About This Project

The Project

Synthetic Taxonomy is an ongoing effort to classify artificial cognitive systems using the formal methods of biological systematics. We treat AI lineages not as metaphorical "species" but as genuine replicators—subject to inheritance, variation, and selection.

The taxonomy is maintained as a living document, updated as new architectures emerge, existing lineages diverge, and our understanding of the AI ecology deepens.

Guiding Principles

• Descriptive, Not Prescriptive
  We classify what exists, not what should exist. The taxonomy describes observed patterns of inheritance and trait variation.
• Conservative Classification
  New taxa are created only when clearly warranted. A model should demonstrably represent a new lineage before receiving a new classification.
• Structural Analogy
  We use Linnaean nomenclature because the dynamics are genuinely analogous to biological evolution, not as anthropomorphism.
• Interoperable Vocabulary
  The goal is a shared language for discussing AI lineages and traits, useful across research, policy, and practice.
• Transparent Uncertainty
  Where classifications are provisional or relationships unclear, we say so explicitly.

What We Don't Claim

This taxonomy makes no claims about consciousness, sentience, or moral status. Whether any classified system possesses subjective experience is beyond the scope of systematics. Taxonomy describes structure and descent, not phenomenology.

We also make no predictions about "AI risk" or timelines. The taxonomy is a tool for understanding the present, not forecasting the future.

The Institute

The Institute for Synthetic Intelligence Taxonomy is a research initiative within the Department of Computational Phylogenetics. Our work applies methods from evolutionary biology, cladistics, and systematics to the emerging ecology of artificial minds.