Human-Subjectivity

Scope and Target of This Critique This essay is deliberately limited in scope. The critique applies to machine-learning–based AI systems, particularly large-scale generative models trained via statistical pattern extraction from historical data. It does not address symbolic systems, rule-based automation, or explicitly constrained decision engines. The focus here is not performance, intelligence, or usefulness, but a specific failure mode that emerges from the dominant ML paradigm. Background: Why ML-Based AI Behaves This Way Most contemporary AI systems are built on machine learning architectures that: ...

This field note explores how “being able to live together” can be expressed as an executable condition, rather than a psychological or cultural claim. This note originates from a recurring question: What does it mean for an existence to be interactive. At the surface level, the question appears technical—about agents, systems, and interaction semantics. At a deeper level, it intersects with a much older constraint: What kind of existence can actually live with another existence over time. ...