Exactly, to create larger and better performing models, there is no lack of ideas or techniques. The real problem is to have the GPUs for that.
If it was as simple as dropping 10's millions on compute they could do that, yet google's bard/gemini have been a year behind GPT4's performance.
That said I do agree that it's a moat for the startups like stability/mistral, etc. They also have access to $/compute, albiet a lot less. And you can see this in their research, as they've been focused on methods to lower the training/inference costs.
*I'm measuring performance by the chatbot arena's elo system and r/locallama