If AI Has Conquered Coding, Why Is Software Getting Worse?

In recent years, AI coding tools have advanced at an astonishing pace. GitHub Copilot, Claude, ChatGPT and others now generate fully functional code from just a few lines of prompts. Yet, the software we use seems to be plagued by more bugs, slower performance, and greater security vulnerabilities. If AI has conquered coding, why is software getting worse? This question is not just a paradox; it reveals a structural contradiction faced by developers living in the era of 'vibe coding'.

The Hidden Bomb of Technical Debt Behind Productivity Gains

AI tools have undeniably accelerated code production. According to a recent Business Insider report, many senior developers say they can prototype and implement features far faster thanks to AI. However, the code produced this way does not guarantee long-term software quality. In fact, generative AI is optimized to provide 'instant answers' to given prompts, without a deep understanding of overall system architecture or maintainability. As a result, superficially working code fragments accumulated in the short term frequently turn into massive technical debt. For example, a payment module in a financial system was quickly built using AI, but failure handling and concurrency control were overlooked, leading to a severe outage months later.

Forbes points out, 'AI Makes Software Cheaper. Quality Still Costs,' warning that reckless AI code production driven solely by cost efficiency can actually skyrocket long-term maintenance costs. Indeed, there are increasing cases where projects that adopted AI-suggested code as-is revealed fundamental design flaws months later, requiring a rewrite. This pattern is akin to taking on debt to increase immediate consumption.

AI Homework Grading: Verification Fatigue and Responsibility Reversal

CNET's column 'On the Special Hell of Marking an AI's Homework' sharply captures the peculiar psychological state of senior developers working with AI. While they welcome AI-generated code, they also complain of 'cognitive burnout' from having to constantly find and fix errors, much like a teacher grading a student's homework. AI-generated code often appears syntactically perfect but may contain subtle business logic errors or security vulnerabilities, like quietly hidden bombs. Since AI takes no responsibility for the code it creates, all final verification and quality assurance falls on human developers—a phenomenon known as 'responsibility reversal'.

Senior developers feel this burden even more acutely. According to The Guardian, experienced engineers are finding new ways to adapt in the AI era by returning to fundamentals and pursuing collective action. Ironically, however, AI tools may hinder the learning curve for junior developers. Another Business Insider article highlights a computer science graduate who feels underprepared for job hunting due to AI, shedding light on the irony that 'AI verification skills' are becoming more important than 'coding skills'.

Conditions for Trustworthy Vibe Coding

So, what does it take to make 'vibe coding' a trustworthy practice? First, developers must recognize AI not as a mere code generator but as a 'conversational design partner,' continuously communicating context and intent. Second, a systematic review system for AI outputs must be established. Rather than generating large amounts of code at once, it's important to break it into small units for verification and accumulate versions. For example, using tools like md-log allows easy human review of AI-generated analysis or work, with immutable versions saved each time, transparently tracking collaboration history. This becomes the core infrastructure for 'human-in-the-loop,' opening AI's black box and placing it under responsible developer control.

Third, companies and educational institutions should emphasize 'software design principles' and 'critical code review' capabilities over 'AI prompt engineering.' As The Guardian article conveys, field engineers are already returning to fundamentals and rebuilding deeper computer science knowledge beyond AI literacy. Ultimately, trustworthy software comes not from simply churning out a lot of code quickly, but from designs grounded in human insight and verification.

The claim that AI has conquered coding is not true. Rather, AI is acting as a catalyst that reveals the essential complexity of software development. If the current structure of shifting responsibility for AI outputs onto individual developers continues, declining software quality may be an inevitable outcome. The real solution lies in clarifying the roles of AI and humans and sharing collective responsibility for quality. Finally, recording the continuous process of verification and collaboration and turning it into a knowledge asset—that's where md-log begins.

References

• Chasing new skills, going back to basics and pushing for collective action: how software engineers are adapting to AI - The Guardian
• For software engineers, AI isn't just changing coding. It's changing careers, confidence, and identity. - Business Insider
• Want a software engineering job? You'll need more than coding skills in the AI era. - Business Insider
• The Human in the Noose: On the Special Hell of Marking an AI’s Homework - CNET
• A college grad said AI has made her feel underprepared for a software engineering job - Business Insider
• AI Makes Software Cheaper. Quality Still Costs - Forbes
• AI writes a lot of software. Now, human code review is starting to disappear. - Business Insider Africa
• Choose Wisely: AI-Generated Coding Risk Varies, A Lot - Dark Reading
• Ford says AI alone couldn't fix its quality problems. It needed to rehire veteran engineers to help. - Business Insider
• The Great Coding Reset: How AI is changing software engineering - Business Insider
• Software vendors aren't losing sleep over vibe-coding — yet - Accounting Today