Beginning fully autonomous operations with the 6th-generation Waymo driver

GM, Cruise, and strategic missteps

• Multiple commenters are baffled that GM shut down Cruise just as Waymo was proving large‑scale autonomy is real.
• Ex‑employees say Cruise had just cleared tougher internal safety benchmarks and was close to relaunch when GM abruptly pulled the plug.
• Theories: GM’s risk aversion post‑2010 crisis, fear of “Silicon Valley style” huge, long‑horizon bets, and reputational damage from the SF pedestrian‑dragging incident.
• Some argue GM could have spun Cruise out or kept it semi‑independent instead of dismantling it and redirecting staff to lower‑ambition driver‑assist projects.

Waymo vs Tesla: sensors, safety, and “vision is all you need”

• Waymo’s blog explicitly touts multi‑modal sensing (cameras, lidar, radar, audio) as essential for the “long tail” of rare events; many see this as a direct dig at Tesla’s camera‑only approach.
• Pro‑Tesla voices argue vision‑only is ultimately cheaper, easier to scale, and more widely applicable (e.g. to general robotics); they cite Tesla’s large fleet and data advantage.
• Critics counter that all actually‑deployed robotaxi systems (Waymo, Chinese players, etc.) use lidar and that lidar costs are now low enough to be practical even in mass‑market cars.
• There are conflicting anecdotes: some report Tesla FSD completing long trips without intervention; others describe multiple “very scary” failures and argue Tesla is far behind Waymo in real, commercial robotaxi service.

What counts as “fully autonomous”? Fleet response and remote help

• Big argument over whether Waymo is “fully autonomous” if it uses remote “fleet response” staff.
• Waymo’s own blog says humans can indicate lane closures, suggest paths, or propose routes, while the “Driver remains in control of driving.”
• One camp says these are remote safety drivers by another name, so claims of “fully autonomous” are misleading marketing.
• Others insist this is materially different from a traditional safety driver: the car handles safety; humans only resolve rare edge cases, so for economics and safety Waymo is effectively autonomous.

Market structure, economics, and competition

• Debate over whether autonomous ridehailing is “winner‑take‑all.”
  • One side points to Uber/Didi‑style dominance and argues a “Waymo but worse” (like Cruise) was never viable.
  • Others note multiple regional players can coexist and that labor cost savings dwarf hardware cost differences, so there’s room for several winners.
• Tesla’s massive valuation vs GM/Waymo is used both as evidence of the perceived upside and as an example of irrational “meme stock” pricing that may never be justified by taxi economics.

Urbanism, traffic, and social consequences

• Some fear ubiquitous robotaxis will worsen car‑dominance: empty vehicles cruising for rides, more land for vehicle flow/parking, faster car‑only corridors, and pedestrian/bike space squeezed into isolated pockets.
• Others respond that cities are already car‑dominated; replacing private cars with shared robotaxis could reduce parking needs and support more density, if paired with good transit and regulation (e.g. congestion pricing, holding areas).
• Autonomous systems may enable safer cycling (fewer distracted humans), but there’s concern regulators could instead prioritize high‑speed automated traffic over human‑scale streets.

Technical package, behavior, and legal compliance

• Confusion over what “6th‑generation Waymo Driver” means: commenters infer it’s a standardized sensor+compute stack that can be retrofitted across platforms (Zeekr “Ojai”, Hyundai Ioniq 5, etc.), not a single vehicle.
• Some praise Waymo’s tech but complain about real‑world behavior: cars blocking lanes with hazards on, awkward pickup spots, long delays before departure, and occasional red‑light running.
• There’s disagreement on whether autonomous cars should strictly obey written traffic law or match human “norms” (rolling with the flow even when technically illegal).

Beyond cars: robotics and AGI

• Several argue that the real prize is not taxis but high‑fidelity world models and perception stacks reusable for home, factory, and military robots.
• One view: true robust autonomy ultimately depends on advances in general intelligence, not sensor choices or proprietary driving data; once AGI‑level models exist, no single company will have a durable moat.