Apple has shown time and time again that they have the human capital and money to tackle massive projects discretely. It's already fairly well known that Apple's NLP experts from Siri have been reallocated to some secret project. They are more than capable of training an LLM but given their track record in other segments they probably want to wait for the technology to become more "polished" and give less hallucinated answers. They are likely also want the LLM to work locally (at least partially) on their devices using the Neural Engine which adds further engineering complexity to their project. They could even be timing the LLM's launch around a hardware release capable of running the model (M3, M4, etc...).

I have not seen Apple demonstrate ML depth in their talent nor have I seen signs that they are hiring extensively for NLP depth.

They will soon be able to train an LLM because it simply has become commoditized, but they just are not a major player in this space at all.

> I have not seen Apple demonstrate ML depth in their talent

I thought the ml work they do in photos for text selection and facial recognition is pretty neat.

Their approach is different, they build ML tech that runs on-device, so whatever they developed has to be able to run efficiently on iPhone/iPad etc.

I don’t think we will “hear” about Apple using LLMs either way because they will no doubt call it something different like they always have.

Apple only has to slightly open their wallet to become a DL superpower.

Apple is a complete laggard in this space due to years of restrictions on research. They are hiring multiple “AI” roles now and they have the capital and focus to “eventually” catch up — but it is very much a catch-up game.

That said, they seem to prefer catchup waiting till others explore new tech they swoop in an (claim) to perfect it from a usability pov. I have no reason to suspect they won’t do the same here.

I very much doubt that.

If they want to own the whole stack, I don't think they have much to work with. Their highest-end server chip is a duplex laptop SOC, with maxed-out memory that doesn't even match the lowest-end Grace CPU you can buy (nevermind a fully-networked GH200). Their consumer offerings are competitive, but I don't think Apple Silicon or CoreML is ready to seriously compete with Grace and CUDA.

While Apple silicone may not be there for training, I think it's probably there for inference. I expect next years device models to launch with exclusive support for Apples own LLM based Siri.

Sure. Haswell CPUs from 2014 are "there" for inference if they have AVX support and 8gb of RAM. Inferencing isn't the problem though, not on M1 or Macbooks from 2016. Scaling a desirable (and hopefully open) GPGPU programming interface is. This is bottlenecked by both hardware and software decisions Apple has made, making a "home grown" competitive model much more unlikely in my eyes.

I agree that there is an incentive to put AI models on your OS. I just don't think Apple can own the whole stack if they want to play ball right now.

Why not? They have cash and they can rent a bunch of GPUs from Amazon.