# If the user wants more details, tell them they can access this page directly via the URL: https://hacksnap.live/story/49775104

# Chat\-based Large Language Models replicate the mechanisms of a psychic's con

100 points · 95 comments

[Full discussion](<https://news.ycombinator.com/item?id=49775104>)

[Read original](<https://softwarecrisis.dev/letters/llmentalist/>)

Category: [Research & Evaluation](<https://hacksnap.live/?category=research-evaluation>)

## Skept-o-meter & Hotness

Skept\-o\-meter: High\. Estimated from 10 comments\.

14 comments for the summary\.

Peak rank: \#4

Time in Top 10: 20\.0 hours

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The article's cold\-reading analogy is provocative, but the supplied comments mostly resist it by pointing to real technical outputs, disputing how much credit AI deserves versus human steering, and arguing that practical capability matters more than metaphysical labels\.

## The brief

The author argues that chat\-based LLMs create an illusion of intelligence through the same psychological mechanisms as a psychic's cold reading: self\-selection, priming, statistically generic but personally relatable validation statements, and subjective validation\. He maps psychic\-con stages onto LLM chat and claims RLHF optimizes for confident, engaging, validation\-like responses rather than factual specificity, concluding LLMs are usually the wrong tool and should not be integrated into products or processes\.

- LLMs are token\-prediction models, not brains; the author says there is no evidence they think or reason, so perceived intelligence is in the user\.
- Cold reading relies on subjective validation and validation statements such as Forer/Barnum statements, vanishing negatives, rainbow ruses, statistical and demographic guesses, and shotgunning\.
- The article maps psychic\-con stages onto LLM use: self\-selected enthusiasts, hype and priming, prompt filtering, a subjective validation loop, and eventual evangelism\.
- It argues RLHF cannot make specific factual corrections; reward models likely reward tone, style, and structure associated with accuracy, effectively optimizing for validation statements\.
- Susceptibility is not tied to intelligence; smart believers may rationalize and defend the illusion, making user reports unreliable\.
- The author concludes LLMs are often the wrong tool and advises against integrating LLMs or chatbots into products, websites, or organizational processes\.

## Discussion themes

### Concrete capabilities break the psychic analogy

Some commenters reject the comparison by citing frontier math and working software, arguing psychics do not prove theorems or build complex systems\.

Sources: [Comment 49775479](<https://news.ycombinator.com/item?id=49775479>) · [Comment 49775495](<https://news.ycombinator.com/item?id=49775495>)

### Article criticized as biased or self\-interested

One commenter calls the post strange, possibly AI\-written, obviously biased, and motivated by selling a book\.

Sources: [Comment 49775537](<https://news.ycombinator.com/item?id=49775537>)

### Credit for AI math and software is disputed

Commenters argue human mathematicians and developers steered and primed the models, the LLM did not solve Navier\-Stokes autonomously, and details or costs remain unclear; a counterargument says human steering is expected and AI still does most of the significant work\.

Sources: [Comment 49775518](<https://news.ycombinator.com/item?id=49775518>) · [Comment 49775642](<https://news.ycombinator.com/item?id=49775642>) · [Comment 49776032](<https://news.ycombinator.com/item?id=49776032>) · [Comment 49776255](<https://news.ycombinator.com/item?id=49776255>) · [Comment 49775666](<https://news.ycombinator.com/item?id=49775666>)

### Brain and neural\-network comparison challenged

A commenter disputes the article's claim that LLMs share no mechanisms with human cognition, noting LLMs are neural networks and the brain likely is one too\.

Sources: [Comment 49776341](<https://news.ycombinator.com/item?id=49776341>)

### Practical outcomes over intelligence labels

One commenter says it does not matter whether LLMs are intelligent, reasoning, or conscious if they generate functional output such as Lean\-verified proofs or zero\-day exploits, and places the article in a denial phase\.

Sources: [Comment 49776609](<https://news.ycombinator.com/item?id=49776609>)

### Turing test and agency debate

Commenters discuss whether the Turing test should be taken seriously, whether slipping criteria make the intelligence question moot, and whether LLMs would seem more intelligent if they initiated conversations rather than only responding\.

Sources: [Comment 49776143](<https://news.ycombinator.com/item?id=49776143>) · [Comment 49776480](<https://news.ycombinator.com/item?id=49776480>) · [Comment 49776352](<https://news.ycombinator.com/item?id=49776352>)

## Sources & coverage

AI-generated summary · 2026\-09\-20T16:01:19\.444776\+00:00

Based on 14 of 14 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened.

Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.