The idea of human-level skilled labor being completely free is an alluring one, but one that may never be completely true so long as humans maintain agency (likely in my optimistic worldview).
Even if production processes were completely automated, making value judgements about the usefulness of the outputs was useful and where to go next is something that humans will probably want to continue to maintain some level of control over where material resource costs and timeliness still matter (i.e. feedback loops as you mention). The work involved in this decision-making process could be more than many people assume.
As discussed in our other thread, modeling how much time is required to define requirements/prompt and evaluate output will be an important component of forecasting how far and fast AI advancements might take us. Realistic estimates of this will likely support your hypothesis of the bottlenecks being in R&D and design and coordination, rather than physical throughput limits.
The idea of human-level skilled labor being completely free is an alluring one, but one that may never be completely true so long as humans maintain agency (likely in my optimistic worldview).
Even if production processes were completely automated, making value judgements about the usefulness of the outputs was useful and where to go next is something that humans will probably want to continue to maintain some level of control over where material resource costs and timeliness still matter (i.e. feedback loops as you mention). The work involved in this decision-making process could be more than many people assume.
A good example of this is how Google claims that 30% of their code is AI-generated, but coding velocity has only increased by 10%. Deciding what work to pursue and evaluating output, particularly in an industry where the outputs are so specialized, is already a substantial percentage of labor that hasn’t been automated to the same degree as coding.
As discussed in our other thread, modeling how much time is required to define requirements/prompt and evaluate output will be an important component of forecasting how far and fast AI advancements might take us. Realistic estimates of this will likely support your hypothesis of the bottlenecks being in R&D and design and coordination, rather than physical throughput limits.