How Union Square Ventures Built an AI Brain for Venture Capital - Ep. 36 with Matt Cynamon

[00:00] You mean I can just like make prototypes with AI all day while working here? Like, [00:04] Yeah, okay. Let's do it. Let's go. There are these great stories that are happening in our meetings that just end up sitting in our heads. We had Chad Hurstwit, who directed the Eno film that was running at the Film Forum in New York. He came in and just like talked to us about his process. Obviously, I attended that meeting, but it was super fun to just like also turn that meeting into a podcast and then like give it to my wife and be like, how cool was this meeting that we just had? [00:34] the last six months of my life. Just make stuff because the tools are there to do it. [00:52] Matt, welcome to the show. Hey, really happy to be here. Psyched to have you. So for people who don't know you, you are what you call a regular at USV, which is UnionScore Ventures, which is one of the top venture capital firms in the world. And you work on all of the AI projects. And I love having you on because USV just keeps putting out these really, really cool projects. You have this one where you tweet out all the meeting notes from meetings that have happened at USV that I read. It's really good. [01:22] do a live version of that today. They usually get done on Fridays. And so we could do it on the podcast and then publish them live. Let's do it. That would be so great. I think that you have this thing that I try to have and everyone, I think at every tries to have, and that I think is just really good for working in this sort of like AI wave right now, which is the spirit of

[01:42] tinkering. You're just playing around with stuff. You're making stuff. You're constantly shipping new things. Um, and it sounds like you're discovering some cool things along the way. So I want to talk about that, I guess to start, um, tell us how you got into this. Like, how did you start? How did you become the AI guy at USB? So, um, mostly just by, I would say following my own curiosity. So I've been at USB for six years now, or just about six years now. Um, [02:07] And for a bulk of that time, I was working on the talent side. So, you know, meeting people who are looking for new opportunities and connecting them to the right people in our portfolio, which was always kind of a weird fit for me because I never came from a talent background in the first place. I'd never been a recruiter. I'd never hired beyond just like hiring people for my own team. But it was still a lot of fun meeting all those people. [02:37] Like, [02:38] here's everyone I met with last week that's looking for a new job. Here's everyone I'm meeting with this week. Who's a good fit for someone in your portfolio? But then I would sort of cold open all of those emails with, and here's this weird thing I made over the weekend that you might want to play with. And at first it was mostly just what I would call like fake movie trailers that I was making in Runway ML. And I would like outline my whole product. This was supposed to be an email about talent. And it was like, so I had this idea about a rom-com set [03:08] and I gave that to ChatGPT and I asked ChatGPT to write a 30-second script and then timestamp each part of the script. And then each timestamp needed to be a digestible prompt that I can feed into RunwayML and would just run that process. And everyone would just be like, okay, looks cool. And then I went on paternity leave. We had our first kid. And when I came back, I was having a conversation with...

[03:35] you know, Andy and Rebecca are two of the partners here. And they're like, [03:40] like, look, the world is changing really fast. And there's a lot of really interesting technologies out there that both we need to be experts in, but at the same time, like, we need to be deploying on our own to make us better at our jobs. Like, you are a tinkerer by nature. That's kind of what it is that you do. [03:58] would you be interested in just kind of doing this full time? And I think maybe I played it a little cool, but in the back of my head, I was like, you mean, I can just like make prototypes with AI all day while working here? Like, [04:10] Yeah, okay. Let's do it. Let's go. And so yeah, that's kind of that's kind of how we got here. I love that. It's such a good story. Some of the best people like stumble into things like that by like they're they're doing their job and then they're like just sneaking in the thing they actually want to do. And somehow it becomes their main thing. It was really inspired by this analyst who worked here. [04:33] many years ago, who's now the CEO of a company called Jam. Her name's Danny Grant. Maybe you've met her. [04:38] And when she was an analyst, she would come in over the weekend and just be like, hey, look at this hologram that I made. That sort of made me realize that this was the type of place where that type of experimentation and just like having fun on your own time and following your curiosities was really rewarded. [04:54] And I think it's proven to be the case. That's great. So it sounds like you started with runway clips. What was the first thing that got your eye where you're like, this AI stuff, I kind of want to just start playing around with it. And what was it?

[05:06] You know, I don't remember whether it was Dolly or whether it was ChatGPT first, but I do have this distinct memory. Like I... [05:19] You know, I like I've always been interested in sort of the more creative side of AI and using it as a creative assistant. And I remember I was staying up at a friend's upstate and we were writing a musical about the life of Jeff Koontz using ChatGPT and like giving it feedback. It was probably two plus years ago and giving it feedback. And like we're like, this is outrageous. [05:49] and it's getting better and better. And so I think that was a really pivotal moment. And then the other is, you know, my wife is an illustrator who I wouldn't call her a Luddite, but she's definitely not terminally online like I am. And my first thought was like, oh, yeah. [06:04] I wonder if she's going to be to feel threatened by this. And like the first thing that she did was that she had Dolly design a cross stitch pattern that then she could do in real life. And I was like, Oh, [06:14] You know, wow, like the these aren't just like these are tools that actually help bring out our creativity if we if we use them right. What's the thread that turned you into someone that can be like a tinker and and like wants to make all these different types of projects with AI? Like, tell me tell me about that. [06:31] Sure. I probably, if I had to guess, I probably get it from my dad, who was like the type of person who always had some like broken down piece of machinery, either like a car or like a computer that he was building from scratch or something.

[06:47] or a ham radio. Like I remember he went through a real ham radio phase. So he was a real tinkerer. So I think it was always in my nature. [06:54] to want to make things. And the problem is like, I just had so few skills. I was quite the generalist always. I mean, my whole career, I, you know, I was, I worked in startups, but in like weird general managerial kinds of positions, even when I was really young. And so there were all these things [07:13] you know, in my spare time, like I'm trying to learn piano and I'm trying to learn all these different like hard skills that I never sort of gained growing up. And then all of a sudden these tools came about where, um, [07:24] Um, [07:25] You know, I wouldn't say that you need no skill, but you can develop the skill alongside seeing your creativity come to life. Like if you want to learn how to play a Beatles song on the piano, like it's going to take you a long time of learning your scales and then like building up to the position where to the point where like you can actually play it credibly. And then if you want to sing on top of that, like, my God, we're talking about three years of lessons and whatnot. [07:55] rewarding and the sort of the feedback loops are so fast. And so, you know, a great example of this is, um, [08:05] You know, I'm not a software engineer, [08:07] But I had made something like I had made a custom GPT. And I was like, you know, this would really work so much better if it read off a live spreadsheet, but I don't know how to [08:16] you know, connect to APIs so that the GPT can read off the live spreadsheet. But then,

[08:21] Because you're already so far down the process, it's just like adding another little element to it. And the GPT can walk you through how to do that. And it was just kind of, I don't know, it's kind of been this magical experience for me. [08:32] Yeah, this is this is one of the things I see a lot to in stuff is like I teach a programming course like programming with AI. [08:42] And one of the things that's very different about that course from, for example, the way I learned to program, which is like, the way I learned to program is the exact same way you're talking about learning to play piano. It's like, okay, what's a variable? What's a function? And you're like, I want to make a website. I want to make an app. And you're like, okay, no, no, you have to spend a year learning about these really basic building blocks that you don't know how they translate into the full thing. [09:09] And, you know, [09:10] What I can do with my students and what anyone can do, regardless of whether they're taking a course with AI, is... [09:17] within the first 30 minutes, you can make something that looks pretty much like the thing that you want to make. And then you can be like, whoa, wait, how does that even work? And then you can go down into the, okay, here are the nuts and bolts of details of how it gets built up. And I feel like, [09:33] And there's a whole generation or crew of people who have not been able to, for example, program because they don't want to go through that whole thing, like learning all the basic things before they see something that they connect to. And now they can connect to that and then they can go back and learn. I think that's so powerful. You know, it reminds me a lot of the personality archetype who as a kid takes everything apart to learn how it works. Like they take apart the clock radio to learn how it works.

[10:03] do that now. You can build the clock radio and then you can take it apart to see what it is that you just did. And then like sort of build your skills that way. And I think a lot of people learn much better that way. Totally. Totally. I know, I know I do. Um, and, and I feel like I have, I have a similar thing where I have all these different interests. I have all these different things I want to make and build and AI just like, [10:22] I can now do it. And yeah, it's just, it's the best. Like literally the only thing holding you back is time and patience at this point. [10:30] Yeah, exactly. I want to get into some of the practical stuff that you're making, because I think it's so cool. We could start with the librarian. What do you think is the best thing to start with? [10:39] Well, there's like six different ways that we can sort of talk about the librarian, because I feel like that's sort of the persona that's taken off and I'll pull it up. You know, when [10:50] started working on all of these projects, I think like a lot of people, my, my imagination just like exploded. And it was like, Oh my God, like we are going to build this like, [11:01] all-in-one monolithic, like super app that's going to live with us wherever we go. [11:07] And it's going to have a name and a face and a personality because like we all believe that metaphor is really powerful and helping people understand things. And I think over time. [11:16] What we realized was that if we wanted to build things that work now, [11:20] Then rather than trying to build like a monolithic app, let's use the tools that already exist. Let's break them down into their component parts and let's build individual agents that do different things. And so there is a version of the librarian that we're still building that still kind of resembles that. But honestly, like...

[11:38] My favorite version of it is just like a simple GPT that we cloned and like called the librarian. And I can pull that up if you want. Yeah, pull it up. [11:46] And give us some background on what it is and where it came from and all that kind of stuff. [12:16] - Yeah. [12:17] A lot of that has been driven by one of the partners, Fred, wrote every single day without fail for 13 straight years. [12:35] I've actually already written that. [12:38] And so what I wanted to do as a starting point was just build a bot that allowed them to be conversational with their writing. And I shared it and... [12:51] Everyone sort of looked at it and they played with it. I think the original name I gave for it was called "Conversations" because we like to say that USB is a conversation. [13:02] And everyone's like, okay, but what does it do? [13:05] And I couldn't really give a good answer. And then I renamed it to At The Edge because I was like, you can ask it questions about our thesis and really like, you know, try to synthesize and advance our thinking. And everyone's like, ah, ah, ah, ah.

[13:18] And then they're like, but can you explain what it is? I was like, I don't know. It's kind of like a librarian. Yeah. [13:24] And then it was like this light went off and everyone's had to like, oh, librarian. Well, that's interesting. And that was sort of this moment, I think, where we realized how powerful metaphor can be and sort of explaining what it is that these chatbots can do for us. And so, you know, I started building out what was the librarian. And initially, like I said, we had sort of put everything into this one. [13:50] chatbot that was sort of its own standalone application that I actually used NoCodeMBA to build, which was an awesome program. [13:57] and then a lot of help also from Ben's Bites. But just maintaining our own UI was such a pain in the butt. [14:05] So, you know, at least for between now and November, I was like, you know what? Let's break down all the component parts of the librarian into the individual things that the librarian was doing. Let's make them all their own standalone GPTs and give them very specific names so people know exactly what they do. So this is an example of the librarian. So one of my favorite things to do, and I did this recently, we can do it live, is like ConsenSys is a company that, [14:32] that we recently invested in. And so we can go to the about like, I'm just going to go, I'm going to copy everything. [14:41] on this page. [14:43] I'm going to type here and say, you know, below is the about page of a company we're considering investing in.

[14:52] pull out any relevant blog posts, [14:57] that we've written. [14:59] that might pertain to this company. [15:03] And then I have like an actual, because you know, this is going to take a little bit, so... [15:09] I can show you... [15:12] Here we go. [15:13] So this was one that I did the other day. It was the same thing, consensus and sort of it brought back, you know, these are the major themes that we've written about that would be relevant for consensus. Can you stop for one second? I just want to, I want to, um, I want to go back and read it. So if you go to the top, okay, so basically, so what you did is you put in just like the, the about page and you're like, you know. [15:34] uh what are the relevant like what's the relevant writing from usv and and it had this this gpt has in its knowledge base all of the blog posts you guys have published right yeah um and so it says here are a few key insights from usv writings that are highly relevant ai unlocking knowledge one of usv's core beliefs is that ai can unlock vast amounts of knowledge from data making it easier for people to access and synthesize information [15:59] Thank you. [15:59] This aligns closely with ConsenSys' mission. And then it keeps going. Democratizing science. USV has supported platforms that democratize access to scientific resources. Trust in AI systems. For AI-powered systems to gain widespread adoption, they must earn users' trust. So this seems pretty good, right? Tell me about your reaction to this response. Oh, I think it's spot on, right? And these are all things that both we've talked about and written about.

[16:29] AI systems. In this instance, I think it did quite a good job of interpreting what it is [16:34] the consensus does, what their core brand promises and how that relates to both things that we've written about that might be relevant to the tech they're building, but also to the brand they're building as well. But maybe I'll pause just for one second and explain like, so why is this even important? Like, why why do we need to pull up our writing about a company that we're considering investing in? And I think for us. [16:58] Um... [16:59] We don't like chase after deals at USV. I think we're quite patient. We like to talk about ideas, or we like to go out into the market and do our research. We like to formulate ideas over time. And sometimes we can spend, you know, six, seven, eight years finding the right company that fits within the thesis that we're looking for. And so a lot of the times we're trying to prove to entrepreneurs like, hey, we're not just chasing the hot thing. Like you are actually what we've [17:29] this tool allows us to do. [17:31] That's interesting. And one thing that I noticed though is like in these summaries, it's not saying like, [17:36] Fred wrote in, you know, 2015, blah, blah, blah. Um, yeah. Yeah. So, um, [17:44] I said, can you just share the URLs to those posts? They don't seem to be working, right? Because that's obviously the first thing that always happens with any of these bots is it gives you back-- it cited the articles for me, and then it gave me these actual URLs. [17:58] And this is like a little trick I've kind of learned about ChatGPT.

[18:01] One of the ways that it likes to hide the fact that it's lying to you is it'll give you a fake URL. And if your cursor is showing up like this, it means it's a fake URL that doesn't exist. Wait, wait, wait. Really? Really? [18:13] That's so interesting. Okay, so if the cursor shows up as the same cursor that you see when you're highlighting... [18:21] text that you can type in instead of like that mouse, that mouse hand, that means that it's, that means that's lying. Yeah. Let's go. Look, oh my God, that is a completely made up link. So I was like, [18:35] Then you can see, I said, none of these are real. Let's try again with a smiley face. And now look at these. You see how my mouse cursor is changing? Trust in AI, science exchange. And then this one's still fake, AI and crypto. [18:47] But like, you know, this is a blog post about that time we invested in Science Exchange. I trust in AI. This is a blog post that, you know, Andy wrote about, you know, trust in AI, as it says. And these are real links. Yeah. It's like you can now use links to the consensus team. And I was like, well, let's get a couple more. Maybe something that's more about search because we've written. In this case, I'm using my own knowledge about what we've done. [19:13] which is that I know we've written a lot about search in the past. I know we've invested in search. So let's try to pull some of those in as well. Because you can't, I say this all the time, but if you're relying on the AI to produce the final product for you, you're always going to be disappointed. But if you're relying on the AI to help you get to a final product, then like,

[19:34] I don't know, I find it extremely powerful because normally this type of work would have actually taken quite a long time. [19:40] Right. [19:41] Right. So here we have the fragmentation of search DuckDuckGo, or, you know, our investment in DuckDuckGo. And I was like, OK. [19:48] Now let's rewrite the email, but include these articles, the real ones. Leave out the AI and crypto one because that wasn't really relevant. And then also make sure to differentiate between the blog posts you pulled around DuckDuckGo and Science Exchange, which were relevant investments we've made. [20:04] And then the other articles, which are more like kind of what we're actively looking for. And I had instructed it earlier to write it in the voice of [20:11] one of our partners, Jared Haft. [20:13] And I think it did a really great job. Like been following what you're building with consensus. I'm really impressed by your mission. [20:20] Um, we were, we were long believers in the transformative potential of AI to unlock knowledge and make it more accessible, which by the way, was like a core part of our thesis for a long time. It did a good job of pulling that in. We've made investments in companies like science and change, DuckDuckGo, both of which share ethos of lowering barriers to specialized knowledge and rethinking how people engage with information, you know, in terms of what we're actively thinking about. Here's a couple of other examples. [20:50] the email for them. I kind of did that just to... [20:53] show what's possible, but, um, [20:56] And basically like demo this to the team like, hey, these things that you do to sort of prove to an entrepreneur that you're aligned with them that take you a long time, we can now do it.

[21:06] way quicker and kind of in a fun way. And then I added one extra step to this, which I'd love to show you, which is I have this other GPT, which again, this used to be folded into the librarian and I separated out called portfolio tracker, which is kind of like, you know, it categorizes all the companies that we've invested in when we invested in them, what our ownership percentage is, all of that. [21:30] And so in this consensus example, [21:33] Uh, do do do. I asked the portfolio tracker. [21:37] Build a chart that plots out all the companies we've invested in, in education, search, and AI over time. Because those are sort of the three areas that were most relevant to this team. [21:48] And you kind of have this chart that goes all the way back, you know, 12 years of us investing in these spaces. But from my own, again, first party knowledge of what we've done, I knew that Indeed, which was job search, which is, you know, a big part of the disaggregation of search early on was missing here. [22:05] But, you know, first, actually, first I asked it to make the colors more pronounced so that it was clearer what was going on. [22:12] And then, you know, I said the only company that isn't [22:15] on there that falls into the search category is Indeed. Make sure you include that one as well. But the reason why it didn't, Indeed didn't have a date, so I gave it the date, blah blah blah. And now we have this chart dating back to 2005 of all the relevant investments that we've made in the space that this company is working in. Again, I don't think we would actually send this chart to them, but this is all knowledge that would have probably taken a really long time to gather up, but now we can do really quickly.

[22:41] would you send the GPT to them and be like, hey, you can ask questions about what we think. [22:45] Yeah, absolutely. We haven't done that yet. But I think... [22:53] It is highly likely that if you come to our website at some time before the end of the year, that all that's going to exist is the librarian. [23:01] I think that's really cool. Like, what I want is, like, I want, like... [23:05] Andy and Fred voice mode. [23:08] Um, and just like, just get to ask them like questions about ideas that reference and reference all their blog posts. Yeah. Um, yeah. [23:18] To date, everything that I have built has been more for internal use. And when it's internal use, you have a certain level of tolerance for... [23:28] information being wrong because you know it's all first party data for you. [23:32] So like, for instance, Indeed didn't show up, but I knew we invested in Indeed in 2005, so I could correct it in that way. And so we're just thinking through how to get this to a place that it's just a tool for public consumption. I mean, what I really want to do, not just like get Fred's opinion on things, but... [23:50] and I think this is also on the roadmap for relatively soon, is that you can just dump your deck [23:56] into here and it can equip you with what we've written about in the past. And that like sort of enables you to be able to come to the meeting also ready, ready to have a conversation in the way that, you know, we're sort of thinking about it as well and kind of get aligned before you even walk in the door. And what are you thinking about? Like the, the place where my mind goes always for this kind of thing is like, um, I'm,

[24:21] Investing is this activity that you can... [24:26] talk about in terms of rules or maxims, but really it's a highly intuitive, like pattern matching type thing where, you know, Fred or Andy has like developed over many, many, many, many years, the ability to like select the kind of opportunity and the kind of person that they like have a taste for. And to some degree, like, um, tools like this might be able to, um, replicate [24:56] of it, but some of it. And have you explored that at all? Which is like, if I wanted to get a rough idea of like what Fred would think about this, like I could throw it in there, like for internal use? Or is that not really on your radar? No, there's two reasons why. Number one is the team is highly accessible here. So like, if I want to talk to Fred, I'll text Fred right now and ask him [25:26] to be able to do that. [25:26] to internally at least abstract away the way that the different partners like to think about deals and then build a bot to sort of get their opinion because they're so accessible. What if they're gone? You know, like, I hope this doesn't happen, but like, you know, Andy could like, you know, get sick and I don't know, like whatever, like, you know, people don't last forever.

[25:56] history. [25:58] And we've talked to a lot of companies that are building digital clones and [26:02] For whatever reason, it's just not what we're interested in. [26:06] And I think we believe that in general, venture capital is a highly relational business. [26:13] And we don't necessarily want [26:16] abstractions of those relationships to replace or even augment the [26:22] kind of the real relationships themselves. [26:25] Um... [26:26] And... [26:27] In general, our approach to AI has been enhance, not replace. [26:31] So, you know, knock on wood, if someone were to disappear, you [26:36] Like... [26:36] Mm-hmm. [26:37] It's not our intention to be able to build something that can replace them. We would just mourn them. [26:44] That's a very good answer. I will say, I think there's room for there to be compatibility between embodying someone's perspective in an AI like this and strengthening human relationships. Yeah. [27:00] I don't think it's an and or a question of augment or replace. I think you can use that. Even Fred or Andy could use that. What would five years ago Fred have thought about this and how have I changed or whatever? There's lots of different ways to use that tool. [27:22] I don't think we would ever build that tool [27:25] to help us decide what to invest in. I think we would build that tool to help us

[27:34] help our companies more. And so what do I mean by that? [27:38] Every week we get together and we discuss all the potential new investments that are on the table. And then we walk through the portfolio and each partner gives an update on not necessarily every company in their portfolio every week. But for any company that there's something newsworthy or something that they need help with. [27:58] So we'll have those conversations and the sticky situations that get thrown around in there are worth, you know, 30 MBAs. [28:06] And so what we are working on right now is how to capture, you know, how would Fred handle this particular situation so we don't have to have that conversation 6000 times. Right. That's really interesting. So for, you know, for example, like. [28:24] you know, a CEO wants to step down. [28:27] And [28:29] or sell the company. Like, have you ever gone through this before? [28:34] And then there's a wealth of knowledge that gets passed back and forth in that Monday meeting that isn't necessarily captured and is not something we ever want to lose. [28:44] Yeah, that makes sense. This reminds me of one of the core ideas that I think is coming out of this particular era of AI for me. It particularly applies for me to creative work, but I think it applies in all areas of business. [28:59] which is it starts to reveal how much we actually repeat ourselves.

[29:06] Um, because it's very good at when something has happened before, um, in the, in a similar context, like bringing it, bringing it back and, and, and, and changing and giving the answer for that particular context. Um, and so when you know that you have a tool for that, you start to see the world through that lens. Um, and I think it's, it's there all the time in creative work, you know, it's like, uh, doing headlines or like making a tweet or out of an article or like whatever. [29:36] but I think it's the same thing in investing. It's like, [29:39] when a CEO wants to step down from a company, that feels like a very unique situation. And there probably are some unique variables, but... [29:49] If Fred has seen that like 5000 times, like he pretty much knows like the three cases and three ways to respond or whatever. And and having that available, like is helpful. Yeah, I mean, like the the actual idea that the actual conversation that comes to mind that happens a lot is like, [30:06] The CEO wants to say the CEO steps down. We need to bring in a replacement CEO. Should that CEO's goal be to retain the entire executive team or bring in their own team? [30:17] Right. Can I can I try to answer that question? I'm curious what the USP perspective is. Yeah, sure. I would say bring in your own team. I would say get rid of everyone. I think it depends. So actually, the way that you answered the question is kind of right. But I think it's the answer is you have to bring in your own team. But that doesn't mean that the people who who are there are.

[30:41] aren't your team. That's right. Yeah. So that, that's what you have to suss out pretty. That's what it seems like you have to suss out pretty quickly. Like, are, is the team that's remaining like, are they, are they ready to ride with you as well? And if the answer is yes, then you keep the team. [30:55] And if the answer is no, then, you know, for better or for worse, the only way you're going to be successful is probably bringing in your own team. And of course, you know, there's nuance to all of this. And that's where, you know, I've been throwing this idea around with my colleague, Nick Grossman. We're calling it playbooks. And like there are all these playbooks for these really tricky situations that we've never written down before. And so how do we trigger like our AI transcribers to make notes? [31:20] of like when those sorts of conversations are happening and then recording those as playbooks is a, is a project that we're working on right now. That's really cool. I love that. I've definitely, you know, I, I've, I worked at an incubator before, um, before every, and we had a similar thing of like trying to write down playbooks of how we made decisions. Um, when I think it's really interesting about all of this is that, um, USV can have a playbook for something like what to do if you're an incoming CEO of a startup that just fired their CEO,

[31:50] write is different from the playbook that like founders fund would write. Yeah, sure. And, um, what I think is so interesting about that is, uh, even, even though they're quite different, they both work, um, but they probably work in slightly different contexts. And, um, USV is probably going to attract founders that like, and, and types of companies that work for USVs, [32:20] more likely and Founders Fund is going to kind of like... [32:24] be giving advice in a context that's more suited to the Founders Fund style of advice. [32:30] And I don't know, that whole thing kind of... [32:33] Because I think I've always been sort of fascinated by if you ask five successful people how to handle a situation, you get five different answers. And they all kind of work, but they're all very contextual. And I think AI helps you kind of see that a little bit. [32:46] Well, I would say this. I mean, in the world, one of the things that we like to say is, [32:53] is, you know, this is very much it's very much like a marriage event, like [32:58] At the end of the day, the product that we sell is money. And our money is just as green as Founders Fund, as A16Zs, as, you know, whoever else. Like everyone's selling the same product. [33:09] Um, sure. You can dress it up with all these sorts of value added services, which a lot of [33:15] Funds do, I think Founders Fund, if I'm correct, [33:19] Uh, sorry. Well, so, you know, some provide none and that's actually what they believe is the value add that they get out of your way.

[33:26] But at the end of the day, the real differentiator is the relationships and perspectives. [33:31] that and like do you align [33:33] with the people that you're doing business with. And so, yeah, I think, you know, [33:38] Obviously, you can suss that out. [33:42] through like the diligence process and reading historical writing and whatnot. But that stuff happens so fast. Like founders sometimes are making decisions over the course of two weeks about people that they're going to be in business with for the next 10 years. And like, they don't even make decisions that fast when it comes to hiring. [33:59] And so it could be I and, you know, hiring is not as permanent sometimes as a cap table, adding someone to the cap table, which is kind of a lifelong commitment to the company. And so, yeah, I think conversational tools that can work. [34:13] like really help you get a sense of the type of person you're going into business with, how they think, how they like to work, I think could be really helpful for founders making that decision. Totally. Okay. I want to keep going. I know you have like a ton of different tools. Like let's, uh, let's, uh, let's keep going. What's the next thing you want to show us? Um, well, I mean, my favorite thing that I've made recently was this meeting notes. So we've started transcribe, I mean, we've been doing this for a while. We've been transcribing [34:43] all of our meetings for the last like four months. It's actually not true. It's not all of our meetings. We do these sort of special topical meetings around like AI, crypto, [34:54] uh climate and then we do sort of this miscellaneous one and that's sort of where we hash out ideas and discuss specific companies and so we've been transcribing those and now we're sort of porting automatically pulling those into this gpt so that you can ask questions to the meetings i kind of want to make two sort of separate points you know one is well why do why are we doing this all here when there are better purpose-built tools

[35:19] And I think the one of the main reasons, like, for instance, like we could do all this in Granola, you don't need to build a special GPT in order to ask questions to meeting notes. But what I think is really nice, and I can't necessarily show this to you because I'm recording myself on my phone. But if any member of USB opens up like their chat GPT or their Claude on their phone, like there is a conversational interface with like every part of the business in the left hand column that they have access to. [35:49] right there without having to toggle between a million different apps. That's really cool. Yeah. So like, sure. Like I actually really prefer notebook LM for instance, for, for analyzing meeting notes, but for sometimes like ease of access is the best feature. And I think whether it's Claude projects or whether it's, you know, GPTs on chat GPT, like for our purposes, I think that just works a little bit better. Wait, I gotta go back. Well, shots fired on notebook LM. Like, [36:19] prefer it for meaning notes. [36:21] Have you ever just like looked at the ques-- like, so, okay, so there's three reasons why I like Notebook LM better. [36:29] Number one is I think it comes up with really insightful questions to ask the notes. [36:37] I think number two is, by the way, are you saying this is shots fired on Spiral? [36:44] No, no, no, no. Shots fired on ChatGPT. Oh, I was going to say. I mean, all these products are amazing.

[36:50] Um... [36:51] Number one is I think it prompts you with really thoughtful questions about the meetings that I sometimes would have not come up with myself. Number two is I think it does a much better job of citing the transcript. [37:03] or, you know, citing the things that have happened in the meeting. So I can verify whether or not that actually happened. And then the third thing is, my new favorite thing in the world is not to read meeting notes or read meeting recaps, but it's to listen to meeting podcasts. And so I have been anytime we have a really like important meeting, I mean, I can just pull one up. Like, we had this guy, Chad Hurstwitt, who directed the Eno film, it was a generative AI [37:33] film about the life of Brian Eno that was running at the film forum in New York. And like, he came in and just like, talked to us about his process. Obviously I attended that meeting, but it was super fun to just like also turn that meeting into a podcast and then like give it to my wife and be like, how cool was this meeting that we just had? Dude. Yeah. I love that. [37:52] Or... [37:53] You know, we had a really, really dense, like really dense AI meeting on Monday. [38:00] And I shared the notes around. I was like, but I would honestly recommend listening to the podcast because it's a lot more fun. And it's something, you know, they're all 10 to 15 minutes long. So like you can do it on a subway. You can do it on a city bike. And what I like about podcasts as a meeting recap. [38:16] um is you know you can do it while you do other things like i can't read and do other things at the same time

[38:23] But I can listen to two people talk about a topic while I'm responding to emails or like riding on the subway or whatever it might be. [38:30] Totally. We're so on the same wavelength here. So I think you've probably seen this, but two days ago, we officially launched our studio where we do product incubations. And one of the ones I'm most excited about is we built something that... [38:46] It takes, we record all of our meetings. And it also takes, we use Discord instead of Slack, but it takes all of the messages that happen in important channels. And then it creates a podcast of like what happened. And it's like, it's a notebook on style thing. And it's so good. Like I, cause we're at a scale now where like, I can't be in every meeting, which is like, it's fucking wild to me. Cause I'm like, I write, I write a newsletter and do a podcast. Like that's, it's weird. But, but yeah, we're at a scale now where I can't be in every meeting. [39:16] I just love listening to a podcast about a meeting that I deeply wanted to go to, but didn't have time to attend. And it's so helpful. And one of the things I love most about it is... [39:30] I'm going to go. [39:31] You feel so good when the host mentions your name. [39:39] I'll tell you what. Do you know what makes you feel even better? What? When the host is like, whoa. That's a crazy idea.

[39:49] It's so good. I'm so excited for this product. I really think so. I think we're going to do something external where you can listen to every meetings. [39:58] But I think we're going to just release it as a product or any... Or use it. [40:02] Yeah, I would love to have you guys try it because I think you're the perfect place to give it a shot. But it's really cool. And it seems like you're doing it already. Can we listen to a little bit of the Eno podcast? Yeah, for sure. [40:15] ever catch yourself hitting that replay button on a movie? Not necessarily because it's brand new, but like, [40:20] because it's familiar. Like you just want to settle in with an old friend. Yeah, totally. But what if you could have that [40:26] that comfy familiarity, but but also a totally fresh take every time. Kind of like seeing a band play your favorite album live. That's an interesting thought. Yeah. Like the core. [40:37] The core is there. [40:38] But there's a unique energy to it each time. And that's, believe it or not, what filmmaker Gary Hufftwitt is, is messing with in his documentary E&O. Which is wild because it's been playing for like 12 weeks straight at Film Forum in New York City. And people... [40:55] All right. Let's, uh, that's so cool. I love, I love that. I honestly, like I would keep listening to that. I'll send it to you. Yeah. Send it to me, send it to me, please. Like, well, I actually, I really want to start. Cause like we're trying, what we're trying to do is like, let people into a lot of our processes earlier because we're, you know, we're sitting in our office and we're like talking about ideas and it sometimes can be months before we really

[41:25] little bit sooner. And so I might just start releasing all of our meetings as a podcast that people can listen in. You should. Totally. I love that idea. I think, um, we'll release one today then. Perfect. Let's do that. I, one of the things that I feel, um, I feel really strongly right now is, um, do you remember the startup Justin TV? Yeah, of course. So I think that Justin TV would actually work now because the reason why Justin [41:55] And for people who don't remember Justin TV, it's like Justin Khan, who eventually started Twitch, was just like wearing a video camera around and like streaming videos of himself, like doing his whole life, basically. And the reason why that doesn't work is because like out of any given day for a normal person, there's like 12 interesting minutes. And you have to like string those minutes together into a story in order to make them compelling. [42:25] not that interesting. And I, [42:28] One of the things that I think is the cost of storytelling, of good storytelling, is going down dramatically. And what that will do is not necessarily... You're not going to necessarily read novels written by AIs. It's not going to necessarily just replace all human novelists, but we will be able to tell stories, high-quality stories, about places that we normally would not be able to afford to tell stories about. And one of those places is your company meetings or whatever Justin TV was.

[42:58] was 20 and not a billionaire can afford to have like incredible stories told about his life. Um, because AI can pick out the, like the interesting things and leave the rest out. [43:09] You know, it's funny that you say that because like we talked at the beginning about like the little overheards that we do. I mean, a lot of that was born from this idea that. [43:20] Hey, [43:21] we're not producing enough content [43:24] And I was of the opinion that like, well, actually we're producing a ton of content. Like every day we are getting together and we are producing a ton of content. We're just not capturing it and we're not necessarily like curating it enough. And so that was really the impetus behind that was like, there are these great stories that are happening in our meetings that just like end up sitting in our heads for weeks on end until we can put pen to paper to get our ideas out there. [43:54] writing as our primary mode of getting our thoughts and ideas out into the world for a variety of reasons. But there's a lot of things that we can do to tell, let's call it tell stories in the meantime. And then there are lots of things that we can build that will help us write quicker and easier than we've done in the past. And I think that's kind of where our focus has been. [44:13] That's really cool. I would love, I know that you, you said we could, um, we could get an, an OH at a US fee out. I would love to see you go through that process. [44:22] Well, so I'll start by saying this process has changed so many times since we started doing it. It used to be fully automated, then it was super hand done. And now it's kind of somewhere in the middle, but let's do it as automated as possible.

[44:37] So I'll go back to share my screen again. [44:40] We'll open up ChatGPT. [44:42] Let's do our meeting notes. [44:44] So I'm having a little bit of an issue with meeting notes understanding the [44:49] what most recent meeting means. So I've been a little explicit recently of, you know, let's produce five OHS from the AI meeting on October 15th and the three meetings on October 18th. [45:11] don't, you know, explicitly mention any companies or [45:18] anything specific to a company. Because, you know, [45:22] When we were doing things a little more automated, sometimes companies would come in and pitch and [45:28] I would I would we wouldn't really want that. Like we wouldn't want their pitch to to go out into the world and make it look like, hey, these are our ideas. [45:36] So let's see how we do. [45:41] So it's saying, here are the five OH posts based on the AI meeting on October 15th and the three meetings from October 18th. One, commoditization is coming. The race for dominance in AI models may lead to a future where foundational technologies become a commodity. Okay. [45:55] It's a reminder that differentiation is key to survival. [45:58] Two, decentralized to survive. We debated if decentralized AI models can compete with their centralized counterparts. It might be the only way to break free from the current tech giants hold. I think that's.

[46:07] I think that's a really good debate. Three, hydrogen risk or reward. As we push a greener tech, hydrogen's potential for clean energy remains controversial, both promising and dangerous safety concerns. Keep the debate heated. [46:18] Four, uncorrelated bets matter. The unpredictability of AI's evolution means diversifying investments is crucial. Interesting. And then five, microgrid revolution. Microgrids are gaining traction in their potential to revolutionize local energy is undeniable. These are cool. What's really interesting is I want the ability to be like, okay, tell me more about uncorrelated bets. Yeah. I mean, I could tell you more about uncorrelated bets. Well, I guess I'd throw this question back to you. It's like... [46:46] how do you think we should release this into the world so that people can ask follow-up questions into our meetings? You know, like we tweet these out and they get some engagement, but yeah, [46:55] I don't know if it's that people would rather have a private conversation with our meeting or whether they want to have a public conversation with our meeting. Like, you know, how can we get people to ask us more questions about uncorrelated bets? [47:05] I think the key thing is when you read this, so like uncorrelated bets matter. In venture, the unpredictability of AI's evolution means diversifying investments is crucial. The real question, which bets will prove to be the game changers? [47:19] So... [47:19] That is posing a question, but it's not giving me any hint at all about what your take might be, right? Like, USV's perspective is completely hidden there. So I know the topics, but I don't have any concrete idea of like, oh, you think that if you're going to invest in AI, you also have to invest in the Japanese whaling industry because they're completely uncorrelated or whatever.

[47:49] the summaries are um and i think this is just a problem generally with ai generated summaries is that they're good at finding the the interesting topic um but they're very good at like telling you like saying things without saying very much at all um yeah you know oftentimes if something pops up as like an overheard at usb which means it's something that we're discussing in a meeting the reason why we're discussing it in the meeting is because we don't always have the answer yet but [48:18] Typically, when we feel like we have the answer, that's when we write about it. [48:23] Now, [48:25] With uncorrelated bets, I could, you know, we are at a point in our thinking where I think we have a strong idea of the direction where we want to go, which is that like, [48:37] You know, there are there's like this X and Y axis of different things that could happen within generative AI. Like on one end of the spectrum is we could be living in a model in a world of like an oligopoly when it comes to models where all the power is within four companies. Or we could be living in a world like a multimodal world. [48:59] So that's, you know, one axis. And then another axis is like, we reach a plateau in terms of AI's ability versus, you know, we get runaway AI or self-improving AI, and that's another axis. And so, you know, [49:11] there are a range of different outcomes that can happen based on the answers to those two questions, which are questions we don't necessarily have the answer to yet. And as a venture firm, it's important that you make bets.

[49:22] that [49:24] occur sort of within each scenario so that if one of those scenarios does happen, [49:29] Like, [49:30] you've made the right bet and hopefully you've backed the right team. And one of those scenarios doesn't happen. Then, uh, [49:36] you know, and you only, and you put all your eggs in one basket, then that's not really good portfolio management. Yeah, no, I think that makes sense. I think the, then even if you don't have like a particular perspective, if I knew like, okay, um, Andy brought up uncorrelated bets because like he's concerned X, Y, Z. And so that became an open question. Or if I knew like, um, Andy and Matt argued about like what it means to be uncorrelated and Andy's position was this, [50:06] is even if it's not a statement about what your full perspective is, even that gives me enough detail to be like, oh, wow, that's actually really interesting. I don't know the answer to that question or here's what I think, you know? Yeah. So I think because we said that we were going to push these live this week, we should do that. But I think what I'm hearing from you, and we'll do this next week, is let's alter the system prompt a little bit to make sure whether we're identifying whether something is, you know, a topic where we have a really strong opinion on. [50:36] If it's one where it's still an open question, let's leave it as an open question. Do you think we could try it? Not necessarily even modify the system prompt, but just follow up and see if we can make these better? Or if you want to modify the system prompt, we can. Oh, well, I mean, I'm happy. What would you like me to ask?

[50:53] Okay. [50:53] In this case. [50:55] I think that I would say... [50:58] Um, none of these tell me anything about the like positions that were actually taken by anyone in, in the meeting. Can you be more concrete about what the different positions were or what people thought? So I can tell you now it will not be able to tell you who said what. That's okay. And that's not for privacy reasons. That's just limitations on the technology. Yeah. I think that's totally fine. It doesn't have to have the names. It's more like, um, I just want to know [51:28] what the perspectives were. [51:30] Okay, so it says... [51:34] Commoditization is coming. The concern about commoditization of foundational models was voiced by multiple participants. [51:41] One side argued that these models driven by high burn rates are becoming indistinguishable. [51:46] leading to a race to the bottom. [51:48] Thank you. [51:49] Another perspective emphasized the need for differentiation, particularly through proprietary data to escape this trend. The consensus leaned towards cautious optimism for companies that can find unique data sets or applications. See, like that actually like, I'm like, wow, these are the two, the two ideas. And then now I kind of understand like where the conversation went. And then the thing that I would maybe think about is how to express that as part of the headline, you know, um, [52:17] But yeah, I mean, I'm more interested in that personally. I don't know. I don't know what you think. So in this case, you think it's better to...

[52:28] re-prompt a few times until you get something that's extremely valuable. [52:32] versus... [52:34] publishing the unfiltered results of the LLM. Yeah. [52:38] Let's do that then. Great. So I mean, decisions get made here too. Do you think that, I mean, cause, cause the, the first one, which is publishing the unfiltered results to me, that's more like a barometer of AI progress than it is like what's going on at USV. Right. Um, and I think the, the idea for you guys is not necessarily like creating a barometer of, [53:08] but more like actually just get out like what are we actually thinking about and using this cool technology to like tell that to people but you tell me. [53:16] No, I mean, I well, so here here's where it gets tricky for me is like when we look at this one, decentralized to survive, the the AI actually got this very slightly wrong. [53:31] because we were not talking about decentralized AI models. We were talking about specifically decentralized training. [53:38] Which is, you know, it's different. And it doesn't really... [53:44] Because the general idea here is that the really big companies are well-suited [53:53] to win because they build out these enormous facilities, these enormous clusters where they're doing all their training. But if you can figure out a way to do decentralized or distributed training, then you don't necessarily need the same...

[54:06] um, like high capital investment to get started, uh, in order to compete. And so like, that seems like a really important area to be looking for investments so that we can build towards a world, a multimodal world. [54:18] And like that isn't really captured here. [54:22] But it's close. [54:23] Yeah, I think you're totally right. I wonder how it would do if it wasn't a one-shot thing and it was like... [54:31] each time you did it for one topic as opposed to all of them at once. Yeah. But yeah, I think that that makes total sense. And at least for me, that would be something that would annoy me. I would be like, we need to get this right. And that's so interesting. No one's talking about that. No one is talking about that. And it's really important that more people do. The thing that I think about is like, well, all those people that were mining Bitcoin should just be like, [55:01] financing AI training runs. And that's cool. [55:06] Yes. [55:07] And that's where our head is at with number two. [55:10] Okay. [55:11] Got it. Cool. Well, I guess we're on the same page or at least I can follow your line of thought once you explain it to me. Right. You know, it's this fine line. The conclusion that I've kind of reached and I think you've kind of helped me see it here as well, is that I kind of think that the market for purely AI generated media is like the size of your largest group chat. [55:35] And...

[55:37] I think... [55:38] I think any time we do something new, [55:42] What people are actually interested in isn't necessarily the content that was produced, but how we did it. [55:47] And so I tend sometimes to over-index on the... [55:52] "Let's keep as many hands off as possible." But in reality, [55:57] If we go back to sort of our first principles of enhance, not replace, [56:02] when it comes to our own internal AI projects, you know, we should be using these tools in partnership with humans to create better content that's actually valuable for the people who are consuming it. [56:13] And so I think you changed my thinking a little on this. Amazing. I love that. I mean, I love I love the, you know, the value of purely AI generated content is the size of your biggest group chat, which at work is your Slack channel is your whole Slack. And that's why I think the work podcast thing is really interesting, because to your point, like. [56:30] Um, [56:32] you don't have to read all the emails you want to read. [56:37] like while you're sitting at your desk, you can like, you can listen to that, listen to all the things you missed while you're doing the dishes, which, which is another point that, um, I was talking to, um, um, [56:48] I was talking to someone else a few days ago on this podcast about, do you know Yohei Nakajima? Yeah, of course. My inspiration for everything. Yeah, he's absolutely amazing. And obviously he does like all these like really cool AI side projects, like has a similar Tinkerer vibe to you, to me, to like a lot of the people I really like hanging out with. Yeah.

[57:10] One of the things that he said that I think is so interesting is that, because I asked him, how do you do all this stuff? You have a day job as a VC, you have kids, and you're programming all the time. How is it even possible? And he was like, well, first of all, I only do it at night after they're in bed, which makes sense. I mostly do it in the morning. Okay, there you go. [57:31] But also, he said, because it's doing a lot of the coding for me, [57:38] um, I can do it with divided attention. I can do it with fractured attention. So I can say, okay, go build this thing. And then I go do some dishes and I come back and I see if it did it. And then if it did, I say, okay, do the next thing. Um, and he doesn't necessarily need to be in that like full flow state all the time. And so I think a lot of these tools are, um, going like the effect, for example, of, um, AI driven storytelling is, uh, you can listen to stories, uh, [58:06] about content that wouldn't have necessarily been storified before. And that means you can consume that content in places that you wouldn't ordinarily have been able to consume it, like listening to your meetings that you missed while you're doing your dishes. And in programming cases, you can create things in situations where you wouldn't have been able to create them before because you don't have to do all the work in this super zoned and flow state.

[58:36] should I just do the dishes? Totally. That's a really important question, which I think is really interesting. And it's an extension of a question that we've had to ask ourselves since cell phones or since the internet or whatever. We're bringing all this stuff and integrating into every aspect of our lives. I think one of the things that I feel that makes me optimistic is... [59:06] Because you can now talk to computers, [59:10] you can express with much more fine grain control what you want to see and when. [59:19] Um, [59:20] And so for example, uh, [59:24] I really think we're going to get to a world pretty quickly where like, instead of seeing like the trash Twitter algorithm that everyone else sees, that's like optimized for like the like most amount of engagement possible on a mass scale. I'll be able to say like here, I want, I only want to see if you show me an article, if you show me an article on Twitter and it's about science, I only want to see articles where if, where it was not just a mouse study. So like. [59:53] Or don't show me any finance articles unless the market has moved more than 3%. Stuff like that, where as things get more integrated, we also have the ability to have more fine-grained control about what we see when, which hopefully is healthier. But also people do dumb stuff and yeah, there's trade-offs here. It's not all good.

[1:00:23] But [1:00:25] So much of our internet lives are owned by these handful of data monopolies like [1:00:31] you know, meta like Twitter. And it requires either breaking those monopolies or behavior change on their part in order to kind of build a more customizable Internet, in my opinion. [1:00:43] And so I think it's worth backing. [1:00:47] Lots and lots of businesses that can potentially break those monopolies. And in addition to that, I think kind of forking over full control of LLMs to, you know, four or five major tech conglomerates is really problematic for society in the long run. I think you're totally right. [1:01:17] where they make all of their data open to researchers. [1:01:23] Because I think... [1:01:26] It's so silly to me that researchers at universities are doing studies with a sample size of 16 people when Facebook is sitting on trillions of terabytes of the most valuable behavioral data ever to understand what it is to be human and what works and what doesn't. [1:01:47] And people hate tech companies right now. I think there should be a social movement for them to donate it for the public good.

[1:01:56] Um, [1:01:57] while we're at it to make sure that all data can be exportable by an individual. Because suddenly, like the sequence data, for example... [1:02:06] that I get from my whoop, if I can throw that into an AI model, it can make predictions about what is good for me or what I might want to do with my health that I can't make unless I have that data. And I don't think any company should be able to keep that away from me. Yeah. I mean, this is and has been [1:02:28] a major, major part of our firm's thesis for 10 plus years when it comes to like Web3 applications, which is composable data that can exist anywhere you want it to exist and that blockchains can enable that. But [1:02:42] you know, [1:02:43] I think there's a long way to go on sort of the UX of a lot of those applications. So in the meantime, let's push the big tech companies to make our data at least available for research purposes. [1:03:13] to show it to people. So set the scene for us. What are we about to see? Sure. So I am actually sitting in front of like 120 something, maybe I don't know how big it is. A really big television.

[1:03:28] And hooked up to this television, there's a bunch of different things, but one setting is hardwired directly into a piece of machinery that is locally running a visual diffusion model that is connected to the Whisper API that listens to what we are saying. And then in real time generates moving images based on what we're talking about. It's called the Dream Machine, and it was developed by an artist who I only know as human. [1:03:58] You can follow him on Twitter. He was a member of a group called the Bright Moments DAO, where he developed this, which was [1:04:04] an investment of ours. And this kind of just sits in the background in our office from time to time. And it's one of the coolest things because it is one of the few things that we have here that both like, [1:04:15] visiting CEOs or whoever or other investors will come and have their minds blown. But then also, I like very often catch the building super bringing his friends up to kind of play with it. So I'm going to turn it on. The one caveat I'll say is that, you know, this thing tends to get pretty backloaded. So sometimes it's a little slow in terms of its responsiveness, but let's just throw it on and see what it's doing anyway. Great. Perfect. [1:04:38] Thank you. [1:04:39] There we go. Ooh, we've got an aquarium. Okay. Like what I want to know, so I'm just going to name some things and see what it does. Like I've been reading Moby Dick. [1:04:47] And I it's really good. I actually just finished it and I just love [1:04:53] I love all the whale and oceanic iconography. You know, he gets really into the whaling industry. It's like it's like such a detailed account of how whales work. And I guess we've got the we've got the aquarium. So we're getting close to whales. Well, what I was going to say is every once in a while you have to reset this thing's memory because it becomes so backlogged with things that people have talked about in the past.

[1:05:17] that it can sometimes take a minute or so before what you're talking about actually shows up. But there is our whales. Here they come. Look at that. That's so fucking cool, dude. Like, yeah. If we, by the way, if we had wiped its memory before we started, like those whales would have just popped up immediately. [1:05:34] And one fun thing I'll say is that we had some event here with Senator Gillibrand and she allowed us to have this going in the background while she was talking. So it was basically transcribing a senator's speech with visual imagery. [1:05:53] So here's what it reminds me of is... [1:05:57] Um, [1:05:58] The way fiction works, [1:06:01] is when you're describing the setting around a character, typically the way that you describe it will be a reflection of the character's internal state. [1:06:10] And that's why it's like a cliche is that like when something bad happens in a movie, like it's raining outside. Like it always reflects how they how they're actually feeling. And you can create this real sense of drama and concreteness. And that's Jill Brown behind you, which is crazy. [1:06:30] I'm sorry. [1:06:40] with that. And that's what this feels like to me. It's reflecting this in our internal states and our conversations in this visual way that reminds me a lot of fiction.

[1:06:50] Yeah. [1:06:51] Um, well, yeah. [1:06:52] I would just say compliments to the chef. Like, I... But... [1:06:58] I also think that it is... I think this is one of the first truly [1:07:06] Native AI artistic art forms that could not exist in a world of [1:07:10] prior to AI. Yeah. [1:07:13] which I think makes it really fun. And, you know, obviously there's cats coming up. And, [1:07:22] I don't know. It's just like, oh, geez. [1:07:25] Uh, Gary. [1:07:26] I know I have such a hard time communicating when this is on because I'm just looking at it. I tried to have a serious meeting here the other day with someone while this was running and it did not go well. [1:07:39] I don't know, like bring your ideas to life. I feel like that is kind of what the lesson of, of, [1:07:45] of the dream machine is and sort of my perspective on everything that's happened over the last six months of my life is just like, [1:07:51] Just make stuff because the tools are there to do it. [1:07:55] Fuck yeah, dude. That's exactly how I feel. I love it. And I think that's a great place to end. Thank you so much for coming on and showing us all this stuff. It was amazing. No problem. And by the way, if we meet again in a year, my hope is that the librarian will be here with us because that is what we're working on. Let's book it. I definitely want to have you on in the coming months and years. [1:08:16] Cool. Thanks so much, Dan.

[1:08:46] and laughter that will leave you on the edge of your seat. [1:08:49] 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 so do yourself a favor hit like smash subscribe and strap in for the ride of your life [1:09:03] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.