OpenAI applies the same strategy, but they’re using their equity to buy compute that is critical to improving their core technology. It’s circular, but more like a flywheel and less like a merry-go-round. I have some faith it could go another way.
If they don't then they're spending a ton of money to level up models and tech now, but others will eventually catch up and their margins will vanish.
This will be true if (as I believe) AI will plateau as we run out of training data. As this happens, CPU process improvements and increased competition in the AI chip / GPU space will make it progressively cheaper to train and run large models. Eventually the cost of making models equivalent in power to OpenAI's models drops geometrically to the point that many organizations can do it... maybe even eventually groups of individuals with crowdfunding.
OpenAI's current big spending is helping bootstrap this by creating huge demand for silicon, and that is deflationary in terms of the cost of compute. The more money gets dumped into making faster cheaper AI chips the cheaper it gets for someone else to train GPT-5+ competitors.
The question is whether there is a network effect moat similar to the strong network effect moats around OSes, social media, and platforms. I'm not convinced this will be the case with AI because AI is good at dealing with imprecision. Switching out OpenAI for Anthropic or Mistral or Google or an open model hosted on commodity cloud is potentially quite easy because you can just prompt the other model to behave the same way... assuming it's similar in power.
Even if that weren't true having your software be cheaper to run is not a bad thing. It makes the software more valuable in the long run.
This is a pricey machine though. But 5-10 years from now I can imagine a mid-range machine running 200-400B models at a usable speed.