According to Sawyer Merritt on X citing Morgan Stanley, the bank grew more optimistic about Tesla’s path to an unsupervised robotaxi rollout after a site visit to Giga Texas, noting specific progress on edge cases in pickup and drop-off handling; as reported by Morgan Stanley via Merritt, the firm views Tesla’s end-to-end autonomy stack and data engine as key to scaling deployment and unit economics for autonomous ride-hailing; according to Merritt’s post, this progress could accelerate commercial viability in geofenced zones where high-volume data helps refine corner-case performance.

According to Sawyer Merritt on X, citing a new The All-In Podcast interview, Travis Kalanick said Waymo is “obviously ahead” in self-driving but faces challenges in manufacturing, scale, urgency, and fierceness, while Tesla is tackling “fundamentals, science, hard mode times 100,” and he questioned when a “ChatGPT moment” will arrive for vision AI. According to The All-In Podcast interview referenced by Sawyer Merritt, this framing highlights two distinct go-to-market strategies: Waymo’s robotaxi-first approach with geo-fenced deployments and deep safety validation, and Tesla’s consumer-scale software-first Full Self-Driving strategy that bets on end-to-end neural networks and fleet learning. As reported by Sawyer Merritt referencing The All-In Podcast, the business implications are clear: Waymo’s constraint is industrialization and rapid city expansion, whereas Tesla’s key risk is the timeline for vision-only breakthroughs to achieve broadly reliable autonomy. According to the same source, Kalanick also noted many smaller players “don’t really have the stuff yet,” underscoring consolidation risk and a capital-intensive path to Level 4 at scale.