Comment by Fiveplus

My guess is that this is bigger lock-in than it might seem on paper.

Google and Apple together will posttrain Gemini to Apple's specification. Google has the know-how as well as infra and will happily do this (for free ish) to continue the mutually beneficial relationship - as well as lock out competitors that asked for more money (Anthropic)

Once this goes live, provided Siri improves meaningfully, it is quite an expensive experiment to then switch to a different provider.

For any single user, the switching costs to a different LLM are next to nothing. But at Apple's scale they need to be extremely careful and confident that the switch is an actual improvement

> 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.

          LLMs are now commodities and the least important component of the intelligence system Apple is building

That's not an "obligatory HN dig" though, you're in-media-res watching X escape removal from the App Store and Play Store. Concepts like privacy, legality and high-quality software are all theater. We have no altruists defending these principles for us at Apple or Google.

Apple won't switch Google out as a provider for the same reason Google is your default search provider. They don't give a shit about how many advertisements you're shown. You are actually detached from 2026 software trends if you think Apple is going to give users significant backend choices. They're perfectly fine selling your attention to the highest bidder.

Apple 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.

Reportedly, Meta is paying top AI talent up to $300M for a 4 year contract. As much as I'm in favor of paying engineers well, I don't think salaries like this (unless they are across the board for the company, which they are of course not) are healthy for the company long term (cf. Anthony Levandowski, who got money thrown after him by Google, only to rip them off).

So I'm glad Apple is not trying to get too much into a bidding war. As for how well orgs are run, Meta has its issues as well (cf the fiasco with its eponymous product), while Google steadily seems to erode its core products.

No, I doesn't beat Gemini in any benchmarks. It beats Gemma, which isn't a SoTA even among open models of that size. That would be Nemotron 3 or GPT-OSS 20B.

No, 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.

my prediction is that they might switch once AI craze will simmer down to some more reasonable level

100%. 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?

> You can't attract world-class talent in research with a culture built around paranoid secrecy.

Would giving more money/shares help?

I 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.

It 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.

is it that surprising? they're a hardware company after all.

> 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.

> 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.

Apple uses user data to target ads

That 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.

Well DRAM prices aren't going down soon so I see this as quite the push away from local inference.

That 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.

Rumor 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.

We 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)

A 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.

Mullvad 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.

They 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.

Because 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

The 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.

That would be more expensive in the long run and Apple is all about long game

absolutely, right now they can avoid any risk but get benefits as they recollect themselves

Of 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.

Of 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.

You don't need to join every fight you see, even if you would do well.

I believe both Nvidia and Google have higher market caps

They 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.

Not 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.

That doesn't make lighting it on fire a great option.

Perhaps 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

The 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.