Sequoia Capital

launched a model accidentally served trillions of tokens a day 29.4% of the fortune 500 showed up raised a really big series A from @a16z it's been 3 weeks we would like to sleep now

Bending Spoons got 800,000 job applications last year. They hired fewer than 300. @luke10ferrari, CEO, has built one of the most unusual companies I've studied. @bendingspoons buys iconic companies (@evernote, @WeTransfer, @Vimeo, @AOL), rebuilds them from the ground up, and never sells them. Often with a 25-year-old in charge. 10 things I took away from my conversation with Luca: 1/ A Harvard or Stanford degree predicts performance a lot less than you'd think. Entitlement is usually what cancels the edge. 2/ People should fluidly move between IC and lead. Org charts should be tools, not religions. 3/ Slope > experience. Extreme ownership + raw smarts beats “I’ve seen this movie” experience almost every time. 25 years olds > 45 year olds more often than people admit. 4/ Throw high-talent people into the deep end. Formal training is 10% of it. The other 90% is giving them extreme responsibility and surrounding them with other high caliber people to emulate. 5/ Interviews are the worst predictor in the hiring process, so let's stop pretending. Unless you script every question and grade the answers separately 6/ Default to “no” on complexity. The person who wants to add anything carries the full burden of proof. 7/ Never lie. Never omit something important. Even when it costs you. Be the kind of person you can trust is telling you things as they are. 8/ Teenage political activism that stops in your early 20s is a weirdly strong predictor of agency. 9/ Most serial acquirers keep companies semi-independent. Bending Spoons turns them into products on one operating system. 10/ Org charts should be tools, not religions. People should fluidly move between IC and lead. New episode of Long Strange Trip. Full episode in the comments. 0:00 – Introduction 1:15 – How Bending Spoons works: buy, rebuild, hold forever, and why operating beats picking 8:00 – Building a talent flywheel from nothing: calling hundreds of graduates 13:34 – Hiring signals: why Harvard barely matters and teenage activists do 17:56 – HubSpot's sales role-play and the reference question that gets the truth 21:33 – Strategy beats org charts: management by chaos vs. eight direct reports 25:14 – Good leader, bad manager? Head in the clouds, feet in the trenches 30:06 – Slope over experience: why 25-year-olds run the companies he buys 40:29 – Radical candor, one improvement a year, and no executive team 50:23 – "Approximately mediocre": first principles and relentless simplification 55:42 – Why Milan: building a world-beating company outside Silicon Valley 59:47 – Never lie, never omit: integrity, plus Brian's takeaways

Bending Spoons got 800,000 job applications last year. They hired fewer than 300. @luke10ferrari, CEO, has built one of the most unusual companies I've studied. @bendingspoons buys iconic companies (@evernote, @WeTransfer, @Vimeo, @AOL), rebuilds them from the ground up, and never sells them. Often with a 25-year-old in charge. 10 things I took away from my conversation with Luca: 1/ A Harvard or Stanford degree predicts performance a lot less than you'd think. Entitlement is usually what cancels the edge. 2/ People should fluidly move between IC and lead. Org charts should be tools, not religions. 3/ Slope > experience. Extreme ownership + raw smarts beats “I’ve seen this movie” experience almost every time. 25 years olds > 45 year olds more often than people admit. 4/ Throw high-talent people into the deep end. Formal training is 10% of it. The other 90% is giving them extreme responsibility and surrounding them with other high caliber people to emulate. 5/ Interviews are the worst predictor in the hiring process, so let's stop pretending. Unless you script every question and grade the answers separately 6/ Default to “no” on complexity. The person who wants to add anything carries the full burden of proof. 7/ Never lie. Never omit something important. Even when it costs you. Be the kind of person you can trust is telling you things as they are. 8/ Teenage political activism that stops in your early 20s is a weirdly strong predictor of agency. 9/ Most serial acquirers keep companies semi-independent. Bending Spoons turns them into products on one operating system. 10/ Org charts should be tools, not religions. People should fluidly move between IC and lead. New episode of Long Strange Trip. Full episode in the comments. 0:00 – Introduction 1:15 – How Bending Spoons works: buy, rebuild, hold forever, and why operating beats picking 8:00 – Building a talent flywheel from nothing: calling hundreds of graduates 13:34 – Hiring signals: why Harvard barely matters and teenage activists do 17:56 – HubSpot's sales role-play and the reference question that gets the truth 21:33 – Strategy beats org charts: management by chaos vs. eight direct reports 25:14 – Good leader, bad manager? Head in the clouds, feet in the trenches 30:06 – Slope over experience: why 25-year-olds run the companies he buys 40:29 – Radical candor, one improvement a year, and no executive team 50:23 – "Approximately mediocre": first principles and relentless simplification 55:42 – Why Milan: building a world-beating company outside Silicon Valley 59:47 – Never lie, never omit: integrity, plus Brian's takeaways

Listen Labs is joining @Salesforce! A year and a half ago we launched Listen, an AI platform for understanding customers, and it’s been a crazy ride ever since. Today, we work with some of the largest companies in the world, including Microsoft, Anthropic, and Sweetgreen. What started as an AI interviewer is now a full platform for understanding customers. Listen finds the right people, interviews them, analyzes what they say, and even simulates how they'll behave. Listen’s growth quickly accelerated and while we were raising our next round, we met @Benioff. Marc is a hero of mine. I've taken countless ideas from Behind the Cloud and watched him framemog every AI CEO on the planet. We're honored to get framemogged next. With Salesforce, we can bring Listen to every company in the world, much faster. To our customers, our mission remains the same.I stay CEO, @Florian stays CTO and the same team will keep building the product you rely on, now inside Salesforce AI Labs. Thank you for believing in us early. To our team, families, and everyone who believed in us before there was much to believe in, thank you. These years have been the most fun years of my life, and it’s still only just the beginning for Listen. Now back to work. 🚀

Managers spend way too much time trying to convince the sales teams to change behavior. @AliGhodsi’s rule at @Databricks is to never engage in the debate. If sales doesn't want to do something, and you know it's the right move, just put it in their comp plan. Align the incentives and the friction disappears.

Find the bottleneck. Swarm the bottleneck. Shatter the bottleneck. If every CEO that I coach focused like @alighodsi, I wouldn't have a job. His strategy: - Identify the main bottleneck - Focus the whole company on it (to an extreme) - If you overdo it, thats great. You did it right. @databricks put all their attention on one thing at a time, and in this story it was figuring out how to get the commercial engine humming. Complete obsession. Now at a $7B run rate. Link to full episode in the comments

We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

Well.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals instead of pacing the frontier… Simpler times! But Aaron’s advice on reinventing yourself and your company for AI is timeless. Aaron founded @Box 20 years ago. It sits on hundreds of billions of enterprise files, and he's bet the company on agents that can read every one of them. He's also one of the most wired-in people in AI, on every cap table and, by his own admission, 95% Twitter-educated. He’s the rare CEO who can straddle both the internet AND has the ear of CIOs. His core argument: (1) the gap between what a model can do and what an enterprise workflow actually needs is vast, and closing it is a lot of software; (2) diffusion of AI outside of coding will take far longer than Silicon Valley thinks, and that slowness is exactly where the applied layer's value comes from. The conversation covers: — why application companies are the hottest neolabs, and why the LLM-wrapper thesis is finally working — the fox-guarding-the-henhouse problem with letting model providers route your tokens — work slop, and why we accept AI-written code but flinch at AI-written decks — how Box built its agentic harness and why it beats raw API access on accuracy and latency — the open-weights paradox: closed labs and open models both growing exponentially at once — what continual learning has to solve before it works for a lawyer with five matters and a Chinese wall — why 90% of enterprise tokens in five years will come from tasks no human kicked off — the mandate for founders right now: whoever gets it to the customer wins 0:00 – Introduction 1:55 – Are application companies the hottest neolabs? 6:56 – Will the labs move up the stack? 12:34 – Box and betting the company on AI 16:50 – Hero use cases: reading a million contracts and long-running agents 18:42 – Work slop: why AI code is embraced but AI content isn't 24:08 – Building Box's agentic harness and the evals that matter 27:23 – The state of the model race 29:25 – Open-weight model adoption in the enterprise 32:34 – Memory, continual learning, and what belongs in the weights 37:29 – Box Labs and systems of record in a world of agents 44:55 – Will chat be the dominant UI for enterprise AI? 48:00 – Why coding diffused fast and the rest of knowledge work hasn't 54:31 – Staying wired in, making a company AI-first, and what it takes to win

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"Does S1 exhibit physical prompt steerability: different prompts induce distinct behavior in the same environment?" Yes! Watch S1 follow 4 different video prompt recipes in the same kitchen. The first one is something far out of distribution -- "putting a plate in a toaster". x.com/SkildAI/status…

Will scrolling on X help you build the next tech giant? To find out, at @Sequoia we dug into 15 years of hype data. Here's our hype analysis 2.0 ... X edition. @ochonaut and I ranked the most hyped topics on X for every year since 2011, with the most valuable company founded that year underneath. What immediately stands out is the strong duration of Crypto on X. Crypto & Web3 held the #1 spot on X from the mid-2010s through to 2022 peak. But the biggest companies founded in those years had nothing to do with crypto - OpenAI, Anthropic, Wiz, Anduril. So does hype on X help us spot the next unicorn? For most of the last 15 years, no. But xAI, SSI and Prometheus were all founded at the peak of AI hype. Maybe that's changing. We'll see how this plays out over the next decade.

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Very special Training Data drops today: Making Cities Awesome ☺️ We haven’t featured an application company on Training Data in a while. The diffusion of AI is the Most Important Thing, and the highest-stakes application category of all is the one sitting near the base of Maslow’s hierarchy: safety. Today I am excited to bring Nick Noone and Ben Rudolph of Peregrine onto the podcast. You may not have heard of Peregrine yet, but their technology is almost certainly keeping you and your loved ones safe, and they are rejecting the surveillance state while doing it. Nick and Ben talk about all the interesting ways that AI is being deployed in customer environments at Peregrine, which include some of the coolest use cases of long-horizon agents and vibe coding that I’ve seen: * 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 * a semantic search tool for identifying active threats to a synagogue * a vibe-coded hurricane simulator to help one Forward Deployed Engineer root-cause an inflection in weather-related incidents Nick formerly ran Palantir’s Special Operations Command business unit and deployed into high-stakes situations in the Middle East. Ben comes from almost the opposite background: building humanitarian technology for refugees and disease prevention in Africa and India. But the two of them converged on a similar thesis: that this technology could be used to make cities work better for the people who live in them. Today, Peregrine powers law enforcement, emergency medical services, fire and rescue, and other services in more than 400 communities globally. Public distrust in surveillance has reached a boiling point, for good reason. Other 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, with permissions, policy and auditability from day one. Authorized users make sense of existing data when they have a legitimate reason to access it, and not otherwise. Data governance, access controls and audit logs are unglamorous work, but they are the safeguards that let a city protect safety and privacy at once. Nick and Ben’s deep integrity shines through in the episode. I am thankful for their stewardship of this critically important category. 00:00 Introduction 02:07 What Forward Deployed Engineering Means 03:58 What Silicon Valley Gets Wrong 05:23 Humanitarian Work, Refugee Work, 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

Rebuilding the American grid is a prerequisite for AI to achieve its full potential. @FormEnergyInc spent nearly a decade building a fundamentally new battery technology and a US manufacturing base to produce it. @sequoia is investing $100M. I first met @mateojaramill in spring 2020. He shared an ambitious idea: build a new battery technology that could store energy for days, not hours, using two of the cheapest and most abundant inputs imaginable – iron and air. Since then, Form has proved that its batteries can compete with gas and can scale, winning its first big hyperscale contract earlier this year. We believe Form Energy can become America’s battery champion.