Hi! I'm not sure I fully understand your question, but if you're asking whether the rate of progress in competitive ML is slowing down, I think probably not. A lot of the key areas of debate (gbdt vs nn in tabular data, cnn vs transformers in vision) are seeing a lot of research still and I expect the competitive ML community to adopt new advances when they happen. Also in NLP there's a move towards more efficient models, which would also be very useful.