The fundamental bottleneck in robotics is the lack of data for training and evaluation — unlike language models where data is abundant on the internet, robotics requires a real-to-sim-to-real pipeline to unlock scaling laws.
Yunzhu Li identifies the lack of data in both training and evaluation as the core bottleneck in robotics, contrasting it with language models where internet data is abundant. His company, Scenix, builds a real-to-sim-to-real pipeline that maps real environments into digital worlds with high alignment, enabling scalable data generation. ✦ AI generated
Yunzhu Li · a16z Podcast · 2026-07-28 · original ↗
starts at this moment · 6:52
“Maybe Fei-Fei could just quickly describe what Marble is.”
What Synix team is doing is trying to solve this extremely difficult problem in robotics which is the lack of data — the lack of data in training, the lack of data in evaluation. This is very very different from language models where data is abundant on the internet. And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from? This is a profound problem that everybody's battling with in robotics. As Cynics, we are developing what we call a real to sim to real pipeline. We want to map the real environments into the digital world that has the best alignments with the real environments. By alignments we mean that whatever happens in the digital world is also going to happen in the real environments, such that we can replace all the data, all the evaluation we need in the real environment by using the data that we can generate at a scalable way in our digital world.
verbatim transcript · starts at 6:52
6:52very different from language models where data is abundant on the internet. >> And we know that um in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from? This is something that that is a profound problem that everybody's battling with in in robotics. It'd actually be great to talk about this energy like you have put together a very very talented team.
7:17You have put together a very talented team and so like to what extent is there overlap to what extent is this an extension? Maybe talk a little bit about that. Yeah, that's actually like how complimentary it is. >> It's it's actually the the TLDDR is is very complimentary and with the shared mission. So is one of the three uh technical co-founders. The other two are Changi Jan, another Colombia professor
7:39who has been a world-class technologist in simulation. Wow. And Changi has his background in also um VFX. He worked at Weta, he worked at Tencent, he's being an entrepreneur. Uh then there's Sunonni who is a phenomenal engineering leader who was also in a uh startup uh that was acquired by Amazon many years ago. So he worked in many different tech stacks in the computer vision field in uh in
8:10Amazon. So when when we started talking more seriously I recognized that uh a couple of things that Phoenix has from a talent point of view is extremely complimentary to to uh worldaps. one is obviously incredible um uh thought leadership and and just technical prowess in robotics right so uh from really from hardware full stack robotics and even when he was my posttock at Stanford at that time you
8:43already had your faculty offer so you were there only for one year I wanted you for more than one year but he had to go become a have the real job so uh he was a full stack researcher in in robotics from modeling to to hardware. Uh and of course uh VR and his student uh students at Synenix was that pool of talent world hasn't had yet. Then on the