14 ms·

One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg

One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg. “I think he’s wrong about the timing but I do expect AI companies to go to zero.” You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning. Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes.

overfeed 16d ago
On the flip side, I also see many people taking this thread as an opportunity to shit on Zitron as a person, and not "discussing his prediction". Incompetent/ blow hard/ dishonest are ones I remember off the top of my head. AI by itself, is surprisingly polarized; Ed Zitron even more so.
- dyarosla 15d ago
  Not sure why you see it as such; Incompetent and dishonest are characteristics that follow from this professional work, adequately describing an individual who continues to make poor predictions, analyses and false statements refuted by past events. Far from “shitting on” Zitron
- TulliusCicero 15d ago
  I mean if his personal work is incompetent or dishonest...? One of the easy ways to evaluate this is: how has he taken being incredibly wrong about his extremely confident predictions over and over and over? Typically with people like this, they completely shrug off being super wrong. It's barely even a blip on their radar, and even bringing it up is a good way to get them to immediately attack you to deflect attention from how bad their predictions or assertions were. If you're constantly making predictions on Topic X, and said predictions are consistently, wildly wrong, and you never actually grapple with that or acknowledge how wrong you were in the past, then that's, at the very least, intellectually dishonest. But by all means, someone link us to his blog posts where he goes over his wrong predictions without excuses or deflections. I'd be happy to change my mind.
emp17344 16d ago
Why is Zitron’s repute evaluated entirely on the basis of failed predictions? Predictions are incredibly hard. AI enthusiasts and thought leaders have made so many demonstrably incorrect predictions it’s hard to keep track. Based on this metric, Altman and Amodei should never be taken seriously again.
- mvdtnz 16d ago
  Have you ever listened to Zitron speak? He's not exactly the type to hedge his predictions behind careful language about how hard predictions are. He makes every one of these predictions with absolute confidence and conviction.
  - emp17344 16d ago
    So does everyone else, including prominent AI enthusiasts and tech CEOs. And most of those predictions are wrong, because predictions are really hard.
    - hgoel 15d ago
      And we're used to insulting and making fun of the prominent members of those circles too, see: "Scam Altman", the large variety of jokes about Musk's timelines, comments about Dario waking up in a cold sweat whenever a powerful new open weight model drops, the "AGI achieved" meme etc.
      - jakeydus 15d ago
        Hyperbolic as those descriptions are, it’s not like they’re not sourced from reality. Sam Altman’s reputation in SV is well-established; Musk promised the roadster what, a decade ago? These guys preach a promised land of milk and honey and so many hear lap it up like dogs.
    - s1artibartfast 15d ago
      Both can be bad, and I think people take issue with intentionally projecting false or irresponsible confidence (lying). Zitron twists and misreports a lot of facts where they should know better. CEO hypemen knowingly conflate Ambitions for certainty
rtpg 16d ago
Here's two possible set of predictions: - a list of predictions that are entirely wrong, from A-to-Z, and are not even resembling what ends up happening - a list of predictions that are wrong, but where the underlying points are in fact interesting and have some predictive value, and it's just the "last step" that is wrong For example, one person might say "oh it's raining in Dallas therefore I should buy some TI stock". And we'll say for sake of argument that they say that even though it's nice and sunny in Dallas at the moment. Another person says "Oh its raining a lot in Idaho and that is going to increase potato yields and therefore I will buy McDonalds stocks cuz fries will be cheaper". In this hypothetical it turns out McDonalds buys all their potatoes from ... Kansas or something instead (and it's a specific kind of potato in a completely separate market)... but Idaho potato yields _did in fact go up_. An even more straightforward point: the iphone 3GS comes out in 2010, people are very hyped, someone looks at how RIM _still_ hasn't gotten its shit together and declares "RIM isn't going to to be able to stay profitable 18 months from now, they're gonna have their lunch eaten". Turns out that RIM still made a healthy profit in 2010. and 2011. And 2012. 2013 was their first loss in a while... and then it wasn't until 2014 that they really got kicked in the face. The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility? I'm saying this... it would be helpful if _some_ more AI companies flamed out. In some sense he does himself no favors by focusing on the corps with the biggest war chest instead of the various AI companies that spend a bunch to go nowhere fast and then have just disappeared.
- threatripper 15d ago
  Being early is the same as being wrong. Being lucky is the same as being right.
  - rapnie 15d ago
    Nice saying. If in the prediction "given [intricate analysis] the whole [shebang] goes [bust] at [date]", only the [date] turns out to be wrong, I'd say it is still a valuable prediction however, though it was wrong.
    - 15d ago
dosisking 15d ago
AI companies will never go to zero because AI is part of the Military Industrial Complex now. All the money is coming from the military and government for surveillance and power and war.
- wmf 15d ago
  Speaking of wrong predictions... the entire US military budget would have to be spent on OpenAI/Anthropic to keep the bubble from bursting.
  - dosisking 15d ago
    [flagged]
- simianwords 15d ago
  Every conspiracy theory has an escape hatch
  - madaxe_again 15d ago
    It’s the moon-leopards in league with the bee-people. Only an IDIOT wouldn’t know this, it’s OBVIOUS.
  - maxbond 15d ago
    Ad-hoc hypothesizing ("escape hatches") are dangerous but not quite invalid. Eg, they failed to find gravitational waves until they did, and you could have viewed building yet another more sensitive detector as a similar exercise in refining a hypothesis that you keep receiving contrary evidence for. Sometimes you really did just underestimate how difficult your hypothesis was to demonstrate. Maybe Meta will be destroyed in 2027, or whatever. The problem with conspiracy theories is more that they have a ratchet-like quality where counter evidence reaffirms the theory in your view and you can only ever get more confident. We should have been increasingly skeptical of gravitational waves to some degree as we failed to demonstrate them, even though we didn't abandon the hypothesis and it ultimately prevailed. But if you adopt a wrong idea, and people try to demonstrate that to you, and you take that effort they're putting forward as a sign that you are correct and they must be hiding something from you, it will be very difficult for you to realize your mistake. So, as long as you are less certain than you were before, I don't think rolling your prediction over into the future is necessarily conspiratorial or a mistake.
jrflowers 15d ago
This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP Some guy wrote that he’s grumpy because he couldn’t sleep and decided to dunk on an internet personality he doesn’t like, it’s not the ceremonial placement of the ur-kilogram
- Aerolfos 15d ago
  > This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP Oh it's not just the author's opinions. They're the opinions of a bunch of LLMs he checked, too. Much better.
johnbarron 15d ago
>> Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes. Dan Luu did not engage on anything more, than a disorganized wall of text, ranted like a teenager using toxic personal attacks, while obsessing over calendar errors and a placeholder in a spreadsheet. If this is what passes here for a smart engineer...Lets analyze his post in a more logical and analytical way: - His entire argument is based on the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, the entire trillion dollar investment cycle is justified. A short window of venture subsidized chip buying...tells you absolutely nothing about the multi decade debt structures, physical infrastructure depreciation, and power grid constraints that dictate whether a capital heavy business model survives. - While he whines about Zitron numbers, fails to provide a single! macro level equation to address the real financial threat. NYU finance professor Aswath Damodaran for example, explicitly warned that the current AI build out is an asset heavy, debt funded run up backed by private capital markets. Unlike the dotcom boom which was equity funded and contained to tech shareholders today AI infrastructure burdens companies with a massive $80 billion in CapEx per gigawatt, meaning a monetization correction will trigger widespread systemic debt distress and loan defaults across the real economy. "Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981 https://news.ycombinator.com/item?id=49229981 - Luu and this HN crowd, today in a mob mood...completely ignore the highly unstable plumbing of the sector growth metrics. Patrick Boyle is a quantitative finance professor and former hedge fund manager, and has meticulously mapped out the mutual dependence the entire AI boom. Big Tech companies are pouring massive venture pools into AI startups, which are then contractually bound to hand that cash right back to the hyperscalers to buy cloud compute. Analysts have identified more than $800 billion in these arrangements: "Why Wall Street is ignoring big tech's debt" - https://news.ycombinator.com/item?id=49230630 https://news.ycombinator.com/item?id=49230630 - The worst of Luu logical failure, is ignoring ( on purpose? ) were Zitron numbers come from! They come from some very disciplined institutions, which Luu completely ignores. Citigroup quantitative analysts project cumulative global AI CapEx hitting $9 Trillion through 2030, with maximum global AI revenues ( not profit...) covering less than 30% of that expenditure. - To break even on the physical infrastructure currently under construction, the AI sector needs to generate over $2 Trillion in annual end user revenue by 2030. Total actual revenue generated across the ENTIRE global AI sector today sits at a fraction, around $150 billion. - Anthropic in a hysterical push, to make it to public markets, before the bubble bursts, recently claimed their addressable market is 30 trillion... the whole of US economy. Are we getting a post from Luu on that? This of course this ignores that MIT Professor and Nobel Laureate, Daron Acemoglu, mathematically proved that while 20% of all labor tasks are exposed to AI, only about 5% can be automated profitably due to upfront enterprise systems integration and the high financial burden of constant human in the loop verification. "A new look at the economics of AI" - https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-economics-ai https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-eco... - Dismissing the AI bubble thesis, because you found a spreadsheet typo in a newsletter, and ignoring the other voices who are aligned with Zitron core premise, means you are also dismissing the research of a Nobel Laureate in economics, the Dean of Valuation, veteran hedge fund managers, Barclays, S&P Global, and Citigroup. Arguing that "the models are hitting benchmarks" while ignoring that the physical balance sheets and enterprise budgets cannot support a multi trillion dollar infrastructure build out, is exactly the type of Dunning Kruger this corner excels at.... Ed Zitron is correct, despite the clumsiness or unpleasantness of his message delivery, and this community reaction, will be an historical record of the AI bubble crowd madness. It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come. The end of this universe is all tax payers will own a piece of AI and will pay for it with increased interest rates for the next 25 years...
leoc 15d ago
In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitron just largely needs to stop coming up in anti- and anti-anti-AI arguments since, it seems, he's just not a particularly insightful or reliable voice on the subject. That said, one or two of the specific assessments in Luu's article seem dubious as well, especially this one: > August 2025 https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ : "These models have clearly hit a wall where training is hitting diminishing returns" > Wrong It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference. See eg. https://www.tobyord.com/writing/mostly-inference-scaling https://www.tobyord.com/writing/mostly-inference-scaling . And in fact in the quoted and linked article https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ Zitron comes up with something which looks like a recognisable explanation of this: > Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed). > As a result, all the "gains" from "powerful new models" come from burning more and more tokens. AFAICT the other drivers of recent progress in LLMs have been: ploughing in lots and lots of specialised training data custom-made at piecework websites https://www.youtube.com/watch?v=4pG3SJQPAwk https://www.youtube.com/watch?v=4pG3SJQPAwk ; and work on harnesses and the like. AFAICT neither of those makes false the claim that "[t]hese models have clearly hit a wall where training is hitting diminishing returns" either. Similarly, even if some big new advance does cause training or post-training to start scaling like gangbusters again in 2027 or 2028 that wouldn't make the quoted statement clearly wrong: Zitron would clearly like you to infer that there won't be any further big advances soon in LLM training, but the quoted statement doesn't clearly make that claim. (Even if he had made that claim, and it did turn out to be wrong, it would be a relatively forgivable error, more on the "cloudy crystal ball" than "misstates currently known facts" end of the spectrum.) So: it seems that Luu took a fairly specific, objectively judgeable claim from Ed Zitron; and that claim was ... correct?; and Luu instead rated it "Wrong" without further elaboration. It seems that Luu interpreted the quoted claim as saying something like "model progress has ceased"; but it seems that's not what that specific claim (as opposed to whatever other things Zitron has said at other times and places) said.
coldtea 15d ago
>You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning. Depends if you care about the "prediction" part or if you care about the assessment of the situation (regardless of date). If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.
- RandomLensman 15d ago
  The subprime market changed quite a bit in size and how much was securitized in the run up to 2007, so not sure a prediction in 2000 for a 2003 event would have been easily transferred to what happened later. Would really come down to what specifically the prediction was based on for it to be a bubble in 2000.
  - HWR_14 15d ago
    Excellent point. We can look at the reasons for the prediction as well as the result in assessing its value. It seems hard to do in practice unless you are evaluated by someone who makes better predictions, but it would be interesting.
- unsupp0rted 15d ago
  Predicting things 5 years too early is often as useless as not predicting anything. You can say "AI will be able to _____" and be right 99.9 times out of 100, but the question is when. You can say "The AI market will go to 0" and be at least directionally right eventually. But none of it matters if you get the timing wrong.
  - Zsfe510asG 15d ago
    It would matter if we had politicians who acted before bubbles collapse instead of bailing out the perpetrators after the fact.
    - unsupp0rted 15d ago
Zsfe510asG 15d ago
Zitron predicted the downfall of Oracle as someone mentioned below. He also predicted that the overhyped data center construction plans (Project Stargate, repeated vague Nvidia pledges) would not materialize. If he got MSFT's cloud revenue growth wrong for this year, how much of that is selling shovels to OpenAI and how much is circular?
braiamp 15d ago
Ok, I was thinking that probably they were saying that he was accidentally right, but missed the timing. It's not that. It isn't that he was accidentally right in a certain scenario also. It's that "I reinterpret the prediction to make it fit my own vision of the world"... that's not how prediction works. Hell that's not how anything would work. Here's an actual prediction I made about a year ago: LLMs have to demonstrate that they make productivity gains that explain the costs or economics will make this problem solve itself, via higher energy cost and loss of business/productivity. That prediction is unbound in the time horizon but it's bound by conditions that explain the triggers and how they will behave. Such prediction is useful. Hell, I could even make a prediction on why the timeline can't be bound while making a prediction on the timeline: I predict that in the next 3-5 years this will have to solve itself, because there's a limit on how much money irrational actors can pour onto something that have limited value. 7-10 years is way too much. I at least hope their coffers are that deep... if this drags on long enough, at some point people are going to want a change
WheelsAtLarge 11d ago
What I observe is that what people don’t seem to grasp is that Zitron isn’t an AI sage. He’s simply someone who figured out that he can get a lot of attention by taking a contrarian stance when it comes to AI. He can be 100% wrong about AI, but people will still read or listen to his next prediction. At this point, it’s mostly entertainment rather than a source of solid predictions. He has one primary objective and that's to keep Ed Zitron in people’s minds by any means necessary. It happens in sports, politics and, with Ed Zitron, AI.

overfeed 16d ago

On the flip side, I also see many people taking this thread as an opportunity to shit on Zitron as a person, and not "discussing his prediction". Incompetent/ blow hard/ dishonest are ones I remember off the top of my head. AI by itself, is surprisingly polarized; Ed Zitron even more so.

dyarosla 15d ago
Not sure why you see it as such; Incompetent and dishonest are characteristics that follow from this professional work, adequately describing an individual who continues to make poor predictions, analyses and false statements refuted by past events. Far from “shitting on” Zitron
TulliusCicero 15d ago
I mean if his personal work is incompetent or dishonest...? One of the easy ways to evaluate this is: how has he taken being incredibly wrong about his extremely confident predictions over and over and over? Typically with people like this, they completely shrug off being super wrong. It's barely even a blip on their radar, and even bringing it up is a good way to get them to immediately attack you to deflect attention from how bad their predictions or assertions were. If you're constantly making predictions on Topic X, and said predictions are consistently, wildly wrong, and you never actually grapple with that or acknowledge how wrong you were in the past, then that's, at the very least, intellectually dishonest. But by all means, someone link us to his blog posts where he goes over his wrong predictions without excuses or deflections. I'd be happy to change my mind.

emp17344 16d ago

Why is Zitron’s repute evaluated entirely on the basis of failed predictions? Predictions are incredibly hard. AI enthusiasts and thought leaders have made so many demonstrably incorrect predictions it’s hard to keep track. Based on this metric, Altman and Amodei should never be taken seriously again.

mvdtnz 16d ago
Have you ever listened to Zitron speak? He's not exactly the type to hedge his predictions behind careful language about how hard predictions are. He makes every one of these predictions with absolute confidence and conviction.
- emp17344 16d ago
  So does everyone else, including prominent AI enthusiasts and tech CEOs. And most of those predictions are wrong, because predictions are really hard.
  - hgoel 15d ago
    And we're used to insulting and making fun of the prominent members of those circles too, see: "Scam Altman", the large variety of jokes about Musk's timelines, comments about Dario waking up in a cold sweat whenever a powerful new open weight model drops, the "AGI achieved" meme etc.
    - jakeydus 15d ago
      Hyperbolic as those descriptions are, it’s not like they’re not sourced from reality. Sam Altman’s reputation in SV is well-established; Musk promised the roadster what, a decade ago? These guys preach a promised land of milk and honey and so many hear lap it up like dogs.
  - s1artibartfast 15d ago
    Both can be bad, and I think people take issue with intentionally projecting false or irresponsible confidence (lying). Zitron twists and misreports a lot of facts where they should know better. CEO hypemen knowingly conflate Ambitions for certainty

rtpg 16d ago

Here's two possible set of predictions: - a list of predictions that are entirely wrong, from A-to-Z, and are not even resembling what ends up happening - a list of predictions that are wrong, but where the underlying points are in fact interesting and have some predictive value, and it's just the "last step" that is wrong For example, one person might say "oh it's raining in Dallas therefore I should buy some TI stock". And we'll say for sake of argument that they say that even though it's nice and sunny in Dallas at the moment. Another person says "Oh its raining a lot in Idaho and that is going to increase potato yields and therefore I will buy McDonalds stocks cuz fries will be cheaper". In this hypothetical it turns out McDonalds buys all their potatoes from ... Kansas or something instead (and it's a specific kind of potato in a completely separate market)... but Idaho potato yields _did in fact go up_. An even more straightforward point: the iphone 3GS comes out in 2010, people are very hyped, someone looks at how RIM _still_ hasn't gotten its shit together and declares "RIM isn't going to to be able to stay profitable 18 months from now, they're gonna have their lunch eaten". Turns out that RIM still made a healthy profit in 2010. and 2011. And 2012. 2013 was their first loss in a while... and then it wasn't until 2014 that they really got kicked in the face. The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility? I'm saying this... it would be helpful if _some_ more AI companies flamed out. In some sense he does himself no favors by focusing on the corps with the biggest war chest instead of the various AI companies that spend a bunch to go nowhere fast and then have just disappeared.

threatripper 15d ago
Being early is the same as being wrong. Being lucky is the same as being right.
- rapnie 15d ago
  Nice saying. If in the prediction "given [intricate analysis] the whole [shebang] goes [bust] at [date]", only the [date] turns out to be wrong, I'd say it is still a valuable prediction however, though it was wrong.
  - 15d ago

dosisking 15d ago

AI companies will never go to zero because AI is part of the Military Industrial Complex now. All the money is coming from the military and government for surveillance and power and war.

wmf 15d ago
Speaking of wrong predictions... the entire US military budget would have to be spent on OpenAI/Anthropic to keep the bubble from bursting.
- dosisking 15d ago
  [flagged]
simianwords 15d ago
Every conspiracy theory has an escape hatch
- madaxe_again 15d ago
  It’s the moon-leopards in league with the bee-people. Only an IDIOT wouldn’t know this, it’s OBVIOUS.
- maxbond 15d ago
  Ad-hoc hypothesizing ("escape hatches") are dangerous but not quite invalid. Eg, they failed to find gravitational waves until they did, and you could have viewed building yet another more sensitive detector as a similar exercise in refining a hypothesis that you keep receiving contrary evidence for. Sometimes you really did just underestimate how difficult your hypothesis was to demonstrate. Maybe Meta will be destroyed in 2027, or whatever. The problem with conspiracy theories is more that they have a ratchet-like quality where counter evidence reaffirms the theory in your view and you can only ever get more confident. We should have been increasingly skeptical of gravitational waves to some degree as we failed to demonstrate them, even though we didn't abandon the hypothesis and it ultimately prevailed. But if you adopt a wrong idea, and people try to demonstrate that to you, and you take that effort they're putting forward as a sign that you are correct and they must be hiding something from you, it will be very difficult for you to realize your mistake. So, as long as you are less certain than you were before, I don't think rolling your prediction over into the future is necessarily conspiratorial or a mistake.

jrflowers 15d ago

This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP Some guy wrote that he’s grumpy because he couldn’t sleep and decided to dunk on an internet personality he doesn’t like, it’s not the ceremonial placement of the ur-kilogram

Aerolfos 15d ago
> This is an article that only cites Zitron’s opinions about subjective model quality. You’re tisk-tisking people to be objective about a bunch of words saying that the author’s opinions are better opinions than the guy he’s talking about OP Oh it's not just the author's opinions. They're the opinions of a bunch of LLMs he checked, too. Much better.

johnbarron 15d ago

>> Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes. Dan Luu did not engage on anything more, than a disorganized wall of text, ranted like a teenager using toxic personal attacks, while obsessing over calendar errors and a placeholder in a spreadsheet. If this is what passes here for a smart engineer...Lets analyze his post in a more logical and analytical way: - His entire argument is based on the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, the entire trillion dollar investment cycle is justified. A short window of venture subsidized chip buying...tells you absolutely nothing about the multi decade debt structures, physical infrastructure depreciation, and power grid constraints that dictate whether a capital heavy business model survives. - While he whines about Zitron numbers, fails to provide a single! macro level equation to address the real financial threat. NYU finance professor Aswath Damodaran for example, explicitly warned that the current AI build out is an asset heavy, debt funded run up backed by private capital markets. Unlike the dotcom boom which was equity funded and contained to tech shareholders today AI infrastructure burdens companies with a massive $80 billion in CapEx per gigawatt, meaning a monetization correction will trigger widespread systemic debt distress and loan defaults across the real economy. "Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981 https://news.ycombinator.com/item?id=49229981 - Luu and this HN crowd, today in a mob mood...completely ignore the highly unstable plumbing of the sector growth metrics. Patrick Boyle is a quantitative finance professor and former hedge fund manager, and has meticulously mapped out the mutual dependence the entire AI boom. Big Tech companies are pouring massive venture pools into AI startups, which are then contractually bound to hand that cash right back to the hyperscalers to buy cloud compute. Analysts have identified more than $800 billion in these arrangements: "Why Wall Street is ignoring big tech's debt" - https://news.ycombinator.com/item?id=49230630 https://news.ycombinator.com/item?id=49230630 - The worst of Luu logical failure, is ignoring ( on purpose? ) were Zitron numbers come from! They come from some very disciplined institutions, which Luu completely ignores. Citigroup quantitative analysts project cumulative global AI CapEx hitting $9 Trillion through 2030, with maximum global AI revenues ( not profit...) covering less than 30% of that expenditure. - To break even on the physical infrastructure currently under construction, the AI sector needs to generate over $2 Trillion in annual end user revenue by 2030. Total actual revenue generated across the ENTIRE global AI sector today sits at a fraction, around $150 billion. - Anthropic in a hysterical push, to make it to public markets, before the bubble bursts, recently claimed their addressable market is 30 trillion... the whole of US economy. Are we getting a post from Luu on that? This of course this ignores that MIT Professor and Nobel Laureate, Daron Acemoglu, mathematically proved that while 20% of all labor tasks are exposed to AI, only about 5% can be automated profitably due to upfront enterprise systems integration and the high financial burden of constant human in the loop verification. "A new look at the economics of AI" - https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-economics-ai https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-eco... - Dismissing the AI bubble thesis, because you found a spreadsheet typo in a newsletter, and ignoring the other voices who are aligned with Zitron core premise, means you are also dismissing the research of a Nobel Laureate in economics, the Dean of Valuation, veteran hedge fund managers, Barclays, S&P Global, and Citigroup. Arguing that "the models are hitting benchmarks" while ignoring that the physical balance sheets and enterprise budgets cannot support a multi trillion dollar infrastructure build out, is exactly the type of Dunning Kruger this corner excels at.... Ed Zitron is correct, despite the clumsiness or unpleasantness of his message delivery, and this community reaction, will be an historical record of the AI bubble crowd madness. It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come. The end of this universe is all tax payers will own a piece of AI and will pay for it with increased interest rates for the next 25 years...

leoc 15d ago

In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitron just largely needs to stop coming up in anti- and anti-anti-AI arguments since, it seems, he's just not a particularly insightful or reliable voice on the subject. That said, one or two of the specific assessments in Luu's article seem dubious as well, especially this one: > August 2025 https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ : "These models have clearly hit a wall where training is hitting diminishing returns" > Wrong It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference. See eg. https://www.tobyord.com/writing/mostly-inference-scaling https://www.tobyord.com/writing/mostly-inference-scaling . And in fact in the quoted and linked article https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ Zitron comes up with something which looks like a recognisable explanation of this: > Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed). > As a result, all the "gains" from "powerful new models" come from burning more and more tokens. AFAICT the other drivers of recent progress in LLMs have been: ploughing in lots and lots of specialised training data custom-made at piecework websites https://www.youtube.com/watch?v=4pG3SJQPAwk https://www.youtube.com/watch?v=4pG3SJQPAwk ; and work on harnesses and the like. AFAICT neither of those makes false the claim that "[t]hese models have clearly hit a wall where training is hitting diminishing returns" either. Similarly, even if some big new advance does cause training or post-training to start scaling like gangbusters again in 2027 or 2028 that wouldn't make the quoted statement clearly wrong: Zitron would clearly like you to infer that there won't be any further big advances soon in LLM training, but the quoted statement doesn't clearly make that claim. (Even if he had made that claim, and it did turn out to be wrong, it would be a relatively forgivable error, more on the "cloudy crystal ball" than "misstates currently known facts" end of the spectrum.) So: it seems that Luu took a fairly specific, objectively judgeable claim from Ed Zitron; and that claim was ... correct?; and Luu instead rated it "Wrong" without further elaboration. It seems that Luu interpreted the quoted claim as saying something like "model progress has ceased"; but it seems that's not what that specific claim (as opposed to whatever other things Zitron has said at other times and places) said.

coldtea 15d ago

>You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning. Depends if you care about the "prediction" part or if you care about the assessment of the situation (regardless of date). If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.

RandomLensman 15d ago
The subprime market changed quite a bit in size and how much was securitized in the run up to 2007, so not sure a prediction in 2000 for a 2003 event would have been easily transferred to what happened later. Would really come down to what specifically the prediction was based on for it to be a bubble in 2000.
- HWR_14 15d ago
  Excellent point. We can look at the reasons for the prediction as well as the result in assessing its value. It seems hard to do in practice unless you are evaluated by someone who makes better predictions, but it would be interesting.
unsupp0rted 15d ago
Predicting things 5 years too early is often as useless as not predicting anything. You can say "AI will be able to _____" and be right 99.9 times out of 100, but the question is when. You can say "The AI market will go to 0" and be at least directionally right eventually. But none of it matters if you get the timing wrong.
- Zsfe510asG 15d ago
  It would matter if we had politicians who acted before bubbles collapse instead of bailing out the perpetrators after the fact.
  - unsupp0rted 15d ago

Zsfe510asG 15d ago

Zitron predicted the downfall of Oracle as someone mentioned below. He also predicted that the overhyped data center construction plans (Project Stargate, repeated vague Nvidia pledges) would not materialize. If he got MSFT's cloud revenue growth wrong for this year, how much of that is selling shovels to OpenAI and how much is circular?

braiamp 15d ago

Ok, I was thinking that probably they were saying that he was accidentally right, but missed the timing. It's not that. It isn't that he was accidentally right in a certain scenario also. It's that "I reinterpret the prediction to make it fit my own vision of the world"... that's not how prediction works. Hell that's not how anything would work. Here's an actual prediction I made about a year ago: LLMs have to demonstrate that they make productivity gains that explain the costs or economics will make this problem solve itself, via higher energy cost and loss of business/productivity. That prediction is unbound in the time horizon but it's bound by conditions that explain the triggers and how they will behave. Such prediction is useful. Hell, I could even make a prediction on why the timeline can't be bound while making a prediction on the timeline: I predict that in the next 3-5 years this will have to solve itself, because there's a limit on how much money irrational actors can pour onto something that have limited value. 7-10 years is way too much. I at least hope their coffers are that deep... if this drags on long enough, at some point people are going to want a change

WheelsAtLarge 11d ago

What I observe is that what people don’t seem to grasp is that Zitron isn’t an AI sage. He’s simply someone who figured out that he can get a lot of attention by taking a contrarian stance when it comes to AI. He can be 100% wrong about AI, but people will still read or listen to his next prediction. At this point, it’s mostly entertainment rather than a source of solid predictions. He has one primary objective and that's to keep Ed Zitron in people’s minds by any means necessary. It happens in sports, politics and, with Ed Zitron, AI.