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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? Apple has the best e
by Fiveplus 9mo ago
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?
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.
- haritha-j 9mo agoAgreed, 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since this is a competitive space with multiple players, with a high price of admission, and where your model is outdated in a year, so its not even capex as much as recurring expenditure.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.
- ysnp 9mo agoCould 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.
- tempodox 9mo agoThe gov’t can force them to reveal any user’s data and slap them with a gag order so no one will ever know this happened.
- MontyCarloHall 9mo agoAll user data is E2E encrypted, so the government literally cannot force this. This has been the source of numerous disputes [0, 1] that either result in the device itself being cracked [0] (due to weak passwords or vulnerabilities in device-level protection) or governments attempting to ban E2E encryption altogether [1]. [0] https://en.wikipedia.org/wiki/Apple%E2%80%93FBI_encryption_dispute https://en.wikipedia.org/wiki/Apple%E2%80%93FBI_encryption_d... [1] https://en.wikipedia.org/wiki/Crypto_Wars https://en.wikipedia.org/wiki/Crypto_Wars
- greentea23 9mo agoWhat you cited is for data on a device that was turned off. Not daily internet connected usage. No one is saying you have no protection at all with Apple, it is just very limited compared to what it should be by modern security best practices, and much worse than what can be achieved on android and linux.
- nozzlegear 9mo ago> much worse than what can be achieved on android and linux. * Certain types of Android
- mmh0000 9mo agoMaybe E2E, but the data eventually has to be decrypted to read it. Then you learn that every modern CPU has a built-in backdoor, a dedicated processor core, running a closed-source operating system, with direct access to the entire system RAM, and network access. [a][b][c][d]. You can not trust any modern hardware. https://en.wikipedia.org/wiki/Intel_Management_Engine https://en.wikipedia.org/wiki/Intel_Management_Engine https://en.wikipedia.org/wiki/AMD_Platform_Security_Processor https://en.wikipedia.org/wiki/AMD_Platform_Security_Processo... https://en.wikipedia.org/wiki/ARM_architecture_family#Security_extensions https://en.wikipedia.org/wiki/ARM_architecture_family#Securi... https://en.wikipedia.org/wiki/Security_and_privacy_of_iOS https://en.wikipedia.org/wiki/Security_and_privacy_of_iOS
- drnick1 9mo agoBecause Apple makes privacy claims all the time, but all their software is closed source and it is very hard or impossible to verify any of their claims. Even if messages sent between iPhones are E2EE encrypted for example, the client apps and the operating system may be backdoored (and likely are). https://en.wikipedia.org/wiki/PRISM https://en.wikipedia.org/wiki/PRISM
- greentea23 9mo agoMullvad is nothing like Apple. For apple devices: - need real email and real phone number to even boot the device - cannot disable telemetry - app store apps only, even though many key privacy preserving apps are not available - /etc/hosts are not your own, DNS control in general is extremely weak - VPN apps on idevices have artificial holes - can't change push notification provider - can only use webkit for browsers, which lacks many important privacy preserving capabilities - need to use an app you don't trust but want to sandbox it from your real information? Too bad, no way to do so. - the source code is closed so Apple can claim X but do Y, you have no proof that you are secure or private - without control of your OS you are subject to Apple complying with the government and pushing updates to serve them not you, which they are happy to do to make a buck Mullvad requires nothing but an envelope with cash in it and a hash code and stores nothing. Apple owns you.
- MrDarcy 9mo agoThis comment confuses privacy with anonymity.
- whilenot-dev 9mo agoAnonymity is an inherent measure to preserve ones individual privacy. What value did you intent to add with your remark?
- greentea23 9mo agoNot for all points. And not being anonymous means your identity is not private...
- asadotzler 9mo agoAnonymity is a critical aspect of privacy. If you cannot prevent your name being associated with your data, you do not have real privacy.
- Melatonic 9mo agoAgreed on most points but you can setup a pretty solid device wide DNS provider using configuration profiles. Similar to how iOS can be enrolled in work corporate MDM - but under your control. Works great for me with NextDNS. Orion browser - while also based on WebKit - is also awesome and has great built in Adblock and supposedly privacy respecting ideals.
- natch 9mo agoThey transitioned from “nobody can read your data, not even Apple” to “Apple cannot read your data.” Think about what that change means. And even that is not always true. They also were deceptive about iCloud encryption where they claimed that nobody but you can read your iCloud data. But then it came out after all their fanfare that if you do iCloud backups Apple CAN read your data. But they aren’t in a hurry to retract the lie they promoted. Also if someone in another country messages you, if that country’s laws require that Apple provide the name, email, phone number, and content of the local users, guess what. Since they messaged you, now not only their name and information, but also your name and private information and message content is shared with that country’s government as well. By Apple. Do they tell you? No. Even if your own country respects privacy. Does Apple have a help article explaining this? No.
- dpoloncsak 9mo ago>Also if someone in another country messages you, if that country’s laws require that Apple provide the name I don't mean to sound like an Apple fanboy, but is this true just for SMS or iMessage as well? It's my understanding that for SMS, Apple is at the mercy of governments and service providers, while iMessage gives them some wiggle room. Ancedotal, but when my messages were subpoenaed, it was only the SMS messages. US citizen fwiw
- threatofrain 9mo agoIf you want to turn on full end-to-end encryption you can, if you want to share your pubkey so that people can't fake your identity on iMessage you can, and there's still a higher tier of security than that presumably for journalists and important people. It's something a smart niece or nephew could handle in terms of managing risk, but the implications could mean getting locked out of your device which you might've been using as the doorway to everything, and Apple cannot help you.
- classicsc 9mo ago[dead]
- richwater 9mo agoYou people will never be happy until the only messaging that exists is in a dusty basement and Richard Stallman is sleeping on a dirty futon.
- drob518 9mo agoYea, 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.
- Fiveplus 9mo ago100%. It feels like Apple is perfectly happy letting the AI labs fight a race to the bottom on pricing while they keep the high-margin user relationship. I'm curious if this officially turns the foundation model providers into the new "dumb pipes" of the tech stack?
- drob518 9mo agoIt’ll be interesting to see how it plays out. The question is, what’s the moat? If all they have is scaling to drive better model performance, then the winner is just whoever has the lowest cost of capital.
- raw_anon_1111 9mo agoThis isn’t a mystery - it’s Google
- drob518 9mo agoYea, I think that’s probably right, unless something unexpected changes the game.
- ivell 9mo agoGoogle seems to thrive on commodity products. Search, EMail, etc. It is their strength to take commodity products and scale it well.
- whywhywhywhy 9mo agoAs if they really have a choice though. Competing would be a billion dollar Apple Maps scenario.
- whereismyacc 9mo agobest inference silicon in the world generally or specialized to smaller models/edge?
- properbrew 9mo agoNot even an Apple fan, but from what I've been testing with for my dev use case (only up to 14b) it absolutely rocks for general models.
- whereismyacc 9mo agoThat I can absolutely believe but the big competition is in enterprise gpt-5-size models.
- fooblaster 9mo agocalling 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.
- scotty79 9mo ago> 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?
- internetter 9mo agoPerhaps spending it on inference that will be obsoleted in 6 months by the next model is not a good idea either. Edit: especially given that Apple doesn’t do b2b so all the spend would be just to make consumer products
- greentea23 9mo agoApple of course does an emormous amount of b2b.
- ceejayoz 9mo agoThat doesn't make lighting it on fire a great option.
- turtlesdown11 9mo agoThe cash pile is gone, they have been active in share repurchase. They still generate about ~$100 billion in free cash per year, that is plowed into the buybacks. They could spend more cash than every other industry competitor. It's ludicrous to say that they would have to burn 10 years of cash flow on trivial (relative) investment in model development and training. That statement reflects a poor understanding of Apple's cash flow.
- _joel 9mo ago> without burning 10 years of cash flow. Don't they have the highest market cap of any company in existence?
- fumblebee 9mo agoI believe both Nvidia and Google have higher market caps
- jayd16 9mo agoYou don't need to join every fight you see, even if you would do well.
- turtlesdown11 9mo agoThey have the largest free cash flow (over $100 billion a year). Meta and Amazon have less than half that a year, and Microsoft/Nvidia are between $60b-70b per year. The statement reflects a poor understanding of their financials.
- deleted 9mo ago[deleted]
- concinds 9mo agoAn 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.
- blitzar 9mo agoApple might have gotten very lucky here ... the money might be in finding uses, and selling physical products rather than burning piles of cash training models that are SOTA for 5 minutes before being yet another model in a crowded field. My money is still on Apple and Google to be the winners from LLMs.
- lamontcg 9mo agoAnd when the cost of training LLMs starts to come down to under $1B/yr, Apple can jump on board, having saved >$100B in not trying to chase after everyone else to try to get there first.
- Melatonic 9mo agoApple has also never been big on the server side equation of both software and hardware - don't they already outsource most of their cloud stack to Google via GCP ? I can see them eventually training their own models (especially smaller and more targeted / niche ones) but at their scale they can probably negotiate a pretty damn good deal renting Google TPUs and expertise.
- ghaff 9mo agoXserve was always kind of a loss. Wrote a piece about it a number of years back. It became pretty much a commodity business--which isn't Apple.
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- dabockster 9mo agoIt also lets them keep a lot of the legal issues regarding LLM development at arms length while still benefiting from them.
- ceejayoz 9mo ago> 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!
- apercu 9mo agoI dunno, my take is that Apple isn’t outsourcing intelligence rather it’s outsourcing the most expensive, least defensible layer. Down the road Apple has an advantage here in a super large training data set that includes messages, mail, photos, calendar, health, app usage, location, purchases, voice, biometrics, and you behaviour over YEARS. Let's check back in 5 years and see if Apple is still using Gemini or if Apple distills, trains and specializes until they have completed building a model-agnostic intelligence substrate.
- maxloh 9mo agoIs 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 https://allenai.org/blog/molmo2 [1]: https://allenai.org/blog/olmo3 https://allenai.org/blog/olmo3 [2]: https://huggingface.co/amd/AMD-OLMo https://huggingface.co/amd/AMD-OLMo
- turtlesdown11 9mo agoNo, of course the training costs aren't that high. Apple's ten years of future free cash flow is greater than a trillion dollars (they are above $100b per year). Obviously, the training costs are a trivial amount compared to that figure.
- bombcar 9mo agoI have no idea what AI involves, but "training" sounds like a one-and-done - but how is the result "stored"? If you have trained up a Gemini, can you "clone" it and if so, what is needed? I was under the impression that all these GPUs and such were needed to run the AI, not only ingest the data.
- esafak 9mo agoYes, serving requires infra, too. But you can use infra optimized for serving; nvidia GPUs are not the only game in town.
- tefkah 9mo agoTheoretically it would be much less expensive to just continue to run the existing models, but ofc none of the current leaders are going to stop training new ones any time soon.
- ChildOfChaos 9mo agoThe 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.
- raw_anon_1111 9mo agoOf course there is going to be an abstraction layer - this is like Software Engineering 101. Google really could care less about Android being good. It is a client for Google search and Google services - just like the iPhone is a client for Google search and apps.
- CharlesW 9mo ago> 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.
- hadlock 9mo ago> what their actual 5-, 10-, and 20-year plans are Seems like they are waiting for the "slope of enlightenment" on the gartner hype curve to flatten out. Given you can just lease or buy a SOTA model from leading vendors there's no advantage to training your own right now. My guess is that the LLM/AI landscape will look entirely different by 2030 and any 5 year plan won't be in the same zip code, let alone playing field. Leasing an LLM from Google with a support contract seems like a pretty smart short term play as things continue to evolve over the next 2-3 years.
- IgorPartola 9mo agoThis is the key. The real issue is that you don’t need superhuman intelligence in a phone AI assistant. You don’t need it most of the time in fact. Current SOTA models do a decent job of approximating college grad level human intelligence let’s say 85% of the time which is helpful and cool but clearly could be better. But the pace at which the models are getting smart is accelerating AND they are getting more energy efficient and memory efficient. So if something like DeepSeek is roughly 2 years behind SOTA models from Google and others who have SOTA models then in 2030 you can expect 2028 level performance out open models. There will come a time when a model capable of college grad level intelligence 99.999% of the time will be able to run on a $300 device. If you are Apple you do not need to lead the charge on a SOTA model, you can just wait until one is available for much cheaper. Your product is the devices and services consumers buy. If you are OpenAI you have no other products. You must become THE AI to have in an industry that will in the next few years become dominated by open models that are good enough or to close up shop or come up with another product that has more of a moat.
- aurareturn 9mo agoSeems 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.
- elzbardico 9mo agoIt is more like Apple have no need to spend billions on training with questionable ROI when it can just rent from one of the commodity foundation model labs.
- nosman 9mo agoI don't know why people automatically jump to Apple's defense on this.... They absolutely did spend a lot of money and hired people to try this. They 100% do NOT have the open and bottom-up culture needed to pull off large scale AI and software projects like this. Source: I worked there
- elzbardico 9mo agoWell, they stopped. Culture is overrated. Money talks. They did things far more complicated from an engineering perspective. I am far more impressed by what they accomplished along TSMC with Apple Silicon than by what AI labs do.
- tech-historian 9mo agoIs Apple silicon really that impressive compared to LLMs? Take a step back. CPUs have been getting faster and more efficient for decades. Google invented the transformer architecture, the backbone of modern LLMs.
- Terretta 9mo ago> Google invented... "Google" did? Or humans who worked there and one who didn't? https://www.wired.com/story/eight-google-employees-invented-modern-ai-transformers-paper/ https://www.wired.com/story/eight-google-employees-invented-... In any case, see the section on Jakob Uszkoreit, for example, or Noam Shazeer. And then… > In the higher echelons of Google, however, the work was seen as just another interesting AI project. I asked several of the transformers folks whether their bosses ever summoned them for updates on the project. Not so much. But “we understood that this was potentially quite a big deal,” says Uszkoreit. Worth noting the value of “bosses” who leave people alone to try nutty things in a place where research has patronage. Places like universities, Xerox, or Apple and Google deserve credit for providing the petri dish.
- segmondy 9mo ago10 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?
- andreyf 9mo agoRumor has it that they weren't trained "from scratch" the was US would, i.e. Chinese labs benefitted from government "procured" IP (the US $B models) in order to train their $M models. Also understand there to be real innovation in the many-MoE architecture on top of that. Would love to hear a more technical understanding from someone who does more than repeat rumors, though.
- 4fterd4rk 9mo agoA lot of HN commentators are high on their own supply with regard to the AI bubble... when you realize that this stuff isn't actually that expensive the whole thing begins to quickly unravel.
- usef- 9mo agoWe don't really know how much it cost them. Plenty of reasons to doubt the numbers passed around and what it wasn't counting. (And even if you do believe it, they also aren't licensing the IP they're training on, unlike american firms who are now paying quite a lot for it)
- hmokiguess 9mo agoI 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?
- pests 9mo ago> is entirely self-contained in the weights produced at the end, right? Yes, and the knowledge gained along the way. For example, the new TPUv4 that Google uses requires rack and DC aware technologies (like optical switching fabric) for them to even work at all. The weights are important, and there is open weights, but only Google and the like are getting the experience and SOTA tech needed to operate cheaply at scale.
- bayarearefugee 9mo ago> and the only opportunity window they are giving away here is "first to market" right? A lot of the hype in LLM economics is driven by speculation that eventually training these LLMs is going to lead to AGI and the first to get there will reap huge benefits. So if you believe that, being "first to market" is a pretty big deal. But in the real world there's no reason to believe LLMs lead to AGI, and given the fairly lock-step nature of the competition, there's also not really a reason to believe that even if LLMs did somehow lead to AGI that the same result wouldn't be achieved by everyone currently building "State of the Art" models at roughly the same time (like within days/months of each other). So... yeah, what Apple is doing is actually pretty smart, and I'm not particularly an Apple fan.
- hadlock 9mo agoSeems 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.
- mr_toad 9mo agoAnd if you wanted to build your own data center right now there’s only so much GPU and RAM to go around, and even all the power generation and cooling manufacturers are booked solid.
- stronglikedan 9mo ago> 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.
- overfeed 9mo ago> 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.
- sumedh 9mo ago> You can't attract world-class talent in research with a culture built around paranoid secrecy. Would giving more money/shares help?
- Melatonic 9mo agoPersonally 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.
- fuzzy_lumpkins 9mo agoabsolutely, right now they can avoid any risk but get benefits as they recollect themselves
- semiquaver 9mo ago> without burning 10 years of cash flow. Sorry to nitpick but Apple’s Free Cash Flow is 100B/yr. Training a model to power Siri would not cost more than a trillion dollars.
- manquer 9mo agoOf all the companies to survive a crash in AI unscathed, I would bet on Apple the most. They are only ones who do not have large debts off(or on) balance sheet or aggressive long term contracts with model providers and their product demand /cash flow is least dependent on the AI industry performance. They will still be affected by general economic downturn but not be impacted as deeply as AI charged companies in big tech.
- baxuz 9mo ago> bill of materials for intelligence There is no intelligence
- Sevii 9mo agoApple'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.
- kennywinker 9mo agoThat was their goal, but in the past couple years they seem to have given up on client-side-only ai. Once they let that go, it became next to impossible to claw back to client only… because as client side ai gets better so does server side, and people’s expectations scale up with server side. And everybody who this was a dealbreaker for left the room already.
- WorldMaker 9mo agoApple thinks they can get a best-of-both-worlds approach with Private Cloud Compute. They believe they can secure private servers specialized to specific client devices in a way that the cloud compute effort is still "client-side" from a trust standpoint, but still able to use extra server-side resources (under lock and key). I don't know how close to that ideal they've achieved, but especially given this announcement is partly baked on an arrangement with Google that they are allowed to run Gemini on-device and in Private Cloud Compute, without using Google's more direct Gemini services/cloud, I'm excited that they are trying and I'm interested in how this plays out.
- kennywinker 9mo agoGiven the snowden leaks, i think it’s naive to believe that any data that leaves your phone is NOT ingested by gov data collection. Maybe private in the sense that it isn’t funneled into your ad profile, but not private in the sense that nobody else can access it.
- WorldMaker 9mo agoI stated that I am not naive and am not entirely convinced by Apple's sales pitch that the Private Cloud Compute containers are encrypted with keys in a way that only your hardware device can read in such a way that the PCC is an extension of your device. I just think it is useful that Apple is trying something along those lines and wishful the guarantees work half as well as they claim they do, because that's a good goal to have in theory even when it fails in practice against dedicated threat actors. And yes, to be fair my personal day-to-day threat model currently is much more concerned with the evil advertising company known as Google than it is with government actors. Even if Apple's Private Cloud Compute only means "private from Google" that's still a win for me (and most of the information I was looking for when I saw this headline, because my first fear was that the advertising company Google was involved).
- chatmasta 9mo agoIt’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?
- hashta 9mo agothis 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"
- LeoPanthera 9mo agoGoogle 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"?
- AuthAuth 9mo agoApple uses user data to target ads
- LeoPanthera 9mo agoDo you have a source for this claim?
- AuthAuth 9mo agohttps://www.apple.com/au/legal/privacy/data/en/apple-advertising/ https://www.apple.com/au/legal/privacy/data/en/apple-adverti...
- LeoPanthera 9mo agoThis very page explains that they use "local, on-device processing" and a "random identifier not tied to your Apple Account." So while the letter of your claim is technically true, it's also very misleading.
- AuthAuth 9mo agoNo, its not misleading it says right there in the private policy. Apple isnt suddenly private just because they have enough data about you that they dont need to link to 3rd party data. They do exactly what 3rd party sites that are considered privacy invasive do. They serve you ads based on your private data like what you watch, what you read and what things you do on your device. It doesnt say that they only store all this information on device. Apple is only using a random identifier when its sharing information about your habits and personal data on its ad platform, that info btw is shared with 3rd parties. But dont worry that data suddenly becomes non personal because they used a random identifier.
- robotresearcher 9mo agoFor 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-intelligence-foundation-language-models https://machinelearning.apple.com/research/apple-intelligenc...
- gilgoomesh 9mo agoThat 3B model is a local model that eventually got built into macOS 26. Gemini 3 Pro is a frontier model (cloud). They're very different things.
- robotresearcher 9mo agoSure. And the same paper describes a ‘larger’ cloud-served model.
- PunchyHamster 9mo ago> 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
- kernal 9mo ago>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.
- goalieca 9mo agoApple 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.
- caycep 9mo agoHonestly, I'm relieved...it's not really in their DNA and not pivotal to their success; why pivot the company into a U turn into a market that's vague defined and potentially algorithmically limited?
- mschuster91 9mo ago> Am I missing the elephant in the room? Apple is flush with cash and other assets, they have always been. They most likely plan to ride out the AI boom with Google's models and buy up scraps for pennies on the dollar once the bubble pops and a bunch of the startups go bust. It wouldn't be the first time they went for full vertical integration.
- derefr 9mo ago> 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. Why does Apple need to build its own training cluster to train a frontier model, anyway? Why couldn't the deal we're reading about have been "Apple pays Google $200bn to lease exclusive-use timeslots on Google's AI training cluster"?
- m3kw9 9mo agoThat would be more expensive in the long run and Apple is all about long game
- SergeAx 9mo ago> without burning 10 years of cash flow AAPL has approximately $35 billion of cash equivalents on hand. What other use may they have for this trove? Buy back more stocks?
- moondev 9mo agohttps://github.com/search?q=org%3Aapple%20cuda&type=code https://github.com/search?q=org%3Aapple%20cuda&type=code
- sitzkrieg 9mo agothe year is 2026, the top advertising company is in bed with the walled garden device specialists and the decision is celebrated
- cluckindan 9mo ago>Am I missing the elephant in the room? Everyone using Siri is going to have their personality data emulated and simulated as a ”digital twin” in some computing hell-hole.
- jedimastert 9mo ago> I'm oversimplifying but this effectively turns the iPhone into a dumb terminal for Google's brain I feel like people probably said this when Google became the default search engine for everyone...