What "Reasoning" Means (and What It Doesn't)

Human reasoning involves building and maintaining a mental model of a situation, applying rules to that model, testing conclusions against it, and updating it when new information arrives.

ChatGPT does none of this. What ChatGPT does is predict the next token in a sequence based on statistical patterns learned from training data. When it encounters a logic puzzle, it does not build a mental model of the constraints. It recognizes the format of the puzzle, retrieves patterns from similar puzzles in its training data, and generates output that looks like a solution.

Sometimes that output is correct, because the puzzle resembles training examples closely enough. Sometimes it is wrong. And the model has no way to tell the difference.

The Autocomplete Analogy

The clearest way to understand ChatGPT's "thinking" is to consider the autocomplete on your phone's keyboard. When you type "I'll meet you at the," your phone suggests "airport" or "restaurant." It does this by predicting the most likely next word based on patterns. It does not know where you are meeting someone.

ChatGPT is the same mechanism, scaled up by orders of magnitude. Instead of predicting one word, it predicts thousands in sequence. Instead of training on your text messages, it trained on a significant portion of the internet. The scale is different. The fundamental mechanism is identical.

When your phone suggests "airport" and you are heading to a dentist, nobody is confused. When ChatGPT produces a wrong answer to a reasoning problem, the reaction is different, because the output is so elaborate and fluent that it creates the illusion of thought. But the mechanism is the same.

Where the Illusion Breaks

Novel combinations. Ask ChatGPT a question that combines familiar concepts in an unfamiliar way and performance drops sharply. Standard pattern structures work fine. Add a twist that breaks the standard pattern and the model fails, because it is being asked to compute, and it cannot.

Multi-step state tracking. Any problem that requires maintaining and updating state across multiple steps is a minefield. Move objects between boxes in a specific sequence, then ask where each object is. ChatGPT will frequently lose track because it is not maintaining a mental model. It is generating text that looks like tracking.

Logical negation. The model struggles with negation in ways that reveal the absence of real reasoning. Ask it to list animals that are NOT mammals, and it may include a mammal. The model is drawn toward the statistically dominant pattern rather than the requested one.

Mathematical reasoning. ChatGPT can solve math problems that match training data patterns. But give it a problem requiring genuine step-by-step computation and it fails at rates that would be impossible if it were actually computing. A system that can explain calculus but cannot reliably multiply 37 by 84 is not doing math. It is mimicking math.

The Benchmark Illusion

OpenAI publishes benchmarks showing ChatGPT performing at "human level" on standardized tests. These results are real but deeply misleading. Standardized tests follow predictable formats that appear throughout training data. A sufficiently powerful pattern matcher will perform well on standardized tests without understanding the underlying material.

Researchers at Apple published a study ("GSM-Symbolic") testing language models on grade-school math problems with minor modifications. Changing names, rearranging structure, or adding irrelevant information caused accuracy to drop by up to 65%. The models were not solving the math. They were matching the format. Change the format and the "reasoning" evaporates.

Chain-of-Thought: A Workaround, Not a Fix

Asking ChatGPT to "think step by step" improves accuracy on many tasks. But this is not the model reasoning. This is the model generating text that looks like reasoning, and then pattern-matching against its own output to produce a more constrained final answer.

The intermediate steps are themselves predictions, not computations. They can be wrong. They can skip critical logical connections. Chain-of-thought prompting improves accuracy the way training wheels improve balance: by constraining the output into a format that is more likely to arrive at the right place, not by changing the underlying capability.

Why This Matters for You

Use it for: First drafts, brainstorming, summarization, translation, code boilerplate, explaining well-documented concepts, generating options you will evaluate yourself.

Don't trust it for: Logic puzzles you cannot verify, mathematical computations you will not check, legal or medical reasoning where wrong answers have consequences, any multi-step analysis where you rely on the model to track state correctly.

The distinction is not about difficulty. ChatGPT can handle some difficult tasks while failing at some easy ones. The distinction is about whether the task can be solved by pattern matching or requires genuine computation.

The Uncomfortable Implication

If ChatGPT does not reason, then every benchmark showing "human-level performance" is measuring something other than intelligence. And every product built on the assumption that language models can think, every autonomous agent, every AI decision-making system, is built on a foundation that does not exist.

ChatGPT does not think. It predicts. When those predictions align with correct answers, it looks brilliant. When they do not, it looks broken. Both impressions are wrong. It is doing the same thing in both cases.