“The organizers even worried DeepMind may have been cheating somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. ‘We couldn’t solve it.’”
“But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two long helical arms in the middle. The model enabled Lupas and his colleagues to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold’s predicted structure. ‘It is almost perfect,’ Lupas says.”
The difference comes in developing a theory for generalizing the study of protein folding as a scientific pursuit.
If you can approximate an algorithm with error that is "below the threshold that is considered acceptable in experimental measurements" (to quote another HN comment), then you have something as good as the algorithm itself for all intents and purposes.
Therefore the use of the word "solve" doesn't qualify as hype-rhetoric, and the distinction you're making does seem somewhat pedantic (even if technically true).
(I'm speaking as someone with only the tiniest amount of stats/ML experience, so I could be totally wrong!)