00:11:42 · Eric Landau, Sebastian Johnson
[00:11:42] S1: Most mostly in The US. Yeah. We... And and most of the growth is now happening in The US. US is just the... You know, it's the biggest producer of AI now outside of outside of China. So you just have to be close to where your your customers are. [00:11:56] S0: And as you've changed from the sort of data annotation play where, you know, Scale AI was the big sort of, like, competitor Yeah. Who do you see yourself going up against when you're kind of pitching to clients? We haven't seen [00:12:09] S1: many, like, full kind of comprehensive end to end solutions from the the data side. I... Not to say that they they don't exist, but we're we're just not seeing in a lot of the the deal cycles that we're doing. So, usually, when we have these types of conversations, it's, will that... The developer at the company try to, like, vibe code something themselves, or will they, you know, yeah. Go with us. [00:12:31] S0: Okay, Justine. So you are still filming in London, but you must be selling a lot in The US. Yes. And especially if you're now having, like, an FTE model, you must be hiring more people in The US. Yeah. Now that you've raised this money, how do you see the the balance between Europe and US changing in terms of your footprint?
00:12:31 · Sebastian Johnson, Eric Landau
[00:12:31] S0: Okay, Justine. So you are still filming in London, but you must be selling a lot in The US. Yes. And especially if you're now having, like, an FTE model, you must be hiring more people in The US. Yeah. Now that you've raised this money, how do you see the the balance between Europe and US changing in terms of your footprint? [00:12:45] S1: So we're growing, quite aggressively on both sides of the pond. The sales team is growing in both places, but I think the, the higher growth rate for go to market in particular is is in The US. Yeah. And, we have, the bulk of our product engineering team based in London, that will kind of, continue for the foreseeable future. We're also adding more technical resources, in, The States as well to be closer to customers. [00:13:14] S0: Nice. And then we talk about some of this growth that you've seen. We saw... You know, you mentioned 10x on the physical AI side. [00:13:19] S1: Yep. [00:13:19] S0: Is there anything else that you can share? What kind of what are the key metrics that you look at in the business and how are they performing? [00:13:24] S1: Yeah. One of the the metrics is just kind of data flux. So how much data is flowing in and out of our system because that's a good proxy for for value, and, that's gone up by, an order of magnitude. We're operating at the multiple petabyte scale of data that's, being managed and used by our system, and, that is probably maybe, like, 50 times more than all the data that was used to train GPT three or four. Wow. So multimodal data in particular is very, very dense
00:13:24 · Eric Landau, Sebastian Johnson
[00:13:24] S1: Yeah. One of the the metrics is just kind of data flux. So how much data is flowing in and out of our system because that's a good proxy for for value, and, that's gone up by, an order of magnitude. We're operating at the multiple petabyte scale of data that's, being managed and used by our system, and, that is probably maybe, like, 50 times more than all the data that was used to train GPT three or four. Wow. So multimodal data in particular is very, very dense [00:13:54] S0: Yeah. [00:13:54] S1: Which makes it quite tricky from a scalability perspective, and it's why we spent a lot of years developing our solution. So just kind of seeing the data number go up, that is something that we track quite closely. [00:14:06] S0: Amazing. You know, you must have been one of the first companies to go through YC that was very much focused on AI because you've around since really before. Yeah. This whole AI moment, the GPT release Yep. That really blew up. Yeah. What have you got right and what have you got wrong about AI over the time, and what have you seen that's changed that you didn't expect?
00:14:26 · Eric Landau
[00:14:26] S1: Well, one thing we we didn't really expect, the ChatGPT moment, it just kind of came, and I remember very very precisely, like, where I was when I first saw it. It was, like, the day of... I was in Boston for... I was seeing a customer, and I was actually talking to, Victor from Synthesia the next day for my podcast. And, I remember what I did was I took... I was preparing a bunch of questions. I thought, oh, what would be fun is I create half the questions, and this new ChatGPT thing creates the other half of questions. And then I asked Victor, which one's human, which one was, AI? And he was shocked they were so close together. I mean, that was where where we were Wow. That's, like, real. Three, yeah, three years ago. So that's something that we were, like, very surprised that Yeah. This vector of AI in particular would be the one that would take off. And even people at OpenAI were... Like, we've talked to some of the people that were, heading the ChatGPT project, and they were all all... Also surprised. So that's something that we weren't expecting. Yeah. I think what we got right in general was just the importance of data, because, again, just from first principles, there's only three ingredients that go into an AI system. It's models, compute, and data. And, if you don't get one of the ingredients right, then the output is not going to work. Mhmm. So you need to make sure that you're getting high quality data for whatever AI system that you're training.
00:15:41 · Sebastian Johnson
[00:15:41] S0: What do you think about the LLMs at the moment in general? We're seeing a plateauing of the improvements across all models, we're seeing increased level of competitiveness, seeing like Gemini is now often talked about as the better model compared to OpenAI, it's managed to catch up. What's your view? Do you do you see a world in which the models become commoditized?
00:15:59 · Eric Landau, Sebastian Johnson
[00:15:59] S1: Yeah. So I I think I have a different frame on it. One is, like, where does the scaling law actually, like, manifest? Mhmm. And one, kind of metric that I I track and a bunch of other people track is, from the METR Institute in terms of the average length of task that an AI system can, successfully complete. And if you look ten years ago, the average length of task would be something like recognizing a cup, which takes a human, like, a a millisecond to do. So an AI could do something that takes a human a millisecond to do. And then a couple years later, it might be to, write an email, without actually, having the the context lost in the email, and that might take, like, five minutes. Now we're at the place where an AI system can successfully successfully do something like... That takes a human two hours, and this number doubles every seven months. So now these AI systems are doing things that humans take two hours. In seven months, it'll be four hours. Once it hits to the level that it's actually two weeks of time, that's the typical kind of cadence cycle of what we do in our company, like, two week sprints. So that's where you'll have an AI, like, as a as a coworker, as a colleague, as an employee, and that will look like a very different world. And that... This, exponential, this doubling every two months, that hasn't slowed down at all. So while it's easy... Like, you know, in certain metrics, there's saturation. It's hard to see, like, the the deltas of improvement. [00:17:23] S0: Yeah.