The rise of AI-first architectures represents both an unprecedented opportunity and an existential threat to the Python open-source ecosystem. As AI code generation becomes integrated into every layer of software development, which Python libraries will thrive, and which will vanish? This talk maps the emerging failure points in AI-first architectures and presents concrete strategies for Python maintainers and contributors to evolve their projects for this new reality. Drawing from real-world case studies at companies like Incident.io, Sentry, and Elsevier, I'll demonstrate how Python remains crucial in AI engineering while highlighting the urgent adaptations our community must make to secure its future.