I don’t think we should use current prices as landmarks for large scale demand. That Studio’s current prices is inflated because of a (presumably) short term supply crunch, not because the average user is willing to pay $24k for a home AI inference device.
It assumes that RAM remains supply constrained and that none of the existing RAM contracts are cut short.
But Meta and xAI putting A TON of AI compute onto the market. OpenAI and Anthropic are raising the costs of inference (by reducing how much inference users get via subscriptions). And we haven’t seen Oracle / CoreWeave struggle to pay their debts yet, but they will be selling assets once they get close to that point.
If demand doesn't fall down or current manufacturers supply go up, somebody (presumably in China) will spin up fabs. Apple wanted to use blacklisted Chinese RAM already.
Ebay and Amazon are flooded with it. Especially if you are looking for anything prior to DDR5. DDR2 and DDR3 are especially flooded with weird brands you've never heard of before.
Unfortunately its not so cheap anymore as everyone ramped prices up of course.
Last year I could still get 32GB of DDR4 for under $60 from chinese brands.
If the increased demand is not short term, production capacity will eventually increase. In the meantime, the logistics disruptions and industrial material shortages and energy inflation will disappear as soon as the wars disrupting them stop, which should bring prices down.
If demand and prices keep rising without production capacity being built fast enough, there will likely eventually be a rush leading to overinvestment and price crashes, but there are too many other factors involved; state investment for security, international politics and trade relations, the possibility of an AI bubble burst, etc.
There are wars coming. The prices are not going down.
We are in a bubble which will be burst the moment the world starts retaliating against the US' 20+ year history of supporting genocide and committing war crimes unabated.
Unless the raw materials have an inherent limit on mining/production due to the amount present on the planet, why should or would companies not ramp up to eventually meet demand?
Edit: Okay, this doesn’t mean that that’s actually possible in the short-term, so I think you’re right. But that means as the silver lining, in the medium term horizon there’ll be enough supply again? :’)
For existing producers expanding capacity would be a risky move. But it's the perfect time for any newcomers to enter the market. Low yields and worse product don't matter as much right now, and by the time the market cools down you have everything dialed in and can compete on even ground
> it's the perfect time for any newcomers to enter the market
This is a good hypothesis. Curious if anyone has data on the failure rates of new entrants in semiconductors based on how frothy it was on founding.
On one hand, more demand makes selling easier. On the other hand, a shortage makes your input costs (consumable and capital) pricier.
EDIT: It seems like the 2 to 3 year lead time and a crowding effect from new entrants historically made booting up a fab into a boom a bad bet [1]. (The article argues, convincingly, that this time may be different.)
I heard that China was spinning up DDR5 (but not HBM?) production in the next couple of years, with the hope of outcompeting Korea and Taiwan in the mid to long term.
Thanks for the link (and underlying thoughts), I really hadn’t considered that.
So essentially, due to technological progress and other factors inducing price collapses (or at least cycles), you can’t start stockpiling insane amounts of finished-product semiconductor, which means you can’t scale production at current technology levels to infinity either?
In 2010 one of the standard configurations for the Mac Pro was $4,999. Once you customised ram, storage, peripherals and software it could easily end up above $15,000, or $23k today accounting for inflation. Apple hardware is one thing that has actually got cheaper over time.
Or they could use that same amount of memory to ship 64x Macbook Neos, and probably make higher margins off the hardware volume.
Those Macbook Neo users would be very reliant on Apple intelligence, enough maybe to pay for a service with it. I think Apple's much happier going this path.
> Or they could use that same amount of memory to ship 64x Macbook Neos, and probably make higher margins off the hardware volume
If it's an "or," absolutely. But if it's an or, they should be prioritising Macbooks over the Mac Mini Doug Brooks is discussing.
When we breach the "and" of memory supply sufficient to allow for more Mac minis (and Mac Studios), I think it would make sense to consider relaunching Xserve (with new branding, of course) as a consumer/small business product.
Apples on stage use cases for their hardware and software makes me wonder if they actually use computers over there, or what a "job" at apple entails.
I am unsure that apple themselves understand why their hardware (top end & bottom end) has been so successful, without this understanding leaning into these use cases isn't really going to be possible.
Obviously they are playing 12d chess. They stopped selling high memory machines, they stopped selling pro machines. They are the king of local Ai compute, definitely not stumbling backwards into a product category they didn't know existed.
With their apple finger right there on the pulse, they are going hard on the VR/AR glasses (following the lead of the visionary CEO of facebook), cars and folding phones. By the end of the year (tm) we 100% will have all the features that were showcased and demonstrated 2 releases ago.
Ages ago, back when the Macs would come out, my co-workers and I would take a bit of time to configure the most expensive possible configuration --- time was, it was pretty easy to hit six figures, but over time, that has gradually come down.
Sadly, that is an outdated PoV. It has probably not been valid, since last century.
It's just that Apple isn't really focused on software development professionals, and it's still fashionable to throw shade on them, so we hear a lot of kvetching about it, in communities like this.
> cannot imagine a personal computing usage which can justify a 10k machine
For me, the privacy pitch wins. I have a friend visiting, however, who spends like $2,400 with Anthropic every year. That's a solid ROI even if the thing becomes obsolete after a couple years. (I'm still on my 2020 MacBook Pro. I love it and will be sad when I have to replace it.)
In addition to privacy I’d like to be able to burn as many tokens as the hardware will let me 24/7 without getting a surprise bill at the end of the month. I don’t care if it is slower that the cloud, I’m not in a hurry.
> That's a solid ROI even if the thing becomes obsolete after a couple years.
How can that be a solid return on investment? There's no model you can run locally to have frontier model level performance. Also who spends 2.4k yearly for personal AI usage, like what's the usecase? If your friend is spending that money for his business then it's not personal computing.
> There's no model you can run locally to have frontier model level performance
I'm betting he doesn't need a frontier model. Sonnet, today, is likely good for 80% of his tasks, which largely involve repretitive, tedious work.
> Also who spends 2.4k yearly for personal AI usage, like what's the usecase? If your friend is spending that money for his business then it's not personal computing
Apple is a premium brand with high brand loyalty. Do you not think even 1 billionaire would want something like that? Even to just say that they bought it. Apple could sell things at a price point much more than $10k.
At the end of the day, it’s just an Apple computer, not a Ferrari or an Aston Martin. I hardly think an Apple computer can be considered as a luxury item, unless they release it as a limited edition
There's only so many billionaires and at Apple's scale you would not offer such a product for public sale even if you do custom builds for the rich and famous.
Apple makes product lines with assembly lines, its not a hand fab or custom build type of place.
What I’m not sure to understand is that if you want to just run Claude code or openclaw type software with llm apis or subscriptions (and not run local models) to benefit from a local file system and always-on capability for ‘second brain’ type of workflows, I guess you don’t need a Mac mini but can run it on a raspberry pi or an old laptop ? Does anyone have experience with that ?
Yeah I find the Mac mini trend is kind of baffling.
It seems like it's driven either by 1) people hearing Macs are good for AI, buying one, and using Claude for inference, not realizing that you interact with the anthropic API from an internet connected hair dryer. Or 2) people want their agents to have blue bubbles.
I find it hard to believe that enough normal people are doing on device inference is driving Mac Mini's out of stock. And even if they were the Mac mini is not actually a very good platform for it.
One aspect you're missing is that people running a claw type agent thing need to run it on a Mac to automate software in the Apple ecosystem.
Neo-Siri in iOS 27 removes the need for a lot of this, but before then, if you want to ask a robot about information that is stored in Apple notes, or to send an iMessage, a Mac mini is your only practical option.
Correct. You do benefit from some headroom for things like launching browsers etc but refurbs or mini PCs (with at least 16gb; ideally 32gb of memory) from the likes of Minisforum or GMKTec work well enough if you're wanting to spend a little bit of money.
It's anecdotal but the kind of people I know that bought Mac Minis for this purpose are what I'd call "light techies." They definitely know how to use an iPhone or a Mac but would struggle on the CLI of a Linux box.
Anyone who wanted the OpenClaw use case that is comfortable with Linux probably already has several Linux machines (including a few Raspberry Pis) on-hand.
With how apple seemed to be caught by surprise when it came to Macbook Neo demand, I'm not sure they have the quantities of SoC's around to handle the demand a Mini Neo could drive. Especially if they could do it for $299.
A Pi running macOS more or less. Not dissing it though. Killer machine for those who don’t need a lot of power locally. Also a great kiosk for some things.
Yup, for openclaw and APIs you dont need a big PC. I run something lightweight on the RPI4 8gb. Many people run local LLMs which is where a mac is useful. Frankly I dont think you can beat the value of an openrouter subscription and API calls.
> Many people run local LLMs which is where a mac is useful
Unless you go for the very expensive options, most of the Mac Minis really aren't suitable for running local LLMs, they're painfully slow with prefill/processing input, and the models you are able to run don't handle long context very well, which these sort of long-running agents perform very differently with when you can.
I'll agree with your latter point, hard to beat the value of using something like OpenRouter or similar remote inference.
Even with local models, you can run the agent software and the inference workload on different hosts, which is what I'm doing at home. Beefy server responsible for inference, tiny VM on other server is running the actual agent software + RPC + bridges and what not.
Why not go direct to the source instead of paying an extra 5.5%? Seems like it'd be trivial to have AI wire up connections to your preferred inference providers and save yourself some money over time.
If you're referring to the markup charged by OpenRouter, you can use harnesses like OpenClaw/Hermes without it and go direct like you're saying. If you're talking about actually "routing", then you don't get that out of the box. However, the popular use of those harnesses doesn't often use the smart routing approach with a single agent. Instead, the approach is to create multiple agents, each with a role and a model tailored to that role by cost and functionality.
Pretty much. Running a local harness calling an llm via APIs doesn't necessarily take a lot of resources. But whatever tasks you want that agent to do via tool calling will be limited by the resources of the machine it runs on if you run those locally, so that's what should inform your choice of specs in this case
I do exactly this. You can run the whole thing on a PI. I have actually installed asahi Linux on my Mac and I connect to it remotely so you can be sure I will never upgrade my Mac again because it’s already overbuilt.
...or any cheap VPS? I now do most of "second brain" things via pi harness with Opencode Go subscription, and it costs me like 20 bucks a year, with added benefit of "you can have tmux and open session realtime on whatever device".
If I had the capital I’d make an household inference appliance.
No peripherals except Ethernet, integrated compute (cpu+gpu+mem) and secondary storage (+mobo, psu). No accoutrements, just the minimum amount of hardware to run a model as a utility.
Even the appliance faceplate would be a display showing stats like an old HiFi stereo.
Edit: something like a series of modules consisting of a RISC-V CPU + Vortex GPGPU + memory
Yes. Solar thermal heaters on the roof are common in Florida and other parts of the south. Some people also use heat recovery devices attached to the AC condenser. Further north I've only seen natural gas heating (e.g. in very rich NYC exurbs). The amount of shade over the pool has a big effect.
Unfortunately their chatbot, while amazingly fast, doesn't know anything about the company running it.
Anyway I wouldn't mind an ASIC running a diffusion language model locally. Even if eventually it would become dated. Beats outsourcing all that to a company that's running on VC money which in the future might either perish or worse - dominate the market and charge whatever they wish.
Fairly sure most iGPUs these days are zero-copy and can dynamically allocate memory so what does "unified memory" mean to you exactly? A wider bus would be nice but it's not exactly a groundbreaking new invention.
> Unified memory in Linux creates a single address space accessible to both the CPU and GPU, eliminating the need to manually copy data between system RAM and video memory. It is enabled via NVIDIA's CUDA, AMD's ROCm/HIP, or generic kernel-level Heterogeneous Memory Management (HMM).
So it does exist and is available for platforms that matter.
Running models on-device on a Mac is immensely annoying though. Figuring out what will work out of BF16, FP8, BF16+FP8, NVFP4, INT8, GGUF ... the list goes on ... is 'non-obvious' at best. Apple do little to support with tooling. There's MLX, but unless you're happy to transform a model to that format yourself you'll be lagging a long way behind.
Apps like LMStudio, Ollama, Draw Things, etc do a great job of simplifying it but it's still a pain.
How is it a pain exactly? It’s just learning and only takes a day or two to get up to speed. We seem to have forgotten that for the past fifty years doing all kinds of tasks on computers has been tedious and involved and time consuming to even get working. My first computer had 48kb of RAM and to play a game you had to load it off cassette for five minutes. That was annoying. Having LM Studio download a model and load so you can chat or attach an agent it is effortless and easy in comparison.
For some models like the popular coding and chat models, things move faster. For things like images, voice, sound etc they definitely lag a long way behind.
It’s not for the AI inference, it’s for the tool calls and desktop GUI app workloads and browser. There aren’t any on-device models capable enough of real work that can run on lower end Mac Minis. But for running a few browsers and GUI apps, you’re much better off buying a Mac Mini than paying for a more expensive and worse-performing container in the cloud. Browsers were not designed to run in Linux containers but they run optimally on baremetal desktop OSes. An M4 Mac Mini beats the single core performance of any VM you might rent in the cloud, in terms of raw compute per dollar (Geekbench scores).
> Apple's Mac mini and Mac Studio have become the machines of choice for running AI agents, according to Doug Brooks, Apple's senior product manager of Apple silicon.
This is mostly an US phenomenon, no Mac mini nor Mac Studio around here.
Only Thinkpads and Macbooks laptops talking to hyperscalers.
Around where? They're pretty popular for it in the UK right now given our obscene energy pricing as they end up being one of the best low power options for local llm. If you're not in the local llm space you obviously wouldn't see it. It's like saying Tennis isnt popular around here then admitting you dont frequent a tennis court so wouldn't even know.
Ok, I didn’t want to take the bait but this one’s just too much.
> “He also described a shift toward running AI locally rather than in the cloud – a move motivated by privacy, security, and the rising cost of inference as agents consume more tokens.”
Classic Apple. No more just beating the “security and privacy” drum, now its “tokens are expensive!”
<neanderthal voice/> Cloud scary. Cloud expensive. Mac good. Buy Mac!
> “He also singled out what he calls ‘transparent AI’ on iPhone and iPad, referring to features scattered throughout the operating system and third-party apps that work quietly without announcing themselves as AI.”
<neanderthal voice/> Apple use AI, Apple just not say it. Apple smart, not lagging behind industry! Buy iPhone!
How about you invest in developing your own models, correctly? And provide a secure and private inference cloud service on your fancy Apple silicon? And integrate that into your platform so Siri gets smarter without you farming queries out to Google Gemini? Bill me for it in iCloud+ I’ll probably pay for those tokens.
I’m not seeing how it’s bad that a company is pushing in the direction of user hardware ownership. Of course it’s self-serving, that’s what companies do, but with most of the rest of the industry increasingly leaning in the direction of eliminating powerful general purpose computers in favor of thin dumb clients with useful compute being gated by subscriptions, it’s nice to see some dissent.
AI features not being constantly shoved in my face and just selectively silently integrated where it’s most useful is preferred to what the rest of the industry has been doing, too. I think most of us are pretty sick of AI getting tacked onto things that don’t need it and then given prominent promotion and UI positioning, potentially at the cost of features we actually use.
They could be doing more, sure, but directionally this all seems fine?
But why should apple invest in developing their own models? Why would it be correct?
Or phrase it in a very similar ask, why don't they invest in power plants? The model space is truly crowded, what do they gain or recover suppose they are SOTA? Across the Pacific they are pumping out free models that are only 6-12 months behind. What business sense does it make for Apple to develop their own models?
They don’t believe in this model and never had. You sound like the people who screamed that Apple was dying because they were not making a netbook style Mac in 2009.
Apple is the only big tech company with a non existent financial exposure to the current capex bubble.
Let big dogs bark at the moon. They are the loud ones, at least until the moon implodes.
The issue with the Apple is that they didn’t really develop any competitive local AI machine. Their strategy/marketing falls flat when you ask them how exactly they implement AI: they buy it from Google cloud. In the future local AI may become a thing but that’s 4-5 years away. I count the 2q-4q and atrocious performance as “local ai” only for the enthusiasts crowd not for people doing competitive work.
Because they killed the market, no one would now buy a macOS server, when Linux distributions, and to a lesser extent FreeBSD, own the server room.
They would even sell less than Windows Server licenses.
By the way, they are down the same path with the workstation market, now that they only top level answer is the Mac Studio.
Workstation market wants flexible towers that they can customise to their own liking and special use cases.
The main reason Swift exists for Linux, is that app developers need to have servers somewhere, and if they want to share Swift code with the backend, well it isn't going to be on macOS Server.
Target audience - B2B. There are multiple videos of Steve Jobs saying that he hates B2B, because the people using the devices are not the ones making the purchasing decision. It is pretty much against Apples DNA, and all their B2B they have today is a means for them to sell more B2C.
My guess is the OS. People who want a server often enough want to choose the OS, Apple wants to supply the OS and the hardware together so they're not blamed every time the two turn out to be incompatible, as happened the other way round in the 90s.
whos going to buy one? You cant trust them not to kill it within a few years and cease all software updates AND make it impossible to install a different OS to keep it going. Until they stop being dicks about what you can do with the hardware you own it's a non starter.
Apple itself was a major user of Xserve, Apples needs for cloud compute are massive and growing, and Apple could probably rent Xserve as a cloud to justify the cost and sell it to privacy focused consumers and businesses.
Being a Mac switcher since 2003 I am as much of a fanboy as anybody else but this quote from the article caught my attention, and smells like PR.
> Many AI tools are also Mac-first or Mac-only
I fail to recall AI tools Mac-only general purpose AI or agentic tools. Most of the claws, harnesses, studios and inference engines seem to be multiplatform. You can say you can run then in a Mac with a nicer UI wrapper or whatever, but "Mac-first" or "Mac-only"?
oMLX[1] is the only one that comes to mind but it's not exactly unique to mac, it just runs MLX models and provides a nice gui. It does have the whole paged SSD KV cache thing, not sure if thats working on other platforms.
Apple could dominate this niche if they decided, for a while until prices fall, to eat some margin and bump up RAM in high end models. Couple that with a new M series chip with even faster AI performance.
It’s not a huge niche but it’s an influential one. They’d get the engineers and CXOs of AI ventures and a lot of academics and hobbyists.
For the platform it would keep them cemented as the high end vendor. In the long term it would position them to take advantage of any software or training breakthroughs that deliver frontier model performance at that scale.
You think Apple is a saint, but they have a completely locked down mobile computing/phone platform. Why the trust in them doing what is best for the user?
> "The speed of AI development right now is just crazy," Brooks said. "I can't imagine where we're going to be a year from now, three months from now, or even a month from now," he added.
I don't think I'm taking this out of context when I say this is unintentionally correct. Apple still doesn't know what to do about AI.
Luckily, it doesn't matter because it's a solution in search of a problem. Most consumers aren't using AI apart from google search.
Everyone else is using it as a content scraper and praying nobody will step in to end the piracy/fraud.
> Luckily, it doesn't matter because it's a solution in search of a problem. Most consumers aren't using AI apart from google search.
This is... a view.
Maybe I live in a strange sphere of strange ("normie"-ish) people, but the people around me are for sure using AI. Mostly chatgpt to be fair. They use it to compare products that they intend to buy, identify plants in nature, create travel plans, find interesting places to visit nearby, give movie suggestions based on what they have previously enjoyed and so on and so forth. AI is becoming a very integrated part of their reality. To "google" something and digging through the search results manually is very rapidly being replaced by asking chatgpt, for better or worse.
Chatgpt is so damn good for cooking it is unbelievable. It will learn your family's tastes over time, you can tell it what pantry staples you keep is stock, and you can take any recipe you find online and ask for stuff like "find a way to make this preparable in 45 minutes instead of 2 hours, what trade offs will I be making?"
I agree! I think it's because there's so much cooking material in its training data. I wonder what proportion of the internet is food blogs and recipes... Probably a lot!
This is true but I can do the same on free ChatGPT without even logging in. I wouldn't pay $5 a month for that functionality, much less $20 (or >$1000 to be able to run it at home).
SOTA AI for "serious" work is in a different position, used by fewer people but with big pockets and sometimes a pathological dependence on it.
The new Siri isnt that exciting at least on my iPhone 15 Pro Max. I know it's beta but it's sluggish and often says try again. I watched many videos on Youtube saying its amazing but maybe not so much on older phones? Also, I need a Siri I can talk in an unfettered manner from my lock screen while Im driving without having to unlock the screen. Probably a big ask for Siri to know my voice via voice fingerprint allowing unfettered access from my lock screen.
Others running the beta now on newer iPhones and enjoying it more so?
> The new Siri isnt that exciting at least on my iPhone 15 Pro Max
On my Pro 16 it has its ups and downs - I still can't get it to "play my running playlist on shuffle" whilst running (this is the only thing I used Siri for before the beta and it would improve my life immeasurably if it worked). But it responds to things like "how long will it take to drive to the AirBnb booking in my inbox", and "when is X playing a concert in Y - add a calendar entry with details" perfectly.
This is a beta and I have hopes, but I can imagine it will run better on a 17 and later
I use it on my 15 pro max. It's a major improvement, but I still don't use siri much. It's gone from murderously infuriating to passably usable. I still use gpt for any sort of exploratory conversations. I haven't bothered trying to use siri for that because to me that's not what siri is for.
Although given how effed up the voice for chatgpt is now with the latest updates I might talk with siri more.
Because I use carplay in tandem with my phone where the map is on the carplay screen and turn by turn directions are on my phone, it's always unlocked so I haven't run into whatever lock screen issue you brought up.
Nobody does what to do, they are just throwing trillions at it betting they’ll figure out.
If they don’t they’ll be screwed, if they do Apple will quickly catch up with a better and more refined product, as they’ve always done.
Oh please the neural engine is mostly useless for LLMs. Siri in iOS 27 is laughably pathetic and slow compared to GPT Live DESPITE sending personal context to their (attested) cloud to execute anything but the most basic queries. Still years behind.
I think the relevant comparison is the developer beta, which has access to the Gemini-powered Siri that will roll out publicly later this year. From the reviews I've seen, Apple won't be "years behind" (which surely they were) for long.
> "people often want a system that's under their control, isolated from their primary machine, and capable of running 24 hours a day, seven days a week," said Brooks. "A Mac mini is an amazing system for that," he added.
These execs are so out of touch they believe Apple hardware to be "a system that's under their control", how does it come to this? Besides, a VM without bi-directional sharing of data gives you pretty much the exact same thing.
Did hundreds/thousands of developers really go out there and bought Mac Minis just because one prominent technology semi-celebrity happens to have used a Mac Mini for the development of their thing? Seems bananas people would spend hundreds on monies on something they barely grasp how it works.
The remote access story for macOS is absolute sadness, without Jump Desktop there would be zero performant ways to access that “system under my control”.
And all of that because Tim Apple fears any feature that could mean people could have less than one iDevice per person.
I'm curious what you mean. I have been accessing Macs remotely over SSH and VNC for like 20 years and it's always been easy and as performant as the network would allow.
The built in high rez screen sharing between Macs works well too. Through Tailscale I’ve accessed my main Mac from both the opposite coast of the US and from the other side of the Pacific and it works great.
Host resolution automatically matches that of client, image quality is great, framerate is decent, latency is minimal. The host creates virtual screens for the connection so connected screens don’t light up and the machine remains locked to anybody accessing it physically too, which is a nice privacy assurance.
It would need a path to a $2,500 machine, I think. But this is a niche I don’t think another consumer-facing brand could do like Apple.
[1] https://security.apple.com/blog/private-cloud-compute/
It assumes that RAM remains supply constrained and that none of the existing RAM contracts are cut short.
But Meta and xAI putting A TON of AI compute onto the market. OpenAI and Anthropic are raising the costs of inference (by reducing how much inference users get via subscriptions). And we haven’t seen Oracle / CoreWeave struggle to pay their debts yet, but they will be selling assets once they get close to that point.
Unfortunately its not so cheap anymore as everyone ramped prices up of course.
Last year I could still get 32GB of DDR4 for under $60 from chinese brands.
https://www.techspot.com/news/112502-memory-prices-tipped-fa...
what if demand keeps rising faster than production capacity is deployed?
We are in a bubble which will be burst the moment the world starts retaliating against the US' 20+ year history of supporting genocide and committing war crimes unabated.
Buy the AI toys while you still can.
Edit: Okay, this doesn’t mean that that’s actually possible in the short-term, so I think you’re right. But that means as the silver lining, in the medium term horizon there’ll be enough supply again? :’)
Memory is a cyclical market that has historically rewarded conservatism [1].
Counterpoint: there is enough demand from enough capital-rich customers that they may be willing to shoulder the capital risk.
[1] https://www.ldeepai.com/tech-hub/dram-industry-consolidation... Sorry for the slop link, it has a good chart from a solid source
This is a good hypothesis. Curious if anyone has data on the failure rates of new entrants in semiconductors based on how frothy it was on founding.
On one hand, more demand makes selling easier. On the other hand, a shortage makes your input costs (consumable and capital) pricier.
EDIT: It seems like the 2 to 3 year lead time and a crowding effect from new entrants historically made booting up a fab into a boom a bad bet [1]. (The article argues, convincingly, that this time may be different.)
[1] https://www.uncoveralpha.com/p/every-memory-cycle-ends-the-s...
So essentially, due to technological progress and other factors inducing price collapses (or at least cycles), you can’t start stockpiling insane amounts of finished-product semiconductor, which means you can’t scale production at current technology levels to infinity either?
[1] https://en.wikipedia.org/wiki/DRAM_price_fixing_scandal
Like, take out the price sheets for the Apple Car. Then sell me an AI tower at those price points.
https://www.macworld.com/article/209019/macguide2010.html
Those Macbook Neo users would be very reliant on Apple intelligence, enough maybe to pay for a service with it. I think Apple's much happier going this path.
If it's an "or," absolutely. But if it's an or, they should be prioritising Macbooks over the Mac Mini Doug Brooks is discussing.
When we breach the "and" of memory supply sufficient to allow for more Mac minis (and Mac Studios), I think it would make sense to consider relaunching Xserve (with new branding, of course) as a consumer/small business product.
https://www.wired.com/2011/04/amazon-flies-24-million/
I am unsure that apple themselves understand why their hardware (top end & bottom end) has been so successful, without this understanding leaning into these use cases isn't really going to be possible.
I trust they know more about their business model than some rando on the internet, sorry.
You have a bold career as a technical journalist ahead of you!
With their apple finger right there on the pulse, they are going hard on the VR/AR glasses (following the lead of the visionary CEO of facebook), cars and folding phones. By the end of the year (tm) we 100% will have all the features that were showcased and demonstrated 2 releases ago.
It's just that Apple isn't really focused on software development professionals, and it's still fashionable to throw shade on them, so we hear a lot of kvetching about it, in communities like this.
I think "buy this $10,000 box and to easily grant every Macbook Neo on your team safe, private, free AI" could be a real winner.
For me, the privacy pitch wins. I have a friend visiting, however, who spends like $2,400 with Anthropic every year. That's a solid ROI even if the thing becomes obsolete after a couple years. (I'm still on my 2020 MacBook Pro. I love it and will be sad when I have to replace it.)
How can that be a solid return on investment? There's no model you can run locally to have frontier model level performance. Also who spends 2.4k yearly for personal AI usage, like what's the usecase? If your friend is spending that money for his business then it's not personal computing.
I do the $100/mo for myself, then about ~$200/mo for startup.
I'm betting he doesn't need a frontier model. Sonnet, today, is likely good for 80% of his tasks, which largely involve repretitive, tedious work.
> Also who spends 2.4k yearly for personal AI usage, like what's the usecase? If your friend is spending that money for his business then it's not personal computing
Combination of business and personal.
Apple makes product lines with assembly lines, its not a hand fab or custom build type of place.
See https://en.wikipedia.org/wiki/The_Cult_of_Mac
It seems like it's driven either by 1) people hearing Macs are good for AI, buying one, and using Claude for inference, not realizing that you interact with the anthropic API from an internet connected hair dryer. Or 2) people want their agents to have blue bubbles.
I find it hard to believe that enough normal people are doing on device inference is driving Mac Mini's out of stock. And even if they were the Mac mini is not actually a very good platform for it.
Neo-Siri in iOS 27 removes the need for a lot of this, but before then, if you want to ask a robot about information that is stored in Apple notes, or to send an iMessage, a Mac mini is your only practical option.
OpenClaw supports all the mainstream (and free) chat apps like Discord, WhatsApp, Signal, Telegram... None of them requiring a MacOS machine.
Is it a lack of knowledge from the users or do they really value iMessage integration that much?
Anyone who wanted the OpenClaw use case that is comfortable with Linux probably already has several Linux machines (including a few Raspberry Pis) on-hand.
Unless you go for the very expensive options, most of the Mac Minis really aren't suitable for running local LLMs, they're painfully slow with prefill/processing input, and the models you are able to run don't handle long context very well, which these sort of long-running agents perform very differently with when you can.
I'll agree with your latter point, hard to beat the value of using something like OpenRouter or similar remote inference.
Even with local models, you can run the agent software and the inference workload on different hosts, which is what I'm doing at home. Beefy server responsible for inference, tiny VM on other server is running the actual agent software + RPC + bridges and what not.
No peripherals except Ethernet, integrated compute (cpu+gpu+mem) and secondary storage (+mobo, psu). No accoutrements, just the minimum amount of hardware to run a model as a utility.
Even the appliance faceplate would be a display showing stats like an old HiFi stereo.
Edit: something like a series of modules consisting of a RISC-V CPU + Vortex GPGPU + memory
https://taalas.com/products/
Unfortunately their chatbot, while amazingly fast, doesn't know anything about the company running it.
Anyway I wouldn't mind an ASIC running a diffusion language model locally. Even if eventually it would become dated. Beats outsourcing all that to a company that's running on VC money which in the future might either perish or worse - dominate the market and charge whatever they wish.
> Unified memory in Linux creates a single address space accessible to both the CPU and GPU, eliminating the need to manually copy data between system RAM and video memory. It is enabled via NVIDIA's CUDA, AMD's ROCm/HIP, or generic kernel-level Heterogeneous Memory Management (HMM).
So it does exist and is available for platforms that matter.
Intel and AMD had been doing this for years already, and had linux support for it from day 1.
Otherwise, AMD is quite close to what Apple has, and Strix Halo is honestly incredible.
Not sure what RDMA brings to the table.
All have unified memory. Linux runs just fine on all of those.
That's a new one.
A bit too expensive for a home appliance though, isn't it?
95% of the price is going to be in GPU+CPU+RAM
Apps like LMStudio, Ollama, Draw Things, etc do a great job of simplifying it but it's still a pain.
[1] https://github.com/antirez/ds4
This is mostly an US phenomenon, no Mac mini nor Mac Studio around here.
Only Thinkpads and Macbooks laptops talking to hyperscalers.
People are buying apple unified as electricity costs in many countries are very high, so cheaper to run than Nvidia setup.
As non-apple unified memory options increase, many people will have more choose those
> “He also described a shift toward running AI locally rather than in the cloud – a move motivated by privacy, security, and the rising cost of inference as agents consume more tokens.”
Classic Apple. No more just beating the “security and privacy” drum, now its “tokens are expensive!”
<neanderthal voice/> Cloud scary. Cloud expensive. Mac good. Buy Mac!
> “He also singled out what he calls ‘transparent AI’ on iPhone and iPad, referring to features scattered throughout the operating system and third-party apps that work quietly without announcing themselves as AI.”
<neanderthal voice/> Apple use AI, Apple just not say it. Apple smart, not lagging behind industry! Buy iPhone!
How about you invest in developing your own models, correctly? And provide a secure and private inference cloud service on your fancy Apple silicon? And integrate that into your platform so Siri gets smarter without you farming queries out to Google Gemini? Bill me for it in iCloud+ I’ll probably pay for those tokens.
Was that so hard?
AI features not being constantly shoved in my face and just selectively silently integrated where it’s most useful is preferred to what the rest of the industry has been doing, too. I think most of us are pretty sick of AI getting tacked onto things that don’t need it and then given prominent promotion and UI positioning, potentially at the cost of features we actually use.
They could be doing more, sure, but directionally this all seems fine?
Or phrase it in a very similar ask, why don't they invest in power plants? The model space is truly crowded, what do they gain or recover suppose they are SOTA? Across the Pacific they are pumping out free models that are only 6-12 months behind. What business sense does it make for Apple to develop their own models?
I agree, apple shouldn't invest in their own models. But they should have close to the best inference + end user design.
https://machinelearning.apple.com/research/introducing-third...
they just suck
They would even sell less than Windows Server licenses.
By the way, they are down the same path with the workstation market, now that they only top level answer is the Mac Studio.
Workstation market wants flexible towers that they can customise to their own liking and special use cases.
The main reason Swift exists for Linux, is that app developers need to have servers somewhere, and if they want to share Swift code with the backend, well it isn't going to be on macOS Server.
but Apple needs to change the licensing model, currently you are allowed to run only 2 macOS VMs for every physical one you buy
> Many AI tools are also Mac-first or Mac-only
I fail to recall AI tools Mac-only general purpose AI or agentic tools. Most of the claws, harnesses, studios and inference engines seem to be multiplatform. You can say you can run then in a Mac with a nicer UI wrapper or whatever, but "Mac-first" or "Mac-only"?
[1] https://omlx.ai/
It’s not a huge niche but it’s an influential one. They’d get the engineers and CXOs of AI ventures and a lot of academics and hobbyists.
For the platform it would keep them cemented as the high end vendor. In the long term it would position them to take advantage of any software or training breakthroughs that deliver frontier model performance at that scale.
So the ad free Apple on device experience will be welcome.
https://ads.apple.com/maps
For example Apple radio is a free product with no ads, Apple TV and Apple Music don’t have an ads supported tier.
I said as they don’t push ads as much as others, the customer may be better
As ads focused services are designed to keep you there as long as possible rather than delivering what you are actually want quickly
1-2% is rounding error.
I don't think I'm taking this out of context when I say this is unintentionally correct. Apple still doesn't know what to do about AI.
Luckily, it doesn't matter because it's a solution in search of a problem. Most consumers aren't using AI apart from google search.
Everyone else is using it as a content scraper and praying nobody will step in to end the piracy/fraud.
This is... a view.
Maybe I live in a strange sphere of strange ("normie"-ish) people, but the people around me are for sure using AI. Mostly chatgpt to be fair. They use it to compare products that they intend to buy, identify plants in nature, create travel plans, find interesting places to visit nearby, give movie suggestions based on what they have previously enjoyed and so on and so forth. AI is becoming a very integrated part of their reality. To "google" something and digging through the search results manually is very rapidly being replaced by asking chatgpt, for better or worse.
SOTA AI for "serious" work is in a different position, used by fewer people but with big pockets and sometimes a pathological dependence on it.
Others running the beta now on newer iPhones and enjoying it more so?
On my Pro 16 it has its ups and downs - I still can't get it to "play my running playlist on shuffle" whilst running (this is the only thing I used Siri for before the beta and it would improve my life immeasurably if it worked). But it responds to things like "how long will it take to drive to the AirBnb booking in my inbox", and "when is X playing a concert in Y - add a calendar entry with details" perfectly.
This is a beta and I have hopes, but I can imagine it will run better on a 17 and later
Although given how effed up the voice for chatgpt is now with the latest updates I might talk with siri more.
Because I use carplay in tandem with my phone where the map is on the carplay screen and turn by turn directions are on my phone, it's always unlocked so I haven't run into whatever lock screen issue you brought up.
That said, Siri seems a little bit better now - my subjective opinion. It is a little bit less frustrating.
These execs are so out of touch they believe Apple hardware to be "a system that's under their control", how does it come to this? Besides, a VM without bi-directional sharing of data gives you pretty much the exact same thing.
Did hundreds/thousands of developers really go out there and bought Mac Minis just because one prominent technology semi-celebrity happens to have used a Mac Mini for the development of their thing? Seems bananas people would spend hundreds on monies on something they barely grasp how it works.
And all of that because Tim Apple fears any feature that could mean people could have less than one iDevice per person.
Host resolution automatically matches that of client, image quality is great, framerate is decent, latency is minimal. The host creates virtual screens for the connection so connected screens don’t light up and the machine remains locked to anybody accessing it physically too, which is a nice privacy assurance.