What Happened
A post by @KatanaLarp on X, shared on Hacker News on March 7, 2026, reframes a fundamental issue with AI-assisted coding: LLMs don't produce correct code. They produce plausible code. The distinction matters more than it might seem.
Large language models generate code by predicting what tokens are most likely to follow the ones before them. The output looks syntactically correct. It follows patterns the model has seen in training data. It often compiles and runs. But "looks right" and "is right" are different things, particularly when dealing with edge cases, business logic, security constraints, or performance requirements that the model has no context for.
This is not a new observation, but it keeps resurfacing because the gap between plausible and correct is where real bugs hide. As AI coding tools move from autocomplete to agentic workflows that write entire features, the stakes of this distinction keep rising.
Why It Matters
If you use Cursor, Claude Code, GitHub Copilot, Cody, or any other AI coding assistant, you have probably experienced this firsthand. The code looks clean. The variable names make sense. The structure follows best practices. Then you run it and discover a subtle logic error, a missing null check, or an API call with wrong parameters that happens to match the shape of the right one.
The problem compounds in two ways. First, plausible code is harder to review than obviously wrong code. Your brain pattern-matches against what looks familiar, and LLM output is optimized to look familiar. Second, as developers become more dependent on AI-generated code, the skill to catch these errors atrophies. Junior developers who learned to code with AI assistants may never develop the debugging instincts that come from writing incorrect code yourself and fixing it.
Studies from 2025 showed that developers using AI assistants accepted generated code without meaningful review roughly 40% of the time. That number has likely increased as the tools have gotten better at producing plausible output.
Our Take
The framing of "plausible vs. correct" is the clearest way to think about AI coding tools. It sets the right expectations. These tools are pattern completion engines with enormous training sets. They are very good at producing code that fits the pattern. They have no mechanism for verifying whether the code actually does what you need.
This is why the best AI coding workflows treat generated code as a first draft, not a finished product. Write tests before generating code. Review generated code line by line. Run it against edge cases the model would not have considered. The tools that will win in this space are the ones that build verification into the workflow, not the ones that generate the most code the fastest.
The practical advice has not changed since AI coding tools first appeared: trust, but verify. The difference now is that "verify" needs to be a formal part of your process, not something you do when you feel like it.