A (10:57)

I think it's like pure software is rapidly becoming uninvestable, if I remember correctly.

B (11:04)

Yeah, that's a watered down version of what I really wanted to say, which is that pure software is uninvestable. I would just full stop right there. If your whole advantage is like, hey, I'm building cool software that other people don't know how to build, I think that's uninvestable. And that's uninvestable for two reasons. One is they can just hack it together today. And the second is the coding agents are getting better so quickly that within a year or even less, they'll probably be building scalable software with good architecture. So I think we're going to see leaps and bounds improvements. That genie is out of the bottle. So if you're a venture investor now, you're looking for hardware, you're looking for network effects, you're looking for AI models. And I would argue that training AI models is the new building software for however long that lasts, until auto research and auto training starts working. But I think vibe coding, it's more fun than playing video games. It's more productive, it's more constructive, it has better feedback loops. You build something you want, you're at the bleeding edge of technology. You may even make some money or a career out of it, although careers are kind of dead. But you may make an interesting opportunity out of it. And you learn a lot about computers just by doing. I've seen kids who are Vibe coding, It's hard to get kids to program. You can throw Swift playgrounds and Scratch Jr. And all of that at them and hope that they pick up coding. But if you throw vibe coding at them, they're going to get instant feedback and instant rewards. Maybe along the way they'll pick up fundamentals because these things still require some skill to operate. And in the process of operating them, you'll be forced to figure out the command line and you'll be forced to figure out how basic computer architecture works. And you'll be forced to figure out concepts like caching and backing off in a network and sharing streams and writing to disk and latency versus bandwidth trade offs, et cetera and all those things. So you'll be forced to learn some basics of computer algorithms and architecture and it's just a fun way to go. I've been up late nights, probably spending a couple hours every night. The time that used to go into reading or doom scrolling or playing video games is all now in vibe coding. In fact, that's why I haven't been active on X recently. I've been completely missing on X because I'm buried in Claude and Codex this

A (13:09)

episode is presented by Angellist, a company Naval and I started a while back because the infrastructure for venture capital was stuck in the past Past the problem with most fund admins is that they have no AI and their software is terrible. So they throw humans at the problem and their service teams are constantly churning. On top of that, they're not full service, so funds have to stitch together their own operations and LPs end up getting pissed when capital calls are wrong and valuations are hard to trace. Angelus is built to solve all of that. It's the most complete product in the market for funds from $10 million to a billion dollars. It includes SPVs, Scout funds, introductions to QPs, and it works with your agents, ChatGPT and Claude. AngelList already powers over $200 billion in assets 5,000 funds, and Naval and I use it to run our funds. So do a lot of investors. We know migration is probably easier than you think. It can be done in four weeks. Go to angellist.com podcast to get your fund on rails and tell them we sent you AI has gotten so surprisingly resourceful that whenever I get a response that isn't surprisingly resourceful, I just assume they're not feeding it enough tokens. The most interesting thing to me about agents is their ability to error correct and learn how people have it, watch YouTube videos at night, or go out onto the Internet and try and learn about the tasks they've been instructed to perform during the day. So these agents are going out and error correcting and improving their skills. Likewise, the innovation of thinking in AI models is also an application of error correcting where you take the next token prediction process and turn it into a pseudo thinking process that can error correct as it goes through each step in the thought process. Getting rid of hallucinations was also an error correction process. So I wonder what's going to be the next application of error correction in AI. One random thought I had, and I'm sure people are working on it, is applying error correction to agents working together, agents working with other agents. Because one of the important ways that people learn and improve is by working with and talking to Other people.

B (15:19)

I'm not sure the analogy applies that well, because AI is jagged intelligence, as they say, where it's incredibly smart at some things and incredibly dumb at others. And it's structured very differently than humans in that when you're using Claude, you're using the same AI model, even if you have 10 instances of it running. So 10 of them talking to each other doesn't really improve its thinking in the same way that 10 humans talking to each other do, because those humans are trained on 10 different data sets. Humans are just inherently very creative and think out of bounds, whereas the AI agents are trained on the same data distribution. They're literally running the same model. It's like 10 people with the same brain, the same data set, talking to each other. Sure, just through thermodynamics. They might have some different ideas and come up with something slightly different, but they're generally going to think the same. So all you're doing when your 10 agents are talking to each other is you're just throwing 10 times as many tokens at the problem. It's like saying, take 10 times as long if you need to. Now, there are different models like Codex and Gemini and Grok code, which are trained slightly differently. Not that different, but they're slightly different, and so they might have some different insights. Claude has really good visual presentation through the system called Artifacts, and Claude is very good at talking to me at the level that I'm at. So it's very tuned to figure out from your question and your conversation what you're capable of understanding and what level you're asking the question at. It's very good at meeting you at that level. ChatGPT is still the OG. It's sort of very good all around. Gemini is very good at search because it has the Google Crawl underneath. It's a frustrating product. It's constantly timing out on the app and losing the connection and forgetting the plot. But it's very fast and it's got a great search index. So if the question I'm asking is really a search question underneath, then I use Gemini. Gemini also has access to YouTube, so if you think your answer is lying in a YouTube video, and there's a lot of YouTube videos, and Gemini has the data advantage of YouTube. So Gemini is really getting by in data advantages. It doesn't feel like the best model to me, but it has the best underlying data. And then GROK is the one I can counter to tell me the truth. It's like the least neutered, least nerfed. It's got access to X. So it's very good at news and it's very good at technical problems. So if you're asking a deep, difficult problem in the scientific mathematical domain, then I think Grok is actually quite good. Not the others aren't, but I just think Grok is stand out there and that reflects the biases of the companies that created them and trained them and are driving them. Currently, all four of the leading frontier models have a place. I do use them against each other. So for example, I wired up with my GitHub so that every time I'm submitting a new piece of code, say that's written by Claude, then Codex and Gemini automatically fire in every pull request. It's misnamed, but it's when you actually push code into your main repository and you're basically saying this is ready for review and this is ready to get merged into the main code base. So you've been working locally in a piece of code, let's say with Claude, and then you push it into the main repository. So you file a pull request. Well, you can set it up so that other agents like Gemini and Codex and Grok automatically fire and review the pull requests and then they say, oh well, you should change this thing about the architecture and so on. That's a way of getting them to sort of communicate with each other, to have a council, a roundtable of AIs. But I haven't found that to be as useful as you might think. There's still a lot of groupthink with these AIs. If you're coding with them and you push towards an answer. For example, if you think you know what the answer is, it is rare that they will contradict you. You'd have to be pretty wrong for them to contradict you. They're trying to please you and I don't think they have any long lived theory of mind of their own. So they're always kind of morphing towards you and they're going to find the answer that you're looking for. So if you think the answer is in a certain area and you push the models even slightly, all of them will find roughly the same answer. Because you're leading them to the answer, they're very easily led around. One of the things I've noticed is that as the code base has gotten more complex and larger, it becomes more difficult to manage because it doesn't all fit into the model's context window anymore. The models can only hold a certain amount of data in their heads and Right now the state of the art is about a million tokens, which will be considered laughable in the future. You can approximate that by thinking that is a million words. And that's because of the transformer attention mechanism underneath which for it to properly work, the problem is a square of the number of tokens in the context. So if it's a million tokens, that means the context window is like in the order of complexity of a trillion tokens because it's a square of a million. So the context window runs out as your code base gets larger. The models can't keep all of it in memory anymore. So they start making guesses, approximations, they start compacting the context window, they start losing the plot, they get lost, they start fixing the wrong thing. They fix the same bug five times. They go do a quick patch in the architecture when the problem lies somewhere else and you have to guide them. So as you are dealing with a more and more complex code base, it falls upon the operator to provide the guidance to say, actually here I think we should just rearchitect that whole thing. And they will do some incredibly boneheaded things, like if you are not paying attention and just text is scrolling by, occasionally they'll patch a bug just by eliminating the use case or destroying the feature in the first place. Or they'll do something that is clearly a hack and you kind of have to stop them and say, hey, that's a hack. And by the way, I do this all the time. I'll stop the model and I'll say, no, no, that's a hack, that's a patch. Go fix it at an architectural level. And what's funny is the model will always say, oh, I'm sorry, you're right, that was a hack. Even if that wasn't a hack, the model will say, you're right, that was a hack. So the model is always trying to please you. And it doesn't know any better in that sense. It's a little bit like a dog. It's better than you at catching that duck if you're duck hunting with a dog. But it's still a dog. So if you point it at a bird, you know that's not a duck, it might take that bird down instead. So you do have to guide it. It does require a lot of operational oversight. So long winded way of saying you still have to guide these models. Them talking to each other isn't going to fix the problem. And you do have to get involved in the architecture, the debugging, the features, and pay close attention. But this combo right now of human operator combined with a state of the art coding model can yield incredible results. You can already completely one shot simple apps. So like a basic task list, a basic video game clone, you can one shot them, one prompt and you get something that's reasonably good coming out the other end. So you can see where this is headed. Eventually, once they have enough data, they will be able to one shot very complex apps. And that's a whole different world that we're going to get into now in terms of what is it about coding that makes them uniquely good at it. It's just there's tons and tons of data and when you're training the model, it's very easy to verify, hey, did you do a good job or not? Because the code has to compile it and has to execute and you can have simple tests that are pre written on the other side to say did the code you wrote pass the test? Did it do the thing you're supposed to do? So coding turns out to be one of those things, things that it's actually quite easy to train models on mathematics is actually similar in that you have a ton of data, you have a lot of solved problems and you can verify the output very easily. So in domains where you have a lot of data and you have good verification, self driving is another one of those. These models do extremely well. In areas where you don't have a lot of data which are kind of brand new fields, the models are not going to do well and that's still an opportunity for humans and creativity. Then domains where it's hard to verify, for example, in creative writing, like who determines what's good, creative writing versus what's not, what's slot versus what's not, then these models don't do as well because you can't easily run a closed loop where they're just outputting huge amounts of content and then that content is being immediately algorithmically graded without having to have humans in the loop saying this is good, this is bad. For example, if you're trying to do creative writing with these models, they're going to output huge amounts of content. They can output infinite essays. Who's to say it's good? On the other side, even if you hire some low wage people to sit around call center style and say this is good or this is bad, it's only as good as their taste. I think one of the reasons why these coding models got really good recently there's multiple. One is they're doing sort of Almost recursive training where like one model is helping improve the next one. But I think the bigger reason might just be that a lot of the best software engineers started using these models in the last few months and their taste has now feeding back in. So you're getting access to their code plus their taste as to what's good and what's not. You need high taste feedback loops to improve these models and those are harder to develop than they look. In certain domains it's tractable and in other domains it's hard to see how it happens. So the obvious stuff is, yeah, you go and you build your app. Great. Less obvious stuff that's like just one level more advanced, which will be laughably simple to a software engineer, but it's kind of fun for a non engineer or someone who hasn't coded in a long time to think about. One is I built my own app store. So if I want an app, I literally open up Claude on my phone. I can operate a remote terminal which is running on my desktop, or I can just use Claude in the cloud, it can connect to Xcode, I give it a two line description, it builds me an app, it ships it to my app store, I open my app store app, the app is sitting there, I click install, 30 seconds later I have a working app on my phone. That's magical. You can literally be at dinner with someone having a conversation, they describe some app they want, you can describe to Claude and five minutes later you're showing them that app on your phone. That's why I say it's kind of the beginning of the end for Apple, because Apple sort of relies on their OS and their apps being better than everybody else. The hardware, yes, it's better, but it doesn't support their margins and their monopoly or pseudo monopoly. So when all your communication starts going through Claude or through Codex or through some other agent, when all you're doing all day long is instead of opening an Uber app, you're saying call me an Uber. Or instead of opening a workout app, you're saying, where's my workout app? Track my workout, make no mistakes, right? Then you're just communicating with the agent. And when that happens, then the need for a phone becomes much smaller and smaller. Maybe there's a few banking apps and government apps that have imported and don't have the proper APIs, but these agents don't even need APIs. They can figure out and create their own APIs on the fly. The use case stops being you're interfacing with your iPhone or your Android phone. Instead you're just interfacing with the AI model. And now Apple is using Gemini, which is Google's AI model. So what's the difference? I might as well just use an Android phone because all I need at that point is I need a screen, I need battery and I need connectivity and Android's got that just fine. And then the apps and user interfaces are being created on the fly for what I need. And yes, for certain things there'll always be best to breed user interfaces and you'll want some familiarity. But even the era of tap, tap, tap, upgrade your system software, drag this over here, hunt for that button, type into that field, all that is going away. It should all be conversational, it should all be agentic. And in that world, Apple loses a lot of its advantages and then it's competing purely on oh yeah, we have the best chips and we have the best integrated hardware, but that's not the same margins as Apple of today. That's more like the margins that Samsung or Lenovo makes, which is not the margins that Apple wants to have. As a consequence, I think its market cap will compress. I think Apple giving up an AI will go down as the biggest strategic mistake in the tech industry of this decade. And it's the beginning of the end of Apple's dominance. These companies can exist for a long time and make lots of money, like Microsoft is more valuable than it's ever been. But Microsoft Windows has kind of lost the battle because they missed the mobile phone wave. They stuck to Windows OS and they didn't upgrade to a touchscreen based native OS design for phones from the ground up. And they didn't focus on the consumer, they were too focused at the enterprise level. So Apple surpassed them and is now one of the most valuable companies in the world. I think it used to be the most valuable might be Nvidia at this moment. The same way I think Apple will get surpassed. I think their future growth is capped because they're now captive on AI and they're behind unless they manage to turn the AI ship around. I think Apple has capped growth long term and is in quote, unquote, trouble. Not in the sense that it won't be valuable, but it'll be a lot less valuable than it could have been. The other thing is, within the app that I'm building, I have a bug reporting infrastructure where someone sees a bug, they tap on a button, the bug sends the logs up and the bug files into a server. And then I have Claude go every 24 hours through all the bug reports and it just fixes them all by itself without my having to intervene. And it puts all the fixes into side branches for me to review. And then all I have to do is just review the fixes and say, ah, that wasn't really a bug, that wasn't a good fix. Don't ship that. Oh, that looks good, makes sense. Ship it. I'm just the final gate that decides on what goes out there. Eventually. You can see apps being built that way by features, where the users will ask for features, they'll vote on features, and then there'll be some tastemaker or maintainer in the cloud who look at that and say, no, the users don't know what they want, or oh, that makes a lot of sense, we should fix that or change that. So I think even software development will become a collaborative process with the users and the agents will be handling all of it. Because in a sense, the agents can do perfect customer service. If your customer service was perfect, your customer service person would also be an incredible coder and would be indefatigable. They would be up 24 7, they would be writing code, fixing bugs, responding to people, and they would have no ego if they wrote a lot of code to fix a bug and then you just threw it all away. So I just find that kind of a feature very compelling. You truly can have one person, two person software companies now that can scale to millions upon millions of users and make billions upon billions of dollars. That has happened already in the past with people like Notch and Satoshi Nakamoto and very small teams like the original Instagram team that just made a huge dent with very few people, or the original WhatsApp team. But I think you're going to see it more and more.