Specialist AI doesn't scale. When you train separate models, each one starts from scratch, so you get no transfer / informative priors. So your specialist models wind up collectively scaling much worse than the power law of the generalist. And because of that, your collection of specialized models winds up being leapfrogged by a generalist model which avoids being 'penny-wise pound-foolish'. In the real world, boundaries are not sharp, and everything is "hallucinations of modern artwork depicting polypeptides"; this is precisely why GOFAI approaches failed!