Google's research arm has made leaps and bounds in a particular field (deep learning) and then managed to apply it successfully to a very, very hard problem (protein folding). That other companies failed to adapt Google's successes in deep learning faster than Google is not surprising at all to me.
One might argue that the impact of academic-big pharma collaboration (in the form of funding for research projects related to CASP) is what enabled a company like Google, with no independent desire to collect the massive amounts of wetlab data required to evaluate or develop a tool like AlphaFold, to even participate.
More importantly, AlphaFold hasn't really solved the protein folding problem well enough for drug development. So, the entire debate might be moot.
Did they though? They did extremely well at CASP14, and much better than competing groups. But does this solve protein folding? Deepmind's marketing department would have you think so, but for those of us that work in the field we know that this is not the case.
Furthermore, does protein folding solve the relevant problems of drug design? It solve the forward problem, given an amino acid sequence predict its 3d structure. But for drug design we need the inverse problem, given a specified structure predict an amino acid sequence that produces that structure.
Note that DeepMind drafted off the work of a protein folding guy from academia who had been doing this for decades.
Well, let's remember that the AlphaFold team at Google solved the protein folding problem with a relatively small team in a relatively small number of years after extremely large, well funded companies whose primary business was drug development failed to do so for decades.
So yeah, it's been demonstrated to be possible that the current leaders in a field might be significantly less capable than a different team.