Y Combinator Startup Podcast - Tokenmaxxing: How Top Builders Use AI To Do The Work Of 400 Engineers

Episode Date: May 8, 2026

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams.In this episode of Lightcone, the hosts break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways.

I think that's like the defining question. Like, will you have control over your own tools or will your tools have control over you? 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 and 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 fix it.

You're going to have to fix it yourself. And so this is a very exciting time in computer science and technology. Welcome back to a special episode of The LightCone. In this episode, we're going to talk about how Gary Tan got back to building. If you follow us on Twitter, you'll know that after a multi-year hiatus to become an investor, Gary Tan is back to being a builder. And in the last couple months, he's shipped hundreds of thousands of lines of life. Alliance of Code and built popular open source projects that have gone from nothing to more than 100,000 stars on GitHub.

And he did all of this while having a very demanding job running YC full time. A lot of people on the internet don't even think that this is possible and are somewhat like in disbelief. But it actually happened. We know because we were here to see the whole thing. And so today we're going to talk about how he did it. Well, I'm relatively shocked myself. I'm amazed as well, it was 13 years of not coding. And then suddenly, boom, I'm doing about 400x the amount of work that I was that year, the last time I was even sort of like two-thirds of the time writing code.

Maybe to start things off, how will we go back to the project that started it all off, which was Gary's list? Oh, yeah. And just like talk about a few months ago how you powered up Claude Code and like started to get back to coding. And it was right after one of the Lycan episodes, right? Oh, yeah, definitely. I realized that I wanted to bring together all the people who believed what I believed. particularly for California. And so I started a 501C4, and now it's a C3 and a PAC,

which is sort of what a lot of political groups do. It's a very common way to bring people together. You know, everyone focuses on the money, but we're trying to bring together smart people. You know, what I learned in the years of working in San Francisco politics is that bringing together people is so powerful. And that's what a mass social movement is. And I said, okay, well, why?

don't I just make a website where we start doing that? And it would just start with, why don't I start writing about the issues that I'm worried about? It's like I want children in school, you know, people watching this from all around the world might find it very, very strange, like I find it strange, that it was not possible and still very, very hard for a seventh grader or eighth grader in middle school in San Francisco public schools to be able to take a. algebra. And that was, you know, a math education thing. Like, you know, if I didn't get to do that

when I was in public schools in the East Bay of the Bay Area, there's no way I would have studied engineering at Stanford. I never would have written code. I never would have been able to do any of these things. So it was close to my heart and I realized like, hey, it's time to write code. And I ended up building Posterous my first YC startup from 2008. What was Posterous for people who don't remember it? 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, you know, 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 sort of issue or, piece of news that was happening. And so, you know, anyone can go to gary's list.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 L.A. And like how do we build a better government? This is the thing I feel like people missed about Gary's little don't fully get.

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 was like you build a blogging platform and people like write blogs and maybe like 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. Yeah.

I mean, basically for the equivalent of like five or ten dollars 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 the web? I mean, the great thing now is, like, you don't have to just do that. Like, you can get perplexities API and you can do deep research there. You have X's API. You can do deep research there. you know, GROC's API, if you need to, like, do research on X using the GROC 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 agenic 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 boil the ocean? Like, What is 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, in the case of this type of writing, like, we wanted to be more representative of reality.

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. It's not just in generating 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, it hurts me and pains me to think about 10, 12, 13-year-old kids who don't know algebra and, like, 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. 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 G-stack.

Like, I actually did not plan to make G-stack. 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 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 test. 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 100% test coverage.

I've since learned that 100% is probably too much. Like hitting 80 to 90% is usually the best practice at this point. But yeah, this is basically the first version of plan dash Eng dash review. I know everyone knows the office hour skill, which is 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 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 mega plan.

And then I ended up renaming it to the CEO plan. We've probably talked about metaprompting before. I used metaprompting 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 three-star experience. There's a four-star experience. And he, like, 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.

So I'm planned 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 GSTAC 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. 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. Yeah, that's right. So I dropped like 13 PRs in the last 48 hours. And then you know, you just cue them up. Like anytime I come up with a new idea, I come in and. 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 at the end of the day, I still need to, you know, for Gary's list, it's like pop open the rail 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 InCP. And it was very, very slow. Two to three seconds for every turn. And it was 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 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 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 one of the things that emerge with GSTAC is that like this is how I create new features now. Of course, 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 GSTAC 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 slash codex. And I recently added a slash clod 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 went to one of our batch events and we were just shooting the shit about what's going on with Claude Code versus Codex. And at the time, I was a total ClaudeCode-only guy. And I realized, oh, a lot of people actually prefer codex? Why is that? And I 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, explain 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, you know, G-Stack skill that takes whatever your plan is, or if you're out of plan mode and you're already implemented, it'll take your repo, and it'll run Codex in a command line prompt with the prompt that says, find all the problems and all the bugs. And it reports it back to Cloud Code, and then you and Cloud 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 slash Claude and have Claude come and be the CEO briefly, if you want. want as well. The cool thing about G-stack is when I run it through this program, like I always,

I start with office hours, see a review, like I do design if there's UI, if I know a developer needs to use it, which is like practically all of G-stack and G-brain stuff. I run the developer review, and then I do End review and then Codex. Once that plan is done, I've worked through all of the issues. The G-stack relies very heavily on ask user question. So, because, you know, and that to me is like really important. That's where the human, 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. It's 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. So basically QA is a good example. And I mean, that's hilarious. Coming back to the demo, it's like I type something into the modern version of G-Stack

and it's like, dude, what are you doing? Like we already built that. We have browse. Brows is a long-lived HP demon with 70 commands as a CLI. And then QA is just browse. But in the prompt for QA, it says, look in your context. What do 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. 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, as a builder, 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. YC startup school is back. We're hands selecting the most promising builders in the world and flying them out to San Francisco for July 25th and 26 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.

Oh, yes. Which actually encompasses all of this philosophy on how to token max. Yeah. I mean, some of it came out of being trolled on the internet relentlessly about markdown. And like, you know, I'm just like peddling 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 Coogan. We have had to build an internal agent. and we call that the harness over and over again. And then at some point, using cloud 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 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 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 sort of all of the difficulty in angentic engineering today is when people 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 much of it is over there in code land. And then, you know, if you combine that with the other thing I learned, which is like, get to 80 to 90 percent tests. Like, if it's not tested and you're just throwing users in there, like, it's sloped. You know, 10x worse than like human written code because, like, you just have no idea what's going to happen. And so that's like 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, like, it's, you know, individually tested and then the integration is tested.

And then going back to boil the ocean, like the machine doesn't care, it'll just do it. It's amazing. Like, just zap the rocks more and you can get to 90% test coverage. And 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 and 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 fix it. You know, you're going to 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, you know, that's a lot of maybe like $500 or $1,000 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 to shout to the hills like, hey, I got a Ferrari. In the part about fixing yourself, I feel people, it's just like one of those things until you've like pushed through. You just don't quite get. If I really zoom out, it's almost like things that move so quickly.

Like 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 like amazing. And then it's like a like chat GPT launches like, oh, now I've got this like interactive thing that's way better than Slack Overflow. But you're still sort of doing the same thing. You're like 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 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 like actually like executes and runs the code. And even open claw I found out when I set it up, yeah, it's an normal. because it can effectively brick itself and it does a bunch of annoying things.

But if you actually have like clawed code like sort of fix it. Yeah, like just have called code running. It will just like fix it. And it's clearly not the way things will be long term. But there's just like mentality shift of it. It doesn't actually matter if it's brittle and requires fixing because you can actually just have another agent like sat there like fixing it all the time. Yeah.

I feel like this evolution. I was like completely clawed code pilled and still am, but like probably only like 50% or 60% of my time like building product or agentic engineering is in Cloud Code now at some point basically almost half of it is through OpenClaught now

yeah which is very interesting I mean then again I'm also spending a lot most of my time working on G Brain itself so G Brain came about because I met you know obviously we had Peter on the show and then I finally got around to it and it was like one weekend I said I got to check this out like what's going on with OpenClaught let's get it going

And this was about the time Carpathie wrote his ex 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 G-Stack built in the 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 RRA. 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 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. think we're entering the golden age of open source. I could just open, you know, this project and conductor. And then the first thing I write is like, you know, go look at, you know, Tilda slash git slash Gary's list. Like, look at how we do chunking, embedding, you know, hybrid RRF, rag, like, 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 G brain and I'm just like at it.

What's cool about open claw, I mean, maybe this is a good example. This is actually my open claw. 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 This Week has awakened my 25-year-old self.

the one that checked Red Bulls and stayed up till Don coding, we're so back. The builder identity resurfaces. Yeah, 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. Yeah, this might be like a good quick aside to talk about, like, 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 measure developer productivity.

It doesn't, right? But it also does. It also kind of does, right? Yeah. It's clearly, and, you know, what's interesting is you can actually, 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,x 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 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. writing in 2013 by like 70%. And so I think that that's sort of the mismatch here. Like people get very upset because 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 100 lines of code. It's like 50. It's like 30. A day.

Yeah, a day, right? Like, for me, it was like 14, but I was like part-time. I don't know. 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 the line of code. So I, yeah. If I trolled you on the internet, I'm very sorry for that. Like, 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 what you're capable of doing. 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 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 would benefit the most from getting this. All someone has to do is, you know, believe. Right.

So stop fighting. Just open cloud code and try it, you know. I think another thing that's potentially going on is just like the experience is very dramatically depending on like the models and the harnesses. Like certainly something I've noticed is any sort of like semi-complicated programming task I try and do through my open claw agent just like kind of fails. Like it's exactly the same model. and saw like opus 4.7 as Claude Code, but it just like, like, anything above like a simple script, I just find like it's not like that great at. So I'll go back into like Claude Code.

And then it was sort of a moment from me where I realized, oh, like, this is how it used to feel. Like this is how like, even six months ago, it used to feel like, oh, like you try and like these things. Yeah, these things aren't quite there yet. And then Claude Code with like Opus 4.5 was like, oh, like it's actually like here. It's about to recur. Like right now people. people sort of are feeling like OpenClaught 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 I think I wish Pete Cooman 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 tools have control over you? 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 basic clot pro subscription only. Yeah. And part of it 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 token, the whole token maxing paradigm.

It actually reminds me of rent, San Francisco rent. Like 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, like, I don't want to move to San Francisco because it's like so expensive to live there. But it's like... It's so expensive to not live there. Yeah, exactly. That's the whole point, right?

Like, early on in a YC badge, like, I'm just used to like a founder being like, like, this apartment is like thousands of dollars a month in rent. Like, this seems ridiculous. Like, should I, like, pay it or not. And it's like, no, you should absolutely pay it. And if anything, you should pay more to not just be in San Francisco, but being 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 like get the like

most utility out of it versus treating it like the office desk or something. Like sure, you can economize on that or you don't need like a super expensive like couch. But like when it comes to like actually using the models and your token spend, you should probably be like pushing pretty hard on that. Yeah. One of the key maxims for YC is, you know, how do you find good start? 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. Gary, I have a weird question. Do you think that in some ways the fact that you tried to build all of while also being the CEO of Y Combinator actually helped you. Because like your time is so scarce, you have to like try to figure 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 automate everything. Yeah, 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 man like you know you can just like do thing 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 can 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 like cone and like talking about this stuff, like, sitting side by side with Boris Churney right here was a very powerful moment for me because I realized,

like, 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 Mac, 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. Well, Gary, I think that was a beautiful quote that should be retweetable. It shows, got to get it on X right away. You could have infinite time by borrowing the time from the machines. Yeah, what a time to be alive. That's beautiful thought to end on. Gary for showing us the future thanks Gary all right thanks for watching and we'll see you on the next episode of the like home