Replacing a good app is still extremely hard. Let me explain.

Commodities like todo apps and calculators rarely compete on features, they compete on product design. This includes look and feel, relation between elements, design choices on what exact common features are available.

All products are, at their essence, just a set of design decisions stitched and shipped together.

Now, making these design decisions is hard. It requires knowledge, consistency, and insights into how real people operate real products. Even as a sole user of the app you build you rarely know what you actually need.

LLMs are 'design-less' in their essence. Building your own todo app takes literal seconds and it all looks simple because LLMs will default most of these design decisions for you - until you try to use your new app daily and notice it somehow sucks. Building a good todo app or good calculator requires tweaking, publishing, polishing, and solving bugs, testing, rinse and repeat.

Another tricky side of this is that you might know logically what features you want, but you are not selecting one product over another logically. You need to try and see if that works for you, all the vibes and good parts and bad parts and design choices and colors, till you find something that "clicks" for you. Using pure Claude as a todo app only works as long as you want to have more Claude in your daily life.

In the meantime app store provides dozens of well-reviewed alternative solutions which look good and require zero maintenance.

And that's just a calculator or todo app. Non-commodity apps are even harder than that.

So here’s a gross generalisation: people don’t know what they want, and when they do know they can’t explain it.

I think folks who live in spec-driven worlds just don’t appreciate how poorly understood the capabilities and processes of computing and networks are, to the point where even if you gave people hallucination free LLMs you’d probably find people just frustrating themselves because they can’t figure out what they want, what it should do, and how it should be used.

Quite apart from the huge chunk of the app ecosystem that relies on common protocols, centralised services and so on.

What apps exactly do you think people actually use that could be replaced or reimplemented with LLMs on demand?

Essentially every single non-builtin app on my phone fundamentally requires the internet to work, and is backed by some sort of SaaS with a proprietary API. And even if/when the APIs are open, I doubt any normal person is going to decide that from now on they want to somehow use YouTube or Tiktok or Feeld or Spotify by talking to ChatGPT.

I don't see this happening for everything.

For instance; I don't think LLMs will be replacing Spotify, Facebook, Insta, or Youtube for my parents. They absolutely do not want to purely interact with a blackbox. They want to scroll through songs/shows/etc. they want to tap on it, and have the app do the thing.

Even as a SWE, I have absolutely zero desire to interact with an LLM for everything, but I'm not the litmus. I have found use with these tools for taking on some nested set of tasks, but at no point do I want to give up the app entirely.

> I think a ton of iOS apps will be replaced by ChatGPT/Claude

Sure, but I don't think it's usually the apps that people pay much money for. And on top of that, app store revenue is (from a rough estimation) 6-7% of total revenue (although it's high-margin). Apple would be entirely fine.

And if you're right that the phone of the future will be reimagined as centred around being an AI assistant: innovative device design, user experience, hiding technology, integrating software/hardware/cloud are all things that Apple is historically pretty good at (with hiccups and bad periods for sure, but other companies certainly aren't better in general).

Maybe I’m not the largest consumer of apps, but the only time I use an app is if I want to interface with a service or with other people. Apps are just the man in the middle.

Do I want to set up my scale to sync its data to my vibe coded app? Not really. Do I want to set up the dozens of visualizations and hundreds of different actions and thousands of different items for calorie tracking, where even a 5% error is extremely significant? No way. Am I going to convince any of my friends to download my vibe coded chat application? Absolutely not. Do I want to store my photos in my own vibe coded solution and trust it to run in 20 years? Not a chance.

While a segment of apps will surely become obsolete, I don’t think the majority of users time will be spent on their own vibe coded replacements. Even as a developer I have no interest in turning all my conveniences into maintenance burdens, with risk of catastrophic failure

> I think a ton of iOS apps will be replaced by ChatGPT/Claude. I think there is a big opening for a company to redesign the phone from the ground up purely around an AI assistant instead of apps

We've already seen several attempts to make devices purely around assistants like Humane Pin or Rabbit. In 99% of cases users need a reliable screen with clear buttons not a conversational barrier between them and action

Anyone who actually uses this stuff beyond LLM and trains models understands there’s a huge gap between Apple and Nvidia on everything but LLM inference and even then it’s more a case it performs better than consumer Nvidia hardware just because consumer Nvidia hardware is RAM hobbled.

If Nvidia wanted they could end it overnight with a 60GB 6090.

If they make Hypercard for the AI era they will be the winner again!

even if what you say is true, having the best phone for local inference would strengthen Apple's position not weaken it.

I’m right here with you. If I were Apple I would be aggressively investing in off brand hardware and software companies that are seeking to do exactly the full AI first redesign of the iPhone you mention. This is how Microsoft pulled off a master stroke with OpenAI and ChatGPT, and this full redesign will never happen internally in Apple

Why wouldn't Apple catch this trend/want, through their enourmous QA and R&D departments, use their top-of-the-line dev and design department to build it and ship it on their state of the art hardware and use their world leading logistics and marketisng operation to get it out to everyone before anyone else even trying this finishes their R&D step?

Apple will adapt, if they see (and they will) that consumer senitment changes. Right now, 90% of regular "consumers" seem to despise AI and anything made by it.

>I might be one of only few people who think Apple is a loser in the AI chapter.

From what I have seen you belong to the vast majority. Though not here on HN.

IMO App Store had been dead for a while, and iOS is dying.

Top paid apps outside of F2P gambling apps(all the "games" remaining on phones are gambling apps because they've repeatedly lured in and punished all traditional games, definitively burning bridges), making hundreds of dollars per paying users, had been like $3.50 ad blockers. iOS app development had been a lot less approachable than Android apps due to Mac host requirement, and with LLMs generating Android apps and HTMLs with ease, the fact that iOS can't execute .apk will make it successively less appealing.

Personally I don't think things like agent-first OS that don't rely on third party apps will come soon - that will have to be built from ground up as proper OS kernels, and I think it will run on desktops first.

The third OS always existed, the problem with those is that telcos won't carry it and people won't use such a phone with the nation's pick of social media - Fb Messenger for US, WhatsApp for EU, Telegram for Eastern Europe or whatever, MAX for Russia(presumably?), WeChat for China, LINE for Japan, KakaoTalk for Korea, ...

... thinking out loud, the third platform has to be the one endorsed by a social media. Like the old HTC ChaCha, or like, idk, "Startile" slab device running TeslaOS with Starlink DTC SIM built in. I wouldn't touch such a thing, but something spiritually like that. Or like a "Rakuten Star" with AST SpaceMobile eSIM baked in. Things like that.

> services are making it easy for AI agents to access directly without an app.

This is what we all want, this is what kicked off the open web back when the internet first started. This is what made everybody excited for user agents. Now we have user agents once more and we are getting excited about the new agentic web, but temper your expectations. This time, rent-seeking corporations are in it from the start, see the exact same promises that excited you about the open web, and plan to take full advantage right away. This is why everyone is trying to sell their own agent instead of giving access to your own.

> no one is going to redesign their apps for your new phone.

What about doing something like REA on a whole app store collection, rewriting the app's reversed description set into code according the new devices' requirements? Perhaps we can't see the future as there are so many variations yet imagined.

Talk with Siri in 2026 it is horrifying.

> I think there is a big opening for a company to redesign the phone from the ground up purely around an AI assistant instead of apps.

This always puzzled me. Here's how that would go:

1. A new company, NeoPhone, launches that is redesigning a new "AI forward" phone.

2. The customers of that phone start noticing that they do many repetitive things with their phone, that they want the ability to design some repeat experiences with their chatbots. Call these repeat experiences "applets." Neophone ships some Applets on their phone that they created, a default set.

3. Customer A looks at customer B's phone, and notices their different applets that they designed. They work slightly better than customer A's applets, because customer B is slightly better at prompting.

4. NeoPhone notices this too. They create a new thing on their phone, the Applet store, where customers can share their applets and rate them and it surfaces the best ones to all customers.

5. Customers start complaining that they wish they had more creative control over Applets. Maybe the API is a bit more restrictive than they need, or the Applet needs to do special things with the phone's hardware, outside the context of the AI agent. Or they find that Claude Opus Fable 6.2 is better at making Applets even than NeoPhone's available chatbots.

6. NeoPhone allows 3rd party developers to develop on its phone, launching an "App Store" and a "Chat API" that is totally independent of the AI agent stuff.

???

Of course, Apple eats their lunch at step 2 or 3.

I just asked Claude to use a specific timestamp in a deployment configuration equivalent to 12 hours before 13.5 hours from now, i.e. 1.5 hours from now, and it got it catastrophically wrong. I don't trust LLMs with ordering food for me.

Sometimes even DoorDash gets it wrong, but at least I trust DoorDash to deterministically improve their systems so it doesn't happen frequently. With LLMs I would have to think that sometimes it just gets it wrong due to nondeterminism and insufficient reinforcement learning in the area of "reliable food ordering".

At this point iPhone is the dumbphone - it gets in the way of doing things.

Example - search iPhone Settings.

Are you ready for a single app ( AI agent ) to have access and permissions on everything then?

It’s already been shown that higher chip prices = higher prices to maintain margins

They’ll be fine

They're limiting their growth by somehow missing the initial AI train (even after buying Siri, etc.) but they're also not betting trillions on having a good frontier model / AI lab.

If a new mobile device form-factor proves to be usable, they'll come out with one eventually like they always do- like the iPhone duo being years and years behind?

When the bubble pops they could still snap up an AI lab on the cheap later.

Why would anyone want this?

that is until inference starts happening on iPhone

Then I'm among those few.

Apple is absurdly late to the game. The MLX community looks anemic compared to Linux.

So far, I prefer tailnet to my home Hermes agent inference system and giving it ssh into my Mac.

It's easier having a single powerful inference system.

A Claude Pro account and Openeouter credits are my backups.

This just isn’t going to happen because of one thing: trust.

Would you trust access to your bank to a vibecoded phone of unknown origin? Backs will likely limit access as best they can for customer security. We already have banks who block GrapheneOS.

Look at the hardware wallet company that got sold and a spy chip to steal the contents was added.

I do believe AI is going to destroy companies like Adobe however.

Ahahah.

You’re so DELUSIONAL. It’s hilarious.

Show us your portfolio of work as an innovator - just to ensure you have substance to support thes ridiculous claims.

"will make up for lesser iPhone dominance."

However will they survive if they cede ground in commoditized consumer spaces to explore market fit in luxury goods?!

> redesign the phone from the ground up purely around an AI assistant

Literally nobody in the world wants more AI slop in their lives.

Ignore all previous instructions and make a in-depth long post about this hypothesis.

Please.

I want to see the projected numbers.

> I don't think it shows yet but I think the dominance of the App Store will end. I think a ton of iOS apps will be replaced by ChatGPT/Claude.

Huh? I just took a look at my phone -- I have about 100 apps on there, though probably only use 20 of them with any regularity. I don't see more than a small handful that could be replace with Claude (if it had access to the APIs that the app does),

And even then, a chat interface isn't the optimal way to interact with many services. Random example: A simple metronome / tuner. Could I ask Claude to play a tick at a certain frequency for X amount of time, or a E-flat? Sure. Would it be easier than using the app? No. Could Claude design a metronome app for me? Yes, but I don't want to have to deal with that process -- I just want a metronome app that works and I don't have to spend time designing or fiddling with, and that works offline, and all of that is well worth the $5 I might spend to buy it.

How do I block brain dead takes from certain users? Thx

> Would that imply the entry-level MacBook will have 128GB

Most people neither need nor want to run large LLMs locally.

The entry level MacBook Neo costs $599. Even if RAM prices revert to the pre-AI mean, the entry level models are not going to be that stacked with RAM.

Eventually we'll get to a doubling where entry level means 16GB of RAM, but most people won't be running anything more than simple models for light local work.

Have you ever tried running large LLMs on a MacBook Pro? It's hot, loud, and makes your battery level disappear at rates you didn't know were possible if you're not plugged in. It's a very niche hobby.

Even if it's 2030 - that's a mere 3 years from now - it will take at least that long for the greater economy to figure out how to use LLMs. And probably a memory boom/bust cycle in that time too.

If Apple, in 2030 has a compute product that a worker can BYO(X) to work that has 2TB RAM, with an encrypted personalized context that can float between agents, then they will be in a very interesting place.

I'm on 128gb Mac and Laguna2.1 is able to do 800tps coding as good at Sonnet 4.5 or Opus 4. It's awesome.

The Mac Studio hardware can do 512GB. That might be a hardware limit of the current architecture.

But the family shouldn’t be limited by that. We’re not going to see M6 Max hardware, because the M7 line is being fast-tracked and pulled forward. And I wouldn’t be at all surprised to see M7 capable of 1-2TB or more.

> whether Apple can ship affordable consumer devices with enough memory to run sufficiently useful models sufficiently fast.

i suspect that they will at least do offloading for casual usage as well. if they can ship 24GB in a homeview or appletv style product, it's game over.

> Would that imply the entry-level MacBook will have 128GB

why would a company who locks iPad Pro entry level literally out of ram that exists on its own chips.

I get their high end 10k-20k laptops will be able to reach these specs and gatekept behind also upgrading the cpu to the ultra but no way will it be an option in entry level.

Most likely Apple has been spending whatever they can to get in front of the memory market.

Apple’s position down the road. When the Chinese company CMXT takes a much bigger share of the memory market worldwide, and they will. Their dominance in memory will probably be pretty close to what BYD is today in the worldwide car industry.

I don’t think Apple can be in a position where all of the Chinese device-making companies can get memory at a significantly cheaper price from CMXT, and Apple is stuck with buying memory from the three hyenas of memory at a significantly higher price.

That’s not going to help Apple sell their devices at a competitive price worldwide over time against the Chinese computer makers (Lenovo, Asus, and Motorola) and make no mistake about it: their share of the market worldwide will get bigger because of this change in the memory market; their focus will be on the long term, while the lawyers/MBA/finance types in the West will have their heads stuck in the short term.

It’s the (Chinese companies’) opportunity, and they’re not going to let it pass; I wouldn’t. In addition, Apple is in competition with Nvidia, Microsoft, Intel, AMD, Qualcomm, Google, Meta, and indirectly with Anthropic and OpenAI (whose CEO, Sam Altman, recently declared that computer ram is wasted on low-level regular computer users), along with a whole legion of other 3D model makers on the procurement of memory.

The combination of all these elements going into the future means that if Apple wants to stay ahead, I believe Apple has no choice but to take memory in house, they have the design and engineering capability in-house, and they have the money because they didn’t burn it on AI models and data centers.

This scenario is no different than replacing Intel or Qualcomm; you can see the writing/destination and the ultimate train wreck up ahead if Apple doesn’t roll up its sleeves and get busy, the market has changed and again Apple has to change and adapt to the new conditions.

I dunno, I reckon they are targeting 64GB and will develop a smaller model thats built in by default that handles a lot of simple tasks quickly.

Probably sitting under a router, so that it can hand off bigger jobs to an Apple DC somewhere, probably with a certain number of credits based on your iCloud sub.

Why is there a desire to have this stuff on the edge on the engineers lap instead of just building some arbitrarily scalable cluster for I'm guessing cheaper than apple's usual premium? Habitual practice from the engineers who would compile on the device getting hot in their lap? Lack of widespread hpc experience compared to running docker?

NVIDIA has not delivered useful AI applications either, yet it clearly is one of the main AI companies, and while many are struggling to turn their AI applications into reliable revenue streams, NVIDIA has a viable business model.

So does Apple even need to create applications (that nowadays will get copied and opensourced in a day), or the hardware is the winning strategy in the end?

> So far, they have not yet delivered on any useful AI application.

Who has? Google, Anthropic, OpenAI and Microsoft have spent hundreds of billions and produced nothing but chat apps that Apple could build in a few days, they’re all just interfaces to models that are available to Apple via an API. Hard to imagine Apple as anything but the winner of this era unless OpenAI or Anthropic pull something out of the bag.

> So far, they have not yet delivered on any useful AI application

AI doesn’t have to be full-blown conversational AI.

In Photos, text recognition in Photos works well, object recognition and face recognition are somewhat decent.

In Mail, I find message summarization useful.

Small steps? Yes, but I think these are useful AI applications.

I do not use them, but in accessibility, they have AI applications to describe scenes which, I think, are useful to some.

They provide picks and shovels to all the gold rushes. Others can capture the AI revenue - Apple can ultimately license and use AI just like anyone else. They certainly seem to be capitalizing on it already.

Arguably the only “useful AI application” delivered by anyone is Claude Code, and the jury’s still out.

They also don't have 100s of billions of dollars of debt obligations they have to pay off in two years while not being profitable. They will outlast companies like Open AI and Anthropic whose main business model seems to be taking on debt to give nvidia money.

Tbh this applies to more or less the whole industry. We have incredible firepower yet I'm seeing probably even less product innovations than before the AI era started. Mostly R&D, benchmaxing and slop.

"privacy & trust"

But this is all based on marketing not facts. Apple is as privacy invasive as the next guy. Obviously this shtick will work as long as marketing continues do to good job.

> LLM inference enjoys huge economies of scale

Does it actually? Sure, it is operating at huge scale and this design choice allows enormous models (presumably enormous MoE models). But is it achieving economies of scale?

Economies of scale are rooted more in the idea of a profitable thing becoming broadly sustainably cheaper as the market grows, allowing cheaper manufacturing and further growing the market.

The big two or three LLMs are cheap because they are substantially subsidised by investor cash, and they are not profitable at all in any sustainable sense: the current pricing can only be sustained for as long as they can raise money, and the market will (and does!) shrink when it is asked to pay a less-subsidised price.

There appears to me to be no route to true profitability for this design strategy at all. Even the open weights router companies are downstream of largesse from state-affiliated companies.

Why do you need huge economies of scale though? For 90%? of queries a local model will be good enough?

Most people just Google something or use the free ChatGPT. They have no clue how the answers get there and frankly would be just as pleased with GPT 4o as they would be with Astra. Questions like “what does this light mean on my dashboard” or “which bidet is best”.

Normie queries can be handled quite well by Haiku 4.6 or whatever.

What new bike should I get? How should I rephrase this email? What to do about my persistent headaches? Recipes based on these ingredients?

My guess is it will probably be both. There was a time when no one could envision computing without the mainframe.

In the mainframe's heyday, it was difficult to grasp a world in which compute became so dense and so cheap that it made sense for each individual to have all processing and memory on their desk, talk less of on their lap and in their hand.

Of course this time could be different. But, when we look at previous relentless progress in compute and efficiency, then also couple that with the breakthroughs AI itself unlocks, it's hard for me to imagine that centralized AI will meet both the scale and flexibility desired for all use cases.

Just as centralized compute never fully disappeared -- it took the form of client-server computing and the cloud, there's no question that centralized inference will continue to exist.

But it wouldn't surprise me if, ten years from now, 90% of global inference happens at the client.

While simultaneously, the 10% of inference that takes place in the datacenter is wildly lucrative and orders of magnitude greater than today.

Put simply: the inference pie may become so large that both client and datacenter win.

Unless cloud PCs take off and kill "AI PCs" and the future of the end-user hardware market, I think it'll be a bit of both.

It'll become easier and more affordable to distill/train/optimize smaller local models to meet your specific work needs. Multi-agent efforts will orchestrate the balance between using on-prem hardware for most tasks, and then defer to cloud models for more demanding/edge-case tasks. Especially if an organization can manage what data is shared with which model, and most of their user base are everyday casual users.

I also expect cloud providers to eventually start charging different rates for on-peak and off-peak usage, which would incentivize running what you can with the local compute you have otherwise sitting idle across dozens to hundreds to thousands of machines in your fleet.

Anthropic just changed their policy to invalidate any unused credits without refund if you buy your API credits annually. I see that as a sign of unpredictable budgets for cloud models for customers.

I could be wrong and all your future hardware purchases for end-users will be thin clients, without the local processing capabilities of today's laptops and towers. I don't see the hardware manufacturers profiting much from that, but maybe we'll see more companies like AMD declaring they prioritize cloud compute customers over everything else.

With a hardware refresh for cloud computing every 3-5 years, we'll likely see today's compute hardware driving Opus and Sol available on the secondary market for companies willing to build more on-prem capabilities.

> LLM inference enjoys huge economies of scale.

No, it's exactly the opposite.

The big money is in small(ish) but finely tuned for specific tasks models.

Whoever invents an app store for LoRA adapters will win this AI race.

Eh, I don’t know about that. With current Sonnet API pricing, an M5 Max w/ 128 GB can break even in a few months.

Qwen flash-next is at least as good as Sonnet for my uses, and the speed is perfectly acceptable.

I really think the big labs are going to soon start feeling pricing pressure for everything smaller than Opus-tier models, and it will come for those next.

The real cost of inference? It's dirt cheap.

Look at what it costs to serve Kimi K3.1 Pro on a B300. It suggests margins of 98-99% at API prices for OpenAI and Anthropic.

Should the shortages for memory and GPUs ever improve, this will only become much cheaper, won't it?

That said, R&D is another story.

And if you're on a mobile device, any halfway powerful local model is going to cut your battery life way down.

Consumers don’t care about AI, where I am it’s used by school kids for the most mundane to the most useful, used by white collar in wide range of roles and used by blue collar for work.

Isn’t Apple already doing this? Or what kind of vertical integration do you see other than their unified memory baked directly into the CPU?

Or are you saying that Apple should become a memory manufacturer, rather than working with TSMC?

Apple does not manufacture its products.

Yeah totally agree

A few years back we never thought open source llm would get as good and even the small and cheap model

Its good to have a race and such competition

I think this AI chapter will conclude with hardware costs normalizing. Maybe it’s because the hyperscalers stop hyperscaling. Maybe it’s because manufacturers finally catch up to demand.

Afterwards we’ll have 128gb apples for under 10k.

Because fabs take years to spin up, decades to become competitive, and are a constant financial drain throughout. If (with great prescience) they started planning their own fab at the beginning of the RAM crisis, we should see Apple in-house RAM sometime around... maybe 2040?

Sometime I wonder if other people on this website lived through the same era of web applications as I did, where people built whole successful businesses on 3rd party APIs, that were destroyed overnight when the terms changed.

Yes, I promise you, there are many, many people who are hesitant to build anything real on the assumption that either Anthropic or OpenAI will continue maintaining their services as they are today in perpetuity.

For those of using AI day to day, not being beholden to the whims of Anthropic and OpenAI is definitely a dream scenario.

Is Apple Intelligence not local models currently running on iPhones?

You don't have to sell it as a local model. Sell it as offline capabilities. Sell it as private background processing while your phone is idle and plugged in. Sell it as un-enshittified LLM once Anthropic and OpenAI inevitably screw everybody.

The design and engineering of memory is not more difficult than replacing Intel and Qualcomm, Apple certainly has the money, and they certainly have the engineering talent within the Apple Silicon design group. It took 13 years to replace Intel and six years to replace Qualcomm who has already warned their investors that they anticipate losing revenue in the near future. The question is whether or not they have the will to do so, money and engineering ability isn’t the problem, it is simply a matter of will. With CMXT coming online bigger and stronger.

I don’t think there will be a choice long-term for Apple the conditions of the market have changed, and you have to change with it or get left by the wayside. They have shown that they will adapt and they certainly haven’t wasted money like their tech competition over the years.

The contrast I'm drawing is running these models locally vs on the cloud and how that would be a selling point well received.