AI Summary
→ WHAT IT COVERS Transportation policy writer Andrew Miller joins Ross Douthat on Interesting Times to examine the accelerating shift toward autonomous vehicles. The conversation covers Waymo's safety record, Tesla's competing approach, liability frameworks, the 2035 timeline for mainstream robotaxi adoption, and what Americans stand to lose culturally when machines replace human drivers. → KEY INSIGHTS - **Safety baseline:** Autonomous vehicles already outperform human drivers in safety metrics according to California's mandatory transparency data, even within San Francisco's complex road environment. With 40,000 Americans dying annually in road incidents — nearly all caused by driver error — Miller argues that any AV system performing better than the human average saves lives on net and should be permitted to scale. - **2035 adoption timeline:** Miller projects that by 2035, most major North American cities will have substantial robotaxi fleets operating commercially. Waymo has already announced expansion into 15-plus cities. The key scaling variables are cost reduction on Waymo's sensor-heavy lidar approach versus Tesla's camera-only bet, which would allow mass vehicle production at significantly lower per-unit cost. - **Liability as the critical unlock:** The single regulatory barrier slowing AV adoption is unresolved liability. Miller's proposed framework is direct: manufacturers must accept 100% liability when their automated driving system is at fault. Waymo already accepts this standard for its current fleet. Tesla has resisted equivalent accountability for its driver-assist systems, which Miller identifies as a credibility problem requiring regulatory enforcement. - **Remote assistance vs. full autonomy:** Waymo vehicles are not fully independent — they use remote human operators who provide navigational guidance when the system encounters ambiguous situations, such as partially displaced traffic cones. Operators do not remotely drive the car but instead send directional waypoints. This distinction matters for understanding actual autonomy levels versus marketed capabilities across competing platforms. - **Bad scenario trigger points:** The dystopian AV outcome involves robotaxis becoming cheap enough to pull riders away from public transit, triggering a death spiral where transit agencies lose revenue, cut service, raise fares, and lose more riders — leaving low-income populations with reduced mobility. The fork between good and bad outcomes depends on whether transit agencies integrate robotaxis as feeder vehicles rather than treating them as competitors. → NOTABLE MOMENT Miller notes a political paradox: traditionally libertarian red states like Texas and Tennessee are currently the most permissive toward autonomous vehicle deployment, while progressive blue states outside California impose the most regulatory friction — meaning the states most culturally attached to individual freedom are accelerating the technology that may eventually eliminate it. 💼 SPONSORS [{"name": "Atlassian Rovo", "url": "https://rovo.com"}, {"name": "Laradyn", "url": "https://laradyn.com"}, {"name": "Adobe Acrobat", "url": "https://adobe.com"}, {"name": "OneTrust", "url": "https://onetrust.com"}, {"name": "IBM", "url": "https://ibm.com"}] 🏷️ Autonomous Vehicles, Transportation Policy, AI Regulation, Robotaxi Industry, Future of Mobility
