Chat-based Large Language Models replicate the mechanisms of a psychic's con
For the past year or so I’ve been spending most of my time researching the use of language and diffusion models in software businesses. One of the issues in during this research—one that has perplexed me—has been that many people are convinced that language models, or specifically chat-based language models, are intelligent. But there isn’t any mechanism inherent in large language models (LLMs) that would seem to enable this and, if real, it would be completely unexplained.
- ▪For the past year or so I’ve been spending most of my time researching the use of language and diffusion models in software businesses.
- ▪One of the issues in during this research—one that has perplexed me—has been that many people are convinced that language models, or specifically chat-based language models, are intelligent.
- ▪But there isn’t any mechanism inherent in large language models (LLMs) that would seem to enable this and, if real, it would be completely unexplained.
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| Original publisher | Out of the Software Crisis |
| Canonical URL | https://softwarecrisis.dev/letters/llmentalist/ |
| Publication time | Sun, 20 Sep 2026 12:20:13 +0000 |
| Retrieval time | 2026-09-20T13:08:46.573Z |
| Last seen | 2026-09-20T13:08:46.573Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1AxZSOi6C5Tu · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
For the past year or so I’ve been spending most of my time researching the use of language and diffusion models in software businesses. One of the issues in during this research—one that has perplexed me—has been that many people are convinced that language models, or specifically chat-based language models, are intelligent. But there isn’t any mechanism inherent in large language models (LLMs) that would seem to enable this and, if real, it would be completely unexplained. LLMs are not brains and do not meaningfully share any of the mechanisms that animals or people use to reason or think. LLMs are a mathematical model of language tokens. You give a LLM text, and it will give you a mathematically plausible response to that text.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Out of the Software Crisis.
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