Building And Structuring An AI Native Company

1. 0:00 – 1:53

   Intro

   1. SPSpeaker0:00

      [upbeat music] How is your day going so far? Good. Great. Um, so I've got about twenty-five minutes or something, um, and I'm gonna try and talk about building AI-native companies. This is based on a talk I gave about a month, maybe six weeks ago. It's on YouTube. Um, I've updated it. I've added some thoughts. I think the main caveat I wanna start with is no one knows how to do this. And so this is, um, very theoretical. It's based on, uh, hundreds of YC companies that we're working with right now. Um, lots and lots of people are trying things, and I think if anyone tells you they've got it all figured out, they are probably lying. I think internally at YC, we have an amazing software team. We've been pushing the boundaries of, I think, what LLMs can do for a couple of years now. I think we have a bunch of really interesting use cases, um, which I... And I'll give examples of those as we go through. But I, I don't think we've got it f- I don't think we've yet got it fully figured out. But I expect, um, by the end of twenty twenty-six or possibly by the first batch in twenty twenty-seven, we... It will be possible-- I don't know if we'll take the PR risk of it. It will be possible to, from end to end, have an AI fully read all the applications, pick the companies to interview, conduct the interview, select the companies to fund, give them advice throughout the batch on how to do... like, run their companies more effectively, introduce them to investors, review their pitch decks, and then debug their pitch meetings. I think an AI will be able to do all of that end to end probably by the end of this year, if not by the first three months of next year. We've got quite a lot of it working already, and we're exposing it a lot to our founders.

2. 1:53 – 3:56

   Why Roman Legions Built Your Org Chart

   1. SPSpeaker1:53

      I'll tell you more about that, but that's sort of where this talk has come from. So what is an AI-native company? Well, the first thing to start with is what is not an AI-native company. This is how the Roman Legions were, were structured. The smallest unit was the Contubernium, so eight soldiers who share a tent, equipment, and a mule, and there's a Decanus, and then ten of these things called a Contubernia form a century, which is actually eighty men, not a hundred, which is very confusing. But y- y- you see this kind of hierarchical, um, way of organizing humans, right? The Roman Legions used this to project power across North Africa, across, um, uh, near Asia, up into the UK, up to Hadrian's Wall. This was the way that the Roman Empire projected power. And they would pass information down and receive reports up, and a human being was responsible for being that conduit the whole way. And bizarrely, like two thousand years later, we're still basically running the same kind of thing. And it-- like, they look slightly different. This is a Karpathy tweet, uh, which I thought was very good. But, um, like, they're all organized slightly differently. But the same principle is true, which is that humans are the conduit for information going up and down, um, up and down the rungs, right? And so there's a Jack Dorsey tw- tweet which kicked this whole thing off about two months ago. There's an underlying assumption that organizations have to be hierarch- hierarchically organized with humans as a coordinating mechanism. And I think basically AI breaks that apart. So you don't have to have humans as a coordinating mechanism any longer. And I basically think most companies have mis-- well, misunderstood is the wrong word. Most companies sort of experience ChatGPT as a Q&A bot. They put, put ans-- they put questions in, they get answers out, and then maybe they've sort of moved to longer running, perhaps what you might call an agentic system or something. But basically,

3. 3:56 – 5:28

   Humans as the Bottleneck

   1. SPSpeaker3:56

      you tell... you ask the agent to go and do something. It goes away until it gets stuck, and it comes back to you. And you as a human are, like, the gating mechanism. And so while you are asleep, the, the thing can't work, right? Like, if it stops halfway through at three in the morning, like, it's waiting on input from you, it can't go anymore. You as a human are the gating mechanism. And so you, you know, you can do things like making engineers twenty percent more productive. You can add co-pilots for lawyers to make your existing workflows go a little better. You can ship more software with AI. But really all of these things, um... The men in the suits are coming for me. I don't know what to... I know. Um, uh, really it's dependent on humans being the coordinating mechanism in all of these places. And if you think about it not just as, like, increasing productivity, but in terms of new capabilities... Huh, that's interesting. Oh, there we go. Um, w- where basically a single person can do way more work than previously thousands of people. Your entire company becomes queryable, and you're not just shipping more software, but the, the software you ship is, like, agent native. I'm-- These are a lot of buzzwords. I'm gonna go into more detail about what I mean. AI... At the moment, AI... a lot of companies are, like, bolting AI on, onto the side of their company, right? In all the ways we've spoken about. They're Q... question answer bots. They're sort of very, um, lightweight agents that can call tools, effectively like a web search or something like that. Um, but if you

4. 5:28 – 7:51

   What a Real AI Loop Looks Like

   1. SPSpeaker5:28

      think about the company as a series of self-improving AI loops from the ground up, you actually get to a very, very different conclusion, okay? Previously, AI was like, make each person twenty percent more productive or thirty percent more productive. What happens if we reimagine the company as a series of AI loops? What is an AI loop? It's basically like, um... So at the top you've got, um, product telemetry, inbound messages. This is, like, data from the real world, right? Like billing signals, support tickets, code changes, stuff that gets you information in. And then you go down to the, uh, perhaps a policy layer, which is like, what rules can it, um- What rules constrain the AI? What things it must ask for approval for? What does it have to log? Um, you have a tool layer where it can call perhaps internal AI-- APIs, um, send emails, uh, uh, update the billing, um, and you might have APIs for this or MCP or whatever. And then you have a series of quality gates, which, which could be a human, and I'd argue probably should not be a human in, in all but the most extreme cases. But the quality gate, it can often be a second adversarial LLM. It might be something that's, that's looking at the output saying, "Are we suffering from prompt injection here?" Or i-in a case of a bank, "Are we giving financial advice here, yes or no? And if we are, we can't, you know, like constrain it." Or a really easy example for engineers is like, um, the quality gate might simply be a second model doing code review. Um, and then a learning mechanism, which is basically you loop back round, right? You deploy the thing, you make the change, and you see how it impacts the real world. If you can do this entire loop without a human, your product starts improving itself when you're sleeping. Can you scroll down a little bit to the bottom of this, please? Yeah, all the way to the bottom. Thank you very much. So, um, the first time I saw these things, it, like, broke my brain a little bit. Um, internally at YC, we've got a lot-- we've got data on seven thousand companies, twenty thousand founders, like many hundreds of thousands of applications. So a year or two ago, we built an AI agent that it basically human... It's like English to SQL, right? Like, it's a simple data querying agent. You can be like, "What was a split of European versus, uh, American founders this batch?" And it would go and translate that human query into SQL and run it and come back with the answer. It's like, yeah, pretty,

5. 7:51 – 8:39

   The Data Agent That Changed Everything

   1. SPSpeaker7:51

      pretty magical. But that feels like a tool that kind of makes a data analyst twenty or thirty percent more productive, or m- actually makes me as a partner t- ten or twenty percent more productive, and I don't have to hire a data analyst. It was fine, and it would keep hitting edge cases or, like, not be able to do certain things, and I'd be like, "Oh, that's annoying." And then we shipped something about two or three months ago, which for me was like the head explosion moment, which was a second agent which, uh, sort of lives on top of this, um, data query agent. And the second agent runs overnight, and it looks for all the queries that any human has done during the day, and it looks for f- success and failure. So the success is like, yeah, I copy it, I send the email, I do whatever. The failure is like, ah, shit, this didn't work, or it-- I hit a permission issue or whatever. Like, the database index

6. 8:39 – 10:32

   The Self-Improving System

   1. SPSpeaker8:39

      doesn't work, or it has to... It requests some permission it doesn't have. And then that second AI agent overnight goes and puts in pull requests to fix all of the problems from the day before. And so if you go back as a human on the second day and run the same query as yesterday, it now works. And so this process of, like, evaluating what the system is doing and then proposing changes to the system and updating the system makes the whole thing self-improving. And that, for me, was like a head explosion moment. I think if you were here earlier for, um, James's talk from PostHog, they basically have the same thing for products now. They take in all your product telemetry, and they see where it's breaking, and then they put in pull requests, and eventually you'll auto-merge those pull requests. So your entire product surface will become self-improving. It will just dynamically... You'll have a, a bunch of headline metrics you want to optimize for. You'll have a bunch of product, like, vision documents which says, "This is the kind of thing we want to build. Here, this is in scope and out of scope." And the AI will basically look at your product telemetry, come up with ideas, deploy those ideas, test them with humans, and see if it moved the needle or not, and just self-improve. Karpathy, um, uh, b- had a great tweet about three weeks ago now on, like, the auto research thing, basically applying this to machine learning. Just having the AI, I think it was on GPT-2 equivalent or something like that, but coming up with research ideas, testing it, running it overnight, and just hill climbing. All this is, is hill climbing. If you have a, an end measurable outcome, th- you can just come up with ideas, you can test it. Did we go up the hill or down the hill? If downhill, discard. If up, like, keep, and just keep hill climbing. And computer systems are really, really good at this kind of hill climbing work because they never stop. They will just run indefinitely, indefinitely until you reach a local maximum, which is kind of cool because you don't... They don't have s-s- to sleep. Um, another

7. 10:32 – 12:22

   Office Hours → Living User Manual

   1. SPSpeaker10:32

      example from YC, we started recording office hours about six months ago. Um, and I was given the job of rewriting a section of... We have a user manual internally, um, which is written over the last fifteen years, and it's, like, five hundred pages long. And a lot of it was very, very good, like, five years ago, and then AI happened, and, like, honestly, a bunch of the advice is just not relevant anymore. And I had the job of rewriting a bunch of this stuff, and I pr- like, procrastinated, procrastinated, and procrastinated. And we started recording the office hours six months ago. Now we have, like, I don't know, three or four thousand hours of office hours recorded. And someone on the team, Harj, I think it was, had the idea of, like, what if we just transcribe and then mine all of the advice we actually give in office hours and then use that to rewrite the user manual? And so when the advice changes, the AI will see what the humans are saying, extract the insight, and just rewrite the user manual and push it. So it becomes this, like, living guide of how we actually advise in companies. And then obviously, once you've got it in the user manual, you just make the entire thing queryable to an advice agent, right? Like, if a, if a founder asks us a question in Slack, and we get a lot of questions that are pretty similar, honestly. Like, "How should I price my first customer?" Or, "My third customer is, is about to churn. What should I do?" Or, "This customer's asked for this esoteric feature. What sh..." And it's just like we've answered versions of this question, like, five hundred times. And so now the AI can give not just Tom's answer, but, like, here's how Nicola would answer it, or here's how Harj would answer it. I think you can actually get superhuman advice from this thing because it's able to recall perfectly and access the intelligence of sixteen partners, not just one with fallible memory.

8. 12:22 – 14:08

   The AI Employee With a VM

   1. SPSpeaker12:22

      And so you can kind of see how this, these like loops can run in the background and get better and better and better and better. And then I think you give them a virtual machine. So you give your agent a virtual machine with things like tools. So search the web, crawl your internal company ad-directory, search your Slack history, persistent file storage, so it can write... It can come up with a plan and write the plan to disk, so if it fails halfway through, it can resume. And then it, if it needs to, it can write code and execute the code to solve a problem, and then see how that worked against the plan and adjust and repeat. And now you've basically got an AI employee. And I-- Uh, we're at the very, very early stage of this with something like OpenClaw or Hermes, is it basically Vms with an agent living inside on a, on basically on a repeating loop. So where we are now, right, I think YC has a few of these loops internally. I think the next thing that happens, like it-- occasionally a human has to intervene, right? And be like, "Well, the loop's going wrong," or whatever, or like, "Let's take some insight from there and put it over there." I think the thing that happens afterwards is the loops start talking to each other, or the AI employees start communicating with each other to share ideas and replan and assess and, and whatever. And you end up with this like... I mean, company brain is, is like an overused term now. It got very, very popular about six weeks ago, and as of like two weeks ago, it's... I see people in the audience with company brain written on a T-shirt now. There he is. [laughs] Um, but what-- So what

9. 14:08 – 15:52

   What "Company Brain" Actually Means

   1. SPSpeaker14:08

      is this as an idea? It's the... I'm gonna steal some of Jack Dorsey's words. But in a conventional company, intelligence is spread throughout the people, and the hierarchy roots it. So in this new model, intelligence lives in the system. What I mean by that basically is that, um, lots and lots of companies trying to write down standard operating procedures, right? Like the way things should be done. But then when you actually observe people, how they actually do the job, they were like, "Oh, well in, in this edge case, really I should do this other thing." And there's a lot of stuff in people's heads that if it weren't in people's heads and it was actually written down, you'd get a lot of value out of it. And so this idea of a company brain, I think is the combination of like basically taking all of the data in your company, and that's things like YC's application data. It's, it's transcripts of every call with, uh, with every meeting, with every founder. It's the advice we give, um, the way we pick companies, and then you record that and make it legible. You make, you make it so that the AI can access all of it. And then you have a bunch of these reinforcing loops that run indefinitely and then are able to talk to each other, and the entire thing becomes a self-improving company. I think that's basically where we're going, that you just pump tokens in, you get access to all of your data, and the entire thing becomes self-improving. And people live at the edge, right? So you have this kind of company brain, this data, these self-reinforcing loops, and then the human beings live around the edge where this intelligence makes contact with reality. So I think Jack Dorsey said, "People reach into places the model can't yet go. They sense

10. 15:52 – 17:44

    Humans at the Edge

    1. SPSpeaker15:52

       things the model can't perceive. Intuition, opinionated direction, cultural context, trust dynamic, the feeling in a room. Um, they make the calls the model shouldn't make on its own, especially ethical decisions, novel situations, and high-stakes moment where the cost of being wrong is existential. A world model, this company brain that can't touch the real world is just a database." And so these humans... What, what he's saying, I think, is the humans are in the sales calls, right? They're like visiting the client office to reassure people, to convince the CEO, to pitch the investor. It's all of the emotional, uh, kind of interpersonal stuff that the humans are there for. But the humans are no longer necessary to do the information routing, right? Like all of the information is routed automatically to the person who needs it. They don't need to go in through layers and layers of management to get approved. Um, and so that's basically what I think companies are gonna be. They're gonna be much, much smaller. They're gonna be centered around a kind of company brain, and the people will interface with the real world and then feed all that information back into, into the brain. A reasonable question is, sounds great in theory. How do... Like this seems like sci-fi, kind of. Like will it happen? Who knows? What can we actually do now to make it happen? Um, and so practically, these are the tips we're giving founders right now. The first is I'd burn tokens, not headcount. So we're seeing founders get to demo day with a million dollars of revenue and some getting to series A, um, with ten million of revenue with like a fraction of the, the people they had before. You can argue about whether it's a thousand X or whatever, but it's multiple times more, right? Like someone, a polyglot who really knows how to use these tools is worth many, many times, uh, the people before. Um,

11. 17:44 – 18:29

    Burn Tokens, Not Headcount

    1. SPSpeaker17:44

       and you can just do this. As a result, it means the headcount of the whole company is gonna be less. In particular, it means no middle management. I just don't think you need layers and layers of like directors and VPs anymore. The key roles for me are the, like the IC. Everyone has to be a, a individual contributor actually doing the work, coming to meetings with working prototypes, not decks. And DR... Like DRIs is, for me, actually the biggest thing a lot of companies are missing, which is like a single person whose head is on the block to make something happen. As soon as you have a committee... I worked at a bank, and we had so many committees for everything, and it grinds things to a halt. So having a single person who's directly responsible for the outcome, I think these are most two important-- the two most important roles. And I think middle management is gone. Practically

12. 18:29 – 19:29

    Make Everything Legible to AI

    1. SPSpeaker18:29

       speaking Um, I would try to make... This is, by the way, easier when you're starting out. I would make everything in your organization legible to A... Legible is like a really popular word in Silicon Valley right now. It means you can read it. Make all your data readable to AI. That means recording everything, transcribing literally every meeting. Um, if you can't get access to s-Slack DMs, I would ban Slack DMs. Make everything, um, in public channels the AI can access. Every action needs to create an artifact. It needs to create a, a written or recorded version, otherwise it basically didn't happen to the AI. Ob-obviously, I think on-demand internal software for all inter- in, uh, all company operations is, um, absolutely necessary. A really cool thing we've just started doing, which I don't think we've spoken about yet, um, is, uh... And it's inevitable it comes out anyway, so, um. Obviously, you record all your, um, sales meetings, right?

13. 19:29 – 21:19

    Simulating Investor Calls

    1. SPSpeaker19:29

       And you can feed those sales meetings back into an AI to be like, "How did I do with my sales meeting?" What if you also recorded all your meetings with investors, and you took those investor calls, and you had access to several hundred or thousand companies all, all meeting all the same investors every three months? And the first thing you can do is tell founders, "Here's what's going wrong with your pitch. You spent ninety percent of the time talking," or, "You really fumbled this question," or, "You need to be much more concise on your go-to-market." Very, very easy to do, and many of our founders are taking forty or fifty or sixty investor calls. You just pump them in, and the AI will, if you're recording them, tell you what's going well and what's going badly. But we also get data on the other side of that conversation. These founders are meeting the same investors. So Investor X will definitely... Here's how... Like, we could simulate the investment call, right? We could say, "Here's exactly how Andreessen Horowitz does every single investment call." Whereas Founders Fund will ask you this question, this question, or this particular partner will always say, "What's your wedge?" Or, "What is like... What's your moat if Anthropic comes after this?" And here's a great way to answer that. And I'm su- I'm, I don't feel bad about doing this at all because I absolutely know that those investors are also recording this and pumping it into their AI. And so anyway, basically, if you can record it, if you can make it legible, then the AI can start to comprehend it and give you superpowers. I will finish on this. If you are starting a company today, would you build it like this? And for... This was a talk to very early-stage YC founders. Most of you are even earlier stage, and you're small enough to just build it right from the start. So anyway, I'll finish there. I think we have... Uh, no, we'll just do questions with all of us, I think, rather than me taking questions on my own. Why don't we do that? Thank you. [audience applauding]

Episode duration: 21:19