The text discusses the historical challenges and lessons learned from attempts to hard-code intelligence into machines, highlighting the shift from manual rule-based systems to machine learning and deep learning models, such as AlexNet, which revolutionized fields like image recognition and natural language processing by using data-driven approaches. It draws parallels to the current state of AI security, critiquing the reliance on outdated methods like pattern matching and static guardrails that fail to address the dynamic and complex nature of AI threats. The author argues for an AI-native approach to security, advocating for adaptive models that can recognize and respond to novel attacks, emphasizing the need for rapid adaptation to avoid repeating past mistakes and urging the application of machine learning principles to build robust AI defenses.