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Training Data · 3 min · Oct 20, 2026
Robotics can unlock commercial tasks before reaching generality
Josh Gao
Founder & CEO, Mecka AI
Sonya HuangHost · Partner, Sequoia Capital
Works with OpenAI and Anthropic; co-wrote Sequoia's essay on generative AI.
About this segment
Gao says robotics data is still far behind autonomous driving in scale, but progress need not wait for one breakthrough. Commercial tasks can unlock gradually, with in-domain data closing reliability gaps.
From “Inside the Robot Data Factory: Mecka's Josh Gao on Sensorizing Humans to Train Robots”, Oct 20, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
“So relatively, we're still many orders of magnitudes away from where we have to be, ultimately.”
Takeaways
- Robotics has only reached millions of data hours.
- A robot's many degrees of freedom raise the data burden.
- Constrained commercial tasks can arrive before general-purpose robots.
In this episode
Full episode- 31:56Robot training is converging on a mixed data recipe3:56
- 12:07Running a robot data factory requires custom hardware and logistics2:45
19:14Robotics can unlock commercial tasks before reaching generality2:40
- 4 more moments in the full episode
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 19:19
and how these products are created will also change. Really interesting. What scale of data has already been collected, do you think? And how much data do you think is going to be collected over the next few years? I think we finally entered a world where we are in millions of hours of data. And coming from the world where Ego4D of 4,000 hours two years ago where we were then is a huge step up but then comparatively and you think about autonomous driving as an example tesla spent over a decade solving for two degrees of freedom ultimately of acceleration and direction millions of cars on the road, they have a lot more than a few million hours of video data at this point.
- 20:06
So relatively, we're still many orders of magnitudes away from where we have to be, ultimately. So on one hand, that sounds like opportunity for you. That sounds like a lot of revenue for the robot data factories. On the other hand, as somebody that invests in the robot foundation models, I look at that and I say like, oh, shoot, are we ever going to get to the promised land? Because I've done the same math as you on like a car has two or three degrees of freedom.
- 20:32
A robot has even dozens in the hand. Yeah, exactly. And so you combine that with like, you probably need force data and there's a way more, you know, it's just a way harder environment to operate in. And I get to like, you might need billions, trillions, quadrillions of hours. Is there even enough GDP in the world to support the cost to bring that in? Like, what do you think? I think it's about unlocking commercial use cases as we go. So it doesn't have to be so much binary in that, oh, one day we've unlocked robotics or not. It is going to be a slow creep up into the world. A great ways to get there is that with some of this space pyramid built up where we are seeing the generalizations, we are seeing these scaling laws at play, that gives people the courage to scale up? And with that courage, can we, one, have some generalizability in some of these
- 21:26
environments? Can we then also collect a lot of the in-domain, in-task data that closes that gap in post-training? So maybe I can collect data exactly from this room, exactly in this podcast demonstration, to actually close the gap of what is, in a lot of cases, four nines of reliability in these real-world environments. That's a helpful framework.