Why the future of AI-native infrastructure will be open

"Large language models (LLMs): GPT-3.5 and GPT-4 have been at the forefront so far, but we’ve seen a number of new LLMs get released in the last couple months, including LLaMA and StableLM. To date, the big knock on open-source LLMs is that they haven’t come close to rivaling the performance of GPT-3.5.  ‍ Even this latest batch of models does not have truly competitive performance. The only model that is nearly as good is LLaMA, which is not licensed for commercial use. But this is going to change. RedPajama recreates the LLaMA dataset, so it’s likely just a matter of time until an open-source (and commercially usable) replica of LLaMA is released, providing something that can more widely compete with GPT-3.5. The public preview of OpenLLaMA is one example of the work happening specifically in this area. Even then, we’re still a long way from an open-source model that rivals GPT-4. But does that matter a whole lot? Maybe not. There are many applications for which GPT-3.5-equivalent quality could prove more than sufficient right now. The closed options may still be best-in-class but most people might just be looking for “good enough” right now. We expect to see open-source LLMs make significant inroads in adoption relative to closed LLMs....   Given the above, how do we explain why open source has become such an integral part of advanced AI-native infrastructure when proprietary solutions have the early lead overall capabilities? There are several reasons, not dissimilar from why open source is valuable to end users generally...."