> I mean by hand waving you are focusing on discussing the future robots that are going to take over and perform x task or y task or duplicate themselves.

Invert your causality.

The discussion so far was "oh no, oh woe, we shall have no jobs!" — this can only happen if AI is good enough to do all that humans can do. Until that point, we're fine, it's the status quo, and also it doesn't matter how long we stay in this state. I'm not making any strong claim about the start date of the transition (I have a 20 year spread which I think is pretty vague), only the duration of such a transition.

When AI can do that, when, then it's obvious they can do things like "build a robot body", which is obvious because we can, and the definitional requirement of there not being any more work for humans is that the robots can do all the things we can. It's a necessary precondition for the scenario, not a prediction.

> Obviously these robots doing that is predicated on them having AGI

No, it isn't. "AGI" isn't even a well-defined term, each letter of the initialism means a different thing to different people.

And self-replication has much, much lower brain power requirements than full AGI, even for simple definitions of AGI: an AI-and-robot combo with all the intellect of the genome of E. coli is also capable of self-replication. The hard part of self-replication right now isn't the brain power.

So again, invert your causality: the brain power to replace all human workers includes the knowledge of how to self-replicate, but the knowledge of how to self-replicate does not require the brain power to replace all human workers.

> so it’s pointless to talk about what these AGI robots will be doing if we haven’t established that AGI is even possible in the near term or at all.

The specific things an AI needs to do, is learn. That's all. And they already can. The weaknesses of current models still, even if left unresolved, result in AI learning to do each thing humans do eventually when given enough examples, which limits humans to the role of teaching the machines. This is still a form of employment, so it's not economic game-over.

> It’d be like me making a prediction that we will have begun to colonize another galaxy in 10 or 20 years time and then only talking about how there’ll be trade between Earth and the colonies and maybe even wars and revolutions. Meanwhile completely skipping over how our spacefaring technology will advanced to the point we can even travel those distances in a reasonable timeframe.

No. That would require a change of the laws of physics. We don't need a change to the laws of physics for AI, because no matter what definition is used and whether or not current models do or don't meet any given standard, the chemistry in our own bodies definitely demonstrates the existence of human-level intelligence.

> I’m not an expert on LLMs

Are not the only kind of AI. You can't use an LLM for OCR, tagging photos, blurring the background of a video call, driving a car, forecast the weather, or predict protein foldings, and shouldn't use one for route finding or playing chess (although they're surprisingly good at the latter two, all things considered). Other AI do those things very well.

But LLMs will translate between languages as a nice happy accident. And they can read the instructions and use other AI as tools. And, indeed, write those other AI, because one of the things they can translate is English to python.

> but there doesn’t seem to be very much about them that is even approaching AGI.

Then you are one of many whose definition of "approaching" and "AGI" is one I find confusing and alien.

Between all AI, every single measure of what it means to be intelligent that I was given growing up has been met. Can machines remember things? Perfectly. How big is their vocabulary? Every word ever recorded. How many languages do they speak? Basically all of them. Are they good at arithmetic? So good that computers small enough and cheap enough to be given away for free, glued to the front of magazines, beat all humans combined and still would even if everyone was trained to the level of the current world record holder. How well do they play chess? Better than the best humans, by a large margin. Go? Ditto. Can they compose music? Yes, at any level from raw sound pressure levels to sheet music. Can they paint masterpieces? Faster than the human eye's flicker fusion rate. Can they solve Rubik's cubes? In less than the blink of an eye. Can they read and follow instructions, such they can use tools? Yeah, now we have LLMs, they can do that great. Can they make software tools? Again, thanks to LLMs, yes. Do they pass law school exams, or medical exams, can they solve puzzles from the International Mathematical Olympiad? Yup.

We're having to invent new tests in order to keep claiming "oh, no, turns out it's not smart".

> They’re a useful tool and it’ll definitely disrupt certain sectors of the economy mostly white collar jobs, but we’re in the middle of a peak of inflated expectations. This has happened before with other technologies.

LLMs, probably so. I often make the analogy with DOOM, released 30 years back, and the way games journalists kept saying each new 3D engine was "amazing" or "photorealistic", and yet we've only just started to really get that over the last decade. Certainly all the open source models are gushing over each other as "ChatGPT clones" or "ChatGPT killers" in the same way games were "DOOM clones" or whatever the noun was in the cliché "${noun} killers".

And yet the field of AI as a whole, including but not limited to GenAI, has been making rapid progress and doing things which are "decades or centuries away" every few couple of years since I graduated in 2006. Even just the first half of the 2010s was wild, and the rate of change has only gone up since then, this last 18 months has felt like more than that entire decade.