I would assume that they would fold this type of classification into their responses API next to existing ancillary tooling that they already ship. I think OpenAI positions themselves as being wholly focused on AGI - but I think their business model leans towards lock-in via superior tooling (Codex, ChatGPT, etc.). This feels like an easy win for them without muddying the larger vision.
I think your last point is exactly why I'm so interested in local models. The current landscape doesn't feel sustainable. The last few months we've seen the big providers (OpenAI, Anthropic) start to play with usage limits, resets, banked resets, pulling models, etc. I think local models are close to the point where, with a sufficiently well-architected harness, you can get results that are on par with the experience you'd have with cloud inference. It is nice to know that I have hardware under my desk that I control with open weight models that I can interact with on my terms.
It is certainly not sustainable but that is exactly why I want to use $400-$800 of resources a month for $20 while the deal last.
It is really a once in a lifetime deal.
Once the deal is over the local models will be better than what I am using now anyway and the hardware will be all the better than what I can get now for the price.
> local models are close to the point where, with a sufficiently well-architected harness, you can get results that are on par with the experience you'd have with cloud inference
In my opinion, 98% of the work most devs would send to an AI can be capably achieved with a local model and a frontier-level model is overkill.
The goalpost moving feeds right into Anthropic and OpenAI's interests.
They highlight:
- core design team
- start with an idea for an unmet customer need
- drive innovation from imagining user’s experience
- define essentials for “insanely great”
- simplify to great lengths to hide complexity
- grow user involvement through evolution
https://repo.aalto.fi/uncategorized/IO_7b149a54-2085-4f7d-9b...