Light Cone Host 1 (3:26)

What? What was Posterous for people who don't remember it?

Gary Tan (3:29)

Yeah, Posterous was dead simple. Blogs by email. It grew to be a top 200 website on the Internet. And then Twitter ended up buying it for about $20 million. So that was sort of like my first bag, really. I actually built it again as Post Haven. When Twitter bought it for the amazing people that we had hired and they shut down the startup, it would have cost a couple million dollars to buy it back from Twitter. And at the time, I had no money in the world. So the next best thing was, why don't I write it again? And then in January of this year, I ended up writing it a third time. Only, you know, the first time it took about, you know, $4 million and, you know, six or seven people and about a year and a half. And then the second time it, you know, took about, I don't know, 100 grand and two people. Me and my co founder, Brett Gibson, who now runs Initialized and maybe like three months or so. And then in this case, it took about $200, which was my Claude Code Max account, and probably five days. Full featured blog platform, does everything you want. And then on top of that, like full rag, full agentic retrieval, like be able to sort of go out and read all of the Internet, like every tweet I've ever Done recursive crawl, deep research of any topic. The algebra thing is just one of a whole lot of different issues that we really, really care about. And to be able to go ingest the Internet, you know, see all the arguments for and against and then to craft incredibly detailed reports on the back end about what are all the quotables like. I think people who are big followers of the light cone might remember one of our first episodes about agentic systems with Jake Heller actually. So Jake created case text and he described exactly what I ended up building for basically journalistic long form articles about any, you know, sort of issue or you know, piece of news that was happening. And so, you know, anyone can go to garyslist.org today and you know, we do about two or three relatively, you know, researched all fully sourced articles about what's going on in California and San Francisco and LA and like how do we build a better government.

Light Cone Host 2 (5:50)

This is the thing I feel like people missed about Gary's little don't fully get is that it's like the classic thing we've been talking about here, which is like software was you build software to let people use it. So it's like you build a blogging platform and people write blogs and maybe they'd start their own substacks eventually or they write articles, but Gary's list is both. Blogging platform, but it actually does the work of a high quality investigative journalist. It's not just something that a journalist uses to publish their articles.

Gary Tan (6:17)

Yeah, I mean basically for the equivalent of like 5 or $10 of Opus calls, I mean I would estimate that it does the work of like, you know, a real human being that would have to like go painstaking through dozens of articles, read entire books about certain subjects, annotate them. I mean going back to the case text example, like the thing that Jake taught me was that you need to think about what a human would do with the context given. Like what would it retrieve? Like does it go to the library? What kind of book would it look for? What does it search on? For search, you know, on the web. I mean the great thing now is like you don't have to just do that. Like you can get Perplexity's API and you can do deep research research there. You have X as API, you can do deep research there. You know, Groq's API if you need to like do research on X using the Grok API is actually very, very good. And you can just grab all of the context. This is sort of going back to the philosophy of Boil the Ocean, which is one of my essays, it's like, particularly when building Agentix software now you don't have to settle for what we did when we were humans writing the code. Like, and that goes for research as well. What if you absolutely boiled the ocean? Like what is, you know, the total completionist? Like if you were a human, this would take you about a month to do this research. You can just, you know, zap the rocks harder. You know, you pay more money and you might be token maxing, but you should token max. Like basically if there is incremental work that makes something more complete, more awesome, more, you know, in the case of this type of writing, like we want it to be more representative of reality. Like, like, you know, we don't just settle for one source. When we can get 20 sources and we can cross reference them, we can figure out like, well these 13 sources say this and seven sources disagree with that. And then you know, you want to feed all of that context into like your core prompt and then you can basically make a better decision than what you would. Like just, you know, a human being clicking on a link, reading a headline and that's all you understand. And I think if you token max, like that's actually the coolest thing you can do now. And it's not just in, you know, generating art articles. It's not, you know, it's clearly in writing code, right. I think now it's going to permeate every part of society. Like every thing that we would call knowledge work could be token maxed. And I don't think that it means that we're going to get rid of people. I think it means that people need to still supply the agency. Like I need this. Like I'm the one who's sitting here caring about algebra. Like I want kids like me who couldn't afford private school. You know, San Francisco is the one city in the world that has the highest rate of private school attendance probably in the entire country actually. And that's not okay. Like you shouldn't have to be rich to have a good education. And you know, I don't know why that's controversial. And so for me it's like this, you know, mass sort of shift in technology was happening and then I had a need and a want and a desire and it was a burning desire. Like I, it hurts me and pains me to think about 10, 12, 13 year old kids who don't know algebra. And like I could have but some bureaucrat or you know, some virtue signaling person in Power says, like, actually, I don't want that kid who wants to learn algebra to learn it.

Light Cone Host 3 (9:47)

So I think in this process of basically solving your own pain and need from the young Gary and building Gary's list, you sort of discover a lot of patterns on token maxing and this new way of building that led you to the next project, which was Gstack.

Gary Tan (10:07)

Like, I actually did not plan to make Gstack. All I did was like, I realized that I was doing the same things over and over again. And then I got sick of typing the same thing. So I went into my Apple notes, I typed in all the things that I found myself writing over and over again into cloud code. And it was pretty simple stuff. It's like, here's the plan review. One of the things I started doing is I really love asking Claude to make ASCII art diagrams. One of the things I discovered is sometimes Claude would just get confused and like write bugs or not be complete. But once I started saying, actually, before you start your work, make an ASCII diagram of all the data flows, all the inputs and outputs, what are the user flows, what are the error messages? And you can see this. It's like data flow, state machines, dependency graphs, processing pipelines, decision trees. Once it did that, it loaded all of the context in and then it just did the work more completely. Like, it boiled the ocean better and it broke down into a bunch of different sections. Like, here's architecture review, code quality test. I mean, one of the things I learned building Gary's list was that when I was writing the code myself, I would always do the minimum amount of testing because it's just like, not very fun. I knew I needed to have it, but I'm here to write, you know, fun new code. I, you know, did not like to write tests. And then honestly, like, I hit all the things that everyone else hits when they start vibe coding, which is like, this is slop. It's not working that well. Like, it works fine for the 80% case, but if any users actually touch it, it starts falling over. And then that's when I realized, oh, I can get to a hundred percent test coverage. I've since learned that a hundred percent is probably too much. Like hitting 80 to 90% is usually the best practice at this point. But yeah, this, this is basically the first version of Plan Eng Dash review. I know everyone knows the office hours skill, which is, you know, what people can use. And I still use when I'm trying to make a brand new product or a brand new feature. It simulates what, what we do when we're working with a company. It's like, how do you know that people want this? You know, who's it for, what does it do and what's the impact, right? But this is like the proto skill. Like, this is. I didn't even know skills existed. And I posted this and it went viral. Like, you know, 200,000 people saw that. And then I made another version of it that was a much more expansive version. I called it the Megapl and then I ended up renaming it to the CEO plan. We've probably talked about meta prompting before. I used meta prompting here. I took the other review plan that we had and then I said, okay, well let's do a version of this. But like, imagine Brian Chesky sitting with you, right? Like, Brian Chesky has this great line about what is a 10 star experience? So, and you know, the point of it is everyone thinks about hotels in terms of like three. This is a two, three star experience, this is a four star experience. And he likes goes, you know, through the list, like five stars. It's like everyone, you know, yeah, cool. Like, he's like, what's a six star and what's a seven star and what's an eight star? And like he goes all through that entire list. And that's one of my favorite, like product and design exercises to go through, like as a mental exercise. And then the cool thing is like, you can do that every single time now. And so that's what this is. You know, this prompt basically tries to figure out what is the platonic ideal of what this is. These are sort of like the three, the two things that are pretty awesome. One is what is the 10x check? What is more ambitious and delivers 10x more value for only 2x the effort, right? And so for whatever reason, coming out of latent spaces helps the model, like really visualize. Like, so I'm plan CEO Skill. I actually really enjoy because I'm an ADHD CEO and I love potential, like pure potential. And so this is like the one, like, I can't believe this is just literally two little sentences. But like, this unlocks an incredible amount. And so that's how Gstack started, actually. Not as, you know, I didn't want it to be anything other than like, well, I just need to make some skills. And I had heard that people were making like skill repos. But then the third thing I did was I started using these two skills so much that my conductor instance was getting very backed up. So this is how I use Conductor. This is actually my real setup.

Light Cone Host 1 (14:35)

This is your daily workflow. This is how you've been shipping hundreds of thousands of lines of code a month. It's all in here.

Gary Tan (14:40)

Yeah, that's right. So I dropped like 13 PRs in the last 48 hours, and then you just queue them up. Like, anytime I come up with a new idea, I come in and here it is. You know, I loved using the CEO skill, I loved using the ENG skill to, like, really make it super well tested. I did that all in Plan mode, and then I'd click Approve here and then, you know, Claude would go and do all the stuff. And then I did that so much that I ended up having like 15 different features that were all queued up, waiting for me to manually test it. Like, it passed it. You know, it passed end to end testing, it passed integration, it passed unit tests. But, like, at the end of the day, I still need to, you know, for Gary's list, it's like pop open the Rails server and like, you know, load that user and like, make it into that configuration for that particular user and, like, manually just make sure it works. And I got sick of doing that. And I was trying to use Claude encode mcp and it was very, very slow. Two to three seconds for every turn. And it's like, this is not usable for qa. But I had heard that Microsoft had released Playwright, which is sort of an alternative testing framework. In retrospect, it's like, actually there was like, Agent there, like Agent Harness, and like, all these other, like, tools that I could have used. But the upside and downside of Claude code is it's so easy to just start something that I just popped open. Like, I literally went in here and this is probably what I did. It's like, I'm so sick of using Claude Claude in. In Chrome mcp. It's too slow. Let's go ahead and wrap Microsoft's Playwright. Can we do that? And then I just pressed Enter. And then, you know, one of the things that emerged with Gstack is that, like, this is how I create new features. Now. Of course, you know, what it's going to do now is like, hey, dude, you already did that. Which is hilarious. You know, I have bug fixes right next to Giant Features. And then the way Gstack works, there's a CEO, there's a designer, there's actually a Developer experience person in there, there's a number of design tools, and then Plan Eng is the last one. And then I actually Usually run codecs and I recently added a slash Claude in Codex. So one of the cool things that I actually learned from YC alums, I came to an event and brain totally frazzled. But you know, went to one of our batch events and we were just shooting the shit about what was going on with Claude code versus Codex. And at the time I was a total Claude code only guy. And I realized, oh, a lot of people actually prefer Codex. Why is that? Discovered that Claude code is ideal for the ADHD CEO, but once in a while there's a, you know, Claude code will just BS a bunch of stuff like Claude models are very, very good, but like they are not the smartest, it turns out. And so a lot of people, you know, explained to me that if you have a problem that's much crazier, you need the 200 IQ nearly nonverbal CTO. So you can just call in a friend and then that's what like slash Codex is. It's a GSTACK skill that it takes whatever plan your plan is, or if you're out of plan mode and you already implemented, it'll take your repo and it'll run codecs in a command line prompt with the prompt that says find all the problems and all the bugs and it reports it back to Claude code and then you and Claude code can work through that feedback. And then I have since added, if you use Codex as your main coding agent, you can actually go and type Claude and have Claude come and be the CEO briefly if you want as well. The cool thing about GSTACK is when I run it through this program, like I always I do, I start with office hours, CEO review like I do design if there's ui, if I know a developer needs to use it, which is like practically all of GSTACK and GBRAIN stuff. I run the developer review and then I do ENG review and then Codex. Once that plan is done, I've worked through all of the issues the GSTACK relies very heavily on. Ask user question. So because, you know, and that's. That to me is like really important. That's where the human, you know, vibe, coder operator, agentic engineer, needs to supply their understanding of what's going on. What are we building? There's not really a substitute to that. It would surprise me very much if someone really, truly did manage to make a thing that could just make software without the human in the loop like that. You know, it's a controversial take, I think, but I never want to be entirely out of the Loop. I just want the machine to do the stuff that I don't want to do. And so, you know, basically QA is a good example. And you know, I mean, that's hilarious. Coming back to the demo, it's like I typed something into the modern version of Gstack and it's like, dude, what are you doing? Like, we already built that. We have browse. Browse is a long lived HTTP demon with 70 commands as a CLI. And then QA is just browser. But in the prompt for QA it says look in your context. What did we do on this branch? If there's UI or any mutation of data, go and use the browser to test that thing. Which is cool. It's like having a black box browser. It blew my mind when it first worked. It's like mini AGI is already here, you know. You know, I realize this is not true AGI. True, true AGI would be like, I'm not even here. And actually that's fine in this respect. Like as a builder, you know, selfishly, I hope that we never have to stop. I hope that the machines never figure it out because that would be really cool. Like then, you know, humans are really important and like engineers who know how to do this, who have taste in design and product feedback and you know, the real customer in mind, like, we're going to be like, we basically have wings for as long as we do.

Light Cone Host 3 (20:32)

YC Startup school is back. We're hand selecting the most promising builders in the world and flying them out to San Francisco for July 25th and 26th to discuss the cutting edge of tech. Apply now for a spot. Okay, back to the video. I think you crystallize a lot of these thinking in this post on X about thin hardness and fat skills.

Gary Tan (20:54)

Oh yes.

Light Cone Host 3 (20:54)

Which actually encompasses all of this philosophy on how to token Max.

Gary Tan (20:59)

Yeah, I mean, some of it came out of being trolled on the Internet relentlessly about Markdown. And like I, you know, I'm just like peddling a markdown, a set of markdown and it's like, you know, I guess my lived experience at this point is that markdown is actually code. It's just like this compiled in a different way. But like you can get the computer to do really astonishing things. Like, I mean, even this, it's like, could we have imagined that I would be talking to something that has replaced Visual Studio for like, I don't use Visual Studio at all. Like, there's no reason to. Like when I can talk to my agent and my agent can do this, right. The article actually, the name actually came from our partner, Pete Kuman. We have had to build an internal agent and, you know, we call that the harness over and over again. And then at some point, using Claude code all day, we realized, like, you know, why should we rewrite a version of that over and over again? Like, you know, we should just use the things that are really awesome as, you know, harnesses. Like, a harness is the core loop that takes the user input, gives it to the LLM, runs what the LLM does. Like, it can do tool calls and things like that. I mean, why would we build that? Like, what we should be spending all our time doing is thinking about what markdown should there be. And the way to think about markdown is if you were an event planner and throwing a wedding and you were trying to write down a checklist of how to throw a wedding again, like, what would you, what would you write in plain English to teach the next person who had to do it what to do? All of that should be in the markdown, whereas all the things that should, you know, be deterministic, like, I mean, or is. Is a real action. Like a wedding planner might have to call like 20 venues, right? But you wouldn't use Markdown for that. Like you would make a, you know, a call to Twilio, for instance. Right. There's like a, you know, sort of. All of the difficulty in engine engineering today is when try to do things that should be in markdown in code, and it fails because code is brittle. It doesn't understand special cases. It does actually, you know, code literally doesn't understand what you want or who you are. It is like, you know, executing deterministic zeros and ones in a Turing complete loop. Right? Like it doesn't know. But then now we have LLMs that have latent space and they know who you are and it knows what your motivations are and it can handle generic cases. And then, you know, a lot of the magic right now as an engineer is like figuring out, okay, how much of it is over here in LLM land and how, how much of it is over there in codeland. And then, you know, if you combine that with the other thing I learned, which is like get to 80 to 90% tests, like if it's not tested and you're just throwing users in there, like it's slop, you know, 10x worse than like human written code because you just have no idea what's going to happen. And so that's one of the things that people have to do. It's like, all right. Not only do you need to figure out what's going on in latent space and deterministic space, you also have to make sure that it's individually tested, and then the integration is tested. And then going back to boil the ocean, the machine doesn't care. It'll just do it. It's amazing. Just zap the rocks more, and you can get to 90% test coverage. Then you can have a system that, you know is not quite perfect. Like, you know, openclaw. Right now, there are lots of, like, failure cases, but it's 95% there. You know, it's. I feel like using OpenClaw these days is like driving a Ferrari. And it's, like, exhilarating. It's insane. Like, you get to do things. Like, it figures things out you would never think a machine could figure out, and it does it so quickly. But then it's also like a Ferrari in that you better be a mechanic. Like, it's a Ferrari that will break down on the side of the road when you most need it. And you need to get out with your wrench and pop the hood and, like, fix it. You know, you're gonna have to fix it yourself. And so this is a very exciting time in computer science and technology because it's like, this is Homebrew Computer Club. You know, the moment when the Apple one came out, like, the Apple one created by Steve Jobs and Steve Wozniak, was a breadboard inside, like, literally a wooden case hammered together with, like, nails and duct tape, you know, and if you wanted a personal computer, that's what you had to do. And that's where we're at right now. Like, you have relatively, you know, smart, technical, and, you know, people who had to study computer science have to spend, like, two or three hours and, like, maybe, like, 500 or a thousand dollars in both tokens and cloud to actually get something like that running. But, like, once you get it, it's like we're sort of in the kit car Ferrari phase. It's like, then you can drive and you can go anywhere, and you know, you want. You want to shout to the hill. Feels like, hey, I got a Ferrari.

Light Cone Host 2 (25:52)

Even the part about fixing yourself, I feel people. It's just like one of those things until you've pushed through, you just don't quite get. If I really zoom out, it's almost like things have moved so quickly. If you think way back, just having stack overflow as a website that you could consult when you got Stuck on a programming problem felt amazing. And then it's like ChatGPT launches, like, oh, now I've got this interactive thing that's way better than stack overflow. But you're still sort of doing the same thing. You're asking questions and you're copying and pasting code and you're running the code and seeing what happens and copy and pasting it back. And then you sort of, with clawed code, you sort of push through and you realize that you don't need to do the copy and pasting anymore. It just actually executes and runs the code. And even with openclaw, I found that when I set it up. Yeah, it's annoying because it can effectively brick itself and it does a bunch of annoying things. But if you actually have clawed code, it'll fix it. Yeah, just have clawed code running it will just fix it. And it's clearly not the way things will be long term. But there's this mentality shift of it doesn't actually matter if it's brittle and requires fixing because you can actually just have another agent sat there fixing it all the time.

Gary Tan (26:53)

Yeah, I feel like this evolution, I was like completely Claude code pilled and still am, but probably only like 50% or 60% of my time, like building product or agentic engineering is in Claude code now at some point.

Light Cone Host 1 (27:09)

Basically almost half of it is through open CLAUDE now.

Gary Tan (27:12)

Yeah, which is very interesting. I mean, then again, I'm also spending a lot, most of my time working on gbrain itself. So gbrain came about because I met, you know, obviously we had Peter on the show. And then I finally got around to it. It was like one weekend. I said, I gotta check this out. Like, what's going on with openclaw? Let's get it going. And this was about the time Karpathy wrote his X post about Knowledge LLM wikis. And so I was like, okay, well I have a repo full of markdown. All my, you know, I should put all of my context into that markdown. And then at some point I realized, oh shoot, it's just using grep and grep is not that good. Like it's, you know, wasting context. It's loading a lot more into context than it needs to. And then I sort of fell into a rabbit hole. I just went into Conductor, click, quick start. And then I had gstack built into Conductor already. And you know, basically this was how I started. I, you know, it was actually much more interesting than that. So I didn't start off from nothing. One of the things I've learned as you write, like a larger and larger corpus of code is like you have it loaded in your brain, you're like, oh well, in order to build an agentic newsroom for Gary's list, I actually had to learn about vector embedding and hybrid RRF and chunking. Like when you're in there trying to make it work, you're just like very applied. It's like I have an output that I want, I want the article to look like this. It needs to be of this quality, it needs to have these citations like, like you start building up your, you know, your tests and integration tests and like you end up with like a product that's like battle tested from like the output that you want. And so I sort of put two and two together and I, you know, and this is something that, you know, anyone can do actually. It's like this, this is why I think we're entering the golden age of open source. I could just open, you know, this project in Conductor and then the first thing I write is like, you know, go look at, you know, Tilda, Git Gary's list. Like look at how we do chunking, embedding, you know, hybrid rrf, rag, all of this and then just like extract it. And then I want to use Postgres with PGvector and like I want a, you know, full rag system for my open claw. And then sort of like one thing led to another. It's like then I have, you know, 10 windows and gbrain and I'm just like at it. What's cool about OpenClaw? I mean maybe this is a good example. This is actually my openclaw. I did go ahead and ask. It's how, you know, how did I actually get into it? January 23rd, also all your emails, I had a tweet that was like, claude code this week has awakened my 25 year old self. The one that checked Red Bulls and stayed up till dawn coding. We're so back.

Light Cone Host 2 (29:58)

The builder identity resurfaces.

Gary Tan (30:00)

You know, I'm basically back to you know, sleeping four hours and you know, coding 20 hours a day. You know, this is also when I started getting myself into trouble, like talking about lines of code. I still believe this by the way.

Light Cone Host 1 (30:12)

Yeah, this might be like a good quick aside to talk about like this. This idea of like lines of code being important measure has been like controversial on the Internet. There's obviously the counter argument like, oh, lines of code doesn't like measure developer

Gary Tan (30:25)

productivity, but it doesn't Right.

Light Cone Host 1 (30:27)

What do you think?

Gary Tan (30:28)

But it also does.

Light Cone Host 1 (30:29)

It also kind of does, right?

Gary Tan (30:30)

Yeah, like, it does. It's clearly. And you know, what's interesting is you can. There's well published git repos out there that you can run to strip away and like, standardize what is actual logical lines of code. And so I actually did go ahead and do that, you know, and I got into trouble for saying, like, oh, I'm coding at like 100x the rate that I was in 2013. And then after I did, the logical lines of code stripped down, it actually went up. It actually went up. So it turns out that I was actually doing. Doing 400x the amount of code. But, you know, obviously I wasn't writing it. I was directing, you know, 15 agents at a time to do so. And then by the numbers, like, it was not that it did, like, knock down my lines of code from Claude code a little bit, but the surprising thing to me was that it knocked down the amount of lines of code that I was writing in 2013 by like 70%. And so I think that that's sort of the mismatch here. Like, people get very upset because is it's easy to, like, pad the lines of code if you're a human writing code. Whereas, like, unless you direct Claude code to literally, like, pad the lines of code, it doesn't necessarily do that. Like, it'll maybe build the wrong thing. Like, you might not steer it very well, it might not do the right thing, but, like, it's not trying to optimize for lines of code the way a human working a job would. Right? Which is, you know, that's just life. And then I guess the really surprising thing is if you look at the literature about software engineering going Back to like 2000, 1990, I mean, it's pretty clear that the average number of lines of code that a professional software engineer that's like, tested and production ready, it's not like a hundred lines of code, it's like 50. It's like 30.

Light Cone Host 1 (32:22)

A day?

Gary Tan (32:23)

Yeah, a day, Right. Like, for me it was like 14, but I was like, part time. I don't know, it's. So that's where the 400X actually came from. You know, the other thing I know is, like, I should have said that instead of just trolling people more on

Light Cone Host 1 (32:35)

the lines of code.

Gary Tan (32:36)

So I, you know, if I trolled you on the Internet, I'm very sorry for that. Like, there, you know, there is a deeper understanding of this, and I did end up releasing a blog post about it that explains this quite a bit more. I mean, and I think it's not a little bit significant. It's very significant for people who are technical because it actually raises the bar on like what you are capable of doing. Like all the people who are attacking me about lines of code, they particularly are the people who are most likely to get wings if you like, let it rip and token Max. This is sort of like the classic problem. It's like if you have taste and you understand technology, you are particularly the people who should would benefit the most from getting this. All someone has to do is, you know, believe, right? So stop fighting. Just open Claude code and try it.

Light Cone Host 2 (33:26)

You know, I think another thing that's potentially going on is just like the experiences vary dramatically depending on the models and the harnesses. Certainly something I've noticed is any sort of semi complicated programming task I try and do through my openclaw agent just kind of fails. It's exactly the same model and sort of like Opus 4.7 as Claude Code, but it just like anything above a simple script, I just find it's not that great at. So I'll go back into clawed code. And then it was sort of a moment for me where I realized, oh, this is how it used to feel. This is how even six months ago it used to feel like, oh, you try and these things aren't quite there. Yeah, these things aren't quite there yet. And then Claude code with Opus 4.5 was like, oh, it's actually here.

Gary Tan (34:15)

It's about to recur. Right now people sort of are feeling like openclaw or Hermes is like not quite there, or it's like a lot of work. And then I guarantee you, like, this time next year, like everyone's going to be saying what you heard here first, which is like, every single person on the planet will have their own personal AI. We could either live in a world where we have our own AI, where we have our own data, our own integrations. Like, we see what's happening, we write our own prompts and we have control over what we see, or it's corporate controlled. It's something, you know, you go to a host, it's kind of like your Facebook feed and like you don't know what the, you know, who wrote that algorithm and who does it benefit and like what business model is behind it. Like, nobody knows. The most powerful idea that like was a gift was the personal computer revolution. And we're about to go through exactly that same shift with personal AI and it's going to be a choice. Like, you know, people are going to have to figure out, am I willing to write my own prompts? And, you know, I think I wish Pete Kohman were here. Like, that's one of the things we learned from him too. It's like, unless you have your own prompts and you can write it for yourself like you are, you know, below the API line for some PM or developer that is not you, who, like, will not understand you, will not understand your needs, will not understand what you uniquely care about. And I think that's like the defining question. Like, will you have control over your own tools or will your tool, your tools have control over you?

Light Cone Host 3 (35:53)

And I think this is one of the disconnects that the public has, I think is a lot of these capabilities. You have to be on the latest and greatest models. And it's actually quite expensive to use them and burn all the tokens. For now it's coming down. But I think maybe people are just trying, like Sonnet or the free model or having the base basic Claude Pro subscription only. And part of is maybe we have to address that. This new way of really getting all this almost asi AGI moment for building is you have to be burning lots of tokens. The whole token maxing paradigm.

Light Cone Host 2 (36:32)

It actually reminds me of rent San Francisco rent. One of the things that I feel like we always have to do with YC founders is that it's like a general thing. It's like, oh, I don't want to move to San Francisco because it's so expensive to live there, but it's like,

Gary Tan (36:45)

it's so expensive not living.

Light Cone Host 2 (36:47)

Yeah, exactly. That's the whole point, right? Like, early on in a YC badge, like, I'm used to, like, a fan of being like. Like this. Like, this apartment is like thousands of dollars a month in rent. Like, seems ridiculous. Like, should I, like, pay it or not? It's like, no, you should absolutely pay. And if anything, you should pay more to not just be in San Francisco, but be in, like, the dog patch and just like being like neighborhoods where you create the serendipity. Like, token maxing is going to be one of those things for founders that we sort of have to teach them where it's not immediately obvious that you shouldn't. This is actually like rent. Like, this is one of the things where you should, like, spend as much as you can to get the most utility out of it versus treating it like the office desk or something. Sure, you can economize on that. Or you don't need a super expensive couch. But when it comes to actually using the models and your token spend, you should probably be pushing pretty hard on that.

Gary Tan (37:37)

Yeah. One of the key maxims for YC is how do you find good startup ideas, live in the future and build what's missing? Right? And so this is a profound version of that where all you have to do is commit your brain to look at, you know, spending $500 in a single day on tokens and say, actually like, you know, as long as I'm building something that's actually of great value to me, you know, and I'm building the right thing, I'm going to do that.

Light Cone Host 1 (38:06)

Gary, I have a weird question. Do you think that in some ways the fact that you tried to build all of this while also being the CEO of Y Combinator actually helped you because like your time is so scarce you had to like try to figure out how to write hundreds of thousands of lines of code with just like spare minutes in between meetings. Unlike a full time software engineer that could, you know, just take the time to like open the website and like click around it, like test it. Like those minutes were like insanely scarce for you and so you were constantly pushing yourself to figure out how to like automatically made everything.

Gary Tan (38:37)

Yeah, I, I envy time billionaires, you know, sometimes look at, I mean, I'm look at my kids and it's like these kids are time billionaires right now. Like, you know, you can just like do things, you know, you know, we run across people at startup school all the time and it's like you're a time billionaire right now. Like, this is incredible. Like you could just do anything you like, learn about anything. This is so great. So, yeah, I'm, you know, personally, like, I think my philosophy is I am in a crazy rush in my brain. I'm like, probably live 10 billion lifetimes live in this body right now and I need every single moment to count. And then if you can token max, it's like, I mean, you could buy millions of years of consciousness, of machine consciousness. Now I can be a time billionaire. It's not, you know, my own time, it's the time of a machine, like doing work for me and like the human entities that I care about, working on the causes that I care about. Right? I care about yc, I care about builders being able to build. Even in a lot of our internal meetings last year, remember in our off sites, we would talk about like, how do we teach the next generation how to use these tools? And so you know, I'd like to. I wish that I could say, like, that was all a part of the grand plan and that's how it started. It's not like. But, you know, subconsciously, I actually think it was like. I think subconsciously from doing Lightcone and, like, talking about this stuff, like, sitting side by side with Boris Czerny right here was a very powerful moment for me because I realized, like, he's. He started saying things that, like, I could do myself. It's like he said, our team doesn't write a single line of code. I'm like, oh, actually, like, I can do that. And, like, the people who are watching right now, it's like, you and I are not different. Right? We're the same. Like, we started in the same place. I don't think of myself as, like, you know, in the sky yet. Even though people seem to talk like, I am, you know, like, I'm just a person trying to do a thing. And if I sit next to Boris, I'm like, you know, this guy is one of the best engineers I've ever met. But also, like, if I just open a prompt, we have the same prompt. We have the same MacBook Pro, and, you know, there's nothing that stands between, like, me or you or any of us from, like, drawing on millions of years, potentially, of, like, tokens to, like, serve humanity.

Light Cone Host 3 (40:55)

Well. Well, Gary, I think that was a beautiful quote. That should be retweetable. It shows.

Gary Tan (41:01)

Gotta get it on X right away.

Light Cone Host 3 (41:02)

You could have infinite time by borrowing the time from the machines.

Gary Tan (41:06)

Yeah. What a time to be alive.

Light Cone Host 1 (41:08)

That's a beautiful plot to end on. Thanks, Gary, for showing us the future.

Gary Tan (41:12)

Thanks, guys. Thanks, Gary.

Light Cone Host 2 (41:13)

All right, thanks for watching, and we'll see you on the next episode of the Light Cone.

Light Cone Host 1 (41:22)

It.