In the article they clearly mentioned that Gemini model will be used for the Foundation Model running on device or their own Server. They are not sending Siri request to Google servers.
There's no reason to think that Apple would have any more skill at making a frontier AI model as they do at making airplanes or growing soybeans. Not much overlap between consumer electronics design and expertise, data, training, and datacenters needed for AI.
See ML research papers from Apple.
Their researchers prefered small models over LLM. So they thought researchers' effort would make up the lack of compute.
Then the scale law hit them hard.
For who? Regular people are quite famously not clamouring for more AI features in software. A Siri that is not so stupendously dumb would be nice, but I doubt it would even be a consideration for the vast majority of people choosing a phone.
Web search is a core part of browsing and Apple is Google's biggest competitor in browsers. Google is paying Apple about 25x for integrating Google Search in Safari as Apple will be paying Google to integrate Google's LLMs into Siri. If you think depending on your competitor is a problem, you should really look into web search where all the real money is today.
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.
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.
> 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!
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.
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.
> 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.
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?
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.
And 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.
> 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.
> 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.
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.
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.
Phones will get upgrades, but then so will servers. The local models will always be behind the state of the art running on big iron. You can’t expect to stand still and keep up with the Red Queen.
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?
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"?
> 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
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.
Honestly, 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?
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.
> 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"?
If nothing else, this was likely driven by Google being the most stable of the AI labs. Gemini is objectively a good model (whether it's #1 or #5 in ranking aside) so Apple can confidently deliver a good (enough) product. Also for Apple, they know their provider has ridiculously deep pockets, a good understanding and infrastructure in place for large enterprises, and a fairly diversified revenue stream.
Going with Anthropic or OpenAI, despite on the surface having that clean Apple smell and feel, carries a lot of risk Apple's part. Both companies are far underwater, liable to take risks, and liable to drown if they even fall a bit behind.
I was more thinking about this being driven by the fact that Google pays Apple $20B a year for being the pre-selected search engine and this way, Apple still gets $19B and a free AI engine on top.
> Gemini is objectively a good model (whether it's #1 or #5 in ranking aside) so Apple can confidently deliver a good (enough) product
Definitely. At at this point, Apple just needs to get anything out the door. It was nearly two years ago they sold a phone with features that still haven't shipped and the promise that Apple Intelligence would come in two months.
I still think Apple should, at least to Apple One customers, offer small, private models, trained on your personal imessage, image and video archives in icloud. With easy-to-use, granular controls for content inclusion/exclusion.
Will make it much easier to find those missing pictures from a few years ago...
Aren't both of those companies also both at the whims of Microsoft for the actual compute hardware? I'm not good at keeping track of who has actual hardware vs. who runs in one of the big clouds
I agree with your point about Google being more stable company then the rest so the decision probably makes sense. But there was a study done by multiple news companies in Czechia by asking about news topics and Gemini was consistently the worst in citations and straight up being incorrect (76% of its answers had "issues", I don't have exact issues specification).
>If nothing else, this was likely driven by Google being the most stable of the AI labs.
I dont think the model is that much different if they thought Siri was half decent enough for so long.
Judging from the past 10 years, I would say this is more likely driven by part of a bigger package deal with Google Search Placement and Google Cloud Services. When everything else being roughly equal.
Instead of raising price again Paying Apple even more per user, How about we pay the less but throw in Gemini with it?
Apple has been very good, if not the best at picking one side and allowing the others to fight for its contract. They dont want Microsoft to win the AI race, at the same time Apple is increasing the use of Azure just in case. Basically playing the game of leverage at its best. In hindsight probably too well into it they forgot what the real purpose of all these leverage are for, not cost savings but ultimately better quality product.
True. Also Gemini is the boring model, heavily sanitised for corporate applications. At least it admits this if you press it. It fits Apple here very well.
Personally I wouldn't use it, it still belongs to an advertiser specialised on extracting user information. Not that I expect that other AI companies value privacy much higher. But clean smell also means bland smell.
Counterpoint: iOS’s biggest competitor is Android. They are now effectively funding their competition on a core product interface. I see this as strategically devastating.
It has nothing to do with how good Gemini is relative to others. Apple is picking Gemini because they don’t want AI to be the selling point for Android phones. Apple execs do not care about innovations. They only care about keeping their monopoly intact.
With Anthropic or OpenAI they would have had to pay for it, but Google already pay them $20bn+ per year to be the default search engine - so they just knock $1bn off Google's bill for Gemini
The original iPhone came pre-loaded with Google search, Maps, and Youtube. Jobs competed with Google but he also knew Google had best-in-class products too.
1. The first issue is that there is significant momentum in calling Siri bad, so even if Apple released a higher quality version it will still be labelled bad. It can enhance the user's life and make their device easier to use, but the overall press will be cherrypicked examples where it did something silly.
2. Basing Siri on Google's Gemini can help to alleviate some of that bad press, since a non-zero share of that doomer commentary comes from brand-loyalists and astroturfing.
3. The final issue is that on-device Siri will never perform like server-based ChatGPT. So in a way it's already going to disappoint some users who don't realise that running something on mobile device hardware is going to have compromises which aren't present on a server farm.
To help illustrate that point: We even have the likes of John Gruber making stony-faced comparisons between Apple's on-device image generator toy (one that produces about an image per second) versus OpenAI's server farm-based image generator which makes a single image in about 1-2 minutes. So if a long-running tech blogger can't find charity in those technical limitations, I don't expect users to.
Siri is objectively bad though. It isn't some vendetta. I am disabled and there are at least 50 different things that I'd love siri to do that should be dead simple, yet it cannot. My favorite one was when I suffered a small but not serious fall, decided to test whether siri could be alerted to call 9-11 while being less than 6 feet away from me, absolutely could not understand let alone execute my request. It's a lot of stuff like this. Its core functionality often just does not work.
> The final issue is that on-device Siri will never perform like server-based ChatGPT. So in a way it's already going to disappoint some users who don't realise that running something on mobile device hardware is going to have compromises which aren't present on a server farm.
For many years, siri requests were sent to an external server. It still sucked.
There are many people who lament that Siri sucks but would be happy to admit if/when this changes. Even if it goes from super shitty (as evidenced by randomly calling people I have never called/texted when I ask it to call my wife) to "pretty good" I will be the first to admit that it is better. I look forward to it getting better and being able to use it more often.
This seems like a pretty significant anti-trust issue. One of the two mobile OS makers is using a product from the other for its AI assistance. And that means that basically all mobile devices will be using the same AI technology.
I don't expect the current US government to do anything about it though.
What antitrust rule do you think would be breached?
I admit I don't see the issue here. Companies are free to select their service providers, and free to dominate a market (as long as they don't abuse such dominant position).
This is one of those announcements that actually just excites me as a consumer. We give our children HomePods as their first device when they turn 8 years old (Apple Watch at 10 years, laptop at 12) and in the 6 years I have been buying them, they have not improved one ounce. My kids would like to listen to podcasts, get information, etc. All stuff that a voice conversation with Chatgpt or Gemini can do today, but Siri isn't just useless-- it's actually quite frustrating!
This is actually a smart and common sense move by Apple.
The non-hardware AI industry is currently in an R&D race to establish and maintain marketshare, but with Apple's existing iPhone, iPad and Mac ecosystem they already have a market share they control so they can wait until the AI market stabilizes before investing heavily in their own solutions.
For now, Apple can partner with solid AI providers to provide AI services and benefits to their customers in the short term and then later on they can acquire established AI companies to jumpstart their own AI platform once AI technology reaches more long term consistency and standardization.
This is good for Siri, in many ways. But I was kind of hoping we would see a time soon when phone hardware became good enough to do nearly 100% of the Siri-level tasks locally rather than needing Internet access.
Google release hints at this being more than just Siri:
> Apple and Google have entered into a multi-year collaboration under which the next generation of Apple Foundation Models will be based on Google's Gemini models and cloud technology. These models will help power future Apple Intelligence features, including a more personalized Siri coming this year.
Does anyone know what Apple's "Private Cloud Compute" servers actually are? I recall murmurings about racked M chips or some custom datacenter-only variant?
I'm really curious how Apple is bridging the gap between consumer silicon and the datacenter scale stack they must have to run a customized Gemini model for millions of users.
RDMA over Thunderbolt is cool for small lab clusters but they must be using something else in the datacenter, right?
Can someone explain to me how this was allowed to happen? Wasn't Siri supposed to be the leading AI agent not ten years ago? How was there such a large disconnect at Apple between what Siri could do and what "real" AI was soon to be capable of?
Was this just a massive oversight at Apple? Were there not AI researchers at Apple sounding the alarm that they were way off with their technology and its capabilities? Wouldn't there be talk within the industry that this form of AI assistant would soon be looked at as useless?
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[ 2.8 ms ] story [ 92.9 ms ] threadBy the way, have any of you ever tried to delete and disabled Siri’s iCloud backup? You can’t do it.
That will be their contract writing AI.
They aren't.
"liquid ass" is how most of my friends describe it
For who? Regular people are quite famously not clamouring for more AI features in software. A Siri that is not so stupendously dumb would be nice, but I doubt it would even be a consideration for the vast majority of people choosing a phone.
Apple weighs using Anthropic or OpenAI to power Siri
https://news.ycombinator.com/item?id=44426643
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.
Wasn't Apple sitting on a pile of cash and having no good ideas what to spend it on?
Don't they have the highest market cap of any company in existence?
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!
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
[1]: https://allenai.org/blog/olmo3
[2]: https://huggingface.co/amd/AMD-OLMo
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.
The moat is talent, culture, and compute. Apple doesn't have any of these 3 for SOTA AI.
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 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.
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.
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.
Would giving more money/shares help?
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.
There is no intelligence
apple to some users "are you leaving for android because of their ai assistant? don’t leave we are bringing it to iphone"
So what does it take? How many actual commitments to privacy does Apple have to make before the HN crowd stops crowing about "theater"?
https://en.wikipedia.org/wiki/CLOUD_Act
https://machinelearning.apple.com/research/apple-intelligenc...
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
Can you cite this claim? The Qualcomm Hexagon NPU seems to be superior in the benchmarks I've seen.
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.
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"?
AAPL has approximately $35 billion of cash equivalents on hand. What other use may they have for this trove? Buy back more stocks?
Everyone using Siri is going to have their personality data emulated and simulated as a ”digital twin” in some computing hell-hole.
I feel like people probably said this when Google became the default search engine for everyone...
Going with Anthropic or OpenAI, despite on the surface having that clean Apple smell and feel, carries a lot of risk Apple's part. Both companies are far underwater, liable to take risks, and liable to drown if they even fall a bit behind.
Anthropic doesn't have a single data centre, they rent from AWS/Microsoft/Google.
Definitely. At at this point, Apple just needs to get anything out the door. It was nearly two years ago they sold a phone with features that still haven't shipped and the promise that Apple Intelligence would come in two months.
Will make it much easier to find those missing pictures from a few years ago...
Just under 16 months since the release of iOS 18. The phones they would have sold this with shipped alongside 18.
Also, the personalized Siri was indicated it would not be available until later and was expected in the spring release (March 2025).
I dont think the model is that much different if they thought Siri was half decent enough for so long.
Judging from the past 10 years, I would say this is more likely driven by part of a bigger package deal with Google Search Placement and Google Cloud Services. When everything else being roughly equal.
Instead of raising price again Paying Apple even more per user, How about we pay the less but throw in Gemini with it?
Apple has been very good, if not the best at picking one side and allowing the others to fight for its contract. They dont want Microsoft to win the AI race, at the same time Apple is increasing the use of Azure just in case. Basically playing the game of leverage at its best. In hindsight probably too well into it they forgot what the real purpose of all these leverage are for, not cost savings but ultimately better quality product.
Personally I wouldn't use it, it still belongs to an advertiser specialised on extracting user information. Not that I expect that other AI companies value privacy much higher. But clean smell also means bland smell.
1. The first issue is that there is significant momentum in calling Siri bad, so even if Apple released a higher quality version it will still be labelled bad. It can enhance the user's life and make their device easier to use, but the overall press will be cherrypicked examples where it did something silly.
2. Basing Siri on Google's Gemini can help to alleviate some of that bad press, since a non-zero share of that doomer commentary comes from brand-loyalists and astroturfing.
3. The final issue is that on-device Siri will never perform like server-based ChatGPT. So in a way it's already going to disappoint some users who don't realise that running something on mobile device hardware is going to have compromises which aren't present on a server farm. To help illustrate that point: We even have the likes of John Gruber making stony-faced comparisons between Apple's on-device image generator toy (one that produces about an image per second) versus OpenAI's server farm-based image generator which makes a single image in about 1-2 minutes. So if a long-running tech blogger can't find charity in those technical limitations, I don't expect users to.
> The final issue is that on-device Siri will never perform like server-based ChatGPT. So in a way it's already going to disappoint some users who don't realise that running something on mobile device hardware is going to have compromises which aren't present on a server farm.
For many years, siri requests were sent to an external server. It still sucked.
I don't expect the current US government to do anything about it though.
I admit I don't see the issue here. Companies are free to select their service providers, and free to dominate a market (as long as they don't abuse such dominant position).
I can't wait for gemini to lecture me why I should throw away my android
The non-hardware AI industry is currently in an R&D race to establish and maintain marketshare, but with Apple's existing iPhone, iPad and Mac ecosystem they already have a market share they control so they can wait until the AI market stabilizes before investing heavily in their own solutions.
For now, Apple can partner with solid AI providers to provide AI services and benefits to their customers in the short term and then later on they can acquire established AI companies to jumpstart their own AI platform once AI technology reaches more long term consistency and standardization.
> Apple and Google have entered into a multi-year collaboration under which the next generation of Apple Foundation Models will be based on Google's Gemini models and cloud technology. These models will help power future Apple Intelligence features, including a more personalized Siri coming this year.
... https://blog.google/company-news/inside-google/company-annou...
I'm really curious how Apple is bridging the gap between consumer silicon and the datacenter scale stack they must have to run a customized Gemini model for millions of users.
RDMA over Thunderbolt is cool for small lab clusters but they must be using something else in the datacenter, right?
Was this just a massive oversight at Apple? Were there not AI researchers at Apple sounding the alarm that they were way off with their technology and its capabilities? Wouldn't there be talk within the industry that this form of AI assistant would soon be looked at as useless?
Am I missing something?