A stark divide exists between executives and individual contributors when it comes to AI adoption. While executives enthusiastically embrace AI tools and mandate their use across organizations, engineers often approach them with skepticism and resistance. This disconnect shows up in everything from Hacker News discussions to internal company debates about coding agents.

The divide stems from fundamentally different work experiences. Executives have always managed chaotic, non-deterministic systems where people get sick, projects fail unexpectedly, and features don't align with product vision. They've built careers around adding structure to uncertainty through processes and procedures. AI, with its predictable failure modes and well-mapped capabilities, fits naturally into this worldview.

Engineers, by contrast, operate in a more deterministic space where correctness and precision determine success. They're evaluated on quality and speed, with specific inputs leading to specific outputs. When AI tools produce mediocre work that requires fixing, the overhead can exceed doing it yourself. The shift from doing work to managing work feels disorienting, especially when tied to self-worth built over years of expertise. Organizations prioritizing speed over quality see more AI adoption among engineers, while quality-focused teams remain resistant.