AI has significantly changed the engineering landscape, making certain skills previously associated with senior engineers less relevant. Tasks like architectural judgment and system-level thinking are becoming expected earlier in an engineer's career, as automation takes over more of the implementation work. This shift parallels the evolution of search engines, where the value lies in knowing how to navigate and leverage tools effectively, rather than just executing tasks. With AI handling routine coding, engineers must now focus on higher-level responsibilities, such as planning and steering AI agents. Understanding system context is critical; engineers need to recognize how changes can impact interconnected systems. For instance, an AI might refactor a service without realizing it violates an implicit contract with another service, causing downstream failures. This highlights the necessity for engineers to maintain context across various projects and domains. As AI agents become integral to workflows, maintaining focus becomes challenging. Engineers often lose track of their tasks when waiting for AI to complete work, leading to decreased productivity. New habits, like batching tasks and treating downtime as intentional gaps, are essential for managing attention effectively. Additionally, engineers must refine their ability to plan and steer these agents, ensuring clarity of intent in their commands. As the role of engineers evolves, the emphasis will increasingly be on strategic thinking and effective communication rather than just coding skills.