AI Reasoning Is Not Mysticism

By Tom Published Jan 1, 2025 5 min read Tech & AI AI Large Language Models

AI “reasoning” can easily feel like a sudden awakening.

It can answer complex questions, write code, solve math problems, and organize arguments. Sometimes it gives the impression that it is thinking. So the question appears: is this ability science, or mysticism?

The better answer is: it is not mysticism, but it should not be simplified into “it understands like a human.”

Large language model reasoning is better understood as a behavior produced by scale, data, training objectives, prompting, and evaluation methods working together.

It is not a hand-written rule engine

Traditional software is easier to picture: programmers write rules, and the machine executes the rules.

Large language models are not built that way.

Their basic training objective is to learn patterns from huge text corpora and predict the next word or token. That sounds simple, but when the model is large enough, the data broad enough, and the training strong enough, this prediction task pressures the model to form useful internal representations: grammar, facts, context, style, code patterns, math steps, and common-sense associations.

The GPT-3 paper, “Language Models are Few-Shot Learners,” showed an important phenomenon: as models scale up, they can perform many tasks from prompts and a few examples without being retrained separately for each task.

That does not mean engineers manually wrote rules for reading comprehension, three-digit arithmetic, translation, or code generation.

It is closer to a model learning transferable patterns from text, then calling those patterns inside a new context.

Why emergence looks like sudden awakening

When people say AI “suddenly learned,” they are often pointing to emergent abilities.

Wei and colleagues, in “Emergent Abilities of Large Language Models,” describe abilities that are not seen in smaller models but appear in larger ones, making them hard to predict by simply extrapolating from small-model performance.

That is where the “quantity becomes quality” intuition comes from.

But caution matters. Schaeffer and colleagues, in “Are Emergent Abilities of Large Language Models a Mirage?”, argue that some apparent sharp emergence may come from the choice of evaluation metric. Nonlinear or threshold-like metrics can make smooth improvements look like sudden jumps.

A more careful view is:

Larger models often become more capable.
Some task performance looks like it appears suddenly.
But the shape of that suddenness can partly come from measurement.
Emergence should not be mystified too quickly.

AI capability has a scientific basis, but “sudden awakening” can turn a complex engineering phenomenon into mythology.

Chain-of-thought shows that prompting matters

Some reasoning behavior is not always stable by default. It may need the right prompt to be elicited.

Wei and colleagues, in “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” showed that asking models to generate intermediate reasoning steps before answering can significantly improve performance on arithmetic, commonsense, and symbolic reasoning tasks, especially for sufficiently large models.

This matters.

It means AI reasoning is not a single switch. It depends on how you ask, whether you provide examples, whether the model is asked to decompose the problem, and whether the answer is checked.

The same model may fail when asked directly and improve when asked to state assumptions, break down the task, and verify steps.

This looks more like a behavior pattern that can be activated by prompting than a permanently online rational soul.

Does it really understand?

This is where arguments become loud.

If a model can answer logic questions, explain code, and solve math problems, can we say it understands?

It depends on the definition.

If understanding means producing correct, explainable, transferable outputs on tasks, then many models show a kind of functional understanding.

If understanding means human-like subjective experience, intention, lived world experience, and stable causal models, then we cannot simply assume that.

Large language models are trained from text. Text contains many traces of human reasoning, but the model did not grow up in the physical world and does not have a human body, feelings, or life history. It can simulate many reasoning forms while still making basic mistakes in facts, counting, causality, and boundary conditions.

Treating AI as if it understands nothing underestimates it. Treating it as a human mind overestimates it.

Why engineers cannot fully explain every answer

AI feels like a black box not because it violates science, but because it is extremely complex.

A large model contains many parameters and layers of high-dimensional vector computation. We can know the training objective, architecture, loss function, and overall behavior. We can also study models with interpretability tools. But tracing one specific answer all the way through every parameter interaction is very hard.

It is similar to weather: we know it follows physical laws, but explaining one exact gust at one exact second is difficult.

A complex system can be studied scientifically even when every detail is not intuitive.

A better way to use it

The best posture toward AI reasoning is neither worship nor contempt.

Use it this way:

Treat it as a powerful tool for generating candidate paths and explanations.
Ask it to break down complex problems, state assumptions, and give counterexamples.
Verify numbers, facts, law, medicine, and finance outside the model.
Do not confuse “sounds true” with “has been proven.”
Let it expand your thinking, while keeping final judgment with you.

AI is strongest when it helps you form candidate paths faster. It is most dangerous when a wrong path sounds smooth.

One line to remember

AI reasoning is not mysticism, and it is not a simple copy of human understanding.

It is a functional ability produced by large-scale models under the joint influence of data, training, context, and measurement: powerful and useful, but still requiring prompting, verification, and boundaries.

Source Boundary

This article is checked against Brown et al., Language Models are Few-Shot Learners, Wei et al., Emergent Abilities of Large Language Models, Wei et al., Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, and Schaeffer et al., Are Emergent Abilities of Large Language Models a Mirage?. It is a technical understanding note, not a real-time evaluation of any specific AI product.