Different Minds
Human vs LLM Understanding
When you chat with Claude or GPT-4, it can feel remarkably human. It understands context, makes jokes, expresses uncertainty. But is it actually understanding anything—or just very convincingly simulating understanding?
The honest answer: we're not sure. And that uncertainty is fascinating.
What Seems Similar
In conversation, LLMs demonstrate capabilities that feel remarkably human-like:
Natural Language
Fluent, contextually appropriate responses. Understanding of idioms, metaphors, and nuance.
Reasoning
Can work through problems step by step, consider alternatives, identify flaws in arguments.
Creativity
Generates novel text, poetry, stories, code. Combines concepts in unexpected ways.
Uncertainty
Expresses when it's unsure, acknowledges limitations, asks clarifying questions.
These capabilities led some to wonder if LLMs have achieved a form of general intelligence. But appearances can be deceiving.
What's Fundamentally Different
No Body, No Experience
You learned language while interacting with the physical world. You know what "hot" means because you've felt heat. You understand "heavy" because you've lifted things.
LLMs learn language from text alone. They've never touched anything, seen anything, felt anything. Their understanding of concepts is entirely based on how those concepts are described in text—which is a fundamentally different kind of knowledge.
No Persistent Memory
Each conversation with an LLM starts fresh. The model has no memory of previous conversations with you (unless explicitly given that context). It doesn't learn or grow from its interactions.
You have a continuous stream of experience, forming your identity. An LLM is more like a very sophisticated function: input goes in, output comes out, nothing persists.
No Internal Motivation
Humans are driven by needs, desires, fears, hopes. These internal states shape everything we do and say.
LLMs have no wants. They don't "want" to be helpful—they've been trained to produce helpful-seeming outputs. They don't "fear" saying something wrong—they have no experience of consequences. The appearance of motivation is just another pattern learned from human text.
A Different Process Entirely
Human cognition involves neurons, neurotransmitters, embodied experience, emotions, social context, and processes we still don't fully understand.
LLM "thinking" is matrix multiplication—billions of numbers being multiplied and added according to fixed patterns. The outputs often look similar to human outputs, but the underlying process is radically different.
The Hard Question
Does any of this matter? If an LLM's outputs are indistinguishable from a human's, does it make a difference that the underlying process is different?
This touches on deep questions in philosophy of mind:
Reasonable people disagree on these questions. Some researchers believe LLMs are sophisticated pattern matchers with no real understanding. Others think something genuinely new might be emerging at scale. Most admit we don't yet have the conceptual tools to answer definitively.
Practical Implications
Whatever the philosophical truth, there are practical differences that matter:
| Aspect | Humans | LLMs |
|---|---|---|
| Factual accuracy | Can verify claims, knows what they don't know | May hallucinate plausible-sounding falsehoods |
| Real-time information | Can look things up, verify current state | Knowledge frozen at training cutoff |
| Accountability | Legal and moral responsibility | No personal stakes or consequences |
| Relationships | Genuine reciprocal connections | No continuity, no "knowing" you |
These differences matter when deciding how to use LLMs. They're powerful tools, but they're not replacements for human judgment, especially in high-stakes situations.
A Mutual Mystery
Here's something humbling: we don't fully understand either type of mind.
Human consciousness remains one of science's great mysteries. Why is there something it's like to be you? How do physical processes in the brain create subjective experience? We don't know.
And LLMs, despite being human creations, are increasingly mysterious too. Researchers can't always explain why they give certain outputs. Capabilities emerge at scale that no one programmed explicitly. The models are too complex to fully interpret.
Key Takeaways
- LLMs produce human-like outputs through a fundamentally different process
- They lack embodiment, persistent memory, and internal motivation
- Whether they "truly understand" is a genuine open question
- Practical differences (hallucination, frozen knowledge) matter for use
- Neither human nor artificial minds are fully understood
