AI development: the good and bad
- AI-assisted development accelerates both coding speed and error propagation, with risks like cascading mistakes and design flaws magnified by AI’s context limitations, akin to Knight Capital’s $500M human-error-driven collapse.
- Real-world failures (e.g., Knight Capital) stem from process gaps, not technology; AI intensifies this by compressing multi-year human error patterns - like developers Alice, Bob, and Charlie inheriting undocumented code under time pressure - into hours or days.
- AI lacks persistent context, acting as a “new developer” per request, which forgets safeguards, amplifies poor documentation, and misses architectural pitfalls unless explicitly guided by experienced oversight.
- Defensive coding practices (e.g., explicit error checks, copy-before-move for files) are critical since AI won’t self-advocate for safeguards - users must enforce quality control as architects and reviewers.
- While AI democratizes development, inexperienced users often overlook foundational pitfalls, making seasoned judgment essential to prevent small oversights from escalating at machine speed.
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