Choosing a tech stack in a world where LLMs write all the code (behan.substack.com)

In a transformative shift for software development, the rise of large language models (LLMs) capable of generating code is prompting companies to rethink their technology stacks. As LLMs like OpenAI's Codex and Google's Bard increasingly handle not just basic programming tasks but complex coding revisions and project management, traditional software development frameworks are becoming more flexible and adaptable. This trend signals a move towards a future where developers can focus on higher-level design and strategy rather than mundane coding tasks. The significance of this development lies in its potential to accelerate the software development lifecycle, reduce the skills gap, and democratize access to programming knowledge. Companies may shift from rigid, technology-specific stacks to more modular and easily interchangeable systems, allowing for rapid experimentation and iteration. Additionally, with LLMs optimizing code and identifying potential bugs in real-time, developers can expect improved collaboration between machine and human, enhancing both productivity and innovation in AI/ML-driven projects. As organizations adapt to these shifts, the implications for scalability, security, and maintainability of software are profound, indicating a new era of coding powered by AI.