You wouldn't use this when building a coding agent.

> while keeping control of the UX.

Extremely tangential, but this is my favourite upshot of AI. For decades, companies have been walling off their services and forcing us into their fuckass UIs. Now over the course of the last twelve months, suddenly everything has an MCP and I can use it through my command line chat interface.

Any company that doesn't adapt gets so hammered by people's AI-DIY web scrapers that they have no choice but to cave.

I think there is an opportunity for a new hardware company to enter the market. I know this is just hypothetical but I believe that AI is revolutionary enough where a new approach to hardware and UI/UX will enable far more value to be derived from AI. I think the incumbents like Apple will stick to their familiar platforms and could get beaten out by a new competitor that is AI native to the core. Maybe? ¯\_(ツ)_/¯

It's been clear for years now that eventually ai will be embedded at the os level. Apple even recognized it way back when they first introduced Apple Intelligence. Yes they're commoditizing llms or whatever. But this has been a user facing feature they've been iterating on for years now

Benedict Evans may be right after all; frontier models look more and more like telecom companies in the 90s. Billions and billions of investment in infrastructure while others further up the stack captured all the value.

Does “the best machine for AI use” apply here considering these models are still server-side?

Now we only need to commoditize the hardware.

How else will I run my coding agent on your Mac without having you download a second LLM and double your memory usage?

The play here seems pretty evidence, if I may assume. Apple creates an interface that is generalized enough so you can easily swap models, and while Claude is preferred by Apple today, it may be any provider or even local models in the future, and the APIs the developers use remain the same, so "migration" becomes easier.

Apple's been trying to make the marketing appeal that "Private Compute Cloud" is also a hardware project. Given it seems to rely on low level details of device Hardware Security Modules, it's maybe even at least a little bit more than just "marketing spin".

for the on-device model, yes it runs on the Neural Engine (at the moment) so a newer chip means faster, cheaper local inference. For the server side path this Claude package is about your machine is irrelevant since it's a network call. The same API covers both, so "best machine for AI" only bites when the session is actually local.

But we can imagine that the balance of what's on-device vs what's remote will move continuously towards the former as time, improved HW and improved local models keep progressing

Check out AMD’s offerings.

They’re typically a bit better on high TDP stuff, and a bit worse on low TDP. They mostly match in the middle. I have a $500 AMD NUC and a slightly older $2000 MBP. Inference throughput is within 2x.

The comparison is a little messy: AMD currently maxes out at 128GB of RAM vs Apple’s discontinued 512. Apple has nothing to rival the Steam Deck.

Check out this WWDC session. Obviously not going to compete with the frontier models (and I think 8GB is too small anyways), but Apple did demo MLX + OpenCode.

https://developer.apple.com/videos/play/wwdc2026/232/ https://www.youtube.com/watch?v=wykPErJ8M-8

You can use OpenCode or Pi with SSD streaming so it technically will have all the features, just unbearably slow.

> all of the existing features of Claude Code but somehow running locally on my laptop's neural engine

You can use environment variables to have claude code query literally any endpoint you choose as long as it has a compatible API.

That's a great opportunity for Apple to provide a universal unique model ID protocol and some shared storage space to allow devs to register models.

That is exactly what foundation models are, yes. Same in Android with AICore which uses Gemma underneath, apps can query the LLM and receive responses back rather than bundling in their own model.

The apps can use the system provided on-device model using the same framework and APIs; but there's no affordances to deduplicate custom models between apps.

Ok but don't expect Anthropic to help with local models, that'll be something apple rolls out themselves if at all

Sounds ripe for block-level deduplication. :D Or an API that lets you request a model and handles caching.

There are already on-device models that you can use through this framework as a developer. Claude would just be an additional one.

Maybe they plan to have the providers pay for being the default model? So basically, what Google is doing right now for search engines. The difference however is that Google is making money with additional search requests while AIs are (as of now) losing money with additional requests. I don't see the business case for them yet though.

> which I think we’ve heard they’ve been spending lots of money on training and might be somehow involved with Siri or current Apple AI

Lol bro this is literally it this is the model they've been training (was Apple Foundation model not a big enough hint?)

UX is just another word for ecosystem building, which is what Apple does best in comparison to their competition and also doesn’t hurt to do hardware to go along with it. Microsoft and Nvidia aren’t teaming up for nothing.

> That'll give apps better UX and save developers money on a bill that Apple doesn't get a cut of.

With other words, it's unlikely to happen as there is no money in it. Better for Apple to create some new subscription "AI" and "AI-lite" plans people can subscribe to, and since Apple is a company and we all know what those care about, it's unlikely to become a utopia of local models running on your phone.

The even bigger hurdle is selling token based pricing to normal (non-dev) users.

"You pay an indeterminant amount of money to ask a question and you might not even get the response you want without spending even more money" doesn't appeal to most people who aren't gamblers and explaining how "thank you" at the end of a long exchange can be expensive due to context is an even harder thing for an average person to swallow.

Token cost going up/down like a yo-yo also doesn't help. Normal users NEED fixed costs and don't want to expend energy constantly keeping up with the AI meta. "My subscription lasted much longer last month" isn't a winning problem either.

I think Apple is correct that Local LLM for most things is the future.

Users don’t give a API key. The docs show how to set up your backend proxy.

> For production, route requests through your own back end with .proxied

Apple is offering developers with less than 2 million downloads free AI models via their servers https://techcrunch.com/2026/06/08/apple-bets-cheaper-ai-will...

The same way you did it before — by proxying the requests to your backend.

Why?

There will be frontier models that are non-commoditized, but they'll be kept guarded and hidden away, and you'll only get the final result, so that they can't be distilled and their harness can't be reverse engineered. They'll be billed like employees, rather than like a tool.

It is much better. Imagine if the whole Manhattan project could have been outsourced and costs you nothing. I expect in a short time that open source models will be almost or almost parity by 2030 and running on consumer devices.

In spite of their deeper pockets, massive datacenters, colosal amounts of user data, and hundreds of thousands of top developers, even Amazon, Meta, Microsoft, and Google are well behind.

I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)

Last I checked the telcos made plenty of money in the 90s. Should Verizon be getting a cut of my Claude Pro subscription, since I use FIOS to access it?

I would think so, as “use” doesn’t specify implementation. If you use a word processor it may be running locally or remotely.

From a user’s perspective, it doesn’t matter.

This is what originally made Microsoft the most lucrative tech company of its day.

Android succeeded at this to an extent with phones, but Apple has been able to keep its products differentiated enough in the minds of consumers to maintain their premium pricing. So far.

How is this Apple keeping control of the UX?

I've found most of the frontier coding models require somewhere between 300GB to 1TB to run with full capabilities.

I think that's what they are trying to avoid. If you need on-device intelligence, their pitch was "The model the device already has is best", and if you need something more specific an adapter (aka, a fine-tune/lora) is best.

They were wrong when their on-device model was way behind. They still might be right in the long term.

While multiple app I use might need Gemma 4 E4B, I use dozens of apps and app devs can choose from hundreds of models. A shared cache might reduce size a little when there's overlap, but the core problem still exists. If each app chooses a model disk and memory-swapping explode.

Its probably be better for device manufacturers to bake in a default. I'm not proposing they limit you from using others, but one shared default might be best developer/user experience for 99% of apps.

- Being warm in memory is the single biggest perf speedup you can get, and a default is much more likely to be warm.

- "Best model" is usually "best model for this device" given both RAM and compute. A developer can't test every device but Apple can/will.

- Each model needs to be optimized for the hardware (what's running on ANE, what's running on Metal, what's running on CPU). The default gets optimized.

- If you need custom model, a Lora is probably best (30MB, benefits from all of the above)

You could say the default should be swappable, but that's more a linux ideal than an Apple one so I doubt we ever see that. Plus there are real downsides: intentional or not, prompts end up optimized to the model they are developed for, so swapping the default system model would degrade every app.

Check out “Bring an LLM provider to the Foundation Models framework” - https://developer.apple.com/videos/play/wwdc2026/339

Do you guys not have phones (with at least 1TB of storage)?

Apple has some clever mechanics to protect user data. I had to work with App tracking stuff lately and their approach to keeping user details private with anonymized cohorts (SKAN, Differential Privacy) before reporting tracking events to third party platforms was surprisingly well thought out. There is value in having them in your loop if you care about privacy.

This is support for a new framework that ships with reality/mac/iPad/watch/tv/iOS 27 (and that they've promised to open-source later in the year, so presumably you'll also be able to lean on this if you ship Swift on your backend).

The framework's whole deal is that it lets you use the same API to target either the device built-in models, the Apple-hosted online models (Private Cloud Computer), or write your own shims to call out to arbitrarily hosted online models.

You can then dynamically route your calls to a different kind of model/provider, using system APIs, without having to write your own abstraction layer over "I want to use local model for this, but I want to use Claude for that", or having to integrate your own API integration with Anthropic/OpenAI APIs.

It abstracts things like tool calling in one place; and has a bunch of other niceties/oddities (it keeps the same "transcript" going, even if you dynamically switch providers/models during a session) and some other things.

A dark, but not totally unfair take: It makes it easier for Apple to take payment for the models others provide, and even allows Apple, if they want to, to use the data to build a dataset for training their own models based on how users use third party models. It's only on Apple devices this API is used, so they split up the market by not letting developers use the same system if they want things to work on iOS, locking users even more in.

The cynic (or realist?) in my thinks this abstraction layer is Apple's way of making sure that users give their own Apple Intelligence credit for the underlying LLM functionality, even if another company is actually providing the LLM.

How does using Gemini lead to better on-device models?

Ugh. It really is. I have allihat.com which is the only safari extension (i think still) that talks to claude. And it's well sought for. But you as a user have to enter a friggin claude api key. :( And I still don't grok their TOS around this. Like you can still type: ```setup-token Set up a long-lived authentication token (requires Claude subscription)``` but this seems like a trap? :) Whose using this? Doesn't this like insta break their TOS if you use that anywhere?

Right now for allihat.com I just let people use the Apple model locally if you don't feel like using the claude key. And my conversions to paying user shot up like 3x! But it really isn't a replacement obviously to claude. I was hoping Apple would make proxying to Claude some kind of thing they do for me so I also don't have to proxy to my own server just to try and manage API to Claude usage.

Isn’t that a privacy and compliance nightmare?

The non-commodity network services of the early 1990’s and the non-commodity 3d graphics hardware of the mid-1990s made the same argument.

Isn't that what they are doing already? The model is already guarded and hidden and i only get to send it what i want. Talk with it to clarify my requirements. And i can switch to a different provider for cheaper/better results.

They tried to do that with operating systems and the browser.

Everything can be distilled, it will just become more painful

I think this will be isolated to highly specialized fields where training data will need to be selectively curated.

Market phenomena like this are a bit like the Manhattan project in that you pay for it, and make use of it, whether you want to or not. It's functionally very similar to the government doing something.

Remember the implicit “pareto” in “frontier models”.

Anthropic and OpenAI are far behind state of the art for the entire curve except the “extremely expensive for barely measurable improvements” part.

GLM is probably the third most expensive frontier model (benchmarks and reviews will say for sure), and is apparently ~Opus 4.6 for 10% the inference cost.

The last I checked, qwen was still owning the 24-32GiB RAM range (it runs reasonably without a GPU!) and somewhere around 3.5-4 generation models.

Also, even anthropic says Mythos ~= ChatGPT 5.5, so it’s unlikely either one is leaving the other behind. The big problem they both have is they asked for the government to gate keep model releases and use cases, and their wish was granted.

That’s knocked them back 6 months already. Anthropic’s only frontier offering has been taken down.

I'm perfectly happy at claude opus 4.6. All improvements since then have not meaningfully improved my day to day. If i can get 4.6 on my laptop for 5-10k, i'd gladly start shifting my ~1k/month Anthropic spend over.

Some of the harness even let you run a local model for most things, and only pay for the latest frontier models when needed, which cuts down cost drastically.

Is Google behind? The general opinions I read suggest Gemini is very competitive with Anthropic and OpenAI's top models.

This is what everybody is TRYING to do. They built something and will do everything they can to charge outsized rent on it far past the value it provides to take revenue from anyone downstream.

The fact that telcos couldn't charge rent was a primary reason the Internet was so successful.

Remember $0.10 per text message? You bet in some alternate timeline AT&T charges $0.10 per webpage visit and we're stuck on 100kbps connections because the monopoly doesn't want to innovate.

Vertical computer company operating system plus hardware under one roof.

I doubt that. What stops the Chinese labs from figuring it out? It’s not like these models are fundamentally different from each other

>I think Evans is completely wrong.

I wish there was a case where I find Evans is wrong. As far as my memory served me, I failed to record a single one.

I disagree that Amazon, Meta, Microsoft, and Google are "well" behind. If anything the frontier model advantage seems to be at best 6 - 9 months. And that the Chinese model are all doing well.

One of Steve Jobs's line, "It is a feature, not a product." Even if Apple were a generation behind or 1 year behind frontier model. The advantage of default is enough to hold a lot of its user.

To put it simply, even if OpenAI or Anthropic were better, there is zero chances they would topple Apple in hardware sales, user or ecosystem. On the other hand, even if Apple's AI were 6 - 9 months or a generation behind, most user would settle for it and damage OpenAI / Anthropic.

I use both Claude and Codex and don’t see any meaningful difference between the two. My use case is modeling semi complex physical processes (energy and manufacturing) in code for simulations. I also have to do a good fair of automation via scripting in Python or PowerShell for manipulating data as well as legacy code analysis (C, Fortran, COBOL). Given I provide the models with the information and documentation they need, both perform very similarly. I recently did a full codebase review (for design patterns and vulnerabilities) and both Codex and Fable agreed 100% about the most critical findings. I do very little front end development, although some of my automation scripts have TUIs and again no problem with either Claude or Codex generating them for me. At this point I go with the less expensive, which seems to be Codex. With the $100 plan I rarely hit the limits. With Claude I max out my plan in about 4-6 hours of work.

I think it's highly likely that there will remain one or two companies on the very bleeding edge of AI development for the foreseeable future.

But what I think a lot of people miss is that the market for the truly bleeding edge (developing bio-tech, building the most sophisticated software stacks (probably with a tilt towards simulation, GPU kernel optimization, etc)) is not the whole market.

There's a plethora of use-cases for models that are not on the bleeding edge. If I can solve my relatively simple problems with an off-the-shelf model for a minuscule fraction of the cost of the frontier, I'm going to.

> I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)

Truly fascinating ecosystem and community in general, as experiences differ so wildly. Anthropic's models seems far behind OpenAI to me, especially when you get into "Pro" territory, and there doesn't seem to be any worthy competition to Pro Mode available at all.

And this is said with someone who use both platforms, and spend a lot of my day interacting with agents and LLMs in various ways. The interesting part is that probably so do you too, and probably your experience and what you share lines up with what you experience! Yet we come away with basically opposite takeaways :) I don't think either of us are wrong either, somehow.

Maybe I’m alone in thinking this but I think the long term victor will be the one that works out pricing best.

Fable might well be a better model but it’s too expensive for everyday AI use. Definitely if we’re talking about the kind of stuff you’re going to want to do on your phone. Even for coding, I’m not going to reach for Fable (well, when I can…) for 95% of the work I do.

I don’t believe a mature AI industry is going to have a one size fits all, single winner.

> On the other hand, even if Apple's AI were 6 - 9 months or a generation behind,

Do you mean Google's AI with Apple wrappers? Apple's in-house AI is further behind Google, amd very far from the frontier according to your ranking. IMO, Google is on the frontier - I recall Altman calling for an OpenAI all-hands-on deck when Gemini was released because of how good it was compared to ChatGPT. I also suspect Google has the lowest operating expenses due to scale, experience and luck/planning (TPUs), there will come a time when AI investments will slow down, and the cost of revenue will become more important.

Anecdotal case in point, but writing mostly enterprise CRUD in C#, I've gotten plenty of mileage out of Sonnet, very rarely do I need to use Opus.

Its somewhat of a myth that you need the most advanced, expensive model for software development.

Yes, and pricing is one of the features of a commodity, because users can jump back and forth between services, it becomes a pricing race to the bottom. Agree also that you don’t need the best model all the time. You could have the most powerful model draft the design, requirements, guidelines, policies or whatnot then get the lower tier models execute it. Then again you can have the most powerful model do the testing and review, and give back feedback, rinse and repeat. Just like in the real world you don’t need an entire staff of lead engineers.

If all you have is the starting point and the finishing point, the lack of the path taken from one point to another limits your ability to train models that can efficiently recreate the work, and increases its cost enough that it's possible the US labs can progress capabilities faster than Chinese labs can distill that behavior.

Even their own employees get frustrated if they can't use Claude or Codex. 6-9 months is a big difference and I think it's closer to 9 than 6. And never mind the harness etc are also many months behind.

Did you find much of a difference between Fable and Opus?

I agree with what you're saying. I have a Claude plan for work and I prefer using Claude more than any other LLM I've tried. Having recently tried the Codex 100€ plan with GPT-5.5 in high/xhigh, I don't think it's worse that the Opus models, just different.

I've noticed that depending on how you talk to it, you get wildly different outputs. This seems to happen less with Opus: it mostly understand what I want. GPT is often a bit too literal.

Just my two cents.

People use a model as their daily driver, get very familiar with it and it's behavior, and then go and use another model and have a hard time. It's very difficult to separate "the model is bad" from "the model works differently".

When you say "Pro" territory, do you include Fable?

Yes, particularly if that memory is designed and engineered by Apple in house like Apple Silicon in house and manufactured by TSMC on shore somewhere in the United States.

That’s exactly how NPM works, and how Cargo works by default. You can make npm install stuff globally, but that’s not recommended except for things like CLI tooling. Cargo builds every project in its own separate targets/ directory unless you manually configure it to share that dir between builds. In both cases, the default is to isolate your current project from everything else on the system.

The main difference is that Python use to make you have to know that the virtualenv existed. Now `uv run` and `poetry run` abstract that away so you don’t have to interact with it if you don’t want to.

The meme phrase “it’s fractally wrong” applies to the entire python ecosystem, IMHO. Virtual environments are just another layer of this fractal wrongness in the layer cake of ecosystem awfulness.

It’s a nice language though.

The article is about (from the eyes of a user) white-labeled usage of Claude models on Apple devices, this subthread is about white-labeled usage of LLMs on Apple devices, how is it not relevant?

Notably good models are not on that list.

> lack of the path taken from one point to another limits your ability to train models that can efficiently recreate the work

Isn’t this the problem inference (training) a model is designed to solve :)))

That’s already the case. Chinese ingenuity allowed them to achieve what they did without access to reasoning outputs

people use outlook when gmail exists.

employees will always suffer.

This is just wishful thinking. I am sure someone from gossip media will also find Apple employees who are ready to leave job if Apple disallows Claude usage.

If anything Apple should notice it is Anthropic has got a really good marketing team and it would be no shame if they pick a trick or two from them.

I constantly hit safety blocks in Fable (I’m trying to write secure software, which is equivalent to finding security holes, so banned).

I didn’t use it on big enough tasks to notice any improvement.

I had been hitting plan limits pretty regularly, but fixed it by changing my workflow. That also increased the success rate of claude by an order of magnitude.

> I've noticed that depending on how you talk to it, you get wildly different outputs. This seems to happen less with Opus: it mostly understand what I want. GPT is often a bit too literal.

Yeah, exact prompting matters a lot, seemingly more than people think. There is definitely tradeoffs between how literal the models takes the prompts, on one hand it's useful for the model to ignore their own instinct when you know better, so they don't go chasing geese randomly, but on the other hand it's useful sometimes when they self-direct, when you misworded something and it's obvious you meant something different because of the context, and similar things. They're basically good at different things.

Really agree every model isn't equal and they aren't as interchangeable without adjusting how you prompt them as people seem to think.

You mean the model that was available for a whole of three days? No, I had played around with it a tiny bit, but not much than that. I guess time will tell if it gets close.

Yes. Fable is much more organized and consistent at taking small bites of the (sorry) apple when solving a problem. Specifically I'm talking about a machine learning problem I'd been working on for awhile with Opus and it was (and is, again) constantly stating that all the signal is exploited, everything is now overfit, etc, etc, etc. The first day I pointed Fable at the situation I got a 10% improvement by paying attention to the little details that Opus instead took slightly negative results and extrapolated to "fully exploited". I've had to drop back, again, to forcing Opus to explain what it's looked at and the detail it has quietly assumed away.

It's like the difference to talking to two smartest kids in a class, but one really belongs a grade higher - and the other hasn't learned yet to ask the questions that encourage it to dig in that little bit more for the additional multi-order effects.

> It's very difficult to separate "the model is bad" from "the model works differently"

At which point it’s fair to reject the commoditization label.

Also missing from these discussions are e.g. Qwen, which is at least as good as one back from OpenAI or Anthropic’s frontiers.

Because that's not what the article is about; this is about a unified API for the _app developers_ to access different kind of models.

That API has no user-facing components, and has no influence over UX of what the end-users are interacting with.

The users won't know if you used Foundation Models API or integrated with OpenAI/Anthropic/Gemini SDK directly.

Yeah, that totally makes them merely assemblers then /s

Had a very similar experience. Opus went "look, t-sne shows your features are neatly clustered" (it didn't) and left it at that. Fable didn't fully explore the problem/data, but it did go much further, implementing models to check for correlations and adjust feature clusters. Opus was able to finish the job after Fable was cut, but required much prodding (doing exactly what you described: pointing it towards things that look off and asking it, are you sure that's all there is to this?).

Neither are other capex heavy items like chip fabs.

> Also missing from these discussions are e.g. Qwen, which is at least as good as one back from OpenAI or Anthropic’s frontiers.

They're missing in the discussion because the ones you can run locally, aren't actually "one step away from other closed-source labs" in practice when you use them. They might benchmark as such, but they're sadly far away from measuring up to those scores except for very specific use cases, even when you have say 96GB of VRAM available to run the bigger models even most (at home) consumers won't be able to run.

Yeah, they also don't mine their own steel and copper. Such mere assemblers!

> The users won't know if you used Foundation Models API or integrated with OpenAI/Anthropic/Gemini SDK directly.

That's the point! That's the whole "white-labeling" part, and what the commentator earlier is talking about. You're very close in understanding the context here!

> the ones you can run locally, aren't actually "one step away from other closed-source labs"

And they probably won’t be for at least another decade. Comparing like with like, flagship model running on the best hardware it can run on, Qwen is close.

I’m sorry, so your position now is that “being completely invisible to the users” is “controlling the UX”?

It's Apple, so it's some revolutionary big brained play, and not just yet another llm sdk.

> Qwen is close

I wish so badly this was true, but sadly today it just isn't.

I think you're taking the written words a bit too literally here. Read it with a more lax filter and less literal word-meaning, and I think the original comment will become a bit clearer.

I can't reply to your child comment for whatever reason, but Siri is part of the Apple Foundation Models framework. The idea is that no matter what backend the developer uses, the end user will always say "Hey Siri." This is analogous to controlling the UX. Siri is independent of whichever model the app developer uses.

To be clear, I’m relaying my subjective experience comparing Opus and Qwen.

No, Siri is entirely separate from this framework.

Are you thinking about Intents? That lets Siri interact with data (and perform some actions in them) from your apps, but it is something completely different.

You can definitely expose things from your app via Intents that will end up calling an external arbitrary LLM somewhere, but it does not require using Foundation Models API whatsoever.

You know what, I've been a bit too snipe-y in my previous comments, and it led to to discussion devolving in unproductive ways.

I'd genuinely like to understand where you're coming from more.

I think we're all in agreement that this framework is very much about letting developers swap the models easily, and treat them as commodities. That seems pretty obvious.

I do however still don't see how this has anything to do with controlling the UX (or the new Siri for that matter! The new Siri doesn't use Anthropic models, and there are no extensions point for it to do so — that's pretty much the whole reason why it won't be available in the EU).

Help me see your point of view!

Thanks for the patience!

The way I see it, isn't about what is immediately there right now today, but what intent it signals, or what path Apple is planning. Yes, today it's ClaudeForFoundationModels, but the FoundationModels stuff will be used to allowed switching between models, probably without users noticing, and who knows what Apple will ultimately surface to users, tends to be in the direction of less user-control.

But there is a lot of assumptions, guesses and extrapolation from that, I think you're right if you focus only what's there right now, rather than trying to "see into the future" which harrouet basically started doing with their root comment.

I don't know if it helps. One way to look at it is branding product. Apple is branding the product. So they supposedly have more value to customers as it stands for quality, awareness, trust etc. As oppose to 100 little components in computer which maybe from different brands, and Apple may switch brand year to year without user noticing. So those components makers have little power over Apple.

Same is happening to Claude software package as it would stand behind branded Apple foundation models. From pure software developer thinking this is exactly what Claude offered here so where is the issue? Issue is in larger space where Apple could take steps to block Claude out of their ecosystem if they so wish at some point and there is little Claude / Anthropic would do if Apple Foundation is the only thing that Apple consumers would know about.

That framing would make sense to me if the thing being discussed was Apple letting _end users_ somehow access Claude models white-labeled as "Apple Foundation Model", sure? Or even letting _developers_ access Apple-hosted Claude or something?

But this is very much _not_ what this is.

Apple showed a bunch of new APIs at WWDC last week. One of this is a way for a developers to interact with LLM's in a way that let's you easily swap out models (with a bunch of other niceties around it), including swapping between on-device and remote models.

This is _Anthropic_ (not Apple!) shipping their support for that framework, so you can also switch between different Anthropic models using the same APIs you'd use to swap between a local or PCC model.

I expect OpenAI will probably ship their shims in the next couple of weeks too? (You can probably vibe-code one in half an hour if you point Codex at the Anthropic one, tbh).

(Apple also doesn't use "Apple Foundation Model" anywhere in the user-facing marketing materials AFAICT, this is strictly developer facing terminology, but I could be wrong?)

My impression is that people are _wildly_ misunderstanding what this _actually_ is, and running wild with speculation/interpretation.