This week, Lyft announced that it is going to be selling its autonomous vehicle division to Toyota for some $550 million. (Apparently $200 million of this will be paid upfront, with the remaining $350 million paid out over a five year period.) This is notable because Uber did the exact same thing last year when it sold its autonomous vehicle business to Aurora (which happens to be working with Toyota), and because the reasons for selling seem clear: getting to full autonomy is going to cost a bunch more money and both Uber and Lyft are determined to reach profitability sooner rather than later.

The other thing that you might be able to glean from these announcements is that neither company seemingly feels like they need to fully own/control the autonomous piece. Presumably the thinking is that someone else can spend the money on developing full autonomy and they'll just stick to building out their ride-hailing network. Once we have autonomous taxis, they'll need a network to run on anyway, right? I guess. But wouldn't this dramatically undermine the network effects of Uber and Lyft?

If you go back to Uber's S-1, there was a diagram that explained Uber's "liquidity network effect." See above. It starts with more drivers and more supply (1), because more cars driving around means that wait times and fares are lower (2) and so more people are likely to use Uber (3). Network size matters. But if you no longer have drivers -- only autonomous vehicles -- isn't it relatively easy to add more supply to any network? I suppose this partially depends on how the ownership structure will end up working for these autonomous taxis. Still, I wonder about the barriers to entry under this scenario.

Last week was CES in Las Vegas. Some or many of you were probably there. One of the things that was announced at the show was a project by Bjarke Ingels Group for Toyota called the Woven City. Situated at the base of Mount Fuji in Japan, the development sits on a 70 hectare site and will eventually house some 2,000 people.

The objective is for it to act as a living laboratory for a number of new city building initiatives, ranging from autonomy and mobility as a service to multi-generational living and hydrogen-powered infrastructure. Woven City is intended to house not only residents, but also researchers who can test out and learn from these new ideas.

Below is a short video from Dezeen. It's entirely visual. No words. There's also an official website, but not much is up there yet. Hopefully there will be more soon. Construction is set to start next year (2021) and it'll be BIG's first project in Japan.

This is an interesting study from a team of AI researchers at Stanford. What they did was use car images taken directly from Google Street View (so images of cars parked on-street) to predict income levels, racial makeup, educational attainment, and voting patterns at the zip code and precinct level.

Admittedly, it’s not a perfect survey, but when they compared their findings to actual or previously collected data (such as from the American Community Survey), it turns out that their study was actually remarkably accurate. Google Street View allowed them to survey 22 million cars, or about 8% of all cars in the US.

Here are some of the things they found:

-  Toyota and Honda vehicles are strongly associated with Asian neighborhoods.

- Buick, Oldsmobile, and Chrysler vehicles are strongly associated with black neighborhoods.

- Pickup trucks, Volkswagens and Aston Martins are strongly associated with white neighborhoods.

Interestingly enough, the ratio of pickup trucks to sedans, alone, is a pretty reliable indicator of voting patterns. If a neighborhood has more pickup trucks than sedans, there’s an 82% chance it voted Republican in the last election.

Perhaps this isn’t all that surprising given that car purchases are highly symbolic. But given that the American Community Survey costs $250 million a year to administer, this study is a good preview of what cheaper and more realtime data collection might look like.