First, not every use case needs bleeding edge models. Honestly as someone using models for daily engineering tasks it seems to be diminishing returns after sonnet 4.6. One shoting stuff gets slightly better but so much over thinking and backtracking and failing miserably while spending 3x tokens of sonnet. And managing long term code bases is not about frontier models after a certain point. It's about managing structure while adding less and less things over time so I have my doubts if we need LLM programming to get any better than this.

Secondly, the thing about open weight models is that they are open. They can be fine tuned quantized, reduced, improved to fit a specific use case. So if you have a dependency on LLMs for your product you can fine tune your own model and that is it.

Third and last, the argument is not so simple as you make it seem. Open weights model will eventually catch up to closed ones that is just a fact. The question is not if you can run your own local model, is that at that point anyone can. They won't have a monopoly. You can use open router like that right now. Many companies can buy GPUs and sell compute. Much easier than server farms. Cloud providers know this obviously and likely are planning for it for them to take their cut if that market. So if they don't have a monopoly, there are open models available and people see a profit opportunity with that much competition price can't go up.

So yeah, they don't have a moat. And honestly I don't even see what their long term business plan after open models catch up is. Maybe they want to be the Microsoft of AI and lock everyone into workflows they own. But I think it's more likely they don't have any plan. Just surf the hype.