Why People Believe the Myth

It’s understandable: LLMs write, joke, and handle follow-ups smoothly, which feels human. Marketing leans on brain metaphors. Demos show continuity within a chat, so it looks like memory. When outputs read as logical, we assume logic produced them. And because they’re fast and fluent, we project intention, emotion, and common sense that aren’t actually present. Reasonable people connect these dots and arrive at a confident, but incorrect, conclusion.

Reality Check

Under the hood, LLMs are trained once by adjusting internal parameters with backpropagation across huge text datasets. After training, their weights are fixed, and they don’t “learn” from your conversations. What looks like understanding is high-quality pattern completion: they process text as tokens and use an attention mechanism to predict what comes next. This is powerful, but it’s not comprehension. 

Session “memory” isn’t durable. A model sees what’s inside the current context window, and when that scrolls out, it’s gone. They can simulate reasoning by producing token sequences that appear logical, especially with step-by-step prompts, but that doesn’t mean the rules are understood.

Hallucinations aren’t lies, but rather confident errors when the pattern seems right, similar to human confabulations. And unlike people, models are disembodied: no touch, sight, smell, or lived causality, which helps explain gaps in everyday common sense. The emerging pattern is clear: great at speed, scale, and synthesis across text while limited where grounding, durable memory, and real-world experience matter.

What People Think

• It learns from each chat: Like humans, AI updates beliefs from new interactions and feedback
• It has durable memory: AI has long-term memories; context doesn't "scroll out" or vanish
• It "understands" ideas: AI reasoning is grounded in comprehensive and real causal models, not just pattern matching

What AI Does

• Weights are fixed post training: Model weights are set during training, and don't change based on your chats
• Ephemeral context: AI's memory is only what's inside the current context window; once it scrolls out, it's gone
• Pattern completion, not comprehension: AI processes text as tokens with attention to predict what comes next

The Kernel of Truth 

There is meaningful overlap at the surface. Language compresses human patterns, so well-prompted models can look thoughtful. In text-heavy domains, such as policies, code, catalogs, they act like superb speed readers and tireless draft-makers. People bring context, ethics, adaptability, and purpose; models bring speed, scale, and encyclopedic exposure. The best results happen when we combine them wisely: add context, constrain scope, and route edge cases to people. That blend keeps the model in its lane while humans supply judgment and accountability. When organizations center people, augmenting rather than automating outright, the work gets better and the risks stay bounded.

What This Means for Your Organization

If you believed the myth, here’s what changes. Plan for prompts, retrieval, and review rather than hoping a bigger model “just learns.” Expect guardrails and human checkpoints where facts or consequences matter. Timelines shift: pilots are fast, reliability comes from iteration and data plumbing, not magic. The quiet competitive move is pairing models with your own context, narrow tasks, and simple escalation paths—quality rises, fire drills fall. In recent client work, we’ve found that small, well-scoped assistants outperform “do-everything” bots because they fail less and are easier to govern. If you’re not ready for that scaffolding, start with low-risk uses like summarization, triage, and first drafts, then expand once your feedback loops and grounding data are in place.

Want your team aligned on what LLMs are great at and where humans must stay in the loop? Let’s talk.