U.S. Ride-Hailing Trips by Autonomous Vehicles

High-forecast respondents commonly emphasize that Level 4 technology “has a proven, real-world track record and is already being commercially deployed,” suggesting that the fundamental technical challenges have mostly been solved. In particular, many high-forecast respondents point to Waymo's demonstrated safety record, noting, “Studies have demonstrated that its vehicles had far fewer airbag deployment crashes and injury-causing crashes as compared to human drivers.” Conversely, a large number of low-forecast respondents emphasize that autonomous driving “is a tech that is absolutely notorious historically for overpromising and snail-like progress” and argue the current technology “is not scalable because it requires lots of case-by-case optimization for a particular region (down to individual intersections).”

High-forecast respondents frequently highlight Waymo's exponential expansion, observing, “Waymo is currently more-than-doubling every year,” which they believe will result in a data flywheel: “Broader deployment will generate more data, which in turn enhances safety—creating a positive feedback loop.” Another states: “Historically, when a technology finally gets to be used in the wild, it improves very rapidly.” Low-forecast respondents often acknowledge progress but stress constraints, arguing that “progress in Phoenix or Miami does not generalize easily to New York, Boston, or Chicago.”

Many high-forecast respondents argue that cost advantages will drive adoption, stating that “once AV ride-hailing proves substantially safer and cheaper, adoption could accelerate sharply.” Low-forecast respondents tend to focus on higher near-term costs, with one noting that “Waymo cars are still 30-40% more expensive compared to Uber and Lyft,” and that “AVs still require high capital expenditure, vehicle downtime, and supervision costs.”

Some high-forecast respondents see Tesla as a game-changer, arguing that “Tesla alone could drive explosive growth if their FSD [Full Self-Driving] vision model is successful in select markets, as their capex is just the mass-produced vehicle cost,” and that Tesla's manufacturing advantage over Waymo could enable rapid scaling. Low-forecast respondents largely remain skeptical, with one predicting, “it is well under 25% likely that Tesla will achieve its dream of all of their 2m cars/year being capable of serving as robotaxis straight out of the factory.”

Some high-forecast respondents believe regulatory barriers will diminish, with a few noting that the Trump administration's anti-regulatory stance may help to speed things up. Low-forecast respondents emphasize fragmented regulation, arguing “the U.S. will continue to have a patchwork of rules on autonomous vehicles,” and that “investors are less likely to invest in areas that present less regulatory certainty.”

High-forecast respondents often downplay geographic limitations, with one noting that even cities like Boston have “more simple grid-like” streets than some might expect. By contrast, low-forecast respondents tend emphasize both weather and geography constraints, noting initial deployment has been “in cities with no or little ice and snow,” and arguing this creates “a chicken and egg problem where they need to train in [icy and snowy] conditions to get better, but [doing so is] really risky and unpredictable.”

Some high-forecast respondents believe “consumer acceptance is likely to rise with exposure,” while some low-forecast respondents warn that “the cities where taxis are most used are also likely to have strong unions and/or lobbying efforts to limit the damage of job losses.” They note growing concern about “the replacement of blue collar labor with more automation,” and predict that “ride share drivers aren't going to go down without a fight.”