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.
Isn’t Apple potentially under a lot of pressure due to the DRAM prices as well as having to compete with AI for fab capacity? Before all the price hikes their products were already priced at the top of the market so I would assume that memory getting more expensive will impact their sales or margin quite a bit as they can’t subsidize hardware like AI companies do (they can of course eat into their own margins a bit which I think they already do). So not sure if what happens right now is great for them, if the memory market collapses it will of course be good.
> Isn’t Apple potentially under a lot of pressure due to the DRAM prices
Maybe. On the one hand, if there is a strong ceiling to what users are willing/able to pay for a smartphone (might not be true, with all those people making $$ on the stock market), they will have to cut prices.
On the other hand, if one part gets more expensive, premium phones need smaller relative price increases than budget phones. Certainly, if they are willing to accept lower margins, premium product might get more attractive.
Also, iPhones tend to have less RAM than competitors, so this might hit other premium manufacturers harder than Apple.
Apparently RAM and SSD costs have risen so much recently that the bill of materials on the next iPhone could exceed the entire cost of the current one just on RAM/SSD alone [1]
> Isn’t Apple potentially under a lot of pressure due to the DRAM prices as well as having to compete with AI for fab capacity?
I'd qualify this with for now. It's not entirely clear what happens in the event the AI industry implodes, but I do think there will be a glut of memory and hardware up for grabs. It very well could crash the consumer hardware market as a result. The AI obsession and investment has burrowed so deeply across the US economy that I can't think of a single industry that won't be impacted, including agriculture.
> It very well could crash the consumer hardware market as a result
Tbh, I hope it does. Prices are outrageous, and I'm a firm believer in personal computing/having access to powerful hardware, privately and locally, is hugely important.
On a more selfish note, I miss the days of being able to build an outrageously powerful desktop for myself for relatively cheap. For now I'm still holding onto my AM4 motherboard w/ 64GB DDR4 and an aging GPU, praying my 7 y/o GPU holds up long enough for prices to drop again.
64 GB! you are lucky :) sitting there with 16GB, a GPU that already shows the signs of strain, and AM4 Ryzen 5 5600G. (MacBook Air M1 equivalent in single-threaded tasks.) apparently the CPU requirements slowly crept up across the line. and then there is looming AVX512. why "looming"? because at some point enough people will move to it that software will require AVX512.
Maybe a year from now we'll all be using MI300As and EPYC 9600s to read e-mails. Or Nvidia Veras, maybe AWS will sell some of their Graviton stuff in an effort to offset their capex.
Unfortunately, I don't think IBM Cloud will suffer enough for me to play Tux Racer on my very own Z17.
in fact, agri sees its share of impact now! all of this investment has to come from somewhere, which means that money is getting sucked up into AI from the entire economy. job cuts in tech are part that, part general "de-bloating" of post-COVID tech. retail investors are a lot more likely to invest into AI than any other industry, although there are signs that big players already consider risks of overexposure to AI.
not to mention, agri saw a "bubble" of its own, surrounding Agtech, which peaked at 2021 and has been cooling ever since. scare quotes here because it was nothing like AI bubble
Sure, but DRAM is a commodity and is classically cyclical. This is a particularly large cycle but so long as the historic pattern holds (and there's not a ton of reason to think it won't unless the hyperscalars can go into greater debt indefinitely which given inflation and interest rates they can't) Apple just needs to get through this peak in prices, which they have a LOT of ammunition to do so.
Having to raise prices isn't a great look but when they come out the other side they can drop pirces and get a boost in volume or they can keep them high and enjoy a bigger profit margin.
Yes it does impact all hardware makers and they raised prices as a result. But again remember the financial reality here… DRAM is in short supply because of the demand for models propped up by VC subsidies and infrastructure providers going deeply into debt to buy all this stuff just as folks are realizing we’re on track to over build capacity. So yes Apple gets impacted a bit in the short term but in the long term they’ll gleefully watch it all burn.
RAM production is historically not a great business to be in. It’s a mint today, but it’s an incredibly tough business to break into & by the time any hypothetical Apple RAM fab is up & running, it will already be losing money.
To give you some idea, here’s an Asianometry video describing Taiwan’s prior DRAM attempts: https://youtu.be/ehT3U935Pww
Let's be real here, Apple does not have to compete with AI for fab capacity, they can outbid anyone if they want. They have more money and longer contracts then anyone else. Apple customers will just end up paying for said DRAM price inflation.
The ones who will actually suffer is Apple 2.0 (presumably a different competing company), start ups, research, companies that are not worth trillions.
Curious, why would Apple want to wait for "all this" to implode? They've had on-device silicon for AI for eons now (in AI-years), so they could have been executing their strategy, whatever it is, all this time, no?
My outsider take is that Apple leadership never really believed in the capability of GenAI, and hence kept pursuing their pre-LLM strategy. An interesting signal about how that's working out now is that their AI org leadership was overhauled recently.
Others are betting on AGI/almost AGI like system. It's a gamble because no one actually knows whether or when it will be possible to create it. But if their bets pay off, they could outcompete Apple.
Basically, other companies are going all in on the next card, while Apple is holding back and waiting to see which cards are dealt.
The problem is even if AI ends up being huge all these companies still have to worry about eachother and the Chinese models that are coming out. It's like a dollar auction and it's very possible we are already at the situation where they have all bid more than a dollar.
In other words, the AGI hype kinda depends on AGI never being achieved. Billionaires don't want the worlds fundamental problems solved overnight for a simple reason: there's no money in that.
Yeah, as an android user myself I switch brands all the time, but the iPhone users in my life all bought iPhones 15ish years ago and have kept doing it ever since.
The good thing of the iPhone is that your next phone is just like the previous one, but faster. Some features are added, some change places and some are even removed or replaced, but it doesn't compare to, say, move from a Motorola to a Samsung - they might as well be different OSs. Last time I had to relearn how to use the camera (and I AM AN EXPERIENCED PHOTOGRAPHER!).
I’ve had a few iPhones. Own 2 right now and I completely agree with your sentiment but while the hardware gets faster the software gets slower and worse. ;)
No Apple was not sitting there and waiting as if its some grand plan. They tried their VR device which they don't even talk about now. They were late to AI and yes if the bubble bursts they will be at a better position but it's not some grand master strategy. Even inside apple there's been a lot of talk about how badly the company handled AI.
As for laughing all the way to the bank, I am sure memory manufacturers and Nvidia was definitely join them regardless of what happens.
Also comparing Apple (literally one of the largest companies in the world) to a company burning VC money makes no sense.
I’m laughing right now, Apple is in a very good position and it was a grand plan because OpenAI, i.e. Sam Altman pitched Apple and got nothing, you cannot tell me going into that meeting with Tim Cook at Apple that Sam thought he could squeeze Apple for 50 billion dollars? Like he squeezed Microsoft?
And what about Google? They probably thought for sure when they met with Apple that they would wipe out that $20 billion yearly payment to Apple every year for the default search position but the best they could do was to come out with a measly $1 billion a year refund both scenarios sound like Apple took it in consideration and determined that it was not worth the price, that does sound like a plan (The Art of War type) when the rest of tech is going crazy over AI.
The Apple Vision is at least two or three generations away hardware wise, it not only has to be several times faster (M8, M9 with a R2 or R3 processors) than what it is. The battery life needs to be better a lot better and it has to be half its current size weight wise.
And the price has to be half as much with all of those improvements. And last but not least the software needs to get even better all of that sounds like iteration over what? 3-4 years.
Fortunate for Apple, the competition is even further away…
It would be a better way to frame it if we were strictly talking about VR. But my point was to show Apple is not some infallible genius company, they make plenty of mistakes. Why are we assuming being behind on AI is not a mistake but rather amazing foresight is the issue? We are only considering yeah the bubble bursts and apple is like oh yeah dude we told you hahahaha, but what if the bubble doesn't burst? What if there isn't a bubble and it just evolves naturally into something less exciting and fundamentally useful? Wouldn't it be fair to say Apple was wrong in that case? Why are we congragulating them for a hypothetical right and not worried about the hypothetical wrong, both of which are possibilities.
Yes Meta spent 80 billion sure, but did you know Meta never had a loss-making quarter in their entire history as a public company?
My overall point is that its not as clear cut as Apple is smart for willingly not being good at AI and they will win in the end.
As for the rest, they’re the only major tech company to not have a serious case of AI FOMO and dumping all their cash into that FOMO. Thats not an accident.
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.
> 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. However, Brooks envisions a hybrid future in which agents decide what runs on-device and what gets sent to the cloud.
And coupled with their efforts in auditably-private cloud computing, that's a strong pitch.
I generally agree. Xserve didn't work out for Apple and they dropped it pretty quickly--think I still have an A/UX coffee cup. Wasn't really in their DNA even if there was an argument before Linux took off. For better or worse, they're basically a client company and barely a software (OK and basically a software services) one--although they spend a fair bit on (roughly) user experience.
It was worthwhile because in the end it taught them that they needed to do something like Apple Silicon. And hopefully the current memory fiasco will convince them that they need to take memory in house if they want to build what they need to build going forward.
I really don’t see any alternative long-term to not engineering and designing memory in house and collaborating with TSMC onshore in America.
Hopefully having an engineer from the Apple Silicon design group as the next CEO will pay off.
They've been investing in NPUs for a long long time. Since the first iPad Pro at least (2018). Apple Silicon is a natural fit for tensor/matrix operations due to unified memory, so if anything them going "all in" on that has been a nice side effect.
> Apple Silicon is a natural fit for tensor/matrix operations due to unified memory,
This is a common misconception. This is NOT true: memory designed for CPU performs horribly for GPU tasks. Mac/Strix Halo/NVIDIA Spark are performing horribly compared to a desktop video card with GDDR.
I spent quite a few weekends optimizing ROCM kernels for strix halo, i WISH I had GDDR instead of high-frequency CPU RAM; the bandwidth would be SO MUCH better.
I'm quantizing weights not to make computation faster, not because I'm out of memory, but because memory cannot move fast enough to be processed and it's cheaper to load quantized version, convert into bf16 compute, and discard; This happens every single time a token is generated for the whole model over and over.
When your competition is currently burning billions of dollars left and right, Apple refunding Google $1 billion and giving OpenAI zero dollars seems pretty smart to me, Apple‘s largest acquisition in their entire history is only three billion dollars that’s planning.
AI is overhyped regardless of whether Apple exists or not. They’re getting attention only because they didn’t jump on the bandwagon, and from a purely investment standpoint their fiscal discipline is honestly astounding.
If they've gone all in on that, it looks even worse for Apple. Qualcomm's Hexagon has been a better NPU since the beginning. Now even Mediatek is better at it than Apple. Blogs like macrumors and daringfireball are notorious for not having any clue about what is going on in the broader industry.
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.
Read a little more of what he's written before you praise him, so you don't end up looking as dumb as I did.
Ed may be right about some of his claims, but he is 100% a crank, and he makes so many incoherent claims I'd say that if he's right, it's in the nature of a broken clock.
If you want to dismiss him for being hyperbolic or under estimating the usefulness of AI go for it.
If you are making the case that he is full of shit, please provide some actual evidence of his main thesis that AI companies are not going about this in any sustainable way
> his main thesis that AI companies are not going about this in any sustainable way
The point isn't that his thesis is fucked, just that his analysis tends to be free with details in a way that shouldn't inspire confidence, e.g. mixing up EBIT and EBITDA.
Someone elsewhere in this thread referenced this guy [1]. His takes properly summarise the lack of care Zitron appears to display for getting detailed arguments right.
If you're deeply familiar with financial jargon, I think he's fine. But if you're not, it's easy to get whisked into woo-woo nonsense that's falsely precise due to mis-using (and in some cases, very clearly mis-understanding) core financial and economic concepts.
Ed's not a crank, but you need to pay close attention to the topic of his proclamations to separate the wheat from the chaff.
He's right about some things -- off the top of my head:
-- OAI's perpetual fundraising, due to their losses
--Coreweave
--Circular financing
--Revenue/capex imbalance
...all forensic economics analyses.
He's often wrong when on the topic of capabilities and adoption -- he mistakes a snapshot for a trajectory, and treats current limitations as permanent:
--Hallucination/unreliability an unsolvable problem
--OpenAI failing
--AI capabilities have plateaued / models will stop getting better
--Saying Microsoft's cancelled leases meant the bubble was popping
--Agents a marketing fad
--Gross miscalculation of OAI 2025 revenue
--Lack of adoption
--AI not getting more efficient (compared to 10-40x reduction in inference costs YoY)
--Repeating "95% of pilots fail" but ignoring massive
bottoms-up adoption
Christensen got this right when he had the insight to judge a technology by its rate of improvement relative to what a market needs, not by whether it's good enough today.
Put Zitron back at the early days of the integrated circuit and imagine what he would have written about the technology.
Some open predictions:
--Bubble is going to burst -- I tend to agree with the thesis that "there is some marginal capacity being planned/built today that will never pay back its capex"
--Losses mean there's no viable business here -- the recent moves by all of the players to some form of usage based pricing show there's price discovery occurring. I don't know of anyone who is stopping using LLMs because of the pricing changes...it's just changing their behavior.
>And yes, that sound you hear is the slow deflation of the bubble I've been warning you about since March...
>How does GPT – a transformer-based model that generates answers probabilistically (as in what the next part of the generation is most likely to be the correct one) based entirely on training data – do anything more than generate paragraphs of occasionally-accurate text?
etc.
Basically the essence is he's skeptical of AI / LLMs being any good so thinks all the investment is down the drain. Meanwhile AI progresses such that Fable can probably beat most humans and IQ tests, maths and the like. And the 'bubble' didn't deflate yet.
My take is that as a PR guy, used to people hyping stuff and not that up on comp-sci he fundamentally doesn't get what's going on. He assumes it's all hype rather than the steady progress in computing reaching brain equivalent levels and beyond.
He's not wrong about the numbers, and you may be seeing what you want to see a little, here.
Fable scoring OK on some benchmarks does not the mean the investment has financially paid off, because that's not how ROI works.
You're right that no bubble has deflated, but I think we can all see that a) there seems to be a bubble - I'm old enough to remember the dot-com era bubble, and this definitely feels like that, b) the only company clearly making a profit on AI right now is NVIDIA, and c) the net economic value of the sector as a whole (i.e. money out > money in), is still unproven
It's not even obvious to me as somebody who is up on comp-sci, that this isn't all hype, that the progress is/will be steady, and that reaching brain equivalent levels and beyond using these architectures will be economically viable.
I can create a perfectly good human brain in 9 months, train it in ~20 years, and have it pay off economically in a more proven way than [waves hands] all of this.
For some reason we've decided paying a hyperscaler the equivalent of a year's salary to do a job in a a day that a similarly paid and skilled human could do in a month is value for money.
You could probably give everyone in the US free healthcare for life and a free ride through college for less money than has been pumped into AI in the last 5 years, and have a more proven economic model.
Sure, time value is a thing, but given the error rates, the energy issues... this direction is not exactly a slam dunk as a net benefit, and I think that's all he's called out in any of the writing I've seen of his.
I also happen to agree with his take on Oracle, as it happens - they're only going to survive if it turns out they're a part of critical national infrastructure deep in the bowels of the US government. They look totally over-leveraged otherwise.
I agree it's a bit like the dot com bubble and I don't think he's wrong about everything but he seems to think it's a bit more like the NFT craze with nothing there rather than dot coms where the web/internet was of real importance.
I think both sides might be cherry picking the pieces that make their arguments the strongest - Pearkes has pulled out a few paragraphs, but there is a lot in Zitron's writing, so it's easy to select 6 bits you don't like/agree with or find fault with, but I'm not sure it undermines the rest.
I actually think Zitron would be better off focusing on those deals where there is clearly something weird going on (SpaceX IPO, Oracle/OpenAI/NVIDIA triangle, the neoclouds, and so on), than picking a fight with the one company that actually seems to be trying to play it straight in this sector (Anthropic), and I can see how he's covering the whole field with the same cynicism which might not be appropriate.
That said, I'm not entirely convinced that some of Pearkes' analysis deserves us all to align to the rosy bullish view he takes either.
Time will tell, it always does, but at least you've put forward some data unlike anyone else, so thank you for that.
Okay, but what’s your counter argument to Jim Chanos? Oh, that’s right, you don’t have one because he’s a tried and true Wall Street heavy who speaks in plain English. Ed and Jim are on the same page looking at it through different sets of prescription glasses. I hope you have your money where your mouth is, so to speak, because I certainly do (avoiding AI trade) and good luck with the SNAP paperwork in two years.
Let me guess, you disagree with his views on the utility of Ai (which he's pretty bad about) and you use that as cover and/or to negate his real journalism that is based in facts (Ai being an unprecedented & unrecoupable bubble littered with unprofitable companies thats likely to crash our economy unless these Ai conglomerates cozy up lawmakers to situate themselves for a bail out)
My disagreement is that every time he turns out to be completely wrong he silently moves the goalposts instead of acknowledging it.
For years he was all-in on "AI is useless, it has no business value at all" claims, and then when that was decisively disproven by Claude Code, he didn't spend one second on self-reflection on why he was wrong and whether this might mean he is wrong about other things. He just smoothly pivoted to "AI companies will never be profitable, tokens are hugely subsidized". And now that that's about to be disproven (word on the street is that Anthropic will soon report profitability, and both Anthropic and OAI have repeatedly said inference is profitable), he's pivoting again to "tokens are too expensive and the ROI isn't there for business". No correction, no reflection, just "we're at war with Eurasia, we've always been at war with Eurasia".
Even if he’s sometimes right, there are better analysts out there who are also right and have the honesty, humility and integrity that Zitron lacks.
At this point I have yet to hear a solid argument against his arguments that use half as much evidence as he does.
That isn’t to say that he’s right, that’s just to say that while he gets creative in his phrasing numbers don’t lie and I haven’t seen anyone else present competing numbers that make sense.
If anyone has them to the degree with which he provides them then please, by all means, I’m interested.
Ya, there are already three pure ad hominem comments here with no examples. Zitron isn't even anti-AI per se. He clearly talks out his ass at times (like AI doesn't work for developers... which he's not saying so much anymore, I don't think) but his main talking points are around numbers and profitability.
As a C++ developer, AI does not work well for software development in my experience. Maybe LLM developer tools work better if you write React or HTML/CSS, but they have been inadequate for my use case since their arrival.
Casey Muratori, who doesn't use AI, made this point on a podcast. Specifically he said that he doesn't blame web developers for wanting to use AI because it's such a mess of an ecosystem and impossible to keep everything you need to know to be successful in your head.
I'm a web developer to myself who has done zero game or systems programming so I can't speak to that side of it, but you reminded me of it!
I find it bad for generating code but alright for review, I put it as a tool to run along static analysis to catch if I missed something on a code change or merge request (being specifically about C++)
You were just attacking his character without backing it up? Do I not know what an ad hominem is? If that's the case, then that's embarrassing for me. Unless you're saying that your remark doesn't technically bash his character?
Yeah, you're off on what an ad hominem is. I would dispute that I attacked his character at all, but that doesn't matter: an ad hominem isn't an "attack on character", it's an argument that is predicated on an unrelated character attack.
My argument here is just that Zitron says things that are wrong all the time.
You could fairly rebut me by saying I didn't substantiate that argument. That's true! That doesn't make the argument fallacious; it just means it isn't facially dispositive.
And I agree that he says things that are wrong much of the time, but not all the time. I also don't like him enough to care, but I do care enough to bring it up when I see multiple random attacks.
> Ya, there are already three pure ad hominem comments here with no examples.
You should probably understand Thomas' comment in part as a reaction to Zitron attacking Thomas in a pretty low-brow fashion, essentially for saying "hey folks, AI coding works and you should be using it":
The bottom line is that Ed Zitron, like Gary Marcus, built their entire brand on being AI contrarians; they can't say anything positive about it without qualifying it with a more damning negative. There's nothing wrong about having voices like that, but it makes them unreliable narrators. I am apprehensive about AI and its externalities, but I don't want to be caught citing either of them for that reason.
I swear to Christ before this comment I had completely forgotten that Zitron had ever written anything about me. Zitron isn't an engineer and doesn't code, so I'm not all that het up by his attempt to rebut me.
> You should probably understand Thomas' comment in part as a reaction to Zitron attacking Thomas
Sure, but an ad hominem is still an ad hominem regardless of one's personal feelings (it took me a good minute to figure out who "Thomas" is).
Otherwise, I agree, and I try not to cite extremists or bullies on either side. I don't even really care that much about defending Zitron, but I do put him on in the background because he does so many podcasts as I find it soothing to listen to someone rant and rave against the tech bro overlords, even if some of the shit he says annoys me. But he does show his work sometimes and wanted to point that out.
I don’t have numbers, but I would like to point out that he's missing a crucial number as well. The fact that maxing out a subscription's limit makes a user generate an amount of tokens that costs much more than the subscription itslef isn't enough to back his claim. What we need to know, is the average use of subscriptions. Think about Gyms. My gut feeling is that, if we run the numbers, we see that by maxing out a gym subscription we can get more value out of it than what we paid for. And still, gyms are profitable because the average customer is using less than what they're paying for. Now, I still think AI companies will have to rise their prices (they are already doing that), but the situation could be less dramatic than what people pointing at those figures based on subscription maxing are claiming.
It could have worked if they weren't leveraged to their necks on spending. All based on the assumption that they can make something like 10x or higher in a few years.
Another point against your gym analogy. When people don't have that much disposable income why would they subscribe to something they semi regularly use? And if it becomes more expensive why would they not cancel it?
You do really need to see the useage numbers for this. Movie pass famously thought they could price movie tickets like gym memberships - I assume they looked at how often the average person watched movies in theaters at the start - but then found out having a subscription to see movies for free / cheap any time you want changed that rate substantially and they ran out of vc money.
Having a subscription might make people run agents overnight, or casually generate images for fun, or start more coding projects. The reason gym memberships work is that people like the idea of going to the gym a lot but don't really enjoy actually doing it very much and fall off over time, leaving the gym for the rare person who's really into fitness to use it cheaply.
is AI like that where most users will get bored? or is it like movies where they'll try to get their money's worth?
> is AI like that where most users will get bored?
User's getting bored will also spell trouble for the subscription model. I don't think we'll see a world where the average normie is going to be running agents in a loop overnight to make software. They'll get bored use it to cheat on their homework and as a Google replacement, generate silly images for a week then get bored of that, and then realize the have no actual reason to be spending $100/month and drop down to a lower tier or just cancel all together and live within the free-tier limits.
So if that happens, the labs are now left only with power users that can actually generate asymmetric cost. The market will bifurcate into free-tier users and "get their money's worth" users. So the risk isn't that the top 5% of users use more tokens than the subscription has any right to give you, it's that everyone else cancels their plans and only those top 5% of users remain.
The labs will have to put stricter rate limiting and token limits on the subscription plans to avoid MoviePass style burn.
Gyms get away with it because most don't let you cancel easily. You pay monthly, but are locked in for 6 months or a 1 year at a time (Adobe subscription style). Maybe the labs will start to do the same? Let you pay $20/month, but you are forced into an annual commitment?
I gotta say that if their best bet is “people forget to use it” I don’t think that that’s going to drive the adoption they need.
Especially if part two of your theory comes to fruition and they have to raise prices. I might of forgotten my Netflix subscription when it was 10 dollars a month but certainly not if it was 200 you know?
You're biasing for quantity of argument vs. quality of argument. That's Zitron's MO: writing 10k word rambles that cover 20 topics, ostensibly with tons of links to sourcing, but with most of the sources not actually saying what Zitron implies they do.
Nobody has the patience to read a comprehensive point by point rebuttal to any of it, it's just too tedious.
You say "numbers don't lie". But they do, when the numbers being presented have been adversarially chosen. You just find numbers (no matter how low quality) that fit the chosen narrative, you throw out the numbers (no matter how high quality) that rebut it. If you're trying to predict the future, you can't afford that of bias. If you're an anti-AI grifter, you can't afford to not have that bias, your entire livelihood depends on only showing things that support the narrative.
So I’ve heard this argument before but I’ve not even seen someone demonstrate HOW the numbers are “adversarial”.
This is the crux of my argument: Zitron provides numbers and he provides a lot of them sourced from reliable and trustworthy sources. He provides counterarguments to numbers-sparse arguments by using even more actual, real numbers with a logical theory formed from them.
This should make it rather easy to dispute his claims, no? So then where are the equally rational disputes?
Some of his numbers are rebutted all the time. It doesn't matter, because he will just ignore the rebuttals and just keep repeating the invalid or irrelevant numbers. For example, a few months ago he did some bad math with a broken spreadsheet to prove that Anthropic's ARR numbers had to be incorrect. That was quickly rebutted[0], but Zitron continued repeating the argument anyway.
Just this article has a dozen instances of numbers being used to prop up the argument. They're mostly irrelevant to whatever argument Zitron is trying to make at the time. Some others are straight up misrepresentations. HN really is not a good forum for point by point rebuttals for that many cases. Do you think there's a particularly high impact use of numbers in this article?
"The biggest thing we’ve learned from the large language model generation is how many people are excited to replace human beings, and how many people just don’t understand labour of any kind"
Really? I think we've heard C-suite blabbermouths and borderline nontechnical tech CEOs being amplified by social media making these assertions, but boy did the weakly efficient market deliver a relatively swift correction to that mindset, no?
Q: "What would it take to change your point of view?"
A: "[AI] would have to solve all hallucinations forever, which they are completely incapable [of]."
Can you produce a human that is infallible? I'll wait.
My hot take is AI is increasingly less unprofitable as the cost of serving tokens drops and Nvidia's ongoing offers to guarantee profitability is a sign that it isn't stopping anytime soon.
You either believe in the underlying science and technology or you don't. But in a world where AI is a fad like cabbage patch kids and beanie babies, what's next?
> But in a world where AI is a fad like cabbage patch kids and beanie babies
AI will almost certainly not be a fad for software developers, or at the very least for web developers, but it's certainly possible that other industries will look back on "experimental AI days" and chuckle.
Even if there is a financial bubble (that pops), the technology is still not going away.
As for what's next, I dunno. I've heard people talking about similar tech that doesn't use neural nets/etc, but that is out of my wheelhouse.
The possibility of AI sticking around is apparently pretty threatening to the Zitron cult or what seemed like an innocuous response wouldn't be in such negative territory so quickly.
But I think AI is here to stay and it has already proven useful. And sure, we will look back on today's models like we look back upon Gordon Gekko and his giant early cell phone. And I cannot fathom how one can not separate AI the science and technology from AI tech bros and CEOs. I agree the latter are going to go through some things as the AGI fails to arrive on their schedule, but IMO there's no turning back on the technology nor should there be.
> But I think AI is here to stay and it has already proven useful.
Yes, I said as much. I've said this is two other places in this thread, but when they are talking AI being useful, they aren't just talking about for software development. The software industry is seeing far more AI adoption, by a very large margin, than any other industry, and the success of the current companies hinges on it being heavily adopted everywhere. I don't really care to defend Zitron because he does speak out of his lane far too much, especially when it comes to software, and those are the points the HN crowd latches onto. But that's not the bulk of what he talks about.
So then what is the deadline for AI reaching into all these new industries (or are you asserting it will never happen)? And you're right, technical people latch onto him sounding like a horse's ass on technical subjects and why shouldn't they? IMO this isn't the same as Elon's endless empty FSD promises (which I called out the whole danged time from the beginning FWIW), but I get why people assume it is, I just don't agree.
I didn't have a lot sympathy for the guy who described the Internet as a series of tubes yet I believe Al Gore caught far more flack for that bit about inventing the Internet than he deserved given the inventor of the Internet defended that very claim.
So if the bulk of what Zitron's talking about is valid, then what are the main points that aren't self-evident i.e. there's definitely either a buildout or a bubble in CapEx and only time will answer that question, not hot air and blatant market manipulation. What is he bringing to the conversation that we're all missing? Seriously, educate me.
Sorry, you're taking this up way too many notches for me to want to continue this convo. But as I said, I have no interested in defending Zitron in general, I made no claims about him being groundbreaking, and I don't even take him too seriously. I was responding to your points around AI's usefulness and how the discussion is almost always localized around "AI as a software development tool" here on HN. For me he's just a soothing counter-voice to the awful and inescapable hype and doomerism coming straight from the American AI companies themselves (as well as people I've been forced to interact with in the programming community). He does show numbers about why he thinks some of them aren't aren't going to survive. This is all speculative (not sure why I feel I have to say this but I feel I do).
> So then what is the deadline for AI reaching into all these new industries?
Obviously the "deadline" would before the US companies go bust (again, IF they do). And I'm confused about your use of "new" here but I'm going to assume you mean "other existing industries that haven't adopted yet." The discussions around this are that several industries already jumped on AI then walked it back, so it's more that we're already past the "reaching in" stage. I don't have links for you but there have been several articles about companies firing people "because AI" then hiring them back. I suppose AI drive-thru order takers would work as another example. Again, I don't have the numbers on this myself. And again-again, none of this is "in defence of Ed Zitron" or "this is why Ed Zitron is great" or any such thing, but these are the things he talks about (and once again, no, it's nothing groundbreaking).
I thought Ed Zitrons argument was just corroborated by Nikkei last Friday with the new report that 1.6 Trillion in SPVs accounts for off-books debt, on top of the $1.4 Trillion of on-books debt that's in these AI companies?
The main argument of Ed Zitron is just that. Huge huge amounts of debt that's due "soonish". Every SPV is different but we are fast approaching the point where this all starts to become real.
Now I dunno why Ed Zitron feels the need to write 10,000++++ word articles that only says "more leverage than people expect", but that's really the crux of things.
It all comes down to the fact that Zitron thinks these AI companies are over levered and have significant chances of collapsing industry wide.
Yeah he uses more words to say it and bad arguments as part of it. But so did Galileo's "The tides prove that the Earth is moving around the sun" argument. Some arguments are bad, even if the overall point was correct.
I think given the new Nikkei report from last week, we are seeing more and more of Ed Zitrons arguments bear fruit, after years (literally) of him calling wolf.
It all is crazy talk until real numbers come out and prove him correct. But it seems to take a long time for those numbers to materialize.
“Per my own analysis, NVIDIA’s predicted $1 trillion in Blackwell and Vera Rubin GPU sales (by the end of 2027) represents around 40GW of data center capacity, which will, assuming a PUE of 1.35, result in around 30GW of usable capacity. At a cost of around $12 million a megawatt, that works out to around *$435 billion in global annual compute revenue to make these data centers necessary.*“
> He's a college drop out with no experience in programming or technology.
I mean Zuckerberg is a drop out.
zitron has been around since before flash was dead, more over hes worked in PR so like anyone with heavy involvement with c-suite, sales and journalism, you get a good nose for bullshit.
That said, do I agree with his assertions? no not entirely. But. I suspect its a bit of an audience capture thing. He makes wild statements, then goes on a flight of fancy to back them up. I appreciate that, but it also lets me decide that he's going down a path that I feel is not backed up by his data.
This but instead it's people insisting that AI will replace my job (been three months out for at least 3 years now) because they know it involves words.
To be fair he has a background in technology PR and Marketing (which he’s been very critical of) which I would count as more worthwhile when assessing the actual value of the product.
I mean you can say he’s not a “science guy” but he’s undeniably “smart”.
While I don't really care one way or another, Zitron isn't arguing AI as a technology is going to fail and go away, but that the numbers of the American companies make no sense and there is a good chance they, or at least some of them, will go away. He does argue that there are large amounts of people who just don't care enough about "AI," and this is likely true. The thing to keep in mind though is that he is not talking about software developers here. What is evident about some of the comments in this thread (and HN comments in general) is that many people on this site seems to think that whenever anyone says anything along the lines of "AI doesn't work for me" that they automatically assume they are talking about "in software development" when they aren't.
the pro-AI side on the other hand has poured billions into ads and marketing and CEOs are forcing it on people due to a combo of FOMO, personal investments in AI (CEO, board, investor), etc
I think AI discourse is revealing some new contours to the information bubbles that we all live in.
folks in the US have been pretty aware that bubbles based on political opinions -- we're all surrounded by news from "our side" yet we know the other side exists in some other bubble. but for AI, we're all either surrounded by pro- or anti- opinions and its a little shocking to learn that theres a parallel internet with the opposite stance. especially shocking when you find somebody that lives in teh same political bubble but opposite AI bubble.
Both can be true. AI is very, very polarizing. The entire Internet is also filling up with AI generated content, some slop, some okay. There is also a very pro-AI managerial class that wants to replace everyone and everything with a Claude project so they can "just ask Claude!"
The vast majority of what I see out there is relentlessly negative about AI. It is kind of absurd to call this the "alternative" unless the implication is that the rest of AI skeptical writing is not "well-argued."
A resolute anti-AI perspective is a breath of fresh air compared to the middle-of-the-road "AI made me 10x more productive BUT"-style comments.
My professional opinion is that LLM technology doesn't work very well for software development and I'm not interested in hemming and hawing over its supposed benefits any longer.
Talking with a friend of mine who's been in development for some time and I had been talking a little bit about the skill required to wield AI effectively and that people were expecting it to be something that required less skill than it does.
He said his version of this observation is "your agents are only as good as you are." I think he's right, and the key is to practice and build the skill.
that doesn't sound like a net benefit at all. I think mostly people just enjoy playing with these toys, and good developers are still good with them, bad developers still bad
As I mentioned in this thread the other day, it's not worth debating AI skeptics anymore in 2026, with step changes like Fable and Sol. Maybe it was the case in 2025 and before but the field has improved rapidly that it doesn't make sense to listen to people with AI experiences prior to that who haven't touched it since. Does AI make mistakes, sure, but increasingly fewer.
My dude. People like you have been making this same claim for literally years now. We've been getting it for every single model that came out ever since 2023. It's never yet been true. Don't act like it's the fault of your interlocutors when they are skeptical of a claim you make, instead pay attention to the fact that you are following up many people who falsely claimed the same thing. It's perfectly rational to be skeptical under those conditions.
I'm generally pretty accepting of trust me bro style arguments where we some claims presented without evidence. Especially for AI, sure some model just rm -rf ed your home directy bro sounds bad. It works fine for online discourse.
This is less agreeable. You kind of just made statements without even having claims (even without requirement of evidence or details) to back them up.
I don’t think people like Linus Torvalds or Stephen Wolfram feel like language models beat them, just they’ve managed to find specific cases where flawed brainless workhorses serve their needs.
Some developers work better with LLMs in their workflows than others. Some problems are easier for LLMs to generate a reasonable solution for than others. Some folks prompt minimally and see what the vibes bring. Others start with a detailed architecture and implementation plan.
The individual results will depend quite a lot on the person, the problem, and the approach. It's not a guaranteed winning formula for everyone.
While that's true - it does not impute Ed's position with greater accuracy or authority. I don't know if Ed is wrong, but I get this vague sense that he is being led on by his emotions.
I am sympathetic to some of his criticisms that the enthusiasm and financial commitment has run far ahead what can be delivered. But I don't quite share his intensity over the doom and gloom. There probably will be a correction. It will probably sting. But I don't expect it to be the near wipe-out that Ed's passionate voice seems to steer towards.
I find it weird/funny you are getting so many hate responses towards Ed, still no counter-arguments at all. When asked why he is wrong, they vanish. From the outside it looks like a coordinated campaign to try to discredit him.
Happy to read why is he wrong, and change my mind, as long as the arguments provide the same level of analysis he provides.
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.
Well hold on. "They" also thought tokenmaxxing was a good idea, until they didn't.
Whether companies pay will come down to the bottom line once the hype around the country club dies down a bit and for that it's still TBD on the actual product cost impact.
Not sure about that. As I replied elsewhere, it's a forcing function:
> People misunderstand the point of the tokenmaxxing time period, it was to force people to use AI so as to not have them stuck in their way, as some people are, and then to evaluate how it can help the company.
That is a truly commendable steelman. But let's be honest, there's basically no chance that was true. Management is frequently stupid enough to not think through the second order effects of their policies, and that is likely what happened this time as well.
Sure, but what you're not taking into account is that $1500/month is subsidised by VC money.
If Uber were asked to pay full price, they'd either get the same value for significantly more outlay, or keep their outlay capped but get significantly fewer tokens.
This would, I'm sure you'd agree, change the ROI outcome.
Zitron is arguing that if we all pay the true cost once the subsidies go, we'll either stop paying for the value we get today, or we'll find AI to be less useful than we do today, and those businesses won't be sustainable, and then <pop> goes the bubble.
I enjoy keeping an eye on https://isaiprofitable.com to see just where we are overall. Interesting reading if you like to think about such things.
that $1.5k is ostensibly not coming out of productivity gains from the AI though.
it's coming out of the salary budget from what i've seen. i've seen a company both say "AI max. it's the future! don't be late" and then go on to increase limits to compensation across the board.
Very true and I don't think you can completely discount the end consumer market either. A large number of the people not currently paying for a subscription are becoming dependent on AI enabled search results. I imagine monetization of those users is in its infancy and will rival the search result ad business eventually
For me, the economics are the exact opposite. I'd pay $1000 for a good gaming VR headset, but not $4000 -- because gaming is just a hobby that isn't really worth that much to me. Gaming VR headsets have been around for well over a decade, I've "been there, done that", and it's just not something that's worth that much money to me. However, I have no problem paying $3500+ for something that creates a new category of productivity and entertainment experiences for me.
Besides that, I actually disagree that the Vision Pro is not good for gaming. It's not good for traditional VR gaming, but using apps like Portal it's phenomenal for, e.g., playing existing PS5 games on a massive virtual screen, which in many ways I actually enjoy more than VR gaming, which is far too limited in comparison.
It's the same decision-making process that tried to do a electric car for years, and canceled it after spending time and money. In that exact timeline, China made great electic cars and new brands, Tesla advanced and Spacex launched dozens or more rockets. Apple is so far from any big new tech, they and market don't even realize it.
Apple had more money and influence than combination of all of them, yet failed with car and VR. Why would anyone think they can't fail much much bigger with AI and LLMs ?
> I will never understand the decision-making process that led to "Let's build an awesome VR headset that can't do gaming".
FOMO, someone else being successful in VR (and now in glasses) is a nightmare scenario for Apple because it's not their platform. They've got a lot to fear from someone else owning the platform, having written the playbook on how that platform would be controlled and exploited at everyone else's expense.
Imagine if Apple doesn't make their glasses: Meta keeps selling truckloads of them, as they get more powerful the smartphone becomes an optional accessory and then an unnecessary one, eventually they become an alternative to a smartphone and an app platform for third party devs.
It's the same story with VR, Apple does nothing and risks Meta or Steam building a viable platform where people want to use software instead of on their iOS devices, until the hardware can replace iOS devices entirely. (although VR is certainly more of a moonshot)
I think the manufacturing analogy is apt. If you consider both the need for minimal COGS and the large R&D expense, it’s kind of like… biomanufacturing. You spend a lot on R&D to develop your bug, which at the end of the day is just a very complicated sequence (DNA vs model parameters), and then you expend a ton of capex (or rent someone else’s capital equipment) to produce a relatively commoditized product (a drug, a protein, food…) at as large scale and low cost as you can.
But that also makes the case for government subsidization + bailout, if you accept it as a capability critical to national security, but one that may not be profitable to run within the US on its own merits.
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.
> AVP wasn't released early (late, if anything), and it wasn't a dud because of Tim Cook's disinterest.
Depends on what you mean by early.
WAs it released before the market was proven? yes.
Was the whole user experience up to scratch before it was released? no.
As someone who worked on the Quest pro/3 ecosystem, We were shitting our selves, because we knew that apple would only release something when the user experience was right.
Oculus just shat out features as and when performance cycles needed juicing.
Sure the UX was good, but it was less good than I expected. The headset was bulky and surprisingly off balance. It wasn't an all day wearable (i mean the quest pro was actually comfortable by comparison)
The resolution wasn't that great and the only thing you could do with it was either look like a twat recording video or have spreadsheets pasted everywhere. I was expecting an OS where we could put things on work tops, create augmentations to my room, decorate or be creative. Instead we got OSX XR.
Tim Cook was probably mostly disinterested because it didn't seem like a real business opportunity. Wherever AVP fell on the VR/AR spectrum, VR (while having other opportunities) was mostly a niche for high-end gamers and AR, whatever the possibilities in some future idealized form with fashionable glasses that give you lots of information about what/who you're looking at is a long way from reality.
I think it's the opposite. None of the players in XR wanted to be Apple'd, least of all Apple. That is, create a base product that someone else would refine in order to drink their milkshake. Apple delayed the AVP multiple times hoping to avoid getting sniped by Meta and Google et al., each time requiring a partial rebuild as the underlying tech advanced. They would have loved to have been able to get something out when Robert Scoble was initially raving in the late 2010s, but everyone was on the same page about not wanting to sink billions into development only to have to cede the market to someone responding to your strategy. So, instead, they all dragged their feet.
If XR companies had been as "reckless" with their product strategies as mobile companies in the 90s and 2000s, AVP would have had its iPod moment 6 years ago and we'd be hurtling towards the facephone. The market suffered because of the entrenchment of incumbents with all-too-clear memories.
Think Apple fell for the trap of assuming they would eventually solve the glasses form factor and then fell for the second trap of convincing themselves that passthrough VR was the same as their real XR vision.
I think you're absolutely right. Apple couldn't be seen as ignoring the gamer geek (or other potential opportunities) space as it wasn't very interesting to them at their scale and that automagical AR was a decade or more away.
> 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.
I'm of the opinion that the market will need to expand, horizontally. That's what's happening, with low-cost LLMs. I already know lots of "regular folks" that are totally hooked on LLMs (mostly ChatGPT, at the "regular mensch" level). They usually use the free tier, but a lot of them are doing the $20/month Pro tier.
If anyone is in my age group, they remember when ATMs were free. It was the "heroin dealer" business model. It worked out well (for the banks). The Chinese did it with manufacturing.
Getting people hooked on subsidized junk, is an age-old (and highly effective) business model.
Think of all the shops that will soon be composed of people that simply can't even get out of bed, if their LLM is not available. If the LLM dealer starts raising the price, there's no choice. I suspect many of the valuations are taking this into account.
> "Junk is the ideal product... the ultimate merchandise. No sales talk necessary. The client will crawl through a sewer and beg to buy."
LLMs are rapidly becoming commodities with open weight and Chinese models. Frontier providers won't be able to raise prices forever, indeed we already see evidence of decreased prices from such competition.
And ATMs can be free still, if you use a bank that refunds all worldwide ATM fees, such as Charles Schwab.
Anthropic's offerings are vertically differentiated. However from an economical stand point, it also be true that they are not the preferred option for many.
> Think of all the shops that will soon be composed of people that simply can't even get out of bed, if their LLM is not available.
I'm biased because I don't use AI, but I don't understand why you wouldn't fire these LLM-addicted people and replace them with people who can code or write an email without the use of an AI assistant.
I use LLMs all the time, but I'm also a highly capable and experienced engineer that can definitely work without. I have just found that the LLM is a force multiplier, like I haven't had before.
Same, though the types of jobs I’m working on now require the speed I get from LLMs. If I had to take my current projects and switch to hand-coding I’d have to extend the timelines and practically add a zero to the price.
If you don't mind sharing, what types of jobs are you working on now?
I haven't personally seen a speed boost from LLMs, but my experience with them suggests that they could be effective in some domains and inadequate in others. E.G., I have observed that LLM tools are inadequate for generating C++ code.
- An electrical component "synthesizer" which takes natural language specifications in (e.g. from an RFQ) and uses material data sheets and core physics calculations to design new components. (Python)
- A transcript processing pipeline which reads what happened on a call, identifies participants from the CRM, drafts structured call notes, and posts them to the CRM with the proper associations after a human-in-the-loop confirmation. Fairly complex server & web app. (Go)
- Interaction design prototypes for a family of smart devices, some with screens. Brought together as an interactive web app to see how each device behaves (vanilla JS). Each prototype generated with the help of an LLM, including 3D animated examples (Claude driving Blender).
Just to counterpoint the adjacent replier. I have claude, chatgpt, and gemini subs and rarely even use even close to 50%. I code c++ everyday. But im not full in on agentic workflows which is probably why. I mostly do fairly targeted things to my various code bases.
Interestingly, that METR study has updated data for 2026 that shows a speed up, although they admit that the data may not be reliable because of changed pay rate for participation, but this quote is telling:
> The primary reason is that we have observed a significant increase in developers choosing not to participate in the study because they do not wish to work without AI, which likely biases downwards our estimate of AI-assisted speedup.
So compared to just last year, they had a hard time finding participants because too many didn't want to work without AI.
I stopped reading at maybe 25% into the article. It's obvious garbage.
He selectively quotes the max theoretical enterprise pricing equivalent of fully using the private subscription. Did SemiAnalysis not also claim a very high margin?
He throws in doubt about the providers having decent margins, which he claims is made up by "AI boosters" rather than leaked financials and open-weight pricing.
Then next he talks about "the real cost" of inference as if it was in any way realistic that labs price the enterprise plans near cost, like he seems to imply.
Then next he claims AI is actually slowing developers down and there isn't much difference between the models.
Welcome to Ed Zitron. There is a reason this man doesn't heavily short the same companies he criticizes. Be wary of anyone that won't put their money where their mouth is.
> There is a reason this man doesn't heavily short the same companies he criticizes
yes, because in order to take a short position you have to predict exactly when the bubble is going to pop, which is different from predicting that at some point it will
Not quite. You could buy a long-dated put if implied vol and current rates are amenable. This protects you against the underlying going up significantly. You might also predict that it won’t go up too much, and just short the underlying with a stop loss.
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
> 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.
Something to consider: would your description also apply to the dot-com boom of the late 1990s? The internet was real, the ideas for internet business were real, and we were not going to the previous reality. But the valuations weren't quite right and a "correction" happened at some point.
When people talk about AI crash, that's what they mean. Not that AI is a hoax, but that the correction could be quite violent and have effects on the broader economy.
But there’s a difference between fearing mongering with a crash and a long term correction. The correction would make all the startups/companies that created a wrapper around frontier models go away, but the ones that truly solved a problem would remain.
Also I remember reading that Anthropic is on its way to be profitable in 2028
With respect, the way you talk about corrections makes me suspect you weren't working in tech in the US at the turn of the century.
A "correction" in this model isn't a reasoned and gradual re-evaluation of the market cap of every company. It's a broad pullback where people panic and no one wants to be left holding the bag. Money shifts into other assets for years and tech employment, incomes, and the availability of funding takes a big hit.
As to your comment about profitability... every unprofitable company is on a path to be profitable. Some even get there.
Plus, look at it this way: Kellogg's is a profitable company and a part of almost every person's life. Does this make them worth trillions of dollars? No, they just provide boring, commodity products, their valuation is basically a low multiple of the assets they hold and the revenues they bring. There's a future where OpenAI or Anthropic are more powerful than all the world's governments combined, but also a future where they're Kellog's.
> [local, but for] extremely niche or higher intelligence tasks
Access to a centralized copy of the web/literature/media, scraped and indexed/integrated, seems another non-local center of gravity. Versus a local model's last minute reaching out to "manually" search and browse.
Also mass parallelism for large ensembles. Perhaps unless/until we get those local models as 10k+ tok/s chips. Local can follow frontier because frontier is still sort of "expensive rack local". If STOA becomes massive burst-parallel ensembles, that following may get harder.
> but don’t think there’s gonna be a crash […]. 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 […]
That’s the crash… that’s pretty much exactly Ed Zitron’s thesis
On device models would work for most of the use cases we have, but we would have new use cases in the future (e.g. with AR/VR glasses, humanoid robots) that would need more advanced models that cannot run on device and frontier models can provide that
> 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.
I think the flaw in this logic is thinking about how AI is currently used only. Yes, Opus is good enough for the task you are asking it to do, but that doesn't mean that is all you will ever need.
As AI gets better and better, it will open up new use cases that require the better performance.
You don’t “need” any LLMs to do software development. Plenty of us were productively writing code for years before these models were released, and many of us continue to do so. The “AI” companies want to sell you hype. But you don’t “need” them - not if you’re a legit, professional programmer.
You could say this for any new technology though, it's a vacuous statement. No one needs a car, they can ride a horse. But step changes in technology do occur and raise the standard for everyone in a society, hence why we don't see horses for transportation anymore.
You don’t need it, but using it would make you more productive. As an example, I have worked in a code base which is pretty much opinionated. I have many Claude skills to build the boiler plate starting code and tests for the feature I have to build following the existing conventions and patterns in the code base and that saves a lot of manual coding time for me.
This is a very personalised use case, but I think everyone can find these kind of use cases that saves a lot of time for you.
The narrative that LLMs are essential to the future of the trade often feels like an assault on my professional expertise.
Whatever your experience is, mine is that LLMs don't work well for software development. I wish people who use AI would be more willing take that perspective seriously.
I have been programming for 35 years and a professional programmer for 25 years. Of course I don't NEED AI to program, because I did it before.
This is completely unrelated to the how useful AI is for programming or how much more efficient you can be with it.
I never said you would need AI to do things you can do today. When I said it will not be 'all you ever need', my point is that NEW uses will come that will need more powerful AI. By definition, you can't NEED a new technology to do something that is already being done, because the fact that it is being done already proves you can do it without the new tech. However, that doesn't mean the tech can't do something new that does need the tech.
For example, no one needed an airplane before they were invented. However, you do need an airplane if you want to get somewhere 6000 miles away in less than a day.
There hasn't been a single bit of technology that is 'needed' if you use your strict definition of the word, because obviously humans existed and survived before the technology existed. Being absolutely necessary is not what makes a technology persist or spread, that is an artificial bar to reach.
Some people have such a natural aversion to LLMs that they will make incoherent arguments as to why they should go away. There are plenty of legitimate concerns and critiques of LLMs, you don't need to articulate arguments that you would never make about any other piece of technology to argue against their usage.
The money being poured into AI infrastructure means there is a market for new ways of doing things that take 1/1000 of the power or are 1000x faster, or both.
And there are many such moonshot startups.
AI on GPUs is an efficient as gaming on CPUs.
All that math where perfect precision is not required means that you can’t tell do things in different ways.
Why is on-device AI the future? What is your reasoning behind this? Look at the proportion of things we compute on someone else’s computer relative to what we compute on our own device. Why would this change for LLMs?
I think the reasoning is similar to why Amazon notification emails are very generic: user data is used for various purposes that a growing number of users and 3rd parties have concerns about.
Back of a very tiny envelope: suppose there are 100 million jobs that benefit from spending $10k/year in tokens. That's a trillion in annual revenue; at a 30% margin that's $300 billion in profits, which can easily support $6 trillion in valuation.
Thats great but this is like when 3g came out. Sure it was the future, but it was half baked, impersonal, expensive, unreliable and required a culture shift to be adopted.
Onboard decent LLM performance thats _power efficient_ is at least two/three hardware generations away. (assuming linear performance)
but, the valuations, with debt trade and private credit obscuring exposure is a recipe for disaster.
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.
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?
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
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 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.
I've never heard a compelling thesis for why this is a bubble that will pop. From my perspective AI adoption is just getting started, and every organization I see that starts on the journey only ramps up tokens dramatically. Even just sticking with current model capabilities we have years of business automation, personal automation, and so much more to saturate data centers. As the models improve that only makes more sense. Rather than Apple watching anything burn I think they are going to end up looking like nitwits who missed the boat.
I think people wish AI would succeed, especially in software, but this appears to be the only argument made. Numerically, AI still costs far more than it returns in value. Its a subscription service. Even if everybody is told they must adopt early and heavily eventually AI has to qualify its expenses to its customers or they aren't going to keep using it.
Think Netflix. If you believe the money spent on Netflix over the course of a year is less than the entertainment you receive back you will likely continue to pay for it. If you believe the money spent on all entertainment subscriptions exceeds the value returned you will likely start cancelling some, or all, of the subscriptions. AI is not immune from economics or human behavior. When customer company budgets get tight the money spent on AI subscriptions might be reallocated or reduced to prevent reductions in head count.
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?
Unpredictable AWS bills are a supply-side problem. He's talking about consumer behavior, and consumers of software do not expect a metered service.
But most consumer's aren't paying per token for access to models, so unless that changes and the labs start charging API pricing to everyone, it's kind of a moot point.
Enterprise certainly wouldn't fit. This argument seems to be targeted at the consumer market, which Apple deals in. Consumers are not paying varying Disney+ subscription prices based on how much they watch. So many points are still interesting. Though, I'm not convinced the consumer market is the primary target for AI firms.
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.
It's fascinating that you simultaneously argue that margins will expand... while posting about personal behavior all of which points toward commoditization and intense price competition.
What’s wrong here ? You push the router , you keep same subscription price , customers are as happy or even more happy because router will make process faster, the costs will go down . The market size will increase several times .
If you have properties of the market where your costs will go down , the size of the market will increase and you are top contender. How is that a bubble or a bad market ?
Sure you have risks of underperforming and lose the competition, but how is that different from any business in the world ?
I think Ed's argument is that given the infra investment the revenue won't be enough to pay back the original investors. In fact cheaper models make this worse for them. Who cares about the investors? Well it turns out the infra was financed in large part by debt that was securitized and a bunch of regarded investors that bought the debt looking for higher returns will be taking a huge haircut when the bubble pops. The amount of securitized debt is roughly the same as the mortgage backed securities back on 2007, ergo the prediction is a big recession.
What's unclear to me is if this is a systemic issue that's going to cause credit to freeze up but imo the opacity of the shadow banking "system" does not help here. If you see one cockroach, etc.
Agents will explode the use 100x or more , so even if you push into your router mix models that dynamically do inference that is 100x 10x 5x cheaper , they are still fine.
In fact spacex story tells you next : investments in infrastructure is the best investment. If OpenAI or anthropic have committed infra in the worst case scenario they can re-sell it with margin .
The only way it will not payout suddenly we wake up in the world where ai fails to deliver . Which does not seems to be the case .
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.
Isn’t Apple potentially under a lot of pressure due to the DRAM prices as well as having to compete with AI for fab capacity? Before all the price hikes their products were already priced at the top of the market so I would assume that memory getting more expensive will impact their sales or margin quite a bit as they can’t subsidize hardware like AI companies do (they can of course eat into their own margins a bit which I think they already do). So not sure if what happens right now is great for them, if the memory market collapses it will of course be good.
> Isn’t Apple potentially under a lot of pressure due to the DRAM prices
Maybe. On the one hand, if there is a strong ceiling to what users are willing/able to pay for a smartphone (might not be true, with all those people making $$ on the stock market), they will have to cut prices.
On the other hand, if one part gets more expensive, premium phones need smaller relative price increases than budget phones. Certainly, if they are willing to accept lower margins, premium product might get more attractive.
Also, iPhones tend to have less RAM than competitors, so this might hit other premium manufacturers harder than Apple.
Apparently RAM and SSD costs have risen so much recently that the bill of materials on the next iPhone could exceed the entire cost of the current one just on RAM/SSD alone [1]
[1] https://images.macrumors.com/t/PtKleXF2po_g3qSuA6VMOE90WEM=/...
Holy hell that’s a crazy image. Can’t even fathom how they will go forward.
> Isn’t Apple potentially under a lot of pressure due to the DRAM prices as well as having to compete with AI for fab capacity?
I'd qualify this with for now. It's not entirely clear what happens in the event the AI industry implodes, but I do think there will be a glut of memory and hardware up for grabs. It very well could crash the consumer hardware market as a result. The AI obsession and investment has burrowed so deeply across the US economy that I can't think of a single industry that won't be impacted, including agriculture.
> It very well could crash the consumer hardware market as a result
Tbh, I hope it does. Prices are outrageous, and I'm a firm believer in personal computing/having access to powerful hardware, privately and locally, is hugely important.
On a more selfish note, I miss the days of being able to build an outrageously powerful desktop for myself for relatively cheap. For now I'm still holding onto my AM4 motherboard w/ 64GB DDR4 and an aging GPU, praying my 7 y/o GPU holds up long enough for prices to drop again.
64 GB! you are lucky :) sitting there with 16GB, a GPU that already shows the signs of strain, and AM4 Ryzen 5 5600G. (MacBook Air M1 equivalent in single-threaded tasks.) apparently the CPU requirements slowly crept up across the line. and then there is looming AVX512. why "looming"? because at some point enough people will move to it that software will require AVX512.
Maybe a year from now we'll all be using MI300As and EPYC 9600s to read e-mails. Or Nvidia Veras, maybe AWS will sell some of their Graviton stuff in an effort to offset their capex.
Unfortunately, I don't think IBM Cloud will suffer enough for me to play Tux Racer on my very own Z17.
actually I'm looking forward to that :3 but that requires Claude et al. going bust and HN is gonna kill me if I wish that publicly
> including agriculture
in fact, agri sees its share of impact now! all of this investment has to come from somewhere, which means that money is getting sucked up into AI from the entire economy. job cuts in tech are part that, part general "de-bloating" of post-COVID tech. retail investors are a lot more likely to invest into AI than any other industry, although there are signs that big players already consider risks of overexposure to AI.
not to mention, agri saw a "bubble" of its own, surrounding Agtech, which peaked at 2021 and has been cooling ever since. scare quotes here because it was nothing like AI bubble
Sure, but DRAM is a commodity and is classically cyclical. This is a particularly large cycle but so long as the historic pattern holds (and there's not a ton of reason to think it won't unless the hyperscalars can go into greater debt indefinitely which given inflation and interest rates they can't) Apple just needs to get through this peak in prices, which they have a LOT of ammunition to do so.
Having to raise prices isn't a great look but when they come out the other side they can drop pirces and get a boost in volume or they can keep them high and enjoy a bigger profit margin.
Yes it does impact all hardware makers and they raised prices as a result. But again remember the financial reality here… DRAM is in short supply because of the demand for models propped up by VC subsidies and infrastructure providers going deeply into debt to buy all this stuff just as folks are realizing we’re on track to over build capacity. So yes Apple gets impacted a bit in the short term but in the long term they’ll gleefully watch it all burn.
Couldn't Apple afford to make it's own RAM fab etc at this point and have a dedicated pipeline of parts for its own products?
RAM production is historically not a great business to be in. It’s a mint today, but it’s an incredibly tough business to break into & by the time any hypothetical Apple RAM fab is up & running, it will already be losing money.
To give you some idea, here’s an Asianometry video describing Taiwan’s prior DRAM attempts: https://youtu.be/ehT3U935Pww
Let's be real here, Apple does not have to compete with AI for fab capacity, they can outbid anyone if they want. They have more money and longer contracts then anyone else. Apple customers will just end up paying for said DRAM price inflation.
The ones who will actually suffer is Apple 2.0 (presumably a different competing company), start ups, research, companies that are not worth trillions.
They are, for now; but also Apple is known for making long term price locked contracts and ensuring availability of semiconductors for their devices.
Everyone is paying more for RAM these days, I expect Apple to maintain their margins.
Curious, why would Apple want to wait for "all this" to implode? They've had on-device silicon for AI for eons now (in AI-years), so they could have been executing their strategy, whatever it is, all this time, no?
My outsider take is that Apple leadership never really believed in the capability of GenAI, and hence kept pursuing their pre-LLM strategy. An interesting signal about how that's working out now is that their AI org leadership was overhauled recently.
Others are betting on AGI/almost AGI like system. It's a gamble because no one actually knows whether or when it will be possible to create it. But if their bets pay off, they could outcompete Apple. Basically, other companies are going all in on the next card, while Apple is holding back and waiting to see which cards are dealt.
The problem is even if AI ends up being huge all these companies still have to worry about eachother and the Chinese models that are coming out. It's like a dollar auction and it's very possible we are already at the situation where they have all bid more than a dollar.
IMO AGI is a bold-faced lie, backed by the age-old American fantasy of free labor. The whole concept seems quite ugly to me.
In other words, the AGI hype kinda depends on AGI never being achieved. Billionaires don't want the worlds fundamental problems solved overnight for a simple reason: there's no money in that.
Moreso what I'm trying to say is that AGI is a return to the eighteenth/ninetheenth century fantasy of slavery as a business model.
AGI means cruelty-free robot slaves.
Cell phones are commodities, so how does Apple maintain that cash flow? It's almost like there is more to it than that...
Cell phones arent wheat. It is a pain to switch between OS's.
Cell phones are not commodities
Cell phones may be commodities, but iPhones are not.
Yeah, as an android user myself I switch brands all the time, but the iPhone users in my life all bought iPhones 15ish years ago and have kept doing it ever since.
The good thing of the iPhone is that your next phone is just like the previous one, but faster. Some features are added, some change places and some are even removed or replaced, but it doesn't compare to, say, move from a Motorola to a Samsung - they might as well be different OSs. Last time I had to relearn how to use the camera (and I AM AN EXPERIENCED PHOTOGRAPHER!).
I’ve had a few iPhones. Own 2 right now and I completely agree with your sentiment but while the hardware gets faster the software gets slower and worse. ;)
I just switched to iPhone and I have to say the UI is so bad. It will be my last.
No Apple was not sitting there and waiting as if its some grand plan. They tried their VR device which they don't even talk about now. They were late to AI and yes if the bubble bursts they will be at a better position but it's not some grand master strategy. Even inside apple there's been a lot of talk about how badly the company handled AI.
As for laughing all the way to the bank, I am sure memory manufacturers and Nvidia was definitely join them regardless of what happens.
Also comparing Apple (literally one of the largest companies in the world) to a company burning VC money makes no sense.
I’m laughing right now, Apple is in a very good position and it was a grand plan because OpenAI, i.e. Sam Altman pitched Apple and got nothing, you cannot tell me going into that meeting with Tim Cook at Apple that Sam thought he could squeeze Apple for 50 billion dollars? Like he squeezed Microsoft?
And what about Google? They probably thought for sure when they met with Apple that they would wipe out that $20 billion yearly payment to Apple every year for the default search position but the best they could do was to come out with a measly $1 billion a year refund both scenarios sound like Apple took it in consideration and determined that it was not worth the price, that does sound like a plan (The Art of War type) when the rest of tech is going crazy over AI.
A better way to frame this might be that Apple tried VR and quickly realized the market wasn't there and moved on.
Compare that to Meta, who went all-in (so much so that they changed the company name) despite apparent overwhelming user apathy toward the product.
The estimates I've seen say that Apple spent ~$10b on their VR program, whereas Meta is in for about $80b.
The Apple Vision is at least two or three generations away hardware wise, it not only has to be several times faster (M8, M9 with a R2 or R3 processors) than what it is. The battery life needs to be better a lot better and it has to be half its current size weight wise.
And the price has to be half as much with all of those improvements. And last but not least the software needs to get even better all of that sounds like iteration over what? 3-4 years.
Fortunate for Apple, the competition is even further away…
It would be a better way to frame it if we were strictly talking about VR. But my point was to show Apple is not some infallible genius company, they make plenty of mistakes. Why are we assuming being behind on AI is not a mistake but rather amazing foresight is the issue? We are only considering yeah the bubble bursts and apple is like oh yeah dude we told you hahahaha, but what if the bubble doesn't burst? What if there isn't a bubble and it just evolves naturally into something less exciting and fundamentally useful? Wouldn't it be fair to say Apple was wrong in that case? Why are we congragulating them for a hypothetical right and not worried about the hypothetical wrong, both of which are possibilities.
Yes Meta spent 80 billion sure, but did you know Meta never had a loss-making quarter in their entire history as a public company?
My overall point is that its not as clear cut as Apple is smart for willingly not being good at AI and they will win in the end.
VR wasn’t AI.
As for the rest, they’re the only major tech company to not have a serious case of AI FOMO and dumping all their cash into that FOMO. Thats not an accident.
Yeah, VR was very clearly useless, and yet...
Do you even know what FOMO is? The money is being spent in physical infrastructure, not the the metaverse.
They certainly were not late to AI, unless you narrowly define AI as “LLMs”. See [1] as a single example among many.
[1]: https://dl.acm.org/doi/abs/10.1609/aimag.v19i1.1355
Given the context of this conversation, one would naturally assume we are talking about commercial public-facing LLMs and not AI as a field.
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.
Here's an interview with the senior product manager of Apple Silicon that more-or-less says the same thing: https://www.macrumors.com/2026/07/06/apple-silicon-exec-expl...
> 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. However, Brooks envisions a hybrid future in which agents decide what runs on-device and what gets sent to the cloud.
And coupled with their efforts in auditably-private cloud computing, that's a strong pitch.
> auditably-private cloud computing
Do those of you who have read the white paper have confidence in this claim (as I’d hope)?
Blog: https://security.apple.com/blog/private-cloud-compute/
I generally agree. Xserve didn't work out for Apple and they dropped it pretty quickly--think I still have an A/UX coffee cup. Wasn't really in their DNA even if there was an argument before Linux took off. For better or worse, they're basically a client company and barely a software (OK and basically a software services) one--although they spend a fair bit on (roughly) user experience.
A/UX predates Xserve by 14 years.
And how much did A/UX sell? I Have an A/UX coffee mug in my kitchen. It even has a phone number on it.
They sold the Xserve for ten years, across the PowerPC and Intel eras.
Still, in the scheme of Apple stuff it was a failed experiment--even if a worthwile one.
It was worthwhile because in the end it taught them that they needed to do something like Apple Silicon. And hopefully the current memory fiasco will convince them that they need to take memory in house if they want to build what they need to build going forward.
I really don’t see any alternative long-term to not engineering and designing memory in house and collaborating with TSMC onshore in America.
Hopefully having an engineer from the Apple Silicon design group as the next CEO will pay off.
I don’t think they need to go all-in on anything related to AI.
> This leverages their core competencies, requires only modest investment, and will likely pay out no matter how the AI market eventually shakes out.
This looks more like a call or a small raise or whatever (I don’t play poker).
They've been investing in NPUs for a long long time. Since the first iPad Pro at least (2018). Apple Silicon is a natural fit for tensor/matrix operations due to unified memory, so if anything them going "all in" on that has been a nice side effect.
> Apple Silicon is a natural fit for tensor/matrix operations due to unified memory,
This is a common misconception. This is NOT true: memory designed for CPU performs horribly for GPU tasks. Mac/Strix Halo/NVIDIA Spark are performing horribly compared to a desktop video card with GDDR.
I spent quite a few weekends optimizing ROCM kernels for strix halo, i WISH I had GDDR instead of high-frequency CPU RAM; the bandwidth would be SO MUCH better.
I'm quantizing weights not to make computation faster, not because I'm out of memory, but because memory cannot move fast enough to be processed and it's cheaper to load quantized version, convert into bf16 compute, and discard; This happens every single time a token is generated for the whole model over and over.
Just feels like the Apple press/podcast circuit just keep trying to spin ways to frame Apples fumble of the AI era as some sort of savvy move.
When your competition is currently burning billions of dollars left and right, Apple refunding Google $1 billion and giving OpenAI zero dollars seems pretty smart to me, Apple‘s largest acquisition in their entire history is only three billion dollars that’s planning.
AI is overhyped regardless of whether Apple exists or not. They’re getting attention only because they didn’t jump on the bandwagon, and from a purely investment standpoint their fiscal discipline is honestly astounding.
If they've gone all in on that, it looks even worse for Apple. Qualcomm's Hexagon has been a better NPU since the beginning. Now even Mediatek is better at it than Apple. Blogs like macrumors and daringfireball are notorious for not having any clue about what is going on in the broader industry.
Not really. Their new Siri is powered by Gemini.
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.
Read a little more of what he's written before you praise him, so you don't end up looking as dumb as I did.
Ed may be right about some of his claims, but he is 100% a crank, and he makes so many incoherent claims I'd say that if he's right, it's in the nature of a broken clock.
If you want to dismiss him for being hyperbolic or under estimating the usefulness of AI go for it.
If you are making the case that he is full of shit, please provide some actual evidence of his main thesis that AI companies are not going about this in any sustainable way
> his main thesis that AI companies are not going about this in any sustainable way
The point isn't that his thesis is fucked, just that his analysis tends to be free with details in a way that shouldn't inspire confidence, e.g. mixing up EBIT and EBITDA.
Someone elsewhere in this thread referenced this guy [1]. His takes properly summarise the lack of care Zitron appears to display for getting detailed arguments right.
If you're deeply familiar with financial jargon, I think he's fine. But if you're not, it's easy to get whisked into woo-woo nonsense that's falsely precise due to mis-using (and in some cases, very clearly mis-understanding) core financial and economic concepts.
[1] https://bsky.app/profile/peark.es/post/3mmhtcib3622i
Ed's not a crank, but you need to pay close attention to the topic of his proclamations to separate the wheat from the chaff.
He's right about some things -- off the top of my head:
-- OAI's perpetual fundraising, due to their losses
--Coreweave
--Circular financing
--Revenue/capex imbalance
...all forensic economics analyses.
He's often wrong when on the topic of capabilities and adoption -- he mistakes a snapshot for a trajectory, and treats current limitations as permanent:
--Hallucination/unreliability an unsolvable problem
--OpenAI failing
--AI capabilities have plateaued / models will stop getting better
--Saying Microsoft's cancelled leases meant the bubble was popping
--Agents a marketing fad
--Gross miscalculation of OAI 2025 revenue
--Lack of adoption
--AI not getting more efficient (compared to 10-40x reduction in inference costs YoY)
--Repeating "95% of pilots fail" but ignoring massive bottoms-up adoption
Christensen got this right when he had the insight to judge a technology by its rate of improvement relative to what a market needs, not by whether it's good enough today.
Put Zitron back at the early days of the integrated circuit and imagine what he would have written about the technology.
Some open predictions:
--Bubble is going to burst -- I tend to agree with the thesis that "there is some marginal capacity being planned/built today that will never pay back its capex"
--Losses mean there's no viable business here -- the recent moves by all of the players to some form of usage based pricing show there's price discovery occurring. I don't know of anyone who is stopping using LLMs because of the pricing changes...it's just changing their behavior.
Can you provide evidence of where he's been wrong?
An example of him in July 2024: https://www.wheresyoured.at/pop-culture/
>And yes, that sound you hear is the slow deflation of the bubble I've been warning you about since March...
>How does GPT – a transformer-based model that generates answers probabilistically (as in what the next part of the generation is most likely to be the correct one) based entirely on training data – do anything more than generate paragraphs of occasionally-accurate text?
etc.
Basically the essence is he's skeptical of AI / LLMs being any good so thinks all the investment is down the drain. Meanwhile AI progresses such that Fable can probably beat most humans and IQ tests, maths and the like. And the 'bubble' didn't deflate yet.
My take is that as a PR guy, used to people hyping stuff and not that up on comp-sci he fundamentally doesn't get what's going on. He assumes it's all hype rather than the steady progress in computing reaching brain equivalent levels and beyond.
He's not wrong about the numbers, and you may be seeing what you want to see a little, here.
Fable scoring OK on some benchmarks does not the mean the investment has financially paid off, because that's not how ROI works.
You're right that no bubble has deflated, but I think we can all see that a) there seems to be a bubble - I'm old enough to remember the dot-com era bubble, and this definitely feels like that, b) the only company clearly making a profit on AI right now is NVIDIA, and c) the net economic value of the sector as a whole (i.e. money out > money in), is still unproven
It's not even obvious to me as somebody who is up on comp-sci, that this isn't all hype, that the progress is/will be steady, and that reaching brain equivalent levels and beyond using these architectures will be economically viable.
I can create a perfectly good human brain in 9 months, train it in ~20 years, and have it pay off economically in a more proven way than [waves hands] all of this.
For some reason we've decided paying a hyperscaler the equivalent of a year's salary to do a job in a a day that a similarly paid and skilled human could do in a month is value for money.
You could probably give everyone in the US free healthcare for life and a free ride through college for less money than has been pumped into AI in the last 5 years, and have a more proven economic model.
Sure, time value is a thing, but given the error rates, the energy issues... this direction is not exactly a slam dunk as a net benefit, and I think that's all he's called out in any of the writing I've seen of his.
I also happen to agree with his take on Oracle, as it happens - they're only going to survive if it turns out they're a part of critical national infrastructure deep in the bowels of the US government. They look totally over-leveraged otherwise.
I agree it's a bit like the dot com bubble and I don't think he's wrong about everything but he seems to think it's a bit more like the NFT craze with nothing there rather than dot coms where the web/internet was of real importance.
Here's some criticism: https://bsky.app/profile/peark.es/post/3mmhtcib3622i (see the full thread)
I'm not qualified enough myself to judge Zitron's reporting, nor this criticism. But George Pearkes is a reasonably respected financial analyst.
I think both sides might be cherry picking the pieces that make their arguments the strongest - Pearkes has pulled out a few paragraphs, but there is a lot in Zitron's writing, so it's easy to select 6 bits you don't like/agree with or find fault with, but I'm not sure it undermines the rest.
I actually think Zitron would be better off focusing on those deals where there is clearly something weird going on (SpaceX IPO, Oracle/OpenAI/NVIDIA triangle, the neoclouds, and so on), than picking a fight with the one company that actually seems to be trying to play it straight in this sector (Anthropic), and I can see how he's covering the whole field with the same cynicism which might not be appropriate.
That said, I'm not entirely convinced that some of Pearkes' analysis deserves us all to align to the rosy bullish view he takes either.
Time will tell, it always does, but at least you've put forward some data unlike anyone else, so thank you for that.
Okay, but what’s your counter argument to Jim Chanos? Oh, that’s right, you don’t have one because he’s a tried and true Wall Street heavy who speaks in plain English. Ed and Jim are on the same page looking at it through different sets of prescription glasses. I hope you have your money where your mouth is, so to speak, because I certainly do (avoiding AI trade) and good luck with the SNAP paperwork in two years.
Let me guess, you disagree with his views on the utility of Ai (which he's pretty bad about) and you use that as cover and/or to negate his real journalism that is based in facts (Ai being an unprecedented & unrecoupable bubble littered with unprofitable companies thats likely to crash our economy unless these Ai conglomerates cozy up lawmakers to situate themselves for a bail out)
My disagreement is that every time he turns out to be completely wrong he silently moves the goalposts instead of acknowledging it.
For years he was all-in on "AI is useless, it has no business value at all" claims, and then when that was decisively disproven by Claude Code, he didn't spend one second on self-reflection on why he was wrong and whether this might mean he is wrong about other things. He just smoothly pivoted to "AI companies will never be profitable, tokens are hugely subsidized". And now that that's about to be disproven (word on the street is that Anthropic will soon report profitability, and both Anthropic and OAI have repeatedly said inference is profitable), he's pivoting again to "tokens are too expensive and the ROI isn't there for business". No correction, no reflection, just "we're at war with Eurasia, we've always been at war with Eurasia".
Even if he’s sometimes right, there are better analysts out there who are also right and have the honesty, humility and integrity that Zitron lacks.
> For years he was all-in on "AI is useless, it has no business value at all" claims, and then when that was decisively disproven by Claude Code...
No such disproval has happened. Claude Code does not provide business value, it provides the illusion of value. Just like everything else LLMs do.
Can you elaborate on what happened that made you look dumb?
There are good sources of well-argued AI skepticism, and then there's Ed Zitron.
At this point I have yet to hear a solid argument against his arguments that use half as much evidence as he does.
That isn’t to say that he’s right, that’s just to say that while he gets creative in his phrasing numbers don’t lie and I haven’t seen anyone else present competing numbers that make sense.
If anyone has them to the degree with which he provides them then please, by all means, I’m interested.
Ya, there are already three pure ad hominem comments here with no examples. Zitron isn't even anti-AI per se. He clearly talks out his ass at times (like AI doesn't work for developers... which he's not saying so much anymore, I don't think) but his main talking points are around numbers and profitability.
As a C++ developer, AI does not work well for software development in my experience. Maybe LLM developer tools work better if you write React or HTML/CSS, but they have been inadequate for my use case since their arrival.
Casey Muratori, who doesn't use AI, made this point on a podcast. Specifically he said that he doesn't blame web developers for wanting to use AI because it's such a mess of an ecosystem and impossible to keep everything you need to know to be successful in your head.
I'm a web developer to myself who has done zero game or systems programming so I can't speak to that side of it, but you reminded me of it!
I find it bad for generating code but alright for review, I put it as a tool to run along static analysis to catch if I missed something on a code change or merge request (being specifically about C++)
I'd be interested in the schematics of where you think the "ad hominems" are in my comment.
You were just attacking his character without backing it up? Do I not know what an ad hominem is? If that's the case, then that's embarrassing for me. Unless you're saying that your remark doesn't technically bash his character?
Yeah, you're off on what an ad hominem is. I would dispute that I attacked his character at all, but that doesn't matter: an ad hominem isn't an "attack on character", it's an argument that is predicated on an unrelated character attack.
My argument here is just that Zitron says things that are wrong all the time.
You could fairly rebut me by saying I didn't substantiate that argument. That's true! That doesn't make the argument fallacious; it just means it isn't facially dispositive.
Ah, well then colour my face red.
And I agree that he says things that are wrong much of the time, but not all the time. I also don't like him enough to care, but I do care enough to bring it up when I see multiple random attacks.
> Ya, there are already three pure ad hominem comments here with no examples.
You should probably understand Thomas' comment in part as a reaction to Zitron attacking Thomas in a pretty low-brow fashion, essentially for saying "hey folks, AI coding works and you should be using it":
https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/
The bottom line is that Ed Zitron, like Gary Marcus, built their entire brand on being AI contrarians; they can't say anything positive about it without qualifying it with a more damning negative. There's nothing wrong about having voices like that, but it makes them unreliable narrators. I am apprehensive about AI and its externalities, but I don't want to be caught citing either of them for that reason.
I swear to Christ before this comment I had completely forgotten that Zitron had ever written anything about me. Zitron isn't an engineer and doesn't code, so I'm not all that het up by his attempt to rebut me.
Oof, you have Thomas admitting he didn't even remember this in the comments.
> You should probably understand Thomas' comment in part as a reaction to Zitron attacking Thomas
Sure, but an ad hominem is still an ad hominem regardless of one's personal feelings (it took me a good minute to figure out who "Thomas" is).
Otherwise, I agree, and I try not to cite extremists or bullies on either side. I don't even really care that much about defending Zitron, but I do put him on in the background because he does so many podcasts as I find it soothing to listen to someone rant and rave against the tech bro overlords, even if some of the shit he says annoys me. But he does show his work sometimes and wanted to point that out.
I don’t have numbers, but I would like to point out that he's missing a crucial number as well. The fact that maxing out a subscription's limit makes a user generate an amount of tokens that costs much more than the subscription itslef isn't enough to back his claim. What we need to know, is the average use of subscriptions. Think about Gyms. My gut feeling is that, if we run the numbers, we see that by maxing out a gym subscription we can get more value out of it than what we paid for. And still, gyms are profitable because the average customer is using less than what they're paying for. Now, I still think AI companies will have to rise their prices (they are already doing that), but the situation could be less dramatic than what people pointing at those figures based on subscription maxing are claiming.
It could have worked if they weren't leveraged to their necks on spending. All based on the assumption that they can make something like 10x or higher in a few years.
Another point against your gym analogy. When people don't have that much disposable income why would they subscribe to something they semi regularly use? And if it becomes more expensive why would they not cancel it?
You do really need to see the useage numbers for this. Movie pass famously thought they could price movie tickets like gym memberships - I assume they looked at how often the average person watched movies in theaters at the start - but then found out having a subscription to see movies for free / cheap any time you want changed that rate substantially and they ran out of vc money.
Having a subscription might make people run agents overnight, or casually generate images for fun, or start more coding projects. The reason gym memberships work is that people like the idea of going to the gym a lot but don't really enjoy actually doing it very much and fall off over time, leaving the gym for the rare person who's really into fitness to use it cheaply.
is AI like that where most users will get bored? or is it like movies where they'll try to get their money's worth?
> is AI like that where most users will get bored?
User's getting bored will also spell trouble for the subscription model. I don't think we'll see a world where the average normie is going to be running agents in a loop overnight to make software. They'll get bored use it to cheat on their homework and as a Google replacement, generate silly images for a week then get bored of that, and then realize the have no actual reason to be spending $100/month and drop down to a lower tier or just cancel all together and live within the free-tier limits.
So if that happens, the labs are now left only with power users that can actually generate asymmetric cost. The market will bifurcate into free-tier users and "get their money's worth" users. So the risk isn't that the top 5% of users use more tokens than the subscription has any right to give you, it's that everyone else cancels their plans and only those top 5% of users remain.
The labs will have to put stricter rate limiting and token limits on the subscription plans to avoid MoviePass style burn.
Gyms get away with it because most don't let you cancel easily. You pay monthly, but are locked in for 6 months or a 1 year at a time (Adobe subscription style). Maybe the labs will start to do the same? Let you pay $20/month, but you are forced into an annual commitment?
I gotta say that if their best bet is “people forget to use it” I don’t think that that’s going to drive the adoption they need.
Especially if part two of your theory comes to fruition and they have to raise prices. I might of forgotten my Netflix subscription when it was 10 dollars a month but certainly not if it was 200 you know?
You're biasing for quantity of argument vs. quality of argument. That's Zitron's MO: writing 10k word rambles that cover 20 topics, ostensibly with tons of links to sourcing, but with most of the sources not actually saying what Zitron implies they do.
Nobody has the patience to read a comprehensive point by point rebuttal to any of it, it's just too tedious.
You say "numbers don't lie". But they do, when the numbers being presented have been adversarially chosen. You just find numbers (no matter how low quality) that fit the chosen narrative, you throw out the numbers (no matter how high quality) that rebut it. If you're trying to predict the future, you can't afford that of bias. If you're an anti-AI grifter, you can't afford to not have that bias, your entire livelihood depends on only showing things that support the narrative.
So I’ve heard this argument before but I’ve not even seen someone demonstrate HOW the numbers are “adversarial”.
This is the crux of my argument: Zitron provides numbers and he provides a lot of them sourced from reliable and trustworthy sources. He provides counterarguments to numbers-sparse arguments by using even more actual, real numbers with a logical theory formed from them.
This should make it rather easy to dispute his claims, no? So then where are the equally rational disputes?
Some of his numbers are rebutted all the time. It doesn't matter, because he will just ignore the rebuttals and just keep repeating the invalid or irrelevant numbers. For example, a few months ago he did some bad math with a broken spreadsheet to prove that Anthropic's ARR numbers had to be incorrect. That was quickly rebutted[0], but Zitron continued repeating the argument anyway.
Just this article has a dozen instances of numbers being used to prop up the argument. They're mostly irrelevant to whatever argument Zitron is trying to make at the time. Some others are straight up misrepresentations. HN really is not a good forum for point by point rebuttals for that many cases. Do you think there's a particularly high impact use of numbers in this article?
[0] https://x.com/binarybits/status/2034376359909130249
I think he actually wrote an article rebutting binarybits and Anthropics claims around that time (though maybe not binarybits directly) here: https://www.wheresyoured.at/anthropics-profitability-swindle...
Either way it looks like he didn't just "repeat the numbers". He updated them and provided more data to back them up.
"The biggest thing we’ve learned from the large language model generation is how many people are excited to replace human beings, and how many people just don’t understand labour of any kind"
Really? I think we've heard C-suite blabbermouths and borderline nontechnical tech CEOs being amplified by social media making these assertions, but boy did the weakly efficient market deliver a relatively swift correction to that mindset, no?
Q: "What would it take to change your point of view?" A: "[AI] would have to solve all hallucinations forever, which they are completely incapable [of]."
Can you produce a human that is infallible? I'll wait.
Finally: https://martinalderson.com/posts/no-it-doesnt-cost-anthropic...
My hot take is AI is increasingly less unprofitable as the cost of serving tokens drops and Nvidia's ongoing offers to guarantee profitability is a sign that it isn't stopping anytime soon.
https://newsletter.semianalysis.com/p/nvidia-gpu-debt-backst...
You either believe in the underlying science and technology or you don't. But in a world where AI is a fad like cabbage patch kids and beanie babies, what's next?
> But in a world where AI is a fad like cabbage patch kids and beanie babies
AI will almost certainly not be a fad for software developers, or at the very least for web developers, but it's certainly possible that other industries will look back on "experimental AI days" and chuckle.
Even if there is a financial bubble (that pops), the technology is still not going away.
As for what's next, I dunno. I've heard people talking about similar tech that doesn't use neural nets/etc, but that is out of my wheelhouse.
The possibility of AI sticking around is apparently pretty threatening to the Zitron cult or what seemed like an innocuous response wouldn't be in such negative territory so quickly.
But I think AI is here to stay and it has already proven useful. And sure, we will look back on today's models like we look back upon Gordon Gekko and his giant early cell phone. And I cannot fathom how one can not separate AI the science and technology from AI tech bros and CEOs. I agree the latter are going to go through some things as the AGI fails to arrive on their schedule, but IMO there's no turning back on the technology nor should there be.
> But I think AI is here to stay and it has already proven useful.
Yes, I said as much. I've said this is two other places in this thread, but when they are talking AI being useful, they aren't just talking about for software development. The software industry is seeing far more AI adoption, by a very large margin, than any other industry, and the success of the current companies hinges on it being heavily adopted everywhere. I don't really care to defend Zitron because he does speak out of his lane far too much, especially when it comes to software, and those are the points the HN crowd latches onto. But that's not the bulk of what he talks about.
So then what is the deadline for AI reaching into all these new industries (or are you asserting it will never happen)? And you're right, technical people latch onto him sounding like a horse's ass on technical subjects and why shouldn't they? IMO this isn't the same as Elon's endless empty FSD promises (which I called out the whole danged time from the beginning FWIW), but I get why people assume it is, I just don't agree.
I didn't have a lot sympathy for the guy who described the Internet as a series of tubes yet I believe Al Gore caught far more flack for that bit about inventing the Internet than he deserved given the inventor of the Internet defended that very claim.
https://en.wikipedia.org/wiki/Al_Gore_and_information_techno...
So if the bulk of what Zitron's talking about is valid, then what are the main points that aren't self-evident i.e. there's definitely either a buildout or a bubble in CapEx and only time will answer that question, not hot air and blatant market manipulation. What is he bringing to the conversation that we're all missing? Seriously, educate me.
Sorry, you're taking this up way too many notches for me to want to continue this convo. But as I said, I have no interested in defending Zitron in general, I made no claims about him being groundbreaking, and I don't even take him too seriously. I was responding to your points around AI's usefulness and how the discussion is almost always localized around "AI as a software development tool" here on HN. For me he's just a soothing counter-voice to the awful and inescapable hype and doomerism coming straight from the American AI companies themselves (as well as people I've been forced to interact with in the programming community). He does show numbers about why he thinks some of them aren't aren't going to survive. This is all speculative (not sure why I feel I have to say this but I feel I do).
> So then what is the deadline for AI reaching into all these new industries?
Obviously the "deadline" would before the US companies go bust (again, IF they do). And I'm confused about your use of "new" here but I'm going to assume you mean "other existing industries that haven't adopted yet." The discussions around this are that several industries already jumped on AI then walked it back, so it's more that we're already past the "reaching in" stage. I don't have links for you but there have been several articles about companies firing people "because AI" then hiring them back. I suppose AI drive-thru order takers would work as another example. Again, I don't have the numbers on this myself. And again-again, none of this is "in defence of Ed Zitron" or "this is why Ed Zitron is great" or any such thing, but these are the things he talks about (and once again, no, it's nothing groundbreaking).
He's a college drop out with no experience in programming or technology.
He has zero numbers and is completely making stuff up to a gullible audience.
The real numbers show high demand for all these services to the point where the companies are rate limiting the services because demand is TOO high.
I thought Ed Zitrons argument was just corroborated by Nikkei last Friday with the new report that 1.6 Trillion in SPVs accounts for off-books debt, on top of the $1.4 Trillion of on-books debt that's in these AI companies?
The main argument of Ed Zitron is just that. Huge huge amounts of debt that's due "soonish". Every SPV is different but we are fast approaching the point where this all starts to become real.
Now I dunno why Ed Zitron feels the need to write 10,000++++ word articles that only says "more leverage than people expect", but that's really the crux of things.
If that's all Zitron wrote, I don't think he'd be as polarizing (or as dunked on). But he goes way, way past that.
It all comes down to the fact that Zitron thinks these AI companies are over levered and have significant chances of collapsing industry wide.
Yeah he uses more words to say it and bad arguments as part of it. But so did Galileo's "The tides prove that the Earth is moving around the sun" argument. Some arguments are bad, even if the overall point was correct.
I think given the new Nikkei report from last week, we are seeing more and more of Ed Zitrons arguments bear fruit, after years (literally) of him calling wolf.
It all is crazy talk until real numbers come out and prove him correct. But it seems to take a long time for those numbers to materialize.
I don't understand how that affects what he has written here? How does his data not make sense?
>zero numbers
You’re thinking of a different person.
Most recent free article: https://wheresyoured.at/the-subprime-data-center-crisis/
Number after number, e.g.:
Ed Zitron is basically two things, numbers and snark. You can disagree with the snark but to say he has “zero numbers” is ridiculous.
Additionally, as another commenter has pointed out, his thoughts and criticisms are being echoed by people who actually have the money.
> He's a college drop out with no experience in programming or technology.
I mean Zuckerberg is a drop out.
zitron has been around since before flash was dead, more over hes worked in PR so like anyone with heavy involvement with c-suite, sales and journalism, you get a good nose for bullshit.
That said, do I agree with his assertions? no not entirely. But. I suspect its a bit of an audience capture thing. He makes wild statements, then goes on a flight of fancy to back them up. I appreciate that, but it also lets me decide that he's going down a path that I feel is not backed up by his data.
Well he's not a developer or science guy or smart at all so in his view, AI doesn't work because sometimes it makes mistakes.
This but instead it's people insisting that AI will replace my job (been three months out for at least 3 years now) because they know it involves words.
To be fair he has a background in technology PR and Marketing (which he’s been very critical of) which I would count as more worthwhile when assessing the actual value of the product.
I mean you can say he’s not a “science guy” but he’s undeniably “smart”.
He also proved during his coverage of the crypto bubble that he’s perfectly able to understand technical issues, way better than most commentators
Cal Newport is another great person to follow on this and I think he understands the CS fundamentals much better.
The entire internet is full of anti-AI discussion and a straight up disbelief in any chance of success.
Implying there is a "gushing torrent" of pro AI narrative is bizarrely out of touch. We both know this isn't true.
While I don't really care one way or another, Zitron isn't arguing AI as a technology is going to fail and go away, but that the numbers of the American companies make no sense and there is a good chance they, or at least some of them, will go away. He does argue that there are large amounts of people who just don't care enough about "AI," and this is likely true. The thing to keep in mind though is that he is not talking about software developers here. What is evident about some of the comments in this thread (and HN comments in general) is that many people on this site seems to think that whenever anyone says anything along the lines of "AI doesn't work for me" that they automatically assume they are talking about "in software development" when they aren't.
that's an organic thing
the pro-AI side on the other hand has poured billions into ads and marketing and CEOs are forcing it on people due to a combo of FOMO, personal investments in AI (CEO, board, investor), etc
I think AI discourse is revealing some new contours to the information bubbles that we all live in.
folks in the US have been pretty aware that bubbles based on political opinions -- we're all surrounded by news from "our side" yet we know the other side exists in some other bubble. but for AI, we're all either surrounded by pro- or anti- opinions and its a little shocking to learn that theres a parallel internet with the opposite stance. especially shocking when you find somebody that lives in teh same political bubble but opposite AI bubble.
It is rather sad, actually.
Both can be true. AI is very, very polarizing. The entire Internet is also filling up with AI generated content, some slop, some okay. There is also a very pro-AI managerial class that wants to replace everyone and everything with a Claude project so they can "just ask Claude!"
The vast majority of what I see out there is relentlessly negative about AI. It is kind of absurd to call this the "alternative" unless the implication is that the rest of AI skeptical writing is not "well-argued."
75% of comments on every article you read about AI on hacker news is talking about the bubble. But everyone thinks the bubble talk is contrarian.
Is it though? He's more of a gushing torrent of anti-AI propaganda. He has an agenda too, it's just the opposite one.
A resolute anti-AI perspective is a breath of fresh air compared to the middle-of-the-road "AI made me 10x more productive BUT"-style comments.
My professional opinion is that LLM technology doesn't work very well for software development and I'm not interested in hemming and hawing over its supposed benefits any longer.
Talking with a friend of mine who's been in development for some time and I had been talking a little bit about the skill required to wield AI effectively and that people were expecting it to be something that required less skill than it does.
He said his version of this observation is "your agents are only as good as you are." I think he's right, and the key is to practice and build the skill.
> agents are only as good as you are
that doesn't sound like a net benefit at all. I think mostly people just enjoy playing with these toys, and good developers are still good with them, bad developers still bad
I think there is a distinction between whether or not they require skill and whether or not they are a net benefit.
>> LLM technology doesn't work very well for software development
This comment is so obviously false (in my experience) I'm wondering what sort of niche environment you're working in
As I mentioned in this thread the other day, it's not worth debating AI skeptics anymore in 2026, with step changes like Fable and Sol. Maybe it was the case in 2025 and before but the field has improved rapidly that it doesn't make sense to listen to people with AI experiences prior to that who haven't touched it since. Does AI make mistakes, sure, but increasingly fewer.
https://news.ycombinator.com/item?id=49051369
> As I mentioned in this thread the other day, it's not worth debating AI skeptics anymore in 2026
This is pretty rude!
My dude. People like you have been making this same claim for literally years now. We've been getting it for every single model that came out ever since 2023. It's never yet been true. Don't act like it's the fault of your interlocutors when they are skeptical of a claim you make, instead pay attention to the fact that you are following up many people who falsely claimed the same thing. It's perfectly rational to be skeptical under those conditions.
Just my personal experience. I'm not really interested in giving up more personal details in this forum, apologies for that.
I'm generally pretty accepting of trust me bro style arguments where we some claims presented without evidence. Especially for AI, sure some model just rm -rf ed your home directy bro sounds bad. It works fine for online discourse.
This is less agreeable. You kind of just made statements without even having claims (even without requirement of evidence or details) to back them up.
This is like the illusion of contribution.
remember, how useful LLMs are is inversely proportional to your skills.
i have yet to have an LLM beat me. i am forced to try regularly so i don't look like an AI anti at a company that is very much SV pilled
I don’t think people like Linus Torvalds or Stephen Wolfram feel like language models beat them, just they’ve managed to find specific cases where flawed brainless workhorses serve their needs.
Like so many things in life, It Depends (tm).
Some developers work better with LLMs in their workflows than others. Some problems are easier for LLMs to generate a reasonable solution for than others. Some folks prompt minimally and see what the vibes bring. Others start with a detailed architecture and implementation plan.
The individual results will depend quite a lot on the person, the problem, and the approach. It's not a guaranteed winning formula for everyone.
What specifically is that agenda?
his agenda is less existential and backed by less money and power than Sam A and Dario's
While that's true - it does not impute Ed's position with greater accuracy or authority. I don't know if Ed is wrong, but I get this vague sense that he is being led on by his emotions.
I am sympathetic to some of his criticisms that the enthusiasm and financial commitment has run far ahead what can be delivered. But I don't quite share his intensity over the doom and gloom. There probably will be a correction. It will probably sting. But I don't expect it to be the near wipe-out that Ed's passionate voice seems to steer towards.
I find it weird/funny you are getting so many hate responses towards Ed, still no counter-arguments at all. When asked why he is wrong, they vanish. From the outside it looks like a coordinated campaign to try to discredit him.
Happy to read why is he wrong, and change my mind, as long as the arguments provide the same level of analysis he provides.
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.
Well hold on. "They" also thought tokenmaxxing was a good idea, until they didn't.
Whether companies pay will come down to the bottom line once the hype around the country club dies down a bit and for that it's still TBD on the actual product cost impact.
Yeah, tokenmaxxing was obviously stupid. Setting a limit is what happens when the adults in the room stop being stupid.
Not sure about that. As I replied elsewhere, it's a forcing function:
> People misunderstand the point of the tokenmaxxing time period, it was to force people to use AI so as to not have them stuck in their way, as some people are, and then to evaluate how it can help the company.
https://news.ycombinator.com/item?id=49047448#49047826
I expect everyone understood that.
The obvious second effect is that if you have a leaderboard people will compete to top it, by running dumb loops or whatever.
That impacts how much you can learn, and costs you more money than you wanted to spend.
That is a truly commendable steelman. But let's be honest, there's basically no chance that was true. Management is frequently stupid enough to not think through the second order effects of their policies, and that is likely what happened this time as well.
It's hard to read this take seriously, when you promoted a company spending $1k per engineer per day on LLM usage.
Sure, but what you're not taking into account is that $1500/month is subsidised by VC money.
If Uber were asked to pay full price, they'd either get the same value for significantly more outlay, or keep their outlay capped but get significantly fewer tokens.
This would, I'm sure you'd agree, change the ROI outcome.
Zitron is arguing that if we all pay the true cost once the subsidies go, we'll either stop paying for the value we get today, or we'll find AI to be less useful than we do today, and those businesses won't be sustainable, and then <pop> goes the bubble.
I enjoy keeping an eye on https://isaiprofitable.com to see just where we are overall. Interesting reading if you like to think about such things.
that $1.5k is ostensibly not coming out of productivity gains from the AI though.
it's coming out of the salary budget from what i've seen. i've seen a company both say "AI max. it's the future! don't be late" and then go on to increase limits to compensation across the board.
Very true and I don't think you can completely discount the end consumer market either. A large number of the people not currently paying for a subscription are becoming dependent on AI enabled search results. I imagine monetization of those users is in its infancy and will rival the search result ad business eventually
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.
For me, the economics are the exact opposite. I'd pay $1000 for a good gaming VR headset, but not $4000 -- because gaming is just a hobby that isn't really worth that much to me. Gaming VR headsets have been around for well over a decade, I've "been there, done that", and it's just not something that's worth that much money to me. However, I have no problem paying $3500+ for something that creates a new category of productivity and entertainment experiences for me.
Besides that, I actually disagree that the Vision Pro is not good for gaming. It's not good for traditional VR gaming, but using apps like Portal it's phenomenal for, e.g., playing existing PS5 games on a massive virtual screen, which in many ways I actually enjoy more than VR gaming, which is far too limited in comparison.
It's the same decision-making process that tried to do a electric car for years, and canceled it after spending time and money. In that exact timeline, China made great electic cars and new brands, Tesla advanced and Spacex launched dozens or more rockets. Apple is so far from any big new tech, they and market don't even realize it.
Apple had more money and influence than combination of all of them, yet failed with car and VR. Why would anyone think they can't fail much much bigger with AI and LLMs ?
Same decision making process behind making a VR headset out of heavy materials like glass and aluminum
Or making a VR headset that has to be tethered but still put all the compute on your face.
> I will never understand the decision-making process that led to "Let's build an awesome VR headset that can't do gaming".
FOMO, someone else being successful in VR (and now in glasses) is a nightmare scenario for Apple because it's not their platform. They've got a lot to fear from someone else owning the platform, having written the playbook on how that platform would be controlled and exploited at everyone else's expense.
Imagine if Apple doesn't make their glasses: Meta keeps selling truckloads of them, as they get more powerful the smartphone becomes an optional accessory and then an unnecessary one, eventually they become an alternative to a smartphone and an app platform for third party devs.
It's the same story with VR, Apple does nothing and risks Meta or Steam building a viable platform where people want to use software instead of on their iOS devices, until the hardware can replace iOS devices entirely. (although VR is certainly more of a moonshot)
Who said it can’t do gaming? All the regular graphics APIs are supported.
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.
I think the manufacturing analogy is apt. If you consider both the need for minimal COGS and the large R&D expense, it’s kind of like… biomanufacturing. You spend a lot on R&D to develop your bug, which at the end of the day is just a very complicated sequence (DNA vs model parameters), and then you expend a ton of capex (or rent someone else’s capital equipment) to produce a relatively commoditized product (a drug, a protein, food…) at as large scale and low cost as you can.
But that also makes the case for government subsidization + bailout, if you accept it as a capability critical to national security, but one that may not be profitable to run within the US on its own merits.
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.
> AVP wasn't released early (late, if anything), and it wasn't a dud because of Tim Cook's disinterest.
Depends on what you mean by early.
WAs it released before the market was proven? yes.
Was the whole user experience up to scratch before it was released? no.
As someone who worked on the Quest pro/3 ecosystem, We were shitting our selves, because we knew that apple would only release something when the user experience was right.
Oculus just shat out features as and when performance cycles needed juicing.
Sure the UX was good, but it was less good than I expected. The headset was bulky and surprisingly off balance. It wasn't an all day wearable (i mean the quest pro was actually comfortable by comparison)
The resolution wasn't that great and the only thing you could do with it was either look like a twat recording video or have spreadsheets pasted everywhere. I was expecting an OS where we could put things on work tops, create augmentations to my room, decorate or be creative. Instead we got OSX XR.
Tim Cook was probably mostly disinterested because it didn't seem like a real business opportunity. Wherever AVP fell on the VR/AR spectrum, VR (while having other opportunities) was mostly a niche for high-end gamers and AR, whatever the possibilities in some future idealized form with fashionable glasses that give you lots of information about what/who you're looking at is a long way from reality.
I think it's the opposite. None of the players in XR wanted to be Apple'd, least of all Apple. That is, create a base product that someone else would refine in order to drink their milkshake. Apple delayed the AVP multiple times hoping to avoid getting sniped by Meta and Google et al., each time requiring a partial rebuild as the underlying tech advanced. They would have loved to have been able to get something out when Robert Scoble was initially raving in the late 2010s, but everyone was on the same page about not wanting to sink billions into development only to have to cede the market to someone responding to your strategy. So, instead, they all dragged their feet.
If XR companies had been as "reckless" with their product strategies as mobile companies in the 90s and 2000s, AVP would have had its iPod moment 6 years ago and we'd be hurtling towards the facephone. The market suffered because of the entrenchment of incumbents with all-too-clear memories.
Think Apple fell for the trap of assuming they would eventually solve the glasses form factor and then fell for the second trap of convincing themselves that passthrough VR was the same as their real XR vision.
I think that's a good shorthand. Apple needed cool glasses that could do useful xR in ways that weren't just useful to gamer geeks.
It was released to appease investors.
Ultimately as a manager you are held accountable to your investors and the cost-benefit analysis is such that it was the right action to take.
I think you're absolutely right. Apple couldn't be seen as ignoring the gamer geek (or other potential opportunities) space as it wasn't very interesting to them at their scale and that automagical AR was a decade or more away.
Agree. Apple Vision Pro was a dud because the whole VR category is a dud.
People are starting to kinda hate tech. People don't seem to be looking for yet another "smart manacle". Time to move on.
> 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.
I'm of the opinion that the market will need to expand, horizontally. That's what's happening, with low-cost LLMs. I already know lots of "regular folks" that are totally hooked on LLMs (mostly ChatGPT, at the "regular mensch" level). They usually use the free tier, but a lot of them are doing the $20/month Pro tier.
If anyone is in my age group, they remember when ATMs were free. It was the "heroin dealer" business model. It worked out well (for the banks). The Chinese did it with manufacturing.
Getting people hooked on subsidized junk, is an age-old (and highly effective) business model.
Think of all the shops that will soon be composed of people that simply can't even get out of bed, if their LLM is not available. If the LLM dealer starts raising the price, there's no choice. I suspect many of the valuations are taking this into account.
> "Junk is the ideal product... the ultimate merchandise. No sales talk necessary. The client will crawl through a sewer and beg to buy."
-William Burroughs
LLMs are rapidly becoming commodities with open weight and Chinese models. Frontier providers won't be able to raise prices forever, indeed we already see evidence of decreased prices from such competition.
And ATMs can be free still, if you use a bank that refunds all worldwide ATM fees, such as Charles Schwab.
The commodity is not in the LLM but the tokens.
Anthropic's offerings are vertically differentiated. However from an economical stand point, it also be true that they are not the preferred option for many.
> Think of all the shops that will soon be composed of people that simply can't even get out of bed, if their LLM is not available.
I'm biased because I don't use AI, but I don't understand why you wouldn't fire these LLM-addicted people and replace them with people who can code or write an email without the use of an AI assistant.
They won't. They'll just pay more for the LLM.
I use LLMs all the time, but I'm also a highly capable and experienced engineer that can definitely work without. I have just found that the LLM is a force multiplier, like I haven't had before.
Same, though the types of jobs I’m working on now require the speed I get from LLMs. If I had to take my current projects and switch to hand-coding I’d have to extend the timelines and practically add a zero to the price.
If you don't mind sharing, what types of jobs are you working on now?
I haven't personally seen a speed boost from LLMs, but my experience with them suggests that they could be effective in some domains and inadequate in others. E.G., I have observed that LLM tools are inadequate for generating C++ code.
Sure, it's a real mix. Most recently:
- An electrical component "synthesizer" which takes natural language specifications in (e.g. from an RFQ) and uses material data sheets and core physics calculations to design new components. (Python)
- A transcript processing pipeline which reads what happened on a call, identifies participants from the CRM, drafts structured call notes, and posts them to the CRM with the proper associations after a human-in-the-loop confirmation. Fairly complex server & web app. (Go)
- Interaction design prototypes for a family of smart devices, some with screens. Brought together as an interactive web app to see how each device behaves (vanilla JS). Each prototype generated with the help of an LLM, including 3D animated examples (Claude driving Blender).
I’m writing Swift (native iOS apps), with PHP backends.
I have found it does good PHP code, and Swift code that definitely needs adult supervision.
Also, very good for things like code documentation, debugging, and writing copy for things.
Also enterprises have to pay per-token.
Just to counterpoint the adjacent replier. I have claude, chatgpt, and gemini subs and rarely even use even close to 50%. I code c++ everyday. But im not full in on agentic workflows which is probably why. I mostly do fairly targeted things to my various code bases.
I max out Claude Code and Codex subs both every week without fail and I’m doing a lot of work but it’s not insane.
> 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.
Interestingly, that METR study has updated data for 2026 that shows a speed up, although they admit that the data may not be reliable because of changed pay rate for participation, but this quote is telling:
> The primary reason is that we have observed a significant increase in developers choosing not to participate in the study because they do not wish to work without AI, which likely biases downwards our estimate of AI-assisted speedup.
So compared to just last year, they had a hard time finding participants because too many didn't want to work without AI.
I stopped reading at maybe 25% into the article. It's obvious garbage.
He selectively quotes the max theoretical enterprise pricing equivalent of fully using the private subscription. Did SemiAnalysis not also claim a very high margin?
He throws in doubt about the providers having decent margins, which he claims is made up by "AI boosters" rather than leaked financials and open-weight pricing.
Then next he talks about "the real cost" of inference as if it was in any way realistic that labs price the enterprise plans near cost, like he seems to imply.
Then next he claims AI is actually slowing developers down and there isn't much difference between the models.
It just seems delusional.
Welcome to Ed Zitron. There is a reason this man doesn't heavily short the same companies he criticizes. Be wary of anyone that won't put their money where their mouth is.
> There is a reason this man doesn't heavily short the same companies he criticizes
yes, because in order to take a short position you have to predict exactly when the bubble is going to pop, which is different from predicting that at some point it will
Not quite. You could buy a long-dated put if implied vol and current rates are amenable. This protects you against the underlying going up significantly. You might also predict that it won’t go up too much, and just short the underlying with a stop loss.
> You could buy a long-dated put
What leveraged? yeahnah thats not going to be profitable.
> you have to predict exactly when the bubble is going to pop
Not true.
please elaborate, I love learning new things
Not everybody is into gambling, some of us find it more troubling than smoking or drinking.
I don't know if his final analysis is right or wrong, but if he believes this, he's completely clueless (about this aspect at least).
Edit: to be clear, I am talking about Ed’s contention that AI coding isn’t net productive.
coding is the one area Ed has acknowledged productivity gains. hard evidence is still lacking though
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
> 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.
Something to consider: would your description also apply to the dot-com boom of the late 1990s? The internet was real, the ideas for internet business were real, and we were not going to the previous reality. But the valuations weren't quite right and a "correction" happened at some point.
When people talk about AI crash, that's what they mean. Not that AI is a hoax, but that the correction could be quite violent and have effects on the broader economy.
But there’s a difference between fearing mongering with a crash and a long term correction. The correction would make all the startups/companies that created a wrapper around frontier models go away, but the ones that truly solved a problem would remain.
Also I remember reading that Anthropic is on its way to be profitable in 2028
With respect, the way you talk about corrections makes me suspect you weren't working in tech in the US at the turn of the century.
A "correction" in this model isn't a reasoned and gradual re-evaluation of the market cap of every company. It's a broad pullback where people panic and no one wants to be left holding the bag. Money shifts into other assets for years and tech employment, incomes, and the availability of funding takes a big hit.
As to your comment about profitability... every unprofitable company is on a path to be profitable. Some even get there.
Plus, look at it this way: Kellogg's is a profitable company and a part of almost every person's life. Does this make them worth trillions of dollars? No, they just provide boring, commodity products, their valuation is basically a low multiple of the assets they hold and the revenues they bring. There's a future where OpenAI or Anthropic are more powerful than all the world's governments combined, but also a future where they're Kellog's.
That's exactly what a crash is, there's not much difference. For example that's exactly what happened in the dot com era.
> [local, but for] extremely niche or higher intelligence tasks
Access to a centralized copy of the web/literature/media, scraped and indexed/integrated, seems another non-local center of gravity. Versus a local model's last minute reaching out to "manually" search and browse.
Also mass parallelism for large ensembles. Perhaps unless/until we get those local models as 10k+ tok/s chips. Local can follow frontier because frontier is still sort of "expensive rack local". If STOA becomes massive burst-parallel ensembles, that following may get harder.
> but don’t think there’s gonna be a crash […]. 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 […]
That’s the crash… that’s pretty much exactly Ed Zitron’s thesis
On device models would work for most of the use cases we have, but we would have new use cases in the future (e.g. with AR/VR glasses, humanoid robots) that would need more advanced models that cannot run on device and frontier models can provide that
> 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.
I think the flaw in this logic is thinking about how AI is currently used only. Yes, Opus is good enough for the task you are asking it to do, but that doesn't mean that is all you will ever need.
As AI gets better and better, it will open up new use cases that require the better performance.
You don’t “need” any LLMs to do software development. Plenty of us were productively writing code for years before these models were released, and many of us continue to do so. The “AI” companies want to sell you hype. But you don’t “need” them - not if you’re a legit, professional programmer.
You could say this for any new technology though, it's a vacuous statement. No one needs a car, they can ride a horse. But step changes in technology do occur and raise the standard for everyone in a society, hence why we don't see horses for transportation anymore.
You don’t need it, but using it would make you more productive. As an example, I have worked in a code base which is pretty much opinionated. I have many Claude skills to build the boiler plate starting code and tests for the feature I have to build following the existing conventions and patterns in the code base and that saves a lot of manual coding time for me.
This is a very personalised use case, but I think everyone can find these kind of use cases that saves a lot of time for you.
Thank you for your comment.
The narrative that LLMs are essential to the future of the trade often feels like an assault on my professional expertise.
Whatever your experience is, mine is that LLMs don't work well for software development. I wish people who use AI would be more willing take that perspective seriously.
You should read this thread and the linked article for a discussion about your last sentence: https://news.ycombinator.com/item?id=49052023
Could you leave a comment expressing your perspective?
I have been programming for 35 years and a professional programmer for 25 years. Of course I don't NEED AI to program, because I did it before.
This is completely unrelated to the how useful AI is for programming or how much more efficient you can be with it.
I never said you would need AI to do things you can do today. When I said it will not be 'all you ever need', my point is that NEW uses will come that will need more powerful AI. By definition, you can't NEED a new technology to do something that is already being done, because the fact that it is being done already proves you can do it without the new tech. However, that doesn't mean the tech can't do something new that does need the tech.
For example, no one needed an airplane before they were invented. However, you do need an airplane if you want to get somewhere 6000 miles away in less than a day.
There hasn't been a single bit of technology that is 'needed' if you use your strict definition of the word, because obviously humans existed and survived before the technology existed. Being absolutely necessary is not what makes a technology persist or spread, that is an artificial bar to reach.
Some people have such a natural aversion to LLMs that they will make incoherent arguments as to why they should go away. There are plenty of legitimate concerns and critiques of LLMs, you don't need to articulate arguments that you would never make about any other piece of technology to argue against their usage.
The money being poured into AI infrastructure means there is a market for new ways of doing things that take 1/1000 of the power or are 1000x faster, or both.
And there are many such moonshot startups.
AI on GPUs is an efficient as gaming on CPUs.
All that math where perfect precision is not required means that you can’t tell do things in different ways.
Why is on-device AI the future? What is your reasoning behind this? Look at the proportion of things we compute on someone else’s computer relative to what we compute on our own device. Why would this change for LLMs?
I think the reasoning is similar to why Amazon notification emails are very generic: user data is used for various purposes that a growing number of users and 3rd parties have concerns about.
In addition to on-device, Apple is making efforts to secure computations that need to occur off-device, see: https://security.apple.com/documentation/private-cloud-compu...
How's it "quite short sighted"? Are you saying the math _does_ make sense? if so, how?
Back of a very tiny envelope: suppose there are 100 million jobs that benefit from spending $10k/year in tokens. That's a trillion in annual revenue; at a 30% margin that's $300 billion in profits, which can easily support $6 trillion in valuation.
The validity and permanence of a technology has literally nothing to do with how irresponsible people have been while placing speculative bets on it.
In this case, it’s really irresponsible.
Thats great but this is like when 3g came out. Sure it was the future, but it was half baked, impersonal, expensive, unreliable and required a culture shift to be adopted.
Onboard decent LLM performance thats _power efficient_ is at least two/three hardware generations away. (assuming linear performance)
but, the valuations, with debt trade and private credit obscuring exposure is a recipe for disaster.
Seriously, you should try Fable. It picks up on subtleties that Opus misses.
I have heard good things about it, but I truly don’t have a use case for it to justify the inference cost. It’s double the price of Opus
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.
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.
???
LOL WHAT.
Actually the right framing is Apple hedged the risk by maintaining good relations with China. They did not need to take any of the risk.
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?
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.
In my experience Sonnet is actually pretty good at coding tasks, at least with Go.
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
Isn't Anthrophic's "recent turn to profitability" just one specific quarter according to non-GAAP practices? How real is that?
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
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.
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.
I've never heard a compelling thesis for why this is a bubble that will pop. From my perspective AI adoption is just getting started, and every organization I see that starts on the journey only ramps up tokens dramatically. Even just sticking with current model capabilities we have years of business automation, personal automation, and so much more to saturate data centers. As the models improve that only makes more sense. Rather than Apple watching anything burn I think they are going to end up looking like nitwits who missed the boat.
I think people wish AI would succeed, especially in software, but this appears to be the only argument made. Numerically, AI still costs far more than it returns in value. Its a subscription service. Even if everybody is told they must adopt early and heavily eventually AI has to qualify its expenses to its customers or they aren't going to keep using it.
Think Netflix. If you believe the money spent on Netflix over the course of a year is less than the entertainment you receive back you will likely continue to pay for it. If you believe the money spent on all entertainment subscriptions exceeds the value returned you will likely start cancelling some, or all, of the subscriptions. AI is not immune from economics or human behavior. When customer company budgets get tight the money spent on AI subscriptions might be reallocated or reduced to prevent reductions in head count.
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?
Unpredictable AWS bills are a supply-side problem. He's talking about consumer behavior, and consumers of software do not expect a metered service.
But most consumer's aren't paying per token for access to models, so unless that changes and the labs start charging API pricing to everyone, it's kind of a moot point.
Enterprise certainly wouldn't fit. This argument seems to be targeted at the consumer market, which Apple deals in. Consumers are not paying varying Disney+ subscription prices based on how much they watch. So many points are still interesting. Though, I'm not convinced the consumer market is the primary target for AI firms.
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.
It's fascinating that you simultaneously argue that margins will expand... while posting about personal behavior all of which points toward commoditization and intense price competition.
What’s wrong here ? You push the router , you keep same subscription price , customers are as happy or even more happy because router will make process faster, the costs will go down . The market size will increase several times .
If you have properties of the market where your costs will go down , the size of the market will increase and you are top contender. How is that a bubble or a bad market ?
Sure you have risks of underperforming and lose the competition, but how is that different from any business in the world ?
Doesn't sound like you've read any of Zitron's analysis
I think Ed's argument is that given the infra investment the revenue won't be enough to pay back the original investors. In fact cheaper models make this worse for them. Who cares about the investors? Well it turns out the infra was financed in large part by debt that was securitized and a bunch of regarded investors that bought the debt looking for higher returns will be taking a huge haircut when the bubble pops. The amount of securitized debt is roughly the same as the mortgage backed securities back on 2007, ergo the prediction is a big recession.
What's unclear to me is if this is a systemic issue that's going to cause credit to freeze up but imo the opacity of the shadow banking "system" does not help here. If you see one cockroach, etc.
Agents will explode the use 100x or more , so even if you push into your router mix models that dynamically do inference that is 100x 10x 5x cheaper , they are still fine.
In fact spacex story tells you next : investments in infrastructure is the best investment. If OpenAI or anthropic have committed infra in the worst case scenario they can re-sell it with margin .
The only way it will not payout suddenly we wake up in the world where ai fails to deliver . Which does not seems to be the case .