Sequoia Capital

•

チャンネル • 3個の番組

テクノロジー

テクノロジー

アップデート：毎週
ビジネス

ビジネス

アップデート：隔週
起業

起業

アップデート：隔週

6日前

Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil

Jerry Tworek led reasoning at OpenAI, convinced that scaling reinforcement learning was the path to AGI. Rohan Anil co-led Gemini pre-training and built the Shampoo optimizer. Now they've teamed up at Core Automation on a contrarian premise: the transformer has carried us as far as it can, and the bottleneck to smarter systems is no longer scale — it's the architecture itself. The missing capability is continual learning, models that adapt at test time, which transformers can't do. In-context learning taps out fast (Codex needs compacting after ~20 minutes) and fine-tuning invites catastrophic forgetting. Rohan argues pre-training and RL should be optimized end-to-end, and that transformers spend computation inefficiently. They lay out why the largest labs won't chase alternatives while locked in the coding-agent race, and why building the world's most automated lab starts with automating kernel generation—the one place frontier models still lose to a high-taste human. Hosted by Sonya Huang and Pat Grady, Sequoia Capital
7月21日

Factory's Matan Grinberg: The Coming ‘Dark Factory’ Where Software Builds Itself

Factory started building fully autonomous coding agents in April 2023, two years before enterprises were ready. Matan Grinberg now says this is indistinguishable from being wrong. The Factory co-founder and CEO explains how the company survived its "journey in the desert," including the decision to hand nearly all of its revenue back to customers when the product wasn't making developers obsessed. Matan makes the contrarian technical case that a model-agnostic harness beats the model-and-harness co-design that labs like OpenAI and Anthropic favor, because exposing a harness to many models keeps it from overfitting to any single one. He argues open-weight models like GLM will capture the majority of tokens by staying one generation behind the frontier at a fraction of the cost, and that CIOs will soon justify every incremental token the way they justify headcount. Looking ahead, he predicts 90% of coding tokens will run asynchronously—the "dark factory" where software builds itself. Hosted by Sonya Huang and Pat Grady, Sequoia Capital
7月23日

The Philosopher CEO | Clay Co-Founder Kareem Amin

I had Kareem Amin all wrong. When Clay first crossed my radar, I thought he was too laid-back: no chip on his shoulder, not the hungry founder I expected to win. Clay has since exploded. Kareem builds from what he calls "a place of wholeness" rather than lack — nothing to prove, no enemy to beat. It makes him sound like a philosopher, but the operating instincts underneath are sharp. He spent roughly five years wandering before Clay clicked, and his takeaway is that the hard part of building isn't working hard — it's the courage to commit to one idea and stop listening to everyone else, customers included. He flipped every assumption in his market: sell to technical "go-to-market engineers," charge for usage instead of seats, pick power over simplicity. We both created categories, in opposite ways. I flogged "inbound marketing" with a book, a conference, and relentless content. Kareem coined "go-to-market engineering" and then refused to take credit, letting agencies own it because a category has to be bigger than you. We also get into his "momentum detective" theory of the CEO job, why he's deleting his standing one-on-ones, and why apologizing is one of his sharpest tools. There's no single way to run a company. Kareem is my proof.
5月21日

Notion’s Ivan Zhao: The Refounder

Ivan Zhao, founder and CEO of Notion, joins me to introduce a new contender in the founder mode debate: jazz mode. Ivan has a different take than Jack Dorsey's circular org chart or Brian Armstrong's player-coach approach. He thinks hierarchy is human nature, and that you can't flatten it away but you can build a company that improvises like a jazz band instead of marching in formation. Notion has roughly 60 ex-founders on staff and a deliberately decentralized structure to make that work. We get into why Ivan rebuilt his engineering org around a barbell — super junior ICs paired with very senior architects — which is the opposite of what most AI-pilled CEOs are doing right now. He explains why building with language models is "more like brewing beer than engineering a bridge," and how Notion's first candidate interview no longer involves a resume. Ivan is the king of refoundings. He's done it twice, once from a small apartment in Kyoto with five employees left, once from Cancun the day he got early access to GPT-4. When I was running HubSpot, I described our scale-up years as boring compared to what's happening now. Ivan's advice for any CEO who's calcified and wondering if it's time to blow it up: feel the AGI first, then trust your body when it tells you to move. 00:00 Introduction 02:22 From Founder Mode to AI Org 11:00 Hiring for Taste and Agency 24:28 Refounding Notion in Kyoto 30:27 Craft Versus Commerce 32:26 When to Refound 34:07 GPT-4 Refounding Shock 45:35 Leadership and Founder Energy 53:17 Sales Culture and Closing Thoughts
7月14日

Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

Katelyn Lesse and Angela Jiang lead the team building Anthropic's developer platform - the layer that both outside builders and Anthropic's own products run on top of. Angela frames the platform as a three-layer stack: knowledge, execution, and coordination. She argues the real leverage is what’s at the top: "strategies," or meta-harnesses that give each token a different job, from advising to executing to reflecting to memory. On the question of open ecosystem vs. walled garden, they say they aren't precious about owning the stack. Katelyn points to Anthropic's self-hosted sandboxes with partners like Modal, Vercel, and Cloudflare. Whether the work runs on Anthropic's infrastructure or someone else's, what really matters to them is that the architecture is sound. The deeper bet is standards: they hand skills and MCP to the whole industry, build connectors on the MCP spec, and help agents (Claude and non-Claude) work together. The one place they stay closed is model routing: they argue harnesses should be tuned to a model family, so they're designing for Claude rather than routing across models. Angela's frame for the ecosystem bet is electricity: transformative only because everyone could plug in, and no company wired it alone.Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital 00:00 Introduction 01:49 Two North Stars 02:27 External Builders And Primitives 03:54 What To Externalize 06:00 From Messages To Agents 08:19 Managed Agents Adoption 09:07 Three Layer Cake 10:22 Execution Harnesses Explained 11:09 Coordination Strategies Roadmap 12:13 Ecosystem Standards And Safety 15:39 Open Ecosystem Not Walled 17:12 Vertical Products And Form Factors 22:26 Claude Tag Under The Hood 26:04 Harness Best Practices 38:13 Token Costs And Whats Next
7月7日

Inside Zipline's Autonomous System: 140M Miles, Zero Incidents

The largest commercial autonomous system on earth isn't a robotaxi fleet — it's Zipline, which has flown 140 million autonomous miles with zero safety incidents. Co-founder Keller Rinaudo Cliffton and Eric Watson, who leads systems engineering and safety, explain why the drone itself is only 15% of the solution. The rest spans inventory management, air traffic integration, and engineering systems such as a dual flight computer failover protocol that recently saved a delivery mid-flight. They trace Zipline's path from launching blood delivery in Rwanda in 2016 (when drone delivery was illegal in the US) to a 51% reduction in maternal mortality in that country, a $550 million commercial diplomacy partnership with the State Department, and a cost curve that fell from $300 per delivery to $12. Zipline is now racing toward a million deliveries a day, and a quiet inflection point when autonomous delivery becomes cheaper than sending a car. Hosted by Alfred Lin and Pat Grady, Sequoia Capital
6月24日

Memory and Continual Learning: Engram's Dan Biderman and Jessy Lin

Dan Biderman and Jessy Lin, co-founders of Engram, are building a neolab around memory and continual learning, which they call two sides of the same coin. Their contrarian premise: instead of stuffing ever-larger prompts into the context window or bolting on RAG, bake a team's knowledge directly into the model's weights, so it knows your company the way an employee of several years does. The payoff: matching or beating frontier models while consuming up to 100x fewer tokens. Working with partners like Microsoft, Notion, and Harvey, the team draws on roots in computational neuroscience and state-space architectures to attack what they see as the real bottleneck in AI — not raw intelligence, but memory and continual learning. In contrast to the frontier labs' race toward one ever-bigger model and AGI, Dan and Jessy imagine a world where everyone has their own model — privately trained, always learning, and good at the things you actually care about. The real ChatGPT moment for memory, they argue, is the day your model feels like an intern that genuinely got smarter overnight. Hosted by Sonya Huang and Shaun Maguire, Sequoia Capital
2025/05/14 · ボーナス

LIVE: How AI is Reinventing Software Business Models ft. Bret Taylor of Sierra

Recorded live at Sequoia’s AI Ascent 2025: Sierra co-founder Bret Taylor discusses why AI is driving a fundamental shift from subscription-based pricing to outcomes-based models. Learn why this transition is harder for incumbents than startups, why applied AI and vertical specialization represent the biggest opportunities for entrepreneurs and how to position your AI company for success in this new paradigm.
6月30日

Why Hardware-Software Co-Design Is AI's Real 100x: Dylan Patel of SemiAnalysis

Dylan Patel, founder of SemiAnalysis, argues the biggest gains in AI don't come from faster chips, they come from software-hardware co-design. Optimizing the model, the kernels, and the silicon together turns a 2x here and a 2x there into 100x. He explains why DeepSeek's experts were shaped for Nvidia's Hopper (and why TPUs struggle to run it), why OpenAI's sparser models and Anthropic's denser ones pull them toward different hardware, and why the so-called CUDA moat was never really about CUDA. Dylan breaks down InferenceX, his living benchmark that runs the latest models on over $50M of donated hardware daily, tracking a roughly 60x annual drop in cost per unit of quality. He makes the case that inference will be a bigger market than oil, that the compute crunch persists because models expand the value of useful work faster than compute grows, and why Jensen Huang is bankrolling neoclouds to engineer a multipolar world. Hosted by Shaun Maguire and Sonya Huang, Sequoia Capital
6月11日

Google DeepMind's Logan Kilpatrick: Why the Model Eats the Harness

The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it. Hosted by Sonya Huang, Sequoia Capital

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