[janalsncm]

> 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.

[jpcompartir]

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 (I believe there's similar work done on vanilla LSTMs, but I'd have to go digging)

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

[janalsncm]

Algorithms do matter because compute is not unlimited in practice. Otherwise we might as well use bogo sort because the result is eventually the same. Yes the platonic ideal of a sorted list looks the same but that doesn’t tell you anything about how to get there or whether you can in this lifetime.

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.