It's plain in reading things over that 3/4s of respondents to this thread have no idea what "AI" or Machine Learning are -- that's typical across the 'tech front' these days. Regardless of having been around for decades, there are basic tenets that remain unknown. (But, having software ask a question does *not* make it "AI"; having software make it's own range of predictions does *not* make it Machine Learning.)
In any event, there is routine alpha to be granted to smaller algos that is of zero interest to parties wishing to efficiently, effectively, deploy hundreds and hundreds and hundreds of millions of dollars. There is stuff to be washed in-and-out-of, in less time than it takes to write it or read it: HFT and all that. And there is stuff that takes a week-to-10-days to enter or exit. But between those to high speed or high wealth playgrounds, is lots and lots of swing time.
Will that alpha-adding swing-time be there tomorrow? I don't see why it should be. Algo design and deployment will have gotten *more* effective than the dribs and drabs by which it is used/profited now. With that drop in fixed cost will come a ramp-up in incidence, and a concomitant decline in required AUM to make it shine.
But it will be YEARS away. 10 at least; 20 years easy. In the meantime, hunt for the alpha while it's yet available.
concomitant
[ kon-kom-i-tuhnt, kuhn- ]
adjective
existing or occurring with something else, often in a lesser way; accompanying; concurrent: an event and its concomitant circumstances.