a whole bunch of doomers and EACC-type people DMing me opposite messages.

So this was just before or just after GPT-4 when you first did this?

This was before GPT-4, I think.

Yeah, I mean, if it was March, it was certainly right around that timeframe. So obviously this set off or helped set off a moment of, oh my God, AI agents. There had been sort of a sense of a big separation between agents and language models, at least for many people, where agents are the kind of thing that DeepMind does and they're very intensively trained. I think of them as hard intelligence. They're extremely good, often superhuman at what they do, but they have these narrow domains and outside of them, they don't do anything. And then obviously the language models are like, I kind of think of that as soft, sort of flabby intelligence for the most part, where It's getting really amazingly good at a lot of things, but it's not really a specialist in anything. But it's the powers in the generality. And I think what you and others kind of showed is that you can get this agentic behavior out of a language model very easily by just prompting it. And as you said, just 3 prompts, you can kind of put it into a cycle where it can kind of keep iterating on its own work and maintaining its own state and updating its own plans and increasingly with tools and affordances of all sorts, actually accomplishing to some degree things in the world. And then everybody was like, oh my God, we're going to have AI agents and they're going to be all over the place before we know it. And then that kind of slowed down for a minute. And I'd love to get your take on kind of the post— obviously there's this huge surge in interest in GitHub stars galore, It seems like you must have seen something early there where some others that had a viral hit raised money or kind of were like, this is going to be a company. You did not do that, as far as I understand, and have kind of kept it as a hobby. So for starters, I wonder how you decided in that moment where the world was beating a path to your door that this was not something you were going to go all in on, even though you probably could have easily pivoted your whole life into that project.

So I think it's pointing out that BabyAGI was 105 lines of code. So it was really simple and it was really more like, hey, here's a simple framework to start working on this idea, was like in my mind was how I open sourced it. It wasn't like— there was clearly a lot of work to do because it was just a large language model. But obviously I definitely fueled the fire in making sure it went wide and spread because I noticed that it was going viral. But yeah, as an investor, well, yeah, that's a good question. I did explore turning it into a company. I talked to a couple of VCs, I talked to a couple of studios. The one thing I knew was that I didn't want to be the CEO of a company while trying to run a venture fund. And to some extent, that could be from PTSD I had when I launched an NFT collection while I was running a venture fund. And it was just like running, you know, I was running an NFT collection called Pixel Bees to learn about Web3. And I was trying to like be the CEO while, you know, basically run a project while doing that. Yeah. And it was just too much. And I was just like, I don't want to do 2 things at once. So, with BabyAGI, I kind of poked around to see if somebody would want to run it. But really, I quickly saw a whole bunch of different people quickly building things that were just much more powerful than what I'd shared, right? Because I shared 105 lines of code and then I went back to work running my VC fund. By the end of the next week, there was multiple dozen projects where teams of 2 or 3 people who are much better developers than I was, than I was building on top of this framework to build really cool stuff. So for me, I actually saw it almost as like, just like as an investor, that's like the ideal opportunity to have all these really cool companies starting and they're all kind of starting to go in different directions. Some were in gaming, some were in research, some was in financial areas. So I thought, I'll keep working on BabyAGI because it's a good way to connect with the community. But ultimately, I want to invest in these different companies. I guess I didn't have a conviction in this space and I like the idea of being able to work with multiple companies tackling different niches.

If nothing else, it's a powerful object lesson that you can take forward with you as a VC, that certainly in this AI wave, the tumble and the kind of things crashing over one another is extremely rapid. So now, and I definitely want to get more into your outlook as an investor in a minute as well, but the project has continued to evolve.

I've noticed a couple of interesting things about it. One is that instead of kind of having a canonical version, you sort of seem to be just forking off these kind of different mods. And the latest is Baby Fox AGI.

It has UI for the first time.

So yeah, and it's distributed remarkably.

People are very confused by the GitHub. So what happened was after I released Baby AGI, people wanted to contribute. That was great. And I pulled like 2 in and I realized, okay, reviewing pull requests is not where I should be spending my time. I don't even understand the code that's being submitted. And I had community members offering help. So I said, okay, go for it. Like, let's pull in stuff that sounds good. And we started pulling stuff. And then when I sat down to want to work on it again, I quickly realized I didn't like reading other people's code. And so I just took my original version and modded it as like my next playtime. And then when I showed it to, you know, some of the community members, they're like, okay, why don't we just create a classic folder and you can just stick it in there? And then that's how it started. And that one I called Baby-B AGI. And then I just at that point just kept building on top of Baby-B, turned into Baby-Cat. So I just stuck it back in the classic folder. And for me, I think one, just having the animal names was the fun way to keep track of different projects because I don't remember numbers. So if I call them Baby-AGI 1.0, 1.2, I wouldn't remember which one I had to go back to find the idea. So having animal names is a way to just make them easier for me to remember. And I guess to some extent, so far all the animals have also been animals that are represented in the Pixel Beast collection. So there's a weird tie-in there that was just for fun.

So what would you say you've learned over these kind of 6 or so iterations that you've done personally? There are obviously a lot of development efforts going on in a lot of different places to try to make agents work. You've implemented some new strategies, But I wonder what your upshot is right now in terms of the prospect for agents and what are the things that have come online over the last 6 months that are really working?

I think there are a lot of different approaches to take them. And I think to some extent, there is a little bit of the specialized agent versus full generalized agent as slightly different things to focus on. The fully generalized agent, I think, will just take longer to do. So as a startup, it's to some extent slightly riskier because it might be harder to get, you know, harder to find the, you know, ideal customer. It might be harder to, to get it to a point where like people are happy with it every time they use it. And then the more niche kind of, you know, ones that start niche probably are easier to monetize, easier to find an ideal customer, easier to build for them. So as a startup, what I'm seeing more, I think, especially the ones that have revenue, are more specialized, starting with a specific customer type and a problem type and working there. And I think that makes sense. And I do believe they will over time generalize more and more, and that's how it seems like they're building. Having the luxury of not needing to monetize BabyAGI, I've decided to just like slowly chip away at the generalized Asian route, even if it takes longer. And I benefit from seeing what everyone else is doing all the time because I'm watching everybody else. So that's kind of my design process. And then I see BabyAGI, what it has been for me and most of my coding has been just a way for me to share ideas with the greater AI and innovation community. So when I think about what to add next, I try, you know, I'm probably like, I don't get excited about the idea of adding something that I've seen 3 other agents do. So I'm usually like purposefully looking for like a novel idea to test is how you think about it. So like with Baby Fox AGI, the novel idea on the UI was, you know, being able to run multiple tasks at once, separating out tasks from chat to make it seem faster. And it does feel faster than ChatGPT when I use it. So yeah, that's kind of my thought process and it's a fun way to collaborate with the greater AI agent development community.

Would you say that the generalist agent model has hit a point where it provides actual practical utility for you personally yet, or is it still more for fun?

I think it is. I mean, I use it all the time, mostly for research. But now that it's connected to not just the web, but like I have a Crunchbase API, so it's connected to Crunchbase, it's connected to my CRM, which is Airtable. So now when I ask it to do research, I can have it look for people who live in San Diego and then like research what they're working on now and like put together a report for me.

Give me that in like real detail. So you sit down, And I noticed this is distributed on Replit, which I love. And we've done a couple of, I've mentioned Amjad already. We did a whole series on Replit and I think it really has a chance to be—

Oh, I have to say something about Replit. My tech stack is starting on ChatGPT mobile because that's usually, I'm like out and about is where I start coding. I copy paste it into Replit to get it running.

Mobile app.

Yeah. Mobile app usually.

The best developer mobile app I've ever seen.

Yeah. And then when it's working, I will go on the computer and copy paste my Replit code into the GitHub UI because I still don't know how to use Git and then open source it.

That's incredible. I think that is also a really good— there's a guy on Twitter also that you probably— I'm sure you see plenty of his stuff. Levels.io is his—

Yeah, I love his style. Yeah, like the one file PHP companies.

It's just a good reminder that you like don't have to master every professional tool to build useful stuff. And I do think that is something that occasionally people need to be reminded of. But so in the actual practical use, okay, so you've coded something up and now you kind of give a short version of the have it do research for me. I'd love to hear that in a little bit more detail. To be totally honest, I have not, outside of ChatGPT, where I have had some pretty agentic experiences with Code Interpreter, where it will explore a dataset for me and kind of iterate on code to get the dataset into some workable form. That's like the one agent experience I've had where I'm like, wow, that thing actually kind of did what I would do, where it ran into a problem and tried a couple of things to poke at it and eventually got to some sort of breakthrough. But I really haven't achieved that with anything else outside of code interpreter. So how do you set it up or talk to it just the right way to get that to actually happen for you on your own little projects?

I mean, I think the one I've used most, because this one's easy to explain too. Also, my versions aren't that stable. So actually sometimes we'll use a different tool called BabyAGI UI that someone else separately created on, I think, Type.js, but it's actually more stable. So I use that one a lot for research. Um, for web research. But I had a lot of companies reach out to me after BabyAGI wanting to know about, you know, how they should be using large language models. This— and one of my very com— anytime I'm jumping on a company, uh, call with a big company that's well known public, I will always ask BabyAGI to research the company, uh, find— research their business units, research their revenue drivers, research their cost drivers, And for each business unit, come up with 3 strategies that leverage large language models that would be impactful to the business. And then I just step away and it takes a few minutes to do because it's a lot of web search, a lot of calls. But when I sit down, you know, if they have 7 business units, it's like this company, 7 business units, 3 ideas each. That's 21 ideas that's prepared for me. And as I jump on a call, I quickly glance through the 21 ideas and I'm fully prepared to like blow them away because I can just pick 3 or 3 to 5 of them and then just expand on it from my own knowledge.

That makes a lot of sense in the context. How reliable do you find it to be? Is it getting all the business units? You know, how often are we kind of missing things? And maybe it doesn't matter in an intro call context where you just want to be like prepared to be impressive, but—

Actually, I think that your question brings up a good point. A lot of people ask me, where should we start with using AI? And what I tell them is start using AI and automations in things that you wish you were doing but don't have time to do. Not the things that you are doing and are critical to. Because at first it's not going to be like doing it yourself is going to be better. And replacing a task that's being done by a person with AI to get worse output is not how you should start using AI. And it's not where you should start experimenting and messing around. Like you have a good human workflow. So like doing deep research into somebody right before you jump on a meeting is something I wish I had time to, but I usually don't. So for me, if it misses a business unit or if it comes up with ideas that aren't good, I can easily like, you know, it's better than not having it.

Don't say the ones that aren't good. Yeah.

Yeah, yeah, just don't say the ones that aren't good. And if it misses one and I notice it during the call, I can probably come up with any ideas on my own. But it's more just like it takes me, you know, a 10-second prompt and a 30-second review to do what would have taken me, you know, 15 minutes of research ahead of time.

So this is actually maybe a perfect intersection. Things that people wish they were doing that they're not doing, or kind of feel like they should be doing and just can't get around to, I think intersects very naturally with something that I understand was kind of a unique approach that you had originally with the VC fund, which was sourcing your deal flow with outbound. So that's fascinating because I think almost every company, almost every organization, Would be like, should I be doing more outbound? Yeah, probably, right? Everybody needs more sales. Everybody has kind of at least experimented with like, yeah, I could go find a ton of people on LinkedIn and message them, but the hit rate's really low and obviously it can be time consuming. Same deal on recruiting. If you're trying to source candidates, man, would it be nice if I could reach out to 10,000 candidates? Yeah, but who has time for that, right? So Take people through that a little bit on, from your expertise originally in outbound, and then now everybody's kind of sitting there thinking, yeah, I should be doing more of this. How would you coach them on how to get started with that near universal problem?

I mean, one, to have an outbound funnel, or I mean, even if it's a Google Sheet, just have a list where you can put people's names if you want to reach out to them. And that's the most important thing is like just have a place you can put it. Once you have that place, if you want to, depending on where you're stuck, if you're having trouble getting people's names on there, then you could connect, you know, a bookmarking tool like Pocket to it using Zapier so that if you come across somebody's LinkedIn and you want to reach out to them, you can just press Pocket and it would take, you know, bookmark it in Pocket and then you connect Pocket to your Google Sheet or Airtable. And it would just start adding people to it. And so like that work, that's the let's get stuff into my funnel really quickly is use like a bookmarking tool of sorts that you can just, and I use Pocket for anytime I find a startup URL, I use Pocket. It's on my Chrome, it's on my mobile, so I can use it from anywhere. So that's kind of the step one is just like get to a point where you have a list and your list is growing. Once you have that, then you need to figure out how you're going to reach out to them. If you are going to reach out to them, I'm using a— I use a tool called Voila Norbert, V-O-I-L-A N-O-R-B-E-R-T. But there's a couple of many other tools to get email addresses, whether it's Clearbit, Hunter. So you need to figure out how you're going to reach out to them. So in my case, if I have their name and domain name, or, you know, name and domain name, Norbert will find their email address for me. So then now you have a list of people and all their email addresses. And then I guess if you don't want to automatically send an email, but you can build a template, you know, you could use something like Zapier. Where you have a checkbox column, or if you— when you check the column, if there's an email address, it'll kick off a Zap and it'll go to a template, and then it can send them an email. And if you want to personalize it more, you can just create a new column in your Google Sheet where you would type in the personalized message and then just pull that into the email message. And you can just do that directly in Zapier. And if you just do what I did, what I said, then now you have— now you can just go through, go look at people's LinkedIn Click Pocket, which will pull their name and maybe company name. And if it's hooked up correctly, it'll automatically find their email. And you can just go through, type in a personalized message, click check, and it would automatically send an email from your email address to them.

Interesting that you're not automating, if I understand correctly there, the personalization step.

Yeah, I guess if you wanted to use AI to personalize it, then you would take whatever data you were fed in and you can just add an OpenAI step in Zapier to like send the personalized info to OpenAI and say, can you write the personalized message? Actually, that would make more sense. That

caught my ear because I was thinking like maybe you didn't trust it to do that or—

I actually write all my outbound emails myself. I did a lot of A/B testing at Techstars on outbound emails and I found that, you know, 2 to 3 sentence emails just perform the best. And so, when I'm like reaching out to somebody, I just send them like 2 to 3 sentences that's directly to the point, that's like personalized. And my bottleneck is my time. Like, I can send a lot of outbound emails. Like, but I just don't have the time to take all the meetings. So that's not where my bottleneck is. So I guess that's probably one of the reasons I write my emails.

Interesting. Yeah, that'd be different for most sales organizations where you probably have a pretty high accept rate on the outbound to the meeting.

Yeah, so yeah, that's probably it. It's like if I send an outbound email, I'm probably gonna get the meeting. So I gotta, if anything, my issue is if I send too many outbound emails at once, my calendar gets too full.

So where would people start today if they want to get into this kind of agents stuff. Obviously, a lot of different projects out there. I saw you tweeted the other day too that you were cited in a new paper that introduced a whole framework for agents. I can imagine you might feel like that's the place to start, or you might feel like, yeah, that's kind of overbuilt, maybe start with something simpler and then graduate to something more full featured.

But what's your advice on just day one with agents for a, let's say, I'll caveat that, like, I haven't gone through, like, the looking at, like, where would I start since the agent space started because I heard I, you know, I was already in it. So, so to some extent, I don't, I'm not fully caught up to date. Obviously AutoGPT, if you haven't heard of it, like really popular project. Again, I haven't looked at it recently, but, uh, that one uses kind of the React style, um, agent where it's kind of doing one task and thinking through. So that's, you know, I think it's one worth looking at. I think there's a lot of integrations. There's probably a lot to learn. I'm personally a big fan of LangChain, Harrison, Chase. You can build an agent with LangChain. They also have a plan and execute agent, which Harrison said was inspired by BabyAGI to some extent, where it does generate a task list ahead of time, right? LangChain agents were initially react style, where it would just do one task, think about it, and do the next. But their plan and execute agent will plan tasks ahead of time. And the reason LangChain, I think, would be a good place to start is because there's so many things built into LangChain that if you start just learning how to play with LangChain, you can actually just learn all the things that you can do. And then if there's something you're really interested in, then you can just do it without LangChain and that would be a good next step.

I guess maybe my last kind of big question on the agents before turning over to the investment side of your activity is, are they all going to wake up when the GPT-4V comes online? It seems like so many of the problems to date have been just getting lost in kind of the friction of the web and all this nonsense. HTML is super bloated these days for all kinds of reasons, but the interfaces are meant to be interpreted visually. It seems like GPT-4V is really good at that. Do you think we're on the verge of sort of a threshold effect where all of a sudden they're going to be able to do like 10 times as much?

I mean, to some extent, yes. I don't know, 10 times is whatever vague number. I feel like anytime a new model, especially something like GPT-4V, when that becomes available as an API, there's gonna be so much you can do that you couldn't do. So it is gonna feel like, wow, these agents have 10x. But I would suspect that we're gonna have many, many moments like that. So it's gonna feel like, oh man, they can do so much more. But we just keep feeling like that every time there's a new model or whatnot. Yeah. It's gonna be many of those moments where we're just gonna continue to be impressed. But the reality is we're incredibly complicated. So Getting where the agents are now to being able to generalize, can do anything a human can do, is going to be a really long ways. And to some extent, the further along we get, the further the bar is going to get. As soon as agents can do everything online for us, it's going to be, oh yeah, your agent still can't make coffee for me. And now we're going to be getting into robotics, which is also moving fast right now. So it's all going to merge together. But again, just because GPT-4B is out, it just moves the bar up to the next one.

Is that a reasonable model? And my first gut instinct is like, yeah, it does kind of seem like a reasonable model. I've been working on this on kind of the safety and control side as well, because there's been some recent research that's like, wow, that seems like it gives you a whole order of magnitude improvement on ability to detect bad behavior in models or whatever. And then you're also kind of like, but order of magnitude, if it's really serious, there's still a long way to go. It's still like multiple orders of magnitude needed. On the agent side, it's kind of coming the other direction where baby AGI can maybe do like 1 in a million things that a human could do in its original form. And maybe we're headed for like 1% pretty soon.

And those next 2 orders of magnitude, I wonder how quickly they come. It seems like you don't think they're going to come that fast, but I wouldn't be shocked if we're sitting here.

I think there will be things that go fast. The question, I don't know. And again, this is beyond my technical capability, but I do feel like there's— I mean, like, I'm experimenting what you call the orchestration layer, right? That you're kind of stripping it out of the model and doing it in orchestration. But it does seem like a lot of the stuff, the learning from building in the orchestration layer, can just basically be baked into the model. Again, that's, that's a little bit beyond my technical capability, but I think if we start seeing stuff like that, like models that are building, you know, that can, that can, you know, multimodal, that can Do a web search from within the model somehow. I don't know if that's even possible, but to decide to do a web search and then write new skills and store it, the stuff that we're orchestrating, if that can be done within the model, I do think that's going to move stuff fast. But again, I don't know if and when that's possible.

It does seem like it's at least somewhat possible and somewhat coming soon. But yeah, we'll have to see just how fast. So let's turn then to the investment angle. And you could either take this from a sort of what you're looking for or maybe more of an advice to founders. But I'm really interested in just kind of, everybody has this big question, right? Where is the value going to accrue? Is anything defensible? Everything's moving so fast. It's, I think, become pretty fashionable to say the incumbents will probably keep all the value in this wave because they've already got the distribution and it's pretty easy to implement the AI. So what's your take?

I think at a high level, we can all agree that a lot of value is going to be created. Compared to previous tech innovations, incumbents will get a larger share of that value. And that comes from largely noticing that both the C-levels at every large company know they need to do it. And like every, like many engineers are just curious and want to play with it themselves. And I think if you have those 2 things, it's enough to kind of It's a lot more than we've ever seen. It's usually like the engineers are interested in a technology or the C-level wants to do it but the other side doesn't. But you have both in AI. So incumbents will move fast. They will capture value. There's no question about that. That being said, I don't think there's any technological innovation where incumbents are going to capture 100% of the value. It's just a matter of what's the ratio going to be. And because there's so much value to be created, I think there's a ton of value to be captured by startups. compared to even other past technical innovations. Because even if incumbents took 90%, which I don't think they will, of the value created, literally AI is probably 100x bigger in terms of value creation than many technological innovations we've seen. So in that sense, there'll be plenty of value to be accrued by startups. So I think it's still a great time to be investing in startups, but also to look at public markets to some extent, I guess.

I don't know that I've ever heard anybody put it quite that way. Senior leadership at companies and rank-and-file developers Both being inclined to embrace a technology at the same time. I don't know that I've ever heard it described that way, but I do think that is really apt. And I just had a little bit of an experience like this over the last day at Athena. And this is an executive assistant company where I'm the AI advisor. I was just struck in the last 24 hours that talking to founders and then talking to a person who was hired at the company as a software developer, not as an AI role, Both are obsessed with AI and literally reading papers. You don't see people reading work out of academic labs in their professional life super often. And now we have people going way out of role to do it. It is really a remarkable moment. So I think that's a fantastic observation.

And just to even add to that, I think Just like, I mean, to some extent, like agents, just because you have an idea and want to do it doesn't mean you're going to do it well. So there are going to be a lot of large companies who are going to lean in and try to do AI. And again, I don't know what percentage, but there's going to be a percentage of them that are not going to do it well, which means they're going to end up possibly buying a startup, buying from startups. And some of those people who learned about the technology and the industry and tried to build it internally but ran into whatever issues are going to spin off and build their own company. to tackle that market or something like that. So there's still going to be— even if incumbents are leaning in, just because they're leaning in doesn't mean they're going to do it right. And the people who don't do it right, that energy will shift into, I believe, the startup market.

Okay, so I give you super strong marks on your analysis on the incumbent versus startup split. Let's try the layer version of it. You know, people are again like, well, the chips are gonna, you know, make a lot of money. But maybe the applications do, maybe they don't, you know, it's so easy to spin up Maybe it's the tooling, maybe it's the model providers. Do you have a similar thesis on how you think about the layers of the stack?

I guess so to some extent. I do think hardware, just because of scale benefits, there's probably not going to be too many. You're not going to see a world with tons of really, really, really big hardware companies building these chips. I would guess it would be somewhat consolidated. Models, I would say maybe a little bit less consolidated. It still takes a lot of money to build, but we're seeing more and more tooling to like spin up models of your own, open source models, smaller, more specialized models. There's probably going to be some variation, but as far as like the core foundational models, it'll probably stay to some extent consolidated is my guess. Not saying there's no room for new ones, but it is— but as a pre-seed investor, I'm noticing that all of these rounds are know, $100 million kickoff round. So it's one to keep an eye on, but not one I'm diligencing too deeply. Infra is the most confusing one where you have these extremely popular, you know, infrastructure projects that are fully open source and making no money that have raised a lot of money. And you're seeing that and they're clearly adding a ton of— creating a ton of value. But their ability to capture value, I think, is still in question. I'm not saying they can't, but I don't think we've seen them all. I think that it'll be interesting to follow that. And so I think there's— because people aren't sure what's going to happen, to some extent, I think there is opportunity if you can come up with conviction and invest. And if you're right, you'll probably make money on the infra layer. And then the application layer, I think there's going to be a lot of value created. There's going to be a lot of companies. A lot of them are going to be niche-specific use cases that are core AI. And then there's going to be some that barely use AI, but because they use AI, they're going to beat everyone else who doesn't use AI. And I think there's a lot of different business models in the application layer and I don't know which one's going to be the best, but I think a lot of them are good ideas. So one of the ways I'm thinking about it is diversifying, at least in the application layer, the types of business models so that I have some hedging on that as well.

Multiple follow-up questions there, but do you have a taxonomy for the business models?

That's probably a good idea to write down. None specifically, but there's some using LLMs on the backend, for example, to take unstructured data or whatever data around the web to build clean structured data, but then the front end doesn't use that much AI. Those actually have much higher margins because they're just using LLM once to collect the data and they're not using LLMs on every usage. So the margins are higher. So that's an interesting model. That being said, it might be less defensible to some extent because if the unstructured data is public, for example, someone else could in theory collect that structured data as well. The most common one is the wrapper, right, is the you know, which is less defensible from a product standpoint. But if you can ingrain deeply into the user's workflow and be something they need and grow fast enough and get market share, then that market share could be defensible, especially if you can keep building value on top of it that makes the switching cost higher. That's probably the most common model. Scaled services is what I'm looking at. I think the idea that, you know, the reason SaaS is so popular amongst VCs and what is popular is because SaaS is scalable. Software is scalable, but now that we have with LLMs, we can make customer service scalable. We can make sales scalable. We can build companies where it feels like a service to the end user, but on the back end, they're using AI and automations to to scale rapidly. And I think that's an interesting business model, and I'm seeing more of those that kind of hint at that. Those are a couple couple examples. Oh, and then I think one this isn't really a business model, but more so like I do think there's probably room for companies that just use LLMs well internally. And it's not even like, if you look at it from the outside, it's not an AI company, there's no AI in the product, but like, you know, the way the company operates, the way the leadership guides their employees to use LLMs and AI tools is just so efficient that they're just operating as a company, you know, 10 times more efficiently than their competitors.

You know, even down to the local business level, you know, just having a decent chat on your website, you know, that nobody else in your, you know, kind of immediate competitive set has, Huge advantages to some simple stuff these days.

How about the enterprise kind of retail divide? I think you may have even better information on this than I do, but my general sense on the retail side right now is that all these apps are posting really good growth numbers, but typically with pretty poor retention, with maybe few exceptions. And I wonder if that's kind of what you see as well. And if so, does that lead you to think more about trying to invest in enterprise-serving companies?

I think it's a fair generalized observation. I think part of it is because people are interested. If there's 5 legal AI tools and a lawyer is really interested in AI, they might jump in and try all 5, but they're not going to keep using all 5, which means, like, you know, if the average user is trying 5 tools, then, like, there's going to be 20% retention, assuming they end up on tools. So to some extent, retention being low is just because people are interested in actually exploring and trying to find the right tools for themselves. That's also a good thing. I think with— I think, uh, I think it was Sarah who mentioned this in her TED AI talk. Sarah Go, she had a good one too. I'm excited to watch it again when she— when it comes out. But she talked about minimum viable quality for AI tools, and I do think that's a thing, right? It's not just about getting the tool out there but making sure it has quality output. Um, so I think with, uh, with these companies, it's not just about getting distribution but also about ensuring quality because people will switch, people will churn. But to answer your question from a more high level, I do think, yeah, there's the retail, um, is, is that— but then I— one aspect, one kind of theme I'm seeing is more kind of consulting-first enterprise approaches. I mean, I've heard the name Palantir come up in a handful of conversations, probably not surprisingly, but they're kind of doing more stuff there. There's a company called Distill that's like some ex-Palantir guys doing similar. And I think Reca, there's the kind of enterprises want handholding and consulting more, and they have the money to throw down to it. So it actually is an interesting go-to-market right now to start enterprise consulting, to like be able to bring in money, hire good talent, work on really good problems, and then, you know, maybe automate that process a little bit and like decrease cost to target a larger audience as you go, which I think is a It's definitely a good time for consultants in general because there's a lot of people with a lot of questions.

And it is one of these rare moments where, and I'm not a huge believer in consultants in all cases by any means, but there is this just huge knowledge gap where I find so often, I'm sure you have this too, where people will come out of the woodwork and ask me a question and Often it's pretty easy to answer and I can just kind of answer pretty quickly and be like, oh, you want to set up the simplest possible chatbot that has access to your documents?

Like, boom, go to chatbase.co. You'll be up and running in 15 minutes. I've tried 10 of these and this one really is really simple and good. And people are like, oh my God, you're a genius. And I'm like, honestly, there's not even that much genius there. I'll keep— I'm honest. I usually do tell them. But yeah, it's kind of like just a knowledge gap that will close, but for the moment, definitely makes a consultant often pretty valuable.

I had one more. Oh, new things versus kind of old things. I mean, this is another Sarah comment. She said that they're looking at some of these kind of fundamentally new behaviors, like talking to an AI bot all the time. We've had Eugenia from Replica on the show.

Character obviously is like a leader in this space to the point where Facebook is potentially going to try to do their Facebook thing and suddenly ship it to 3 billion people. How much do you find yourself thinking about ways to apply AI to current things versus just fundamentally new experiences or products or ways to spend your time that are only possible now with AI?

I think the latter excites me more, but I spend my time thinking about both equally to some extent. Because to some extent, I think the first one is easier to at least make money in the short run. And it's a fuzzy question because An old thing with AI can look like a very new thing.

Coding, honestly,

going back to one of your things at the top where you said you have all these projects and you go pull out a snippet from whatever, I find myself doing that these days when I'm coding for the purpose, not even so much of me to adapt it, but you may have a better term for this, but I've been calling it coding by analogy. And basically what I'll do is take whatever I have, to GPT-4 and say, hey, here's what I have. It helps if it's working.

Here's what I want. And it is damn good at making that jump. So I think that is a fundamentally new experience. I basically don't code manually anymore almost at all. My fingers barely work.

Especially on mobile Replit, it's hard to copy paste specific lines. So I'm usually copy-pasting whole files back and forth.

Yeah, that's a pretty profound shift, I think, in what that experience looks like.

How about out of your portfolio, anything that you would want to highlight as either, I mean, really examples of any of these things we've talked about, but I'd love to hear kind of some of the things that have sparked enough conviction in you that you've actually invested in them.

Obviously AI is, it was one of my first AI investments. Actually, that was back in 2020 during the no-code time. They had a no-code AI tool, and they built out this incredible tool that can do both kind of traditional AI in terms of predicting columns based on past data, but also LLM tools. But the approach they've taken recently is actually just acting like a service. So they have a data scientist as a service for $1,000 a month, and they have internal people who know how to use their internal tools really well and really efficiently. So from a company perspective, they just work with their data scientists within Obvious.ai who will do all the cleaning of data, all the predictions, all the setting up whatever APIs or whatever you need. But really, it's just team members internally who are good at using a tool they've really robustly built out. So that falls into this kind of scaled services model that they've kind of pivoted into. But I think that approach is really, really clever and they're doing well.

Not to ask you for any numbers that they haven't disclosed, but you got to have pretty serious leverage. I guess it obviously depends on what your SLA is in terms of what service commitments you're making, but $1,000 a month doesn't buy you a big fraction of a data scientist in today's market.

So, you know, kind of sounds like inherently they've got to have one data scientist supporting like at least 20 companies, right?

Probably more than that.

I'm not going to go into specific numbers like you said, but I mean, there's definitely scaled benefits to having, you know, having a platform that can do a lot of the work. I mean, if you ask a data scientist at a slightly old company, I mean, the amount of time they spent on cleaning data and doing stuff that they don't need to do is pretty high. So if you can, if you have built tools to automate that part, then you can definitely be more efficient.

Yeah, that's fascinating. I do think that sounds like a pretty compelling offering at a minimum.

I've spent a lot of time in media. I used to work with the Disney Accelerator. I've consulted for Nintendo a lot. So that's an area I just get kind of, you know, attracted to. I have 2 companies and I guess 3 companies in the media space. I have AugX Labs, That does prompt to video. They started really more around, you know, grabbing media clips from around the web to put them together for you based on a prompt or based on transcribing a video, but they've layered on LLMs so you can kind of just describe a video you want. It'll write a script, it'll create an audio output, it'll chunk it up and find media from online to basically put together a full video for you. Space I know you're familiar with. Spekst is kind of in the podcast slash YouTube space. They started as more closer to kind of, um, what's the one that a lot of people use? Descript. Um, but they've, uh, really layered on clever LLMs. So, um, for all the videos that, you know, and you can have it in Discord or Zoom, you know, they kind of bake— you can bake it into Discord or Zoom to record stuff. It'll do chapter summaries, it'll do takeaways, and you can ask questions directly on the video. More recently, they've expanded to being able to do full libraries. So if you just give it a YouTube channel it'll just go transcribe all the videos. You can ask it a question and it'll give you all the short clips that are most relevant to that question that you can just jump to from anywhere in their library. Um, and also you can just kind of ask general questions against the YouTube library. So that one's pretty powerful. And then they've also built out video editing features on the back end. So the video creators, once the library is connected, can say, hey, can you find a clip about, you know, AI doomerism and like clip out a 10-second clip and then like add our And so just kind of natural language engagement against the whole video library is what they've been building out recently.

It seems like there's a tremendous possibility, although I'm not sure it's necessarily what people want, but the opportunity for sort of semi-dynamically generated content experiences, whether it's games or video, but kind of choose your own adventure style media. Do you think that is going to be a thing?

I do. I thought the Black Mirror one was pretty interesting, especially with LLMs. To some extent, there's a blurred line between a choose-your-own-adventure media experience and a video game. Video games, to some extent, is that, I think. Blurring the line is interesting, and especially from an education angle, if designed correctly, I think it can accelerate somebody's learning if you can keep them both engaged and interested and layering on content at the right level for that person in the right way could really accelerate learning or just, you know, I guess, learning about anything.

One that I noticed in your portfolio is this company Senior Sign. And I don't know if they have an AI component to what they're doing right now. But I love the fact that you invested in a technology company specifically focused on the senior market, because that's been a longstanding thesis for me, especially as demographics shift is underway in some parts of the world and getting underway here in my part of the world as well. It seems like this disconnect between the technology that we have and what seniors actually could benefit from is just so huge.

AI seems like a great way to bridge that gap. Have you been seeing any deals or do you have any kind of ideas as to what might take shape there?

That's fascinating. Seniorstown was more my no-code phase. I mean, the fact that people are onboarding onto senior care centers and like huge paper digital binders today is just mind-blowing to me that that still exists. But yeah, I mean, I think AI can be very powerful there, and I'm excited to see what kind of products people build around that. I think there is some hesitation, I think, from generally from the older people get against technology. So it'll be interesting on the right go-to-market and approach on creating AI that seniors would want to talk to.

Yeah, that talk to, I think, is the key phrase because it— my grandmother, for example, has an Alexa device at, you know, her side table at the end of the couch, and she'll use it to play music. And she does kind of personify it quite a bit, and she's like fully with it. But, you know, she'll say, oh, she wasn't playing it for me today, and stuff like that.

And I'm excited. And she spends most of her time alone. She actually has a pretty good support network, as individuals in their 90s go, for sure, but still spends most of her time alone. And I do think just a little more conversation, and you can imagine kind of creating a profile in the background that the AI could kind of— it doesn't have to be that customized. The smallest gesture from the staff where she lives, is something that really makes her day.

And I also think there's something interesting about seniors where they're like— this is— my grandfather was especially this way— not at all cynical about things that were in fact kind of contrived. Sometimes you'd go to this kind of faux downtown or some historical building or whatever, and he's like, this is so fake, Pavon. But he loved it.

He didn't care if it was made for this purpose or not. He would still find great pleasure in it. Old people, in many cases, are really endearing in that way, and maybe in some ways could be more receptive to these AI conversation partners because they're just not jaded.

I have this mixed emotional reaction to this, though. On one hand, I'm like, yes, I want people to feel heard and connected. And the idea that someone doesn't have to sit there lonely especially, I think that's happening more and more, um, is good. But at the same time, like, there's also like probably the old school part of me that's like, I want to make sure people have human relationships. And if they get too close, if there's an AI that's too empathetic, like, are they gonna not want to connect with humans anymore? And is that, is that good for us, um, is, is a question. And maybe the answer is it is fine, but I don't know.

I think it's a very, very good question to ask.

And I felt this way about our I spent a decent amount of time actually using the Replika app when I had the interview with Eugenia, the CEO there, and it was fascinating in many ways. But one of the ways that was honestly maybe the most interesting was this was still pre-GPT-4 and pre a big price drop. So she didn't have the ability to do super advanced conversation as cheap as it is today. And my actual experience of the app was like, it wasn't super sophisticated in conversation. And yet she'd grown a big user base and lots of people really cared about it and were genuinely emotionally invested in this. And my takeaway from that was like, a lot of people really need this kind of stuff. And for the audience that she had at the time, I came away feeling like this has got to be very positive for a lot of people because It's a tough world out there, and a lot of people don't have a lot of things that I'm maybe privileged to take for granted. But then I also, just like you, I was like, but as this stuff gets really good and it becomes the norm, or it starts to— it's one thing to use this as almost like a treatment for people that are struggling, and it's quite a different thing for it to be the norm among—

There are going to be couples who break up because the girlfriend is upset at the boyfriend for sharing too much with his AI partner.

I bet that's already out there. Yeah.

Probably, right? Like, it's good and it's gonna— yeah, it's gonna get weird. And then, I mean, this— I feel like this is gonna just go deeper, but this was an interesting conversation that came up with— actually, I had a panel, the awesomest panel, the day after TED with Jim Fan from Voyager, June from Stanford Smallville, and then Noam who did the AI for Diplomacy game. So, it was like a gaming for AI panel, but the conversation did go— at some point, it did touch on like If you have an emotional connection with an AI, they also inherently to some extent have some influential power over you. And so, if you give companies the scaled ability to control what a chatbot might nudge a person to think, then it does kind of take that whole like, you know, there's a lot of responsibility on that to nudge humans in the right direction or not take advantage of it. Just something to keep— keep an eye on, I guess, from the safety standpoint.

Yeah, absolutely. There's a great article on LessWrong from early this year by a person who had a pretty consuming and borderline deranging experience of kind of falling in love with a character on character AI. And the fact that this was posted on LessWrong is remarkable.

And the person up front was like, I'm someone who should have known better because I have a pretty good understanding of what these systems are and loosely how they work, at least. And yet it was so compelling experientially that he kind of lost himself in it for a while and eventually somehow pulled himself out of it and wrote this post. But I would definitely encourage anyone, and certainly if you doubt the potential for these sorts of things to happen or think it would only happen to someone who is dumb or whatever, Read this one and you'll hear an account from an intelligent person who was not super naive going in and still kind of ended up in a strange place.

I'll check it out.

A couple last questions and then we can let you go. You've been super generous with your time. Building in public, you've done it and you've had great success with it. Do you advise your companies to do it? I'm struck that there are some, I think of like Harvey, Lindy, who's a former guest, who basically are allowing no users outside of, in Lindy's case, I think it's almost all internal still. Harvey has big customers, but they basically don't say anything. In a world where 1,000 people created derivative projects of your baby AGI in 2 seconds, do you advise people to still put it all out there in public? Or maybe it's just situational, but what's kind of your guidance on how much to build in public if you're building an AI?

I think it's situational. Absolutely. I mean, if you have a strong technical team that you're confident can build the solution you want to build, and you have connections, and you have deep connections and intro, like ways to connect into large enterprise customers who can pay you a lot of money, the incentive to open source actually, I think, is pretty low. You should just build the product and sell it. On the flip side, for example, if you're, you know, if you grew up sometime outside of like a major hub, you don't have any connections, you don't know anybody, and you want to build a dev tool, then open sourcing makes a ton of sense because if you can get people excited about your open source project, suddenly you've built a network of people who are familiar with you and your work and they're even probably contributing to you and suddenly you have friends that you can build alongside. And then a lot of companies fall into the middle of like somewhere between that, but not a lot of people starting companies are like, I have all my customers handy, like ready to go. A lot of people are trying to sell to customers. So open source is a decent market, you know, it's a good marketing technique. Especially if there's, if there's a technical aspect where you need developer buy-in from your buyers, then if you can have a popular open source project, then you've already gained credibility in some of the key players of your potential buyers. So it can decrease your sales cycle, right, if you decide to go commercial open source and build a commercial layer on top. But there are a lot of approaches. I don't— I'm not going to say I know what the right way is. I'm observing and learning a lot. Actually, interestingly, if you're— if you are interested in this open source commercializing question, a group to follow is OSS Capital. JJ there, they run the Commercial Open Source Conference. So he's put out a lot of content around approaches to commercializing open source. So I've learned a lot from him and actually he was the person who convinced me to open source BabyAGI.

Okay, cool. That's a great recommendation. 2 more questions.

One kind of super big picture, one a little more personal. I used to ask this question every interview and then I kind of went away from it. But just recently at the AI Engineer Summit, there was a survey result presented where seemingly in a pretty neutral— I took the survey, otherwise pretty neutral survey, which was like about what tools do you use and do you like LangChain or LlamaIndex or whatever?

Those were kind of most of the questions. Then there was this question of, it was simply put, P doom? And people filled it in and then they showed the results and the results were all over the spectrum. Basically, you could call it the uniform distribution to a first approximation. I thought that was extremely striking because it was like, there's definitely no agreement on this. And it's obviously super important if there is a serious chance of P doom. What's your take on that? Do you spend time worrying about it? Do you think about it?

Somebody actually asked the question during a dinner and I had an answer. I think I think about it slightly differently. I think just asking P-doom actually as a question to me doesn't make sense without a specific timeline. Although if you gave me a timeline, I would tell you I don't have an answer. I think of it more as P-doom, which is P-doom not specific to AI. I think of it as 100, meaning I don't think humans will be around in a billion years. And so the question just becomes, what is the likelihood that doom comes from AI? Because there's a lot of other reasons for doom. It could be a comet, it could just be time, it could be a bigger pandemic with a worse disease or whatnot. And then when you start thinking about it that way, you do quickly realize AI does touch on a lot of other things, but ultimately it's like a combination of AI and human. I think the most likely scenario is some sort of combination of human using technology wrong, whether it's nukes or creating diseases or whatnot, and maybe AI is part of building that solution. So I guess my answer becomes P doom 100. But then if you're going to ask that question, I think it's also important to think about the benefit of AI, which is that it can actually solve against some of the other doom scenarios. So actually, if it decreases the likelihood of other doom scenarios, from at least the way I'm thinking about it, it increases the P doom of AI because if the other likelihood goes down, the likelihood of AI goes up. It's interconnected is what I'm trying to say, and it's a hard question to answer, but that's the framework I think about it, but I don't actually have a good answer.

Yeah. Do you have a sense for which direction we're travelling relative to 5 years ago? Do you think that there is a, let's just say, like our natural expected lifespan, do you think there is a notably higher or lower P-doom in light of all the AI developments?

I believe we are on a path to a much better future. But I say that knowing that I'm an optimist and I believe the pessimists against it are also just as important. So I say I'm hopeful, but that's how I feel personally. But I think the discourse is extremely important amongst both sides.

I think I sort of share that perspective. I don't know that I would say I'm an optimist necessarily, but I do see, and I get so excited about the tremendous upside potential of AI.

And I guess maybe my answer would be, it feels like the distribution has shifted toward more of a bimodal where things could go really amazing or we could have some really crazy shit that could be really bad. But definitely both are very much in play in my mind.

But again, it's like saying, oh, should we do healthcare research? Because What's the likelihood that if we continue to research healthcare, we're going to eventually create a disease that can wipe out humanity?

Well, I would shut down some of that gain-of-function research. I do take a position on that one.

I think there are certain health types of research that maybe you should consider, but that is a good differentiation between calling it health research versus a specific type of health research. I think saying, should we stop AI progress, or if it is a specific type of AI that's dangerous, Let's talk about what that should be. But I think broadly saying, should AI slow down, is similar to saying, should we broadly slow down healthcare research? I think the answer everyone would say is wrong.

What would you say you have learned about yourself in the process of creating all these AI projects and Mini Yohei and all those things?

I think I realize how not complicated we probably are to some extent. We are very complicated, but at the same time, a lot of the way we work especially is simplified, like our work selves, like because of, to some extent, I think, the industrial revolution and making jobs ones that you can replace one person with another. So, the way our industry has evolved, just industry in general has evolved, is like, let's make jobs that we can easily replace one person for another, which means simplifying the job and tasks to a point that I actually think is actually pretty easy to map. So, what I figured out, I guess, to some extent, I— I've been fascinated at how simple we are when it comes to how we work actually to some extent.

Yeah. How much of what we do is in fact pretty routine.

Yeah. How much of what we do can be automated is mind-blowing. And it's not easy to get to a point where we've automated everything, but a large portion of work in most jobs can be automated with the tools we have today if we just built it and integrated it the right way, which is the difficult part.

Yeah. Well, you've taken a nice little bite out of that grand challenge personally and been someone that I've really enjoyed following and inspired many with your projects, including BabyAGI.

So we will look forward to the release of the TED Talk coming soon. And for now, I'll just say, Yohei Nakajima, thank you for being part of The Cognitive Revolution.

Thank you for having me. That was fun.

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