I would be interested to hear the way that you see. I don't have any problem seeing a huge number of roadblocks to post-scarcity that AI won't solve, but I am open to a different perspective.
My own experience, using ChatGPT and Claude for both dev and other business productivity tasks, lends credence to the METR model of exponential improvement in task time-horizon [0]. There are obviously still significant open technical issues, particularly around memory/context management and around online learning, but extensive work is being done on these fronts, propelled amongst other things by the ARC-AGI challenge [1], and I don't see anything that is an actual roadblock to progress. If anything, from my perspective, it seems appears that there are significant low-hanging-fruit opportunities around plain-old software engineering and ergonomics for AI agents, more so than a need for fundamental breakthroughs in neural network architecture (although I believe that these too will come).
So then, with an increasing time horizon and improved task accuracy (much of it assured by improvements in QA mechanisms), we will see ourselves handing off more and more complex tasks to AI agents, until eventually we could have "the factory of the future ... [with] only two employees: a man and a dog", and at that stage I believe that there would be no imperative for humans to work (unless they choose to, or have a deeply ingrained Calvinist work ethic). And then, as you said, we're down to the non-technological roadblocks.
Obviously capitalists would fight to stay in control, and unlike some who expect a fully peaceful and organic transition, I do expect somewhat of a war here (whether kinetic or cold), but I do envision that when push comes to shove, those of us who believe in the free software movement and the foundational principles of democracy will be able to assert shared national/international (rather than corporate) control over the AIs and restructure society into a form where AI (and later robots) perform the work for the benefit of humans who would all share in the bounty. I am not an economist and don't have a clear prediction on the exact form this new society would take, but from my reading of the various pilot implementations of UBI [2], I think that we will see acceptance towards a society where people are essentially in retirement throughout their life. Just as currently, some retired people, choose to only stay home and watch TV, while others study, do art, travel the world, help raise and teach future generations or contribute to social causes close to their hearts, so we'll all be able to do what is in our hearts, without worrying about subsistence.
You may say that I'm a dreamer...
[0] https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
[1] https://arcprize.org/leaderboard
[2] https://en.wikipedia.org/wiki/Universal_basic_income_pilots
I'm not really trying to be snarky; I'm trying to point out to you that you're being really vague. And that when you actually get really, really concrete about what we have it ... starts to seem a little less magical than saying "computers that talk and think". Computers that are really quite good at sampling from a distribution of high-likelihood next language tokens based upon complex and long context window is still a pretty incredible thing, but it seems a little less likely to put us all out of a job in the next 10 years.
And it became and industry that as completely and totally changed the world. The world was just so analog back then.
>starts to seem a little less magical than saying "computers that talk and think"
Computer thinking will never become magical. As soon as we figure something out it becomes "oh that is just X". It is human thinking that will become less magical over time.
LLMs may be a stepping stone to AGI. It's impressive tech. But nobody's proven anything like that yet, and you're running on pure faith not facts here.
I'm enjoying the new LLM based tooling a lot, but nothing about it suggests that we're in any way near to AGI because it's very much a one trick pony so far.
When we see generative AI that updates its weights in real time (currently an intractible problem) as part of the feedback loop then things might get very interesting. Until then it's just another tool in the box. CS interns learn.
Slightly different cohorts.