Different models have different strengths and weaknesses, especially here in the early days when models and their capabilities progress several times per year. The apps, programs, and systems based on models need to know how to exploit their specific strengths and weaknesses. So they are not infinitely interchangeable. Over time some of that differentiation will erode, but it will probably take years.
AWS having customers using its own model probably improves AWS's margins, but having multiple models available (e.g. Anthropic's) improves their ability to capture market share. To date, AWS's efforts (e.g. Q, CodeWhisperer) have not met with universal praise. So for at least for the present, it makes sense to bring customers to AWS to "do AI" whether they're using AWS's models or someone else's.
I don't think there will be one model that will rule them all, unless there is a breakthrough. If things continue on the same path, I think Amazon, Microsoft and Google will be the last ones standing, since they can provide models from all the major LLM players.
1. A company the size of Amazon has enough resources and unique internal data no one else has access to that it makes sense for them to build their own models. Even if it's only for internal use
2. Amazon cannot beat Anthropic at this game. They are far a head of them in terms of performance and adoption. Building these models in-house doesn't mean it's a bad idea to also invest in Anthropic
Not sure if this was the goal, but it does work well from a product perspective that Nova is a super-cheap model that is comparable to everything BUT Claude.
AWS having customers using its own model probably improves AWS's margins, but having multiple models available (e.g. Anthropic's) improves their ability to capture market share. To date, AWS's efforts (e.g. Q, CodeWhisperer) have not met with universal praise. So for at least for the present, it makes sense to bring customers to AWS to "do AI" whether they're using AWS's models or someone else's.