The author argues against the necessity of reading code line-by-line, suggesting that it’s becoming less effective for ensuring software quality, especially as AI tools take on more coding tasks. Instead of focusing on the code itself, they emphasize the importance of specifications, tests, and the overall system architecture—what they call a "harness." The author cites examples like OpenAI's development of a product entirely coded by AI agents, where the focus was on creating a robust infrastructure rather than reviewing the code itself. They also reference a successful solo project, OpenClaw, which achieved significant popularity without the creator reading the code directly. Critics raise valid concerns about the risks of a black box approach, security vulnerabilities, and the potential for bugs. The author addresses these points by arguing that relying on automated tools like static analysis and dependency scanning can mitigate risks better than manual code reviews. They acknowledge that AI-generated code can have defects, but contend that using well-structured processes and high test coverage can lead to fewer bugs compared to traditional coding methods. The author emphasizes that their approach involves detailed specs and a comprehensive verification system, ensuring that the output remains reliable without needing to scrutinize each line of code.