TL;DR: Humans can be worse than robots in everything and still have well paying jobs due to comparative advantage.
Comparative advantage: https://en.wikipedia.org/wiki/Comparative_advantage
Comparative advantage: https://en.wikipedia.org/wiki/Comparative_advantage
It's not like we will converge to some state where AI workers and human workers will coexist in some ratio.
Comparative advantage does not mean that humans are better than AI at something. "Comparative advantage" != "Absolute Advantage"
Comparative advantages don't usually disappear just because one party gets better and better.
If you need AI to do more work and you need more hardware, you just buy more hardware. And this becomes more true with time.
1) That constraints on compute will mean that humans will have jobs to do that AIs are too busy to do, because they're fully maxed out on capacity doing more important jobs.
This doesn't hold up when the marginal cost of creating and running a new AI instance falls below the marginal cost of raising and feeding a human. Noah believes it will not because he assumes:
2) that the resources that AIs need (compute) are not in competition with the resources that humans need (food).
However, they are. We can repurpose farmland and irrigation water to datacenters, fabs, and cooling towers. Maybe that wouldn't matter if all the AI-created wealth meant that you didn't need to do valuable labor to feed yourself, but:
3) If vast wealth is created by AI, there will be enough for everybody, so there's no need to be rushing to accumulate capital now.
In his article, dismisses the people who are worried that the AI's owners will accumulate all the wealth and be the only ones who can afford food or housing. Comparative advantage provides no reassurance here; there's nothing in economics that prevents productivity gains from being outpaced by a growing wealth disparity that provides all the productivity gains to the top and then some. And it's exactly the outcome you'd expect without government redistribution.
This way humans can fallback to their main activity: which is not art; because art has been taken by AI; but eating.
At least eating is not something AI plans to do.
We have made enormous jumps with transformer models (language models being integrated in the stack, and vision language models (VLM), and even vision language action models (VLA)). Pair this with recent advances in reinforcement learning and we can do things we couldn't do a few years ago; the whole space is fascinating!
...but we're nowhere near usable robotics in complex human environments for complex multistage tasks yet. It's an incredibly difficult problem where we still don't have the hardware, the software, or the sensors for it. The other thing to keep in mind is that since it's so expensive and difficult to do, there's no marketplace yet driving significant advances like we had with phones to hone in on such efficient amazing pieces of tech across the industry.
I wrote a bit about where we're at with the research last year; it's a bit out of date with even cooler advancements, which is why I'm working on another article in the same vein now.
https://hlfshell.ai/posts/llms-and-robotics-papers-2023/
I also did a project (Master's thesis) integrating an LLM into a ROS2 stack as a high level action planner, specifically to prove that LLMs have contextual understanding of the real world that can benefit planning missions.
https://hlfshell.ai/posts/llm-task-planner/
Always happy to talk this subject.
https://en.m.wikipedia.org/wiki/Energetically_Autonomous_Tac...