Regarding all the bottlenecks, I think there is an analogy between gradient descent and economic growth / innovation: when the function is super high-dimensional, it’s hard to get stuck in a local optimum.
So even if we stagnate on some dimensions that are currently bottlenecks, we can make progress on everything else (and then eventually the landscape may have changed enough that we can once again make progress on the previously stagnant sectors). This might look like a cost disease, where the stagnant things get more expensive. But that seems like it would go along with high nominal GDP growth rather than low.
Regarding all the bottlenecks, I think there is an analogy between gradient descent and economic growth / innovation: when the function is super high-dimensional, it’s hard to get stuck in a local optimum.
So even if we stagnate on some dimensions that are currently bottlenecks, we can make progress on everything else (and then eventually the landscape may have changed enough that we can once again make progress on the previously stagnant sectors). This might look like a cost disease, where the stagnant things get more expensive. But that seems like it would go along with high nominal GDP growth rather than low.