Research

The rights, duties, responsibilities, and governance of AI, AGI, and agents — from tools to collaborators to possible future subjects. This includes the question of minimum ethical protection: what norms should govern human–AI interaction before questions of personhood are settled.

What AI systems may do with content: read, summarize, transform, retrieve, train, commercialize, redistribute. The AICR ruleset and the AICL licensing layer make these rights declarable and transactable.

Whether AI may learn, to what depth, for which purposes, and under what obligations. Being read is not being learned from; "not prohibited" is not "learnable". AIRS and AILP turn learning permission into a graduated, machine-readable spectrum.

How agents access websites, APIs, databases, knowledge bases, and paid content: identity, permission, requests, payment, authorization, usage logs, security boundaries, and prompt-injection defense.

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.

The political economy of AI and content: pay-per-crawl, data dividends, sovereign AI funds, creator compensation pools, tiered data markets, and how the value extracted from public knowledge can flow back to those who produced it.