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➚ MOVE FAST: Breakout. After more than 10 years of Apple or Microsoft being the world’s largest company by market value, Nvidia has now taken the crown. Five years ago, the chipmaker wasn’t even in the top 20 ranks, but the AI boom has pushed it, and its CEO Jensen Huang, to new heights.

➘ BREAK THINGS: Crackdown. A US House panel on competition with China is shifting its focus to American manufacturing industries, with an eye on critical technologies. Semafor’s Morgan Chalfant scoops that a hearing next week will focus on semiconductors, shipbuilding, and drones.

Self-driving trucking company Waabi raised $200 million from investors who are in the middle of the AI boom, including Khosla Ventures and Nvidia. Waabi said it will launch fully autonomous trucks (with no safety driver) in 2025.

What’s different about Waabi is that its products are powered by generative AI. The problem with previous methods of self-driving was that they don’t “generalize.” It takes a herculean effort to teach a car to drive in one city, and engineers have to do a lot of that work again when a new city is added for service. Google-owned Waymo, for instance, offers driverless taxis in San Francisco and I’m amazed every time I take one. But the vehicles don’t know how to drive themselves outside of San Francisco.

That’s why Waymo taxis are considered “level 4” autonomy. Level 5 is when a driverless car can go anywhere.

It took Google about two decades to make those taxis. Waabi is three years old, and generative AI is still in its infancy.

Waabi is about to answer the big question about that technology and autonomous driving: Can you make a model that can reliably reason about driving, grasping the concepts that govern the full gamut of skills needed to be behind a wheel?

Waabi co-founder and CEO Raquel Urtasun told me, unequivocally, “yes.” And if Waabi can make a generative AI model that can reliably drive because it has a generalized understanding of the world, it can probably do a lot of other things, too.

“We’re going to do much more than trucking in the future,” she said. “We can do humanoids. We can do warehouse robotics. We can do robo taxis, drones, you name it. So, the world is our oyster.”

Nancy Xu is CEO of AI-driven recruiting firm Moonhub.

Reed: How do you measure success when it comes to more diverse hiring?

Nancy: A lot of people talk about mitigating bias within AI systems. I think there are two other ways you should think about bias. The first is how to use AI to make humans less biased, and then how to use AI to make AI less biased. At Moonhub, we give most of the decision-making power to the human today, and I think for most applications of AI, a lot of the ultimate decision making is still with the human.

The first step is to build AI systems to make the human less biased. In the recruiting world, the first step in any search is to ask what the hiring manager is looking for. An example might be: My hiring manager wants someone who has ‘signs of excellence.’ The recruiter, based on all their priors of how they’ve hired in the past, might say: ‘I’m going to assume this means they’ve worked at Google or Meta.’

Obviously, that is not the only sign of excellence, and what we’ve found is that people — no fault to them — can be lazy. Sometimes it is unintentional — they might be applying those two filters because it’s the two things they know of on how to find people that are excellent. But you might also find excellent people who went to a state school, never worked at Google, never worked at Meta, but happened to have paid their way through college with three engineering jobs, graduated and had an amazing career at a place where they got promoted three times.

Because recruiters especially tend to go for the easiest path, almost every recruiter reaches out to the same 20,000 people even though there’s probably a million people out there they could be reaching out to. The power of AI is to help humans think about how to go from the 20,000 to the million. We have helped some of our customers hire people in Wisconsin that they never would have met otherwise, because their pool is very much focused on people they know in Silicon Valley. One of them told us one of the best candidates they ever hired was one of these people, whose background they had never really considered before.

So it’s almost like your service is finding the off-the-beaten-path candidates, and that will just generally be more diverse.

Yes. And some of this is also how you message an opportunity. If you say, ‘I want people with at least five years of software engineering experience,’ there are many studies that show men are more likely to apply, even if they have three or four years, while women are less likely to apply. We will work with the customer to help them understand that these types of restrictions within their search may lead them to have a more biased pool of candidates.

Back to that person in Wisconsin. You said there were certain metrics, like promotions. That seems like the kind of thing that they would have posted about on social media. What are these data sources?

There are many different things you can look for. In the [Wisconsin] example, the way you were able to find that particular signal was by looking at people’s historical career progressions on data sources like LinkedIn. But there are so many other data sources people can consider that they don’t.

An example is these coding communities or open-source coding challenges. Oftentimes we have found they attract a lot of people who have not worked at the Googles or Facebooks of the world and might be a really smart hacker in India or somewhere, who is super passionate about machine learning and decided to enter this challenge and ended up doing super well. That person you would never really find just looking at, ‘Oh, they went to some school in India.’

Most American employers won’t know the school and will write them off. But this person was actually the Grand Prize winner of this competition, and that’s a big piece of signal. Another example is university websites that have names of all their students and who works in which labs. Academics notoriously don’t have LinkedIn profiles, so you can find these university websites, and identify individuals who have a really deep area of expertise that you may never find on a traditional platform.

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