We've seen this before, with for example Amazon where they made a deliberate effort to avoid profitability by spending as much as possible on infrastructure until the revenue became some much they couldn't spend it.
Being in a position where you are highly cash-flow positive and it's strategic investment that is the cost seems like a good position.
Amazon were reinvesting and building scale, breadth and efficiency that has become an effective moat. How do you compete with Amazon Prime free delivery without your own delivery fleet, and how do you build that without the scale of operations?
OpenAI don't appear to have any moat, don't own their own datacenters, and the datacenters they are using are running on expensive NVIDIA chips. Compare to Google with their own datacenters and TPUs, Amazon with own datacenters and chips (Graviton), Meta with own datacenters (providing value to their core business) and chips - and giving away the product for free despite spending billions on it ... If this turns into the commodity business that it appears it may (all frontier models converging in performance), then OpenAI would seem to be in trouble.
Of course OpenAI could stop training at any time, but to extent that there is further performance to be had from further scaling and training, then they will be left behind by the likes of Meta who have a thriving core business to fund continued investment and are not dependent on revenue directly from AI.
Training costs are killing them, and it's obviously not sustainable to keep spending more on research and training than the revenue generated. Training costs are expected to keep growing fast, while revenue per token in/out is plummeting - they need massive inference volume to turn this into a profitable business, and need to pray that this doesn't turn into a commodity business where they are not the low cost producer.
https://x.com/ayooshveda/status/1847352974831489321
https://x.com/Gloraaa_/status/1847872986260341224