A few years ago I was 100% gung-ho about Robotaxis, and I went through a mild depression last winter when I gave way to mounting evidence that it's a really hard problem and the 'it's 30 years out' naysayers are probably correct. It just sucks being wrong.

Google's Koala cars are functional under only the most idyllic, constrained and carefully monitored conditions. There's a huge laundry list of unsolved, and unknown problems between what Google has demonstrated so far and where they need to be technology-wise to run a robust, reliable, profit generating service at the scale needed to cover their R&D.

The casual thought experimenter generally fails to recognize the frequency with which they utilize higher level reasoning when driving that's well beyond the limits of the current state of the art in AI. Nobody has the slightest idea of how to solve this, let alone dig into all the as-of-yet not understood logistical problems inherent in commercializing the technology, an unexplored realm rife with any number of unknown unknowns.

The real world is a very messy place. Unlike Google, Uber is eyeballs deep in the messiness of the real world, so they're probably better poised, though a lot can change in 5 or 10 years. The competitive playing field has been so dramatically altered in the past 2 or 3 years that the days when Google was the only company anyone took seriously feels like ancient history.

With regards to the sensors, my bet is that by the time AI's capacity to reason is where it needs to be, the sensors and software needed to see and interpret the dynamic driving environment will be dirt cheap. Probably all you'll need is cameras, their cost keeps going down and the state of the art in image processing is progressing and will continue to progress.

Sure, they could definitely do similarly to what Uber just announced, in a small scope area like the Bay Area suburbs. But that's not likely to be a high margin product, certainly not something Google would want to hang onto long term. It's far smarter to be selling software licenses or cloud access to millions of units built by other car manufacturers and operated by individuals or other companies at scale, that's where the money is.

But you also have to realize that you'll also highlight the weaknesses of the technology. People may not be able to easily specify to the car where they'd like to disembark. What if people want to be taken just outside the service area? Uber is including a human with their self-driving project for now, which doesn't save them (or you) any money.

Google learned from Glass that a small number of users and a lot of public attention and hype about a product can quickly eviscerate it. The technology was good, but people without hands-on experience misunderstood it, and a couple small incidents became national news. A small rollout can just as easily kill your project as kick it off.