You're reading way too much code

2026-07-22 · Source: Theo - t3․gg · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

The article contends that most engineers, regardless of project criticality, read an excessive amount of code and underutilize AI for code generation. It introduces a "code importance spectrum," ranging from trivial "slop websites" to life-critical "pacemaker firmware." The central argument is that AI has made code generation incredibly cheap, enabling engineers to produce vast quantities of "slop" code—such as custom debuggers, extensive test harnesses, and one-off verification scripts—to rigorously validate mission-critical production code. This approach suggests that if code is important enough to cause death or financial ruin if incorrect, it demands an "unbelievable amount of slop" for verification, which does not require human review. The author emphasizes that this paradigm shift allows for more thorough testing and exploration of ideas, moving beyond the traditional model where every line of production code was manually written and reviewed.

Key takeaway

For software engineers responsible for critical systems, if you believe every line of your production code is vital, you should significantly increase your use of AI-generated "slop" code. This cheap, disposable code can create custom debuggers, test harnesses, and verification tools, allowing you to rigorously validate your core systems without needing to manually review every generated line. Embrace this infinite code generation to enhance verification, not to merge unreviewed code.

Key insights

AI-generated "slop" code can vastly improve verification of critical systems, shifting focus from reading to generating.

Principles

• Code importance exists on a spectrum.
• Cheap code enables extensive verification.
• Verification systems can be layered.

Method

Generate vast amounts of "slop" code using AI (e.g., custom debuggers, test harnesses, one-off scripts) to verify mission-critical production code, exploring theories and testing assumptions without human review of the generated verification code.

In practice

• Build custom debuggers with AI.
• Generate extensive test harnesses.
• Use AI to port code for testing.

Topics

• AI Code Generation
• Code Verification
• Software Engineering Practices
• Test Automation
• Debugging Tools
• Code Quality

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• How to Avoid AI Code Slop — AI-accelerated development necessitates shifting code quality gates upstream to intent-driven verification, not post-code review.
• How to Avoid AI Code Slop — Treating AI as a junior pair programmer, not an autopilot, improves code quality through human oversight and iterative refinement.
• Stop Asking Which AI Writes Better Code. Start Making Them Compete. — AI code generation models should compete in automated pipelines, proving their implementations with evidence, not benchmarks.
• Coding is solved? Software is not. — AI shifts coding from generation to verification, making system-level correctness the new bottleneck.
• Dev Propulsion Labs podcast with Dr. Kate Holterhoff — AI agents selecting tools and AI-generated "slop" in open source are key emerging challenges.
• The Code I Didn’t Read — Over-reliance on AI-generated code without thorough review can introduce critical, hard-to-detect bugs.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Theo - t3․gg.