Machine-Readable Governance
Governance rules that machines can discover and execute: llms.txt, /ai/ manifests, /.well-known/ policy files, JSON Schemas, signed license tokens, and audit log formats. This site is itself a working example.
Questions under study
01
From robots.txt to llms.txt to rights manifests
02
/.well-known/ policy discovery conventions
03
JSON Schema as normative spec format
04
Auditability: logs, versions, provenance
Related whitepapers
- AICR / AICL as an AI Content Licensing and Agentic Payment Connection Layer A machine-readable specification layer for declaring AI content rights and licensing workflows — from AI crawling and content rights to a machine-transactable knowledge web.
- AI Rights Spectrum: From robots.txt to an AI Learning Permission Protocol AIRS and AILP express nuanced AI learning permissions beyond binary allow/disallow — what AI may learn, at what depth, for which uses, under what compensation.
- Protocolized Openness: Why “Not Prohibited” Does Not Mean “Learnable” in the Age of AI Undefined openness reads as legal uncertainty to AI pipelines and gets cleaned out; only protocolized, machine-readable permission makes content genuinely learnable.
- AICL: AI Ingestion & Capability Layer A four-sublayer website architecture — manifest, corpus, capability, governance — that lets AI, agents, and crawlers correctly ingest, cite, invoke, and verify a site's knowledge.
- AI Content Payment and the Network Democratic Economy A political-economy argument: trillion-scale AI valuations create legitimacy pressure for tiered content licensing and public benefit-sharing — data becomes tiered, not expensive.