According to Andrej Karpathy, building a thriving open source AI community can be achieved by writing code modeled after bacterial genomes—emphasizing small, energy-efficient, modular, and self-contained code components (source: @karpathy, Twitter, July 5, 2025). This approach encourages higher reusability, easier contribution, and rapid innovation in AI projects by making codebases more accessible and swappable. The strategy enables faster adoption and differentiation in the open source AI ecosystem while reducing maintenance overhead and increasing collaboration opportunities, especially in large-scale, community-driven AI initiatives.