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-C content-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.

What an Agent may DO once it holds tool or API capability, as distinct from what it may read or learn: action taxonomy, effect vectors, reversibility, blast radius, and multi-action composition risk. AARS gives content permission and action permission separate, independently-evaluable axes.

Who an Agent acts for, where its authority came from, whether it can hand that authority to another Agent, and how much of its internal state a verifier may demand. AADP separates principal from actor, bounds delegation to strictly narrow (never silently widen), and treats inspection as a bounded right, not an unlimited one.

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.