Past performance is not indictative of future results across distinct domains. Within a single problem space (or sub-space) past performance can generalise qui

Past performance is not indictative of future results across distinct domains. Within a single problem space (or sub-space) past performance can generalise quite well. There's a problem with scaling solutions and expecting performance to continue to increase in a continuous exponential manner: growth that we perceive as exponential is often only on a long-life S-Curve. We've seen this in silicon, where what appears to the layman to have been exponential growth has in fact been a sequence of more limited growth spurts bound by the physical limits of scaling within whatever model of design was active at the time. The question of where the bounds to the problem domains are, and when new ideas or paradigms are required is much more difficult in AI than it has been in microprocessors. It's easy enough to formulate the question "how small can this be before the changes in physical characteristics at scale prevent it from working?", if rather more difficult to answer. AI is so damned steeped in the vagaries of the unknown that I can't even think of the question.