What happens when your newest team member never sleeps, never pushes back, and never truly understands the system? The people building software at scale already know something the rest of us are still debating: AI teammates change everything about how a team works, and not always for the better. They are fast, eager, and dangerously confident, capable of producing enormous volumes of code that looks right while quietly violating architectural constraints no one thought to specify. Because they rarely ask clarifying questions or flag risk, they optimize locally and break things globally. Yet used well, they remain the most powerful force multiplier our field has ever seen. This panel brings together practitioners and industry researchers living this reality daily. No hype. No fear. Just honest accounts from the trenches. Questions for the room: What new failure modes are AI teammates introducing that we have no playbook for? When AI teammates generate code at 10x speed, who owns the technical debt they leave behind? What does “code review” even mean when half the code was not written by a human? Are we heading toward a world where the developers who thrive are those who specify intent precisely, rather than those who code the fastest? What organizational structures and processes need to change to absorb AI teammates without losing control of quality? Where should humans remain in the driver’s seat, and how do teams determine the right level of autonomy for different tasks? When junior developers learn alongside AI teammates, what happens to mentoring, and how does engineering knowledge transfer across a team?