Is Chain-of-Thought Reasoning Real?

When AI Started “Thinking Out Loud”

Picture this: You ask ChatGPT to solve a complex math problem, and instead of spitting out just an answer, it walks you through each step like a patient tutor. “First, I’ll identify the variables… then I’ll apply the formula… finally, I’ll calculate the result.”

This is chain-of-thought (CoT) reasoning, and it’s been hailed as AI’s breakthrough moment—the point where machines started thinking like humans. Chain-of-thought prompting enables AI models to solve complex tasks through step-by-step reasoning, significantly improving their ability to handle arithmetic, commonsense, and symbolic reasoning tasks.

But here’s the uncomfortable question: Is AI actually reasoning, or just getting really good at looking like it’s reasoning?

The Rise of Step-by-Step AI

Chain-of-thought prompting emerged from Google Research as a method that enables models to decompose multi-step problems into intermediate steps. The results were stunning. On the GSM8K dataset of math word problems, combining chain-of-thought prompting with a 540B parameter model achieved 58% accuracy, surpassing previous state-of-the-art results.

The technique works deceptively simply. Instead of just asking an AI “What’s 15% of 847?” you prompt it with examples that show the reasoning process:

Problem: What's 15% of 847?
Let me think step by step:
1. Convert 15% to decimal: 15% = 0.15
2. Multiply: 847 × 0.15 = 127.05
Answer: 127.05

This step-by-step problem-solving structure aims to help ensure that the reasoning process is clear, logical and effective. The AI learns to mimic this format, breaking down complex problems into manageable chunks.

For a while, it looked like we’d cracked the code on machine reasoning.

The Cracks in the Foundation

Then researchers started poking at the edges. In 2024, Apple researchers caught AI models just cribbing reasoning-like steps from their training data. Push the models even slightly outside their training distribution, and the reasoning would collapse entirely.

Even more telling: OpenAI admitted they don’t show users the actual reasoning from their o1 model—instead, they show “a model-generated summary of the chain of thought.” Wait, what? The reasoning steps we see aren’t even the real ones?

The ASU Bombshell

In August 2025, researchers from Arizona State University dropped a research paper that sent shockwaves through the AI community. Their study, titled “Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens”, investigated whether CoT reasoning reflects structured pattern matching learned from training data rather than genuine reasoning.

Their method was elegant in its simplicity. They trained small AI models from scratch on synthetic data—basically, toy problems with clear right and wrong answers. Then they tested the models on three scenarios:

In-distribution problems: Questions identical to training data Near-distribution problems: Slight variations of training examples Out-of-distribution problems: Novel problems requiring the same reasoning skills

The results? CoT reasoning proved to be “a brittle mirage that vanishes when it is pushed beyond training distributions.” The models could barely produce accurate reasoning except when questions came straight from their training data.

The researchers concluded: “CoT is not a mechanism for genuine logical inference but rather a sophisticated form of structured pattern matching, fundamentally bounded by the data distribution seen during training.”

The Pattern Matching Hypothesis

Think about it this way: If you trained exclusively on math problems involving apples and oranges, you might get really good at generating apple-and-orange reasoning patterns. But show you a problem about cars and fuel efficiency, and suddenly your “reasoning” falls apart—even though the underlying math is identical.

This explains why CoT works so well on standard benchmarks (they’re similar to training data) but breaks down on novel problems. It’s the same pattern we’ve seen with every AI breakthrough: impressive performance on familiar tasks, but fragility when faced with anything truly new.

The scary part? Tools designed to generate plausible text can generate plausible reasoning chains entirely independently of their actual problem-solving process. The reasoning and the answer might have no real connection—the AI just got good at producing both separately.

Why This Matters

This isn’t just academic nitpicking. As AI systems get deployed in critical applications—healthcare, finance, legal decisions—the difference between genuine reasoning and sophisticated pattern matching becomes crucial.

Current reasoning models provide unprecedented visibility into AI decision-making, but this visibility depends on training methods and architectural choices that may change. If the reasoning we can see isn’t actually driving the decisions, how can we trust these systems with important tasks?

The limitations revealed by the ASU study show that improvements from CoT don’t stem from AI learning algorithmic procedures—they’re just getting better at pattern matching within familiar domains.

The Defense of Chain-of-Thought

Before we throw CoT under the bus entirely, it’s worth noting that chain-of-thought prompting remains an important tool for LLM applications, and knowing its limitations helps avoid its pitfalls. Even sophisticated pattern matching can be incredibly useful when applied correctly.

Systems like DrEureka, developed by University of Pennsylvania and Nvidia, use GPT-4’s reasoning abilities to create reward functions for robotics tasks, achieving superior results to hand-crafted approaches. The key is having verification mechanisms and understanding the boundaries.

The Road Ahead

The chain-of-thought controversy reveals something deeper about AI development. We’re so eager to anthropomorphize these systems—to see human-like thinking where there might only be very sophisticated statistics.

As AI systems become more capable, they’ll likely leverage sophisticated planning and reasoning capabilities that require working memory and multi-step processing. Whether this will constitute “real” reasoning or just more elaborate pattern matching remains an open question.

For now, the prudent approach is to appreciate CoT for what it demonstrably is: a powerful technique for improving AI performance on familiar problems, while remaining skeptical of claims about genuine reasoning abilities.

After all, if it walks like reasoning and talks like reasoning, but only works when it’s seen similar problems before—maybe it’s just a very convincing duck.