The lack of data in training and evaluation is the most profound problem in robotics, fundamentally different from language models where data is abundant, and unlocking scaling laws for robotics depends on solving it.
Fei-Fei Li identifies data scarcity as the core obstacle in robotics — unlike LLMs, robotics has no abundant internet-scale dataset, making it impossible to naively apply scaling laws. ✦ AI generated
Fei-Fei Li · a16z Podcast · 2026-07-28 · original ↗
starts at this moment · 6:21
“Maybe Faye could just quickly describe what Marble is.”
Really what Synix team is doing is trying to solve this extremely difficult problem in robotics which is the lack of data. M >> the lack of data in training, the lack of data in uh evaluation. This is very very 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.
verbatim transcript · starts at 6:21
6:21can be a few images and uh or a text and turn that into a geometrically consistent world that can be represented in 3D geometry whether it's gausian splat or mesh. Really what Synix team is doing is trying to solve this extremely difficult problem in robotics which is the lack of data. M >> the lack of data in training, the lack of data in uh evaluation. This is very
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