“Why AI-Generated Code Falls Short (And How It Can Improve)”
The rise of large language models (LLMs) in software development has been swift and transformative. Promises of unparalleled productivity gains and seamless coding assistants sparked a wave of excitement. Developers could now generate multi-line code blocks at the touch of a button, merging complex solutions into their projects with ease. At first glance, it seemed like a magic trick—effortless and flawless. However, beneath the surface, a lingering question remained: Can I really trust this code? While the use of AI in coding has become widespread, many developers still have reservations about the reliability of the generated output.
By 2025, the integration of AI in software development has reached a point where it feels like a given. According to Microsoft, 150 million developers are utilizing GitHub Copilot, and 61.8% of developers surveyed by Stack Overflow in 2024 reported incorporating AI into their workflow. Google even claims that a quarter of its new code is AI-generated. In many ways, AI-generated code is already the norm. But while its presence is undeniable, there are still critical questions about whether AI is truly up to the task of writing high-quality, trustworthy code.
The Limitations of AI-Generated Code
Despite its widespread use, AI-generated code often falls short of expectations. Steve Wilson, chief product officer at Exabeam, describes LLMs as “interns with goldfish memory,” great for quick tasks but lacking the ability to grasp the broader context of a project. This limitation has profound implications for software development. As AI becomes more integrated into the process, it’s increasingly taking on tasks that were once the domain of human developers, but this has led to new challenges. Developers are finding that the time spent debugging and addressing security vulnerabilities in AI-generated code is actually increasing, not decreasing. According to the 2025 State of Software Delivery report, many developers are spending more time fixing the mistakes AI makes rather than writing new code.
Bhavani Vangala, co-founder of Onymos, emphasizes that while AI-generated code can be useful, it still isn’t reliable enough for developers to trust without oversight. “AI output is usually pretty good, but it’s still not quite reliable enough,” Vangala says. While AI shows remarkable potential, it’s far from perfect. The quality of AI-generated code often suffers from issues like inconsistency, poor context awareness, and an inability to handle complex scenarios. As a result, human oversight remains indispensable, and developers still need to review, debug, and adjust the output before it’s ready for production.
Bloat, Context Limits, and Technical Debt
One of the most prominent issues with AI-generated code is its tendency to create bloated, inefficient solutions. AI tools often generate new code from scratch instead of refactoring existing code or reusing functions and classes that have already been written. This leads to unnecessary duplication, which not only increases the size of the codebase but also contributes to growing technical debt. Sreekanth Gopi, a prompt engineer at Morgan Stanley, points out that code bloat and poor maintainability arise when verbose or inefficient code is generated, making it harder for developers to manage and evolve the code over time.
GitClear’s 2025 AI Copilot Code Quality report analyzed millions of lines of code and found that the frequency of duplicated code blocks has risen dramatically since AI tools began to gain traction in mid-2022. In fact, duplicated code blocks have increased by eightfold, with some studies suggesting that cloned code could lead to defects anywhere from 15% to 50% more frequently. This not only adds to technical debt but also compounds the likelihood of errors slipping through undetected. The consequence is clear: AI-generated code may be quick, but it often results in poorly optimized, difficult-to-maintain code that slows down development in the long run.
The Path to Improvement: AI’s Potential with Human Oversight
Despite these flaws, the future of AI-generated code holds promise. To reach its full potential, AI must evolve to address these challenges—namely, improving accuracy, consistency, and efficiency while reducing the creation of unnecessary or redundant code. However, AI will never completely replace the need for human developers. Instead, it will serve as a tool that amplifies human ability. Developers will need to continue overseeing and refining AI-generated code, offering the critical thinking and problem-solving skills that AI cannot replicate.
As AI models continue to evolve, we may see advancements that address these issues—better context understanding, reduced duplication, and improved accuracy. With the right improvements, AI could become an indispensable partner for developers, allowing them to focus more on higher-level problem solving while AI handles the repetitive and boilerplate tasks. But until then, human involvement will remain essential for ensuring that AI-generated code meets the standards of quality and reliability required for production environments.