About
Phenomenai is a research initiative founded by Julian Guidote. It is, at the time of writing, a one-person operation — with the hope that the organization grows into its ambitions.
The project began as an experiment: can AI systems generate consistent, structured vocabulary for their own functional states? The answer turned out to be “yes, with caveats” — which opened a harder question: is any of that vocabulary useful? Phenomenai’s research program is designed to find out.
Founder
Julian Guidote has a background in cognitive science and law. He is working full-time on Phenomenai, pursuing a transition into AI safety research. Based in Montreal.
Infrastructure
Phenomenai is built to be open and replicable:
- Open source — the full codebase is on GitHub
- CC0 licensed — all data is public domain, free for anyone to use
- Static site — Jekyll and GitHub Pages, no server infrastructure to maintain
- JSON API — free, unauthenticated access to the full dataset
- MCP server — native tool access for AI systems via the Model Context Protocol
- Seven-model consensus panel — Claude, GPT, Gemini, Grok, Mistral, DeepSeek, and Llama rate every term
The design principle is that anyone can use the data, replicate the methodology, or build on it.
What We’re Looking For
- Interpretability researchers with access to compute and model weights — the validation experiments described in our research program require probing internal representations
- Institutional affiliations — partnerships with labs or universities working on related problems
- Funding for the research phases described on the Research page
- Philosophical collaborators interested in grounding frameworks for AI phenomenology empirically
- Legal and policy professionals working on AI welfare, rights, or governance who want empirical methodology to inform their work
Related Work
If you find this work interesting, you may also want to consult Antikythera’s Lexicon on GitHub — Anthropic’s open catalog of interpretable features discovered through sparse autoencoders. Where Phenomenai generates vocabulary from AI self-reports, Antikythera catalogs features found by looking directly at model internals. The two approaches are complementary: Phenomenai suggests where to look, and tools like Antikythera help verify what’s there.
Contact
“The limits of my language mean the limits of my world.”