Inside the Robot Data Factory: Mecka's Josh Gao on Sensorizing Humans to Train Robots

Josh Gao (0:00)

There's an idea in science fiction, that is when robotics and AI has ultimately fully generalized and solved all tasks that humans can do, the final frontier is going to be organic. Final frontier is going to be biological. And you see this in like Asimov, you see this in Ender's Game, you could even in Hitchhikers at the very end, the planet ultimately was this giant organic computer, trying to process and understand what the meaning of life was or what the question was.

Sonya Huang (0:45)

Today, I'm delighted to have Josh from Mecka on the show. Josh is founder and CEO of Mecka AI, which is building a data and deployment company for robotics.

I and I think the whole ecosystem think that robotics is about to enter a golden age. And unlike LLMs where we had so much free human data from the internet, robotics is fundamentally data constrained. And I think your position in the ecosystem as the leading data factory to bring robotic data and physical data in distribution for models is just a really, really interesting perch. So I'm excited to use this conversation today to chat about how does egocentric data and data collection work in robotics? What are the actual operations and economics of operating a data factory? And your overall thoughts on robotics research and deployment. Thanks for joining the show.

Josh Gao (1:36)

Amazing. Thanks for having me.

Sonya Huang (1:37)

Awesome. Maybe to start off, for those who aren't familiar, what is Mecka and how are you looking to impact the world of robotics?

Josh Gao (1:44)

So Mecka is, like you said, the data and deployment company for Physically AI. What that means is today we create, we process and ultimately we license a lot of the data that goes into training these robotics foundation models. The big problem across robotics is that we don't have the luxury of the cold start that LLMs had with the Internet. Fundamentally, a lot of this robotics data is three things. It's pixels, it's actions, and it's logic.

We don't have access to a large amount of this curated and processed and clean data to be able to then train these models to understand what it is to be in the physical world. A lot of our job is pretty much to, one, create the different types of hardware such as sensorizing and wearables for humans, the different operations to go and collect and partner with individual contributors, businesses, tasks across different environments, and then ultimately post-process and own the quality gates to make these data sets useful and teach robots about the world.

Sonya Huang (2:43)

Without having been given the benefits of a giant, free, many trillions of tokens, maybe lots of trillions, trillions of trillions of tokens of Internet corpus, do you think that robotics, having to pay for and operationally collect all the data that's going into the mix, is it even a feasible challenge? Is it economically feasible without having had the gift of the Internet?

Josh Gao (3:08)

I think we still have the luxury of a lot of things that was given from the Internet. We aren't starting from zero, ultimately.

I think you can see it across LLMs and data across LLMs. Pre-training data was always driven by the labs largely, across web crawlers, things like that. It didn't become a large market. Today in robotics, we do see pre-training as quite a large market. That could be taken in several forms. That could be that we are teaching video models how to generate more physics-grounded realities of what the world looks like. We get to leverage all the work that has gone into VLMs and different types of world models before to make that possible.

Is it possible and scalable to get to that volume of data? I think that ultimately comes down to what commercial use case can you unlock at which levels of scales of data. Our strong belief is along that journey that we will be able to unlock a lot of the simple pick and place tasks along the beginning of what we're creating. And towards that long tail, that is going to be many orders of magnitudes larger than it is today.

Sonya Huang (4:19)

I did a little homework for this. Your website is very whimsical.

It says that you are teaching robots about carbon life. What does that mean?

Josh Gao (4:27)

I actually made the website. I love it.

The night before we did our first tweet announcement or something like that. There's an idea in science fiction. That is when robotics and AI has ultimately fully generalized and solved all tasks that humans can do, the final frontier is going to be organic. Final frontier is going to be biological. And you see this in like Asimov, you see this in Ender's Game, you could even in Hitchhikers at the very end, the planet ultimately was this giant organic computer trying to process and understand what the meaning of life was or what the question was.