| The argument appears to be "last-mile" specialisation of AI models by massive-compute-companies will be entirely proprietary, and walled-off to prevent competitor extraction of data. And these scenario/domain/task specific models will be the product sold by these companies. This is plausible insofar as one can find a reason to suppose compute costs for this specialisation will remain very high, and the hardwork of producing relevant data will be done best by those same companies. I think its equally plausible compute will come down enough, and innovations in "post-training re-training" will occur, that you'll be able to bring this in-house within the enterprise/org. Ie., that "ML/AI Engineer" teams will arise like SEng teams. Or that there's a limit to statistical modelling over historical cases, that means specailisation is so exponentially demanding on historical case data production, that it cannot practically occur in places which would most benefit from it. I think the latter is what will prevent the mega players in AI atm making "the model the product" -- at the level they can specialise (ie., given the amount of data needed), so can everyone else. Perhaps these companies will transition into something SaaS-like, AI-Model-Specialisation-As-A-Service (ASS ASS) -- where they create bespoke models for orgs which can afford it. |
I think you are on to something here - and this may very well be what these rumored $20k/mon specialized AI "agents" end up being. https://techcrunch.com/2025/03/05/openai-reportedly-plans-to...