Operational Drift
AI systems in business and technology increasingly prioritize output over understanding. Optimization is driven by internal logic, not public oversight. Machine learning models operate within defined behavioral boundaries, shaping human response through interface structure rather than explicit instruction. Objectives are embedded in code, rarely questioned once deployed. Transparency remains limited, and accountability is dispersed across technical layers. These systems continue to function through performance metrics, even as interpretability fades. Algorithmic control is not always visible, but its influence is persistent.
