But they don't have to be retained frequently at great cost. Right now they are retrained frequently because everyone is frequently coming out with new models and nobody wants to fall behind. But if investment for AI were to dry up everyone would stop throwing so much money at R&D, and if everyone else isn't investing in new models you don't have to either. The models are powerful as they are, most of the knowledge in them isn't going to rapidly obsolete, and where that is a concern you can paper over it with RAG or MCP servers. If everyone runs out of money for R&D at the same time we could easily cut back to a situation where we get an updated version of the same model every 3 years instead of a bigger/better model twice a year.

And whether companies can survive in that scenario depends almost entirely on their unit economics of inference, ignoring current R&D costs