I think cranking out open source projects like this raises Meta AI’s profile and helps them attract attention and people, and I don’t think selling AI qua AI is their business plan, selling services built on top is. And commoditized AI means that the AI vendors don’t get to rent-seek on people doing that, whereas narrowly controlled monopoly/oligopoly AI would mean that the AI vendors extract the value produced by downstream applications.
I've always half-believed that the relatively open approach to industry research in ML was a result of the inherent compute-based barrier to entry for productizing a lot of the insights. Collaborating on improving the architectural SoTA gets the handful of well-capitalized incumbents further ahead more quickly, and solidifies their ML moat before new entrants can compete.
Probably too cynical, but you can potentially view it as a weak form of collusion under the guise of open research.
This particular model has a very low barrier; the model size is smaller than Stable Diffusion which is running easily on consumer hardware for inference, though training is more resource intensive (but not out of reach of consumers, whether through high-end consumer hardware or affordable cloud resources.)
For competitive LLMs targeting text generation, especially for training, a compute-based barrier is more significant.
Yeah that’s fair. I intended my comment to be more of a reflection on the culture in general, but the motivations in this instance are probably different.
> Probably too cynical, but you can potentially view it as a weak form of collusion under the guise of open research.
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
