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There are better and worse algorithms. I'm not sure "there is no free lunch" always applies in a particularly meaningful way. Some things aren't on the pareto frontier.

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Kinda like the aiff -> mp3 conversion process. A lot of data is lost, but we human can really tell the too much of a difference?

There's no reason to think the current next token prediction models are optimal for predicting sentences (they aren't!)

> An algorithm may outperform another on a problem when neither is specialized to the problem

https://en.m.wikipedia.org/wiki/No_free_lunch_in_search_and_...

I would go even further and say there isn't any indication that we are even close to what is possible. My subjective feeling is that with the current rate of progress it is entirely possible that we will have GPT-4 level performance locally on smartphone hardware within 3-10 years (unless companies decide again that they don't want to give this kind of power away)

Probably. Advancements in ML algorithms, like this one, have been outpacing advancements in hardware for awhile now, so both are converging on making ML faster and ubiquitous.

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