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Training Data · 3 min · Oct 20, 2026
Constrained jobs will come before household robots
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 homes demand extra safety diligence, while early deployments will target constrained tasks with established procedures. He gives a timeline for laundry folding and predicts packaging and assembly tasks are close behind.
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.
“Laundry folding, we spent about a year and a half plus trying to solve. And I think it will absolutely get solved in your term.”
Takeaways
- Home deployments require careful safety evaluation.
- Laundry folding took Mecka about a year and a half to work on.
- Packaging and assembly-line manipulation may arrive within a year or two.
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
- 8:29Egocentric data gave Mecka a scalable starting point3:27
- 15:19Robot data factories need adaptive quality gates2:25
21:57Constrained jobs will come before household robots2:50
- 24:38Robot integrators can improve installed bots with data3:31Up next
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 22:03
One curiosity, though, is that, you know, the data that is most plentiful right now is like, it seems like home data, right? Just because like everyone, everyone can film that. It's pretty easy to get. But a robot in the home almost seems like one of the most difficult environments to deploy in. And so I'm curious what applications do you think will hit first in robotics? Cause like where the data is most readily available seems almost the opposite of where robotics might be easiest to deploy.
- 22:29
The home is good. to be some of the environments that we have to be most diligent on by far. And a lot of these benchmarking and evals that have just started to mature across robotics, making sure that the robot doesn't fall on the baby in the homes, for example. I think the initial tasks will be much more constrained as we go along that spectrum in that very goal-oriented where you can hill climb some of these tasks where you have existing SOPs for what it means to do that job and that work.
- 23:02
Like a laundry folding robot. It could be laundry folding. Laundry folding, we spent about a year and a half plus trying to solve. And I think it will absolutely get solved in your term. A lot of these pick and place, these packaging tasks that you see on assembling lines that had to be manipulation bots that couldn't be solved by traditional robotics, we will get there within the next year or two.
- 23:23
It was a good transition to talking about deployments. I'd love to talk to you about robot deployment overall as a concept. You've said that useful robots can already be deployed today. Where do you think that's the case? I think people underestimate how diverse robotics can be. And if we constrain ourselves to, one, humanoids are incredibly popular, it will be a very popular terminal state of robotics as well.
- 23:52
But beyond that, there are so many different form factors that exist in robotics today. You could look at different markets, even across hotels in China or Southeast Asia, for example, where there are robots that are doing services, that are doing cleaning, that are delivering from after your Uber Eats gets to the door, from the front desk to your hotel room door that exists. Many of these bots are widely deployed across the world today.
- 24:18
They are just starting to get popularized across the U.S. and North America. I think some of the first bots that have to get deployed will be even just the industrial cleaning bots, for example, a lot of like thematic robotics that are out there. And then slowly stepping up and up from there, where you start to solve early manipulation tasks along that spectrum, all the way up until we get to that humanoid solving out