Writing Code Was Never The Bottleneck

Overview
LLMs have reduced the time required to generate code, but the real bottleneck remains in understanding, reviewing, and maintaining that code. The article discusses how technical leaders must focus on building trust, judgment, and processes around AI-generated code to ensure quality and long-term sustainability.

Key Takeaways

• Code generation speed is not the limiting factor; human comprehension is.
• Effective review practices and clear standards are essential when using LLMs.
• Trust in AI output must be balanced with rigorous testing and documentation.
• Leaders should foster a culture of critical evaluation rather than blind reliance on tools.

Who Would Benefit

• Engineering managers overseeing AI-assisted development teams.
• Technical leads responsible for code quality and maintainability.
• Software architects designing processes for LLM integration.
• Developers who use LLMs for daily coding tasks.

Frameworks and Methodologies

• Code review best practices
• Continuous integration / continuous delivery (CI/CD)
• Documentation standards
• Ethical AI guidelines