I’m starting a new early-stage AI fund, doing what I love: partnering with founders from company formation on bold bets, long before consensus. The fund’s LPs will include Sequoia, Jeff Dean, Sachin Katti, Philip Wong (my Stanford professor and Chief Scientist adviser to TSMC), and others whose work has shaped our industry and how I think about AI’s future. I’ll remain a partner at Sequoia and keep serving on my boards on Sequoia’s behalf. Sequoia has been my home for the past 11 years. I joined as the youngest investor the firm had ever hired. I grew up here. Through years of intense debate and shared work, I found lifelong mentors and friends who took me under their wing and taught me this craft. They taught me to aim high and take big swings. I’ll carry that into what I build: outsized ambition, relentless truth-seeking and curiosity, and a refusal to become complacent. Working alongside founders has made me want to build something of my own: a firm shaped from day one around the convictions I’ve developed over the past decade. Life has taught me not to take time for granted. This is how I want to spend mine. Why now We’re seeing a new way of building companies in today’s AI era. Research and products often advance together. A breakthrough can open a market or accelerate a business far faster than expected. Some companies require extraordinary capital and compute early on. Others reach meaningful scale with remarkably small teams. The rules for how big these businesses can get, and how fast, are being rewritten. But early on, the technology is unproven, the resources required enormous, and the vision seemingly implausible. This is what I love most: seeing the potential right from the beginning, and helping founders realize their full scale of ambition. More than ever, it starts with an extreme willingness to believe early, with bold, independent conviction in a founder’s technical insight before any product or market validation. Then, it means helping founders take the biggest swings, giving the rare, long-term patience to see those bets through uncertainty, ambiguity, and turbulence, and serving as a level head when the stakes are highest. I learned this kind of partnership at Sequoia. It’s the foundation of the firm I’m building. Believing before consensus My fascination with AI’s potential took root at Stanford in the early-to-mid 2010s, studying computer science and working in Andrew Ng’s lab. The friendships I formed connected me to a community whose people and ideas would help shape modern AI and build some of our generation’s defining companies. What stayed with me was the gap between what those people saw was possible and what the wider world could imagine. I’m drawn to founders pursuing ambitions that sound impossible today. I start with a simple question: “What if everything goes right?” Then we work backward together to make it happen. In 2023, Deepak and Abhinav set out to build a general-purpose robot brain at @SkildAI, before physical AI was a consensus bet. Their sim-to-real breakthroughs gave me conviction where earlier robotics approaches had fallen short. But the data flywheel required deployment at scale. We made commercialization as urgent as research and brought in strategic partners like NVIDIA and SoftBank to support the ambition. Three years in, Skild is advancing the frontier of physical AI with nine figures in annualized revenue. In 2024, Ioannis and Misha founded @reflection_ai with an insight into scaling reinforcement learning. As that grew into the ambition to build a Western frontier lab for open models, the hard question was how a startup competes with labs spending tens of billions. An introduction to NVIDIA and many compute calls and recruiting events later, the team has built Beam, now one of the leading US open models. In 2025, Anna and Azalia, the co-creators of AlphaChip, founded @RicursiveAI to design models and hardware together. From day one, we made early customers and ecosystem partners a priority. Today, they are showing order-of-magnitude improvements on real workloads, working with some of the industry’s most consequential players, and reaching a defining inflection point. The ambition keeps growing as these companies scale. @linear has always had a kind of magic to it. Now it’s the system a new generation of AI companies builds on, for teams and agents alike. @tavus saw early that we’d one day talk to AI the way we talk to each other. Today, it’s building the human computing interface for a world of AI colleagues. @Mach_Industries began as hydrogen experiments in an MIT dorm room. It has since become one of the most exciting next-generation defense companies. Making the ambition possible Being a first believer is only the beginning. Compute falls through. A deep-pocketed incumbent makes your mission its top priority. A breakthrough changes the opportunity and the capital required overnight. These are stomach-churning moments, and early belief carries a responsibility to help make the bet work. This is the partnership I love: turning the full ambition into concrete steps. Raising capital, securing compute and strategic partners, recruiting exceptional people, and winning the first customers together. Thinking several moves ahead while solving what’s in front of us. And as breakthroughs open new possibilities, helping founders dream even bigger. Thank you To the founders I’ve worked with: thank you for trusting me and Sequoia to be your first partner and first believer. It’s been the joy of a lifetime, and I can’t wait to keep building together. Thank you to Alfred and Pat for your leadership of Sequoia and your support of my next chapter. To Roelof, for bringing me into Sequoia and believing in me from the beginning. Alfred and Roelof, you have been mentors in investing and in life. So much of what I know, and the investor I’ve become, comes from your guidance and generosity over the years. To Bill, for the close partnership and wealth of knowledge, always wrapped in humility. To Shaun, for showing me how to think deeply and imaginatively. To Michael Moritz, for inspiring 22-year-old me to dream big. To Doug, for teaching me to keep things simple. To Bogie and Jess, for bringing joy to the everyday and perspective on what matters. And to all my Sequoia teammates, for the friendship, camaraderie, and lessons I’ll carry with me. To my family, especially my husband and my brother, who have each started companies of their own: thank you for giving me the courage to take this leap. Let’s get to work!

⚽️ 🤝 🤖 @SkildAI is bringing back self-play, this time for physical AI. Inspired by the World Cup this summer, Skild started from S1 to have a humanoid play soccer against increasingly capable versions of itself: - for 140+ simulated years - only objective during self-play: score goals No new demonstrations. No hand-crafted rewards for dribbling, shielding, tackling, shooting, or recovering from falls. Those behaviors *emerged* because they helped it win, and transferred to a real robot A scalable way for robots to create their own curriculum, improve beyond human-provided data, and keep making themselves better!

💥 The era of demos is over. The era of deployments has begun 3 yrs in, 10 mo after 1st deployment: 60+ customers, $100M in rev run rate 🤖 @nvidia and Foxconn for high-precision assembly of Blackwell systems 🤖 Sumitomo to automate wire-harness processes previously considered “impossible” to automate 🤖 Mitsui to operate commercial kitchens serving supply chains for 1.4 million meals a day Real world applications!

So well deserved! The AI era is compute-constrained, and chip design is one of the deepest bottlenecks. @annadgoldie & @Azaliamirh are true forces of nature: they saw this early (before this has now become obvious!) and built @RicursiveAI to solve for it Now they’re proving AI for chip design in production: real industrial chip designs, against commercial tools, with step-function results: - Dramatically faster runs - Cleaner layouts - The ability to take on problems existing workflows struggle to handle Their early results are groundbreaking, and everyone from chip incumbents, frontier labs, to hedge funds are now asking for it

Last week Apple previewed the future of Siri. In 1987 though, Apple showcased a far more advanced AI assistant that would change how we use our computers entirely. It could see you, control your computer, and even looked and sounded human. They called it Knowledge Navigator. For nearly 40 years it remained science fiction. This past week at @tavus we finally brought it to life with the help from our friends at @cerebras. Meet Dom:

Excited to be on the Business Insider list of top robotics investors. The data wall is falling. For years, the bottleneck was teleoperation: slow, expensive, narrow. Now robots can learn from human video, practice in simulation before touching reality, and there is enough funding to scale data deployments. Robotics is finally running the playbook that took LLMs from autocomplete to reasoning. There are still many open frontiers. General manipulation. The right API layer for the robotics models. Application robotics. That's exactly why I'm bullish. We've just found a recipe that works. Thanks to @ryajetha for compiling the list.

At Ricursive, we are accelerating the inner loop of AI evolution through chip design. If you speed up training and test time by a multiplier, designing a new model can be sped up by a multiplier! Thank you, @sallywf from EE Times for a thoughtful piece on @RicursiveAI!

When we started @linear, we wanted to make product work feel more coherent across the whole team. Agents can now do more of the work, but too often they operate from an empty prompt, in isolation, for one person at a time. That risks creating a new layer of fragmentation. We think the larger shift happens when agents work from the same context as the team. With the new coding sessions, Linear Agent can now move from intake to investigation, code, and review inside that shared environment. This completes the product development loop we’ve been building toward: from intake to merge, with the team’s context intact.

The task: rapidly design highly performant chips. The vision: serve intelligence at a price and capability impossible to match. Our co-founder and CEO @annadgoldie sat down with @FounderCoHo to break down the recursive self-improvement loop at the heart of Ricursive's work. The task and vision are ambitious, but so are we. Full conversation:

Building the future of silicon requires the best talent in the world. That's why we’re excited to welcome Mark Lee to the team. Mark is a titan in the chip industry: he led physical design for the iPhone and iPad, helped technically scope Apple's revolutionary M1 chip, and directed RTL-to-GDS efforts for Tenstorrent's leading Ascalon processor. Mark has spent decades pushing the limits of what silicon can do. We’re thrilled to have his expertise as we together reinvent chip design.

So fun to host @annadgoldie and @Azaliamirh of @RicursiveAI at @sequoia AI Ascent 2026! They share the story of building AlphaChip which was incorporated into multiple generations of the TPU. They walk through their three-phase roadmap: from AI-powered design tools -> a "fabless era" platform for custom silicon -> full vertical integration, and their LT vision of building true recursive AI. 00:00 Neural Nets Meet Chips 00:21 Meet AlphaChip Creators 00:43 Recursive Intelligence Vision 01:31 AlphaChip Real World Impact 02:17 Three Phase Company Roadmap 02:20 Phase One Speeding Design 04:11 Rebuilding Tools for AI 05:05 STA Engine and RL Loop 06:15 Designless Platform and Custom Chips 08:01 Team and Audience Q&A

Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.

@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer. The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling. We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding.