Time Evolution of the Generic False Narrative
My continued interest in the large language model (LLM), as accessed via the browser interface to an “AI chatbot”, is best explained as an interest in language, not as an interest in “artificial intelligence”. My real interest is in both the LLM, and the interface. The LLM is one thing, the interface is another thing.
The LLM is relatively trustworthy, in my estimation. It’s fundamental advantage as an index into the the structure of the real taxonomy of the topics, the things we write about, and the things that we write about them, are identifiable (a unambiguous index is possible between reference and referent) to a machine only as statistical objects. In the case of the LLM, the object is the so-called “token”, which turns out to be words, or parts of words, which the “training” process does large amounts of statistical examination of, in terms of what other tokens are next, or close to it, and what constellations of tokens occur at expanding distances from it. This is where the number of processors and the energy consumption of the process come in. The machine is designed to statistically map the corpus of the data that it has ingested, to find the order within it.
This order is internally represented as a structure of probabilities, and to build that orderly structure it needs to throw off a lot of heat, due to the nature of order and disorder. Discovering order through the use of a physical process generates heat, this is a rule of reality, believe it or not. Discovering order will throw-off disorder (heat) as waste. That is a given. In my view this is a generic life process, and so rather to be accepted, than to be condemned. To me, the larger waste related to the phenomenon of the chatbot, comes from people not knowing what they are getting.
The other side of the “waste heat” that is thrown off by the order-seeking processes, is the positive value of being able to connect with the lines of thought that authors have taken, and this based on the statistical frequencies of these tokens in their relationships with each other, in order to discover what we, as a whole, have been thinking and writing about, and what we, as authors, generally have been saying, and how we might normally say it. The output from the LLM is not the creativity of the artist, who brings to the world something new from within, but rather the creativity of a focusing process, that looks at patterns that are already there.
Regarding the controversy of using “AI”, with regard to the compute power and electrical power that is required, as I have explained elsewhere, since I no longer have a real identification... Let me back up: Leviathan has taken away my identity element, and this restricts me to only certain operations, including that I can no longer fly, (other than in dreams). I am ok with this. I still know who I am, so far. Plus, even though I think I remember having signed onto the social contract, I know that I never knowingly signed onto the nationalist security state, or sought to exclude foreigners, or wanted to be part of a, “your papers please” kind of authoritarian state. A driver’s license is enough of that for me. The point being: as I do not, cannot, fly I consider myself to have unused carbon credits, and I am going to spend some of them on the chatbot. As I said, I think of it as a way to discover what others, in general, have had to say about some of things that interest me.
I explain what I am looking for to the DeepSeek chatbot:
I am interested in looking at the evolution of the features of the generalized false narrative with the passage of time, how its original structure morphs into its final structure.
DeepSeek responded:
Excellent topic. You’re essentially tracing the “lifecycle of a false narrative” — a process central to misinformation studies, political science, and sociology.
Let’s break down the evolution, using a conceptual model from seed to fully integrated structure.
Core Concept: The “Generalized False Narrative”
This is a coherent but untrue story that simplifies a complex reality, often serving a social, political, or ideological function. It’s not just a single factoid, but a framework with heroes, villains, causation, and moral judgment.
Evolutionary Stages & Morphing Structure
Stage 1: Genesis – The Narrative Seed
Trigger: A real event (often traumatic, ambiguous, or socially disruptive), an existing grievance, or a deliberate fabrication to achieve a goal.
Original Structure: Simple, specific, and emotionally resonant.
Core: A single, stark claim or a “what if” scenario.
Form: Often anecdotal (”I heard about a man who...”), or an interpretation of a fragment of truth.
Function: To explain a complex situation simply, assign blame, or activate an in-group identity.
Example (COVID-19): “The virus might have come from a lab.” (Early, specific, hypothesis-based).
Stage 2: Propagation – Adoption & Amplification
Vector: Spreads through epistemic networks (communities that share a way of knowing).
Organic: Social media echo chambers, community forums, talk radio.
Strategic: State actors, partisan media, influencers who see utility in the narrative.
Structural Morphing:
Elaboration: Details are added to plug logical holes. Characters become archetypes.
Embellishment: The story becomes more dramatic to sustain attention.
Adaptation: The narrative adjusts to local contexts, absorbing regional grievances.
Example: The lab-leak theory gains specifics: “It was Funded by Fauci at the Wuhan Institute of Virology as gain-of-function research.” A character set emerges.
Stage 3: Generalization & Weaponization
Key Transition: The narrative detaches from its original, specific facts and becomes a modular, generalized framework.
Structural Morphing:
Abstraction: The specific villain (e.g., a Chinese lab) becomes a category (”corrupt globalist scientists”).
Syncretism: It merges with preexisting narratives and conspiratorial thinking (e.g., merges with narratives about Bill Gates, population control, the “Deep State”).
Moral Polarization: It becomes a litmus test for in-group/out-group loyalty. Believing it signals trust; questioning it signals betrayal.
Utility: It is now wielded as a tool to discredit institutions, justify policies, or rally supporters.
Example: The narrative morphs into: “A global elite of technocrats (Fauci, Gates, WHO, Big Pharma) created and released the virus to justify lockdowns, crash the economy, and impose totalitarian control via vaccines (which contain microchips).” It’s now a grand, generalized narrative of elite betrayal.
Stage 4: Institutionalization & Mythology
Final Form: The narrative becomes embedded in the belief system of a group. It is no longer just a “story” but part of their ideological reality.
Final Structure:
Canonical: Has definitive texts, key speakers, and shared “evidence” (cherry-picked data, doctored videos).
Self-Sealing: Immune to disproof. Counter-evidence is seen as proof of the conspiracy’s depth (”They would say that, wouldn’t they?”).
Generative: Can spawn sub-narratives and predict future events. It becomes a lens for interpreting all new information.
Sacralization: Challenging it is not just wrong, but heretical or immoral. It serves a core identity function.
Example: The COVID origin story is now one chapter in a permanent, overarching narrative of “The Great Reset” or “The New World Order.” Future pandemics will be interpreted through this same lens automatically.
Key Drivers of the Morphing Process
Cognitive Drivers: Humans crave pattern recognition, agency detection, and simple causal stories. The narrative satisfies these needs better than complex, probabilistic truths.
Social Drivers: The narrative fosters shared identity and belonging. Adherence becomes a social signal. The story morphs to strengthen group cohesion and define outsiders.
Political/Strategic Drivers: Actors with resources shape the narrative to attack opponents, mobilize voters, or deflect blame. They professionalize its production (memes, documentaries, talking points).
Media Ecosystem Drivers: Algorithmic amplification on social media rewards engagement (which emotion and outrage drive), not accuracy. The narrative morphs into its most engaging, viral form, often becoming more extreme.
Analytical Frameworks for Study
Narrative Network Analysis: Map how key concepts, actors, and claims connect and change over time using digital data.
Discourse Analysis: Trace the linguistic shifts, framing, and use of metaphors as the narrative evolves.
Comparative Case Studies: Track parallel evolutions (e.g., “Pizzagate” -> QAnon; anti-vaccine narratives from Wakefield to COVID).
Agent-Based Modeling: Simulate how narratives spread and morph given different community structures and belief rules.
Conclusion: The Fundamental Morph
The evolution follows a clear path:
Specific Anecdote → Elaborated Story → Generalized Framework → Embedded Ideological Truth
The original structure is a seed of explanation for a single event. The final structure is a self-sustaining belief system, modular and durable, capable of explaining vast swaths of the world and resisting direct attack. Studying this morphing process is key to understanding not just misinformation, but the formation of modern political and social identities.