The Hard Truth About AI and Production Code

There’s a seductive story floating around right now: large language models can write code, and therefore, they can build software. It’s a story that sells tools, excites investors, and reassures managers who dream of faster development cycles. But here’s the uncomfortable truth: generating code and engineering software are not the same thing. Treating them as interchangeable is a mistake that will cost companies time, money, and trust.

At its core, an LLM is a pattern machine. It doesn’t “understand” code—it predicts what code should look like based on mountains of examples. That makes it brilliant for small, well-defined tasks. Ask it to generate a utility function, an API call, or a simple test case, and it will impress you. But scale matters. Software is not a loose collection of snippets. It’s a living system where decisions in one place ripple across the entire codebase. That’s where things break down.

We’ve already seen what happens when AI writes long-form text. In the opening chapters of an AI-written novel, the narrative may feel cohesive, but by the time readers reach the middle, they forget characters, plotlines contradict each other, and the story collapses under its own weight. Code is no different. A small script may work beautifully, but as soon as you ask an LLM to generate something closer to an encyclopedia of interdependent modules, inconsistencies creep in. And in software, inconsistencies don’t just confuse readers—they create bugs, fragility, and maintenance nightmares.

Then there’s the problem of verbosity. LLMs are wordy by nature. In prose, that means redundant sentences. In code, it means unnecessary complexity—extra abstractions, bloated classes, tangled logic. Complexity is the enemy of maintainability. Every additional layer is another place for a bug to hide, another obstacle for a developer to untangle later. Software engineering is already a battle against complexity. Injecting more of it by default is not progress—it’s regression disguised as productivity.

The bigger issue is that writing code is only a fraction of what software engineering is. The real work is in system design, trade-off decisions, debugging, testing, integration, and long-term maintainability. These are not pattern-matching problems. They are judgment problems. They require experience, discipline, and often uncomfortable conversations about priorities and constraints. No LLM, no matter how advanced, is built for that kind of reasoning.

Does this mean AI has no role to play in software development? Absolutely not. When used correctly, it is transformative. It can eliminate boilerplate, accelerate prototyping, and suggest alternatives you might not have considered. It can act as an extra pair of hands, or even an always-available junior developer. This potential is what makes AI in software development a source of optimism and hope. But it is not, and will not be, the architect of your production systems. Treating it that way is a recipe for brittle software and broken promises.

The companies that win with AI coding tools will be the ones that understand this distinction. They will utilize LLMs to expedite the non-critical aspects, while relying on human judgment for the critical ones. They will resist the temptation to hand over the keys to their codebases and instead treat AI as a powerful assistant—valuable, but not infallible. The bubble around AI-generated code is real. The sooner we pop it, the sooner we can see the actual value of these models. They are not replacements for engineers. They are accelerators for engineers who know how to wield them wisely. And that difference is what separates hype from real progress.