The difference might be the immense cost of physical infrastructure needed for AI training. People would make open source contributions for free, TSMC, NVIDIA and power companies seems less likely to make free contributions to AI.
Not sure if I'm labouring under basic misconception here (or if you're trolling). First, you note that BPP <= P/poly. Secondly, you note that it is hard to prove P = BPP, but this is not necessary, all we need is the trivial P <= BPP. Then P <= P/poly < NP (if the author is correct).
Perhaps Bret Victor's ideas are comparable to something like formal methods: few doubt their enormous power, but the difficulty is in the extreme effort one needs to implement them in a project. It is tempting to believe that the level of instrumentation that Bret proposes could be achieved automatically, just as it was once dreamed that formal methods could be fully automatic. But experience with formal methods has shown us that while some of their promise can be implemented by automatic tools, and this is valuable, to realize their full potential for a complex project requires substantial effort, non-reusable effort.