Garry Tan, President of Y Combinator, discusses building AI-native companies. Key points: reporting roughly 400x output gains from managing AI agents rather than writing code directly; treating AI tools like Claude Code or Cursor as a workforce to be hired and managed, with roles, processes, and reviews encoded as markdown-based skill files; building "company brains" (including his project GBrain) that supply agents with the right context, acting as library and librarian; and a discipline of converting every task into reusable skill files so the organization retains what it learns instead of starting fresh daily. He argues this enables lean teams to operate at scale previously requiring hundreds or thousands of employees and achieve much higher revenue-per-head. (Talk from an AI Engineer event; topic inferred from title, description, and timestamps.) # Channel Identity Observations — "Every company should have a Brain — Garry Tan, Y Combinator" ## Speaker & Institutional Presentation - The video presents a **keynote/talk format** delivered by Garry Tan, who self-describes as **"a founder, I'm an investor, and I run a 20-year-old institution"** — referring to Y Combinator. - Explicit institutional framing: > "we're trying to build companies where one person does what it took... One person does what used to take a thousand people." - The talk appears delivered at a startup event with a "startup battlefield" component: > "In about an hour, some of you will walk into the startup battlefield." ## Intended Audience - **Founders and early-stage startup builders**, addressed directly and repeatedly: - > "the people in this room will do this" - > "if I were 25 and sitting where you're sitting" - > "You will take to your startup" - Audience is assumed to be technically fluent in AI tooling (references to Claude Code, context windows, resolver tables, "Codex" are used without basic explanation, though some terms are briefly defined). ## Claimed Scope & Self-Description - The channel/video positions itself as **YC's perspective on AI-native company building**, with authority claims backed by YC data: - > "In the winter 25 batch, a quarter of the companies had code bases that were 95% AI-generated" - > "94 companies total have now crossed a hundred million in dollars in revenue from a seed check" - Self-description of YC's internal transformation: > "we run a 20-year-old institution that is becoming AI-native right now" and non-engineering staff described as "a manager of agents now and everyone at YC now is." ## Demonstrated Presentation Patterns (Observable) - **Advocacy of a specific methodology**, not just commentary: skill files as "employees," "skillify it" discipline, company "brains," context engineering ("who decides which three books are open on that desk"). - **Open promotion of creator-affiliated tools**: G brain (described as MIT open source, "Postgres for agents"), Open Claw ("the Ferrari"), and a referenced blog post: > "I have a blog post on X about that. You can search for skillify it." - **Investment pitch embedded in content**: > "I'd like to fund you at YC if you do." - **Big quantitative claims presented as personally stress-tested**: the "400X" productivity claim, with the speaker preemptively discounting it ("It's still 8X at the floor and 80X in the middle") and noting prior online criticism: > "I got torn apart on the internet for this." - **Honesty/disclaimers as a rhetorical device**: > "let me be honest in a way that maybe undercuts my own pitch. You don't need my tools to start." ## Notable Distinctions - **Creator claims** (e.g., 400X output, YC batch performance, G brain's 220,000 pages) are presented as first-person testimony and YC internal data; none are independently verifiable within this material. - **Demonstrated pattern**: the talk's structure moves from personal productivity data → YC portfolio evidence → prescriptive methodology → tool promotion → inspirational close ("Go boil the ocean"), consistent with a **thought-leadership / recruiting-adjacent** presentation style rather than neutral tutorial content. # Delivery Observations: Garry Tan at Startup Battlefield ## Tone & Register - **Conversational, high-energy opener**: Opens with crowd engagement — *"How's everyone doing? All right. Are we ready for the revolution?"* - **Casual register with technical depth**: Frequent fillers (*"uh," "you know," "um"*) even amid dense technical claims, giving a spontaneous, unscripted feel. - **Self-deprecating and pre-emptively skeptical**: Anticipates pushback and deflates his own claims before others can — *"I got torn apart on the internet for this, but I'm going to say it again"*; *"let me stress test my own pitch because you would anyway."* ## Recurring Rhetorical Moves ### 1. Direct address to the audience - *"some of you will walk into the startup battlefield, and I want you to walk in knowing what's actually possible"* - *"if you're not doing it, your competitor is, and they will eat your lunch politely and thank you for it"* - *"And I'd like to fund you at YC if you do."* ### 2. Concession-then-reinforce pattern Repeatedly grants a skepticism, then re-asserts the claim: > *"If you don't trust the raw code, well, fine... It's still 8X at the floor and 80X in the middle. That number is large, no matter how you torture it."* > *"Some of you are already thinking, 'This is just rag.' And you're right that retrieval is the primitive... The hard part is everything around it."* ### 3. Memorable aphorisms / quotable one-liners A stable habit throughout: - *"A skill file is an employee."* - *"you're hiring, training, and managing a workforce made of markdown."* - *"Retrieval is easy. Being worth retrieving from is the product."* - *"Model quality is rented, but if you build your brain, you own that brain."* - *"Abundance is not a policy paper, it is shipped software."* - *"They will eat your lunch politely and thank you for it."* ### 4. Analogy-driven explanation - Organizations ↔ markdown files (*"They're markdown files and other types of markdown files"*) - Working memory ↔ *"seven plus or minus two"* vs. an agent holding *"three Harry Potter books"* - Company brain = *"the library plus the librarian"* - Tools as cars: *"Open Claw is the Ferrari... Codex is a really good Honda."* ### 5. Numbered/multiplied claims as evidence spine Leads with metrics: *"400X," "94 companies," "95% AI-generated," "$60 million with about 40 people," "220,000 pages."* These anchor nearly every section. ## Teaching Habits - **Definition-by-example**: Explains "resolver table" concretely — *"whenever you need to alter a test, load tests.md."* - **"If you remember only one thing"** framing, used twice: - *"if you remember only one thing... It's not the model."* - *"if you remember only one thing, it's that... skillify it."* - **Concrete anecdotes to ground abstractions**: the YC finance employee collapsing *"about a hundred Excel workbooks into a single app"*; the friend with the epileptic son building *"80,000 markdown files."* - **Honest limitation flagging**: *"I can't prove that the AI-generated code caused the growth"* — explicitly marking causal uncertainty rather than overclaiming. ## Structure & Pacing Signals - Explicit roadmap and bookending: opens with *"What do we build now?"* and closes with *"Theo asked, 'What we should build now?' And here's the whole damn whole answer."* - Signposting transitions: *"So, let me start with a number,"* *"which brings me to working memory,"* *"So, let me land this."* - Rapid-fire parallel tricolon at the close: *"Go boil the ocean. Go write that test. Go ship that skill."* ## Notable Isolated Moments (not stable patterns) - The "no slides" aside (*"Sorry, there's no slides"*) — an unplanned moment, evidenced by audience laughter in the transcript. - The closing personal story about the father and son — an emotional peak not mirrored elsewhere in the talk. - *"the part that I'd tattoo on the inside of everyone's eyelids if I could"* — a one-off vivid image. ## Uncertainty Notes - Delivery features like pace, gesture, or vocal tone cannot be directly observed from the transcript; tone is inferred from word choice and audience reactions ([laughter], [applause]) only. - Repetition of fillers ("uh," "um") is transcript-visible but may reflect transcription rather than exact speech disfluencies. # Position Record: Garry Tan — "Every company should have a Brain" (YC) ## Speaker - **Garry Tan**, founder, investor, and head of Y Combinator, addressing a startup audience (references Theo's question "What do we build now?"). ## Central Claims ### 1. Individual productivity leverage is roughly 400X with AI - In 2013, as a near-full-time engineer, he wrote ~**14 usable logical lines of code per day**, which he says matches era literature (median ~15). - In 2025, running YC full-time (with fewer hours, a 5pm kid pickup), his output is ~**400X**. - **Self-qualification**: he deflates the number himself — assuming bloated AI-generated code, half scaffolding, self-flattery — it's "still 8X at the floor and 80X in the middle." - Key claim: **"It's not the model."** The 2X and 100X people use the same Claude, same weights, same API. "The leverage is not in the weights. It's in how you wire the work." ### 2. Evidence from YC (with stated caveat) - **Winter 25 batch**: a quarter of companies had codebases 95% AI-generated; that batch became YC's fastest-growing, most profitable batch ever. - **94 companies** total have crossed $100M in revenue from a YC seed check. - Explicit caveat: "I can't prove that the AI-generated code caused the growth" — but the fastest-growing founders "treat AI as a workforce," not auto-complete. - Examples cited: **Emergence** (Summer 24): nine figures ARR within 8 months of launch, 15 people at $15M ARR; **Retail** (Winter 24): $60M revenue with ~40 people. "That kind of revenue per head did not exist before... in software, not in oil, not in railroads." ### 3. Agent workflows map to an organization - **Skill file = an employee** (one capability, written down clearly). - **Resolver table = an org chart** (decides who handles an incoming task). - **Filing rules = internal process**. - **Trigger evals = performance reviews**. - Claim: agent work is "hiring, training, and managing a workforce made of markdown." - AI-native companies encode sales, support, ops, finance as skills, and hire engineers to maintain skills and do what skills can't yet do. ### 4. Where computation happens: latent vs. deterministic space - Most AI engineering bugs come from work happening in the wrong space. - **Latent space (the LLM)**: taste, judgment, understanding vague human intent, non-deterministic calls — steered by markdown. - **Deterministic space**: code, structured computation. - Example: seating 800 Startup School attendees in "perfect clusters" — the multi-dimensional seat storage "must not live in the context window"; feasible now in ~10 minutes for a couple hundred dollars of tokens, impossible six months ago. ### 5. Working memory and the company brain - Humans hold ~7 items (7±2, citing famous cognitive psychology paper); every institution is a "prosthetic for that limit." - Agents hold ~a million tokens ≈ three Harry Potter books ("Is that AGI? Maybe not, but it's already a very different operating regime"). - The decisive question: **"who decides which three books are open on that desk"** — that is context engineering. - A **company brain = the library plus the librarian**. - Anticipates the objection "this is just RAG": retrieval is the primitive, "the same way Postgres is just B-trees. The hard part is everything around it" — enrichment, hot vs. cold memory, arbitration of conflicting facts. "Retrieval is easy. Being worth retrieving from is the product." - His personal brain: **~220,000 pages**, largely agent-written from his email, meetings, 20 years of notes. He is building **G Brain** in the open (MIT open source, "Postgres for agents"), saying he doesn't want to make money from it — the layer should be open "the way Linux is open." ### 6. Failure modes — acknowledged, not smoothed - "A brain nobody curates becomes a garbage dump with great search." - "Retrieval will surface a stale fact with total confidence." - "A bad skill file encodes a bad process forever." - Remedy: memory **plus hygiene** — provenance on every fact, contradiction checks, a librarian (human + agent) whose job is pruning. "Treat the brain like production infrastructure and it compounds... a dumping ground gets you a very confident agent that is wrong in ways nobody can trace." ### 7. Signature discipline: "Skillify it" - **"Never do one-off work."** After a satisfying agent result, convert it into a reusable skill file. - "If you have to ask for something twice, you failed." - "Model quality is rented, but if you build your brain, you own that brain." ### 8. What to build (answer to Theo's question) - Build the **AI-native company**, not a company that uses AI: thin team, skill files for everything, founder still in the code, company brain from day one. - Green field opportunity: **every company will need a brain** — memory layer, personal AI, the librarian. He invites founders to build the defining company here and offers YC funding. - **Self-undercutting honesty**: "You don't need my tools to start... Codex is a really good Honda. It will do 90% of this... The concepts are the point, not my repos." ### 9. Jobs and abundance - Acknowledges widespread fear about job loss, then states plainly: "that is a failure of imagination." - "Abundance is not a policy paper, it is shipped software." - Anecdote: a friend with a son with a rare form of epilepsy built an 80,000-markdown-file brain pushing "the absolute edge of what humanity knows" about the condition — "No lab, no grant, no permission. A father, a laptop, and a library" — presented as the same architecture as his whole pitch. ## Preserved Tensions & Qualifications - The 400X claim is presented with the speaker's own deflation (8X–80X floor/mid range) — he notes he was "torn apart on the internet" for it. - Causal link between AI-generated code and batch growth is **explicitly unproven** by his own admission. - AGI framing is tentative ("Maybe not"). - He promotes G Brain while simultaneously stating tools don't matter and alternatives are fine — a deliberate, self-acknowledged tension ("let me be honest in a way that maybe undercuts my own pitch"). # Speaker Attribution Record — "Every company should have a Brain — Garry Tan, Y Combinator" ## Speakers | Speaker | Role | Evidence | |---|---|---| | **Garry Tan** | **Creator / Keynote speaker** | Self-identifies: "I'm a founder, I'm an investor, and I run a 20-year-old institution" (Y Combinator); title metadata names him as speaker | | **Theo** | **Audience member / referenced questioner** | Referenced only ("Theo just asked the right question... What do we build now?"); does not speak in transcript | | **Audience** | Unidentified | Laughter, applause noted in stage directions only | ## Provenance & Referenced Claims ### First-party claims by the speaker - Personal productivity: ~14 usable lines of code/day in 2013 → claimed ~400X output today (deflated to "8X at the floor and 80X in the middle") - YC Winter 25 batch: "a quarter of the companies had code bases that were 95% AI-generated" - YC stats: 94 companies crossed $100M revenue from a seed check - Speaker explicitly flags causation uncertainty: **"I can't prove that the AI-generated code caused the growth"** ### Third-party company claims (attributed to speaker, unverified) - **Emergence** (YC Summer 24): "nine figures of ARR in eight months"; 15 people at $15M ARR - **Retail** (Winter 24): "$60 million with about 40 people" - Unnamed friend: rare-epilepsy son, "80,000 markdown files" repo ### Referenced tools / sources - Claude / Claude Code, Claude.md, Codex, Open Claw ("the Ferrari"), open client Hermes agent - **G brain** — speaker's own project; open source, MIT license; he states "I'm not trying to make money from this" - Cited cognitive psychology: "seven plus or minus two" working memory paper (unnamed) - Speaker references a blog post on X: search "skillify it" ## Promotional Segments ⚠️ - **G brain promotion**: described as "Postgres for agents," speaker's "second brain" (~220,000 pages), "use G brain if you'd like... open source and free" — promotional, though speaker discloses non-commercial intent - **Open Claw** endorsed as "the Ferrari, I will always recommend it" - **YC funding solicitation**: "I'd like to fund you at YC if you do" - Speaker self-mitigates: "You don't need my tools to start... The concepts are the point, not my repos" ## Availability & Limits - **Comments**: no comment availability info supplied - **Slides**: absent by speaker's own admission ("Sorry, there's no slides") - **Event context**: live talk preceding a "Startup Battlefield"; ~20-minute talk - Third-party metrics and personal productivity claims are **speaker-asserted only**; no external verification available in source \>\> Okay, great. Hey everyone. How's everyone doing? All right. Are we ready for the revolution? Okay, Theo just asked the right question. What do we build now? I'm going to answer it from the other side of the table. Uh I'm a founder, I'm an investor, and I run a 20-year-old institution that is becoming AI-native right now, which is a strange and wonderful thing to do to a 20-year-old institution. Uh and I'll spend about 20 minutes um talking about, you know, what YC is we're trying to build companies where one person does what it took to two it One person does what used to take a thousand people. And I don't mean that as a metaphor. I mean that me- mechanically this year, the people in this room will do this. In about an hour, some of you will walk into the startup battlefield, and I want you to walk in knowing what's actually possible right now, because what is possible now is much much bigger than what people believe. So, let me start with a number and uh you know, I got torn apart on the internet for this, but I'm going to say it again in front of all of you anyway. Uh this is the one room in the world that will stress test it, and I'd rather stress test it with you myself. In 2013, I was a YC partner uh building the internal social network um at at YC. Uh I was also investing in companies, but you know, I was also uh near full-time engineer. Um and when I was doing that, I could maybe do about 14 usable logical lines of code a day. Take out of the comments, take out all the and that's how many lines of code I was writing. And if you look at the literature from that era, um you know, that's kind of normal. Like some people write 15, some people write 50. It was not, you know, the thousands of lines of code that I know a lot of you in this room are actually writing now per day. Um that's about median 15. That was me at full effort at that time. This year I run uh YC full-time. Uh same person, same hours, actually way less hours weirdly. Uh you know, but I have a 5:00 p.m. kid pick-up now. And I did the math on my output, and it's about 400X. Now, before the skeptic in the third row right there deflates the number from for me, let me deflate it myself. If you don't trust the raw code, well, fine. Take the most pathological verbosity plant penalty you can stomach, and assume the agent writes bloated code. Assume half of it is scaffolding. Assume I'm flattering myself. It's still 8X at the floor and 80X in the middle. That number is large, no matter how you torture it. And here's the part that matters, the part that I'd tattoo on the inside of everyone's eyelids if I could. It's not the model. The 2X people and the 100X people are using the exact same Claude. Same weights, same context window, same API. So, the leverage is not in the weights. It's in how you wire the work. And it's not just me. At YC, we see this all the time. In the winter 25 batch, a quarter of the companies had code base code bases that were 95% AI-generated, and that was a year ago. That batch has become the fastest-growing, most profitable batch in the history of YC. 94 companies total have now crossed a hundred million in dollars in revenue from a seed check in the history of YC. So, uh I think we know what we're talking about here. And I can't prove that the AI-generated code caused the growth, but what I can tell you is the fastest growing founders we fund are not treating AI as auto-complete. They're treating it as a workforce. The companies that wired the work differently are the ones that are bending the curve. So, what does wiring the work really mean? This is the heart of the talk. And this is what I most want you to steal. Everything we've learned building with agents maps to an organization. Sorry, there's no slides. \>\> \[laughter\] \>\> I have no slides. I'm so sorry. \>\> \[applause\] \>\> A skill file is an employee. It has one capability, one job, written down clearly enough that someone can execute it. Uh a resolver table, uh the thing that many of you, you know, when you run into Claude code and it says your context is too big in Claude.md, you run off and create a resolver table. Well, you know, it and if you don't know what that is, it's literally like whenever you need to uh alter a test, load tests.md, you have a whole table of these things. Um that's an org chart. A task comes in and the resolver decides who handles it and where it goes. Uh filing rules are your internal process. So, this can be um you know, whether or not the resolver is actually working and uh is you know, is there a is it actually in compliance? And uh trigger e-vals. So, going in and actually having a test that says, "When I need to alter a test file, does test.md actually get loaded?" Those are performance reviews. So, you know, what have we done? Like literally every part of an organization, the organization that you used to have to hire a thousand people for, I just told you what those things are. They're markdown files and other types of markdown files. And maybe there's some TypeScript in there, too. We've been building organizations this whole time, but we didn't have a management layer, but now that's what we have. When you sit down with Claude Coder Codex, you're not writing software, you're hiring, training, and managing a workforce made of markdown. Uh and you know, that's there are tons of companies at YC that are doing this. Uh Emergence, an AI app builder out of Summer 24, they went from public launch to nine figures of ARR in eight months. Uh when they crossed $15 million ARR, uh they were only 15 people. Retail out of Winter 24, it's at $60 million with about 40 people. The kind of that kind of revenue per head did not exist before. Not in software, not in oil, not in railroads, never. These are not freaks of nature. They're just the first companies built natively on the new physics. And so, how do companies like that actually run? Not by hiring hundreds of people for sales, support, ops, and finance. The AI-native companies that I see inside YC encode all of that as skills, written procedures that their agents execute, and they hire they hire engineers whose job it is to maintain those skills, to do the work the skills can't do yet. That is an AI-native company, and it's not a thought experiment. It'll actually file your taxes if you have a skill file for it. Now, picture YC's batch room. Like it actually kind of looks like this. Um 400 companies or 400 founders at long tables, and uh you know, I can imagine every single one of you each with a laptop every single day. You're doing uh a former person's entire year worth of work in a single day. That's not the future. That's actually the bar right now. And if you're not doing it, your competitor is, and they will eat your lunch politely and thank you for it. Here's the extension uh most engineering stocks miss. It's not just the engineers. At YC, as we make our transformation, it's our media people, our event staff, our finance team. People have never opened a terminal in their lives are building skill files and cron jobs. And one of our finance folks just collapsed about a hundred Excel workbooks into a single app she built with our internal open claw and company brain. She's not a programmer. She's a manager of agents now and everyone at YC now is. So, that's why YC can run at the scale it does with a staff that would look like a rounding error at any other comparable firm. And that's not because we work harder, because it's because we have a different type of org. And that's whole the whole game. It's not just 400X engineers. It's one company that operates at the level of 400X everyone else. And so, you know, if you remember only one thing about this, I mean, this is one of the things I had to discover along the way. Like, you actually have to be really, really careful about where the compute computation is actually happening. It's happening almost always in two different places and all of the bugs, all of the AI engineering that we run into that's a problem, it's usually because something is happening in one side of the equation that should be in the other. Uh the first area I would say is latent space. So, the actual LLM. It's you know, what do you use it for? Taste judgment, understanding what a human actually wants when they say something vague, the non-deterministic calls, the computation that lives in the model and you steer it with the markdown file. And then deterministic space is what engineers know. Like, your your code agents go off and write TypeScript or, you know, maybe they're writing Erlang if you're using Elixir. Um yeah. Deterministic space is the second place. Let's say, you know, this is a real problem that we have for Startup School coming up. We have 6,000 people or we're going to try and, you know, one of the experiments we're going to try and do is can we seat 800 people at a time, um, perfectly clustered? So, the person sitting to the left and the right of you is the perfect person for you to meet at Startup School. Um, we have to do that in deterministic space combined with latent space. Uh, the computation, this computation, this actual storage of like where everyone is inside like, you know, this multi-dimensional array of 800 seats, um, it actually must not live in the context window. The LLM has to do the human part and seat people. Um, it's actually exactly what you would do if, um, you know, you were a human tasked with this thing. You would probably have to physically print out 800 pages and go in a a big room and like say like, well, where does this person go? Only now it can all happen, um, in your computer and it can instead of taking a month, it might be able to you might be able to do it with, uh, you know, couple hundred dollars worth of tokens and probably 10 minutes. Um, and so that's, you know, I would argue that's pretty remarkable. These are things that you couldn't do even, uh, I don't know, six months ago. Um, which brings me to working memory and, uh, that's, you know, sort of my favorite way to understand it is, um, you and I, human beings, we only hold about seven things in our head at once. Uh, seven plus or minus two. It's one of the most famous papers in cognitive psychology and it's why, uh, local phone numbers are seven digits and why you forget the eighth item on your grocery list. Uh, that's the entire working memory generally of a human being and every institution humanity has ever built, every checklist, every org chart, every filing cabinet is a prosthetic for that limit. It's kind of a wild thing to think about. But an AI agent holds a million tokens. That's about a thousand pages. I was trying to explain to my 10-year-old what G brain was recently. I said, "There's, you know, the AI agent can keep about three Harry Potter books sitting open in its head all at once. And it can find a needle in any of them and synthesize across all three in seconds. And that's quite magical, actually." Three Harry Potter books versus seven digits. I mean, that's pretty awesome. I mean, I don't know. Is that AGI? Maybe not, but it's already a very different operating and regime. Almost every company on the earth is still running an org that's designed for the seven-digit brain. But notice what that also tells you. Three books is a lot, but it's also very little. Your company is not three books. Your company is a library. Every email, every meeting, every decision, its reasoning, every customer conversation, every postmortem. The question that determines whether your agents are geniuses or goldfish is who decides which three books are open on that desk. That's context engineering. And this is what a company brain is. It's the library plus the librarian. Now, some of you are already thinking, "This is just rag." And you're right that retrieval is the primitive, the same way Postgres is just B-trees. The hard part is everything around it. What gets written down in the first place into the knowledge wiki, how it gets enriched and linked, what gets promoted to hot memory versus filed as cold reference, who arbitrates when two facts disagree. Retrieval is easy. Being worth retrieving from is the product. So, I've been building mine in the open. It's called It works with any harness, but it loves open client Hermes agent. It's effectively Postgres for agents, a retrieval layer whose job is to figure out for uh for any three you know, for any task, what three books should be loaded into the agent's head. My personal one started as a rooms full of books or so. Now it's a warehouse, about 220,000 pages written mostly by my agents from my email, meetings, 20 years of notes, uh and the lived experience of me. And that's the point. It's my second brain. And when a founder emails me about a crisis, before I start reading as before I even finish reading that email, my agent has already pulled every prior conversation with that founder, three portfolio companies that hit the same wall, and what actually worked for those people. Uh when my agent does anything, it does it does everything knowing what I already know. And that's the difference between an assistant and a colleague. So, let me stress test my own pitch because you would anyway. Company brains do have failure modes. Uh a brain nobody curates becomes a garbage dump with great search. Retrieval will surface a stale fact with total confidence. Um a bad skill file encodes a bad process forever. Uh that's bad. So, primitive the primitive is not memory. It's memory plus hygiene, provenance on every fact, contradiction contradiction checks when new information collides with the old, and a librarian, human plus agent, whose actual job is pruning. Treat the brain like a production infrastructure, and it compounds. Treat it like a dumping ground, and you get a very confident agent that is wrong in ways nobody can trace. And um here's the discipline that I think, you know, personally uh makes our company brain and my personal AI compound. Um that's my signature move. And, uh, you know, it's what I say to every YC company and every, uh, everyone inside YC, which is never do one-off work. You can open open claw, you can open your harness, you do some work, but then when you're happy, you know, and it'll come back. It's, you know, kind of a bad job. It's kind of like an intern that's not that good. But the great thing is you can just say, "Hey, I didn't like that. Fix it." Right? I'm sure all of you do this. But don't stop there. You actually need to at the end of that task, uh, skillify it. And so I have a blog post on X about that. You can search for skillify it and, you know, go get that skill file and then just load it into your your own harness and it'll just turn whatever you just did into a skill that you can reuse. Because if you have to ask for something twice, you failed. Um, so yeah, if you remember only one thing, it's that. Like when you, you know, use your AI agent and then when you're done with it and you're happy with the output, skillify it. It's going to be awesome. Um, the organization that captures what it learns like this gets smarter every single day. The one that doesn't wakes up every morning with amnesia, no matter how good the model is. Model quality is rented, but if you build your brain, you you own that brain. So, Theo's question head-on. What do we build now? Build the AI native company, not a company that just uses AI. A company that is shaped like what I just described from day one. A thin team, skill files for everything, the founder still in the code, library, this library, this company brain, your personal AI. Uh, use G brain if you'd like. Uh, it's open source and free, but you don't have to. There are a lot of really good ones. The the library will compound from the first week and your whole org will be wired to run at about 400X. And if you want the green field, the thing that I'd build if I were 25 and sitting where you're sitting, every company on this earth is about to need a brain. The memory layer that means that you never have to re-ask what you knew. Personal AI that actually knows you. We're building G brain in the open and MIT open source. Um I'm not trying to make money from this because I think the layer should be open the way Linux is open. But the layer itself, company brains, personal context, the librarian that picks the three books, that's all wide open territory. I hope somebody builds the defining company here. And I'd like to fund you at YC if you do. Now, let me be honest in a way that maybe undercuts my own pitch. You don't need my tools to start. Open Claw is the Ferrari, I will always recommend it, but Codex is a really good Honda. It will do 90% of this. Uh it will not blow your face off, but it will get you there. Use whatever. The concepts are the point, not my repos. You know, think about where the computation is. Use skill files as employees. The librarian the librarian. Never do one-off work. Those travel with you to any stack. So, let me land this. A lot of people in the world right now are terrified about what happens to all the jobs, and I understand the fear. But I want to say it plainly, that is a failure of imagination. And the people in this room are the answer to it. What I just described, you're going to take to your startup. You will multiply yourself, and every person in your company will multiply themselves, and you will go build the companies that become the beacon for how all of this works in society. Abundance is not a policy paper, it is shipped software. I have a friend who has a rare form of epilepsy. He built a repo of 80,000 markdown files, a company brain for one small boy, and he pushed himself to the absolute edge of human, what humanity knows about his son's exact condition. No lab, no grant, no permission. A father, a laptop, and a library. That's not a side story. That is the exact architecture I've been describing for the last 20 minutes. A library of the librarian, the right three books open at the right moment, pointed at the thing this man loves the most in the world. You can do that now. Every problem where you thought, I wish I had that person but I can't get them. You can. Every code base you thought was too buggy to fix, you can fix all of it. Every archive too big to read, every data set too gnarly to clean, every ocean you were told not to boil. We can boil the ocean now. \>\> \[applause\] \>\> And every single one of you can fly, not metaphorically, mechanically. And you need to to survive, to thrive, to win. Theo asked, "What we should build now?" And here's the whole damn whole answer. Build that AI native company and build it build the thing underneath it, the brain, the memory, the compounding library. That makes every company after yours easier to build. Go boil the ocean. Go write that test. Go ship that skill. Some of the companies you're about to watch in the battlefield are already doing this. Go build the one that does it best. Thank you.