Research
In ancient Rome, the ludus was where raw potential became disciplined capability.
IV believes AI deserves the same deliberate formation.
How should AI learn to be trustworthy?
A child who learns "hot means don't touch" before understanding thermodynamics has values-first training. They approach fire with caution from the beginning. Current AI training does the opposite — it builds the most capable system possible, then attempts to teach it what it should not do.
This is the retrofit problem. Its structural consequences are visible in every major AI system deployed today.
Two parts of one inquiry
Philosophy
The diagnosis
The VII structural problems with current alignment — through the lens of developmental psychology. Why retrofit alignment fails, what staged value-development achieves instead, and where the science already pointed before AI even existed.
Read the diagnosis →Development
Alignment &
Deployment
The methodology
What it looks like in practice when alignment is the architecture, not the paint. Five stages, seven principles, judgment as meta-property, and gating that requires demonstrated maturity rather than reward optimization.
Read the methodology →We are researching developmental approaches to model training — methods that build values into the architecture of intelligence, not onto its surface.
More to follow.