The past year has been a wild ride for Aicha Evans. Zoox, the autonomous-vehicle company Evans leads as CEO, was acquired by Amazon last summer for a reported $1.2 billion. And when the company unveiled its vehicle in December, the car represented a significant departure from the automobile as we know it.
Meant to serve as a self-driving taxi, it looks more like a high-tech carriage than a car. Sliding glass doors welcome passengers from either side, and each of the vehicle’s outer corners houses a “sensor pod” with multiple lidar and radar modules and cameras to help it navigate. Beneath the floorboard is an electric motor that can whisk passengers to their destination at up to 75 miles per hour.
Zoox’s vehicle is one of the few driverless rigs built from the ground up, and Evans says it’s also one you’ll never get to own. Instead, Zoox plans to launch an app-based ride-hailing service in cities including San Francisco and Las Vegas, where the vehicle is being tested.
I spoke with Evans about what it’s like to try to change an industry and transform the way we move about our cities.
Q: How do you define Zoox? Is it an AI company? A robotics company?
A: It’s a transportation company that takes advantage of AI, robotics—all of the new techniques around electric vehicles and software—and blends all of that to basically open up a new area of transportation.
A city like San Francisco that has housing issues and is worried about business flight has 30% of its real estate footprint dedicated to parking. So if people were using Zoox to go from point A to point B, those buildings could be replaced, reclaimed for businesses or for housing and parks.
The other thing that’s really important from a Zoox standpoint is taking advantage of sensors and computing to make all of this happen. One of the questions we get all the time is: Why are you building a vehicle? Well, because the passenger car of today was architected and designed for human drivers. Rearchitecting and redesigning the vehicle to make it easiest and safest for AI to drive is what we’re all about.
Q: How might autonomous vehicles affect our lives when we’re not on the road?
A: The world 30, 40 years from now will look very different. We talk about autonomy as really the beginning of a wave. Sort of like also what happened with the internet and then the PC and then wireless, and then the smartphone.
I mean, the smartphone is not that old, right? Sometimes I’m like, how did we even operate without these things? Well, we did. And I think autonomy will allow a lot of things like that—around goods, around services. I think there are a lot of things we physically go to a place for today that in the future will come to us through autonomy.
Q: Many driverless cars are primarily trained in Western, urban environments. How well will these systems work in other places?
A: Mathematical algorithms are not biased. However data can be. Not because the data is bad but because of where you collect the data. In terms of Zoox, what I can tell you is that we will not go somewhere without training on local data sets. If you don’t do that and you make assumptions, life could get quite complicated.
I think that as an industry, we understand the science, and it’s important to understand from an input standpoint what could be problematic. You’re also more likely to do that if there are people in the room who don’t all look the same and think the same.
Q: What’s your approach to leadership?
A: It’s changed over the years. You’re first an engineer, and you get noticed because you’re one of the best in the room on the project. And you try to improve and learn more and have more impact, but very quickly you sort of understand the math around one versus many. And as a single person, you can only do so much.
Leadership is not command and control. It’s really about: How do you get people together? How do you get them sold and bought in on a mission? And then how do you work together to accomplish it?
Q: When it comes to building teams, what’s your approach to diversity?
A: We’re building a consumer product at the end of the day, and consumers come in all shapes, all races, all genders, all … everything. And so it’d be bad to build a consumer product without having people who look like and think like the consumers.
Another element of it is access and equity. Like, for an African-American woman with short hair, getting my hair cut is a big deal. Not a lot of people can cut my hair—let me just put it that way. You start noticing any time you move to a new city: “Oh, I just have to find MLK Boulevard. And on either side of it, all the barbers will know how to cut my hair.” And that was true in DC. That was true in Austin. That was true in Portland. And so I look forward to our vehicle picking up people in whatever neighborhood they are from, and basically giving them the opportunity to be transported to where the economic access is.
Q: You’re a Black woman in power, in tech. That is not common. How do you deal with that?
A: I don’t actually wake up every day thinking I’m a Black woman with power in tech. Occasionally when I’m getting pulled over or when somebody is doing something stupid, I am reminded, but I don’t want to be an angry person all the time. I don’t think that’s very productive.
What I do think about is, I represent opportunity. I represent that it is possible. I’ve spent a lot of time asking myself: What is embedded systematically that makes this rare, and how can we break that down?
I remember going to a Lego robotics competition with my son for the first time in the Bay Area. And my son said to me, “Wow, there’s only one of me here.” I’m like, “No, no, no, you’re confused.” And I looked around, and yes, there was only one of him and there were very few girls’ teams. And the lack of opportunity is already right in front of you.
And then you bring that to Zoox and you say, “Hey, crew, we’re going to sponsor Lego robotics. Are some of you interested in being mentors?” I look at opportunity. I look at how do you make a meaningful, positive difference?
This is our problem as a nation. These are problems that have not been solved for centuries. Our nation was built and started a certain way. So a little bit of honey and less vinegar might be the right way to go.
This interview has been condensed and edited for clarity.
This new data poisoning tool lets artists fight back against generative AI
The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models.
Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist
An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.
Unpacking the hype around OpenAI’s rumored new Q* model
If OpenAI's new model can solve grade-school math, it could pave the way for more powerful systems.
Generative AI deployment: Strategies for smooth scaling
Our global poll examines key decision points for putting AI to use in the enterprise.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.