The Impact of Robotaxis on Gig Worker Wages: Early Evidence and Future Outlook (gadallon.substack.com)

Autonomous ride-hailing is already live in multiple U.S. markets (Waymo in Phoenix 2020, SF June 2024, LA Nov 2024, Austin Mar 2025; Tesla piloting “Cybercab” in Austin mid‑2025), and early operational data points to wage pressure for human drivers in those cities. Gridwise analytics shows median pay-per-trip rose +3.4% nationwide (July 2024–July 2025) but fell in AV hubs (Austin −5.3%, SF −3.1%, Phoenix −2.4%; LA roughly flat). Hourly pay was up ~1% nationally but dropped sharply where AVs operate (SF −6.9%, Austin −5.3%, LA/Phoenix ~−4–5%). Drivers’ utilization and trips/hour diverge by city—Austin and Phoenix saw small rises in trips/hour and utilization (+1–1.4%), yet per-trip payouts and heavy incentive cuts (bonuses down ~47% nationally; LA −65.3%, Phoenix −64%) drove monthly earnings down (Austin −7%, Phoenix −9%, LA −18.4%). San Francisco is an anomaly where monthly pay rose ~7.8% as drivers worked more hours, signaling behavioral adaptation rather than market stability. The causal story is nuanced: AVs correlate with wage declines but deployments targeted tech‑friendly, idiosyncratic markets, so causation isn’t established. Economic models (Siddiq & Taylor 2022; Castro & Frazelle 2024) add depth: game‑theoretic analyses predict gradual AV adoption can paradoxically lower platform profits via price competition and trigger strategic driver exit—sometimes prompting higher per‑trip wages even as human participation falls. Rapid AV scaling risks cannibalizing revenue; gradual, strategic rollouts may better preserve profitability and worker capacity. For the AI/ML community this implies the need for models that include strategic agent behavior, market dynamics, and policy levers to mitigate displacement as AV and GenAI capabilities scale.