Speculative, but Emad has heavily signaled that they will be releasing to the public an LLM.

People are doing some really cool stuff with llama right now, but it all lives in a bit of a grey area, for the obvious reasons related to licensing (of both the model weights and the underlying gplv3 code).

If Emad releases a comparable LLM publicly, but with a generally permissive license (which is not a guarantee...), all of this hacker energy will immediately go into a model/platform that is suddenly (in this scenario) widely available, commercially usable (which means more people banging away at it, including with levels of compute that don't make sense for the average individual but are trivial for even a modestly funded AI startup), etc.

Further, SD has done a really good job of building a community around the successive releases, which--done right--means increased engagement (=better tooling) with each release, since authors know that they are not only investing in a model today, but that they are investing in a "platform" for tomorrow. I.e., the (idealized) open source snowball effect.

Additionally, there is a real chance that SD releases something better than llama*, which will of course further accelerate adoption by parties who will then invest dollars to improve it.

This is all extra important, because there has been a lot of cool research coming out about improving models via [insert creative fine-tuning/RL method, often combined with clever use of chain-of-thought/APIs/retrieval systems/etc.]. Right now, these methods are only really leveraged against very small models (which can be fine-tuned, but still aren't that great) or using something like OpenAI as a black box. A community building up around actually powerful models will allow these techniques to get applied "at scale", i.e., into the community. This has the potential to be very impactful.

Lastly, as noted, GPT-4 (even though notionally against ToS) is going to make it (presumably) even easier to create high-quality instruction tuning. That is going to get built and moved into public GPT-3-like models very, very quickly--which definitely means much faster tuning cycles, and possibly means higher-quality tuning.

(*=not because "Meta sux", to be clear, but because SD will more happily pull out all the stops--use more data, throw even more model bells & whistles at it, etc.)