The AI Hater's Manifesto
We recorded 27 claims from this post. 27 can’t be checked. 8 more are held. Read the post at its source.
Claims from this post (27)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“I would never, ever trust this thing that deleted and added lines of code at random with anything mission critical, I could not trust software built with it, and I certainly couldn’t trust it with anything involving my personal data.”
Our reading: Zitron says he would never trust the LLM tool he used, which deleted and added lines of code at random, with anything mission critical, could not trust software built with it, and certainly could not trust it with anything involving his personal data.
No horizon
Scope: the tool he used, which deleted and added lines of code at random
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Here’s how I feel about LLMs. In a vacuum, they’re an interesting technology that can do some interesting stuff, in the right scenarios, but never in a way that involves you fully surrendering your actual work product to it.”
Our reading: In a vacuum, LLMs are an interesting technology that can do some interesting stuff in the right scenarios, but never in a way that involves fully surrendering your actual work product to them.
No horizon
Qualifier: in a vacuum
Assessment: Can’t be checkedSaid
Causal claim
“The further you stretch yourself away from having complete clarity and industry over every element of the output’s purpose, the more likely you are to fall foul to a technology that is mathematically certain to make mistakes”
Our reading: The further you get from having complete clarity and industry over every element of an LLM output's purpose, the more likely you are to fall foul of a technology that is mathematically certain to make mistakes.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“They do not need to be in anything near healthcare or finance or mental health or, really, people.”
Our reading: LLMs do not need to be in anything near healthcare, finance, mental health or, really, people.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“If you think any of this is worth hundreds of billions or trillions of dollars, you are either ignorant or corrupt. On top of how disgusting their outputs feel, the cost is going to take at least a decade to share, and begin the end of hypergrowth in the tech industry.”
Our reading: The cost of AI investment is going to take at least a decade to 'share' (his word, meaning unclear) and will begin the end of hypergrowth in the tech industry.
No horizon
Qualifier: 'share' is his word; its intended meaning is unclear
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“AI research fucking stinks, as does AI writing.”
Our reading: AI research stinks, as does AI writing.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“I have never seen somebody like Nik who is also a huge, drooling fan of AI. In fact, the people most-excited about AI tend to, at best, create distinctly mediocre shit.”
Our reading: The people most excited about AI tend to create distinctly mediocre work at best.
No horizon
Qualifier: tend to
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Generative AI is far more useful as an idea than as a technology, and only ever has to show enough promise to back whatever vile agenda you’re pursuing.”
Our reading: Generative AI is far more useful as an idea than as a technology, and only ever has to show enough promise to back whatever vile agenda one is pursuing.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“The fact any of you take “annualized run rate” seriously is an offense to good sense, and yes, that includes every reporter reporting it, even the ones I respect.”
Our reading: Annualized run rate figures for AI companies should not be taken seriously, including by reporters.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Look, I’m sorry, Anthropic is not worth $2 trillion, and whatever convinced you of that is a mixture of manufactured consent and mistaken trust of the powerful.”
Our reading: Anthropic is not worth $2 trillion, and whatever convinced people otherwise is a mixture of manufactured consent and mistaken trust of the powerful.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“It’s also ridiculous that anyone is talking about “recursive self-improvement.””
Our reading: It is ridiculous that anyone is talking about 'recursive self-improvement.'
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“The push into cybersecurity seems to have happened as a result of training models to excel at coding hitting the point of diminishing returns, at least from the perspective of impressing people enough to be excited about the company again.”
Our reading: AI labs' push into cybersecurity appears to result from coding training hitting diminishing returns in its ability to impress.
No horizon
Qualifier: from the perspective of impressing people
Assessment: Can’t be checkedSaid
Observation
“The fact that non-enterprise customers are still able to buy monthly subscriptions is proof that the AI labs know that regular people won’t pay the actual cost of AI.”
Our reading: The fact that non-enterprise customers can still buy monthly subscriptions proves that AI labs know regular people won't pay the actual cost of AI.
No horizon
Assessment: Can’t be checkedSaid
Observation
“The fact that OpenAI has now cut the costs of all three of its latest models less than two months after their release is a sign that it knows there’s a disconnect”
Our reading: OpenAI cutting prices on its three latest models within two months of release signals it knows there is a value disconnect.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“Guess what folks! Building the infrastructure for all these fucking LLMs just got more expensive, with NVIDIA raising its prices by 17% for systems due to be delivered next year — an important designation, because it’s very likely that much of the revenue for said systems gets booked in this year”
Our reading: After a 17% price increase for systems delivered next year, NVIDIA will very likely book much of the revenue for those systems in 2026.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“Anthropic plans to raise $100 billion at a $2 trillion valuation, and if it does so, it will successfully suck up the remaining liquidity in a market already dangerously close to losing its lunch.”
Our reading: If Anthropic raises $100 billion at a $2 trillion valuation, it will soak up the remaining liquidity in the market.
No horizon
Only if: if Anthropic raises $100 billion at a $2 trillion valuation
Assessment: Can’t be checkedSaid
Forecast
“And said market may not accept that valuation, or want that much stock.”
Our reading: The market may not accept Anthropic's $2 trillion valuation or want that much stock.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“This is why I find it difficult to see a post-bubble future for LLMs.”
Our reading: Zitron finds it difficult to see a future for LLMs after the AI bubble.
No horizon
Only if: after the AI bubble bursts
Assessment: Can’t be checkedSaid
Observation
“We still lack any real killer app for LLMs.”
Our reading: LLMs still have no real killer app.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“These costs are only increasing, likely due to the diminishing returns of pre-training and the massive cost of buying training data for every imaginable new vertical.”
Our reading: OpenAI's training costs keep rising, likely because of diminishing returns from pre-training and the cost of training data.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“I also think that LLMs have created a near-permanent scar in the workforce, and traumatized more people than we’re aware of right now, both in those pressured about AI and those defending it.”
Our reading: Zitron thinks LLMs have created a near-permanent scar in the workforce and traumatized more people than we are aware of right now, both those pressured about AI and those defending it.
No horizon
Qualifier: I also think
Assessment: Can’t be checkedSaid
Forecast
“They’ll have to explain why they ignored a company that burned $5 billion in 2024, $20.9 billion in 2025, and will likely burn $30 billion or more in 2026”
Our reading: OpenAI, which burned $5 billion in 2024 and $20.9 billion in 2025, will likely burn $30 billion or more in 2026.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“This is not a sustainable industry — either economically or emotionally — because it has a fundamentally dishonest relationship with its customers defined by the inconsistency of LLMs both in efficacy, stability (see: Anthropic’s downtime) and training”
Our reading: The AI industry is not sustainable economically or emotionally because it has a fundamentally dishonest relationship with its customers, defined by LLMs' inconsistency in efficacy, stability and training.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“It deliberately subsidized its subscription products because it knew its customers wouldn’t pay the actual cost of AI”
Our reading: The AI industry deliberately subsidized subscriptions because it knew customers would not pay the true cost.
No horizon
Assessment: Can’t be checkedSaid
Observation
“It refuses to explain how it becomes profitable, because nobody knows how or has a tangible plan to do so.”
Our reading: Nobody in the AI industry knows how it becomes profitable or has a tangible plan to do so.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“And it’s losing because, at its core, AI was never built on very much.”
Our reading: The AI industry is losing because, at its core, AI was never built on very much.
No horizon
Assessment: Can’t be checkedSaid
Rhetoric
“It’s so much weirder, and in the end will be so much worse.”
Our reading: This time really is different: the AI bubble is so much weirder and, in the end, will be so much worse.
No horizon
Held from this post (8)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: Zitron estimates Anthropic and OpenAI will spend $200 billion in 2027, and lists their lacking the actual compute demand to justify that spending as one of several points at which the mismatch could happen.