As in bearish on openai if they don't offer cheaper 10m context soonish. Google will.
If raw AI power is the key, Google seems to be in pole position form here on out. They can make their own TPUs, have their own data center. No need to "Stargate" with Oracle and Softbank in tow. Google also has Android, YouTube and G-Suite.
However, OpenAI has been going down the product route for a few years now. After a spout of high-profile research exits it is clear Altman has purged the ranks and can now focus on product development.
So if product is a sufficient USP, and if Altman can deliver a better product, they still have a chance. I guess that is where Ive comes into picture. And Google is notoriously bad at product that is internally developed.
And historically, that's definitely been true. I do think they're doing well on the AI front at the moment, but who knows if that will continue.
What we need is not "long context", we need memory: ability for LLM to address datasets of arbitrary size.
RAG has bad reputation but there's a myriad of different ways for doing RAG. Say, "agentic" tool calls which fetch specific data is essentially a form of RAG. But it's cool because it's not called RAG, right?
Anyway, this definitely requires some innovation, but I doubt "longer context" is exactly what we need.
From my experience, pretty much all coding tools have their quirks.
I generally agree that Gemini is a very strong model, but I don't think we can at this point we can conclusively say Google would win because of the long context.
It's too much to extrapolate from a single case. E.g. I see Gemini struggling with editing files a bit more than other models, but I'd say it's just growing pains rather than something fundamental