Using AI to write better code more slowly
nolanlawson.com
May 25, 2026
4 min read
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77/100
Summary
LLMs can be utilized to produce high-quality code at a slower pace, countering the belief that AI coding is solely about rapid, low-quality output. This flexibility allows developers to prioritize code quality over speed.
Key Takeaways
- AI coding can be used to write high-quality code more slowly, contrary to the belief that it is only effective for rapid, low-quality output.
- Large language models (LLMs) are effective at finding bugs in codebases, with recent models from Anthropic and OpenAI demonstrating significant bug detection capabilities.
- A multi-agent approach to code review, utilizing different models, reduces the likelihood of hallucinations and false positives in bug identification.
- The process of using AI for code review may not increase development velocity but can improve codebase health and deepen understanding of the code.
Community Sentiment
Positives
- Using AI for code design and implementation fosters productive discussions, enhancing engineers' understanding of architecture and leading to better outcomes.
- The ability to rapidly iterate on code variants with AI allows developers to explore multiple solutions, ultimately refining their approach and reducing unnecessary complexity.
- AI tools like Claude and Codex can significantly improve the code review process, catching edge cases that might be overlooked by human developers.
- The intersection of experienced professionals and AI coding tools creates a unique synergy that can lead to exceptional results, highlighting the value of human expertise.
Concerns
- Reliance on AI for coding can lead to a lack of confidence in edge cases and architectural fit, as AI often rushes to complete tasks without thorough consideration.
- Some developers find that the time spent interacting with AI can negate any time savings, leading to frustration and a preference for manual coding.
- The perception that AI coding tools prioritize speed over quality raises concerns about the long-term implications for code maintainability and overall software quality.
