This is the second of a two-part series on ridehail drivers and robotaxis; the first, published last week, was “Who Drives Ridehail?”. If AI interests you more than driverless vehicles, you’ll find lots to chew on here as well.
In August, Anthropic published a review of every randomized trial of American job training its authors could find: 56 of them, going back to 1973. The review found that the typical program raised employment, but only trivially: out of every 100 people who got retrained, only two or three more ended up employed than would have otherwise been the case. Comparing those benefits against the cost of the retraining programs, the authors conclude the result is “a little better than break-even”, but that such programs fall far short of what will be required if AI seriously disrupts job markets.
Such disruption is still theoretical for the sort of AI that Anthropic sells. But for a particular kind of ‘applied AI’, namely driverless vehicles, the disruption is real. The first large group of workers whose work an AI is going to take is on our roads, today, driving for ridehail firms like Uber and Lyft.
As discussed last issue, there are two kinds of ridehail driver. One, making up the larger group, works casually and drives only a little for a few months, rarely lasting more than a year. The other, making up a smaller core, does most of the work, giving 100 or more trips a month, and remains active year over year. In Chicago in July 2026, core drivers were 45% of active drivers, but gave 85% of the trips.
It would be nice if, as the robotaxis enter a market, they displace the casual drivers. If that were the case, casual drivers would churn out, as they would have anyway, but some would not be replaced, as the robotaxis silently absorbed that side of the market. Meanwhile, the core drivers, professional and hard-working, would keep the share of the business they’d always had.
In that world, there would be no trade-off: core drivers would retain what they have, and robotaxis would slowly fill the gap left by attrition among casuals. The public, enjoying more and safer rides, would win, but human drivers would be just as well off as they were before.
Unfortunately, that isn’t likely to be the case. As robotaxis enter the market, it’s the core drivers who will be displaced, while casual drivers keep their share of the business. The folks who are working hard and making a full-time living will see that living go away first.
You may not agree that those drivers have a moral right to compensation for the loss. Seeing what ridehail drivers as a class did to taxi drivers, I understand the view. But I hope you’ll agree that they have a claim on people’s sympathy, and that that claim could endanger the rollout of robotaxis at scale, which would be strictly worse… not just in terms of overall utility, but in terms of lives lost to preventable accidents. To get the politics right, if for no other reason, we need to make smart deals whereby the winners compensate the losers; and correctly identify those losers so we aren’t rewarding the wrong people.
That is worth doing on its own, but also as practice. If AI does indeed have the impact on the job market that some anticipate, proactively addressing AV’s impact on the market will make the coming, harder transition easier to manage.
The Core Goes First
Today, robotaxis’ share of the for-hire market is non-existent in most places, and trivial even in its strongest markets. Inside its service areas in San Francisco and Phoenix, Waymo takes about 15% of what riders spend on ridehail. Those estimates count only trips that Waymo could serve, i.e., trips that start and end inside Waymo’s permitted zones, meaning that its share of the whole city is even lower than that.
But of course this will change in time. How will it change? Absent data, we’ll have to run a thought experiment. Let’s imagine five phases. In Phase One, a robotaxi operator has just begun operating in a city; in Phase Five, it has absorbed the entire for-hire ride market.1 In the intervening phases, robotaxis give 20%, 40% and 60% of trips respectively. To keep things simple, let’s stipulate that regulators don’t cap the size of fleets. That may be too unrealistic… but we could also assume that regulators cap fleet size, but the process for raising those caps is clear and reliable, such that operators can grow their fleets at the speed they wish. For our purposes, either assumption works.
Finally, stipulate that while some riders have idiosyncratic tastes, the bulk of travellers care most about price and, subject to a minimum level of quality, will always choose the lowest fare. This is precisely what we see in commercial air travel, where the finding is so durable that I have dubbed it the Iron Law.
Given these terms, we should expect that robotaxis will outcompete human-driven ridehail on price. We don’t see that yet, but it’s early days, and in San Francisco the cost of a Waymo fare relative to Uber or Lyft keeps dropping. Given that the driver’s wages represent about half of the cost of a ridehail trip, the robotaxis will ultimately cross the line and become the cheaper option, not to mention safer.
On these assumptions, as mature robotaxis enter a market and their driving hours rise, human driving hours fall in rough equilibrium.
But which humans?
