AI Writes Code Fast—Fixing It Takes Years

By Dr. Deepak Kumar Sahu,Editor-in-chief-VARINDIA

The power of AI-assisted coding is undeniable—models today can generate 10,000 lines of code in under two minutes, accelerating development cycles like never before. But developers are increasingly warning that this speed comes with a heavy cost: massive technical debt, hidden bugs, security vulnerabilities, and bloated codebases that could take years to repair.

While AI tools excel at producing large volumes of syntactically correct code, they often lack architectural context, long-term maintainability planning, and nuanced understanding of edge cases. This leads to code that “works” initially but is difficult to scale, optimize, or secure. Developers report encountering duplicated logic, unnecessary functions, inefficient algorithms, and libraries added without justification—all contributing to code bloat.

Security is a bigger concern. AI often reuses outdated or unsafe patterns scraped from public repositories, creating vulnerabilities that attackers could exploit. Missing input sanitization, weak authentication flows, and poor error handling are increasingly common in AI-generated output. Even small oversights can become long-term liabilities in production systems.

Debugging such code becomes a multi-year task:

• Fixing logic errors buried across thousands of autogenerated lines

• Refactoring code to meet organizational standards

• Patching security flaws before deployment

• Reducing tech debt introduced by unnecessary abstractions

Industry experts caution that AI should be a co-pilot, not a replacement, emphasizing human oversight, architectural review, and careful code auditing.

The message from engineers is clear: AI can write code fast—but real software quality still requires time, expertise, and disciplined engineering.