Creative Commons has been exploring questions around artificial intelligence for a long time: how this profound technological shift might affect people’s motivations to share, whether the intentions behind sharing work before AI became mainstream still hold when we consider how those works are being used now, and how we sustain a thriving commons when the fundamental bargain of the open web has shifted.

Since 2002, Creative Commons (CC) licenses have served as legally enforceable tools for digital sharing. Beyond their functionality, they also signal a commitment to open sharing in the public interest.  CC’s general guidance has been to acknowledge and support all valid uses for each of our current six licenses and two public domain tools.  

Attribution has always been a cornerstone of working with other people’s creativity and knowledge. As Creative Commons reminds us, practicing attribution serves many important functions, from providing verifiable evidence of a claim, to paying respect to the work of others, and creating pathways for traffic and financial value to flow. Despite the importance of attribution, many of today’s AI systems fail to acknowledge the sources of knowledge and creativity that make them possible.

At the recent Founders Fireside Chat, Creative Commons founder Lawrence Lessig referred to himself as “hopelessly naive.” It struck me that this sort of naivete—the kind that is about believing in the potential for good when sharing knowledge with other humans—is the glue that holds the entire Creative Commons movement together, across borders and belief systems.

In this post, we outline our plans to build upon and strengthen CC signals in order to support our goal of sustained access to human knowledge. We do not have all the answers yet. What we do have is a framework for how we will work toward them.

It’s been a while since we last shared an update on CC signals and our work around AI and the commons. Over the past several months, we’ve been deep in research, in conversation, and in active collaboration with communities, policymakers, and practitioners. We took the time to understand where power is consolidating, where harms are emerging, and where meaningful intervention is actually possible. We are now at a point where we believe we can act in ways that will have real impact.

Over the past year,  we’ve been engaged in a series of conversations with a small group of researchers specializing in IP, AI policy, and data governance about what CC  licensing means—and does—in African contexts today. What started as an organic exchange in various spaces has revealed something larger: a strong appetite to move these conversations into the open. At stake are not only questions about CC licenses but deeper issues of data sovereignty, equity, governance, and power in global knowledge systems.

As we look back on 2025, it’s clear that the internet as we know it is changing. Information is being removed from the web or locked away. We are experiencing a crisis in the commons, driven in part by current AI development practices. New systems are emerging in response—from content monetization schemes and licensing agreements designed to protect large rightsholders, to the ongoing morass of lawsuits about how AI services are using content as data. We are in the midst of a major reconfiguration of how we share and reuse content on the web.

At Creative Commons, we’ve long believed that binary systems rarely reflect the complexity of the real world—nor do they serve the commons very well. The internet, like the communities that built it, thrives on nuance, experimentation, and shared stewardship. That’s why we’re continuously working to introduce choice where there has been little, and to advocate for systems that acknowledge the diversity of values and needs across the web.

Distorted Forest Path © by Lone Thomasky & Bits&Bäume is licensed under CC BY 4.0 Here at CC, we have the goal of defending and sustaining the digital commons in the face of developments in artificial intelligence. We’ve recently introduced a new framework, CC signals, to offer a new way for stewards of large collections…