What Happens When Driverless Vehicles Break the Law?

[00:00:00] [SPEAKER_00] Countless companies invest in AI tools without tying them to tangible business outcomes. Join McKinsey later to learn how leaders rewire their organizations for sustained impact and value.

[00:00:17] [SPEAKER_04] Welcome to WSJ Tech News Briefing. It's Tuesday, July 21st. I'm Imani Moise for The Wall Street Journal. New research shows that AI-powered startups are smaller and flatter, with about 25% fewer employees than their non-AI counterparts. We're exploring how they make do with less manpower and whether more established companies will follow suit. Then, driverless cars are causing big headaches for law enforcement as they hit the streets across the U.S.

[00:00:44] [SPEAKER_04] We'll unpack why policing robo-taxis is rarely as simple as riding a ticket. But first, corporate giants are rushing to integrate AI into their workflows. But what happens when a company is built around the technology from day one? A new generation of AI-native startups is rethinking everything, from staffing levels to the number of rungs on the corporate ladder.

[00:01:09] [SPEAKER_04] WSJ reporter Lindsay Ellis spoke to founders and researchers to understand what these companies can teach us about the future of work, and joins us now to explain why smaller companies may not mean fewer jobs. So, Lindsay, what jumped out at you when you looked into these AI-native startups?

[00:01:24] [SPEAKER_03] So, these new companies, founded over the last few years, they have this technology woven into their DNA in a way that the big companies who employ thousands of people and who we often see in the headlines don't in the same way. And so, if we look at how they structure operations, how they use this technology, even kind of what they expect out of their staff,

[00:01:50] [SPEAKER_03] it can offer just really interesting clues for what work is going to look like for the rest of us as this technology gains a firmer hold in the workplace.

[00:01:59] [SPEAKER_04] One of the companies you covered is this startup called PointHound. Can you tell us a bit about what it does and how it's structured?

[00:02:06] [SPEAKER_03] PointHound is a website that helps people book flight deals with credit card points. And it's actually the second company that its CEO has started in the last decade. So, he offers just a really interesting kind of petri dish to see how AI has started to change corporate operations. In his old company, at its peak, they had 150 staff.

[00:02:36] [SPEAKER_03] And this company has four. And when I asked him if things were to really take off, how many more people would you need? He was like, maybe one more engineer, maybe one person to help out with marketing. But he really believes that a lot of growth could be handled by just this core four.

[00:02:57] [SPEAKER_04] How do these AI native companies actually use AI to drive those type of proficiencies? I assume it's more than just giving staff access to ChatGPT.

[00:03:05] [SPEAKER_03] That's totally right. And the thing that each of these founders told me was just that it really is woven through every part of the business, even thinking about how individuals see their roles. So, you wouldn't necessarily just have a manager anymore overseeing a team or a team of teams, as we see in some corporations.

[00:03:26] [SPEAKER_03] That manager would need to be contributing individually, overseeing agents who are doing the work, maybe having a direct report. But it really is what they call a player-coach model, altering workflows and altering the reporting lines so that everybody is getting their hands dirty. And there are fewer layers between the bottom rung of the corporate ladder and the top executive ranks.

[00:03:54] [SPEAKER_04] Another change you wrote about is that there are more engineers within companies. How could that reshape the workforce?

[00:03:59] [SPEAKER_03] So, there's this new working paper out of Harvard Business School and INSEAD, and it basically looked at thousands of startups. Basically, the startups either self-identified as AI or didn't. And when you compared those groups, you found that the AI startups had 15% fewer entry-level employees, 15% fewer managers.

[00:04:25] [SPEAKER_03] And they had a higher proportion of engineers on staff than the non-AI companies. And that was a really interesting finding to me. I mean, we talk a lot about how good AI is at coding, and there's been a lot of hemming and hawing about what will the future of the computer science job be.

[00:04:44] [SPEAKER_03] And I think this is indicative of the fact that you really need senior good engineers on staff to make sure things are working to really get down and dirty with this technology, even if the jobs look different than traditional coding jobs did years ago.

[00:05:00] [SPEAKER_04] What do you think this tells us about what the typical American office might look like five or ten years from now?

[00:05:06] [SPEAKER_03] There's an acknowledgment that it could be smaller. When I talked to Rem Koning at Harvard Business School and asked him, is this going to mean an uptick in unemployment? He wasn't so sure and said that, yes, these AI-native startups have smaller headcounts, but the ease of this technology may make it easier to form businesses. And so the uptick in business creation might offset that.

[00:05:36] [SPEAKER_03] But I do think he would agree that smaller teams are likely.

[00:05:40] [SPEAKER_04] That was WSJ reporter Lindsay Ellis. A quick reminder, we want to hear from you. Have you thought something was AI that wasn't? Shoot us an email to tnb at wsj.com or leave us a voicemail at 212-416-2236. That's 212-416-2236. Or if you're a listener on Spotify, leave us a comment.

[00:06:05] [SPEAKER_04] Coming up, a driverless car can't fill a breathalyzer, but it can still break the law. We'll explain how law enforcement agencies are adapting to the robo-taxi era. That's after the break.

[00:06:22] [SPEAKER_00] True AI value comes from rethinking workflows, not just layering on tech. Here's Dan Swann, senior partner at McKinsey.

[00:06:29] [SPEAKER_01] When we talk about rewiring, we're more talking about getting into the chassis of the organization. And that means, yes, there's technology to it, but how are you redesigning the organization or the capabilities of the people that are actually doing the work and linking that to the technology? So for us, the technology, yes, is critically important, but these other pieces are 70-80% of the work to get that right.

[00:06:55] [SPEAKER_04] Self-driving taxis are popping up in more cities across the country, promising to make roads safer by taking human error out of the equation. But they're also creating a new challenge for law enforcement, who are learning in real time how to direct, corral, and when necessary, dole out punishments when there's nobody behind the wheel. WSJ's Ellie Davis joins us now to explain who's on the hook when robo-taxis break the rules and how traffic cops are coping.

[00:07:22] [SPEAKER_04] So Ellie, what happens when there's a traffic violation, but there's no driver to pull over?

[00:07:26] [SPEAKER_02] So this is an important question because there's no federal guidance on what police officers should do. So it's been up to local police departments and states to decide how they should proceed when they have a car without a driver that breaks a traffic rule. In places like Phoenix, when police officers see a robo-taxi like a Waymo make a mistake, they've been able to get in touch with the car manufacturer company so that they can correct the issue without ever having to issue any type of fine.

[00:07:55] [SPEAKER_02] But then in other places like Austin, officers will write a particular type of citation of a Waymo to the municipal court rather than issuing a ticket because they cannot issue a ticket without having somebody there to sign as the driver of the vehicle.

[00:08:14] [SPEAKER_04] What kinds of traffic violations are we talking about typically when it comes to autonomous vehicles?

[00:08:18] [SPEAKER_02] With driverless cars, the issues that police officers see most often are not issues while the cars are moving, but issues of the cars not moving. They often stop in places where they shouldn't be, parking sometimes in disabled spots, sometimes impeding traffic. In Austin in March, a Waymo vehicle blocked an ambulance from reaching the scene of a shooting. And so that was a big concern for law enforcement there.

[00:08:47] [SPEAKER_02] And also the other kind of category of issues that police officers are seeing are Waymo's and other robo-taxis getting confused in unpredictable circumstances like a construction zone or a big event like a concert or sporting event.

[00:09:03] [SPEAKER_04] So who actually gets held responsible when a driverless car gets a citation?

[00:09:07] [SPEAKER_02] When an autonomous vehicle gets a ticket, it's the company that's responsible for handling the ticket and potentially paying a fine. For example, in Austin since September 2024, Waymo, as the company, has had to pay $7,433 in fines. That's according to the municipal court records in Austin.

[00:09:28] [SPEAKER_04] So can you give us a few examples of how this is playing out on local roads? Like, what exactly was the traffic violation that the autonomous vehicle made? And how did local authorities respond?

[00:09:39] [SPEAKER_02] One example of a surprising traffic violation that police officers saw was a Waymo that was briefly stuck in the middle of an intersection, swerved left and went onto the wrong side of the road to get ahead of traffic. And eventually, after a block of driving on the wrong side of the road, merged back into the right lane on the right side of the road.

[00:10:01] [SPEAKER_02] And in this situation, the police officer wrote a ticket to the company, recorded the license plate, and was able to write a citation. Other instances that police officers have had to deal with are Waymo's getting stuck in kind of dead-end zones or places where they shouldn't be during big events.

[00:10:21] [SPEAKER_02] I talked to one officer in Austin who had to ultimately block Waymo's in with traffic cones because they were getting stuck at the dead-end of a road during a graduation event. They're trained to not run over the cones. And he kept those vehicles there until Waymo sent out human representatives to drive the cars away because he didn't want to let the cars go and drive away if they were going to keep causing problems during this event.

[00:10:52] [SPEAKER_04] Now, are these types of encounters with robo-taxis still rare for law enforcement? Or are they becoming an everyday part of the job?

[00:10:58] [SPEAKER_02] Most of the law enforcement officers I spoke to acknowledged that in most cases, autonomous vehicles do a better job than regular drivers. They typically don't speed, they never drive inebriated, and they eliminate a lot of human error. But at the same time, they're causing sort of their own set of headaches when they encounter any confusing or unpredictable circumstances like emergency scenes and construction zones.

[00:11:29] [SPEAKER_02] What do the companies that operate robo-taxis have to say about all of this? So Waymo, which is by far the leading robo-taxi company in America, said that the company's goal is to be a trusted public safety partner and not an additional burden for first responders. And these companies, according to the police officers, do a good job investing in training police officers when they're bringing their robo-taxis to a city for the first time.

[00:11:56] [SPEAKER_02] And so they're trying to work with law enforcement to not create more of a burden and more of a hassle in traffic regulations. But sometimes it's not enough. That was WSJ's Ellie Davis.

[00:12:10] [SPEAKER_04] And that's it for Tech News Briefing. If you're a listener on Spotify, be sure to leave us a comment. Today's show was produced by Julie Chang with supervising producer Katie Ferguson. I'm Imani Moise for The Wall Street Journal. We'll be back later this morning with TNB Tech Minute. Thanks for listening.

[00:12:32] [SPEAKER_00] AI transformation is more than a tech initiative. It requires a broader rewiring of human skills and ways of working. Here's Dan Swann, senior partner at McKinsey.

[00:12:41] [SPEAKER_01] One of the biggest unlocks we see is the human being capabilities around the technology. Whereas before you may need to be really good at analysis, now the skill might be how do you prompt these tools to get the right answers and outcomes? And so for us, it's a really big priority to help companies to understand how do you need to change the operating model of your organization and change the underlying capabilities of the people that are interacting with the technology.

[00:13:08] [SPEAKER_01] And when you see that all humming together, it can be really, really special.

[00:13:11] [SPEAKER_00] Discover how McKinsey helps organizations rewire to outcompete with AI at McKinsey.com slash techmoves.

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