Suspend Disbelief - From Implementation to Intention

Introduction It's a reasonable thing to be sceptical about coding with AI. If you've been burned by earlier models, the hesitation makes sense. But the models really are good enough now. Not "kind of good enough for prototypes" or "good enough if you keep a close eye on them"—genuinely, substantively good enough to write production code. Code that holds up in review. The kind that ships. It's taken a while to get here. Early ChatGPT produced code that looked plausible at first glance but fell apart under scrutiny—wrong outputs, runtime errors, style problems that signalled the model was pattern-matching rather than reasoning. That reputation stuck. And here's the uncomfortable truth: for a significant slice of the developer community, the mental model formed in 2022 hasn't been updated since. The tools moved on. The assumptions didn't. That gap—between what the models can actually do right now and what most developers believe they can do—is what this post is about. The bottleneck is no