Software developer TROJAN reports challenges using AI coding assistants, noting that while they excel at initial development, they often produce code that is difficult to maintain and debug. These tools frequently generate code that appears functional but hides complex logic errors, requiring significant time to troubleshoot. The author highlights that AI tools often generate 'confident nonsense'—code that appears technically sound but fails under real-world conditions.

The fundamental issue lies in the lack of context provided by AI. While AI identifies patterns efficiently, it lacks the ability to understand broader consequences, such as state management conflicts, complex asynchronous logic, and unexpected side effects. The author argues that senior-level engineering intuition regarding trade-offs, technical debt, and business risks remains indispensable. Because AI prioritizes completion speed over architecture and scalability, developers face increased maintenance burdens.

To effectively integrate AI, the author emphasizes the need for rigorous testing and a shift in perspective. Instead of treating AI as a replacement engineer, the author now views these tools as fast, confident junior developers prone to occasional hallucinations (misleading or nonsensical outputs). This approach allows the author to leverage AI for removing repetitive tasks and speeding up experimentation while retaining responsibility for final architectural decisions. Ultimately, the author concludes that AI usage highlights the importance of deep software engineering expertise, as understanding the necessity and implications of code remains a human-driven requirement.

Software developer TROJAN reports challenges using AI coding assistants, noting that while they excel at initial development, they often produce code that is difficult to maintain and debug. These tools frequently generate code that appears functional but hides complex logic errors, requiring significant time to troubleshoot. The author highlights that AI tools often generate 'confident nonsense'—code that appears technically sound but fails under real-world conditions.

The fundamental issue lies in the lack of context provided by AI. While AI identifies patterns efficiently, it lacks the ability to understand broader consequences, such as state management conflicts, complex asynchronous logic, and unexpected side effects. The author argues that senior-level engineering intuition regarding trade-offs, technical debt, and business risks remains indispensable. Because AI prioritizes completion speed over architecture and scalability, developers face increased maintenance burdens.

To effectively integrate AI, the author emphasizes the need for rigorous testing and a shift in perspective. Instead of treating AI as a replacement engineer, the author now views these tools as fast, confident junior developers prone to occasional hallucinations (misleading or nonsensical outputs). This approach allows the author to leverage AI for removing repetitive tasks and speeding up experimentation while retaining responsibility for final architectural decisions. Ultimately, the author concludes that AI usage highlights the importance of deep software engineering expertise, as understanding the necessity and implications of code remains a human-driven requirement.