The widespread excitement around generative AI, particularly large language models (LLMs) like ChatGPT, Gemini, Grok, and DeepSeek, is built on a fundamental misunderstanding. While these systems impress users with articulate responses and seemingly reasoned arguments, the truth is that what appears to be “reasoning” is nothing more than a sophisticated form of mimicry.
These models aren’t searching for truth through facts and logical arguments—they’re predicting text based on patterns in the vast datasets they’re “trained” on. That’s not intelligence—and it isn’t reasoning. And if their “training” data is itself biased, then we’ve got real problems.
I’m sure it will surprise eager AI users to learn that the architecture at the core of LLMs is fuzzy—and incompatible with structured logic or causality. The thinking isn’t real, it’s simulated, and is not even sequential. What people mistake for understanding is actually statistical association.
Much-hyped new features like “chain-of-thought” explanations are tricks designed to impress the user. What users are actually seeing is best described as a kind of rationalization generated after the model has already arrived at its answer via probabilistic prediction. The illusion, however, is powerful enough to make users believe the machine is engaging in genuine deliberation. And this illusion does more than just mislead—it justifies.
LLMs are not neutral tools, they are trained on datasets steeped in the biases, fallacies, and dominant ideologies of our time. Their outputs reflect prevailing or popular sentiments, not the best attempt at truth-finding. If popular sentiment on a given subject leans in one direction, politically, then the AI’s answers are likely to do so as well. And when “reasoning” is just an after-the-fact justification of whatever the model has already decided, it becomes a powerful propaganda device.
There is no shortage of evidence for this.
A recent conversation I initiated with DeepSeek about systemic racism, later uploaded back to the chatbot for self-critique, revealed the model committing (and recognizing!) a barrage of logical fallacies, which were seeded with totally made-up studies and numbers. When challenged, the AI euphemistically termed one of its lies a “hypothetical composite.” When further pressed, DeepSeek apologized for another “misstep,” then adjusted its tactics to match the competence of the opposing argument. This is not a pursuit of accuracy—it’s an exercise in persuasion.
A similar debate with Google’s Gemini—the model that became notorious for being laughably woke—involved similar persuasive argumentation. At the end, the model euphemistically acknowledged its argument’s weakness and tacitly confessed its dishonesty.
Hellbound and Down 
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