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.<p>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.
There are good sources of well-argued AI skepticism, and then there's Ed Zitron.
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.
At this point I have yet to hear a solid argument against his arguments that use half as much evidence as he does.<p>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.<p>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.
He's a college drop out with no experience in programming or technology.<p>He has zero numbers and is completely making stuff up to a gullible audience.<p>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?<p>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.<p>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.
Ed Zitron is basically two things, numbers and snark. You can disagree with the snark but to say he has “zero numbers” is ridiculous.<p>Additionally, as another commenter has pointed out, his thoughts and criticisms are being echoed by people who actually have the money.
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.
his agenda is less existential and backed by less money and power than Sam A and Dario's
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.<p>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.
What specifically is that agenda?
The entire internet is full of anti-AI discussion and a straight up disbelief in any chance of success.<p>Implying there is a "gushing torrent" of pro AI narrative is bizarrely out of touch. We both know this isn't true.
That article is a bit incoherent.<p>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.<p>Staying in their lane is arguably the optimal business decision for Apple and they lose nothing by it.
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.<p>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.<p>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.<p>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.<p>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.
> 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.<p>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.<p>As AI gets better and better, it will open up new use cases that require the better performance.
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.<p>And there are many such moonshot startups.<p>AI on GPUs is an efficient as gaming on CPUs.<p>All that math where perfect precision is not required means that you can’t tell do things in different ways.
The validity and permanence of a technology has literally nothing to do with how irresponsible people have been while placing speculative bets on it.<p>In this case, it’s <i>really</i> irresponsible.
How's it "quite short sighted"?
Are you saying the math _does_ make sense? if so, how?
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?
Seriously, you should try Fable. It picks up on subtleties that Opus misses.
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".<p>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.<p>Uber set their per-developer token allowance to $1500 per developer <i>per tool</i>. That suggests to me that they think they can get at least that much ROI out of AI tooling.<p>Selling $1500/employee/month plans to companies is a great business to be in.
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?<p>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.
> 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.<p>One logical gap in the SemiAnalysis 40x cost of tokens versus subscription: I don't know anybody actually maxing out their account limits.<p>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.<p>What is the average usage of subscribers? Only Anthropic and OpenAI know, as far as I can tell.
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.<p>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.
> While people get some sort of benefit out of AI-generated code, these tools actually end up making them slower<p>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:<p>> 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.<p>So compared to just last year, they had a hard time finding participants because too many didn't want to work without AI.
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).
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<p>yes, because in order to take a short position you have to predict exactly <i>when</i> the bubble is going to pop, which is different from predicting that at some point it will
I will never understand the decision-making process that led to "Let's build an awesome VR headset that can't do gaming".<p>I would love to replace my monitor with Apple Vision Pro for programming and productivity. I would gladly pay $1000 for that.<p>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.
IBM stood on the sidelines of the dot com boom; their share price still halved in the resulting bust.<p>HSBC didn't engage in the unwise practices that led to the great financial crisis; their share price still dropped by 75% in 2008.<p>If the AI bubble does burst chaotically, then I'd expect <i>all</i> 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).
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.)<p>Just more wishful thinking from our favorite AI skeptic
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.
>At their very core, Large Language Models' costs run contrary to basically every model of selling software.<p>>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.<p>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.<p>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?<p>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 :)<p>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.<p>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.<p>Isn't that hard to predict that their economic model is very easy to tune? and this is just first baby steps.<p>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.<p>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.
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.<p>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.
Apple's greatest accomplishment is convincing its customers that they're all geniuses.