Just an off-the-cuff prediction: I fully anticipate AI bros are gonna put their full focus on local models post-bubble, for two main reasons:

1. Power efficiency - whilst local models are hardly power-sippers, they don't require the planet-killing money-burning server farms that the likes of ChatGPT require (and which have helped define AI's public image, now that I think about it). As such, they won't need VC billions to keep them going - just some dipshit with cash to spare and a GPU to abuse (and there's plenty of those out in the wild).

2. Freedom/Control - Compared to ChatGPT, DALL-E, et al, which are pretty locked down in an attempt to keep users from embarrassing their parent corps or inviting public scrutiny, any local model will answer whatever dumbshit question you ask for make whatever godawful slop you want, no questions asked, no prompt injection/jailbreaking needed. For the kind of weird TESCREAL nerd which AI attracts, the benefits are somewhat obvious.

The whole point of using these things (besides helping summon the Acausal Robot God) is for non-technical people to get immediate results without doing any of the hard stuff, such as, I don't know, personally maintaining and optimizing an LLM server on their llinux gaming(!) rig. And that's before you realize how slow inference gets as the context window fills up or how complicated summarizing stuff gets past a threshold of length, and so on and so forth.

Azure/AWS/other cloud computing services that host these models are absolutely going to continue to make money hand over fist. But if the bottleneck is the infrastructure, then what's the point of paying an entire team of engineers 650K a year each to recreate a model that's qualitatively equivalent to an open-source model?

Correct, they're 👆 here

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