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

I think Meta's gameplan is complex. Inspiration as well as adoption, not stepping on the toes of regulators prolly another intention. Have a look at PyTorch for example. Massively popular ML framework, with its lots of interesting projects running.

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".

Well there's some patent offense and defense in making and releasing research papers. There's some recruiting aspects to it. Its also a way to commoditize your inverse if you assume this sort of stuff brings AR and the metaverse closer to reach.

Their main use case for these models seems to be AR. Throwing it out in the open might help getting external entities to build for them & attract talent, etc. Not sure they’re that strategic but it’s my guess

n=1 (as a mid-profile AI researcher), but for me it's working in terms of Meta gaining my respect by open sourcing (despite the licensing disasters). They clearly seem to be more committed to open source and getting things done now in general.