Devendra Chaplot

I'm joining SpaceX and xAI, working closely with Elon and team to build superintelligence. Together SpaceX and xAI combine physical and digital intelligence under a leader who understands hardware at the deepest level. Add a high-agency culture with frontier-scale resources, and you get the possibility to achieve something truly unique. I’m excited to advance the fields I’ve obsessed over for years, from robotics research to building AI models on the founding teams of Mistral and TML. Both were extraordinary journeys with extraordinary people that shaped how I think about building intelligence from the ground up. Grateful for everything that brought me here and can’t wait to get started.

Thank you @dchaplot for kind words. Working with brilliant students such as yourself has been such a privilege.

The mark of a great professor isn’t just the papers they publish. It’s the people they help build.

I’ve been asked several times whether Zhilin Yang, the founder of @Kimi_Moonshot was my PhD student. The answer is yes and he is absolutely brilliant. But I’ve been incredibly fortunate to work with so many outstanding PhD students over the years. So I thought I’d brag a little about them and their career paths (of course there are also many MSc and undergraduate students, sorry if I missed anyone): Founders / Founding Team Members Devendra Chaplot @dchaplot PhD, Founding Member Thinking Machines / Mistral Zhilin Yang PhD, Founder & CEO, Moonshot AI Jimmy Ba @jimmybajimmyba MSc/PhD, Co-founder xAI Hubert Tsai PhD, Co-founder Spuree, Apple Nitish Srivastava @nitishsr PhD, Co-founder Perceptual Machines; Co-founder Vayu Robotics Charlie Tang PhD, Co-founder Perceptual Machines, DE Shaw. Professors Paul Liang @pliang279 PhD, MIT Ben Eysenbach @ben_eysenbach PhD, Princeton University Ruosong Wang @RuosongW PhD, Peking University Bhuwan Dhingra @bhuwandhingra PhD, Duke University Roger Grosse @RogerGrosse Postdoc, University of Toronto Alexander Schwing Postdoc, UIUC Research Scientists Shuyan Zhou @shuyanzh36, Postdoc, Meta Superintelligence Lab Tiffany Min @SoYeonTiffMin PhD, Microsoft AI Murtaza Dalal @mihdalal PhD, Tesla AI Minji Yoon @MinjiYoon90 , PhD, Microsoft AI Shrimai Prabhumoye PhD, NVIDIA AI, Mistral Haitian Sun @sun_haitian PhD, Google DeepMind Emilio Parisotto PhD, Google DeepMind Lisa Lee PhD @rl_agent, Google DeepMind Manzil Zaheer @ManzilZaheer PhD, Google DeepMind Jamie Kiros PhD, Google Brain, OpenAI Yuri Burda PhD, OpenAI, Anthropic Cody Severinski PhD, Amazon

Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. thinkingmachines.ai/news/introduci… Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

Most businesses don’t need just any software. They need a way to turn how they actually work into software That’s what @emergentlabs is becoming: the operating system for businesses Today we’re announcing our $130M Series C at a $1.5B valuation to put that future in their hands

Announcing Grok 4.5, our first model trained specifically for coding and agents. It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency. x.ai/news/grok-4-5

Humanoid robots don't need to look human. Meet Eno, our first general-purpose robot. Not a machine pretending to be human, but intelligence given a body. At Genesis, we’re building a future where robots don’t feel cold or distant, but capable, calm, and ready to help. Available Q4 this year.

We are back again :) After three weeks of quiet building. Introducing Genesis World 1.0, our latest simulation platform, the second release in our full-stack suite. Open-sourced. Robotics is still bottlenecked by the 1× speed of the physical world. Every model, checkpoint, and data recipe eventually needs to be tested on physical hardware, slowly, expensively, and with limited coverage. One hour in reality can become 100 days in simulation. That is how robotics model iteration moves from a wall-clock bottleneck to a compute problem. To make this work, simulation has to be both fast and trustworthy. Over the past year, we rebuilt the entire stack: a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, and a photo-realistic renderer purpose-built for physical AI applications. We built Nyx, a high-performance path-traced rendering engine for robotics application. Genesis World 1.0 achieves near realtime performance with our latest development for penetration-free IPC solver, supporting various types of deformables beyond rigid bodies. It supports contact-rich, dexterous manipulation simulation across different embodiments: unitree, sharpa, wuji, genesis hand and various types of grippers. Under the hood is Quadrants, our effort in pushing forward cross-platform GPU-accelerated computation. Quadrants started as a fork of Taichi, and we rebuilt most of the critical parts for optimizing simulation workloads, giving 10x faster launch time and up to 4.6x runtime performance compared to the initial Genesis release. Together, they bring us to an unprecedentedly low sim-to-real gap, enabling zero-shot real-to-sim model evaluation and much faster iteration of GENE. All available today. Genesis World 1.0: github.com/Genesis-Embodi… Quadrants: github.com/Genesis-Embodi… Nyx: github.com/Genesis-Embodi…

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. thinkingmachines.ai/blog/interacti…

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

We've raised $9.1 million at a $500 million post-money valuation from Marco Mascorro, Adam D'Angelo, and Devendra Chaplot.

Excited to work with the incredible team at Cursor

The iteration speed of Elon companies is indescribable There is nothing else like it in the West

Welcome to @xAI!

Grateful to Jensen and @nvidia team for their support. Together, we’re working to deploy at least 1GW of Vera Rubin systems, bringing adaptable collaborative AI to everyone. thinkingmachines.ai/nvidia-partner…

Four Qwen 3.5 models from @Alibaba_Qwen are now live on Tinker. Qwen 3.5 introduces hybrid linear attention that enables long context windows, as well as native vision input.

To support open and collaborative science, we offer Tinker grants for researchers advancing the field. This week we’re featuring publications by some of our early research grant recipients! thinkingmachines.ai/blog/tinker-re…

Custom fine-tuning has boundless applications. This week’s roundup shows Tinkerers taking models in unexpected directions, from science research to fun and games (literally).