Apple Intelligence Foundation Language Models Tech Report 2025

I mean they also buy from companies with less ethical supply chain practices than their own. I don’t know that I need to feel anything about that beyond recognizing there’s a big difference between exercising good practices and refusing to deal with anyone who does less.

They already partnered with OpenAI, right?

In theory Apple could provide their training data to be used by OpenAI/Anthropic.

Same way as the other parts of their supply chain I suppose.

"Good artists copy; great artists steal"

- Famous Dead Person

Uncharitable.

Robots.txt is already the understood mechanism for getting robots to avoid scraping a website.

I think it is entirely morally consistent to provide alt text for accessibility even if you personally dislike it being used to train AI models.

I mean if you throw out all contrary examples, I suppose you are left with the simple lack of nuance you want to believe

Sorry if I didn’t use the correct terms. Didn’t catch up on all the terminology coming from my native language. ;) But yes, I agree, the fact that parts, different parameters, of the model can be completed asynchronous by streaming the output of the model, is quite unique. Apple/swift was late with async/await, but putting it all together, it probably plays well with the ‘never’ (I know ) asynchronous and reactive coding.

An issue with this is that model quality can get a lot lower when you force it into a structured form, because it's out of distribution for the model.

(I'm pretty sure this is actually what drove Microsoft Sydney insane.)

Reasoning models can do better at this, because they can write out a good freeform output and then do another pass to transform it.

People have been giving Siri a few years for a decade now. Siri used to run in a data center (and still does for older hardware and things like HomePods) and it has never supported compound queries.

Siri needs to be taken out back and shot. The problem with “upgrading” it is the pull to maintain backwards compatibility for every little thing Siri did, which leads them to try and incorporate existing Siri functionality (and existing Siri engineers) to work alongside any LLM. Which leads to disaster, and none of it works and just made it all slower. They’ve been trying to do an LLM assisted Siri for years now and it’s the most public facing disaster the company has had in a while. Time to start over.

First, most of the English speaking world is not native.

"As of 2022, there were about 400 million native speakers of English. Including people who speak English as a second language, estimates of the total number of Anglophones vary from 1.5 billion to 2 billion."

Second, all popular models I tested did well with that query, including Gemini on Android (aka "ok Google"), except Apple's.

https://en.m.wikipedia.org/wiki/English-speaking_world

That exact phrase "What is 75 degrees fahrenheit in celsius, and what is 85 degrees in fahrenheit" given to ChatGPT produces the correct result (it infers that the second degrees must be Celsius) and ChatGPT gives me a nicely laid out formula for the math of the conversion.

So yeah, Apple is way behind on this stuff.

the fact is that gemini responds with this: 75 degrees Fahrenheit is 23.89 degrees Celsius, and 85 degrees Celsius is 185.00 degrees Fahrenheit.

Meanwhile users have been conditioned to expect a system that understand the multiple queries and answers them appropriately.

None of those features are in any way interesting though. Image playground is a joke, Siri is a joke, that generative emoji thing is a joke.

The AI stuff with photography sure, but that’s more like machine learning.

The photo touch up thing is… useable? Sometimes?

What is it you’ve been so impressed with?

It says there’s 2 models - one local. It’s already released to app developers to use locally I think (it was in the keynote for WWDC).

"Sorry we are hilariously far behind everyone else in the industry after having made a huge amount of fanfare about 'Apple Intelligence' for years. It's just that we have shot ourselves in the knee to satisfy Bluesky posters and the NY Time's lawyers"

I wouldn’t go as far as GP, but yes, absolutely, they must compete with large models on the internet. Customers are now used to being able to ask a computer a question and get something better than “I just ran a web search for what you said, here are the uncurated, unsummarized results”.

Yes, this is in fact what people want. Apple is the biggest company in the world (don’t quibble this y’all, you know what I mean) and should be able to deliver this experience. And sure, if they could do it on device that would be aces, but that’s not an item on the menu, and customers seem fine with web-based things like ChatGPT for now. To act like Apple is doing anything other than fumbling right now is cope.

I'm fine with Apple chilling on the sidelines for a bit.

But they have de facto admitted failure of most of the strategy if the rumours are true that they are switching much harder to OpenAI/Anthropic for upcoming LLM products.

This is the first time in 10+ years I've seen Apple so far on the back foot. They usually launch category defining products that are so far ahead of the competition, even by the time they work through the 'drawbacks' in the first versions of them they are still far ahead. OS X, the iPhone and the iPad were all like that. They are still way ahead of the competition on Apple Silicon as well.

I am not very confident on their on device strategy at least in the short to medium term. Nearly all their devices do not have enough RAM and even if they did SLMs are very far behind what users "know" as AI - even the free ChatGPT plan is leap years ahead of the best 3B param on device model. Maybe there will be huge efficiency gains.

Private cloud is used AFIAK for virtually 0 use cases so far. Perhaps it will be more interesting longer term but not very useful at the moment given the lack of a suitable (ie: non Chinese), large (>500b param) model. They would also struggle to scale it if they roll it out to billions of iOS devices especially if they put features that use a lot of tokens.

Then they've got OpenAI/Gemini/Anthropic via API. But this completely goes against all their private cloud messaging and gives those providers enormous potential control over Apple, which is not a position Apple usually finds itself in. It will also be extremely expensive to pay someone per token for OS level features for billions of iOS/Mac devices and unless they can recoup this via some sort of subscription will hit services margins badly.

To me its clear the future of "OS" is going to involve a lot of agentic tool calling. These require good models, with large context windows and a lot of tokens - this will definitely not work on device. Indeed this is exactly what the Siri vapourware demo was.

I'm sure they can potentially get to a great UX (though these missteps are making me question this). But having such a core feature outsourced does not leave them in a good position.

> It's a pity this model won't be directly accessible, only integrated in some apps.

It's already accessible using Shortcuts, even to non-developers "iOS 26 Shortcuts + Apple Intelligence is POWERFUL " (Youtube) [1].

[1]: https://youtu.be/Msde-lZwOxg?si=KJqTgtWjpdNDxneh

Wild to see what improvements might come if there is additional hardware support in future Apple Silicon chips.

What’s the cost of pointing it to Private Cloud Compute? It can’t be free, can it?

It would be interesting to see the tok/s comparison between the ANE and GPU for inference. I bet these small models are a lot friendlier than the 7B/12B models that technically fit on a phone but won't accelerate well without a GPU.

There are even already some local AFM to Open AI API bridge project on GitHub - that lets you point basically any Open AI compatible client at the local models. Super nice for basic summarisation and completions.

I was worried "device" was a Mac mini, not an iPhone. (I already have been running models on my MacBook Pro.)