I can see two potential reasons:
1) Most of the big players seem convinced that AI is going to continue to improve at the rate it did in 2025, if their assumption is somehow correct by the time any chip entered mass production it would be obsolete.
2) The business model of the big players is to sell expensive subscriptions, and train on and sell the data you give it. Chips that allow for relatively inexpensive offline AI aren't conducive to that.
The cloud-based AI (OpenAI, etc.) are todays AOL.
And it produced fake headlines and summaries including the threat of lawsuits from involved person(s).
Apple usually waits until somebody else has refined a technology to "invent" it, but I guess they couldn't wait for this one.
It’s for cloud based servers.
Will your comment age well? We'll see.
We might all be surprised if (somehow, ternary logic?) models come down drastically in size. It doesn't have to be the hardware getting more dense.
Guess who acqui-hired Groq to push this into GPUs?
The name GPU has been an anachronism for a couple of years now.
I would be shocked if Google isn’t working on this right now. They build their own TPUs, this is an extremely obvious direction from there.
(And there are plenty of interesting co-design questions that only the frontier labs can dabble with; Taalas is stuck working around architectural quirks like “top-8 MoE”, Google can just rework the architecture hyperparameters to whatever gets best results in silico.)
Time is money and when you're competing with multiple companies with little margin for error you'll focus all your effort into releasing things quickly.
This chip is "only" a performance boost. It will unlock a lot of potential, but startups can't divide their attention like this. Big companies like google are surely already investigating this venue, but they might lack hardware expertise.