AI tribalism (nolanlawson.com)

In a reflective piece, a software developer examines their transformation in perspective toward Large Language Models (LLMs) over the past year, highlighting a significant shift from skepticism to reliance on tools like Claude Code for coding tasks. Initially viewing LLMs as unreliable and chaotic, the author has come to depend on them for approximately 90% of their coding, recognizing their potential to automate routine tasks and identify bugs more effectively than manual effort. As debates around AI and its impact on software development intensify, the author critiques the tribalism that has emerged in the community, fostering divisive views on LLMs' usefulness or threats. The developer emphasizes the importance of adapting to evolving technologies and encourages fellow engineers to engage with AI tools rather than fear them. They suggest that the current integration of AI in coding practices could lead to a more collaborative future between human developers and AI-driven systems, despite persistent challenges such as security and accessibility. By advocating for experimentation and open-mindedness, the author portrays the landscape of software development as rapidly evolving, with the necessity for collective empathy among developers navigating this transition.