Another possibility not really addressed here --- local LLMs.

AI on hardware you own and control --- instead of a metered service provider. In other words, a repeat of the "personal computing" revolution but this time focused on AI.

TurboQuant could be a key step in this direction.

Yeah, I don't think local LLM's will keep up with what the massive corporations put out. But they might get to a level of performance where it just doesn't matter for most users.

And people would prefer to run a model locally for 'free' (not counting the energy cost) rather than paying for an LLM subscription.

TurboQuant helps KV quantization which is not very relevant to local LLMs, since context size becomes most relevant when you run inference with large batches. For small-scale inference, weights dominate. (Even if you stream weights from SSD, you'll want to cache a sizeable fraction to get workable throughput, and that dominates your memory usage.)

Local LLMs don't sound profitable at all for those building them. If you really wanted a SOTA model, you would be paying eye watering amounts to own it unless you got an open sourced one.