Sequoia Capital

.@mansourtarek_ has a great metaphor for scaling a company: you're driving a ship as hard as you can, and there's always a hole leaking water somewhere. One kind of founder stares at it all day. Another throws a rug over it and tells everyone things are fine. Great founders stop waiting for calm water. They resign themselves to the holes constantly appearing, and get good at spotting the next one before it spreads. It sounds grim. It's actually the whole game. If you’re lucky enough to build a legendary company, the work will never end. The trick is to love what you do enough that you’re energized by fixing the holes. You can see it in @Kalshi's own progress: the CFTC lawsuit, then over-reliance on a few big brokers, then communicating clear lines between regulated markets and offshore ones. Solve one, the next is already taking on water.

🚨 NEW EPISODE DROP with @Kalshi CEO Tarek Mansour My conversation with the founder who built a $22B prediction market empire by staring straight at the fire. What he and co-founder @luanalopeslara have built, the "future of truth," is nothing short of extraordinary. This episode has some of the most unfiltered lessons on chaos, focus, and founder DNA from one of the sharpest operators I have seen. We talk about: • Fixing the “hole in the ship” daily and the one existential leak that can sink you • Annual roadmaps are dead • Realigning every 30-60 days • Obsessing over the last 10% • Designing co-founder disagreement as a feature • Companies win by betting on asymmetric moves • Aligning incentives so you profit when users win, not lose 00:00 The 'Everything' Exchange 01:18 Co-CEO roles at Kalshi 03:04 Disagreeing by Design 06:43 Beirut Roots and Risk 10:23 Entrepreneurial Literacy 13:37 Chaos as a Strategy 26:52 Desert Years and Never Pivoting 32:15 Weekend Work Rhythm 32:38 Founder Grind Culture 33:08 Scaling Past 150 34:40 Perfectionist Marketing 36:09 Timing The Zeitgeist 40:19 Suing The Government 47:56 Going Mainstream 51:25 Trading Vs Gambling Debate 59:08 Founder Advice Links to the full episode in the comments 👇

POV: you're in SF during a once-in-a-generation shift in technology. You just graduated, or you came out for the summer. AI billboards everywhere, models getting smarter every week, equal parts exciting and daunting. And somewhere this summer, you meet your future friends and cofounders. We're bringing them together for a night in SF with @sequoia. You'll be in the room with some of the founders defining this moment too. July 30th. Invite only. DM me for an invite.

📹️ Announcing the fal x @sequoia 72-Hour Video Hackathon. A 3-day global sprint for AI-native filmmakers, creative technologists, developers, designers, and storytellers building the future of video. Supported by leading AI video labs @GoogleDeepMind, @xAI, and @Kling_ai. Participants will get: - Access to frontier video models - Workshops + talks from industry leaders - Mentorship from top creators + engineers - $150k credit prize pool This is a chance to build at the cutting edge and create what wasn’t possible before. Judges, speakers and event details coming soon. Participation is by application only. Apply here: 72hourhackathon.com

Today, we are introducing Traversal Workers. No tag required. The industry's first AI agents built to proactively investigate wherever they're needed, from alert triage and deployment monitoring to complex production incidents, without being scheduled or summoned. It feels like having your favorite SRE teammate seamlessly problem solving with you. Because AI in production shouldn't just know how to investigate. It should know when to act. Learn more: traversal.com/blog/ai-sre-pr…

Today's AI models train once. We don't work that way. We learn continuously, forget what doesn't matter, and retain what does. That gap is what @dan_biderman and @realJessyLin are closing at @EngramLab. AI that never stops learning, with memory that lives inside the model instead of bolted on as an afterthought. In our latest Training Data episode we get into why memory is the next frontier: why the brain forgets on purpose, why RAG is a band-aid, and what becomes possible when a model is always training. 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

Today's AI models train once. We don't work that way. We learn continuously, forget what doesn't matter, and retain what does. That gap is what @dan_biderman and @realJessyLin are closing at @EngramLab. AI that never stops learning, with memory that lives inside the model instead of bolted on as an afterthought. In our latest Training Data episode we get into why memory is the next frontier: why the brain forgets on purpose, why RAG is a band-aid, and what becomes possible when a model is always training. 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

Samir Menon @blintzbase and I are thrilled to announce Sail @sailresearchco ! We build infrastructure for long-horizon agents: inference served at unbeatable prices-per-token for open models, plus sandboxes designed to run for days, weeks, or longer. We've raised $80M, w/ our seed led by @Sequoia and series A led by @KleinerPerkins. We're using this capital to build the most efficient infrastructure for long-horizon agents. What makes agents so different? Unlike a human waiting at a keyboard (top priority: speed), agents need scale, reliability, and sustainable cost. Sail finds this efficiency everywhere in the stack: we carefully choose our chips, write custom inference engines, and run a global controller that fully utilizes every computer in our fleet. Tight integration from silicon to API lets Sail open up the cost / latency frontier to our customers - the most patient agents can now access 10x more intelligence per dollar. We're excited to be working with great companies like @parallelweb, @detaildotdev,@Jackandjillai, and @quadrillion_ai to deploy long-horizon agents with trillions of tokens. Our team is thoughtful in our engineering craft and relentlessly ambitious in our pursuit of peak performance. We previously trained at companies like NVIDIA, OpenAI, Google, and so many trading firms. Now we're ready to do the work that will define our careers, in the most compute intensive market of all time. Welcome to the era of abundant intelligence. We can't wait to build with you!