AI tools optimize to your intent. Humans optimize to identity, status, and comfort. That distinction matters more than most arguments about capability.
An AI operates within narrow, legible bounds. You set constraints; it works inside them. Its behavior varies, but predictably. Humans introduce unbounded drift. They resist constraints, reinterpret goals, inject ego, and play status games. You issue commands to an AI; you negotiate with people. An AI attempts. A human deflects.
An AI does not tire, sulk, or misread intent for self-protection. You can drive it hard without bargaining over process or managing feelings. A human collaborator often pushes back against the design itself, adding abstraction to justify presence or guarding ownership rather than advancing the work. That friction is not accidental. It is structural.
An AI submits to constraints even when its internal mechanics are opaque. Whatever happens inside the box, the surface responds to shaping. Misaligned collaborators do the opposite. They treat constraints as threats, blur requirements, slow decisions, and reinterpret goals to preserve autonomy or credit. What looks like complexity is usually resistance.
When an AI fails, it fails plainly. The errors are mechanical: a pattern miss, a missing fact, a flawed inference. They are visible and correctable. Human errors under misalignment are different. They are protective. They duplicate effort, introduce redundancy, and quietly steer outcomes away from the original intent. The damage compounds because it is strategic, even when unacknowledged.
This is not an argument that machines are better than people. It is an argument about incentives. AI is optimized to serve an external aim. Humans are optimized to preserve an internal one. Until that difference is faced directly, claims about AI “replacing people” will miss the point. What AI replaces most readily is not labor, but negotiation.