Apple Will 'Watch Everything Burn' When the AI Bubble Bursts

Apple looks better and better as every day passes. Other players going deeply into debt to build out massive infrastructure, VCs pumping up the model ecosystem that is now a total commodity.

Apple’s just been sitting there with solid cashflow waiting for all this to implode and then have on-device models with their own chips.

Apple will own the end device while the rest of the world is fighting over a pure commodity. It will burn hard and Apple will laugh all the way to the bank.

That article is a bit incoherent.

I think it is pretty clear at this point that Apple has gone all-in on being the ideal edge silicon for AI. This leverages their core competencies, requires only modest investment, and will likely pay out no matter how the AI market eventually shakes out. They are in one of the only parts of the obvious future AI market where there isn't really a fight for greenfield turf with other big companies.

Staying in their lane is arguably the optimal business decision for Apple and they lose nothing by it.

I don't know if Ed Zitron is right about allhis analysis, but it's nice to have an alternative, well-argued narrative to the gushing torrent of AI company propaganda.

One big difference I have with Ed Zitron is I look at companies suddenly getting worried that they're spending millions of dollars on tokens and think "wow those AI vendors are going to make SO MUCH MONEY".

The best price for a product is what I call the "suck air through your teeth" price. You want your customers to suck air through their teeth... and then pay the full amount anyway.

Uber set their per-developer token allowance to $1500 per developer per tool. That suggests to me that they think they can get at least that much ROI out of AI tooling.

Selling $1500/employee/month plans to companies is a great business to be in.

AI companies are a lot more like traditional manufacturing companies.

They can only subsidize you so far, because they may not be even be covering their variable cost at this point.

There's no software multiplier (build once — pay the cost once — sell many times).

Traditional SaaS is in a middle ground, there are operational costs associated with providing services, but per request they're usually negligible.

AI? I don't know, but it's not looking great from where I sit, unless there's a significant breakthrough in inference efficiency.

His read on the Apple Vision Pro (both the circumstances of its creation and its path forward) are incorrect in quite the discouraging manner for me. AVP wasn't released early (late, if anything), and it wasn't a dud because of Tim Cook's disinterest.

I also think it's irresponsible to not broach the obvious implication of "PC components becoming prohibitively expensive" + "untold amounts of compute sitting in compute warehouses with nothing to do because the AI companies that used to own them folded". You probably won't even notice when everything in Best Buy becomes a thin client.

I will never understand the decision-making process that led to "Let's build an awesome VR headset that can't do gaming".

I would love to replace my monitor with Apple Vision Pro for programming and productivity. I would gladly pay $1000 for that.

But at $4000 it really needs to put me in a Microsoft Flight Simulator cockpit.

> If Anthropic and OpenAI believed customers would actually pay the real cost of AI tokens, they wouldn't have to give away 20 to 40 times the amount of tokens to subscribers.

One logical gap in the SemiAnalysis 40x cost of tokens versus subscription: I don't know anybody actually maxing out their account limits.

Sure, if you're somehow always running stuff, you can max it out, but subscriptions like this allow people to max sometimes (or always), while others never come close to the max.

What is the average usage of subscribers? Only Anthropic and OpenAI know, as far as I can tell.

> While people get some sort of benefit out of AI-generated code, these tools actually end up making them slower

Curious that he references a METR study from July 2025, before the leap in model and harness performance towards the end of 2025/early 2026.

Ctrl+F: "national security".

Didn't see national security mentioned a single time in all the current comments or in the article. Speculations about profit are great and there are many companies that will largely be irrelevant across all the major metrics which will not be in a good position, but there are always these people lost on the profit picture of anything.

Does anyone realistically believe that all of these smart people are rushing into AI to maximize profit? No, they're rushing into it, because it's important and awesome. The potential value, not simply measured in dollars, is enormous.

Despite China aggressively training on western outputs, their cloud services can't be trusted and their open weight models are too large for most people to run. Running any of these open weight models through proxies or 3rd party services isn't really expected to be more secure or more private than any of the big companies. If you aren't running it locally then it's irrelevant and most people cannot do that for any sufficiently big model at a usable performance.

Ctrl+F: "web search"

Almost nobody talking about the giant shift in web search. Web search also remains a hugely profitable business and AI is becoming the new web search. It is the default now for many people and that will likely continue to increase.

Is anyone going to argue that web search is unprofitable? No. Nobody. AI will get better and more efficient as needed. You'll have your efficient to run models and your expensive models, branched as needed. This is the biggest shake up we've seen in search in decades, but even with that, Google remains dominant.

Bing made AI search their default and it actually hurt them in my opinion, because it was very bad and mostly wrong. Google added AI search, but it's been a lot more accurate and grounded. Keep in mind that Google search remains the default for a lot of devices. You don't even have to install ChatGPT or Claude on your phone, because Google is already there and people are getting used to Google AI results.

When I talk to random people, do you think they tell me they're using ChatGPT or Claude? Basically never. They talk about Google Gemini or Google AI. Gamers have an ok chance of saying they use ChatGPT. Programmers have a good chance of saying they're using Claude or ChatGPT. Anyone else it's almost universally Google Gemini or just Google AI. You do a basic Google search and you're already within the potential flow of an AI conversation.

Making money is important so that you can increase the amount of reinvestment into the product, not strictly to maximize profit. From the situation a lot of these AI companies are in, acceleration should still be more important than profit and whether they can focus on acceleration may depend on their investment picture.

OpenAI and Anthropic are going to need a long term reinvestment strategy that accounts for Google having the potential to outpace them and become good enough that nobody bothers launching or loading a separate app anymore. Keeping smart people using your product is an important aspect of it all since you want your product to become smarter too.

Maybe Apple will simply pay for these services the way it does for Google search, but if they want to maintain their privacy philosophy they will either be dependent on models from these companies or have to roll their own.

Either way, AI is sufficiently complex and capable that there will remain many ways for companies to specialize and provide profitable value to people for decades to come. It adheres to physics. It takes energy. It takes time. User experience is important, but the more that private user and company data gets used the more that trust will continue to be relevant too.

I think some of these analysts are too caught up in a few numbers here and there to understand what is happening. Are OpenAI, Anthropic and Google simply going to explode and cry "oh no, we were dumb"? That feels like the picture this guy is painting and it feels a little naive. That also ignores the fact that they are basically developing the smartest AI that can theoretically help them navigate their business into the future.

This is quite a short sighted analysis. I do think the valuations are quite and they would need to meet the reality, but don’t think there’s gonna be a crash or we’d ever go back to pre-AI era. It would more or less would be a correction to valuations.

The future of AI would be on-device models which are as powerful as current frontier models and also I can imagine companies have their own deployments of inference of open weighted models for most of the use cases and use the frontier models for extremely niche or higher intelligence tasks.

As an example I use Claude code heavily for every day development and Opus 4.8 was already good enough for my use cases and never used Fable. Also note that I use AI as a tool to help with my work and I do not offload everything I have to do to AI in a single prompt

There is a difference between "AI is overvalued" and "AI isn't valuable". We've seen entire industries deliver real tech progress while still going through harsh valuation resets.

Needs an ( “says Ed Zitron”) title change haha.

This is an interview with Ed Zitron.

I more or less agree. When Apple starts using Chinese and expanded South Korean production memory chips, their hardware pricing problem will go away.

I am running new betas for macOS/iOS/iPadOS and Siri is actually useful for a much wider set of use cases. I asked Siri last week what models it was using and one of those listed was Gemini which is confusing because I enabled free use of OpenAI in the settings. Regardless, Siri is much more useful than it used to be.

I totally think the AI companies are very overvalued, I feel that's obvious. But I don't understand why apple should have such a high run in the last year. Sure, it avoided the pitfall of overinvesting in AI -- but what did it do for positive development? Avoiding waste isn't (imo) enough to warrant increased price. What's going on, what's the bull case for apple?

Apple failed in their efforts to build their own models, but they got lucky.

China is doing all the R&D for free and the whole thing turned out to be unprofitable anyway.

IBM stood on the sidelines of the dot com boom; their share price still halved in the resulting bust.

HSBC didn't engage in the unwise practices that led to the great financial crisis; their share price still dropped by 75% in 2008.

If the AI bubble does burst chaotically, then I'd expect all tech stocks to decline to at least some extent and for even the strongest survivors to remain in the doldrums for years (in the cases listed above, the share price of both IBM and HSBC remained flat for almost a decade).

Maybe a dumb question, but isn't the idea that the efficiency of the models will improve with time, such that you won't be burning hundreds of dollars of tokens for most queries?

Fable is great, but Opus can handle most coding tasks for a fraction of the cost, and Sonnet is good enough for average questions or word processing tasks.

You’ll note that Mr. Zitron’s analysis is based on unfounded assertions (API prices are the ‘real costs’ of tokens, and model providers are margin negative on subscriptions) and outdated figures (OpenAI negative profit in 2025, ignoring at least Anthropic’s recent turn to profitability.)

Just more wishful thinking from our favorite AI skeptic

And people said that Apple was going to get left behind by not going full throttle on AI. Seems like they took the right approach

Trigger warning: Ed Zitron is asked for his take.

This bubble too will pop, and life will go on. Costs will slowly drop down, just like the dot com days, but some useful things will be come out of this.

I kind of look forward to Oracle's demise. Maybe they sell the remains of Solaris and SPARC to someone who'll make something useful out of that IP, maybe even a nice product.

I sincerely hope, when all this madness ends, there will be enough data centre surplus gear we'll all be able to soup up our homelabs.

> ..because the entire AI bubble is a result of everybody running out of hypergrowth ideas, mostly because we're flat out of new interfaces.

Really? I thought the AI bubble happened because something genuinely novel had been invented. People immediately saw practical uses for it, and then it took on a life of its own - funding, superfunding, $trillions becoming part of day to day lingo.. etc.

AI is not in the same class as Vision Pro, which Ed Zitron is comparing it to. I personally found the overall tone to be a bit hyperbolic.

Unless Apple figures out AI software execution and opens up their walled garden a bit, their hardware edge won't matter.

In 2020 I could write audio apps that worked incredibly well on a Pixel: despite LLMs! I still can get Siri to put tasks in the right todo app 70% of the time.

I updated to iOS 27 beta hoping something had changed, but nope: the biggest change is a massive black orb search bar.

That fabled line one must cross to achieve profit is ridiculously out of alignment with the current valuations on AI. Its why SPCX is falling like a meteor, currently at $111 per share.

The people who are going to end up making the most money on this are creditors and future businesses. When the AI bubble does pop there will be a massive glut of data centers and hardware available. Both Apple and Microsoft are realigning their entire businesses to brace for this. When the AI bubble pops businesses that sell hardware, like Apple and Microsoft, will face an immediate price shock because they have had to raise prices to account for more expensive hardware. That shock will be short lived and prices will fall accordingly with disruption to supply chain but otherwise minimal disruption to margins.

I look forward to the bubble popping because when retail hardware becomes cheap again all kinds of new business opportunities will open in the self-hosted service market.

Watch everything burn?

Hell no.

They'll pick up companies in trouble at bargain-basement prices.

I don't agree with many of Ed Zitron's opinions, but it is so enjoyable to listen to Better Offline and hear him ranting about AI. At least the logic is very coherent within itself, and it's not pretentious. It is a breath of fresh air compared to slop I see on LinkedIn and sometimes here.

>At their very core, Large Language Models' costs run contrary to basically every model of selling software.

>Consumers and enterprises alike have been trained to pay a monthly fee for a service, and while these services might have limits or strictures, basically nobody buying software expects to have a metered service, let alone one that's both metered and with hard to measure costs.

Has Ed Zitron not heard about the cloud? Unpredictable AWS bills?

Apple's greatest accomplishment is convincing its customers that they're all geniuses.

I'm so tired of this shallow analysis claiming vendors are losing tons of money on subscriptions. How do you even judge that?

Users go on vacation, they slack off, they spend the day talking to each other. There are very few people who are really effective at burning tokens. how do you know the ratio? do you have insides? No :)

The biggest target is enterprise, and the economics for an LLM vendor look like this: price per token = R&D + inference + infra investments. When you buy a subscription, you are quite often buying a year ahead. That lets the vendor predict future infra investments against hard commitments, and sell expensive per token pricing to everyone else. And when a hard commitment sits unused because the user is busy, they sell it twice. It is loyalty in exchange for predictability, in exchange for the promise to always deliver SOTA to users.

Vendors control the harness. Tomorrow they simply roll out a router where reading the code and doing the final edits goes to a cheaper model, and their math suddenly becomes very sexy.

Isn't that hard to predict that their economic model is very easy to tune? and this is just first baby steps.

I personally pay per token ( do not have subs for work ). I did have once a $25k/mo worth of tokens, since i knew it was free so i was doing crazy experiments. Now , 2 month later, my bill was barely $1.5k since i moved into different stage with project. I do have team members who burn $500-600. pre router, pre optimization.

I switched recently to grok 4.5 and cursor router and my bill will go even further down. It rotates 4-5 different vendor models cheap and expensive too, depends on the task. Routers will flip entire LLM economy upside down.