For a long time we had a situation where transistors got smaller and cheaper and faster and more power efficient all at the same through the wonders of Denard s

For a long time we had a situation where transistors got smaller and cheaper and faster and more power efficient all at the same through the wonders of Denard scaling and we gestured broadly at the whole thing and called it all "Moore's Law" without needing to distinguish which exponential improvement the term referred to. But in the mid 2000s Denard scaling broke down and now it looks like transistors are still getting smaller and cheaper and more power efficient but they aren't getting exponentially faster any more. So we've mostly settled on the smaller bit as being the true "Moore's Law" and that's more or less kept going but might be running out of steam. It's still very nice from the perspective of very parallel tasks but as consumers we don't see as much benefit from it in our computers except in graphics. But sooner or later Moore's Law will run out too, transistors can only get so small as long as they're made of atoms. Luckily the laws of physics have precise limits for just how efficient computation can be, Landauer's principle, that are just as binding as the laws for how efficient a heat engine can be. Progress in engine efficiency did indeed form a converging sigmoid on what's allowed by Carnot. But we're still quite a distance away from Landauer's limit and if transistors can't get us there we have every reason to believe that other computational substrates are possible and can. In the meantime we might be looking at an interregnum. But while AI is gobbling up computational cycles because they're available there's no reason to think that efficiency gains aren't possible - just look at the orders of magnitude improvement in training resources from AlphaGo to AlphaZero. I don't see that this should stop progress, though it might slow it. [1]https://en.wikipedia.org/wiki/Landauer%27s_principle https://en.wikipedia.org/wiki/Landauer%27s_principle