Zu Wang

Career update: I’ve recently joined a new startup in town called Genesis AI. At Genesis, we’re building generalist robots to unlock unlimited physical labor — so humans can focus on creativity, curiosity, and what we love. First and foremost, I would like to thank @Zhenjia_Xu for his invaluable mentorship and unwavering support. Nothing brings me more joy than continue to work with you! I’d like to thank @zhou_xian_ and @theo_gervet for making the whole process smooth and for believing in my potential to do something great. I’m deeply grateful to my wonderful advisors @yukez and @LerrelPinto, who opened the doors that led me into the world of robotics. I’m endlessly inspired by my incredible friends and colleagues at Genesis, NVIDIA, NYU, and in the early days at SJTU — across the Bay, Paris, New York, and Shanghai. Thank you all for your love and support. For more info about Genesis, checkout genesis-ai.company. Exciting journey ahead!

@LerrelPinto @Meta Big congrats @LerrelPinto!!

ARI is joining @Meta! Over the past year, we have been building ARI (Assured Robot Intelligence) with the mission to build industry-grade physical AI for humanoids. The ARI stack is built on human experience, condensed into actionable tokens that can be rapidly adapted to real-world hardware. But the most rewarding part of ARI has been the people. I feel truly blessed to have worked alongside some of the world's best roboticists, a top-notch investor pool led by @aixventureshq, and the many supporters pushing for us behind the scenes. Starting next week, ARI will join the Meta Superintelligence Labs (MSL) to continue advancing frontier robotics models that advance personal superintelligence in the physical world. We have the potential to transform AI that can think and talk to AI that can do, assisting humans safely and reliably in the physical world. To the many people behind the scenes who supported us: Thank you! This is just the beginning. More in the Bloomberg article:

Wow, this one is surprising. Congrats to @LerrelPinto and the team!!

btw really a master of memes

Just watched the latest episode of AI Ascent by @DrJimFan—easily the most compelling talk I’ve heard this year. Good taste filled with great results, so I highly recommend it .

@hanwenjiang1 A good read.

GPT Image 2 has been deeply unsettling to me in the best way. Some of its outputs make it hard for me to keep using the old criterion of vision, especially the old definition of visual representation learning. Thus, I wrote this essay as a reflection on that shift: why knowledge may be the right name for what vision once called representation, and what can be the ultimate formulation for representation learning. (An unexpected side path: it also led me to think about the relation between knowledge and representation through the old calligraphic relation between spirit and form 😃 hwjiang1510.github.io/blogs/knowledg…

Shizuku AIはこのたび、a16zをリード投資家として、日本のスタートアップとしては初となる資金調達を実施しました。 日本から、世界で一番愛されるAIキャラクター、そして日常に寄り添い、支えとなるAIコンパニオンを全力で作っていきます！！ a16z.com/announcement/i…

@cumulo_autumn Congrats!!

'Take Your Child to Work Day' looks a little different at NYU Courant...

RT @stingning: “Unmoved by praise, unshaken by slander; I follow the Way, I hold myself upright”. We simply like the whale @deepseek_ai th…

@BoyuanChen0 Congrats outstanding work

This is what I’ve been cooking in the past 4 months . GPT Image 2 is over a massive 240 elo jump over the second place model, marking the biggest jump bigger than the rest of the leaderboard combined

🤖Co-training is everywhere (sim↔real[e.g. GR00T, LBM], human↔robot[e.g. PI, EgoScale], even non-robot data[e.g. PI, LBM). But why does it work? How can we improve it further? Taking sim-and-real imitation learning in diffusion/ flow-based models as the test bed, we performed a rigorous mechanistic analysis, drawing on theoretical insights and multi-layered experiments. 😮Key insight: it’s all about representations. - Alignment → enables transfer - Discernibility → enables adaptation ⚖️Both are necessary — it's better to have more aligned representations, but the model must be able to discern the domains. We term this as structured representation alignment. ⬇️Let’s take a deep dive into that: Paper: arxiv.org/pdf/2604.13645 Website: science-of-co-training.github.io

1/ We just released π0.7 — a steerable generalist robot model with emergent capabilities. I want to share a bit of the backstory, because π0.7 taught me something surprising about where robot learning is heading. A thread on bittersweet lessons 🧵

@youliangtan wishing you god speed for what's next.

After an incredible chapter, I’ve decided to leave #NVIDIA - GEAR last week to start a new journey. I’m grateful for the opportunity to work in a frontier AI research lab like GEAR, continuing my passion for robot learning and foundation models. Over the past 1.5 years, we grew from a scrappy team of fewer than 10 researchers into a much larger group, operating very much like a startup. From spinning up robots from cargo boxes, to training VLAs and baselines, to building data pipelines, operations madness, engineering infrastructure, and cross-team collaborations—we did it all. Robots and humans scaled from under 10 to the hundreds. Many things may seem taken for granted now, but building a robotics foundation model lab from the ground up is no small feat, truly a great team effort. 💪 Over the past months, the team delivered exciting work across GEAR, DreamZero, GR00T, EgoScale, Sonic, and more. While robotics is held back by Moravec’s paradox, I hope our work in open research and open source has helped advance the robot learning field. After more than a decade in robotics, I feel grateful to have witnessed and contributed to this progress—and I’m optimistic about what’s ahead. Thanks to @DrJimFan and @yukez leadership, and many teammates and friends across GEAR, NVIDIA, and our external partners who supported my journey 🙏 GEAR is truly a family of passionate individuals, and the torch will continue. It’s been a privilege building alongside such thoughtful and talented people. Now, I’m excited to start building with @jang_yoel and team — more soon... 😀✨

@perryzjia The ability to orchestrate macro data refinement department well is often overlooked, but probably one of the most important things imo.

@zuwang95 Facts! I may be a bit biased 😁

“Even within the organization, how you collect data actually changes over time quite significantly, and then the kind of data we collect changes significantly. Really, everything is a variable. The only thing that stays constant is the people. It’s the same people collecting data, and people have the same needs, the same desires, and are motivated by the same thing. Once you build a good intuition about how people operate, I think things become a lot easier.” — Cheng Chi

Excited for my talk at ETH!

@Scobleizer While others go to conferences, we study the blade

@c_valenzuelab 💯

I am willing to bet anyone that in less than 5 years there will be dedicated categories for AI and that almost all movies will be using AI in some way.

@tonyzzhao Congratulations on the new fundraising! Excited to see what Sunday will ship!

We raised $165M at a $1.15B valuation to stop doing demos. 2026 is about 1) deployment and 2) research. We will start shipping Memo with our new frontier models in a few months. Our series-B is led by Coatue, with Thomas Laffont joining the board. ->🧵