My machine learning professor would always remind us that 50% is analogous to random chance in a binary decision, but in a model where the prediction is not so black and white, 66% doesn't sound half bad!
If it got answer right 1 time in 100, that would be amazing and you'd be foolish not to use it!
Except you have no way of knowing if the answer it gives you is one of the 66 right predictions vs one of the 33 wrong predictions. You could say it's likely correct, but not to a high enough degree of confidence that you could really trust it without verifying using the old established techniques.
Also important - verifying that a given model has a signature that matches the established techniques is far easier than using those techniques to generate the complete model from scratch.
I'm not really sure what your point is.
This isn't really how it works.
To quote the CASP competition organisers:
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’s almost perfect,” Lupas says. “They could not possibly have cheated on this. I don’t know how they do it.”[1]
So you have experimental results, but still don't know how it folds. You aren't trying to avoid the all the experiments, just understand them.
[1] https://www.sciencemag.org/news/2020/11/game-has-changed-ai-...