The popularity of proprietary generative AI systems trained on vast swaths of publicly available content has fueled concerns over exploitation, leading to a rapid decline of the AI data commons and litigation worldwide. Mechanisms like robots.txt are limited, and traditional copyright licensing has limited effects in the generative AI context. This presentation offers an overview of the collaborative, technical standardization efforts underway to establish machine-readable preference signals and shared ground rules for mutually beneficial data reuse. Standardization efforts are being driven by non-profit organizations like the IETF AI Preferences (AIPREF) working group and Creative Commons (CC). Industry-led solutions highlight the need for flexible commercial terms, including the Really Simple Licensing (RSL) standard, and Cloudflare’s AI Crawl Control, which facilitates pay-per-crawl and pay-per-inference licensing. The TDM·AI Protocol introduces ways to attach preferences to content identifiers. The presentation will also touch on legal contexts like the text and data mining (TDM) exceptions in European law.