Disagree. I was on one of these R&D/prototyping teams running ML experiments and you're right, it was the company wanting to present itself as future-leaning, ready to adapt, and I would say that at this point it was a good move to have employees who understand where the tech is going.
Companies with internal teams that are able to implement open source models are in a much better negotiating position for the B2B contracts they're looking at for integrating GPT into their workflow, they won't need GPT as much, if they can fallback on their own models, and they will be better able to sit down with the sales engineers and call bullshit when they're being sold snake oil.
You nailed it, although very few models actually ever got deployed to Prod at Fortune 500 non-tech companies and the few that did delivered little value. I'm a consultant and most internal AI/ML/DS teams that I interacted with were just running experiments on internal data as you said, and the results would get pasted into Powerpoint, a narrative created, and then presented to executives, who did little or nothing with the "insights". Reminded me of the "Big Data" boom a few years earlier where every company created a Big Data Team who then promptly stood up a Hadoop cluster on prem, ingested every log file they could find, and then..................did nothing with it.
Anyway it's a symptom of the hype cycle - AI was the next electricity, but there were no actual products and nothing clear to do with it, just hire a bunch of kids to act like they were in a kaggle competition, or worse a bunch of PhDs to be under-utilized building scikit-learn models.
Now that there are (potentially) products coming along that at least bypass the low-level layer of ML, having an internal team makes no sense. Maybe the most logical thing that will happen is the pendulum will swing too far, and this bubble will consist more of businessy types using chatGPT without remotely understanding it or realizing it's just a computer program.