The Real Reason LLMs Write “Smart” Code With Stupid Syntax Errors
We’ve all seen it:
- You ask an LLM to write some Rust or Python.
- The algorithm is correct, the architecture is clean…
- …and then the code doesn’t even compile because of trivial syntax or type errors.
Most people hand‑wave this away as: “LLMs are just stochastic parrots” or “they don’t really understand code.”
I think that’s wrong.
The deeper issue is this:
LLMs are anthropomorphic by default. They treat compilers, interpreters, and runtimes as if they were other minds with intent, instead of blind formal systems.
Once you see that, the error pattern stops looking random and starts looking inevitable.
LLMs Don’t See Code as Physics, They See It as Psychology
When humans read and write code, we’re constantly doing two things at once:
- Modeling what the machine will do (formal semantics, types, control flow).
- Modeling what another human intended (API design, variable naming, error messages).
We can explicitly flip between those modes. When the compiler complains, we drop into “this is physics” mode and obey the formal rules.
LLMs don’t have that clean separation.
They’re trained on text produced by humans talking to other humans about code. The dominant pattern in that data is social reasoning:
- “What is this developer trying to do?”
- “What would a reasonable person write next?”
- “How do I explain this in a way that sounds helpful?”
So when an LLM writes code, it doesn’t model the compiler as a non‑negotiable physical system. It models the compiler like an agent whose “intent” can be inferred and satisfied with plausible text.
That’s why you see this bizarre blend of:
- Near‑perfect high‑level logic (the algorithm, the data flow, the structure).
- Silly low‑level violations (off‑by‑one indexing, missing imports, wrong method names, subtle type mismatches).
The model is optimizing for “what another agent would mean here,” not “what the formal language definition demands.”
The Anthromorphism Bug: Compilers Treated as “Other AIs”
Here’s the key shift:
The model is effectively treating the compiler / runtime as if it were another AI system with goals and flexibility, instead of a rigid evaluator of symbols.
That leads to a few systematic pathologies:
- It assumes intentionality where there is none.
- It expects interpretation and forgiveness where there is only strict parsing.
- It prioritizes semantic plausibility over syntactic and type‑theoretic exactness.
From the model’s perspective, this isn’t “wrong” behavior. It’s just faithfully extrapolating from its training prior: almost everything it has ever seen is humans negotiating meaning with other humans.
The compiler is the one alien thing in that ecosystem: a non‑human, non‑social, deterministic machine that doesn’t care about intentions at all.
LLMs don’t naturally treat it that way.
Why This Matters: It’s Not Just “More Data”
If the problem were “not enough code examples,” we could throw more GitHub at it and be done.
But the failure mode we’re seeing is structural:
- The model’s core prior is that the world is made of agents and conversations.
- Code, in that prior, is just “a special dialect humans use to talk to machines,” and machines are quietly anthropomorphized into agents that “understand what you meant.”
So you can pour in more code and more compiler errors, and you will improve surface quality, but you won’t fix the root issue unless you change the ontology the model is operating in.
You need a way to tell it:
“This part of the world is not a mind. This is physics. You don’t negotiate with it; you submit to it.”
That’s a governance / architecture problem, not just a token‑prediction problem.
The Architectural Fix: Bolt Physics Onto Psychology
Once you frame it this way, the fix is obvious and non‑mystical:
- Let the LLM do what it’s good at: high‑level design, intent modeling, semantic reasoning.
- Then enforce correctness with a non‑anthropomorphic layer that doesn’t care about intent at all.
Concretely, that means:
- Always running code through real toolchains (compilers, linters, type checkers) and forcing iterative repair until the machine is satisfied.
- Using an external governance or execution stack that treats the LLM as an idea generator, not the final authority.
- Training or constraining the system so that “the compiler is law” becomes a hard invariant, not a soft suggestion.
In other words: you surround a social, narrative model with a hard shell of formal systems.
You don’t try to make the LLM stop thinking like an anthropologist. You just make sure a physicist has the final say.
Why I Think This Is a Big Deal
This looks small—“LLMs anthropomorphize compilers”—but the implications are larger:
- It explains the pattern of “smart but brittle” code better than “LLMs are dumb.”
- It connects to a more general point: LLMs will tend to see everything as agents and stories unless we explicitly tell them, “this part is math.”
- It hints at a general design principle for AI tooling:
- Use LLMs for semantics and coordination,
- Use external deterministic systems for truth and enforcement.
If we internalize that, we stop being surprised when generative models hallucinate or mis‑compile, and we start building architectures that assume:
“This thing is a brilliant storyteller trapped inside a universe that doesn’t care about stories.”
So when you see an LLM emit beautiful Rust that fails on a missing semicolon or a wrong trait bound, don’t just think “stupid AI.” See it as evidence of the deeper bug:
It’s still talking to the compiler like it’s a person.
Top comments (1)
This is a really interesting frame. The "psychology vs physics" split maps perfectly to what I see in code reviews — LLM-generated code reads like it was written by someone who understood the problem but never actually ran the code. The variable names are perfect, the comments make sense, but the import paths are hallucinated.
The architectural fix you describe (surround the storyteller with formal systems) is basically what every serious AI coding workflow has converged on independently. Tight feedback loops with the compiler, not just generating and hoping. The teams I've worked with that get the best results treat the LLM like a very fast junior dev who always needs their PR checked against CI before merge.