Interesting work, but there's a huge sector they're missing - industrial enzyme and catalysis design. Most of this field is concerned with small molecule binding - methane, carbon dioxide, ammonia, methanol, acetic acid, etc. Binding is often just the first step, as you're typically trying to do highly specific chemistry, e.g. attaching a single oxygen to methane or a single hydrogen to carbon dioxide, etc.
Working in this area might also be good test of their technological approach, as small-molecule binding can be somewhat challenging, and even evolved biological systems can struggle to achieve high specificity.
I want to mention an interesting industrial enzyme project. If you ever saw the laundry detergent commercial "Protein gets out protein", this is referring to an industrial enzyme in laundry detergent. Many years ago, Genentech had built up a significant capability in proteases, which are proteins that cut other proteins into pieces. In the course of optimizing proteases, they made a thermostable, thermoactive protease. Although it wasn't super useful for Genentech in a drug discovery context, it was recognized that you could put an inactive enzyme into laundry detergent that would be activated when the hot laundry water hit the detergent, and the resulting protease would be good at cleaning stains (many stains are composed of protein- blood, food, etc).
Genentech set up a subsidiary with Corning (the glass company) that owns the IP for this protease and then licensed it to laundry detergent manufacturers; many billions of dollars in revenue. I think this is one of the original patents: https://patentimages.storage.googleapis.com/d9/ca/6f/2fb89ff...
My guess is that this area is much harder to break into–enzymes facilitate challenging chemical transformations by stabilizing high-energy transition states in chemical reactions. These states are usually highly transient and therefore much harder to capture using the structural biology techniques that generate the structural data that AlphaFold and similar methods are trained on. Even though there are many structures of enzymes in the absence of their substrate, I would imagine that the small number of structures for states that represent actual catalytic intermediates would make it difficult for a model to internalize the features that distinguish a good enzyme/catalyst from a bad one.
Another consideration is that most protein structure prediction methods only generate the backbone, and the sidechains are modeled in afterwards. Enzyme efficiency requires sub-A level structural precision in the sidechains that are actually doing the chemistry involved in catalysis, so it could also be the case that the current backbone-centric methods aren't good enough to predict these fine-tuned interactions.
Working in this area might also be good test of their technological approach, as small-molecule binding can be somewhat challenging, and even evolved biological systems can struggle to achieve high specificity.