Sequoia Capital

Anthropic's Angela Jiang breaks down the abstraction stack behind Claude: knowledge (answering questions), execution (doing real work via Claude Managed Agents), and coordination — "strategies," a meta-harness where tokens get different jobs. Some advise, some execute, some dream. And the roadmap only moves up the stack.

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

Conventional wisdom says continuous disagreement is an anti-pattern. Kalshi's co-CEOs made always taking the opposite side a system — the balance a company walking the regulatory tightrope needs.

Every company has one leak the founder can't hand off. Tarek Mansour explains why most CEOs throw a rug over theirs — and why Kalshi's is proving what separates regulated prediction markets from the offshore players. #shorts #ceo #entrepreneur

Tarek Mansour calls himself a paranoid risk manager - the guy who can list 20 ways a hot air balloon will go down before it leaves the ground. Then he bet his entire company on suing its own regulator. Kalshi spent years walking through the desert. The CFTC pocket-vetoed its election markets ahead of the 2022 midterms, people left, and the company took a layoff while the government piled on audits and enforcement actions. Death by a thousand paper cuts. Instead of pivoting, Tarek and co-founder Luana Lopes Lara sued the federal government against the guidance of nearly all their investors and advisors. They won, three and a half weeks before the 2024 election, and Kalshi now claims 95% U.S. market share in prediction markets. We get into how two co-founders run 150 people with nearly everyone reporting directly to them, why it’s intentionally chaotic, why the two of them disagree by design, and Tarek's poker-player theory of expected outcome vs. outcome. He also breaks down his obsession with marketing timing - like launching the Timothée Chalamet spot 12 hours after the Knicks news broke - and his "hole in the ship" rule: a founder has to be the one staring at the leak. Tarek and Luana's dynamic reminded me a lot of me and Dharmesh at HubSpot: total opposites, and one plus one equals three.

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 00:00 Introduction 02:28 Early Vision and Regulation 04:09 Rwanda Launch Hard Lessons 06:49 Scaling to 24/7 Impact 09:35 Real World Ops Surprises 11:15 Safety Redundancy Failover 20:24 Precision Delivery Pod Tech 25:34 Building the Drone Network 26:51 Fleet Commanders Explained 28:22 Scaling to a Million a Day 29:51 Autonomy Enables 24 7 Ops 31:52 Reinventing Air Traffic Control 36:08 Why Zipline Is Vertical 41:40 First Principles Delete Parts 44:45 Market Explosion and Closing Thoughts

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 00:00 Introduction 01:58 Motel Kid Origins 03:11 Xbox Repair Spark 04:23 Internet Forums to Semis 06:42 From Quant to Founder 09:16 Homeless Research Roadtrip 14:04 InferenceX and Benchmarking 34:35 Sparse vs Dense Models 35:08 Interconnect Shapes Architecture 35:48 CUDA Moat Is Shifting 36:46 Ecosystems and Co-Design 38:46 Cerebras Speed and Limits 42:07 ROI Debates and Hot Takes 44:20 Ten Year Tech Bets 50:48 Compute Crunch and NeoClouds

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 00:00 Introduction 00:59 Always Training Explained 01:51 Beyond Context Windows 03:29 Ngram Product Overview 04:34 Adapters And Training Signals 05:32 Internalize Vs Externalize 06:49 Compute And Token Savings 08:19 Teams First Then Individuals 08:51 Memorization Vs Understanding 12:47 Dreams And Offline Digestion 14:08 Training Beats Curation 15:19 Why Everyone Needs A Model 21:44 Bitter Lesson And Architecture 24:44 RAG Killer And KV Cache 31:38 Future Of Memory And Models

Logan Kilpatrick of Google AI Studio on why he makes all his own content, and the version of gen media worth building toward: keep the personhood, reinvent everything around it. #shorts #ai #genai

Copy: The old question was whether an idea was even possible. Logan Kilpatrick on how AI flips that, and why the hard part becomes resetting your own ceiling on ambition. #ai #shorts #gemini

Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word, image, and video is produced in real time and customized for whoever is asking. In conversation with Sequoia Capital's Konstantine Buhler. #shorts #ai #technology #artificialintelligence #nvidia

The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

Logan Kilpatrick of Google AI Studio on how Google went from ~50 disconnected products, to Gemini as the throughline, to the agent layer as the next one. #shorts #AI #agents #gemini

Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word, image, and video is produced in real time and customized for whoever is asking. In conversation with Sequoia Capital's Konstantine Buhler. #shorts #ai #technology #artificialintelligence #nvidia

Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word, image, and video is produced in real time and customized for whoever is asking. He explains why NVIDIA's AI factories are the dynamos of this era: machines that take in electrons and send out tokens of intelligence, just as Siemens' dynamo once turned motion into electricity. Jensen frames intelligence as the third force to "cocoon" the planet after electricity and the internet. In conversation with Sequoia Capital's Konstantine Buhler. #shorts #ai #technology #artificialintelligence #nvidia

David Senra on what he actually sees when he drops into the Bay Area founder scene. #sfbayarea #founders #vc #startup

David Senra on the line from Atari founder Nolan Bushnell that shaped Steve Jobs from day one. #founder #entrepreneur #startup #shorts

Jensen Huang describes the five-layer cake of AI investment—energy, chips, infrastructure, models, applications—and dismantles the fear that AI will erase jobs, using radiology and software engineering to show how automation raised labor demand instead of killing it. In conversation with Sequoia Capital's Konstantine Buhler. #shorts #ai #technology #artificialintelligence #nvidia

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 00:00 Introduction 01:47 Agentic Gemini Era 03:05 Antigravity Agent Harness 05:07 Cannibalization and Outcomes 08:24 How Agentic Are We 14:22 Gemini vs Codex Claude 19:11 Vibe Coding Games 26:13 What People Build 27:07 Vibe Coding Games Soon 28:01 World Models vs Engines 29:29 Omni World Model Blur 31:10 Single Omni Model 33:50 Authentic Gen Media 35:19 Vibe Coding Android Apps 38:32 Scaffolding and Startup Edge 43:54 Inside DeepMind Culture

David Senra on what he'd actually look for if he ever became a VC. The bar is brutal. #shorts #ceo #founder #startup #vc

Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word, image, and video is produced in real time and customized for whoever is asking. He explains why NVIDIA's AI factories are the dynamos of this era: machines that take in electrons and send out tokens of intelligence, just as Siemens' dynamo once turned motion into electricity. Jensen frames intelligence as the third force to "cocoon" the planet after electricity and the internet. He describes the five-layer cake of AI investment—energy, chips, infrastructure, models, applications—and dismantles the fear that AI will erase jobs, using radiology and software engineering to show how automation raised labor demand instead of killing it. His bottom line: you won't lose your job to AI, but you might lose it to someone who uses AI. Hosted by Konstantine Buhler, Sequoia Capital Recorded May, 2026 00:00 Introduction 00:42 From Chatbots to Generative AI 03:35 Agentic AI That Does Work 05:26 Downstream Industry Impact 06:25 Computing Shifts From Retrieval to Generation 11:26 A Planet Cocooned by Intelligence 14:27 Inside the NVIDIA AI Factory 20:48 AI Five Layer Cake 21:58 Beyond Chatbots to Biology 23:54 Tokens and World Models 24:53 Trillions in Applications 27:13 Ditch the AI Doom 31:32 Jobs Tasks vs Purpose 38:40 Closing the Tech Divide

David Senra on the one skill that makes Rick Rubin a generational talent. #shorts #podcast #ceo #founder #startup

David Senra on the simplest, truest career advice I've heard in years. #ceo #entrepreneur #founder #careeradvice

David Senra has studied hundreds of legendary founders. The common thread is one word — and one maxim. @founderspodcast1⁩ #shorts #podcast #ceo #entrepreneur #leadership

David Senra on the difference between loving your work and actually loving your work. @founderspodcast1 #shorts #podcast #ceo #entrepreneur #leadership

David Senra has spent a decade reading the biographies of 400+ founders for his podcast Founders - and lately he's started interviewing the living ones face to face. He joins me to share what all of them actually have in common, and it isn't what Silicon Valley thinks. His one word is focus — what he calls "mute the world and build your own." He walks through Dana White buying the UFC for $2 million and turning it into a nearly $8 billion TV deal by ignoring everything outside his own arena; why Daniel Ek believes founder-problem fit matters more than product-market fit. We get into the idea that the best founders are driven by control, not money - and why selling your best company and trying to recapture the magic at 60 almost never works. David’s perspective on overcoming negative self-talk: at some point you have to change your fuel source from something that punishes you to something that generates. If you've ever wondered whether the founder mythology is real, David has read more of the source material than anyone alive. 00:00 Introduction 01:11 Focus Above All 01:50 Dana White UFC Focus 04:19 Focus vs Obsession 05:05 Origins in Childhood 06:07 Coppola and His Father 08:48 Assholes and Archetypes 11:14 Autism and Originality 14:55 Immigrant Drive and Grit 16:38 Bet on the Founder 17:52 Solo vs Partners 23:20 Negative Self Talk Fuel 26:39 Platform Shifts and Founder Mode 28:07 Dell Versus IBM 30:02 Infinite Leverage Edge 31:38 Focus Versus Speed 34:20 Taste And Listening 40:52 Founder Traits And Balance 54:22 Closing Takeaways

Online (real-time) RL only works if the model is already great — users won't engage with a bad one, and no engagement means no feedback. Federico Cassano explains why Cursor uses offline RL to bake in reasoning and tool calling first, then layers online RL on top for that final delightful experience. #shorts #Cursor #reinforcementlearning #ai

ame model. Same tokens. Different log probabilities. Federico Cassano explains the "numerical mismatch" problem that plagues async RL on giant sparse MoE models like Kimi — and teases that the next Composer will be trained on Cursor's own base model. #shorts #Cursor #Composer #RL #MoE

Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own. 00:00 Introduction 01:20 How Listen Works 02:23 Customer Wins 03:28 Surveys Versus Reality 05:13 Zoom Like AI Interviews 07:14 Origin Story 08:01 Old World Research 09:50 AI First Benefits 11:32 Finding The Right People 14:30 CRM And Prospecting 15:35 Consulting In The AI Era 20:05 Market Research Simulation 35:33 Closing Thoughts

Dmytro Dzhulgakov reveals the trick behind Cursor's RL infra: not all weights change every step. By compressing the delta between training steps, Fireworks ships updates 20x smaller than the full model — losslessly — across continents. Pure database-systems engineering applied to RL. #shorts #Cursor #RL #aiinfrastructure