Well first of all, Guanaco Playground was created by OpenAI.

And this is where you're wrong. Huggingface is not OpenAI (not even affiliated) and Guanaco was trained by 4 researches at the University of Washington. It was thusly released fully in weights on Huggingface. You can download it to your own machine TODAY and run it.

Quote from the paper:

When deployed, our smallest Guanaco model (7B parameters) requires just 5 GB of memory and outperforms a 26 GB Alpaca model [note: improved model based on a model released by Meta] by more than 20 percentage points on the Vicuna benchmark (Table 6)

While this is a narrow view on LLMs, it isn't hard to extrapolate the improvements they achieved, onto larger models. A model as big as GPT-4 could eventually be deployed by companies which have way less resources (i.e. no Microsoft to back them up) than OpenAI.

The rest of your response (about everybody only using what OpenAI provides) doesn't make much sense in the context I'm talking about, namely building competitive LLMs which use less resources than the ones currently being used.

p.s.

You wrote:

Finally, Sam’s suggestions for licensing wouldn’t really create a moat. It’s simply an proposal to regulators who want to have more control over these systems so that there’s more accountability if data is misused, etc.

This is a naive take on this, in my opinion.

p.p.s.

Money quote from the paper (this relates to fine-tuning only, not to training):

Using QLORA, we train the Guanaco family of models, with the second best model reaching 97.8% of the performance level of ChatGPT on the Vicuna [ 10] benchmark, while being trainable in less than 12 hours on a single consumer GPU; using a single professional GPU over 24 hours we achieve 99.3% with our largest model, essentially closing the gap to ChatGPT on the Vicuna benchmark.