This is Apple commoditizing LLMs while keeping control of the UX.
They are a hardware company and will keep selling the best machine for AI use. Well done.
They are a hardware company and will keep selling the best machine for AI use. Well done.
China will spend all of the money required to catch up, Google and OpenAI will both spend money to catch up as well. NVidia and others will not allow a frontier lab to become the AI bottleneck.
OpenAI and Anthropic may have gone silent on how they build their models, but other companies have different incentives.
Isn’t this the problem inference (training) a model is designed to solve :)))
And it's a hard problem.
What's an easier form of training is being able to see the intermediate results and train to imitate them.
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)
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.
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.
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.
employees will always suffer.
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.
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.
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.
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.
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.
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.
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.
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.
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 wish so badly this was true, but sadly today it just isn't.
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.
Its somewhat of a myth that you need the most advanced, expensive model for software development.
Spend for compute seems like it needs to increase to get the next iterations of models, and even if they IPO the money might run out before they can solidify their revenue streams.
All while Google just needs to survive long enough with their good-enough models and do it without really putting themselves in any existential financial risk.
And ideally the chinese models are also still there keeping everyone honest.
The true dystopic worst case is a Google monopoly on cutting edge AI.
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.
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.
Well, in domains like SWE where Anthropic's putting in the effort. I don't they'll make the claims that OpenAI makes about how their models are pushing the life sciences forward, for example.
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.
Most of the ones that survived did so due to being able to pick up distressed assets and at values that could then be profitably monetized - a move that it would not surprise me to see repeat itself in the LLM space (we'll see).
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.
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
From a user’s perspective, it doesn’t matter.
Anthropic literally says "Requests go directly from your app to the Claude API; Apple is not in the request path and does not see prompts or responses." — Apple straight up lied
the Swift package for Claude for Foundation Models is about sending calls to Claude. That had nothing to do with Apples models which do use local models and models on Private Cloud Compute.
Your accusation that "Apple straight up lied" is based on misunderstanding TFA.
this is a blurry line. when factual statements are so confusing (deliberately? we well never know), there is not much difference between lie and technically-turth ful misdirection. harm is done. ill-intent is likely present.
you can burry "facts" into thousands pages long T&C (apple is infamous for) nobody reads. and claim "you agreed to that!". nobody does, nobody did. technically this is correct and truthful, but harmful ill-intent against user pulling power to corporate entity as much as it can.
same here, apple would "technically" not lie, but annouce their products and build their API confusing to their advantage (upselling features that do not exit).
when you have to fight over word definitions, it is a red flag.
Now if they can further reinforce their angle on Privacy, they might continue to be what they are (or more)
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.
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.
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.
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!
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!
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.
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.
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.
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.