00:01:45 · Eric Landau
[00:01:45] S1: Right. So we are a universal data layer for AI, and what that means is we help companies train and run their AI on the right data. Mhmm. So that involves multiple components. It means managing data, curating data, annotating data, aligning data. When we started the company, we we always knew that we wanted to be the data layer because AI systems only have three ingredients. It's models, compute, and data. And even when we started, a lot of the market value had been realized in models and compute especially. Now, you know, models have multi $100,000,000,000 companies, Compute, you know, NVIDIA is well, north of a trillion. But the largest data company was Scale AI, which, you know, was, kind of bought for 14,000,000,000, which is an order of magnitude less than the other two ingredients. And data, in the long run, will actually be the most important of these ingredients. So we always knew that really tackling the data layer and owning, that part of the stack was going to be extremely valuable. And we just saw that at the time, the biggest bottleneck of the the, bits that I was an annotation. So, we started with a a tool to automate the annotation process, and, we started in health care because turns out that it's very expensive to have doctors labeling data, manually. So a lot of the way that data was being labeled at the time was being sent overseas to factories of literally thousands of people, and they're drawing boxes on cars and dogs and cats. But getting a physician to go label data at know, $2,000 an hour wasn't very economical, so they appreciated an automated solution. And so that was, like, the initial entry point in the market. And over time, it was just kind of realizing more of our vision of owning that entire data layer. And that's kind of what's happening now, and we're seeing that, play out in Physical AI and some other other use cases, but it was something that we had recognized some some time ago.
00:03:36 · Sebastian Johnson, Eric Landau
[00:03:36] S0: And why do you think there is that huge gap between the model layer, the compute layer with these trillion dollar valuations and the data layer being, you know, Scale AI being the biggest, being, like, $15,000,000,000 valuations? [00:03:48] S1: It's because most models are not in production yet. [00:03:52] S0: Mhmm.
00:03:52 · Sebastian Johnson, Eric Landau
[00:03:52] S0: Mhmm. [00:03:53] S1: So one thing that I don't think people realize is that AI has actually barely touched our lives. It's, like, very minimal impact. You might use it as a chat box, or maybe, you want to edit your script a bit or send it to your questions. So things that are, like, minor within your day to day routine. But in the limit, when this stuff is actually working, it will be touching every single aspect of your day to day life. You're gonna wake up to an AI system. An AI robot will be something that has cleaned your house over the night. You're gonna get, your vitamins delivered by a drone that comes in, at at the day of... You're gonna be driven to work in a self driving car. You're gonna get a a health scan from an autonomous system. All the infrastructure will be checked by autonomous systems. So AI will just be in every single aspect of our lives. And to make that work, to make these systems actually work and, be reliable in the real world, the key ingredient is data. And so we've gotten to a point where a lot of these companies are getting funding for demo and POC projects where you can work off of open source datasets or maybe just scraping the Internet, and so it's sufficient to get to the stage that we're at. But data is like the, it's the bit that kind of does the last mile in getting these, systems from, like, you know, 90% accuracy to 99.999% accuracy, which you need for real world reliability. So we're gonna get there, and it'll be there, but, we're we're, you know, we're still kind of getting to that stage.
00:05:13 · Sebastian Johnson, Eric Landau
[00:05:13] S0: And what does that data look like for the physical world? Like, how do you capture and store? What what is that data? [00:05:18] S1: Yeah. I mean, it's all the data that you take in as a person. Right? You're you're seeing me, like, you're hearing me, and you're getting sensory data from, the air around you, from the temperature. So it's sensor data. It's audio data. It's text data. It's visual data. It's multimodal data. And, one of the bets that we made a couple years ago was really investing in, multimodality. This is a time when, there was a lot of focus on just text only, use cases, and we thought that, ultimately, AI systems would kinda move towards more, multimodal native, functionality, which happens a lot within the physical, world data. And so that put us in a strong position to cap... Capture a lot of the, the tailwind in physical AI in particular. So just capturing all of the different types of data that, a person takes in on a day to day basis. [00:06:05] S0: And so you... I saw that you've 10x revenue for, the physical AI work that you do. Can you talk about maybe, like, a client or a customer or two that you're working with in the physical AI space and what that really means for them?
00:06:05 · Sebastian Johnson, Eric Landau
[00:06:05] S0: And so you... I saw that you've 10x revenue for, the physical AI work that you do. Can you talk about maybe, like, a client or a customer or two that you're working with in the physical AI space and what that really means for them? [00:06:16] S1: So maybe a few different examples. One is, woven by Toyota, so the self driving unit of Toyota, where, they have obviously quite a lot of data of, self driving cars in in the real world, and it's very difficult to manage and curate that data at scale. So they use our our platform to kind of manage and curate for those types of, self driving applications. Another example is, Pickle Robot, which is, a very exciting robotics company. They started in, logistics doing, loading and unloading of trucks, and they use us for, like, their, data engine. So, you know, where the model doesn't do well, kinda find the next bit of data, send it over to an automated, pathway to do a mixture of, like, kind of agentic annotation and human loop review, and then send it back to the model to to do retraining. So those are two kind of, like, physical examples in, the robotics and self driving world. One thing about a lot of the, physical AI companies is that they're extremely secretive, because, they're at the very forefront, and they don't wanna know... They they don't want other people to know what they're doing and what their stack is, so I I probably can't share too much about, some of the other customers that we're... But we're working with, like, a lot of the major, robotics companies in the space.
00:06:16 · Eric Landau, Sebastian Johnson
[00:06:16] S1: So maybe a few different examples. One is, woven by Toyota, so the self driving unit of Toyota, where, they have obviously quite a lot of data of, self driving cars in in the real world, and it's very difficult to manage and curate that data at scale. So they use our our platform to kind of manage and curate for those types of, self driving applications. Another example is, Pickle Robot, which is, a very exciting robotics company. They started in, logistics doing, loading and unloading of trucks, and they use us for, like, their, data engine. So, you know, where the model doesn't do well, kinda find the next bit of data, send it over to an automated, pathway to do a mixture of, like, kind of agentic annotation and human loop review, and then send it back to the model to to do retraining. So those are two kind of, like, physical examples in, the robotics and self driving world. One thing about a lot of the, physical AI companies is that they're extremely secretive, because, they're at the very forefront, and they don't wanna know... They they don't want other people to know what they're doing and what their stack is, so I I probably can't share too much about, some of the other customers that we're... But we're working with, like, a lot of the major, robotics companies in the space. [00:07:31] S0: Do you think physical AI companies in this space or maybe physical AI data companies like yourself, can you get to the size and the scale of some of the LLM companies that we've got?