This is a great article, and I've never seen the problem explained better. The solution doesn't make sense, though, and this is almost but not quite obvious in the article by virtue of how well it has stated the problem. The problem with basic income is that it provides people with cash but does not change the underlying supply-demand system. To get the lokal (the room) to work you needed more than cash: you needed a desire for teens to be happy and engaged, the will to help them get there, a sense of what might help them, leadership to set it up, attract them, and keep it going, and then, yes, money to pay rent. If all you provide is the money, you will get people spending more money on the usual things the economy "sees": phones, video games, amusing t-shirts. What you have around the lokal in the story is actually not merely basic income fed into the existing economy: you actually have a separate economy. The separate economy works differently because it sees differently, it has different demands, different desires. The society as a whole (as embodied here by the government, thankfully—though not all governments are predictably good-willed in this way) wants the teens to thrive, to provide for them, to pay for them, to guide them with modest leadership. Though individuals, and the regular markets, cannot think about the teenagers—they don't have the bandwidth, they don't have the time, they have diffusion of responsibility, etc—the society can pool its interests and act upon its collective interest through the market. This may not be the only way to "cut across" the market, to do collectively what no set of individuals will do, but it's one way. So we definitely want to solve the problem: we want to organize, lead, preserve, and fund things that are of collective value but that the market cannot see. Giving individuals a basic income, however, doesn't accomplish that. I wish it did, but it doesn't. It puts the money into the usual demand centers—individuals needing to pay rent and transportation costs and go on dates and so forth—that live within the "blind" economy.
One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
I don't read Ed Zitron, aside from when he appears here on Hacker News, and I also find his tone to be over-the-top. I think we might agree on that much.
These articles are lengthy but, to my understanding, Ed's idea is...
* AI companies have committed to purchasing X amount of compute
* Data centers are being constructed to meet this demand, they'll need to charge amount Y
* AI companies do not have sufficient revenue to pay amount Y
IMHO this isn't surprising, personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls. This doesn't seem like a big enough market for the huge dollar amounts I'm seeing thrown around.
I'm curious why you think Ed is so far off the mark on this. To me, it seems like we are headed for a big correction on the whole AI thing.
• He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
• All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
• He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
> All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
Agreed, but I'd argue that Ed doesn't have much else to work with. I'd like to see journalists take this tack and start asking these executives to either back up their statements or back down from them. They should be held accountable for their statements.
Even if we dial down these numbers by a magnitude they are still insanely large and the AI companies do not seem to be making enough money to balance things out.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
I agree that older hardware from Nvidia doesn't become worthless when Nvidia releases new, more powerful hardware. I have to point out that it certainly loses a great deal of value and that's not nothing.
> He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
Is that really the case? I mean, I read about the build out of these data centers being delayed all of the time. I read this last week and it seems roughly in line with Ed's ravings:
> A JPMorgan analysis last month found that more than 60% of data-center capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed.[0]
H100s installed 4 years ago are more expensive to rent now than they were on day 1. It is not at all clear that older hardware is losing its value in a world where the next gen model is smarter and faster due to improved training+inference algorithms (e.g. custom kernels) but runs on the same hardware.
It's either new GPUs make the old ones worthless or old GPUs make the new ones too expensive because they're still useful, it depends which ranter you're reading at the time.
Just like Michael Burry kept comparing NVDA to CSCO and now he doesn't do so anymore now that NVDA's P/E is ~31 and CSCO's is ~41. Funny that.
It helps if you look at Zitron's work history and experience. He's a hype man and a games journalist. His opinions on this are whatever sells, not exactly whatever is correct.
This is alarmingly obvious whenever he talks out of his depth about things like how companies actually use AI and reason about business decisions.
This doesn't match my experience, in academia I saw ~40-45% utilization NVIDIA GPU clusters that went 6 years with <20% failure rate. Might be a TPU thing?
I'm FAR form an expert on this, but I believe that the operating costs such as power + cooling form a big part of the lifecycle. I have no doubt that at some point within the 6 years that are being booked, that replacing entire working racks won't be more cost efficient.
That is current practice, yes. The economics of replacing racks then selling the old ones to people who will salvage and resell working components works out better than trying to repair/retrofit in place.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless.
I am the OP and I totally agree with you on this one point. In fact the progress being made by open weights models strongly suggests that some of this hardware has much more of a life.
The overarching point he makes about incomplete data centres is that the current offering is running successfully on that very incomplete capacity, right?
What he is saying is that he cannot believe the demand exists to fill any of the unbuilt stuff, but much of it is still commitments that are going to have to be paid for, unless they can be backed out. He points to Nadella essentially confirming there will be overcapacity.
He also makes an interesting point that people tend to think "I can't get a GPU right now" means "there is intense, live demand for GPUs in data centres" when in fact the reason you can't get one is buy-and-hold. Including much of that new replacement hardware: it is being bought even the old stuff would (let us stipulate will) do the job.
I think he (or someone who interviewed him) recently said it reminded them less of the dot com boom and more of the Chinese real estate bubble.
Future demand is unknowable. He might be directionally correct but wrong in magnitude, or right about everything, or wrong about everything. Unfortunately people who call bubbles never make their claims falsifiable or do anything else to build confidence, like take short positions. Zitron attacks the very notion that he might put skin in the game like that as obviously crazy.
I don't know to what extent we can say the current offering is running successfully. Anthropic have had visible capacity constraints for 18 months now with lots of throttling and quota capping going on. Those are good signs that demand does exceed supply at the current price point.
Additionally, Mythos has not launched publicly and one reason seems to be that it's too slow/expensive to make widely available, i.e. is capacity constrained.
But supply/demand is always in equilibrium, in some sense. So you could argue that it's currently balanced, or would be if priced correctly. That tells you little about future demand though.
All of this is fair, but it's also important to weigh your criticisms of Zitron's claims against the absolutely unsupported claims being made by Altman on a regular basis. They never show their working in even the way he does.
FWIW on capacity constraints, my gut instinct is that like every other startup these AI companies are are really only now beginning to do the serious efficiency work, because they had money and resources to throw at scaling without it; never optimise too early is pretty much a startup mantra.
Sure, I'm not claiming that anyone who isn't Zitron must therefore be completely reliable.
I think all the labs have done a lot of efficiency work for a long time, tbh. You can see the evidence in their papers, open source releases and product design choices like model routers. They know they need to reduce their cost base a lot to become profitable.
> personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls.
That seems like a giant paucity of imagination. I can easily name a lot of areas where AI is already having a large impact and it's not hard to imagine the impact growing:
1. Customer service. Yes, we all like to laugh at the silly chatbot mistakes, linked list reversals and Instagram oopsies, but a lot of companies are putting a lot of effort (and spend) into AI for customer service.
2. The legal profession is already spending a lot on AI, and it will only grow. Again, we all like to read about hallucinated case citations, but those are solvable problems (honestly I felt they were more human problems than tech problems to begin with) and there are so many areas in research and document summarization that AI is really good at.
3. Radiology. There are lots of arguments over whether AI will "replace radiologists", but that's besides the point. The largest radiology groups in the country already use AI software to check for specific missed diagnoses, and the expected spend on AI will grow, a lot.
4. Enterprise knowledge management. Services like Glean are popular and growing.
You annihilated your own argument with the inclusion of radiology. The only successfully deployed "AI" in use by radiologists (that I'm aware of) are bespoke image analysis models, not LLMs. And that space is rapidly fragmenting as there's a frustrating and seemingly irresolvable tension between sensitivity, generalizability, and accuracy.
Everyone I know hates AI customer service. A couple of prominent food delivery apps here in India switched to AI chatbot customer services and it’s been horrible since then. It’s been almost impossible to get refunds since then, even when there’s straight up fraud involved without screaming ok twitter.
Now ofc it can be said that they haven’t implemented it properly but at some point it needs to be considered that why isn’t no one figuring it out?
I don't know if Ed is far off the mark. But this article does nothing to help illuminate it.
He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.
He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.
It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.
Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
That's exactly what the first (titled) section does?
Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year. But yes it does call out the unprecedented amount of money getting dumped into AI.
What turned me off though was this paragraph:
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
Really tried to read through this all the way, but man I'm just not in love with this guy. I feel like the frustration is clouding his judgement. This line is another one with a fact that isn't really grounded:
> so, you know, they only need to grow by 496% by the end of 2029!
Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
Idk, not really sure what my point is here. There are just so many facts and numbers quoted in here... It's a bit exhausting to refute a piece like this, when parts are genuinely correct, and parts are maybe subconciously exaggerated due to some emotional leaking into the argument.
> ... Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale...
My 2 year old is 80cm tall now, and was just 50cm tall 18 months ago! So it's not wrong for me to expect him to be over 2 metres tall when he's 8...
I just woke up and THIS! ... you almost owe me a new keyboard! I love it!
This statement cleanly encapsulates the entire problem with all of the frontier models' companies' pre-IPO numbers.
They have something-something "new technology" and we don't know anything about how the market is going to settle on the ethics, the utility, the human capacity opportunity cost impacts of not training and/or mis-educating an entire cohort of intern-engineers for a few seasons to a generation, the full environmental costs of hardware and operations necessary for the training each new larger model, ... and we cant even quantify the unknown-unknowns - the risks we cannot forsee.
To predict market revenues for the next few years based on the curves, that they self report without external disclosure of the underlying numbers, is just like expecting your 2 yr old to continue growing at the same pace in the future and in the past - laughable. Good thing it was just water not coffee and it didnt quite come "out my nose" :- ) Thank you kind stranger!
Dividing by 12 you still have the same problem. They're projected numbers as opposed to real ones as well as being grossly skewed by any short term fluctuations.
Divide both by 12 and you do not get the projected numbers. You get monthly revenue, a real measured number. It is the number being reported * 12 when they state a new ARR.
E.g. When Anthropic stated $1B ARR (an extrapolated value) what they were actually reporting is $(1/12)B Monthly revenue. If it helps their current monthly revenue is 47 times that, for a grand total of $(47/12)B per month in revenue.
Yes it is the current monthly revenue which is a projected number as far as the other 11 months go. That's fine if the overall economy has low volatility, your sector is well established and predictable, and your company isn't undergoing any significant changes. Absolutely none of that applies to the frontier AI labs.
Well, he dismisses any value whatsoever to GenAI. That's immediate bozo bit criteria to me. And, well, if Anthropic revenue doesn't grow 5x between now and the end of the decade, I'll be pretty surprised. But, sure, if it doesn't, then someone will keep them around anyway. AMD almost died in the 2010s as one example, but they kept getting propped up and now they're back in the game swinging. There are people who can see alpha beyond the next 10Q. Ed Zitron isn't that sort.
> Well, he dismisses any value whatsoever to GenAI.
I didn’t read it that way. I see a lot of value in it.
I just don’t see us justifying the amount of infrastructure being built or current valuations. Or in the unlikely event that we do, the societal upheaval is going to take away the ability to monetize it meaningfully.
OpenAI and Anthropic may make it through. But that is different from saying valuations are justified or that all this infrastructure will pay off.
"Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible."
How else would you read the above statement? He's just preaching to his own choir IMO.
My take: like any gold rush, a lot of dumb ideas will get backed and they will all fail. And then we'll keep the ones that worked. SSND. Good luck picking the winners a priori.
I read it in context as being about the market prospects of genai.
The problem is, when there is so much overinvestment, everything gets wrecked. In the aftermath of the dotcom boom there was at least a bedrock of fiber and still useful equipment to build upon amid the rubble. This time we are going so much further; also many of the durable assets are misplaced bets and the depreciating ones will depreciate more steeply.
Someone should do the analysis of a decade and a half of Nvidia datacenter GPUs from Fermi to Kepler to Maxwell to Pascal to Volta to (Turing) to Ampere to Hopper to Blackwell and generate some hard depreciation numbers. Fiddling around a bit, 16-20% annual depreciation (so 5-6 years total and then any further revenue is bonus goods) it would appear, but that's a fiddle number.
But confounding this, K80s and V100s are still offered by cloud providers 13 and 9 years after their releases and academia still loves their GTX 1080 Pascals in their desktops. At companies, the beancounters take a computation and find the best architecture !/$ for that calculation. It does not need to be brand new shiny. It's Nvidia's job to make that case, not them. But anyway, the real data is right there. And those old GPUs demonstrate the dark fiber is already in place (and it's not so dark or they'd pull their racks).
AI is the special case. New GPU generations are the only way to access HW implementations of last year's research on precision modes and matrix math. If that slows down, that would be the first real bellwether of a slowdown. It hasn't happened yet. I'm a little surprised myself, but I also think coding agents are the vanguard of general design agents and that's going to hit a lot of industries at once.
So as long as the next generation of GPU halves the price of tokens and doubles throughput (or better), the demand for tokens will continue to rise IMO.
What I don't think is that AI can come for anyone's job successfully no matter what the C-suite sorts insist.
In summary, if you're a bear, you can point to the depreciation cycle and scream the sky is falling. And if you're a bull you can point to GPUs staying in production for a very long time despite the depreciation. Guess we have to wait for 2030.
Sure, according to an unnamed "GenAI principal at Alphabet" of whom "We could not verify the name of the person who describes themselves as 'GenAI principal architect at Alphabet' and therefore we cannot 100% trust their claims."
But let's run with Deep Layer(tm)'s hot take from 2024, GPUs all die in 2 years, no exceptions*. Poor guy just spent $400,000 on a DGX with 8 B200s, each of those B200s generates a piddly ~$3,000 in profit monthly spewing tokens, netting $576K in 2 years, that's a pathetic 20% annual return. Oh no... Won't someone call Michael Burry!
*Never mind the 3-year warranty or any extended service contract, that GPU is D E D and you're S O L.
You gotta include 50k+ in power plus other expenses. Still looks like an ok return on capital, but you are betting heavily that cost of computing doesn’t fall much.
If token prices fall a bunch then it may not even be worth leaving on, depending on your facility’s relative power, cooling costs.
If we push far into oversupply eventually a bunch of firms building this infrastructure are going to lose out.
Chinese providers realized that LLMs have peaked and have started trying to reduce the price per token. Deepseek pro v4 can easily add tests to my complicated code and costs cents for a million tokens.
I can ask Claude or ChatGPT architecture questions and then use Deepseek for the rest.
How are these businesses going to pay to price of energy and GPU depreciation again?
I love nonsense like this. If using a larger model to plan a chain of thought task for a smaller model works, what makes you think Anthropic can't do the same thing and offer it as one of their effort level settings? It reminds me of all those fallen AI ASIC startups insisting they can crush Nvidia until they found out the hard way that the rules of the game are dynamic.
The real challenge IMO is whether enterprise will want to run the models on-site for 100% security and privacy, but even then, what stops Anthropic from offering such an option on-prem or in the cloud?
He implies $400 billion in revenue by the end of 2029 is unrealistic when in fact it's very doable if you look at the trajectory of this technology since ChatGPT 4.0 launch. Google and Meta bring in around $500 billion in ad revenue between two of them annually. ChatGPT will easily bring 100s of billions in ad revenue if fully monetized given 1. it has billion weekly active users 2. ChatGPT conversation provides even better context for ad targeting vs search or social media. Enterprise AI revenue is going through the roof already, and with computer use companies will literally be able to fire large percentage of white collar workers and replace them with AI agent without updating their software infra.
Does that '100s of billions' come from a big bucket somewhere called 'spare cash', or does it correlate to a commensurate reduction in the 'around $500 billion in ad revenue' that Google and Meta are extracting?
Do your assumptions - " if you look at the trajectory " - factor in a slowing economy, a slowing growth in quality improvements in the tech, and/or the asymptote of market saturation for punters happy to stump up more than $50 a month?
What about a few hundred billion in salary and benefits reductions due to mass layoffs?
Not saying this would be good (qualitatively) or even good business in any sense, but we’ve already seen companies willing to sacrifice headcount to cover CAPEX for these models.
A few hundred billion in salary and benefits reductions equates to millions of layoffs. At minimum, we'd be looking at something about the same magnitude as the 2008 financial crisis. That scale of workforce reduction would have profound implications for the broader economy.
In a consumption-driven economy, businesses need consumers. Any gains from these layoffs would be short term at best.
I mean it almost certainly won't increase unless a major company takes out substantial debt, in which case we just kick the can and have conversations about bigger numbers. I don't quite think you understand, where will these hundreds of billions come from? By 2029 we will be well into a hardware glut and people will run their own models. Anthropic doesn't have the data flywheel to compete with OpenAI or Google. They went all in on special purpose AI and hit a brick wall and had a "do as much evil as possible" strategy which didn't pay off. Hopefully they fail before they get the entire industry regulated.
> Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year.
People like you would be why I put "(titled)" in the reply.
> That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
The premise is that AI is significantly more expensive than current subscription & token fees. Within that framing, yes basically all AI users are getting conned. Tricked into redesigning their workflow around an unaffordable technology, in the hopes there will be too much sunk cost and they'll just eat a thousands-a-month fee.
> Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
"Doesn't guarantee it will continue to grow" is an understatement.
Let's take a generous assumption of the average subscription; $1000/month/seat. This will be quite a bit higher than pretty much everything but hardcore software dev, we'll re-do the math with $200 in a moment. Let's also grab Ed's $60B figure for both Anthropic/OpenAI, as it's more generous.
That's 30 million subscribers for Anthropic, 30 million for OpenAI, 60 million total.
They need to 5x. So 240 million extra subscriptions.
... Are there 240 million people left on the planet who can afford $1000/month?? (Either directly, or their employer) This kind of scaling is already hitting the limits of people on the planet. That sounds ridiculous for "240 million people" against 8 billion, but remember that $1000/month is a lot of money and a lot of jobs just do not benefit from AI. 2/3rds of employment in the US is stuff that happens in the physical world. Claude won't restock shelves, manufacture goods, construct buildings, cook food, or wipe geriatric asses.
Go again with $200/month. While this monthly fee is much more palatable, the sub-count inflates to 300 million subs needing to grow to 1.5 billion. They'd need to sell a sub to everyone in Europe and North America.
(And while there's loads of people in Africa and Asia, most of those are low income. You're not getting expensive AI subscriptions out of them or their employers either. China's obviously not gonna buy US AI, India has a GDP-per-capita of $250/month.)
>They'd need to sell a sub to everyone in Europe and North America.
Yep. Every man, woman and child, and even then provided we include Russia, Mexico, Cuba, Haiti etc, and, out of desperation to get to 1.5 billion, Turkey, which is in Europe a little.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it* how did it disappoint you?
The information you are describing is stuff I would not expect anybody to repeatedly duplicate across periodic blog-posts.
* (Yes, I'm being sardonic, but if you did bother to click them, then I'm legitimately interested in your answer.)
He's right that its all going to pop dramatically and catastrophically for some. But having read a bunch of his stuff, there are two things he's just plain wrong about and they make his martyrdom tone too grating.
- His own objectivity - he consistently throws shade (rightfully) at the pro-AI side being financially 'required' to hold a certain world view, but is completely blind to his own claim to fame effecting him similarly.
- He consistently claims AI can't be made to work, and tries to prove this by calculating with the bubble prices. Its like saying tulips could never be profitable in the middle of the mania because ships were too expensive as proven by their current price to use for shipping tulips.
Add in the semi regular instance downplaying AI's usefulness contradicting my own experience and I mostly dont bother reading him anymore.
Its not like I'll be surprised that shit hits the fan, and he's not going to call the 'when' any better than wallstreetbets or an octopus.
Yeah, to be honest I think his take is a bit nonsense because it's so historically inaccurate.
Most hugely transformational technologies in the past also resulted in giant bubbles that burst, because investors piled into lots of companies in the hope that their particular company would win out. Railroads, automobiles, telecommunications networks, the Internet, etc. etc. were all hugely important, transformational technologies that all caused giant bubbles that burst.
But Ed Zitron seems hellbent on saying AI is a nothing burger, and that's why the bubble will burst. But the latter doesn't necessarily follow from the former, and indeed the examples I gave show that the exact opposite is often true.
I believe that the AI bubble will burst precisely because it is such a transformational technology. AI may not live up to the ways its biggest cultists like to shout ("Feel the AGI flow through you!!!"), but similarly in the .com boom/bust there was tons of nonsense about how we'd do absolutely everything online, we were in a new "eyeball economy", whatever that meant, yada yada, yet I'd argue that in some ways the Internet was actually a bigger impact than originally envisioned, just not necessarily in the way that late 90s boosters envisioned it.
Agreed. Phrases like "journalists are currently gooning over OpenAI and Anthropic" really put me off. It's a poor attempt at modern muckraking; cheeky yet offering little substance.
I'm not a Brit, but I do enjoy British culture, including writing. I haven't been able to read any of Ed's rants to the end despite generally being on the cautious side towards LLMs
I didn't say it was. I'm just observing that his muckraking style is part of a very long British pundit tradition. Americans have never liked it — Intel got very upset about The Register's coverage of "the Itanic".
(And he's not Gen Z anyway is he; he's among the older millennials. He's appropriating it for muckraking purposes.)
Sure, but does that vibe invalidate the argument? What an odd time the middle of an argument is to be clutching pearls and worrying about prose quality.
Style and vibes notwithstanding, is there anything in your view that wrong with the argument itself? Could a better or more polite writer have convinced you with the same shape of logic?
> He is not writing his blog to convince people, his primary audience already agrees.
He's selling a paid newsletter, so at least one of his motivations is to make money. His target subscribers are certainly people who lean towards his viewpoint but he still needs to do some convincing because the market of people who are open if not warm to his thesis is much bigger than the market of people who already share his thesis.
> That doesn't make him wrong.
I think it's way too early for anyone pontificating about AI, the economics of AI, etc. to be declared "wrong" or "right". This is going to take years, if not decades, to play out.
Subscription based writing is all about writing for audience that agrees. Yes it creates bubbles, but economic of it is "people paying to read stuff they agree with".
Well, we don't have to speculate as to whether there is some sort of emotional taint on Zitron's thinking; it's shot through. But again, that does nothing to damage or offset _the argument_, which is available for your inspection and consideration, and you, as a thinking person, are handily capable of vetting. :) There is no need to use a heuristic; you have the thing itself.
The square of the hypotenuse — you multiply it by its bleedin' self — is LITERALLY equal to the sum of the squares of the other two sides. Again. Multiply each of the other sides BY THEMSELVES, and then add them together. It's that bleedin' simple Jim Cramer could do it. That number is the same as the square of the hypotenuse, no matter what idiot CEOs think.
> He is preaching to the choir, if you already hate AI you will love the article, if you don't hate AI already you will find the article insufferable.
I'm neither (or both, if you want - I can hate the direction its taking humanity while not hating my usage of it or opportunities it brings), and I definitely did not find his writing to be either lovable or insufferable.
I enjoyed reading it in a "smells-like-BOFH-but-in-finance" type of way.
It's fully in the BOFH/The Register tradition, yeah.
Some of his manner reminds me also of Max Keiser's "The Truth About Markets" which I credit with telling me about the subprime crisis a couple of years before it happened, even though people thought he was insane… because he kind of is.
I particularly enjoy reading big banners asking me to pay for a newsletter subscription if I "liked" the content. Not if I found it interesting. Not if it actually provided any value whatsoever to me. No, you just have to "like" it. In other words, it is meant to be written in an engaging way and perhaps reinforce your believes like an echo chamber or even stir up certain strong emotions. Not to convey information. So, thanks, but no. I'm sure this opinion blog is very well written, but I don't think it is more well founded than anything else in this sea of opinions that sports a bigger garbage patch than the Pacific Ocean.
I know the HN guidelines discourage commenting on "tangential annoyances" on a website, but I think this issue is more than just tangential and more than just an annoyance.
When an author is this relentless in pushing you to sign up, there is good reason to suspect that financial motives are unduly driving an agenda.
I counted 8 such instances:
1. In the sidebar
2. At the top of the article
3. Popup in the middle of the screen after just a couple of scrolls into the body
4. Several paragraphs into the article
5. At the bottom of the article
6. At the bottom of the page under the comments section
7. Popup at the bottom of the screen after scrolling to the end of the body
8. (My personal favorite) Click the "user" icon in the bottom-right corner, which you'd normally expect to open an AI chat bot these days, and (surprise) you're prompted to sign up for a paid subscription
This sort of behavior just completely tanks any and all credibility this person may have.
Of things to be upset about, an independent journalist asking readers to pay for access ranks very low. Especially compared to LLM companies that are exacerbating the climate crisis, increasing cancer rates among residents, or increasing utilities for residents.
This sort of behavior completely tanks any and all credibility this commentator may have.
Is the OP article “journalism” or more of a rant with self-aggrandizement about how they’re so smart and such a good person that it makes lots of people angry?
What are you talking about? Why is "liking" something mutually exclusive with conveying information? I like lots of things precisely because they convey information!
>I like lots of things precisely because they convey information!
Correction: You may like them because you think they convey information. But without any sort of vetting process, the internet has become a cesspool of "news" or "general knowledge" places that ended up quite successful, but which are essentially just a contest of who is most confident when talking about topics and who can present bullshit in the most engaging way. You can see the peak of this on the JRE podcast. Anyone with actual expertise in a subject would be able to call out many of the guests, but since the host knows nothing about most of their fields he just gives them a platform to spread their opinions as facts. And millions of people who also don't know better will accept them without question.
I like Ed's sense of humor, I also like that he can distill down a lot of messy details into something more cogent, especially with the money side.
But, I also think he has missed the mark on a fair few things in terms of out comes. He may be proven right yet in terms of the general shape of things for some parts of the industry but also will have some big misses.
My general take away usually comes down to, places like OpenAI, Anthropic and Oracle have gone in a little to hard to fast and it may hurt them long term as they struggle to make it work in terms of economics. not that they can't just it will be difficult. But places like Microsoft, Google, Meta, Apple, Amazon; they have a very long runway to endure the growing pains and make it through to a long term business that no longer burns cash.
Oddly suspicious how this comment which was not one of the first comments which does not address the content at all but the tone skyrocketed to the top.
The tone is written as abrasive to anyone who doesn't already agree, which shows this is more of an emotional opinion piece than open minded objective research.
Hype cycles never last forever, but that doesn't mean all the value has been tapped by any means. The fact that modern GPUs can solve ridiculously complex high dimensional functions is a superpower in every possible field of research.
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
Arguments have smells but rigour demands you investigate further. Zitron's smelly prose is, ironically, just the kind of stylistic distraction that AI can help condition; the further irony is that he will one day seem to have been right, for a year or two.
The money is indeed losing its mind over AI, and Zitron is a stopped clock. A correction is coming but the tool isn't going anywhere.
I kinda get it - he's been attacked for his negative views a lot, and that tends to produce a more passionate writing style. It's a little immature, sure, but also authentically human.
> One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow.
This is a super-useful article, and there are many cases where the cost of buying, owning, and selling a home is higher than that of renting. But sometimes ownership costs less than renting, and often—generally when you stay long enough—it can cost a lot less. One place in this article that hints why is when he says that in 2011 his mortgage was $2,329.92, whereas now 15 years later it's $2,440.48. That is a tiny crawl upward, nowhere near the rate of inflation. If he'd been paying $2,329.92 monthly in rent in 2011, he'd now be paying about $4,100. Inflation hits renters much harder than homeowners. Appreciation (a form of inflation) tends to help homeowners more than hurt them. Nerdwallet's rent vs buy calculator is quite sophisticated and can help you reason about your own situation. https://www.nerdwallet.com/mortgages/calculators/rent-vs-buy...
You know, anyone can say things. Rants are cheap. What we need, though, is critical thinking. Not just, “Look, three stars are in alignment; it’s the constellation Triangulum.” This article expresses many opinions. It draws lines between stars. But many other lines could be drawn. Maybe we should believe this analysis because it sounds passionate? “He uses cuss words! He must be invested! He talks about CEOs! He must be an insider!” No. Anyone can point out obvious, insignificant data and rant about it. We need facts and logical arguments, not rants. Less of this.
Ughh..Lets put people, humans, front and center again -I'm in the AI space too but when it comes to content and insight we need to be human focused and not machine driven. Especially opinion and insight pieces.
Bilal is not a writer he is a developer and founder. he founded a two person startup to use AI to produce podcasts. He calls himself a 'Product Engineer' which just means he is using claude and AI to develop..
The article he wrote has all the hallmarks of a lot of AI hyperbole and style. The author has some education in economics but no indication of any writing ability. so who knows what his real writing style is. I too can generate a similar article with Claude in about 30 seconds. Does that need to be read on top of HN too?
e.g this use of AI of the word 'line' is sooo annoying: The "they just overhired during COVID and are self correcting" line was the dominant"
So this ai article about ai is out there.. its out there influencing us even though it is largely speculation, hype and AI-content. The site he has is all AI written (which I am not opposed to but Bilal never worked at Meta, he never spoke with anyone in senior leadership.. So this is an opinion piece driven by AI influencer..that reaches the top of HN
Ok so what about its content here is the points he made (who knows how many he really thought of or that Claude created)
1. AI layoffs structural, not cyclical—"measurers" (middle management, finance, legal) hit hardest
Only producers/sellers and top leadership safe; the coordinating middle gets cut
(WHO knows if this is correct?)
2. Founder-led firms cut first—board trust buys 12–24 month head start
(Everyone is laying off, economy sucks, AI Cap expenditure and uncertianty..)
3. "10x engineer" becomes "1000x"; token data makes leverage brutally measurable
(not right now..yes for quickly built MVPs hardly as fast -1000x is just hyperbole)
4. Inequality explodes—first trillionaire soon, many to follow
(Pure speculation)
5. 2028 political backlash predicted: anti-elite, anti-AI populism, plus xenophobia
(sure maybe)
5. Regulation may cost U.S. the race vs. China
(hype driven influencer speil - AI is barely speeding up as fast as VC Bros are predicting)
The moves from “the subscription model for AI isn’t working given these parameters” to “a subscription model for AI can never work” to “the model was deliberately deceptive” to “it’s a fucking ripoff” is not logical. AI companies are feeling the need to get hold of spiraling costs by increasing prices and limitations. Inference hasn’t gotten cheap enough fast enough, and for some reason they feel they can’t wait longer. That doesn’t mean a subscription service can’t work: only that it will be expensive, maybe vastly so, and will need tiers based on usage with some fluidity for users to move between tiers in a given month. The model is something like HP’s “instant ink” service. Sure, there’s a question whether the moves companies are making now are worth the cost in the eyes of customers. But that’s a question of economics and timing, not a fundamental blow to monthly subscriptions as a model. The article doesn’t deal with these considerations fairly. It’s too much in the direction of a rant, with conspiracy theories thrown in.
Thank you. But the Y test still seems sufficient. Every criterion will have false positives and negatives. With the Y test the false negative (you present as a woman but have a Y chromosome) is rare and the vast majority of cases are handled well. If you have this condition you must compete against men (given the Y chromosome test rule) or not compete. If you’re dying to be in the Olympics as a woman but have the Y chromosome, you’re just out of luck. Not everyone can be a concert pianist either. No rule makes things wonderful for 100% of humans. The Y test gets very close.
But that's a contradiction, no? We're saving women from other women and barring trans people also (ones we consider men) because of a perceived risk that I don't see evidence for (i.e. people choosing to compete as women on a malicious basis or with an 'innate advantage' that makes it dangerous - we've had a long time of running these sports without this sort of regulation, and it seems to be a political choice more than a reaction to evidence that women are being outcompeted by trans people). This is also assuming that having a y chromosome makes it fair for people with a y chromosome to compete against one another, but if you compare people's physiology these people who present as women often have low/no testosterone. Separating on the line of testosterone picks up a lot of female athletes (especially at the olympic level) that are not trans, and overall I just see this hurting women without evidence that it's actually a response to harm. In any case, trans people and gender non conforming women become the victims of this in the public sphere.
It just seems very misguided.
High level sports consists entirely of outliers. That’s kind of the point of the olympics. This newest rule is nothing more than a misogynist rule to turn the women’s division into the “no more than statistically average” division.
Almost every gold medal winner in the past games would not have been affected by this new rule, so that's a biiit hyperbolic. Those athletes are still far outside the normal performance of women (or men, for that matter).
What's the story with the Avro C102 (per law 20)? What's the connection with "A bad design with a good presentation is doomed eventually. A good design with a bad presentation is doomed immediately"? I'm intrigued.
Two references come to mind – the first is that the Avro C102 was beat by the de Havilland Comet, which was an example of a bad design with a good presentation. It had a series of mysterious hull losses that were eventually attributed metal fatigue accumulating around a square cut-out in the hull.
The second, more depressingly, is that the Avro C102 was cancelled to redirect resources to the Avro Arrow: Canada's mythical last jet fighter, of which only a handful were produced before the program was (painfully) cancelled, and Avro wound up. IIRC, the Canadian government was the main-to-sole bankroller of the project, and ballistic missiles became increasingly more important.
I'll argue that Comet was a good design, but with too many unknowns being solved at once - while we had limited experience with pressurization the comet took more cycles, and had a much higher service ceiling.
I dont think the Comet was a materially worse design than the Avro C102 (both aircraft are similar) and the C102 may very well have had other unknown issues only to be found if it went into series production.
This is an area that could really use good research, but this study looks badly designed and completely dismissible. I hope it’s true that video game playing has some mental health benefits, and I wouldn’t be surprised. You’re not going to determine whether it does by asking a bunch of people how they feel about Mario and Yoshi.
A vibecoded (but programmer-supervised and not sloppy) retirement savings forecaster similar to what you see financial advisors using, but you're in control. Could be useful? Feedback please.
No. This perspective is wrong in both directions: (1) it is bad medicine and, (2) the medicine doesn't treat the disease. If we could successfully ban bad ideas (assuming that "we" could agree on what they are) then perhaps we should. If the damage incurred by the banning of ideas were sufficiently small, perhaps we should. But both of these are false. Banning does not work. And it brings harm. Note that the keepers of "correct speech" doing the banning today (eg in Biden's day) can quickly become the ones being banned another day (eg Trump's). It's true that drowning the truth through volume is a severe problem, especially in a populace that doesn't care to seek out truth, to find needles in haystacks. But again, banning doesn't resolve this problem. The real solution is develop a populace that cares about, seeks out, and with some skill identifies the truth. That may not be an achievable solution, and in the best case it's not going to happen quickly. But it is the only solution. All of the supply-based solutions (controlling speech itself, rather than training good listeners) run afoul of this same problem, that you cannot really limit the supply, and to the extent you can, so can your opponents.
What do you think about measures that stop short of banning? Like down ranking, demonetizing, or even hell 'banning' that just isolates cohorts that consistently violate rules?
Not OP, but my opinion is that if a platform wants to do so, then I have zero issues with that, unless they hold a vast majority of market share for a certain medium and have no major competition.
No. You are objectively wrong. It's great medicine that works -- for example, in Germany, and in the US, and elsewhere, it has stemmed the flow of violent extremism historically to stop the KKK and the Nazis. You can't even become a citizen if you have been a Nazi. Even on the small scale, like reddit, banning /r/fatpeoplehate was originally subject to much handwringing and weeping by the so-called free speech absolutists, but guess what -- it all went away, and the edgelords and bullies went back to 4chan to sulk, resulting in the bullshit not being normalized and made part of polite society.
If you want to live in a society where absolutely anything goes at all times, then could I recommend Somalia?
