> But time passes and situations evolve. Ed Zitron, though, clearly does not.
> Over the last two years, he has called the top repeatedly: The AI bubble was definitely about to burst here, and here, and here, and here, and here, and here. His conclusion hasn’t changed, but his arguments have.
> The 2024 and 2025 articles make, basically, the business case against AI: that companies aren’t really using it, it isn’t adding value, and AI investors are betting that will change before they run out of cash. In 2026, the focus is much more on alleging widespread, Enron- or FTX-tier outright fraud.
> This is basically an admission that he can’t make the case in terms of the economics anymore. And in deciding how seriously to take his case in 2026, I think it’s valuable to read it in parallel with his case from 2024 and 2025.
Say what? This is exactly the progression that you'd expect if there was, in fact, outright fraud going on.
* Someone claims to be able to do <impossible thing>
* Critic call them on it
* Rather than folding, the hype machine grows and they start claiming to be doing the thing
* The critics start accusing them of fraud
Also, I note, it's a cute trick to start of claiming "time passes and situations evolve. Ed Zitron, though, clearly does not" and then in the next paragraph object that "his conclusion hasn’t changed, but his arguments have".
I don't have a pony in this race and don't know who Ed Zitron is, but this article makes me suspect he's correct. Acting as if going from "they are wrong" to "they are wrong and lying" is "losing the plot" is anti-convincing.
[edit]
The ending is much stronger:
> I don’t actually think we need less skepticism in AI world. These companies are, indeed, run by people who are not very trustworthy, who often contradict each other or oversell their products.
> And the things they say they’re trying to do are outrageous; people have every right to object to it. Skepticism is more than warranted.
> But we desperately need better skepticism.
In that spirit, I would like to offer this observation. The one substantive difference the author highlights is the claim that generative AI is now offering value that renders the claims that it's all fraud questionable. I would argue that the value it offers is effectively plagiarism-as-a-service, and, just as with the infinite energy machines that secretly harvest power from the wiring of the building, compatible with the notion of fraud.
I'm not familiar with Ed Zitron but failing to call the top of a bubble doesn't mean you're wrong about it being a bubble. People who were calling out the housing bubble in the 2000s were "wrong" right up until they were right. e.g. from 2006 https://www.nytimes.com/2006/01/02/opinion/no-bubble-trouble...
My own feeling is that it is a bubble: AI models are the new virtual machines. They will become commodified and low-margin hosting providers will dominate the market. Investors in OpenAI/Anthropic will lose their shirts.
I'm pleasantly surprised to see this! Last year a few people I know in person, and a podcast I enjoy, talked about or to Ed Zitron and I felt like I was going crazy because so, so much of what he argued was either woefully outdated, or just a fallacy. It's also annoying because it'd be such an interesting topic to explore rigorously and without motive. As mentioned in the article, those analyses _can_ be found. But man, Ed Zitron just seems loud and silly.
my main qualm with Ed is his analysis on the financials is decent, but he absolutely refuses to admit that the technology is useful (especially in the hands of competent users), and that all the labs are extremely compute starved due to overwhelming demand.
He’s hitched his wagon to a thesis and views everything through that lens come hell or high water.
> he does not consider, even to disagree with it, the possibility that the industry is paying for Anthropic’s product for non-psychosis reasons, such as finding it useful)
This is my main problem with Zitron. He is so obviously the epitome of motivated reasoning. He seems constitutionally incapable of admitting the possibility that companies derive usefulness and productivity from LLMs. For anyone capable of doing on the ground reporting this would be trivially obvious (at least when it comes to coding). So he ends up just cheerleading on the “AI bad” side whether the cheers make any sense or not.
> “Nobody wants to talk about the fact that AI isn’t actually doing very much,” he complained, before going on to complain about people saying that agents are able to do tasks independently with oversight. “What tasks, exactly? Who knows!” he wrote.
>
> Ed, thousands of people know and it is your journalistic responsibility to be one of them!
He’s intentionally incurious and doesn’t understand the idea of a general-purpose technology. This would be like looking at the rise of programming and computers in the 80s and 90s and asking “what are computer programs doing? I don’t see any concrete benefits right now, must be a scam”
But saying “I wish the argument was being made better” while using him as the basis for your article is more annoying to me! Just make the argument then.
But publications like The Argument need to take shots to get views, I guess.
Other professional critics like Gary Marcus and Emily Bender are the same way. It doesn't matter what neural networks do, they will always be a dead end that should be abandoned.
I view the AI bubble more like huge investments into something with the goal to profit later, against the likelihood of a open source model (probably even models) running on affordable hardware in any home, making the bet and all the datacenter the real flop.
I dunno if he's lost the plot so much as repeating the "AI is rubbish, the investment is a bubble, it'll all crash" plot at the rate of 10,000 words a month year after year.
ed can be verbose and he can be exaggerated but it's funny to claim that he doesn't come with receipts when his last two articles exhaustively go over the many signs of financial deceptions and other pricing issues that signal manipulation
this whole article was "i wish he made arguments the way i like"... ok then go do that yourself? its word policing at its most annoying
I'm not familiar with Zitron as a character, but the article disagrees with his critique of AI progress slowing with the rebuttle that AI is getting more efficient to build and use. That comports with my perception that much of the recent work in the field has been focused on making the technology more profitable. See special emphasis in the last 2 releases from Google, OpenAI and Anthropic. I do think that's a meaningful change in their messaging. I don't know what it means exactly, but they are clearly sending a message about economics.
I'm not a big user of AI for lack of interest, but have held for several years that I'd be more interested if it were faster and cheaper. If this form of AI is the future, I do hope it gets significantly more efficient, even if the capability caps out. I think there is plenty of room for interesting applications, if so.
As of now, neither side has "lost the plot" but I do understand general dislike for Zitron. This mostly stems from the fact that proof innocence (that AI will succeed) lies with teams making frontier models and one can continue to bash them until they do.
>> You could, if you wanted, use this as an argument that OpenAI is on shaky financial ground: The pressure to come out with the next generation of models and stay in the lead is so intense that models are retired before the company has actually turned a profit on them. Certainly, if I were thinking about investing in OpenAI, I would want to think about when that is expected to turn around.
Yeah but that's the whole point. If LLMs need to keep getting better before they can turn a profit they need to keep scaling. If they need to keep scaling then the LLM companies need to keep spending more to scale them. If they need to keep spending more then they need to be making more money.
Are they? If they're not, then they're toast.
Btw, this is true even if training gets cheaper over time. Cheaper training means more training not less money. Jevon's Paradox and all that.
17 comments
[ 1.3 ms ] story [ 39.3 ms ] thread> Over the last two years, he has called the top repeatedly: The AI bubble was definitely about to burst here, and here, and here, and here, and here, and here. His conclusion hasn’t changed, but his arguments have.
> The 2024 and 2025 articles make, basically, the business case against AI: that companies aren’t really using it, it isn’t adding value, and AI investors are betting that will change before they run out of cash. In 2026, the focus is much more on alleging widespread, Enron- or FTX-tier outright fraud.
> This is basically an admission that he can’t make the case in terms of the economics anymore. And in deciding how seriously to take his case in 2026, I think it’s valuable to read it in parallel with his case from 2024 and 2025.
Say what? This is exactly the progression that you'd expect if there was, in fact, outright fraud going on.
* Someone claims to be able to do <impossible thing>
* Critic call them on it
* Rather than folding, the hype machine grows and they start claiming to be doing the thing
* The critics start accusing them of fraud
Also, I note, it's a cute trick to start of claiming "time passes and situations evolve. Ed Zitron, though, clearly does not" and then in the next paragraph object that "his conclusion hasn’t changed, but his arguments have".
I don't have a pony in this race and don't know who Ed Zitron is, but this article makes me suspect he's correct. Acting as if going from "they are wrong" to "they are wrong and lying" is "losing the plot" is anti-convincing.
[edit]
The ending is much stronger:
> I don’t actually think we need less skepticism in AI world. These companies are, indeed, run by people who are not very trustworthy, who often contradict each other or oversell their products.
> And the things they say they’re trying to do are outrageous; people have every right to object to it. Skepticism is more than warranted.
> But we desperately need better skepticism.
In that spirit, I would like to offer this observation. The one substantive difference the author highlights is the claim that generative AI is now offering value that renders the claims that it's all fraud questionable. I would argue that the value it offers is effectively plagiarism-as-a-service, and, just as with the infinite energy machines that secretly harvest power from the wiring of the building, compatible with the notion of fraud.
When I search for such things I tend to only find claims that claims were made.
My own feeling is that it is a bubble: AI models are the new virtual machines. They will become commodified and low-margin hosting providers will dominate the market. Investors in OpenAI/Anthropic will lose their shirts.
Which seems to be a lot of this article
But saying “I wish the argument was being made better” while using him as the basis for your article is more annoying to me! Just make the argument then.
But publications like The Argument need to take shots to get views, I guess.
this whole article was "i wish he made arguments the way i like"... ok then go do that yourself? its word policing at its most annoying
I'm not a big user of AI for lack of interest, but have held for several years that I'd be more interested if it were faster and cheaper. If this form of AI is the future, I do hope it gets significantly more efficient, even if the capability caps out. I think there is plenty of room for interesting applications, if so.
Yeah but that's the whole point. If LLMs need to keep getting better before they can turn a profit they need to keep scaling. If they need to keep scaling then the LLM companies need to keep spending more to scale them. If they need to keep spending more then they need to be making more money.
Are they? If they're not, then they're toast.
Btw, this is true even if training gets cheaper over time. Cheaper training means more training not less money. Jevon's Paradox and all that.