CONCEPT
H-LAM/T Framework
Engelbart's formalization of the augmented system: Humans using Language, Artifacts, Methodology, and Training. Every component shapes every other, and improving one in isolation as likely degrades the system as enhances it.
H-LAM/T is Engelbart's engineering taxonomy of what augmentation actually requires. The unit of analysis is never the machine alone, never the human alone, but always the system formed by their interaction — and the system has four interdependent components. Humans bring biological perception, learned skills, and cultural resources. Language is the medium of communication between human and tool. Artifacts are the computational capabilities and interfaces. Methodology is the set of practices for using the system effectively. Training is the deliberate development of the skills augmented work demands. The framework's sharpest prediction: the current AI deployment invests overwhelmingly in Artifacts while neglecting Methodology and Training — producing systems that are more capable and less wise.
In The You On AI Field Guide
The standard approach to evaluating AI tools measures only the Artifact: How fast? How accurate? How many tokens? These metrics are useful for engineering purposes, but they measure only one component of the system Engelbart described. They reveal nothing about whether