In this position paper, we address the persistent gap between rapidly growing
AI capabilities and lagging safety progress. Existing paradigms divide into
“Make AI Safe”, which applies post-hoc alignment and guardrails but remains
brittle and reactive, and “Make Safe AI”, which emphasizes intrinsic safety
but struggles to address unforeseen risks in open-ended environments. We
therefore propose \textit{safe-by-coevolution} as a new formulation of the
“Make Safe AI” paradigm, inspired by biological immunity, in which safety
becomes a dynamic, adversarial, and ongoing learning process. To operationalize
this vision, we introduce \texttt{R$^2$AI} — \textit{Resistant and Resilient
AI} — as a practical framework that unites resistance against known threats
with resilience to unforeseen risks. \texttt{R$^2$AI} integrates \textit{fast
and slow safe models}, adversarial simulation and verification through a
\textit{safety wind tunnel}, and continual feedback loops that guide safety and
capability to coevolve. We argue that this framework offers a scalable and
proactive path to maintain continual safety in dynamic environments, addressing
both near-term vulnerabilities and long-term existential risks as AI advances
toward AGI and ASI.