3. The O-Ring models makes AI hard to work with.  The O-Ring model stipulates that, in some settings, it is the worst performer who sets the overall level of productivity.  (In the NBA, for instance, it may be the quality of the worst defender on the floor, since the player your worst defender is supposed to guard can just keep taking open shots.)  Soon enough, at least in the settings where AI is supposed to shine, the worst performer will be the humans.  The AIs will make the humans somewhat better, but not that much better all that quickly.

Why I think AI take-off is relatively slow - Marginal REVOLUTION

This whole article is a good read. The human component of AI adoption is going to be fascinating. There might be many parts to a system before the system can get better and it's important to understand which part of the system is holding back progress.

Consider the technical progress from 1600s and this statistic is fascinating to me.

6. Historically, gdp growth is remarkably smooth, albeit for somewhat mysterious reasons.  North America is a vastly different place than it was in the year 1600, technologically and otherwise.  Yet there are remarkably few years when the economic growth rate is all that far from two percent.  There is a Great Depression, some years of higher growth, some stagnation, and a few major wars, but even in those cases we are not so far from two percent.  I do not pretend to be able to model this satisfactorily (though the above factors surely have relevance in non-AI settings too), but unless you have figured this puzzle out, do not be too confident in any prediction that is so very far from two percent.

Tyler Cowen presents this as his reason to why AI take off will be slow. I think there might be some interesting advantages in being ready for when the final parts of a system that's holding the system back will be ready for a change with AI.