Waymo Co-CEO Dmitri Dolgov: "Move Fast And Ship Safely"

**Dmitri Dolgov** (0:07)
Good afternoon, everyone. It's great to be here. We talk a lot about AI that lives on your screen, lives in the digital world. And today, I'd like to talk to you about a different kind of AI that we've been building at Waymo, AI that lives in the real physical world. So today, I'm going to talk to you about a different kind of AI that lives in the real physical world.
How many of you, by the way, have been in a Waymo? Just raise your arms. Wow, okay. That is impressive, especially, I understand, many of you are out of town. The folks who are visiting and have not had a chance to check out Waymo, I hope while you're here in the Bay Area, give it a try. So this being a startup school, I structured this presentation as a sequence of lessons, seven lessons that we've learned over the years at Waymo around what it takes to build and safely ship today's most mature application way in the physical world, the Waymo Driver.
Let me start with a short video. This is a clip from a ride that I recently took in a Waymo with my kids. So as you see here, we're moving forward, we're proceeding through an intersection, and a couple of human drivers just decide to cut in right in front of us. And the Waymo Driver reacted safely, reacted smoothly, in fact, so much so that the kids, my kids, who are preoccupied in the backseat, they didn't even notice that anything happened. And to me, this was a pretty powerful moment. I've been working on this technology and this product for close to two decades, and it just did something fairly important. It acted safely, it kept my kids safe, it kept everybody safe, and nobody noticed. And that I think will be a bit of a theme in general when it comes to physical AI, that the best AI moments will look like nothing happened. And it just the task got done safely and smoothly.
And these sort of moments where the Waymo Driver kept everyone safe are happening daily across our fleet. Today the Waymo Driver is serving around 500 trips per week and driving over 4 million fully autonomous miles every week in 15 cities across the United States. Just for our comparison, that's over 300 years every week of an average American driver per year.
And the Waymo Driver is accomplishing that with a superhuman safety record. So what does it take to build and deploy an AI agent in the physical world at scale?
Now, in Silicon Valley, there's a common mantra to move fast and break things. However, when you're dealing with atoms instead of bits, breaking things is not really okay. So the thing you have to do is to move fast and ship safely.
And that's a much more difficult thing to do. You have to build systems that are robust from day one. You have to build AI models and you have to build training recipes where safety is the foundation and not an afterthought, not an add-on.
And by the way, the problem itself, a physical AI is different from digital AI. There are four main gaps that you have to contend with if you're building AI for the physical world versus the digital world. First, there is the cost of error gaps. You have a language model or a chatbot or a co-pilot and it makes a mistake, usually it costs you a retry. In the physical world, the cost of a mistake can be measured in human lives, not tokens. There's simply not an undo and a retry button.
Secondly, you have the latency gap. And typically when you're running a VLM or a digital assistant, it can take many seconds, sometimes minutes to come back with an answer to you. A car traveling at freeway speeds moves about 100 feet in one second. So their milliseconds really matter. And you have to run all of your inference, make all of your decisions on board a compute that fits in a trunk of your car.
Next, there's the data gap. Digital AI had the Internet. This wonderful, immense cache of pre-labeled human knowledge and human thought that we've ever assembled. There's no digitized version of the Internet for the physical world.
And lastly, there's the validation gap. In digital AI, often you can ship something that's good enough. And then you'll let your users use your product. They find the edge cases, and that allows you to deploy on day one practically at unlimited scale. And then you can just iterate and hill climb on quality from there. In physical AI, the situation is different. Given the high cost of errors, you need to have a very high level of safety and a very high level of confidence on day one, before you deploy your first robot, before you drive your first autonomous mile.