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.

Recap: What’s At Stake

When it comes to AI, copyright operates in a landscape that is uneven and often unclear. Because of this, the CC licenses, while still important, are not sufficient to address how content is used in AI systems. You can read more on this here. CC licenses also do not fully capture the range of intentions creators and data holders have in an AI-mediated world.

Across the web, creators, communities, and institutions are turning to multiple forms of defensive enclosure to restrict access. These include:

• Legal (e.g. licensing), such as open access publishers recommending CC BY-NC-ND as a mechanism of control, which ACM now does, which negatively impacts human collaboration.
• Technical (e.g. CAPTCHAs, bot blocking, rate limiting), such as what news publishers are doing, which negatively impacts archiving efforts.
• Financial (e.g. paywalled APIs), such as what X did post-acquisition, which negatively impacts researchers. 

The problem is that these tools treat all machine use as the same, regardless of the purpose. In trying to limit large-scale extraction by AI developers, they also block public interest uses like research, preservation, and accessibility.

While our research is ongoing, there are early indications of a more fragmented and potentially shrinking commons, along with a weakening of long-standing public interest protections.

Building the Next Generation Infrastructure of Sharing

Open access through CC licenses created a spectrum of sharing. Today we need something similar for AI: a spectrum of participation, where creators and data-holding stewards are active participants in how knowledge is produced, shared, and used.

The commons we have built over the past 25 years did not emerge on its own. It was designed through legal frameworks, technical standards, and shared norms. The AI era requires the next generation of that infrastructure. We want a future where the global knowledge commons remains accessible, and where AI systems engage with it in ways that are transparent, accountable, and aligned with the public good.

Our Plans

CC is advancing several high-impact interventions as part of the CC signals framework to restore trust, strengthen participation, and embed public interest values into the AI knowledge ecosystem.

1. Helping People Make Informed Decisions in the Current Moment
2. Making Attribution the Norm in AI
3. Building New Tooling that Protects Public Interest Uses while Restoring Agency

Helping People Make Informed Decisions in the Current Moment

AI systems are using CC-licensed works in ways that are causing many to question whether the existing CC license suite still aligns with their goals.

These concerns take different forms: attribution that disappears inside AI systems, sensitive knowledge stripped from its original context, growing concentrations of value and power, and no clear mechanisms for reciprocity or accountability. But they share a common root: uncertainty about what the CC licenses actually mean in this new environment.

We want people who choose to CC license to do so with confidence. We also want institutions with CC licensing embedded in their policies to have a clear picture of what the licenses do and do not cover when it comes to AI.  Over the next six months, we will provide sector-specific interim guidance to support CC licensors in navigating the new questions that AI raises for them. This guidance is not intended to resolve all legal ambiguity. Instead, during this period of uncertainty, we want to preserve the practice of sharing that AI is currently putting at risk, while we develop new tools and practices that address our communities’ concerns.

We will be holding a series of sector-specific virtual events to collect feedback on this interim guidance. Sign up for the CC newsletter for more information as soon as it becomes available. 

Making Attribution the Norm in AI

Attribution has always been a cornerstone of the commons. It supports participation, enables transparency, and allows knowledge to be traced, evaluated, and built upon.

Today’s AI ecosystem is eroding this norm. Most generative systems do not meaningfully acknowledge the sources they rely on. As AI increasingly mediates access to knowledge, this has serious consequences: loss of provenance, reduced trust, and fewer incentives to share. The first iteration of CC signals included attribution as a preference; today we believe that attribution must be a requirement. 

Our plan is to define best practices for attribution in AI contexts. AI developers often claim that attribution is simply not possible in LLMs. But this is a consequence of choices made during design, not a technical inevitability. We believe there is value in envisioning what attribution practices could look like in an AI ecosystem that prioritized them. And while there is no going back in time, we can demand attribution where it is technically possible within existing systems, such as Retrieval Augmented Generation (RAG), a method where AI systems pull from specific, traceable sources to generate responses. 

Our work will involve detailing ideal attribution guidance for AI systems, end users, and creators. We will then demonstrate how attribution can be realized in RAG models. This initiative serves two purposes: building shared understanding of what attribution in AI can and cannot currently achieve, and giving creators and AI users the tools to advocate for attribution as a baseline expectation. Strengthening attribution helps ensure that knowledge can circulate widely without losing connection to the people and communities who created it.

CC is looking to connect with experts working on attribution standards and developers working on AI systems that preserve attribution. If that describes your work, we would love to hear from you. 

Building New Tooling that Protects Public Interest Uses While Restoring Agency

Copyright alone cannot do this work. We believe maintaining a human-centered internet requires meaningful guardrails, upheld collectively. Our goal is to support an ecosystem that balances openness with agency, and access with accountability.

First, we are advocating for the development and usage of carefully scoped AI opt-outs that simultaneously sustain creator agency while protecting public interest uses. In an effort to address this need, we proposed additions to the IETF (the body that sets foundational internet standards) AI Preferences vocabulary that would help strike the right balance between creator agency and public interest reuse. It is essential that opt-out tooling and any related legislation protect public interest uses. This includes enabling cultural heritage institutions to preserve and analyze content, and supporting not-for-profit research and educational organizations in their work.

Second, we are doing research and development for a new tool designed to enable conditional access to openly shared collections and compilations. It will allow data stewards to set terms for accessing and using a collection or compilation that protect the sustainability of their technical infrastructure. These stewards may include libraries, archives, research institutions, data repositories, public knowledge projects, and cultural heritage organizations. Resource-heavy bulk reusers of data may be subject to more conditions, and public interest uses would be excluded entirely.

Without practical legal tools to define conditions for AI development, collections are left with blunt options: allow unrestricted extraction by AI developers, or restrict access entirely. Neither option reflects the goals of most knowledge stewards. This research and development is informed by close consultation with community members and stakeholders, such as dialogue with practitioners in the African context this past year, as well as broader explorations in the movement, such as this analysis on sharing of cultural heritage by Open Future Foundation, and the development of NOODL to rebalance power for marginalized language communities.

Many want to continue sharing their collections while ensuring that AI developers use them responsibly by respecting attribution, ensuring transparency, and meeting other safeguards aligned with their public interest missions. We want to build tooling to enable this in standardized, legally enforceable ways. 

What Happens Next

The exploration of these kinds of tools requires us to look beyond copyright alone, which is a real paradigm shift for CC, and not one we take lightly. We believe that investigating the risks and benefits of legal tools that support conditional access is an essential part of stewarding the long-term health of the commons. We need to preserve access to valuable knowledge resources while ensuring that the institutions and communities who steward them remain active participants in shaping the AI ecosystem.

Here is where things stand. This month, we are convening a workshop in London to begin working through the design and governance questions that new tooling raises. Later this year, we will be seeking pilot adopters to help us test and refine the approach in practice. We will share updates as this work develops. 

We have a clear plan, with these initiatives entering pilot phases within the year. Like many nonprofits, our ability to accelerate depends directly on the resources we have available. Support from our Open Infrastructure Circle has made progress to date possible, and as we mark our 25th anniversary, we have set a goal to raise $5 million to advance the next iteration of CC signals. If you are able, we invite you to support this work. 

Let’s collectively build what the commons needs next.

The post From Signals to Infrastructure: Strengthening the Commons for the AI Era appeared first on Creative Commons.

The biggest reason for the gap between updates is timing. We are deliberately resisting the pressure to move quickly simply because the broader technology landscape rewards speed. Our work touches the infrastructure of the commons. That requires care, consultation, and a willingness to sit with complexity.

So we slowed down. We let the first wave of AI development crest without rushing to respond. 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.

This post is meant to bring you into that journey. Our destination has not changed, but the path we are taking to get there has. Come along!

From Signals to Agency

When we first introduced CC signals, the idea was relatively straightforward. We proposed a set of preferences that creators could use to communicate with AI developers, relying on shared norms to guide behavior. It reflected how CC has historically operated. For 25 years, we have worked within copyright, building tools that expand access while maintaining a balance between creators and reusers. That history shaped our instincts. We assumed that a carefully calibrated, norms-based approach would move the ecosystem in a better direction.

But as we began consulting with our community, it became clear that this approach was not enough. The feedback was direct and consistent in stating that preference signals without enforcement do not meaningfully shift power. Signals alone cannot create agency in a system that many people did not choose to participate in. 

That feedback forced us to confront some of our own assumptions. For a long time, copyright has been our primary tool, and with good reason. CC licenses have enabled the sharing of tens of billions of works and have helped build a more open internet. But relying on copyright as the default lens for every problem has its limits, especially in an AI-mediated environment. 

Beyond Copyright

Over the past four months, we have been reexamining what it means to support the commons in this new context. 

CC licenses remain essential. They will continue to play a critical role in enabling human access to knowledge. However, when it comes to AI, copyright operates in a landscape that is uneven and often unclear. In many cases, CC license conditions do not apply to AI training. In others, they might. In some jurisdictions, broad exceptions mean that using CC-licensed works for AI development is lawful regardless of license conditions. At the same time, the presence of a CC license is often interpreted as permission to use the work in this way. That interpretation follows from how the licenses were designed; they grant broad permissions with limited conditions. 

The CC licenses were not designed with the scale and growing harms caused by the dominant, profit-driven approaches to AI in mind. And CC licenses do not capture the full range of intentions creators have in this AI-mediated world. Some creators are comfortable with their work being used in AI systems; others are not, and many fall somewhere in between.

Why New Tools Are Necessary

We also explored whether updating the CC licenses themselves, in the current paradigm, could provide a solution. Versioning has helped us adapt to new contexts before. But in this case, there are two novel factors at play.

The first is structural. CC licenses were designed not to enable control beyond copyright. They are intentionally scoped to copyright and related rights, and they explicitly do not allow additional restrictions that would limit uses outside that scope.

Our current trademark policy reinforces this. If restrictions are added that limit the permissions granted by a CC license, the work can no longer be presented as CC-licensed. This reflects the critical role that standardization has played in the success of open licensing. When you access a CC-licensed work, you should be able to rely on the terms and conditions written in the license to determine what your reuse obligations are. Expanding the CC license suite beyond its original focus on copyright would represent a significant change to how the licenses operate, and it could have unintended consequences on the existing license ecosystem.

This brings us to the second factor, which is that CC licensors have such a wide spectrum of needs and values about how and whether their works are used in AI. It is possible that new tooling would better address what may be irreconcilable within the open movement: some see any tool that attempts to control AI uses that fall outside of copyright as a betrayal; others see it as an imperative.

With the future of the commons in mind, at this time we believe that the best approach is to innovate with the development of new tools, where we can test and explore more freely. The CC licenses are one part of a larger strategy needed to meet this moment, which is evolving in an undefined legal landscape, just as it was 25 years ago when the CC licenses were first developed. 

The Stakes for the Commons

Our north star remains the same: sustain access to human knowledge. Today, that means more than enabling sharing. It means questioning long-held assumptions, and ensuring communities are in control of their own data. It means holding the tension that, in some cases, conditional access is better than no access. The commons needs guardrails in order to thrive. 

AI systems are being built on an unprecedented scale of knowledge extraction, drawing heavily from the commons. The governance systems that made open sharing possible have not kept pace with this shift. There are limited mechanisms for attribution in AI systems, few pathways for consent, and little transparency.

When the commons weakens, power over information becomes more concentrated. Knowledge moves into private datasets and proprietary systems controlled by a small number of actors. That limits who can access, verify, and build on information. Democracies depend on broad access to reliable knowledge. Public interest AI depends on diverse, high-quality data. 

A healthy commons is governed and sustained through systems that balance access with agency, openness with accountability. AI relies on the commons, not the other way around. If we want a future where knowledge is shared and where AI serves the public good, we need to ensure that the commons can thrive. This is the context in which we evolve CC signals. 

Strengthening CC Signals

Our problem statement has not changed, and neither has our end goal. But what we are building to get there has.

What began as a relatively narrow, tool-focused approach has evolved into something broader and more structural. CC signals is no longer limited to signaling preferences. CC signals is about addressing the underlying conditions that have made creator preferences so easy to ignore. This shift has led us toward work that is more ambitious, and necessarily more disruptive, in confronting the real harms to the commons emerging from dominant, profit-driven approaches to AI.

Check back with us next week, when we’ll share more about the specific interventions we are building from this foundation.

The post Update on CC Signals: What Changed and Why appeared first on Creative Commons.

Since then, the Summit—a major global gathering of policymakers, technologists, civil society leaders, and researchers—unfolded against the backdrop of widespread calls for cooperative frameworks and measurable outcomes. For an excellent summary of the highs and lows of the Summit, take a look at this article by CC Board Member Jeni Tennison.

From CC’s perspective, what became clear in Delhi is that AI governance is shifting. The conversation is moving beyond high-level principles and into harder, more structural questions about infrastructure, stewardship, and power.

Data as a Leverage Point

Concerns about data capture and extraction abounded at the Summit. But alongside those concerns, a persistent theme emerged: data scarcity.

Participants repeatedly pointed to the lack of high-quality, localized, representative datasets as a fundamental constraint on public interest AI. The call for “really good data” came from startups, researchers, governments, and civil society actors alike—many working to build contextually grounded systems. Without accessible datasets, cultural representation is limited, competition falters, open-source development slows, and meaningful innovation remains concentrated in the hands of those with the most resources.

The gaps are especially pronounced across Global South languages and cultural contexts. Researchers are working to supplement large models with local norms and knowledge to address bias and misrepresentation. This is particularly urgent in sectors such as health, agriculture, climate, and development, where high-quality open datasets could unlock substantial public benefit.

There is a real tension here. High-quality open data is required to power public interest AI. At the same time, without guardrails, open data can be exposed to extraction and misuse. Communities are often presented with a false choice: open their data and risk exploitation, or close their data and risk exclusion from shaping AI systems that affect them. Addressing this tension is essential if governance frameworks are to support both individual agency and shared stewardship. In essence, we need to:

• Fill existing gaps in shared governance infrastructure through collaborative frameworks and development of globally accessible tools that balance the tension between agency and access;
• Uphold an understanding of data governance as something that is deeply participatory and democratic, and an absolute necessity for any AI system that becomes part of the public infrastructure, whether privately held or not;
• Rebalance the power inequities in the current landscape overall, with our focus being on the data layer.

We believe that the path forward is not enclosure. It is stewardship. Governance mechanisms, interoperability standards, and access frameworks will determine who participates in the AI ecosystem and who does not. If we want AI systems that reflect diverse knowledge and lived realities, we must build the infrastructure that makes responsible openness durable.

Openness as a Method for Collaboration 

At the Summit, openness was not framed as a philosophical preference. It was framed as a structural necessity and a baseline condition for equity, competition, collaboration, and democratic accountability.

But the mental models we use to think about open versus closed must evolve. Openness cannot stop at model weights. It must extend across code, data, infrastructure, tooling, standards, and usability. And, crucially, openness and guardrails are not opposites. Responsible governance is not in tension with open systems; it is what makes them sustainable.

In this sense, openness is no longer the ceiling of ambition. It is the floor.

The Implementation Gap

Despite widespread agreement on concentration risks, data bottlenecks, and the speed of AI development, there was palpable exhaustion with principles that lack implementation pathways. Participants pointed to attempts like the Hiroshima AI Process and statements from past Summits as being great in theory but missing in practice. What’s missing are durable intermediaries capable of stewarding shared resources and translating shared values into operational systems. 

This is where the conversation becomes especially consequential for Creative Commons.

For more than two decades, CC has built legal and social interoperability at global scale. We have designed data governance frameworks that allow sharing of knowledge to function across jurisdictions and sectors. We have stewarded a commons model that balances openness with structure, enabling participation and mutual benefit through principles like attribution.

While debates about the limits of copyright were not central to most discussions in Delhi, there was significant interest in expanding high-quality open data, strengthening digital public infrastructure, and supporting community-led AI development​​—all areas deeply aligned with our expertise.

AI governance must move from principles to infrastructure. Shared, open digital infrastructure that works across borders is what Creative Commons is known for building. We believe that building the next generation of infrastructure for sharing—which would support the data layer of public interest AI—is not a departure from our mission. It is a timely extension of it and builds on the groundwork we have been laying for the past few years.

An infrastructure like this could include identifying high-impact open dataset initiatives in sectors such as health, agriculture, climate, and education to be opened up and prepared for machine reuse. It would require developing safe and trusted data-sharing models, with nuanced approaches depending on what data are being shared. This isn’t just about legal tools absent the context in which they are used; it is about comprehensive data governance mechanisms that balance openness with accountability and ensure interoperability across jurisdictions. 

Collaborative Construction

As we’ve talked about before, a central challenge in AI governance is avoiding false choices. Overly restrictive guardrails risk enclosing the commons, limiting access to knowledge, and stifling innovation and scientific discovery. Yet the absence of guardrails undermines trust, enables exploitation, and erodes the foundations of openness itself. Creative Commons operates in this critical middle space.

Our interventions at the Summit focused on advancing governance frameworks that protect human agency, cultural context, and trust in information while preserving openness, access, and reuse. An AI ecosystem that serves the public interest must be standardized where possible and contextual where required, especially across diverse linguistic, cultural, and regional settings.

If the Summit made one thing evident, it is that there is readiness for partnership. Policymakers, funders, technologists, and civil society leaders are looking for institutions capable of translating shared values into durable systems.

If We Do Not Intervene

It is worth being explicit about the alternative trajectory.

If sharing of data is only driven by commercial markets and not the public interest, and if data infrastructure consolidates in the hands of a few actors, “sovereignty” risks becoming a commercial product rather than a public capacity. Cultural representation will become extractive rather than participatory. Open models may technically exist, but without access to high-quality datasets, they will struggle to compete. The language of openness could persist while the data infrastructure beneath it quietly closes. What is the value of open weights and open code when the very essence of our cultures and languages isn’t carefully and deliberately shared, through robust open datasets?

The infrastructure phase of AI governance has begun. Creative Commons intends to help build what comes next—in partnership with those who share a commitment to an AI ecosystem that is open, inclusive, and grounded in the public interest. 

A huge thank you to our partners, event organizers, and co-panelists who helped to shape a meaningful engagement for CC during the Summit. We are particularly grateful for the thoughtful welcome provided by CivicDataLab, who ensured balanced dialogue and representation between those attending from elsewhere and those actively engaged on the ground in India. If we chatted during the Summit, we look forward to ongoing discussions. If we didn’t have a chance to connect, our doors are always open—send us a note! 

The post AI’s Infrastructure Era: Reflections from the AI Impact Summit in Delhi appeared first on Creative Commons.

I like to say I am a “writer who lawyers”. I begin here because I want to name my biases up front. I am a lawyer, but I come to this work first and foremost as a writer thinking about the conditions that will allow us to continue to share knowledge publicly. And in spite of—or perhaps because of—the fact that I am a lawyer, I have a healthy skepticism about the power of legal terms and conditions. The law will play a role, but the challenge of keeping the internet human will ultimately be navigated by the stories we imagine and tell. 

We need new stories. 

I spent the first 15 years of my legal career working in intellectual property. For most of that time, I was part of the open movement, fighting overly restrictive intellectual property laws to promote access to knowledge. But over time, I began to feel like the message of open licensing did not resonate with me in the same way, especially in my identity as a writer. Eventually I left the open movement to go into the field of privacy. 

Immersing myself in digital privacy led me to realize why the story of open felt incomplete. We had been undervaluing the role of boundaries around reuse. The tension between the instinct to share and the need for boundaries around reuse is the point. And right now, that tension is completely out of balance. Instead, what exists online is a free-for-all.

If you are familiar with the concept of a commons, you know it requires shared rules that govern reuse of resources. Those shared rules represent a mutual commitment by producers and reusers, and they ensure that the cycle leads to collective benefit and begins again. A free-for-all, on the other hand, has no shared rules. As a result, we are losing the instinct to share. 

What happened to the commons? 

It would be easy to blame AI for this situation, but it is not so straightforward. AI is simply speeding up and exacerbating longstanding challenges with open knowledge. As privacy scholar Daniel Solove has written, “AI is continuous with the data collection and use that has been going on throughout the digital age.” 

In preparation for this talk, I went back and reread the brilliant CC Summit keynote “Open As In Dangerous” by Chris Bourg from 2018 and the seminal Paradox of Open report by the Open Future Foundation. For many years, these and countless other voices have been warning us about the vulnerabilities that open knowledge creates. Whether it is the use of CC-licensed photos for facial surveillance technology or the creation of Grokipedia, it is clear that open content is particularly vulnerable to abuse. 

But of course, it is not just open content that is vulnerable. All content online today has essentially been treated as fair game. The free-for-all extends to everything online. 

This has led to a vast renegotiation of what it means to share publicly, still currently underway. We see this in the massive wave of litigation against AI services, the rise of paywalls and commercial licensing deals, the introduction of new technologies to increase control over content in ways that scale back the open web, and the extreme backlash against AI by creators and the general public.

All of this constitutes a threat to open access to knowledge. It is unlikely that the incentives to share can outweigh all of the growing countervailing forces at play: economic, moral, safety, more. We cannot respond by accepting these risks and harms as inherent and inevitable costs of public sharing knowledge.  

Changing our mental models

To meet the moment, we need to rethink our most fundamental assumptions about open knowledge. 

The old taxonomies no longer apply. 

For a very long time, we have used categories to help us determine the appropriate rules for sharing knowledge. Open content could be licensed one way, while open data had different parameters. This distinction no longer applies when everything online is used as data by machines. Even the difference between copyrighted material and public domain is not very useful, since even copyrighted works are largely used by machines for the public domain material within them (e.g., facts and ideas). 

Copyright is not the main event.

The original “enemy” of the open movement was copyright, and things were simpler back then. Even the most restrictive open license was more permissive than the default under copyright law, so any boundaries we set around the commons were still fighting the copyright war. Overly restrictive copyright laws still cause problems today, but they are no longer the biggest threat against the commons. In fact, it is copyright’s weakness in the context of machine reuse that is the real challenge. The inapplicability of copyright in protecting against unwanted machine reuse guts the CC licenses of the same ability, creating the free-for-all even on CC-licensed content. And importantly, because the aim was to avoid having CC licenses impose restrictions on activity that was otherwise allowed under copyright, this was by design. 

We have to stop confusing property with morality.

This is where I depart from my younger self and from many of my peers in the open movement. I think we have let important principles like the notion that facts and ideas should not be privately owned, or the fact that some permissionless reuse plays a critical role in free expression, convince us that the scope of copyright is an ethical line. The logic goes: if no one can own it, then no rules should apply. This leads to an impoverished sense of morality, where the only justification for constraint is property rights. As Robin Wall Kimmerer says, “In that property mindset, how we consume doesn’t really matter because it’s just stuff and the stuff all belongs to us. There is no moral constraint on consumption.” 

The ethics of sharing—which is what open is about—needs to be broader than what we can own. 

Boundaries benefit us all.

Boundaries on reuse are what create the reciprocity that fuels a commons. Without them, there is no assurance that sharing leads to collective benefit, and people lose their instinct to share. But boundaries can also have social value in their own right. Even when sharing in public, people rightfully expect some boundaries around how their works are used, regardless of what copyright law says. This is foundational in the field of privacy, but somehow we lose sight of it when we are sitting in the realm of content sharing. Daniel Solove writes: “People expect some degree of privacy in public, and such expectation is reasonable as well as important for freedom, democracy, and individual wellbeing.” Similarly, we establish boundaries around reuse of knowledge because those protections serve us all. 

Open should not be a purity test. 

The open movement has had incredible success creating global standards, and this has helped make it so successful. But the emphasis on standardization has led us to hyper-focus on definitions, and this focus is distracting us from the bigger picture. What matters is not open versus closed, or even abundance versus scarcity. We need to focus on values, not prescriptions. Open licensing has always been conditional, and it has always been a spectrum. This means we have to accept that there will be gray areas. What we lose in certainty, we will gain in relevance and moral clarity. As Rebecca Solnit says, “Categories are where thoughts go to die.” 

Where do we go from here? 

All of this leads back to where we began. We have to reconstruct the mutual commitment that keeps the commons cyclical.

Rebuilding the mutual commitment that comes with sharing knowledge requires us to balance opposing values. On the one hand, we must protect important freedoms of the reusing public. On the other, we must establish boundaries around responsible reuse. The goal is to be as open as possible and as restrictive as necessary. And before we start panicking about slippery slopes, we should remember there is an important limiting principle we can leverage:  does the boundary shift power in ways that further concentrate it or redistribute it? We can also ask whether there are ways to mitigate a boundary’s effect on access. 

We already have a good sense of the dimensions of boundaries around responsible reuse. They all have roots in the existing CC license suite.

Attribution: While the AI landscape complicates methods and norms for attribution, the principle is more important than ever for informational integrity, authors rights, and transparency. 

Reciprocity: Molly Van Houweling calls this “extractability,” the idea that those extracting facts and ideas from others’ works have a moral responsibility to ensure that knowledge remains extractable by others. This is essentially about crafting a ShareAlike obligation for the age of AI. 

Financial sustainability: This has been a longtime challenge in the open movement, and it is more urgent than ever. It is not about preserving business models, it is about financially sustaining the production of knowledge and culture as public goods. 

Prohibitions on harmful use cases: This dimension may feel less familiar in open licensing, but the sentiment is one we hear regularly. There are simply some use cases or even actors that feel out of bounds for people sharing knowledge because of the harm they cause. 

How do we catalyze a mutual commitment around prosocial boundaries in the current free-for-all environment? Open Future Foundation’s Paul Keller has written: “For any response to succeed in preserving a diverse and sustainable information ecosystem, collective action is required—both bottom-up, through coordinated action by information producers, and top-down, through political will to enable redistribution via fiscal interventions.” There is no single solution, and we need to tackle it from all directions. 

For the bottom-up efforts, we can leverage the tools we have. Norms and social pressure have a role to play, though it is hard to put full faith in voluntary action right now. We can also explore methods for legal control, including both contract and copyright law. As Nilay Patel has said, “Copyright is the only functioning regulation on the internet,” which makes it impossible to avoid considering it as one lever to employ.¹ Finally, there is the strategy of controlling access. This is the most uncomfortable tactic because of the collateral damage it risks, and it requires extreme care. But if AI companies will not pay attention voluntarily, technical controls around access look increasingly necessary. 

There are many in the open movement already experimenting with these efforts, including the Mozilla Data Collective, the differentiated access model proposed by Europeana and the Open Future Foundation, the NOODL license, and many more. Creative Commons is also actively thinking about how to build a framework that re-instills mutual commitment into the ecosystem. Many of you have been following along as we experiment with an AI preference signals framework we’ve been calling CC signals. While the path we will take is evolving, the goal is the same. We need to come together to define and sustain the boundaries that serve us all. 

I will end with the words of Ruha Benjamin: “We need to give the voice of the cynical, skeptical grouch that patrols the borders of our imagination a rest.” 

We can imagine a better way. 

¹ While copyright law is ill-equipped to function as a method of control over machine reuse (and rightly so, considering the importance of not treating facts and ideas as private property), copyright law still has a role to play because of the uncertainty around its application on a global scale. Granting copyright permission in exchange for agreement to certain conditions could still be a valuable offer to some reusers. 

The post How to Keep the Internet Human appeared first on Creative Commons.

In response, new approaches are emerging to support creators, publishers, and stewards of content to reclaim agency over how their works are used.

Pay-to-crawl is one approach beginning to come into focus. Pay-to-crawl refers to emerging technical systems used by websites to automate compensation for when their digital content—such as text, images, and structured data—is accessed by machines. We’ve recently published our interpretation and observations of pay-to-crawl systems in this dedicated issue brief.

CC’s Position on Pay-to-Crawl

Implemented responsibly, pay-to-crawl could represent a way for websites to sustain the creation and sharing of their content, and manage substitutive uses, keeping content publicly accessible where it might otherwise not be shared or would disappear behind even more restrictive paywalls.

However, we do have significant reservations.

Pay-to-crawl may represent an appropriate strategy for independent websites seeking to prevent AI crawlers from knocking them offline or to generate supplementary revenue. But elsewhere, pay-to-crawl systems could be cynically exploited by rightsholders to generate excessive profits, at the expense of human access and without necessarily benefiting the original creators.

Pay-to-crawl systems themselves could become new concentrations of power, with the ability to dictate how we experience the web. They could seek to watch and control how content is used in ways that resemble the worst of Digital Rights Management (DRM), turning the web from a medium of sharing and remixing into a tightly monitored content delivery channel.

We’re also concerned that indiscriminate use of pay-to-crawl systems could block off access to content for researchers, nonprofits, cultural heritage institutions, educators, and other actors working in the public interest. Legal rights to access content afforded by exceptions and limitations to copyright law, such as noncommercial research (in the EU) or fair use exemptions (in the US), as well as provisions for translation and accessibility tools, have been carefully negotiated and adjusted over time. These rights could be impeded by the introduction of blunt, poorly designed pay-to-crawl systems.

Proposed Principles for Responsible Pay-to-Crawl 

Pay-to-crawl systems are not neutral infrastructure. It’s vital that these systems are built and used in ways that serve the interests of creators and the commons, rather than simply create barriers to the sharing of knowledge and creativity, and benefit the few.

We’re proposing the following set of principles as a way to guide the development of pay-to-crawl systems in alignment with this vision:

1. Pay-to-crawl should not become a default setting.
   Pay-to-crawl represents a strategy that may work for some websites, and not all websites share the same underlying concerns. Pay-to-crawl systems should not be deployed as an automatic or assumed setting on behalf of websites by others, such as domain hosts, content delivery networks, and other web service providers.
2. Pay-to-crawl systems should enable choice and nuance, not blanket rules.
   Pay-to-crawl systems should enable websites to distinguish between—and set variable controls for—different types of content users (such as commercial AI companies, nonprofits, researchers, or even specific organizations), as well as types and purposes of machine use (such as model training, indexing for search, and inference/retrieval). Systems should not affect direct human browsing and use of content, including by restricting translation or accessibility services.
3. Pay-to-crawl systems should allow for throttling, not just blocking.
   Pay-to-crawl systems should enable websites to manage hosting costs and other impacts of heavy machine traffic without walling off content entirely. For instance, systems could allow websites to throttle traffic driven by ‘agentic browsing’ or ‘inference’ undertaken by large AI models, while permitting other forms of machine access that involve far lower traffic, such as for research or archival.
4. Pay-to-crawl systems should preserve public interest access and legal rights.
   Pay-to-crawl systems should not obstruct access to content for researchers, nonprofits, cultural heritage institutions, educators and other actors working in the public interest. Nor should these systems block lawful uses of content protected by copyright exceptions and limitations, and other legal rights afforded in the public interest. The act of deciding not to abide by a pay-per-crawl system should not, by itself, convert an otherwise lawful use into an illegal act.
5. Pay-to-crawl systems should use open, interoperable, and standardized components.
   Pay-to-crawl systems should not become proprietary chokepoints or gatekeepers. We urge particular caution in the use of proprietary components for authentication and payment that might result in websites getting locked into a particular pay-to-crawl system.
6. Pay-to-crawl systems should enable collective contributions to the commons.
   Pay-to-crawl systems that only enable financial transactions between singular websites and content users risk creating a highly transactional future, where the value of content is atomized. Pay-to-crawl systems should support collective forms of payment, such as to coalitions of creators and publishers, and wider conceptions of what it means to contribute to the digital commons.
7. Pay-to-crawl systems should avoid surveillance and DRM-like architectures.
   Pay-to-crawl systems must not introduce excessive logging, fingerprinting, or behavioral tracking related to the use of content. Systems should minimize data collection to only what is needed to authenticate users and settle payments, rather than seek to follow content downstream or dictate how it can be used.

The Path Forward: Showing Up Where the Future Is Being Decided

We believe now is the moment to engage, to influence, and to infuse pay-to-crawl systems with values that prioritize reciprocity, openness, and the commons.

We welcome feedback and dialogue on the principles outlined here. Your input will help guide our engagement with pay-to-crawl systems and related initiatives moving forward, as well as inform the wider CC community’s understanding of them.

Thank you to Jack Hardinges for his contributions to this post.

The post Where CC Stands on Pay-to-Crawl appeared first on Creative Commons.

In June we kicked off a public feedback period on our proposal for CC signals. CC signals is a preference signals framework designed to sustain the commons and ensure the continued sharing of knowledge in the age of AI. 

The goal is to give holders of large datasets a way to set criteria for how their data may be used within AI training models. To give an example, a dataset holder may wish to require that any AI training that uses their data gives credit back to the original source (e.g. attribution), or that the resulting AI model is open. Like the CC licenses, CC signals builds on the idea of ‘some rights reserved’ and that creators and knowledge holders deserve meaningful choices in how their work is used. You can learn more on our website. 

Since our kickoff event, we have been listening closely to feedback. We heard from hundreds of creators, librarians, technologists, legal experts, and open advocates. We asked for feedback and you delivered! Your voices – supportive, skeptical, frustrated, or curious – are essential in shaping how CC signals develops. We’d like to summarize what we heard and how this feedback is being incorporated and addressed.

What We Heard

Across the conversations, several themes emerged: 

Concerns that CC is prioritizing AI companies over creators. A recurring concern is that CC signals seem to give legitimacy to AI training without doing enough to protect creators. 

Confusion and disagreement about the CC licenses and AI training. We heard frustration that the CC licenses are not being interpreted or enforced in ways that some creators expected. 

Strong calls for opt-outs. Many wondered why the draft CC signals did not include an opt-out option. 

Asking politely for AI developers to give back in exchange for datasets is not enough. We heard doubts that CC signals would work in practice, given the widespread evidence of AI companies ignoring copyright, licenses, and even technical protocols like robots.txt. 

Broader critique of AI’s role in society. There is a spectrum of views on AI across the CC community. Many of you stand firmly at the anti-AI end. For these voices, no technical framework, like CC signals, feels adequate without stronger laws and regulations. 

We haven’t been clear on who this tool is meant to serve and the use cases it is meant to address. Naturally, the needs of an individual creator, like an artist, are quite different from those operating at an institutional or collective level. We heard loud and clear that CC signals, as currently conceived, does not meet the diverse needs of individual creators.

Requests for clarity. Many asked for more details about implementation and interoperability, including our long-term vision for CC signals as part of our broader mission. 

We understand how deeply personal these issues are for many of you, especially artists and creators who feel their work is being taken without consent and are looking for ways to fight back. That frustration is real, and we take it seriously. 

What We’re Doing Next

Improving clarity around CC’s position. We know many of you are worried that CC has “taken sides” or is being influenced by AI companies. We want to be clear: the driving motivation of CC signals is to defend and sustain the commons by developing practical tools for knowledge holders. Going forward, we will aim to clarify our guiding principles and positions in ways that translate to product decisions. 

Strengthening messaging and education. We are committed to expanding resources on how the CC licenses and CC signals could interact, examples of how signals could work in practice, and deeper dives into questions of copyright within the AI landscape. If you haven’t already, take a look at our legal primer on understanding the CC licenses and AI training. The better informed the CC community is about AI and the commons at large, the more effective we can be as a community to defend the commons. 

Clarifying the use cases for CC signals. This phase of CC signals is designed to serve large and open dataset holders, not the individual creator. Your feedback helped us recognize that this focus was not easy to square with our decision to leverage technical protocols used by anyone with a website. As a result, the target audience for CC signals was not clear. As we decide on next steps in product development, we plan to focus on specific use cases to put our goals and objectives into practice. 

Deepening global engagement and inviting stakeholders into product development. We plan to continue conversations with diverse audiences to inform the future of CC signals through an iterative process. The rest of this year will be focused on exploring and testing possible integrations of CC signals with pilot adopters. From this, we hope to extrapolate findings as we explore wider adoption of CC signals in the future. 

Maintaining transparency in development. Our GitHub repository will stay open and up to date. We are creating a roadmap that will be shared publicly and will provide consistent updates (either on the blog or via a virtual town hall) on our progress. This feedback loop is not over; it will be built into how CC signals will evolve. 

Looking Ahead

The future of the commons depends on tools that reflect shared values of openness, fairness, and agency. We know many of you remain skeptical. 

CC signals is not final. It is an experiment in building a new layer of choice in an age where the rules are rapidly shifting. We will keep listening, adjusting, and collaborating until we arrive at something that genuinely serves the commons.

Thank you to everyone who took the time to write, question, challenge, and support us. Please stay engaged. Together, we can ensure that Creative Commons continues to stand where it always has: with the community, for the commons.

The post We Asked, You Answered: How Your Feedback Shapes CC Signals appeared first on Creative Commons.

Thanks to everyone who attended our CC signals project kickoff last week. We’re receiving plenty of feedback, and we appreciate the insights. We are listening to all of it and hope that you continue to engage with us as we seek to make this framework fit for purpose.

Some of the input focuses on the specifics of the CC signals proposal, offering constructive questions and suggesting ideas for improving CC signals in practice. The most salient type of feedback, however, is touching on something far deeper than the CC signals themselves – the fact that so much about AI seems to be happening to us all, rather than with or for us all, and that the expectations of creators and communities are at risk of being overshadowed by powerful interests.

This sentiment is not a surprise to us. We feel it, too. In fact, it is why we are doing this project. CC’s goal has always been to grow and sustain the thriving commons of knowledge and culture. We want people to be able to share with and learn from each other, without being or feeling exploited. CC signals is an extension of that mission in this evolving AI landscape.

We believe that the current practices of AI companies pose a threat to the future of the commons. Many creators and knowledge communities are feeling betrayed by how AI is being developed and deployed. The result is that people are understandably turning to enclosure. Eventually, we fear that people will no longer want to share publicly at all. 

CC signals are a first step to reduce this damage by giving more agency to those who create and hold content. Unlike the CC licenses, they are explicitly designed to signal expectations even where copyright law is silent or unclear, when it does not apply, and where it varies by jurisdiction. We have listened to creators who want to share their work but also have concerns about exploitation. CC signals provide a way for creators to express those nuances.  The CC signals build on top of developing standards for expressing AI usage preferences (e.g., via robots.txt). Creators who want to fully opt out of machine reuse do not need to use a CC signal. CC signals are for those who want to keep sharing, but with some terms attached.

The challenge we’re all facing in this age of AI is how to protect the integrity and vitality of the commons. The listening we’ve been doing so far, across creator communities and open knowledge networks, has led us here, to CC signals. Our shared commitment is to protect the commons so that it remains a space for human creativity, collaboration, and innovation, and to make clear our expectation that those who draw from it give something in return. 

Our goal is to advocate for reciprocity while upholding our values that knowledge and creativity should not be treated as commodities. 

Our goal is to find a path between a free-for-all and an internet of paywalls.

Copyright will not get us there. Nor should it. And we don’t think the boundaries of copyright tell us everything we need to know about navigating this moment. Just this week, Open Future released a report that calls for going beyond copyright in this debate, on the path to a healthy knowledge commons.

This is the beginning of the conversation, not the end. We are listening. From what we have heard, CC signals, or something like it, is the best practical mechanism to avoid the dual traps of total exploitation or total enclosure, both of which damage the commons. We have shared our current progress because we want to learn how to design it to meet your needs. We invite you to continue sharing feedback so we can shape CC signals together in a way that works for diverse communities.

In the months ahead, we’ll be providing more detail about how CC signals are developing, including key themes we are hearing, along with the questions we are exploring and our next steps.

The post Why CC Signals: An Update appeared first on Creative Commons.

Creative Commons (CC) today announces the public kickoff of the CC signals project, a new preference signals framework designed to increase reciprocity and sustain a creative commons in the age of AI. The development of CC signals represents a major step forward in building a more equitable, sustainable AI ecosystem rooted in shared benefits. This step is the culmination of years of consultation and analysis. As we enter this new phase of work, we are actively seeking input from the public. 

As artificial intelligence (AI) transforms how knowledge is created, shared, and reused, we are at a fork in the road that will define the future of access to knowledge and shared creativity. One path leads to data extraction and the erosion of openness; the other leads to a walled-off internet guarded by paywalls. CC signals offer another way, grounded in the nuanced values of the commons expressed by the collective.

Based on the same principles that gave rise to the CC licenses and tens of billions of works openly licensed online, CC signals will allow dataset holders to signal their preferences for how their content can be reused by machines based on a set of limited but meaningful options shaped in the public interest. They are both a technical and legal tool and a social proposition: a call for a new pact between those who share data and those who use it to train AI models.

“CC signals are designed to sustain the commons in the age of AI,” said Anna Tumadóttir, CEO, Creative Commons. “Just as the CC licenses helped build the open web, we believe CC signals will help shape an open AI ecosystem grounded in reciprocity.”

CC signals recognize that change requires systems-level coordination. They are tools that will be built for machine and human readability, and are flexible across legal, technical, and normative contexts. However, at their core CC signals are anchored in mobilizing the power of the collective. While CC signals may range in enforceability, legally binding in some cases and normative in others, their application will always carry ethical weight that says we give, we take, we give again, and we are all in this together. 

“If we are committed to a future where knowledge remains open, we need to collectively insist on a new kind of give-and-take,” said Sarah Hinchliff Pearson, General Counsel, Creative Commons. “A single preference, uniquely expressed, is inconsequential in the machine age. But together, we can demand a different way.”

Now Ready for Feedback 

More information about CC signals and early design decisions are available on the CC website. We are committed to developing CC signals transparently and alongside our partners and community. We are actively seeking public feedback and input over the next few months as we work toward an alpha launch in November 2025. 

Get Involved

Join the discussion & share your feedback

To give feedback on the current CC signals proposal, hop over to the CC signals GitHub repository. You can engage in a few ways: 

1. Read about the technical implementation of CC signals
2. Join the discussion to share feedback about the CC signals project
3. Submit an issue for any suggested direct edits

Attend a CC signals town hall

We invite our community to join us for a brief explanation of the CC signals framework, and then we will open the floor to you to share feedback and ask questions. 

Tuesday, July 15
6–7 PM UTC
Register here.

Tuesday, July 29
1–2 PM UTC
Register here.

Friday, Aug 15
3–4 PM UTC
Register here. 

Support the movement

CC is a nonprofit. Help us build CC signals with a donation. 

The age of AI demands new tools, new norms, and new forms of cooperation. With CC signals, we’re building a future where shared knowledge continues to thrive. Join us.

The post Introducing CC Signals: A New Social Contract for the Age of AI appeared first on Creative Commons.

The application of copyright law to AI training is complex. The CC licenses are copyright licenses, so it follows that applying CC licenses to AI training is just as complex. 

The short answer is: AI training is often permitted by copyright. This means that the CC license conditions have limited application to machine reuse. This also means that using a more restrictive CC license in an effort to prevent AI training is not an effective approach. In fact, restrictive licensing may actually end up preventing the kind of sharing you want (like allowing for translation, for example), while not being effective to block AI training. 

For the long answer, read our new guide that provides a legal analysis and overview of the considerations when using CC-licensed works for AI training. 

  For an at-a-glance overview, head over to the Using CC-Licensed Works for AI training webpage. 

  For a more in-depth analysis, check out our handy PDF download. 

For those who love a visual, take a look at our supplementary flowchart. 

If the CC licenses have limited application to machine reuse, what agency do creators have in the AI ecosystem? 

This is an important question. As you’ve heard us talk about before, we’re actively developing a CC preference signals framework to help bridge this gap. The framework is designed to offer new choices for stewards of large collections of content to signal their preferences when sharing their works, using scaffolding inspired by the architecture of the CC licenses. This is not mediated through copyright or the CC licenses. It is governed by something that tends to be even more widely adopted: a social contract. Stand by for the release of the paper prototype of CC preference signals framework at the end of June 2025. 

While you are here, please consider making an annual recurring donation via our Open Infrastructure Circle. This work will require a large amount of resourcing, over many years, to make happen. 

The post Understanding CC Licenses and AI Training: A Legal Primer appeared first on Creative Commons.

If you’ve been following along on the blog this year, you’ll know that we’ve been thinking a lot about the future of open, particularly in this age of AI. With our 2025-2028 strategy to guide us, we’ve been louder about a renewed call for reciprocity to defend and protect the commons as well as the importance of openness in AI and open licensing to avoid an enclosure of the commons. 

Last month, we took some of these conversations on the road and hosted the Open House for an Open Future during SXSW in Austin, TX, as part of a weekend-long Wiki Haus event with our friends at the Wikimedia Foundation. 

During the event, we spoke with Audrey Tang and Cory Doctorow about the future of open, especially as we look towards CC’s 25th anniversary in 2026.  In this wide-ranging conversation, a number of themes were reflected that capture both where we’ve been over the last 25 years and where we should be focusing for the next 25 years, including: 

• The Fight for Technological Self-Determination: Contractual restrictions are increasingly being used to lock down essential technologies, from printer ink to hospital ventilators. The push for openness and economic fairness must go beyond just content-sharing and extend to fighting for the rights of people to repair, modify, and use technology freely.
  
• Shifting from Resistance to Building Alternatives: The open movement is not just about opposing corporate restrictions but also about creating viable, open alternatives. Initiatives like Gov Zero show that fostering decentralized, user-controlled platforms can help counteract monopolistic digital ecosystems.
  
• The Power of Exit as a Lever for Change: Simply having the option to leave restrictive platforms can influence corporate behavior. Efforts like Free Our Feeds and Bluesky aim to create credible exit strategies that prevent users from being locked into exploitative digital environments.
  
• Beyond Copyright: New Frameworks for Openness and Innovation: While Creative Commons began as a response to copyright limitations, the next phase should focus on broader issues like supporting an infrastructure for open sharing, ethical AI development, and open governance models that empower communities rather than just limiting corporate control.
  
• Reclaiming the Ethos of Open Source and Free Software: The movement must reconnect with its ethical roots, focusing on freedom to create, share, and innovate—not just openness for the sake of efficiency. This includes resisting corporate capture of “openness” and ensuring technological advances serve public interest rather than private profit.

Since the proliferation of mainstream AI, we’ve been analyzing the limitations of copyright (and, by extension, the CC licenses since they are built atop copyright law) as the right lens to think about guardrails for AI training. This means we need new tools and approaches in this age of AI that complement open licensing, while also advancing the AI ecosystem toward the public interest. Preference signals are based on the idea that creators and dataset holders should be active participants in deciding how and/or if their content is used for AI training. Our friends at Bluesky, for example, have recently put forth a proposal on User Intents for Data Reuse, which is well worth a read to conceptualize how a preference signals approach could be considered on a social media platform. We’ve also been actively participating in the IETF’s AI Preferences Working Group, since submitting a position paper on the subject mid-2024 .

As CC gets closer to launching a protocol based on prosocial preference signals—a simple pact between those stewarding the data and those reusing it for generative AI training—we had the opportunity during SXSW to chat with some great thought leaders about this very topic. Our panelists were Aubra Anthony, Senior Fellow, Technology and International Affairs Program at Carnegie Endowment for International Peace; Zachary J. McDowell, Phd, Assistant Professor, Department of Communication, University of Illinois at Chicago; Lane Becker, President, Wikimedia LLC at Wikimedia Foundation, and our very own Anna Tumadóttir, CEO, Creative Commons to explore sharing in the age of AI.  A few key takeaways from this conversation included: 

• Balancing Norms and Legal Frameworks: There is a growing interest in developing normative approaches and civil structures that go beyond traditional legal frameworks to ensure equitable use and transparency.
  
• Navigating AI Traffic and Commercial Use: Wikimedia is adapting to the influx of AI-driven bot traffic and exploring how to differentiate between commercial and non-commercial use. The idea of treating commercial traffic differently and finding ways to fundraise off bot traffic is becoming more prominent, raising important questions about sustainability in an open knowledge ecosystem. From CC’s perspective, we’ve found that as our open infrastructures mature they become increasingly taken for granted, a notion that is not conducive to a sustainable open ecosystem.
  
• Openness in the Age of AI: There is growing reticence around openness, with creators becoming more cautious about sharing content due to the rise of generative AI (note, this is exactly what our preference signals framework is meant to address, so stay tuned!). We should emphasize the need for open initiatives to adapt to the broader social and economic context, balancing openness with creators’ concerns about protection and sustainability.
  
• Making Participation Easy and Understandable: To encourage widespread participation in open knowledge systems and for preference signal adoption, tools will need to be simple and intuitive. Whether through collective benefit models or platform cooperativism, ease of use and clarity are essential to engaging the broader public in contributing to open initiatives.

Did you know that many social justice and public good organizations are unable to participate in influential and culture-making events like SXSW due to a lack of funding? CC is a nonprofit organization and all of our activities must be cost-recovery. We’d like to sincerely thank our event sponsor, the John S. and James L. Knight Foundation for making this event and these conversations possible. If you would like to contribute to our work, consider joining the Open Infrastructure Circle which will help to fund a framework that makes reciprocity actionable when shared knowledge is used to train generative AI.

The post CC @ SXSW: Protecting the Commons in the Age of AI appeared first on Creative Commons.