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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.
Devendra Chaplot retweeted
Excited to launch Before They Break Out, where I’ll spotlight promising startups before the rest of the world catches on.
Kicking off with Vals AI, a startup building critical evaluation infrastructure for the future of AI. @ValsAI @RayanKrishnan
Introducing Before They Break Out, where we explore promising startups and why they’re worth joining early. First up: Vals AI, a company building the measurement layer for artificial intelligence. @ValsAI @RayanKrishnan
beforetheybreakout.substack.…
Devendra Chaplot retweeted
Tinker lets customers turn their expertise into cutting-edge models that they own. One year after launch, we look back at what we’ve shipped and what our users are building.
thinkingmachines.ai/news/one…
Devendra Chaplot retweeted
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
🤖 Made with AI
The physical world is the next frontier for AI. Watney has an exceptional team tackling a key bottleneck to AI progress: data center deployment. Glad to support them and excited for what’s ahead.
We’ve raised $80 million in Series A funding, co-led by the Valor Atreides AI Fund and Hummingbird Ventures, with continued participation from Conviction, Abstract, A*, and Grant Gordon. This brings our total raised to over $100 million.
We started Watney by asking a simple question -- what can a robot enable in industries where the work remains valuable on an infinite horizon? We found an answer in the fundamental inputs to civilization: energy, matter, and intelligence.
A robot will never be as charming as a barista or as personable as a housekeeper, but it can be more exacting than a surgeon. And, a breakthrough in one quickly becomes the capability of millions. We don’t imitate human motions or processes. We care about the new, differentiated capabilities robots can unlock. We work on problems where our choice of embodiment gives us an order of magnitude advantage, whether through precision, reliability, or scale.
Compute is just beginning to transform society, but it’s constrained by execution. Since 2025, Watney has been serving the largest hyperscalers in the world, helping accelerate their compute rollout through an end-to-end deployment model.
Across hundreds of thousands of hours in customer facilities, our systems have achieved more than four nines of reliability. They’ve enabled our customers to build data centers faster, at larger scales, and with more ambitious architectures. Today, we operate the largest fleet of dexterous robots running 24/7/365 across the United States.
This is all to say, it's an exciting time at Watney. We’re all here because we want to see more ambition in the physical world. We are hiring across all domains. Join us.
Devendra Chaplot retweeted
Today we are announcing @KeenableAI, an AI-native index of the best human knowledge we have, starting with the open web.
AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average.
We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web.
Backed by a $26M Seed from @Accel and @conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity.
The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September.
Seven days of benchmarks, stories, and launches ahead.
Let your agents search.
Devendra Chaplot retweeted
Introducing Grok 4.6.
It delivers frontier intelligence and is a significant improvement over Grok 4.5 at the same price.
Devendra Chaplot retweeted
Sarvam Epoch brought builders, digital-native teams, and enterprise leaders together in Bengaluru for two days of conversations, demos, and launches across the stack.
We introduced new models, inference infrastructure, agents, and products, alongside partnerships that will help bring these capabilities to more people.
Thank you to everyone who showed up, participated, contributed, and made the first edition of Epoch what it was.
Here’s everything we announced: sarvam.ai/epoch/summary
Devendra Chaplot retweeted
Today, we are releasing Inkling-Small.
Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available.
thinkingmachines.ai/news/ink…
Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
Devendra Chaplot retweeted
Releasing the model weights and technical report of Kimi K3.
Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.
New model architecture: 2.5x the intelligence per unit of compute, not just more params.
Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.
Model weights: huggingface.co/moonshotai/Ki…
Tech report: github.com/MoonshotAI/Kimi-K…
Tech blog: kimi.com/blog/kimi-k3
Devendra Chaplot retweeted
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.
AI will transform every industry, power every company, and be built by every country.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.
The world needs both frontier closed models and frontier open models.
images.nvidia.com/pdf/Open-W…
Devendra Chaplot retweeted
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
Devendra Chaplot retweeted
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
Devendra Chaplot retweeted
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Devendra Chaplot retweeted
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
Devendra Chaplot retweeted
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
Devendra Chaplot retweeted
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.
Devendra Chaplot retweeted
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-Embodied-…
Quadrants: github.com/Genesis-Embodied-…
Nyx: github.com/Genesis-Embodied-…
Devendra Chaplot retweeted
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/int…