No, but since you mentioned it: https://en.wikipedia.org/wiki/Hutter_Prize

Anyway, OP is about lossy compression. I can't fully follow it but they talk about techniques for mitigating loss later in the paper.

Parents thinking was probably: If you can achieve similar results with a fraction of memory/compute usage then capability at the same hardware level will increase even more.

"Hardware overhang" is the term of art.

My meek opinion is this is obvious. Human-level intelligence requires at most 20 watts and substrate no more complicated than can be constructed from simple organic molecules in a dirty environment.

What is possible with 20 kilowatts and wafer fabricators?

It's specifically the fact that the network is directing its own optimization. Which yes, could then potentially be used to get more capability from the hardware, but that's true of manually optimized networks as well. Needing less human help is the... interesting part.

Compressing understanding (not just information) in a way that uses semantic links in information is a big part of intelligence, I'd say.

We're doing a double search - searching for experience outside, collecting data - and searching for understanding inside, by compressing the data. Search and learn, they define both AI and us.