Both seem around the corner for me.

For robo-taxis it is more a society-based problem than a technical one.

• Robo-taxis have problems with edge cases (like some situations in some places with some bad circumstances). Usually in those where human drivers also have even worse problems (like pedestrians wearing black on the road at night with rainy weather - robo-taxi at least have LIDAR to detect objects in bad visibility). Sometimes they are also prone to object detection hacking (by stickers put on signs, paintings on the road, etc.). In general, they have fewer problems than human drivers.
• Robo-taxis have a public trust problem. Any more serious accident hits the news and propagates distrust, even if they are already safer than human drivers in general.
• Robo-taxis and self-driving cars in general move responsibility from the driver to the producer. The responsibility that the producer does not want to have and needs to count toward costs. It makes investors cautious.

What is missing so we would have robo-taxis is mostly public trust and more investments.

For AGI we already have basic blocks. Just need to scale them up and connect them into a proper system. What building blocks? These:

• Memory, duh. It is there for a long time, with many solutions with indexing and high performance.
• Thoughts generating. Now we have LLMs that can generate thoughts based on instructions and context. It can easily be made to interact with other models and memory. A more complex system can be built from several LLMs with different instructions interacting with each other.
• Structuring the system and communication within it. It can be done with normal code.
• Loop of thoughts (stream of thoughts). It can be easily achieved by looping LLM(s).
• Vision. Image and video processing. We have a lot of transformer models and image-processing techniques. There are already sensible image-to-text models, even LLM-based ones (so can answer questions about images).
• Actuators and movement. We have models built for movements on different machines. Including much of humanoid movements. We currently even have models that are able to one-shot or few-shot learning of movements for attached machines.
• Learning of new abilities. LLMs are able to write code. It can write code for itself to make more complex procedures based on more basic commands. There was a work where LLM explored and learned Minecraft having only very basic procedures. It wrote code for more complex operations and used what it wrote to move around and do things, and build stuff.
• Connection to external interfaces (even GUI). It can be translated into basic API that can be explored, memorized, and called by the system that can build more complex operations for itself.

What is missing for AGI:

• Performance. LLMs have high performance in reading input data, but not very much inferring the result. It also does not scale very well (but better than humans). Multi-model complex systems on current LLMs would be either slow and somewhat dumb and make a lot of mistakes (open-source fast models, even GPT 3.5) or be very slow but better.
• Cost-effectiveness. For sporadic use like "write me this or that" it is cost-effective, but for a continuous stream of thoughts, especially with several models, it does not compare well to a remote human worker. It needs some further advancements, maybe dedicated hardware, 
• Learning, refining, and testing is very slow and costly with those models. This makes a cap for anyone wanting to build something sensible. Rather slow steps are done towards AGI by the main players.
• The scope for the model is rather short currently. Best of powerful models have a scope of about 32 thousand tokens. There are some models that trade quality for being able to operate on more tokens, but those are not the best ones. 32k seems a lot, but when you need a lot of context and information to process to have coherent thoughts on non-trivial topics not rooted in model learning data... then it is a problem. This is the case with streams of thoughts if you need it to analyze instructions, analyze context and inputs, propose strategy, refine it, propose current tactic, refine it, propose next moves and decisions, refine it, generate instructions for the current task at hand, and also process learning to add new procedures, code, memories, etc. to reuse later. Some modern LLMs are technically capable of all that, but the scope is a road blocker for any non-trivial thing here.

If I would be to guess I would say that AGI will be sooner in scale - just because there is hype, there are big investments and the main problems are currently less like "we need a breakthrough" and more like "we need refinements". For robo-taxis we still need a lot more investments and some breakthroughs in areas of public trust or law.

I think these problems are much more similar than you do.  Perhaps not purely equivalent, but the hard parts of each are very much the same.

Note that here's a lot of fuzz in the definition of AGI, which could lead it to being easier, harder, or orthogonal to self-driving taxis.

a computer program that functions as a drop-in replacement for a human remote worker, except that it's better than the best humans at every important task (that can be done via remote workers).

"a human worker": one, some, some parts of some jobs, only script-based call-center work, with liberal escalation to humans available, or some other criterion?  One could argue that a whole lot of jobs from the last milleneum have been automated away, and that doesn't really qualify as AGI.   It might be useful to instead compare on the same dimensions as self-driving cars: amount of output with minimal human operational intervention for a type of job.

But that problem aside, the thing that's going to be difficult in both branches is the long tail.  Human cognition is massive overkill for 99% of things that humans actually do.  But the small amount of unexpected situations or highly-variant demands are REALLY hard to handle.  The reasons humans are hired for office/knowledge/remote-able jobs is that there are undocumented, non-obvious, and changing requirements for the tools in use and the other people involved in the enterprise.  The old joke that goes "why would I pay you just to push buttons?" being answered with "you don't, you pay me to push only the right buttons at the right times" points to this very difficult-to-define capability.  

This difficulty of definition applies to training, documenting, and performing the tasks, of course.  But it ALSO applies to even identifying how to measure how well an agent (human or AGI) is capable of handling it.  Or even noticing that it's the crux of why a human is there.

Further, both branches have to extra hurdle of human-level performance being insufficient for success.  Self-driving cars are ALREADY better than (guesses, no cite) the 75%ile human driver, in 90% of condions.  But that's not good enough to make the switch, they need to be near-perfect, including the decisions of when to break rules in order to get something done.  Chatbots are ALREADY better than the median level-one customer support agent, and that's not good enough either - the ability to decide to go beyond, or to escalate, needs to be way BETTER than human before the switch gets made.

Here's a post I made on this to one of your colleages:

https://www.greaterwrong.com/posts/ZRrYsZ626KSEgHv8s/self-driving-car-bets/comment/Koe2D7QLRCbH8erxM

To summarize: AGI may be easier or harder than robotaxis, but that's not precisely the relevant parameter.

What matters is how much human investment goes into solving the problem, and is the problem solvable with the technical methods that we know exist.  

In the post above and following posts, I define a transformative AI as a machine that can produce more resources of various flavors than it's own costs.  It need not be as comprehensive as a remote worker or top expert, but merely produce more than it costs, and things get crazy once it can perform robotic tasks to supply most of it's own requirements.  

You might note robotaxis do not produce more than they cost, they need a minimum very large scale to be profitable.  That's a crucial difference, others have pointed out that to equal OAI's total annual spending, say it's 1 billion USD, then they would need 4 million chatGPT subscribers.  It only has to be useful enough to pay $20 a month for.  That's a very low bar.

While each robotaxi only makes the delta between it's operating costs, and what it can charge for a ride, which is slightly less than the cost of an uber or lyft at first, but over time has to go much lower.  At small scales that's a negative number, operating costs will be high, partly from all the hardware, and partly simply because of scale.  To run robotaxis there are fixed costs with each service/deployment center, with the infrastructure and servers, with the crew of remote customer service/operators, etc.

As for is the AGI problem solvable, I think yes, for certain subtasks, but not necessarily all subtasks.  Many jobs humans do are not good RL problems and are not solvable with current techniques.  Some of those jobs are done by remote workers (your definition) or top human experts (Paul's definition).  These aren't the parameters that matter for transformative AI.  

"Normally when Cruise cars get stuck, they ask for help from HQ, and operators there give the vehicles advice or escape routes. Sometimes that fails or they can’t resolve a problem, and they send a human driver to rescue the vehicle. According to data released by Cruise last week, that happens about an average of once/day though they claim it has been getting better."

From the Forbes write-up of GM Cruise's debacle this weekend. I think this should update people downward somewhat in FSD % complete. I think commenters here are being too optimistic about current AI, particularly in the physical world. We will likely need to get closer to AGI for economically-useful physical automation to emerge, given how pre-trained humans seem to be for physical locomotion and how difficult the problem is for minds without that pre-training.

So in my opinion, there actually is some significant probability that we either have AGI prior to or very soon after robotaxis, at least the without-a-hitch, no weird errors, slide-deck presentation form of it your average person thinks of when one says "robotaxis".

Note that even if robotaxis are easier, it's not much easier. It is at most the materials and manufacturing cost of the physical taxi. That's because from your definition:

By AGI I mean a computer program that functions as a drop-in replacement for a human remote worker, except that it's better than the best humans at every important task (that can be done via remote workers).

Assume that creating robo-taxis is humanly possible. I can just run a couple AGIs and have them send a design to a factory for the robo-taxi, self-driving software included.