The author appears to fall into that category. They seem to acknowledge this: The above graph is misleading in one way though because it is dependent on a spe

The author appears to fall into that category. They seem to acknowledge this: The above graph is misleading in one way though because it is dependent on a specific metric, GDT_TS, which only measures gross topology. If we care about high resolution topology, which we certainly do for most practical applications, then a more appropriate metric is GDT_HA, and using it the picture looks a bit different: [graph] Still a good trendline, but much further down from a “solution”. Another caveat is that both of these metrics measure global goodness of fit, which is important in terms of the basic scientific problem, but is often not indicative of functional utility. Local accuracy, for example the coordination of atoms in an active site or the localized change of conformation due to a mutation, is what is often sought when answering broader biological questions. Global metrics hide local discrepancy by diluting it in the sea of generally good agreement between experimental and predicted structures. and Even for MSA / family-level predictions, there is the question of desired accuracy, which hinges on the biological application. If one is predicting protein structures to ascertain their general fold for function classification, then high accuracy is unnecessary. If on the other hand the objective is to design small molecule drugs that bind proteins, which require ~1Å accuracy in the local pocket, it is unclear if we have made any detectable progress.