I completely disagree with his interpretation. It would be surprising if group that concentrates some of the top expertise in AI weren't able to make a big imp

I completely disagree with his interpretation. It would be surprising if group that concentrates some of the top expertise in AI weren't able to make a big impact on a well-defined optimization problem that has been studied for decades. I think a lot of the commentary is missing two essential points: 1. Protein structure prediction is to a large extent a solved problem for small-ish, soluble targets. AlphaFold is a significant improvement on the current state of the art, but the state of the art was already far enough along that the best computational models in 2007 were good enough to bootstrap experimental structure determination (https://www.ncbi.nlm.nih.gov/pubmed/17934447 https://www.ncbi.nlm.nih.gov/pubmed/17934447). In other words, it's not like the entire academic community was stumbling around helplessly in the dark. 2. The value of these predictions to pharmaceutical companies is extremely marginal. Having a high-accuracy model is very helpful but it's rare that the researchers have so little information available that a completely de-novo prediction is necessary. And when they really don't have much information at all, it's usually because the target is sufficiently messy to defy traditional structure determination methods - which means it's almost certainly more than AlphaFold can handle too.