Deepseek completely changed the game. Cheap to run + cheap to train frontier LLMs are now in the menu for LOTs of organizations. Few would want to pay AI as a Service to Anthropic, OpenAI, Google, or anybody, if they can just pay few millions to run limited but powerful inhouse frontier LLMs (Claude level LLMs).
At some point, the now fully packed and filtered data required to train a Claude-level AI will be one torrent away from anybody, in a couple of months you could probably can pay someone else to filter the data and make sure it has the right content enabling you to get well the train for a claude-level inhouse LLM.
It seems the premise of requiring incredible expensive and time demanding (construction), GPU especialized datacenters is fading away, and you could actually get to the Claude-level maybe using fairly cheap and outdated hardware. Quite easier to deploy than cutting edge newer-bigger-faster GPUs datacenters.
If the near future advances hold even more cost-optimization techniques, many organizations could just shrugg about "AGI" level - costly, very limited - public offered AI services, and just begin to deploy very powerful -and very affordable for organizations of certain size- non-AGI inhouse frontier LLMs.
So OpenAI + MS and their investments could be already on their way out of the AI business by now.
If things go that way - cheaper, "easy" to deploy frontier LLMs - maybe the only game in town for OpenAI could be just to use actual AGI (if they can build it, make it to that level of AI), and just topple competitors in other markets, mainly replacing humans at scale to capture reveneau from the current jobs of white collar workers, medics from various specialties, lawyers, accountants, whatever human work they can replace at scale with AGI, for a lower cost for hour worked than it could be payed to a human worker.
Because, going to "price war" with the inhouse AIs would probably mean to actually ease their path to better inhouse AIs eventually (even if just by making AI as a service to produce better data with they could use to train better inhouse claude-level frontier LLMs).
It is not like replacing onpremise datacenters with public cloud, because by using public cloud you can't learn how to make way cheaper onpremise datacenters, but with AGI AI level services you probably could find a way to make your own AGI AI (achieving anything close to that - claude-level AIs or better- would lead your organization to lower the costs of using the AGI AI level external services)