alt Hacker NewsThe writing was on the wall the moment Apple stopped trying to buy their way into the server-side training game like what three years ago?
Apple has the best edge inference silicon in the world (neural engine), but they have effectively zero presence in a training datacenter. They simply do not have the TPU pods or the H100 clusters to train a frontier model like Gemini 2.5 or 3.0 from scratch without burning 10 years of cash flow.
To me, this deal is about the bill of materials for intelligence. Apple admitted that the cost of training SOTA models is a capex heavy-lift they don't want to own. Seems like they are pivoting to becoming the premium "last mile" delivery network for someone else's intelligence. Am I missing the elephant in the room?
It's a smart move. Let Google burn the gigawatts training the trillion parameter model. Apple will just optimize the quantization and run the distilled version on the private cloud compute nodes. I'm oversimplifying but this effectively turns the iPhone into a dumb terminal for Google's brain, wrapped in Apple's privacy theater.
Replies
Apple sells consumer goods first and foremost. They likely don't see a return on investment through increased device or services sales to match the hundreds of billions that these large AI companies are throwing down every year.
An Apple-developed LLM would likely be worse than SOTA, even if they dumped billions on compute. They'll never attract as much talent as the others, especially given how poorly their AI org was run (reportedly). The weird secrecy will be a turnoff. The culture is worse and more bureaucratic. The past decade has shown that Apple is unwilling to fix these things. So I'm glad Apple was forced to overcome their Not-Invented-Here syndrome/handicap in this case.
Is the training cost really that high, though?
The Allen Institute (a non-profit) just released the Molmo 2 and Olmo 3 models. They trained these from scratch using public datasets, and they are performance-competitive with Gemini in several benchmarks [0] [1].
AMD was also able to successfully train an older version of OLMo on their hardware using the published code, data, and recipe [2].
If a non-profit and a chip vendor (training for marketing purposes) can do this, it clearly doesn't require "burning 10 years of cash flow" or a Google-scale TPU farm.
[0]: https://allenai.org/blog/molmo2
Yea, I think it’s smart, too. There are multiple companies who have spent a fortune on training and are going to be increasingly interested in (desperate to?) see a return from it. Apple can choose the best of the bunch, pay less than they would have to to build it themselves, and swap to a new one if someone produces another breakthrough.
Google says: "Apple Intelligence will continue to run on Apple devices and Private Cloud Compute, while maintaining Apple's industry-leading privacy standards."
So what does it take? How many actual commitments to privacy does Apple have to make before the HN crowd stops crowing about "theater"?
For some context with numbers, in mid-2024 Apple publicly described 3B parameter foundation models. Gemini 3 Pro is about 1T today.
https://machinelearning.apple.com/research/apple-intelligenc...
Apple's goal is likely to run all inference locally. But models aren't good enough yet and there isn't enough RAM in an iPhone. They just need Gemini to buy time until those problems are resolved.
I always think about this, can someone with more knowledge than me help me understand the fragility of these operations?
It sounds like the value of these very time-consuming, resource-intensive, and large scale operations is entirely self-contained in the weights produced at the end, right?
Given that we have a lot of other players enabling this in other ways, like Open Sourcing weights (West vs East AI race), and even leaks, this play by Apple sounds really smart and the only opportunity window they are giving away here is "first to market" right?
Is it safe to assume that eventually the weights will be out in the open for everyone?
> I'm oversimplifying but this effectively turns the iPhone into a dumb terminal for Google's brain, wrapped in Apple's privacy theater.
This sort of thing didn't work out great for Mozilla. Apple, thankfully, has other business bringing in the revenue, but it's still a bit wild to put a core bit of the product in the hands of the only other major competitor in the smartphone OS space!
Seems like there is a moat after all.
The moat is talent, culture, and compute. Apple doesn't have any of these 3 for SOTA AI.
> The writing was on the wall the moment Apple stopped trying to buy their way into the server-side training game like what three years ago?
It goes back much further than that - up until 2016, Apple wouldn't let its ML researchers add author names to published research papers. You can't attract world-class talent in research with a culture built around paranoid secrecy.
It’s also a bet that the capex cost for training future models will be much lower than it is today. Why invest in it today if they already have the moat and dominant edge platform (with a loyal customer base upgrading hardware on 2-3 year cycles) for deploying whatever future commoditized training or inference workloads emerge by the time this Google deal expires?
10 years worth of cash? So all these Chinese labs that came out and did it for less than $1 billion must have 3 heads per developer, right?
It also lets them keep a lot of the legal issues regarding LLM development at arms length while still benefiting from them.
Could you elaborate a bit on why you've judged it as privacy theatre? I'm skeptical but uninformed, and I believe Mullvad are taking a similar approach.
Personally also think it's very smart move - Google has TPUs and will do it more efficiently than anyone else.
It also lets Apple stand by while the dust settles on who will out innovate in the AI war - they could easily enter the game on a big way much later on.
Seems like the LLM landscape is still evolving, and training your own model provides no technical benefit as you can simply buy/lease one, without the overhead of additional eng staffing/datacenter build-out.
I can see a future where LLM research stalls and stagnates, at which point the ROI on building/maintaining their own commodity LLM might become tolerable. Apple has had Siri as a product/feature and they've proven for the better part of a decade that voice assistants are not something they're willing to build a proficiency in. My wife still has an apple iPhone for at least a decade now, and I've heard her use Siri perhaps twice in that time.
> Seems like they are pivoting to becoming the premium "last mile" delivery network for someone else's intelligence.
They have always been a premium "last mile" delivery network for someone else's intelligence, except that "intelligence" was always IP until now. They have always polished existing (i.e., not theirs) ideas and made them bulletproof and accessible to the masses. Seems like they intend to just do more of the same for AI "intelligence". And good for them, as it is their specialty and it works.
Agreed, especially since this is a competitive space with multiple players, with a high price of admission, and where your model is outdated in a year, so its not even capex as much as recurring expenditure. Far better to let someone else do all the hard work, and wait and see where things go. Maybe someday this'll be a core competency you want in-house, but when that day comes you can make that switch, just like with apple silicon.
> To me, this deal is about the bill of materials for intelligence. Apple admitted that the cost of training SOTA models is a capex heavy-lift they don't want to own. Seems like they are pivoting to becoming the premium "last mile" delivery network for someone else's intelligence. Am I missing the elephant in the room?
Probably not missing the elephant. They certainly have the money to invest and they do like vertical integration but putting massive investment in bubble that can pop or flatline at any point seems pointless if they can just pay to use current best and in future they can just switch to something cheaper or buy some of the smaller AI companies that survive the purge.
Given how much AI capable their hardware is they might just move most of it locally too
The trouble is this seems to me like a short term fix, longer term, once the models are much better, Google can just lock out apple and take everything for themselves and leave Apple nowhere and even further behind.
this also addresses something else ...
apple to some users "are you leaving for android because of their ai assistant? don’t leave we are bringing it to iphone"
> without burning 10 years of cash flow.
> bill of materials for intelligence
There is no intelligence
> without burning 10 years of cash flow.
Don't they have the highest market cap of any company in existence?
>Apple has the best edge inference silicon in the world (neural engine),
Can you cite this claim? The Qualcomm Hexagon NPU seems to be superior in the benchmarks I've seen.
calling neural engine the best is pretty silly. the best perhaps of what is uniformly a failed class of ip blocks - mobile inference NPU hardware. edge inference on apple is dominated by cpus and metal, which don't use their NPU.
best inference silicon in the world generally or specialized to smaller models/edge?
> without burning 10 years of cash flow.
Wasn't Apple sitting on a pile of cash and having no good ideas what to spend it on?
> I'm oversimplifying but this effectively turns the iPhone into a dumb terminal for Google's brain, wrapped in Apple's privacy theater.
Setting aside the obligatory HN dig at the end, LLMs are now commodities and the least important component of the intelligence system Apple is building. The hidden-in-plain-sight thing Apple is doing is exposing all app data as context and all app capabilities as skills. (See App Intents, Core Spotlight, Siri Shortcuts, etc.)
Anyone with an understanding of Apple's rabid aversion to being bound by a single supplier understands that they've tested this integration with all foundation models, that they can swap Google out for another vendor at any time, and that they have a long-term plan to eliminate this dependency as well.
> Apple admitted that the cost of training SOTA models is a capex heavy-lift they don't want to own.
I'd be interested in a citation for this (Apple introduced two multilingual, multimodal foundation language models in 2025), but in any case anything you hear from Apple publicly is what they want you to think for the next few quarters, vs. an indicator of what their actual 5-, 10-, and 20-year plans are.