In the past few years, large language models (LLMs) have burst onto the software development scene like a meteor bright, exciting, and full of promise. They can write entire applications in seconds, generate boilerplate code with ease, and explain complex algorithms in plain English. It’s hard not to be impressed.
But after spending serious time testing various AI platforms as coding assistants, I’ve reached a clear conclusion:
AI is not yet suitable for generating long-term, maintainable, production-grade software.
It’s fantastic for prototyping, disposable tools, and accelerating development, but when it comes to real-world, evolving, multi-developer systems it falls short. And the root cause is simple but fundamental: non-determinism.
The Non-Determinism Problem
At the heart of every LLM lies a probabilistic process. When you ask an AI to write or modify code, it doesn’t “recall” what it said before it predicts the next most likely word or token based on the context it sees. Even when you give it the exact same prompt twice, you often get subtly (or wildly) different answers.
In casual conversation, this doesn’t matter much. But in software engineering, determinism is sacred. A build must produce the same binary every time. Tests must behave consistently. A function’s output must depend solely on its input.
LLMs break this rule by design.
When you ask AI to “add a new field to this API,” it might add the field but it might also rename unrelated variables, adjust indentation styles, reorder imports, or subtly alter unrelated logic. These incidental changes make it almost impossible to track what actually changed and why. In version control, that’s noise. In production code, that’s risk.
The Illusion of Velocity
Using AI for coding can feel like flying until you realize you’ve lost track of where you’re going.
AI-generated code feels fast. You type a prompt, and it spits out a function that looks plausible. But as any experienced engineer knows, code that looks correct is not the same as code that is correct.
Worse still, AI often gets 90% right just enough to lull you into trusting it, but that last 10% (the edge cases, performance issues, or security vulnerabilities) can be costly. In long-lived systems, those flaws become ticking time bombs.
So yes, AI saves time but only if you’re ready to spend that saved time reviewing, refactoring, and making it consistent with your project standards. Otherwise, you’re borrowing technical debt against future maintenance.
“Vibe Coding” vs. Real Engineering
There’s a growing trend I like to call “vibe coding” relying on AI to produce code that “feels” right without understanding it deeply. It’s seductive, especially for less experienced developers or under time pressure.
But the truth is: software longevity is built on understanding, not vibes.
A healthy codebase is not just functional it’s coherent, documented, and maintainable. Every class, function, and comment exists for a reason that another human can later understand. AI-generated code often lacks that intentionality. It can mimic style, but it doesn’t comprehend architecture, team conventions, or long-term evolution.
AI doesn’t “see” the whole system it only sees your current prompt.
Where AI Does Shine
Despite these limitations, I’m not anti-AI. In fact, I use it daily strategically.
AI is brilliant at:
- Prototyping ideas getting something working fast, even if it’s messy.
- Generating boilerplate writing repetitive CRUD or setup code.
- Explaining code translating complex logic into human-readable summaries.
- Brainstorming solutions helping you think through alternative approaches.
- Writing tests drafting coverage you can refine manually.
In other words, AI accelerates cognition, not automation. It’s a thinking partner, not a replacement for engineering discipline.
What It Means for the Future
As LLMs improve, we’ll likely see more deterministic, context-aware systems perhaps ones that can “anchor” to a codebase and learn its structure persistently. But until then, the responsibility for coherence, maintainability, and correctness still lies with us the humans.
AI might be the apprentice, but we’re still the architects.
My takeaway after months of experimentation is simple:
Use AI to accelerate development, not to abdicate responsibility.
Treat its output like an intern’s draft: useful, fast, and full of potential but never production-ready without review, cleanup, and integration into your project’s ecosystem.
The Bottom Line
AI coding tools are a revolution but like every revolution, they require balance and maturity to use effectively. They’re not replacing software engineers; they’re augmenting them.
So go ahead, let the AI write your prototypes, mock APIs, or test scaffolds. But when it comes to the production systems that real users depend on make sure there’s a human behind the keyboard who understands every line.
Because in the end, the difference between disposable and durable code isn’t who (or what) wrote it it’s who owns it.