This same phenomena is happening in physical AI Even after GPT-4 came out, I remember talking to computer scientists/ML experts that were so confident that general purpose models were overhyped and wouldn’t be able to produce real useful work soon. A lot of reductionist talk about stochastic parrots and next token predictors. It is surprisingly difficult for a lot of career technologists to grasp non linear technological trends and how slopes can change. Different experts in the same domain can have widely different expectations for technological advancement even 1-2 years in the future. There is also an element of information asymmetry. Organizations on the frontier move at vastly different paces than those that don’t. They are privy to key know how and training/data collection approaches that can potentially compress R&D pace by orders of magnitude. It’s important to remember that those that aren’t on the frontier are somewhat blind to the full state of the world even though they might not think that they are. We see time and time again that research paradigms are always changing and the same bottlenecks that existed yesterday get solved by smart brains (and AI models) motivated enough to tackle them. Laggards claim “we are so far”. We bet on the people that respond with “skill issue”.