What Happened
Developer @katanalarp posted a thread on X (formerly Twitter) that surfaced on Hacker News with a concise thesis: "LLM doesn't write correct code. It writes plausible code." The distinction sounds subtle but it points at a fundamental limitation in how language models generate code.
LLMs are trained to predict the most likely next token given the context. When generating code, this means the output will statistically resemble correct code from the training data. Variable names will make sense. Function signatures will look reasonable. The structure will follow common patterns. But "looks like code that works" and "actually works" are two different things.
The failure modes are specific and consistent. LLMs will confidently call APIs that do not exist, reference parameters in the wrong order, generate logic that handles the happy path but breaks on edge cases, or produce code that compiles and runs but produces subtly wrong results. The output passes the eye test in a way that makes these bugs harder to catch than obviously broken code.
This is not a new observation, but it continues to be validated as AI coding tools move from autocomplete into agent-driven development where models write larger blocks of code with less human oversight per line.
Why It Matters
If you use Cursor, Claude Code, Aider, or any AI coding assistant, this framing should shape how you work with these tools daily. The plausibility problem scales with code complexity. A five-line utility function is likely correct. A fifty-line function with multiple branches, API calls, and error handling is far more likely to contain plausible-but-wrong code.
The practical risk increases as AI coding tools get better at producing code that compiles and passes basic tests on the first try. When code fails obviously, you catch it. When code looks right, runs without errors, and produces output that seems reasonable, the bugs hide longer and cost more to fix.
This is especially relevant for developers who are newer to a codebase or technology. An experienced developer reading LLM-generated code will often catch plausible-but-wrong patterns because they have seen the real patterns enough times. A developer who is learning will accept plausible output as correct, reinforcing wrong patterns in their mental model.
Our Take
The "plausible vs. correct" framing is the most useful mental model for working with AI coding tools. Not because these tools are bad - they are genuinely useful - but because understanding how they fail changes how you use them.
The practical adjustment is straightforward: treat LLM-generated code the way you would treat code from a confident junior developer. Read every line. Question API usage. Test edge cases. Do not assume that code which looks clean is code that works correctly.
The tools that are getting this right are the ones adding verification layers. Claude Code's ability to run tests against its own output, Cursor's inline diff review, and Aider's test-driven workflow all address the plausibility gap by adding feedback loops. The raw generation is not trustworthy on its own. The generation plus verification pipeline is where real value lives.
If you are relying on AI to write code you do not review, you are accumulating plausible bugs. If you are using AI to write code you do review, you are saving time. The difference between those two workflows is the difference between a useful tool and a liability.