People hear frontier model and they think highest-end most expensive intelligence. They then use this to make the argument: Not every task needs PhD therefore "the frontier" will lose share to others. The reality: Frontier lab models are more efficient across the entire intelligence range. PhD level tasks and mundane tasks are more efficiently routed across the frontier labs because that's where the end user gets best bang for buck. The high end is important because those highest end models are what allow the frontier companies to compress and distill intelligence into more efficient lower horsepower models. The argument for open weight models is that the economic penalty of using them can be overcome by the strategic benefit of having more control over supply and/or you can extract more performance per dollar by finetuning particular to your task. Given the pace at which the frontier is improving, however, your turn-rate on finetuning/deploying the model custom to your org has to be extraordinarily fast, or else you will be even further cost-offsides. OpenAI and Anthropic are growing as fast as they are because their customers get best value per dollar for their services. Both Grok and Gemini are potential contenders given the compute resources they have. I think it is a fairly safe assumption that spend will continue to accrue to those at the frontier and that by dent of economic gravity alone this will cause openweight capability to lag rather demonstrate any sort of catch-up.