What Happens When Driverless Vehicles Break the Law?

**SPEAKER_1** (0:01)
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.

**Imani Moise** (0:17)
Welcome to 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 US. We'll unpack why policing railroad 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. WSJ reporter Lindsay Ellis spoke to founders and researchers to understand how 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?

**Lindsay Ellis** (1:25)
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, 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.

**Imani Moise** (2:00)
One of the companies you covered is the startup called Point Hound. Can you tell us a bit about what it does and how it's structured?

**Lindsay Ellis** (2:07)
Point Hound is a website that helps people book flight deals with credit card points.
It's actually the second company that its CEO has started in the last decade. 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, he had 150 staff, 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.

**Imani Moise** (2:57)
How do these AI native companies actually use AI to drive those type of efficiencies? I assume it's more than just giving staff access to JAT GPT.

**Lindsay Ellis** (3:05)
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. 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.

**Imani Moise** (3:54)
Another change you wrote about is that there are more engineers within companies. How could that reshape the workforce?

**Lindsay Ellis** (4:00)
There's this new working paper out of Harvard Business School in Insayad and it basically looked at thousands of startups. Basically, the startups either self-identified as AI or didn't. When you compared those groups, you found that the AI startups had 15 percent fewer entry-level employees, 15 percent fewer managers, and they had a higher proportion of engineers on staff than the non-AI companies. That was a really interesting finding to me. 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.
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.

**Imani Moise** (5:00)
What do you think this tells us about what the typical American office might look like five or ten years from now?