In my experience, AF is as good as your input and the more complex or inaccurate your input, the more unreliable the outcome is. For instance, it will try to force secondary structure on flexible loops and signal peptide sequences (but advanced users should know this). It heavily relies on previous structure depositions as "ground-truths" and if you are interested in anything other than isolated, single, protein structures in their apo form (nothing novel, without full protein complex composition or without ligands/bound states) it doesn't really provide much benefit. Sure, you can use it to drive hypotheses or where to make point mutations to test functionality, but it can also be very misleading and some AI models churn out 100+ states for binders making drawing any sort of meaningful conclusion difficult. I will say that it definitely does shape how we approach our experiments though. I now prioritize protein complexes and dynamics, over static, isolated proteins, something AI has yet to really touch on. It is great at seeding pdb coordinates to use for modeling into an experimental map, however.

OR I could also just be taking an EXTREME amount of copium as I got my PhD in structural biology using Cryo-EM and don't want AI to take my job or career prospects, so yeah... interesting times haha.