PJ Vogt

There would also be Tesla, which by 2020 was publicly marketing a product the company called full self-driving, but which absolutely was not.

Meanwhile, Waymo had slowly continued to develop its tech.

Their robotaxis would be ready for riders by 2020.

The team had gotten an unexpected boost from a technology that was, at the time, very little understood.

In 2026, when most people talk about artificial intelligence, the conversation defaults to products like ChatGPT and Claude.

But artificial intelligence has been a core part of driverless cars going back two decades.

In the 2010s, neural net advances meant that you could now begin to feed a computer system large amounts of data and watch as its perception, prediction, and decision-making abilities improved.

Here's Sebastian Thrunn.

That technology of massive data training was with us from the get-go, but has become more and more and more and more important.

The surprise for all of us has been that size matters.

When you put a million documents into an AI, it's fine.

100 million is fine.

But when you put 100 billion documents into an AI, it is unbelievably smart.

And that, I think, shocked everybody, myself included.

The Google Brain team, the deep learning people, started working with the driverless car team to use training data to help the computer driver learn things, like how to better predict when another car was about to suddenly switch lanes, how to more reliably spot pedestrians.

Over the years, as the car drove more miles, as the team gathered more data, plugged that data into their AI systems, and tweaked those systems, the engineers say the robot driver kept improving.

As they tested the car in new weather conditions, they discovered problems that required hardware fixes.

For instance, in Phoenix, Waymo had to design miniature wipers for their car's LiDAR sensors to deal with the dust storms and heavy rains.

In 2020, Waymo finally debuts to the public in Arizona.

In the years after, it'll roll out to 10 more American cities.