To your point, I've been watching the slow uptake of basic ideas in science departments. In this case, neural networks are still largely looked at either with suspicion (wrong principles) or as magic (too difficult) by top non-CS departments at top institutions. There are exceptions, but they're the ones that prove the rule.
The big ~5 (Apple, Microsoft, Google, Facebook, maybe Amazon), and then an even bigger universe of Chinese companies + consulting companies, are interesting to me because they solve scaling these ideas. They've been putting money into projects that academic teams largely aren't, collecting global-scale data that these teams aren't, and doing global-scale real-world experiments that academics aren't.
Ex: For something like healthcare, there isn't a good reason for Flatiron Health to be a leader while, to almost a rounding error, most real-world clinical data researchers are data-poor & AI-poor. While scientists are squabbling around open access for papers, imagine if the NIH+HHS+CDC+... required all publicly-funded genomic + health data be unified into a national database and the DOE provided AI compute infra for working with it. Instead, we have an area PI at every research university getting grants to make believe that their regional hospital network's tiny genome/ehr database will be the one that becomes that.