00:00:00 · Sebastian Johnson, Eric Landau
[00:00:00] S0: Hello, and welcome back to the Scaling Europe show presented by Deel. I'm Seb Johnson. Today, we are live in the very new, brand new Encord offices here in London. I'm joined by cofounder, co CEO Eric, has got some amazing news to share. [00:00:12] S1: Yes. Thank you, Seb. So this is a very auspicious day because it's literally the first day that we have in the new office in London, and we're announcing our series c, a $60,000,000 series c led by Wellington management, brings our total funding to a $110,000,000. [00:00:27] S0: Amazing. See, series c, get a new office, time to scale up. [00:00:31] S1: Yeah. [00:00:32] S0: Can we talk about the the the raise itself? So $60,000,000 is is a is a big round. [00:00:36] S1: Yeah. [00:00:36] S0: What has unlocked this for you? [00:00:39] S1: So for us, it was a lot of the, just market pull that we were seeing, especially in physical AI. Mhmm. So we're at a bit of an inflection point where a lot of the AI workflows that were happening in kind of digital spaces are now moving into real world and things like autonomous transportation, robotics, logistics, manufacturing. And we just so happened to kind of put ourselves in a very strong position to to capture that, that tailwind, and we felt like more funding could help us scale even faster.
00:00:39 · Eric Landau, Sebastian Johnson
[00:00:39] S1: So for us, it was a lot of the, just market pull that we were seeing, especially in physical AI. Mhmm. So we're at a bit of an inflection point where a lot of the AI workflows that were happening in kind of digital spaces are now moving into real world and things like autonomous transportation, robotics, logistics, manufacturing. And we just so happened to kind of put ourselves in a very strong position to to capture that, that tailwind, and we felt like more funding could help us scale even faster. [00:01:10] S0: Amazing. I've been following the company for a couple of years now. You know, when I when I first came across you guys, it seemed that you were a data labeling company, and you're doing a lot of almost, like, medical stuff, it felt like. Yeah. That was your, like, initial use cases. Last time we spoke was Samit, and you were saying the next big thing is going be physical AI. We're here like seven or eight months later, [00:01:27] S1: and [00:01:27] S0: you're saying physical AI is now kind of here, you're getting that big pull. But I was wondering if you could kind of just talk a bit about what it is that you do now in the sense that data labeling is quite easy to understand. Now it feels like you're a much broader data infrastructure play. Yes. For those who don't know, can you kind of summarize what it is that you kind of help your your clients with?
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?