Formerly known as the Google self-driving car project and now an independent subsidiary of Google parent-company Alphabet, Waymo has been operating in some capacity since 2009. Five years ago, the company launched what it billed as the “world’s first commercial autonomous ride-hailing service” in the metro Phoenix area, then last year expanded to San Francisco. The company soon plans to launch commercially in Austin, its fourth city, and also recently began test-driving vehicles in the winter weather of Buffalo, New York.
For much of this time, Cruise has seemed to be competing neck-and-neck: When Waymo raised funding at a $30 billion valuation in 2020, Cruise followed in 2021 with the same valuation. When Cruise began offering fully autonomous rides in San Francisco in the winter of 2022, Waymo followed in the fall. In August, California regulators voted to approve round-the-clock robotaxi service in San Francisco from both companies, making it the first major U.S. city to allow two robotaxi companies to compete for service “at all hours of day or night.”
Now, after a barrage of safety concerns and incidents with Cruise self-driving cars in recent months, the landscape looks starkly different. Cruise has paused all public road operations – both supervised and manual, laid off contractors and recalled nearly 1,000 robotaxis after a pedestrian collision. In October, the California Department of Motor Vehicles suspended Cruise’s deployment and testing permits for its autonomous vehicles, effective immediately, and last week, GM announced it would significantly cut spending on Cruise in 2024.
Amid the news, Waymo’s chief product officer, Saswat Panigrahi, told CNBC that the self-driving car unit hasn’t seen a change in tone from regulators or a shift in the company’s public perception.
There are no shortcuts. I mean, this is not a question you’re asking an app or a web page, which is giving you an answer. This is a multi-thousand pound vehicle that’s moving through the physical world – yes, it’s an application of AI but a very different kind of application of AI. And there’s something to be said about time and experience and just rigor that no matter how hard you work, it takes time to do this.
So I would say that the amount of data you’ve tested yourself against – you could always test more, but the staggering scale of testing that has been brought to bear – I sometimes say that building the Waymo Driver is a hard thing, but it’s almost as hard to evaluate the Driver. The amount of simulation we have had to do… has taken a decade. It took Google’s level of infrastructure because even to simulate at that scale, as you and I are speaking right now, 25,000 vehicles in our simulator are learning to drive better. To bring that, you need incredible infrastructure capability because even if you had the AI capability, without the infrastructure, it’d be very hard to bring that skill to bear – a decade of investment into AI before AI was cool.
Compute infrastructure, to power those simulations?
Yeah, some of it is just raw scale of compute, how many computers can you bring to bear, that kind of thing. But some of it is also – think of the old-school video game versus how realistic video games have become now, that’s a metaphor for how things are. Let’s say we saw a person in Phoenix speeding at 60 miles an hour on a 45 mile-per-hour [street], and then imagine that we saw a very tight intersection in SF – can you realistically mix these two to challenge your driver to a harsher situation that may occur many millions of miles later in the real world?
[On top of that], being able to add rain, for example – all right, you’re safe enough when you’re driving through good weather, through this tight intersection with a speeding agent. Can you do that as well in rain? Can you do that at night? You can’t wait for the rain in real life to occur exactly when you want to push your system in that way, but being able to simulate rain requires that infrastructure but also enough algorithms and realism on top to be able to push this.
Can you get specific about how much compute that requires?
I have worked with pretty high-scale systems before Waymo, at Google and Ericsson, and this is a pretty staggering scale. But the only number I can tell you is 25,000-plus virtual vehicles driving continuously, 24/7, learning from each other, and [tens of] billions of miles in simulations. Think of how much you or I drive in a year – we drive, what, 10,000 miles in any given year…? Now think of billions of miles of experience – close to seven orders of magnitude difference.
Let’s talk about the shift in ridership over the past month. Have you seen an increase? Decrease?
Things are growing – to give you an idea, this year we have more than 10x’d [trips with public riders]… The ridership is increasing in both Phoenix and SF. We are well ahead of 10,000 trips [in each city] every single week… So it’s going well. We’re taking the time to respond to feedback and thoughtfully expand.