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If it ever goes bad, well I hope that that’s an impetus for new open source projects to be started — and with improvements over and lessons learned from incumbent technologies, right at the v1 of said projects.
If LLMs turn out to be such a force multiplier, the way to fight it is to ensure that there are open source LLMs.
I think the issue is that LLMs are a cash problem as much as they are a technical problem. Consumer hardware architectures are still pretty unfriendly to running models which are actually competitive to useful models so if you want to even do inference on a model that's going to reliably give you decent results you're basically in enterprise territory. Unless you want to do it really slowly.

The issue that I see is that Nvidia etc. are incentivised to perpetuate that so the open source community gets the table scraps of distills, fine-tunes etc.

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The problem is even if an OSS had the resources (massive data centers the size of NYC packed with top end custom GPU kits) to produce the weights, you need enormous VRAM laden farms of GPUs to do inference on a model like Opus 4.6. Unless the very math of frontier LLMs changes, don’t expect frontier OSS on par to be practical.
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That would be accepting the framing of your class enemy, there is no reason to do that.
unless they are also pirate LLMs, I don't see how any open source project could have pockets deep enough for the datacenters needed to seriously contend
If it goes bad? It’s too late by that point. And how is open source going to compete with billions of investment dollars?
If AI tools are as good as the CEOs claim, we should have no friction towards building multiple open source alternatives very quickly. Unless of course, they aren’t as good as they are being sold as, in which case, we have nothing to worry about.
What would the new open source projects do differently from the "old" ones? I don't think you can forbid model training on your code if your project is open source.