> Progress in AI has always been a step function.
There's decisively no evidence of that, since whatever measure you use to rate "progress in AI" is bound to be entirely subjective, especially with such a broad statement.
We don't expect steel to suddenly have new properties, and we don't expect bubble sort to suddenly run in O(n) time. You could ask -- well what is the evidence they won't, but it's a silly question -- the evidence is our knowledge of how things work.
Saying that improvement in AI is inevitable depends on the assumption of new discoveries and new algorithms beyond the current corpus of machine learning. They may happen, or they may not, but I think the burden of proof is higher on those spending money in a way that assumes it will happen.
There are signs, though. Every "AI" cycle, ever, has revolved around some algorithmic discovery, followed by a winter in search for the next one. This one is no different and propped up by LLMs, whose limitations we know quite well by now: "intelligence" is elusive, throwing more compute at them produces vastly diminishing returns, throwing more training data at them is no longer feasible (we came short of it even before the well got poisoned). Now the competitors are stuck at the same level, within percent points of one another, with the difference explained by fine-tuning techniques and not by technical prowess. Unless a cool new technique come yesterday to dislodge LLMs, we are in for a new winter.
Just as AI has killed off all demand for lower-skill work in copywriting, translation, design and coding, it will do so for manufacturing. And that will be a dangerous bloodbath because there will not be enough juniors any more to replace seniors aging out or quitting in frustration of being reduced to cleaning up AI crap.