I can hail a robotaxi. So can anyone living in San Francisco, Phoenix, Beijing, Shanghai, Guangzhou, Shenzhen, Chongqing or Wuhan. The barriers to wider rollout are political and regulatory, not technological.

Waymo cars, and I believe Apollo and Cruise as well, are "level 4" autonomous vehicles, i.e. there is no human involved in driving them whatsoever. There is a "human available to provide assistance" in roughly the same sense that a member of the American Automobile Association has an offsite human available to provide assistance in case of crashes, flat tires, etc.

I don't see any reason to think AGI is imminent but this particular argument against it doesn't go through. Robotaxi tech is very good and improving swiftly.

There is probably a big difference between "doing something reliably well" and "doing something, hit and miss".

I wonder if this will also be true for the first AGIs. Maybe they will greatly surpass humans in all areas randomly, and will occasionally do something utterly stupid in any area. And the probability of doing the stupid thing will slowly decrease, while the ability of doing something superhumanly awesome will already be there.

That alone sounds scary, even ignoring all the usual worries about alignment.

Without intending to, I wrote a response to your question a few months ago: AGI is easier than robotaxis [LW · GW]
 

I wrote about this previously here [LW(p) · GW(p)]. I think you have to break it down by company; the answer for why they're not globally available is different for the different companies.

For Waymo, they have self-driving taxis in SF and Phoenix without safety drivers. They use LIDAR, so instead of the cognitive task of driving as a human would solve it, they have substituted the easier task "driving but your eyes are laser rangefinders". The reason they haven't scaled to cover every city, or at least more cities, is unclear to me; the obvious possibilities are that the LIDAR sensors and onboard computers are impractically expensive, that they have a surprisingly high manual-override and there's a big unscalable call center somewhere, or they're being cowardly and trying to maintain zero fatalities forever (at scales where a comparable fleet of human-driven taxis would definitely have some fatalities). In any case, I don't think the software/neural nets are likely to be the bottleneck.

For Tesla, until recently, they were using surprisingly-low-resolution cameras. So instead of the cognitive task of driving as a human would solve it, they substituted the harder task "driving with a vision impairment and no glasses". They did upgrade the cameras within the past year, but it's hard to tell how much of the customer feedback represents the current hardware version vs. past versions; sites like FSDBeta Community Tracker don't really distinguish. It also seems likely that their onboard GPUs are underpowered relative to the task.

As for Cruise, Comma.ai, and others--well, distance-to-AGI is measured only from the market leader, and just as GPT-4, Claude and Bard have a long tail of inferior models by other orgs trailing behind them, you also expect a long tail of self-driving systems with worse disengagement rates than the leaders.

Insufficient onboard processing power?  Tesla's HW3 computer is about 70Tflops, ~0.1% of estimated 100Pops human brain.  Approximately equivalent to a mouse brain.  Social and predator mammals that have to model and predict conspecific and prey behaviors have brains that generally start at about 2% human for cats and up to 10% for wolves.

I posit that driving adequately requires modelling, interpreting and anticipating other road users behaviors to deal with a substantial number of problematic and dangerous situations; like noticing erratic/non-rule compliant behavior in pedestrians and other road users and adjusting response, or negotiating rule-breaking at intersections or obstructions with other road users, and that is orders of magnitude harder than strict physics and road rule adherence.  This is the inevitable edge-case issue that there are simply too many odd possibilities to train for and greater innate comprehension of human behaviors is needed.

Perhaps phone-a-smarter-AI-friend escalation of the outside-of-context problems encountered can deal with much of this, but I think cars need a lot more on board smarts to do a satisfactory job.  A cat to small-dog level H100 @4Pflops FP8 might eventually be up to the task.

Elon's recent announcements about end-to-end neural nets processing for V12.xx FSD release being the savior and solution appears to not have panned out - there's been months of silence after initial announcement.  That may have been their last toss of the die for HW3 hardware.  Their next iteration HW4 chip being fitted to new cars is apparently ~8x faster at 600Tflops.[edit, the source I found appears unreliable, specs not yet public]  They really wouldn't want to do that if they didn't think it was necessary.

As usual you fall onto the trap of neglecting the engineering and social organisation problems and the time required to solve them. We don't need AGI for autonomous car, it will just take time.

My argument (which I'm not all that sure I believe myself):

• You need computation done "on-board," not in the cloud, and that requirement drives up prices to an order of magnitude too high to be feasible. 

I think the use of the term "AGI" without a specific definition is causing an issue here - IMHO the crux of the matter is the difference between the progress in average performance vs worst-case performance. We are having amazing progress in the former, but struggling with the latter (LLM hallucinations, etc). And robotaxis require an almost-perfect performance.