Core Viewpoint - The choice of programming languages is becoming increasingly important in the AI era, as it directly impacts the quality of code generated by AI agents [19][28]. Group 1: Programming Language Comparison - Go is favored in AI scenarios due to its thin abstraction layer and structured nature, making it easier for models to understand and rewrite code. In tests, Go outperformed Python and Rust in generating code for similar small programs [2][27]. - Python remains essential for any company, especially for tasks involving machine learning or data processing, even if it is not used for core services [12][16]. - JavaScript and TypeScript are also unavoidable in the current landscape, with TypeScript often accompanying JavaScript [12][17]. Group 2: Language Evolution and Future Trends - The industry is witnessing a trend towards creating "next-generation languages" designed for human-agent collaboration, as existing languages may not be optimal for this new paradigm [3][29]. - The migration from Python 2 to 3 serves as a cautionary tale for future language transitions, highlighting the complexities involved in such changes [4][6][7]. - Rust has learned from Python's migration challenges by implementing an "edition system" that allows for incremental feature adoption without breaking compatibility with older versions [7]. Group 3: Practical Considerations in Language Choice - The choice of programming language should be pragmatic, focusing on the product being built rather than the code itself. Early-stage companies should limit their technology stack to three or four languages [11][18]. - The emergence of AI tools has shifted the focus from the necessity of a unified codebase to maintaining clear boundaries between systems, enhancing development efficiency [18][20]. Group 4: AI's Impact on Software Development - AI tools are significantly changing the software development landscape, allowing for more efficient coding and problem-solving. A substantial portion of code (over 80%) in some companies is now generated by AI [21][24]. - The role of human developers is shifting towards creative and thoughtful tasks, while AI handles more routine coding responsibilities [21][24]. - The democratization of programming is occurring as AI lowers the entry barrier, enabling more individuals to engage in coding without extensive prior knowledge [25]. Group 5: Error Handling and Language Design - Different programming languages exhibit varying error handling characteristics, which can significantly impact system reliability and user experience [34][35]. - The design of programming languages often involves trade-offs between performance and error handling capabilities, which can affect the overall robustness of applications [40][42].