The Illusion of Intelligence: Large Language Models vs Human Cognition

Originally posted: 4/30/2025, Updated: 9/14/2025

Large Language Models (LLMs) have rapidly evolved from niche Natural Language Processing (NLP) systems to widely used language tools appearing in productivity apps, customer support systems, creative workflows, and everyday conversations. Their output is fluent, responsive, and often surprisingly well-structured, which has led many people to ask a familiar question: Are systems like these beginning to approach real intelligence?

This confusion is understandable. The surface-level behavior of an LLM can look intelligent, especially when it responds in a way that aligns with the user’s expectations. When a model generates a helpful answer, follows an extended thread, or uses technical language correctly, it can feel like something more is going on under the hood. That impression is often reinforced by repetition, tone, formatting, or the use of formal-sounding language, even when the content is speculative or flawed.

As these systems become more capable, the misconceptions around them are becoming increasingly common. People describe LLMs as “black boxes,” suggest they are beginning to reason or evolve, or assign qualities like memory, understanding, or even consciousness. In some cases, these ideas are framed metaphorically. In others, they’re presented as literal explanations for how the system operates. In either case, they point to a growing disconnect between how these systems work and how people interpret what they’re seeing.

The purpose of this article is to clarify that gap by walking through the architectural and functional differences between large language models and the human mind, and explain why the comparison, while tempting, is inaccurate and often misleading.

Human Learning vs. Machine Training

For nearly all modern societies and cultures, humans are educated slowly, starting with basic understanding from early life experience, followed by foundational education that includes both knowledge and critical thinking skills upon which further learning builds.

This education fosters understanding through direct experience and teaches children how to synthesize that experience. Fundamentally, we teach children to learn, preparing them to succeed in society both in terms of culture and employment. Humans also never stop learning, making new connections in knowledge from life and work experience.

Transformer Large Language Models do not learn in any cognitive sense. They are statistical language models trained for text completion that learn the probabilistic relationships between words through ingesting large batches of data. Training occurs over multiple steps until these models are able to accurately predict the next expected words (or actually tokens).

Once the model has properly generalized language to a point where its predictions are accurate, the model’s weights are frozen and the model becomes static, or read-only, and may be used to predict next tokens for context it hasn’t seen yet. While there is human knowledge and logic embedded in these learned language patterns, the model doesn’t actually have the capability of understanding, and what looks like reasoning is simply the reuse of learned statistical patterns.

LLMs learn through the mathematically complex process of forming statistical relations in language for the sole purpose of predicting next likely tokens. They have no true understanding, no internal ability to learn more (more on fine-tuning and RAG later), and no self-awareness, emotion, desire, or intent.

The differences here are crucial: Humans don’t just learn and solve problems through memorization or breaking down embedded patterns or reason and logic in language to solve new problems, but through experience that includes all five senses, with the ability to update their knowledge and beliefs according to that experience and their own desires.

Author’s addendum: Just as “hallucination” has a different meaning in AI/ML when describing AI/ML concepts and operation, so does “learning.” For example, researchers use the term “In-Context Learning (ICL)” to describe how models can temporarily adapt to tasks from examples in the prompt without changing their weights. This is not lasting learning like training or fine-tuning. See linked post for a deeper dive.

Deterministic Machines, Not Minds

There’s a common misconception that the unpredictably of output from LLMs implies they are black boxes, but it’s more accurate to describe them as complex opaque boxes. While the calculations to predict text, even token by token, makes output impossible to predict, the mechanisms are still largely understood and the output deterministic. That is, given the same prompt, model weights, settings, and random seed, the output will be identical every time.

The only variability comes from the sampling process, controlled by parameters like temperature or top-k sampling. These do not indicate creativity—they are simply methods to control randomness during output generation. When people encounter variation in a model’s output, they are seeing the influence of that controlled randomness.

In short, we know how the mechanisms work, and the deterministic nature of the architecture and ability to trace operations aids in that understanding, but the complexity and unpredictability create actual interpretability and interpretability challenges.

This differs from human unpredictability, which stems from memory, emotion, and awareness. LLMs do not operate with those faculties. They are large, high-dimensional mathematical functions that are traceable and deterministic, even if their size makes them complex to interpret intuitively.

Static Systems, Dynamic Minds

Once trained, LLMs are static. They do not change, adapt, or learn from new experiences during use. Their parameters are frozen, and they have no memory of previous conversations or external events unless reintroduced in the prompt.

Humans, on the other hand, constantly adapt. Even brief interactions can alter future responses due to emotional or contextual integration. LLMs do not have this ability.

There are a number of system (application layer) approaches that attempt to overcome this critical limitation:

Some platforms simulate memory by storing user interactions and resubmitting the whole growing session with each prompt, giving the illusion of statefulness.
Applications build around LLMs (like ChatGPT’s web interface and app) may even store bits of user context or pull context from other sessions by the same user.
Many popular LLM systems use Retrieval-Augmented Generation (RAG) to pull data from external sources to be included with the user’s prompt.

While these methods can improve usefulness by introducing additional context behind the scenes, they do not change the underlying model itself: The weights remain fixed, no true learning occurs, and the system has no awareness of the retrieved content beyond its appearance in the prompt.

In short, these techniques enhance performance pragmatically but do not transform the architecture into something capable of real-world experience, memory, or understanding.

Language Without a World

LLMs operate almost entirely within language. They do not perceive or interact with the real world in any human sense. Some modern systems can also take in images or audio, but their inputs are still limited and all their knowledge comes from training data, not real experience. They cannot see, hear, touch, or experience the world the way people do.

Humans connect language to sensory and emotional experiences. Words are grounded in physical and emotional reality. LLMs, by contrast, replicate patterns without actual understanding or reference to the world.

This limitation explains why models can confidently generate incorrect or fictional information. They’re not evaluating truth; they’re continuing patterns.

Pattern Mimicry, Not Reasoning

LLMs appear to reason because they mimic language structures associated with logic and analysis. But what they’re doing is predicting the most likely next word—not applying internal models of logic or inference.

Chain-of-thought prompting may help with complex outputs, but this is not true reasoning. It works by prompting the model to lay out intermediate steps in the same way it learned them from training data. There is no awareness, strategy, or verification happening inside the model.

Humans reason symbolically and abstractly, applying general rules across contexts. LLMs do not. They simulate the form of reasoning, not the process.

No Memory, No Self

LLMs do not have memory between sessions. Any reference to past conversation is due to context included in the prompt. Once that context is gone, the model has no access to it.

Some platforms implement memory features by storing interactions and injecting them later, but this is external to the model. Internally, it is stateless and unaware of prior exchanges.

This also means there is no sense of self. LLMs don’t recognize themselves or others, and don’t track identities or relationships. Their responses are based only on the current input.

No Goals, No Motivation

LLMs do not have goals, beliefs, or intentions. When they produce responses that seem helpful, it’s because similar prompts in the training data led to similar completions—not because the model “wants” to help.

Human communication is purpose-driven. LLMs imitate that pattern but have no internal states or objectives. They do not understand or care about the results of their output.

When users interpret politeness, resistance, or emotion, they are projecting. The model is just continuing a text pattern based on probability.

Why These Differences Matter

Understanding how LLMs work affects how they’re used and trusted. If users believe the model understands them, they may rely on incorrect or misleading outputs caused by any and all of the following:

While this is especially risky in domains like medicine, law, or finance, using LLMs without this proper understanding can negatively affect not just business decisions but personal relationships, reputation, and mental health.

Misunderstanding the system can also lead to poor regulation, misplaced fears, or inappropriate system designs. Models should be treated as tools, not collaborators or agents.

The more realistic the expectations, the safer and more effective the applications will be.

Conclusion

LLMs are powerful tools for generating language, but they are not minds. They do not reason, understand, or remember. They are statistical systems that generate plausible continuations of text based on learned patterns.

These limitations are not bugs to be fixed but architectural facts. Responsible use depends on understanding what they can—and cannot—do.

LLM Misconceptions and How to Stay Grounded

Misconception	Reality	What to Do
“The model understands me.”	It’s matching patterns, not building a model of your thoughts.	Be clear in your prompts. Don’t assume shared memory or understanding.
“It remembers what I told it yesterday.”	LLMs are stateless. No information carries over unless you include it.	Keep key context in the current prompt or start a clean session.
“It’s reasoning through the problem.”	It’s continuing language patterns that resemble reasoning.	Ask for step-by-step logic—but verify it yourself.
“It wants to help” or “It’s being evasive.”	There are no goals or emotions—just tone and structure.	Don’t interpret tone as intent. Read output as formatting, not feeling.
“It’s learning from our conversation.”	No learning occurs at runtime.	If it loops or gets confused, restart with only the latest and correct input.
“It passed the Turing Test, so it must be intelligent.”	It imitated human conversation convincingly in a narrow setting.	Treat the Turing Test as a measure of mimicry, not cognition.
“It repeated my idea, so it must agree with me.”	It’s reflecting your language back, not forming opinions.	Ask the model to critique or challenge, not just confirm.
“It said something confidently, so it must be true.”	Confidence is a function of text pattern, not correctness.	Cross-check facts. Fluency is not accuracy.

Bottom line: If a response sounds human, ask yourself whether it’s echoing your words—or expressing real understanding. The answer is almost always the former.

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