Apple doesn't like to be held hostage, it has the cash coffers, so it wouldn't surprise me if they're somehow buying dedicated production capacity for the future.
Not that they will start making memory themselves, but they have bankrolled production expansions in their suppliers before in exchange for guaranteed supply.
In any case, if my guess is right, it would take years to take effect.
90% of my model use is on local open-weights models.
The things that I need to automate do not need frontier models. Heck, even a gemma-4-12B-it-qat-UD-Q4_K_XL can deal with a lot of complexity if properly guided (it can run on 16GB of unified memory, for example on a base model Macbook Air).
I've been using it to translate Javascript to a custom scripting language in a product I work for, just by providing a system prompt and an MCP tool to call the target compiler to check for errors.
Sometimes it converges faster than Opus 4.6 (I've tried) because it doesn't over-think stuff.
If it were a person I would say it knows less, but it's still smart.
I mean, you don't need the most powerful tool at all times.
We treat AI as one-size-fits-all, and once cost gets in the way, it will matter.
All leadership is like that. Even if you're not a people manager.
I'm an IC in a technical leadership position, all of these hold true with the added constraint that I cannot tell anyone what to do. I hold no carrot or stick.
I have to persuade, convince and influence, I have no reports (nor I want them) so to get anything done I need to get people to align and understand the value on its merits.
It's still too unpredictable trying to be transparent IMHO.
Scalarization can fail in surprising ways just due to what a maximal atomic write can be on the target platform, and then it fall back to heap allocated objects.
Even if there's type erasure.
I much rather have the compiler balk at me than let me write something that may or may not work as expected.
I'm trying to figure out exactly how to best use AI, but not just as a chatbot user.
How to integrate it to solve useful problems. Make delightful products.
So many things that used to be intractable now are probabilistically solvable and that needs some things to be changed fundamentally and a lot of assumptions dropped.
The randomness that AI introduces poses a plethora of challenges.
I think local models will be a big thing eventually, not everything needs a frontier model and a lot of useful work can be done with surprisingly little hardware.
Not really, I've been using linux since the Slackware on diskette era. I know my way around recompiling a kernel (used to do it on a 486).
I also did dev on Windows, I know the internals pretty well (I even did embedded on CE, writing portable code across CE/95/NT family runtimes).
I still prefer Mac, it's much less likely to randomly break, mainly due to the fact that the hardware the software runs on is extremely predictable. I also like the care about muggle usability that Apple tends to pursue (granted, not perfect, but way better than the other two)