don't let AI rob you

2026-06-06 · Source: Hussein Nasser · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

An editorial analyst argues that AI, specifically Large Language Models (LLMs), should primarily serve as a "search accelerator" in software engineering, aiding in quick lookups for syntax or specific queries (e.g., SAP HANA log retrieval). The author emphasizes the intrinsic joy and "art form" of debugging and building intricate systems, asserting that relying on AI to fully solve problems or build entire products diminishes the engineer's learning, personal connection, and the valuable "collateral knowledge" gained through struggle. Using an Elden Ring analogy, the piece illustrates a spectrum of AI assistance, advocating for minimal use to overcome minor obstacles rather than delegating the core problem-solving journey. The author also critiques the quality of modern software and encourages engineers to embrace the process of "reinventing the wheel" to cultivate their craft and find deep satisfaction in their work.

Key takeaway

For software engineers seeking deep satisfaction and skill development, resist delegating core problem-solving to AI. While AI can accelerate minor lookups, fully relying on it for debugging or building projects you care about robs you of valuable "collateral knowledge" and personal connection to your work. Instead, embrace the struggle of the engineering journey, even "reinventing the wheel," to cultivate your craft and find genuine joy in creating.

Key insights

AI should augment, not replace, the intrinsic joy and learning derived from the software engineering journey.

Principles

• Software engineering is an art form.
• Struggle yields "collateral knowledge."
• AI functions as a search accelerator.

In practice

• Use AI for quick syntax lookups.
• Debug complex issues manually.
• Prioritize personal interest in projects.

Topics

• AI in Software Engineering
• Large Language Models
• Software Development Philosophy
• Debugging Strategies
• Collateral Learning
• Developer Experience

Best for: Software Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hussein Nasser.