ChatGPT can seemingly reason through problems logically, like humans may. But is this genuine reasoning, or is it just sophisticated pattern matching?
This question has genuinely profound implications for how we use AI systems and how much we can trust them with critical decisions.
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Open Table of contents
Do Humans Actually Reason?
We are super logical In the most charitable sense, we draw logical conclusions from assumptions, and follow step-by-step logic to reach those conclusions. That way, we can explain why a conclusion is true rather than just… stating the conclusion as a fact.
Just kidding
In reality, we are emotional decision makers. It’s like when you tell yourself “I’m going to have only one Oreo”. Then 45 minutes later, you notice you’ve eaten 15 Oreos. How the heck did that happen?
But as the saying goes, “you live and you learn”. Despite our emotionality, we can still generalize principles to new situations and create new decisions, concepts, and things. You could be an emotional eater and a technical genius.
We take a hybrid approach So humans really reason in a hybrid sense. Like a sliding scale between emotion and logic. We can be purely logical enough to develop extreme ultraviolet lithography, or to grow diamonds in a lab. But we’re also emotional enough to buy a Ukulele and never learn to play it.
So are we truly reasonable? I’d argue no. And neither are LLMs. They draw conclusions differently.
Do LLMs Logically Reason?
Elite pattern matching LLMs learn correlations in training data where “X often follows Y”. But they don’t always understand the underlying reasons why, so asking “What’s the capital of France?” triggers the highest probability output “Paris” through pure pattern matching rather than any actual reasoning about geography or political structures.
They’re fundamentally just learning statistical patterns: LLMs learn correlations in their training data where “X often follows Y” without understanding the underlying reasons why, so asking “What’s the capital of France?” triggers the highest probability output “Paris” through pure pattern matching rather than any actual reasoning about geography or political structures.
They lack genuine world models: LLMs consistently fail at basic physical reasoning tasks because they lack any causal model of how objects move through space and time, relying instead on surface-level pattern matching.
Their understanding is superficial: Lacking an understanding of causality is a fundamental flaw of probabilistic thinking. But this way of thinking is also a blessing, in that it is incredibly flexible and creative. So it is not all bad. But it is not reasoning in the logical sense.
Does It Even Matter?
The Turing Test: Alan Turing helped build a machine to break Nazi Germany’s Enigma cipher. It was their key message encryption device in World War 2. And in doing so, he allowed the Allies to understand all their war plans.
He was a pioneer in AI. Turing came up with a test to see if a machine was truly intelligent:if a machine behaves intelligently in conversation and we genuinely can’t distinguish its responses from human intelligence through questioning, then it IS intelligent in every meaningful practical sense.
This is a pragmatic functionalist view where intelligence is defined entirely by behavior rather than internal mechanisms, summed up as “intelligence is as intelligence does.”
So Alan Turing would likely argue that it doesn’t really matter, that LLMs are not logical. Because they’ve been shown to pass the Turing Test.
Searle’s Chinese Room argument John Searle imagined a person locked in a room who follows detailed rule books to answer Chinese questions flawlessly, despite having absolutely no understanding of Chinese.
His argument was that syntax alone doesn’t equal semantics, and following formal rules doesn’t constitute genuine understanding regardless of output quality.
The provocative question for LLMs is whether they’re just doing this: receiving input tokens, applying statistical rules learned during training, outputting tokens, all without any actual understanding or genuine reasoning happening.
Maybe LLMs are just tools and should be appreciated as such I tend to think that the practical answer is that reasoning or not, the world’s best pattern matching technology, that can write a new song on the fly is pretty cool. Let’s use these tools to create new things, and respect them for what they are, and not idealize them for what they’re not.