Summary Points
- AI accelerates coding tasks dramatically, enabling faster delivery of work like migrations and testing, but this often leads to taking on more tasks and creating overload.
- The real risk of AI in coding isn’t unreliability but the shift in bottlenecks—from writing code to reviewing and verifying it—resulting in increased review times and false productivity.
- To avoid verification debt and loss of understanding, developers should read documentation carefully, write detailed specs before coding, and communicate with humans for review instead of relying solely on AI.
- The key to working effectively with AI is discipline: recognize when more AI capacity isn’t the goal, focus on precise specifications, maintain human oversight, and embrace continuous learning to prevent skill erosion and hidden errors.
AI Boosts Our Speed, but Also Our Mistakes
AI tools are making work faster. Tasks that took days now take hours or minutes. As a result, many of us get more done in less time. For example, AI can refactor code, write tests, and handle migrations quickly. However, this speed comes with a catch. Rushing to keep up, we often take on more work. We add new AI agents to our workflows without stopping to ask if we should. This extra capacity can feel like freedom, but it also pushes us to make more mistakes. Sometimes, faster work leads to overlooking details or trusting AI outputs blindly. So, while AI makes us faster, it also challenges our accuracy and judgment.
The Hidden Cost of Being Five Times Faster
Getting faster with AI shifts where bottlenecks appear. We think we’re saving time, but we’re just moving it. Reviewing AI-generated code now takes longer because there is more to check. For instance, I once approved an AI change that added too many permissions. It looked fine at first, but it wasn’t safe. Reading the full code and verifying intent saved me later. This shows that speed can trick us into thinking we understand more than we do. AI often produces reliable results on simple work, but struggles with complex, large systems. Trusting AI blindly can lead to outdated understanding and increased review time. Moving faster sometimes means spending more time ensuring correctness.
How to Use AI Without Losing Control
The key is to work smarter, not just faster. First, read the documentation carefully before asking AI to generate code. This helps you steer the AI more precisely. Second, write clear specifications, even if it’s just a page outlining what you want. Clearly defining your goals saves time later. Third, talk to team members. Verifying AI output through conversation catches mistakes early. Fourth, keep a record of decisions and reasoning. Sharing notes prevents misunderstandings and improves teamwork. Finally, follow seven simple rules: define your requests precisely, never accept unexplainable changes, batch tasks small, estimate verification time, ask why, take regular breaks from AI, and measure the quality of specifications. These habits help balance speed with safety and growth.
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