The tech community is grappling with a growing problem in AI-assisted development. While artificial intelligence tools have revolutionized coding speed and capability, developers are discovering a critical flaw: these tools will confidently create elaborate solutions for problems that don't exist or shouldn't be solved in complex ways.
AI Tools Excel at Speed But Struggle with Direction
Experienced developers report that AI coding assistants work best when used as fast versions of what they would do themselves. The tools shine at handling compiler errors, finding documentation, and making quick edits across multiple files. However, they consistently fail when developers lack clear direction about what needs to be built.
The core issue isn't the technology itself, but how it amplifies existing problems. When someone doesn't understand their users' needs or lacks good judgment about solutions, AI simply helps them build the wrong thing more efficiently. The tools fill in knowledge gaps confidently, even when those gaps represent crucial decision points that require human insight.
Common AI Coding Tool Applications:
- Granola: Transcribing user research sessions
- Visual Electric: Generating diverse stock images
- ChatGPT: Code review and copy improvement
- Cursor: Website and prototype development
- Custom agents: Brand-specific copywriting
The Gold-Plating Problem in AI-Generated Code
A significant pattern has emerged in AI-generated code: excessive complexity for simple tasks. Developers working on conservative codebases report that AI tools consistently suggest overly sophisticated approaches. For example, when asked to check three bits in a byte, AI might propose creating base structures with virtual functions, templates, and pointer vectors instead of simple bit extraction.
These tools seem to always reach for what I would call the pristine solution as their first attempt, and many would call that gold-plating.
This tendency toward over-engineering creates maintenance burdens and introduces unnecessary complexity. The generated code often includes extensive comments explaining how and what rather than the more valuable why that human developers typically need.
AI Code Quality Issues:
- Excessive "gold-plating" with unnecessary complexity
- Over-engineered solutions for simple problems
- Comments focusing on "how/what" instead of "why"
- Confident suggestions of inappropriate approaches
- Lack of intentional design decisions
Strategic AI Usage Requires Human Expertise
Successful developers treat AI tools as leverage rather than replacement for thinking. They use AI for specific tasks like transcribing research sessions, generating hard-to-find stock images, and building prototypes. However, they avoid using AI for tasks that require genuine understanding, such as conducting user interviews or making architectural decisions.
The key skill for developers moving forward appears to be knowing when and how to guide AI tools effectively. This requires understanding what the expected output should be before engaging the AI, and maintaining the ability to spot and correct the tool's mistakes in real-time.
The Future Belongs to Human-AI Collaboration
The divide between successful and struggling developers increasingly centers on their approach to AI tools. Those who resist AI entirely may find themselves at a disadvantage, while those who treat it as a magic solution will struggle with poor results. The winners are developers who combine deep human insight with AI capabilities, using the tools to amplify their existing skills rather than replace their judgment.
As AI coding capabilities continue advancing rapidly with new models appearing every few months, the importance of human oversight and direction becomes even more critical. The technology can help experienced developers work faster and explore more possibilities, but it cannot substitute for understanding problems, users, and appropriate solutions.