Not true at all. The whole point of ML is to find better mappings from X to Y, even for the same X.
Many benchmarks can’t be solved by just throwing more compute at the problem. They need to learn better functions which traditionally requires humans.
And sometimes an algorithm lets you tap into more data. For example transformers had better parallelism than LSTMs -> better compute efficiency.
So the bottleneck was compute. Which is compatible with 'data or compute'. But to accept your point, at the time the algorothmic advances were useful/did unlock/remove the bottleneck.
A wider point is that eventually (once compute and data are scaled enough) the algorithms are all learning the same representations: https://arxiv.org/pdf/2405.07987
And of course the canon: https://nonint.com/2023/06/10/the-it-in-ai-models-is-the-dat... http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Scaling compute & data > algorithmic cleverness
I bring up transformers because scaling compute and data was unlocked by a better algorithm. It matters a lot because scaling compute isn’t always an option.