I'm an Amazon tech lead who uses AI to write code daily. There's one situation I hesitate to use it in.

Anni Chen, a tech lead at Amazon with over three years of experience, shares her insights on utilizing generative AI for coding. In her role, she focuses on implementing large-scale systems powered by AI, particularly in enhancing personalization features across the platform. Chen finds that using AI tools for coding has significantly boosted her productivity. For routine tasks and debugging, she likens the process to a lottery—sometimes yielding impressive results. This approach allows her to brainstorm potential solutions even if the AI-generated ones aren’t always perfect. When employing AI to assist in coding, Chen emphasizes the iterative nature of the process. She inputs basic requirements, reviews the generated code, and suggests improvements—similar to a collaborative code review with colleagues. Although AI can quickly provide solutions, it often introduces new issues that require careful monitoring. For intricate problems, additional scrutiny is essential; nonetheless, the speed of AI assistance remains a considerable advantage. For example, while dealing with complex locking issues alongside a partner team, Chen was able to leverage an AI model to generate multiple potential solutions in just 15 minutes. This swift brainstorming session allowed her to refine these ideas and quickly create a proposal for her team, a task that would have traditionally taken a full day. Despite the advantages, Chen expresses caution about deploying AI-generated code directly into production environments. Large language models (LLMs) can make assumptions that may not align with the specific requirements of a project, such as multi-threading capabilities. This oversight can lead to critical failures when scaling applications to handle significant user loads. While non-technical users can benefit from AI tools, Chen believes that having a solid technical background enables one to foresee potential constraints and navigate the complexities involved in coding. This understanding not only aids in mastering AI tools but also helps in recognizing their limitations, particularly in tasks such as mathematical problem-solving. Chen notes a shift in attitudes toward AI coding within her team, especially among engineers who were initially skeptical. As more team members experienced the benefits of AI-assisted coding, acceptance grew, leading to broader adoption across the organization. Now, with the productivity gains evident, it’s increasingly challenging for employees to resist utilizing AI in their workflows. Ultimately, even for those who may be reluctant to embrace AI coding directly, interactions with AI tools are becoming inevitable, especially as these technologies weave into standard practices like code reviews. The evolution of coding with AI is not only changing how developers work but is also reshaping the landscape of software development itself.