> The bottleneck in AI/ML/DL is always data (volume & quality) or compute.

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.

Fair push back, but I do think the LSTM vs Transformers point kinda supports my position in the limit, not refutes. Once the compute bottleneck is removed, LSTMs scale favourably. https://arxiv.org/pdf/2510.02228

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