I suspect AI will be somewhat similar where even if the linear scaling laws continue to hold the practical utility of a model flattens for almost all conceivable use cases.
In some ways I already feel this has begun to happen. The marginal utility of opus class models and fable has in my perception begun to flatten. While I can tell the differences they aren’t earth shattering. I could continue to use the present models for the rest of my life and be ludicrously more productive simply by adapting within their constraints through ever more sophisticated applications.
What holds back the open weights IMO is hardware scaling and industrial production. As the enormous transfer of wealth in debt and equity markets unfolds with semiconductor and adjacent companies and the corresponding capital investments are made, and the eventual bubble pop leading to over capacity and market flooding, as well as advances in technology, math, techniques, and efficiencies, will make very large open weight models more directly attainable. This will also lead to chimera models that MOE very large models to get very close to the 1-2T parameter dense models, at which point I suspect utility for almost all uses is nearly fully saturated.
There will be areas where more capable models are needed but they will be frontier models on frontier problems. This, IMO, is inevitable, and without some criminalization of weights (see the attempts to criminalize encryption algorithms in the 20th century and all the wonderful tshirts that emerged). It’ll be harder to print a trillion parameter model on a shirt but I’m sure someone will try, as will governments try to keep us in our boxes slaving for food coupons and basic rights like health care.