Probably too cynical, but you can potentially view it as a weak form of collusion under the guise of open research.
For competitive LLMs targeting text generation, especially for training, a compute-based barrier is more significant.
I think that argument falters when the weights are released, which lowers the barrier by a lot as training of large models is much more expensive than inferences. A weak form of collusion would be publishing papers that explain enough for the practitioners to fill in the gaps (so casuals are left out) and not publishing the weights so only other large companies can afford to implement and train their versions of models.
My own view is that open-publishing in AI is mostly bottom-up, and the executives tolerate open publishing for the reasons you gave.
Incidentally most companies won't publish their crown jewels i.e. Camera apps on Google and Apple phones had great segmentation on the usual photography subjects, would rather not publish them. I'm not holding my breath for video Recommendation models from TikTok or Facebook either
If Meta frequently shares their "algorithms" they take the blame out of its usage. After all, who is to blame when everybody does "it" and you are very open about it.
Use cases, talent visibility as well as attraction also plays a role. After all, Google was so fancied, due to its many open source projects. "Show, don't tell".