Don't like the coding vibe, but not against it either.

2026-06-22 · Source: AI on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

A programmer reflects on their evolving relationship with AI tools, acknowledging their utility in daily work while expressing a growing unease. The author, an "idealist," finds that AI's pervasive presence diminishes the personal satisfaction derived from independently solving complex coding problems, leading to a fear of skill erosion. They advocate for "reasonable AI use," likening AI to a calculator that speeds up repetitive tasks while the human retains core understanding and problem-solving. To counteract perceived skill dullness, the author actively practices coding problems, particularly with low-level languages, aiming to restore their "memory muscles" and the enjoyment of the programming process, noting that while results are not yet significant, the direction is positive.

Key takeaway

For software engineers integrating AI tools, recognize the risk of skill erosion and diminished satisfaction from core problem-solving. You should consciously limit AI to repetitive tasks, ensuring you fully understand every line of code generated. Actively engage in independent coding challenges, perhaps using low-level languages, to maintain and sharpen your fundamental programming abilities and preserve the enjoyment of the craft.

Key insights

Over-reliance on AI can diminish a programmer's problem-solving satisfaction and erode core skills.

Principles

• AI should augment, not replace, human thinking.
• Understanding every line of code is crucial.
• Preserve the joy of independent problem-solving.

Method

To counter skill dullness from AI use, actively practice coding problems, especially with low-level languages, to force deeper thinking and rebuild "memory muscles."

In practice

• Use AI for repetitive tasks, not core logic.
• Regularly solve coding challenges independently.
• Engage with low-level languages for deeper thought.

Topics

• AI Ethics
• Programmer Productivity
• Skill Erosion
• Low-Level Programming
• Coding Practice
• Human-AI Collaboration

Best for: Software Engineer, AI Engineer, AI Ethicist

Related on AIssential

• I Spent Two Years Building a Template That AI Replaced in Seconds — Over-abstraction and static templates hinder productivity in dynamic software development, even with AI tools.
• Should you still learn to code? — Learning to code now means understanding systems and directing AI agents, not just typing lines.
• The Dangerous Illusion of AI Coding? - Jeremy Howard — AI coding offers an illusion of control, hindering genuine understanding and knowledge growth in software engineering.
• don't let AI rob you — AI should augment, not replace, the intrinsic joy and learning derived from the software engineering journey.
• You're Using AI to Learn Faster. That's Exactly Why You're Not Learning — Over-reliance on AI for learning impedes deep understanding and skill development by circumventing necessary cognitive struggle.
• Grief and the Nonprofessional Programmer — AI-assisted coding offers efficiency but risks diminishing programmers' fundamental understanding of algorithms.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.