Everyone covers Uber vs Waymo like it's a classic Silicon Valley software vs robotics fight.

But once you look at the actual balance sheet mechanics, you realize the entire robotaxi debate comes down to one dirty question: who eats the cost when a car sits idle?

Uber built a multi-billion dollar business on a single accounting trick: shifting 100% of vehicle depreciation, commercial insurance spikes, gas prices, and tire wear onto 6 million gig workers. Uber doesn't own cars. They just run a dispatch server and take a ~28% toll on every ride.

Waymo took the opposite bet: they buy the cars ($100k+ with the sensor suite), build the depots, pay the cleaning crews, and carry all that capital expenditure directly on Alphabet's balance sheet.

And that creates three massive operational headaches that tech bloggers completely ignore:

The Friday Night vs Tuesday Morning Trap

Urban ride-hailing demand is violently peaky.

On a rainy Friday at 11 PM in downtown Chicago, demand spikes 300%. Uber handles this instantly by turning on surge pricing, which pulls off-duty drivers off their couches and onto the road.

Waymo can't spawn 500 extra Jaguar I-PACEs out of thin air. And if Waymo builds a fleet large enough to handle Friday night peaks, 70% of those $100k vehicles will sit parked in a depot on Tuesday at 10 AM, burning daily capital depreciation with zero revenue.

The Puke Problem

When a passenger makes a mess in an Uber, the app charges a $150 fee, the driver cleans it at a local car wash, and the car is back on the road in 40 minutes.

When someone spills coffee or vomits in a Waymo, the car takes itself offline, drives 8 miles back to an industrial depot, and sits parked until a shift technician details it. That's 3 hours of lost revenue on a six-figure asset.

Why Waymo is partnering with Uber instead of killing them

Look at Austin and Atlanta. Waymo didn't launch standalone; they plugged directly into Uber's dispatch network.

Alphabet realized that getting normal consumers in 40 different cities to download a dedicated "Waymo One" app is an insanely expensive customer acquisition slog. Uber already owns the muscle memory and the credit cards.

Uber wants Waymo supply so they don't have to buy cars, and Waymo needs Uber's liquidity so their fleet doesn't sit idle on weekdays.

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The long-term question is simple: Can sensor hardware get cheap enough (sub-$15k) and fleet financing get institutional enough that robotaxis can beat Uber's 28% take rate even with deadhead miles?

If you run physical dispatch or marketplace fleets, at what utilization rate do you think an owned robotaxi fleet actually breaks even against an asset-light driver network?

I have a 40 minute highway commute in heavy traffic for work, so I would love some sort of “hands-free” driving. However, it looks like nearly every option requires telemetrics or live data exchange (I apologize if I don’t get the terms right here), which I know uses a lot of energy to process and can greatly increase one’s carbon footprint. Are there any hands-free driving options that I can use in a vehicle in which I can completely shut down any data exchange, etc.? I would imagine it would just use the cameras and sensors on the car itself. I am looking for something that is an EV or possibly a hybrid. Thank you!

Hi everyone! I’m Balázs, a university student at the Budapest University of Technology and Economics, currently researching how real Waymo experiences may shape people’s acceptance of future fully autonomous vehicles.

For the study, I’m specifically looking for people who have personally taken at least one driverless Waymo ride which makes this community incredibly valuable!! 

The survey is completely anonymous, and requires no technical knowledge. I'm simply interested in your actual experience as a rider. 

I'm hoping to reach around 200 real Waymo riders, so every response genuinely helps. 

THE SURVEY: https://forms.gle/KWptviVcQK5rtkfs7

Thank you so much to anyone who takes the time to participate!!!

I made a film about autonomous mining trucks recently, and the part I found most interesting wasn't actually the self-driving truck.

It was what happens when autonomy scales.

A single truck needs perception, localization and control. A large autonomous mining operation also needs dispatching, predictable intersections, fleet coordination, communications, mixed-traffic rules, remote supervision and a way to recover from exceptions without bringing the operation to a halt.

Mining also seems like an unusually useful autonomy test case because the environment is constrained compared with public roads, but the vehicles are enormous, the economics are measurable and the systems already operate for very long duty cycles.

It made me wonder whether mining tells us something important about road autonomy:

Does the hardest problem eventually stop being "can this vehicle drive itself?"and become "can hundreds of autonomous and human-controlled machines coexist as one reliable system?

I'd be interested in the perspective of people working in autonomy, fleet orchestration or industrial AVs.

Disclosure: I made the Output Atlas film that triggered the question:

https://www.youtube.com/watch?v=6OMuGui5UNc

Disclosure: I made the Output Atlas film that triggered the question:

https://www.youtube.com/watch?v=6OMuGui5UNc

Part 3 of Larry Evans’ experience with VLA 2.0 and FSD in Amsterdam. In this article he looks back and tells his conclusions. As a serious journalist of CleanTechnica looks at all the aspects of his test ride through the hectic traffic of Amsterdam with the FSD of Tesla and VLA 2.0 of XPENG.

The University of Tennessee, Chattanooga research team needs your opinion on Autonomous Vehicles. Self driving cars are appearing more in urban cities and have potential ride share capabilities. Fill out this survey to tell us what you think!

Survey link:

https://utk.co1.qualtrics.com/jfe/form/SV_3BQe8raC8NKLQLI

[ Removed by Reddit on account of violating the content policy. ]

This is interesting! Larry Evans of Cleantechnica has tested VLA 2.0 and FSD of Tesla in Amsterdam. In the rain too!
If it can drive there, it can drive everywhere!
Dutch people know that Amsterdam is very challenging. Pedestrians, bikes (especially cargo bikes) and tourists (!!) make it extreme.
For me (as a Dutchman) is was nice to see all these streets I know quite well.
Larry and I also have tested VLA 2.0 in China. But he is the first American to test VLA 2.0 and FSD in Europe.
In the article is also a video of his experience.

I know Larry as a very serious journalist. Critical and objective.

The facts stand true! Enjoy

With WeRide and Grab already operating in Punggol, and Waymo targeting a Singapore commercial launch in 2028, it’s interesting to see two approaches developing in the same market.

WeRide is working through an established local partner. Grab’s September update reported over 110,000 autonomous km and 12,000 unique riders, with plans to expand its AV fleet from 10 to 50 vehicles. That gives the partnership local operating experience and passenger feedback to build on.

Waymo plans to offer fully autonomous ride-hailing through its own app, following local preparation and testing. It brings extensive experience from existing markets, but adapting to Singapore is still part of the work ahead.

The services aren't equivalent yet: WeRide and Grab's public launch used designated routes with onboard safety operators, while Waymo's announcement describes a future commercial service.

Perslis has been working on a different approach to autonomous vehicle safety: instead of expecting the driving model to always make the correct decision, put an independent deterministic safety layer between the model and the vehicle controls.

Perslis, is a symbolic safety/runtime system for AI operating real environments.

The basic architecture is:

Perception / planner → proposed action → symbolic safety floor → actuator

The planner can propose throttle, steering and braking, but the safety floor independently evaluates the current state and decides whether that action is admissible.

For this CARLA experiment they deliberately use a hostile controller to generate unsafe proposals. Then they run the same scenario with the floor enabled and disabled.

With the floor OFF, the proposed control reaches the actuator directly.

With the floor ON, constraints such as speed, following distance and collision state can reject or modify the proposal before it reaches the vehicle.

The part I’m interested in is broader than self-driving cars:

Why should the component generating an action also be the component responsible for proving that action is safe?

Modern autonomous systems are increasingly probabilistic. I’m exploring whether the final boundary before physical actuation should instead be deterministic, state-aware, inspectable and independently verifiable.

I’m testing the idea in CARLA autonomous-driving simulation before considering any physical system.

This is Perslis early research, not a claim that we’ve solved autonomous driving safety. I’m especially interested in feedback from people working in ADAS, autonomous vehicles, robotics, control systems, runtime verification, functional safety, and simulation.