We are all selectively ignoring the elephant in the room when it comes to AI coding.

The first half of this article was written by humans, and the second half was optimized with AI-assisted language refinement.

Today, whether it's capitalists or liberal arts students, everyone is immersed in grand terms like vibe coding and technological democratization, loudly proclaiming that "software engineering will cease to exist," while stocks hit new highs!

The Fragility of Code

But everyone seems to be selectively ignoring the "elephant in the room" in the Coding Agent space: code degradation. Whether it's codex 5.4 or Claude Opus 4.6, as code repositories grow larger, architectures become increasingly chaotic, with massive amounts of duplicated logic and bugs scattered across every corner, inevitably piling up into a mountain of technical debt faster than ever. This rapid accumulation of "spaghetti code," caused by completely letting go and giving AI full creative freedom, will eventually reach a breaking point.

No Silver Bullet in Software Engineering

Many people cite examples: "My project has already reached 100,000 lines, all written by AI." Some of them even appear to be fairly senior programmers.

But if you take a closer look at your AI-programmed projects, haven't you noticed that this project could have been done in 50,000 lines or even less? It could have been written more robustly, with better performance, better architecture, and better maintainability.

AI Programming Is More Like Copying

This is another major realization I've had recently, though it may not be entirely accurate, so take it with a grain of salt.

• Many coding models showcase sample projects at release, like quickly building a Snake game or Flappy Bird.
• A product manager can use it to rapidly create a webpage, a TODO app, or even a more complex email-sending program.

And then we're all amazed that AI can write this on its own, that it "created" this code.

But is there a possibility that this thing already existed on GitHub? More accurately, AI just copied it. There's no need for us to marvel at AI writing a Cloudflare email-sending program in 30 minutes, because that already exists on GitHub. If you found the original repository, git clone would take just one second, and it wouldn't cost you a dime.

The vast majority of your needs have already been implemented by someone on GitHub. The problem is that among tens of millions of repositories, you can't find it, and it doesn't have a README. If you did find it, you wouldn't need to have endless conversations or pay for Claude.

The Flaw in Reward Functions

Let's go back and examine the underlying AI of these Coding Agents and how it's actually trained.

Current Coding Agent training primarily relies on functional implementation rates (such as fixing bugs or passing unit tests) as the core reward signal. This approach tends to learn "patch-style" solutions from massive repositories, and its essence is a binary, result-based feedback loop: as long as the code runs and passes, the AI receives positive reinforcement.

This "single-reward" mechanism ignores the non-functional requirements that are crucial in software engineering. The code generation process often lacks consideration for the following dimensions:

• Architectural soundness: Does it conform to design patterns? Is there excessive coupling?
• Performance boundaries: Optimal time and space complexity, rather than merely "it works."
• Maintainability and extensibility: Naming conventions, comment quality, and support for future feature evolution.

The Long-Term Technical Debt Caused by "AI Laziness"

This model that only focuses on "instant feature delivery" is essentially AI performing "greedy search" at the execution level. It leverages massive parameter counts to find shortcuts to implement features while stripping away engineering aesthetics and systemic depth. This short-termist efficiency gain is essentially a form of "R&D loan": although initial development speed is astonishing, the low-quality code it produces quickly transforms into heavy technical debt.

The Time You Save Will Be Paid Back Tenfold

A healthy system should be fully planned with a well-designed architecture. When we let AI hastily complete each feature in the early stages without even looking at it, the resources required for subsequent maintenance will often be several times greater than the development time AI saved.