> We mainly build small closed-down AI compute-chips we can control, sell them for-profit to individual consumers and then orchestrate data-processing on those chips, with setup and operational cost all paid by the consumer

I wish they did but they don't. They have been for decade so stingy on RAM for iPhone and iPad. There are at current point that only small percent of their userbase have iPhone or iPad with 8GB RAM that somehow can run any AI models even open source and be of any use. Not mentioning they don't compare to big Models.

They don't even provide option to sell iPhone with bigger RAM. iPad can have max 16GB RAM. Those mainstream macbook air also can have max 32 GB RAM.

And for the current price of cheap online AI where e.g. perplexity provides so many promo for PRO version for like less $10 per year and all ai providers give good free models with enough rate limit for many users I don't see apple hardware like particularly bought because of AI compute-chips - at least not non-pro users.

If the loose AI though and because of that won't have good AI integrations they will loose also eventually in hardware. e.g. Polish language in Siri still not supported so my mum cannot use it. OSS Whisper v3 turbo was available ages ago but apple still support only few languages. 3rd party keyboard cannot integrate so well with audio input and all sux in this case because platform limitation.

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rickdeckard 4 days ago | parent | next [-]

Their strategy is not to sell you a device that YOU can use for AI, they sell you a device that THEY can use for AI.

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bigyabai 3 days ago | parent | next [-]

Some lot of good that's done them. The Neural Engine is dark silicon on most devices I've seen, and now we're getting another product segment with M5's matmul GPUs.

To me, it feels like Apple should have supported CUDA from the start. Sell the ARM-hungry datacenter some rackmount Macs with properly fast GPUs, and Apple can eventually bring the successful inference technology to cheaper devices. Apple's current all-or-nothing strategy has produced nothing but redundant hardware accelerators, while Nvidia's vertical integration only gets stronger.

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robotresearcher 3 days ago | parent | next [-]

> The Neural Engine is dark silicon on most devices I've seen

At the very least it's used by the Photos app[1]. Likely other Apple apps too.

[1] https://machinelearning.apple.com/research/recognizing-peopl...

	▲	buildbot 3 days ago \| parent [-]
		I have a little rust script that uses the built in vision toolkit to do ocr of pdfs, it spins up the ANE to a full 1W compared to 0 as measured by the power profiler. So it is used! IMO, It’s a very apple strategy, stuff just works and is slowly more accelerated/lower power.

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rickdeckard 3 days ago | parent | prev [-]

Maybe. But Apple tried the server business and found that they can't compete there.

Not because of Engineering deficiencies, but because datacenters buy based on facts, not fluff.

Now their ARM silicon is top-notch, no doubt about that. But will they earn a higher margin if they put it in a datacenter instead of a consumer device which is then used to consume Apple Services? I don't think so.

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bigyabai 3 days ago | parent [-]

> But will they earn a higher margin if they put it in a datacenter

Nvidia is a five trillion dollar business right now. The total sum of Apple's profits from services, hardware and servicing/repair costs all fail to crest Nvidia's total addressable market. We've been past the point of theorizing for almost two years now.

Apple has the means to break into that market, too. They don't need the silicon (iPhone/iPad are way overpowered, Vision Pro and Mac are low-volume), they have thousands of engineers with UNIX experience, and hundreds of billions of dollars in liquid cash waiting to be spent. If the China divestment and monopoly case happen, Apple needs a game plan that guarantees them protection from US politicians and secures an easy cash flow.

From the consumer perspective, it seems simple; stop shipping the latest silicon in the iPhone. Nobody uses it. They're not playing AAA-games or inferencing the latest AI models, and the efficiency gains haven't been noticable for a decade. You don't need TSMC 2nm to browse the App Store, or watch AppleTV. The only opportunity cost comes from selling consumers hardware they can't appreciate.

	▲	rickdeckard 3 days ago \| parent [-]
		> From the consumer perspective, it seems simple; stop shipping the latest silicon in the iPhone. Nobody uses it. From a vendor-perspective, ~200mn iPhones are sold each year, the end-user will pay for it. The scale of this is financing the entire development and supply-chain for the silicon itself, and it contributes not only to hardware but also service revenue of the entire company. nVidia owns 94% of the GPU market and shipped 11.6mn GPU's in Q2/2025, let's say they ship 60mn GPUs in 2025 total. --> Why should I stop shipping the latest silicon in the iPhone? Even without stopping production, why should I enter and compete in a market that is currently dominated by a single player, has a total size of ~60mn units/year, with each product deprecating almost instantly as soon as a more efficient product is announced? Apple's silicon is not magically more efficient than everything else, their products are efficient because they are vertically integrated. I doubt that Apple Silicon is competitive to nVidia in a datacenter setting

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amelius 3 days ago | parent | prev [-]

> Their strategy is not to sell you a device that YOU can use for AI, they sell you a device that THEY can use for AI.

How will that work out with the battery?

I mean, they could have mined crypto on our phones but that would have been a bad idea for the same reason.

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robotresearcher 3 days ago | parent | prev [-]

> They don't even provide option to sell iPhone with bigger RAM. iPad can have max 16GB RAM. Those mainstream macbook air also can have max 32 GB RAM.

That's a selective list. High RAM Macs are available. MBPro goes up to 128GB. Mac Studio goes up to 512GB. Not cheap, but available.