Sequoia Capital

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9月1日

Making Cities Awesome: Peregrine’s Nick Noone & Ben Rudolph

Most public safety technology companies grow by collecting more data. Peregrine inverted the model: no sensors, no new data, a business built on connecting the data and information cities already own. Co-founders Nick Noone and Ben Rudolph received more than two dozen no's before San Pablo PD let them in the door in February 2018. Today, Peregrine powers law enforcement, emergency medical services, fire and rescue, and other services in more than 400 cities and communities globally. Nick and Ben explain their north star for data sovereignty, and discuss how Peregrine's philosophy and privacy-first approach to data access and ownership preserves individual privacy and cities' sovereignty. They walk through how AI and long-horizon agents are being deployed: a cold case agent that reproduced an exoneration detectives had reached by hand, a Wisconsin county that placed a suspect using cell records buried in 300GB of evidence, identifying threats to a synagogue, root-causing an escalation in weather-related incidents, and more. 00:00 Introduction 02:07 What Forward Deployed Engineering Means 03:58 What Silicon Valley Gets Wrong 05:23 UNHCR, Dimagi And Downstream Data Problems 08:25 Why Cities, Why Safety 10:45 Two Dozen Nos And San Pablo PD 14:19 Building Through Defund The Police 18:16 The Inversion Of The Collection Model 21:20 Data Ownership And Governance 22:57 From Nice Search To Deep Analysis 29:59 Agents Writing The Integrations 31:45 The Cold Case Agent 35:02 The Anti-Network-Effect Proposition 38:40 Facial Recognition And Hard Decisions 40:48 Technology For The Underdogs 42:54 Trusting The Individual Contributor 48:50 Ten Thousand Cities
8月18日

Rich Sutton and Khurram Javed: Why AI Models Stop Learning, and How to Start It Again

Rich Sutton, who helped pioneer reinforcement learning and wrote the seminal AI essay The Bitter Lesson, has now cofounded Oak Lab with his former student Khurram Javed. Their goal: to build agents that continuously learn from their own experience rather than from us. Rich doesn't think he holds a radical view: "I'm not weird. The field is weird." He says all learning is continual, and the field is the one that needed a new name for it. Rich and Khurram argue synthetic data is "a big mistake." Their "big world hypothesis" is that the world is massively more complex than any agent or simulator, so approximations have to be updated continuously rather than frozen at deployment. Rich calls LLMs an unanticipated scientific breakthrough, but says they represent roughly a quarter of intelligence. He says catastrophic forgetting is "totally curable" with the ideas behind their continual backprop algorithm. Khurram explains why the frontier labs can't follow: they sit in a local minimum where a new paradigm gets worse before it gets better. Their target, five to ten years out, is a trillion-parameter mind that keeps learning, stays coherent, and runs on 20 watts. Hosted by Sonya Huang and Alfred Lin, Sequoia Capital 00:00 Introduction 02:10 An AI winter, a cancer diagnosis, and the move to Alberta 07:07 Writing "The Bitter Lesson," and what people get wrong 09:53 Are LLMs a positive or a negative example of it? 11:03 Synthetic data is "just a big mistake," and the Big World Hypothesis 18:01 AlphaGo, human priors, and why prior knowledge and learning should be friends 22:37 "Their weights never change": do LLM assistants actually learn? 26:09 Babies, squirrels, and why no animal learns by supervised learning 32:02 Rockets, imagination, and where paradigm shifts come from 36:42 The Alberta Plan and its 12 steps 38:53 Catastrophic forgetting and the cure 43:43 Oak's biggest ambition: a self-maintaining mind 47:56 Why the big labs are stuck in a local minimum 49:13 If everything goes right: LLMs, many minds, and hiring
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.
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
8月25日

Parallel’s Parag Agrawal: Building a New Web for AI Agents

Parag Agrawal is making a bet that goes against two decades of web search: agents will query the web a thousand times more than humans ever have, and the infrastructure built around human clicks is wrong for them. The former Twitter CEO, now founder and CEO of Parallel Web Systems, explains why Parallel treats human click data as a bug and trains on agent feedback instead. He unpacks the counterintuitive choice to ship a search agent before a search engine, building an index incrementally, and how the new Turbo product cut agentic search to 200 milliseconds. But the problem Parag keeps returning to is economic: the ad-supported internet collapses when agents show up instead of people. His fix draws on Shapley values to pay content owners for the value their pages provide agents, with real dollars reaching publishers, he predicts, within 12 to 24 months. Hosted by Sonya Huang and Andrew Reed, Sequoia Capital 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision
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
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
2月26日

Ben Horowitz On What Makes a Great Founder

A16z’s Ben Horowitz joins me for a raw, unfiltered conversation on what actually breaks founder CEOs, and what separates the great ones from the rest. We unpack founder mode, where it works and where people are taking it too far. Ben shares why overly deferring to experienced executives creates politics and fiefdoms, but avoiding senior talent altogether is just as risky. Founder mode is not about micromanaging. It’s really about taking responsibility for outcomes and having the confidence to manage people who may have more experience than you. Ben goes deep on “constructive confrontation” and why running away from the truth to preserve feelings is one of the most dangerous things you can do in a tech company. He explains why bad news has to travel fast, how decision debt paralyzes organizations, and why hesitation, not lack of intelligence, is what usually gets CEOs replaced. We also dive deep into hiring, especially the VP of Sales role founders mess up more than any other. Ben breaks down why great sales leaders qualify you in the interview, why references matter more than charisma, and why selling a hard product builds a different kind of operator. Along the way, we cover the psychology of being a first-time CEO, what Zuckerberg, Jensen, and Elon actually have in common, why culture is defined by behavior not values, and why feeling like you don’t know what you’re doing is more normal than most founders admit.
4月2日

Jack Dorsey: Every Company Can Now Be a Mini-AGI

Jack Dorsey (Block CEO) and Roelof Botha (Sequoia partner and Block board member) join to discuss a bold claim they wrote about recently: the traditional corporate hierarchy isn't just inefficient — it's obsolete. Jack made one of the toughest calls in recent business history: cutting 40% of his workforce and rebuilding the company from the ground up around what he calls an AI "intelligence layer." We get into how that conversation went down, the math they used to land on a number, and why he's convinced that acting from a position of strength beats reacting from one of weakness. Jack breaks down his vision for simplifying into just three roles, and what it means to replace a pyramid org chart with a circle — AI at the center, and people at the edge. Roelof, who helped think through the restructuring, shares his perspective on how AI-native startups are building differently, and what CEO qualities are timeless. I've eliminated org charts before. I know how hard this is. But Jack is doing something I never had the tools to pull off. If you're a founder wondering whether your hierarchy is working for you or against you, this one will make you uncomfortable in the best way.
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

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