00:05:06 · Ruby Justice Thelot, Brandon Basso
[00:05:06] S1: Because you're doing London and all across [00:05:08] S0: the city. Literally everywhere. Literally... Quite literally everywhere. When we're operating like we've done with our road show last year in the Arctic Circle or just around the world, it's it's crazy how different it is. And regulators and our customers expect that we're performing safely, comfortably, compliantly, and competently. Meaning that when you ask the car to do a lane change, it does it in a way that people expect. Now your question is, well, how do we grade all those things? And that is the challenge, and there's no one answer. There's no one metric just in the way that you go to any AI leaderboard today. There's a ton of different metrics, but we try our very best to make sure that those metrics are aligned with human expectation, our expectation when we say what it means for a driver to be safe. [00:05:55] S1: Yeah. Something you alluded to at the start of our conversation is you mentioned a tough piece is gathering enough data to train how... These cars to drive autonomously. Famously, even a decade ago, I remember I grew up here in the Bay Area, and you would see the people inside the Waymo cars just gathering data about what's happening on the streets. Where are we now? What's different about 2026 about how we gather all the simulation data?
00:06:21 · Brandon Basso
[00:06:21] S0: Yeah. Absolutely. So one of the core advantages, and I I would say core aspects of Wayve's approach going back to a decade ago, is we very much recognize both the driving problem and the validation problem as a data problem, and we secured all sorts of interesting diverse data early on. We did this roadshow campaign where we drove well north of... I I forget if it's the Arctic Circle or not, so I'm not geography expert, where it was very cold, and collected diverse data. And that is so critical because our system is fundamentally one that gets better with data just like any frontier LLM today. And people in robotics, general robotics will tell you this. There is no general dataset for robotics today, for physical AI. There's no general dataset like there is for language. You know, humans have developed all this language data throughout the years, books and, you know, videos and stuff. And we don't have a corpus yet of driving data, so we work very hard on collecting that. That's both our first party collection, purchasing data going back to ten years ago, and then partnering with our fantastic OEM partners and working with them on getting the data so that we can deliver back to them the best the best products.
00:07:34 · Ruby Justice Thelot, Brandon Basso
Ad[00:07:34] S1: I wanna shout out our sponsor, Lovable. You know that app you've been meaning to build? The internal tool, side project, or product you'd otherwise lose a weekend to? With Lovable, you get software you can ship today. That includes an editable code base you can inspect and change, plus two way GitHub sync. Lovable also handles the backend and infrastructure, including managed Postgres, auth, storage, hosting, payments, and more, so you don't have to wire it all up yourself. And through Lovable's MCP server, you can create and deploy your project directly from the agents you already use. Turn ideas into software people love with Lovable, lovable. Dev. Now onto the episode. So I know today during our interview, you brought along a demo. Ad[00:08:18] S0: Tons of demos. [00:08:19] S1: Tell us a little bit about what this demo is supposed to be. [00:08:22] S0: Yeah. Let's talk about demos. Let's talk about data. So I'm gonna jump to to one to just highlight that last point. This is in Mhmm. The center blog, the validation blog. So we'll come back to some of these videos. But just speaking to the diversity Yeah. Here is a very simple scene that you can see anywhere in the world, and it's behave... It's stopping at a stoplight. Now the way that those stoplights look in The US and Germany is slightly different. The nuance behavior for how far you might stop behind a stop line is not only geography dependent, but it's also local rules dependent style [00:09:00] S1: You know, they say it is a California stop compared to
00:09:00 · Ruby Justice Thelot, Brandon Basso
[00:09:00] S1: You know, they say it is a California stop compared to [00:09:03] S0: Yeah. And this really gets back to sort of the the overall thrust of what we're putting out there and we're trying to say, which is there... It is literally impossible to write rules for how this should work. We didn't learn to drive by digesting... We digested a... Well, it depends on where you grew up. But we digested a rules book that said, okay, here's the driver test, and we took it. But ever since then, when we were 16 or 14 or whatever, we've learned, we've extrapolated from those rules. We've taken trips to countries where you drive on the other side of the road road, and we've become better drivers. And that's exactly how we view the driving problem for Wayve. I wanna highlight one other great example in here where... I think it might be in the first post. Here we go. Okay. So this this example is about many things, but I just wanna highlight one thing that is kind of a little bit different, but also a little bit Yeah. The same throughout the world, which is construction scenes. So I think when you look at these scenes, you probably have a sense of what to do from just looking at the image. Okay. It's like there's [00:10:09] S1: a light of a driver. So, honestly, I look at these scenes. I'm like, thank god I'm not driving in London or Japan right now. Well,
00:10:09 · Ruby Justice Thelot, Brandon Basso
[00:10:09] S1: a light of a driver. So, honestly, I look at these scenes. I'm like, thank god I'm not driving in London or Japan right now. Well, [00:10:16] S0: this is this is why we say that the the driver should be better than better than humans because I I was actually looking at this image on the left. I'm like, do I do I... Should I know what to do here? Do I got it right The point is that construction scenes are all a little bit different. You have to understand language. You have to understand that language both from a system one thinking level, reactionary, but also a system two thinking level, which is, like, long term strategy. So, like, it's not just enough to parse the sign, you but also need to understand, like, what are the consequences of par... Parsing that sign? How do I make sure I'm not driving over here into this, like, left region? And the... And then if you look at the right, you know, there's there's signs in in in different characters. There's a person there that's like, maybe it doesn't even look like he's necessarily trying to tell you what to do. So this is an example of the diversity of data that we have, and it's... The point is that if we learn how to drive in situations like this in Japan on the right and this in The UK on the left, we're gonna make a generally better driver that works worldwide. [00:11:19] S1: How do you gather all of this data?
00:11:19 · Ruby Justice Thelot, Brandon Basso
[00:11:19] S1: How do you gather all of this data? [00:11:22] S0: We have a fantastic operations team that thinks about that night and day, twenty four seven, three sixty five globally. So that's pillar one is our operations team. Now, Wayve is decidedly not a hardware and operations business. That's a... It's a very hard business to be in. People who have been working on self driving, especially Robotaxi forever, have massive operations footprints. You know, kudos to them for how they've built that out, but that's not how Wayve is planning on solving the problem. We're planning on solving the problem by having a minimal internal operations footprint purely for the purpose of geographic and vehicle diversity data collection and also high fidelity human reference data. So we have expert drivers. We see how they drive. However, we wanna primarily generate data with simulation over time. Mhmm. Maybe not primarily, but over time. That's one aspect of getting data. Two is partnering with our OEMs who have a much richer set of data than we do, and three is purchasing data. Yeah. So there's a rich set of data out there that we can go license or purchase from a variety of vendors. Dashcam data, for example, we have a huge corpus of crazy stuff that's happened on highways. Man. So if we can get that, we can incorporate, something that happened on highway ten years ago into our corpus, then we can build a better driver. So multipronged. [00:12:41] S1: Alright. With that said, I do wanna see the other demos that you have on here.