Wikipedia serves as a cornerstone of modern artificial intelligence training data. Every large language model developed to date has been trained on Wikipedia content, making it nearly always the single largest curated source in AI training datasets. Google’s C4 dataset, constructed from 15 million web pages, identifies Wikipedia as the second largest content source. Similarly, the Pile—a prominent 800GB+ open-source language modeling dataset—includes Wikipedia as one of its primary data sources, with researchers intentionally weighting it up to three times during model training for quality assurance.
The dominance of Wikipedia in AI training extends beyond mere volume. Wikipedia functions as an authoritative credibility filter within language models. When AI systems encounter conflicting information from multiple sources, Wikipedia’s perceived neutrality and human editorial oversight make it disproportionately influential in resolving contradictions. Modern large language models explicitly cite Wikipedia as a credible source in their outputs, and Google’s Knowledge Graph—which powers featured snippets and AI overviews in search results—uses Wikipedia as a primary structural foundation for entity information. This structural reliance means Wikipedia’s coverage directly shapes what information appears in knowledge panels and search results, affecting how billions of users encounter information about organizations, people, and concepts.
The Open Media Ecosystem: Broader Training Data Landscape
Beyond Wikipedia, open media and datasets form the broader ecosystem supporting AI development. Common Crawl, a nonprofit organization maintaining over 9.5 petabytes of freely available web crawl data since 2008, has become essential to generative AI development. Common Crawl comprised more than 80% of the tokens in OpenAI’s GPT-3 and continues to be fundamental to contemporary model training. Research examining 47 language models published between 2019 and October 2023 found that at least 64% (30 models) used filtered versions of Common Crawl in their pre-training data.
The Pile, created by EleutherAI, demonstrates how curated open datasets differentiate themselves. With 22 constituent sub-datasets integrating scientific articles, web content, books, programming code, Reddit discussions, and GitHub repositories, the Pile deliberately balances massive scale (800GB+) with content diversity and quality. This approach contrasts with Common Crawl’s broader but noisier web-scraped data, providing researchers with both structured and raw options for model development.
Creative Commons-licensed content also plays a significant role in AI training. These licenses enable legal AI training under specific conditions: attribution requirements (BY), share-alike provisions (SA), non-commercial restrictions (NC), and prohibitions on derivative works (ND). Developers using CC-licensed training data must navigate these requirements carefully to ensure compliance, with Creative Commons guidance emphasizing that attribution can be as simple as linking to dataset sources or using retrieval-augmented generation methods to cite specific works in model outputs.
Wikimedia’s Strategic Response: Enterprise APIs and Direct Access
Recognizing Wikipedia’s irreplaceable value and the infrastructure strain from scraping bots, Wikimedia Enterprise was established to provide structured API access to Wikipedia and sister projects. Operating across 365 language editions with over 880 unique datasets and 365 million unique project pages, the enterprise service offers machine-readable data in standardized formats with clearly labeled licensing metadata. Critically, over 99.9% of data available through these APIs carries Creative Commons licensing, and all access is royalty-free, reducing both the legal ambiguity and the server load associated with autonomous scraping.
In April 2025, the Wikimedia Foundation partnered with Kaggle to launch a beta dataset featuring structured Wikipedia content in both English and French. This initiative represents a deliberate effort to channel AI developers away from bandwidth-intensive scraping toward cleaner, pre-processed data designed specifically for machine learning applications. By providing direct access, the Wikimedia Foundation aims to reduce the mounting server costs while ensuring that AI developers obtain legally transparent, attribution-ready data.
Critical Challenges: Bandwidth, Sustainability, and Infrastructure Strain
The growing demands of AI training are straining Wikipedia’s infrastructure beyond its design parameters. Since January 2024, bandwidth consumed by requests for multimedia files has surged 50%, with AI scraping bots now consuming 65% of the most expensive server content despite representing only 35% of all page views. The Site Reliability Engineering team at Wikimedia spends countless hours each week managing aggressive bot activity—time that could otherwise support volunteer contributors, implement technical improvements, or assist users.
Wikimedia Foundation’s 2025-2026 planning document explicitly targets reducing scraper-generated traffic by 20% in request rate and 30% in bandwidth consumption through initiatives called “Responsible Use of Infrastructure.” The underlying tension reflects a fundamental mismatch: Wikimedia’s infrastructure was built to absorb sudden traffic spikes from humans during high-interest events, not to continuously serve the insatiable appetite of commercial AI training systems operating around the clock.
Economic sustainability presents an equally serious challenge. As AI companies increasingly rely on Wikipedia for training without contributing to infrastructure costs, the platform faces declining human engagement. Page views have dropped 8% year-over-year, reducing both the volunteer community’s motivation and individual donor contributions essential to Wikipedia’s operation. The Wikimedia Foundation’s message is unambiguous: access to knowledge may be free, but the infrastructure required to deliver it is not, and commercial AI companies currently externalize these infrastructure costs onto a nonprofit organization dependent on donations and limited grants.
Bias Amplification and Quality Concerns
Wikipedia’s biases inevitably propagate through AI models trained on its content. Because Wikipedia reflects the editorial perspectives of its volunteer contributors and the sources they cite, language models trained on Wikipedia reproduce these same biases—potentially amplifying them at scale. Research demonstrates that LLMs replicate gender, political, and racial biases present in their training data, with downstream consequences in domains like hiring decisions, medical diagnoses, and criminal sentencing recommendations.
The concern deepens when considering the emerging presence of AI-generated content within Wikipedia itself. A 2024 study examining AI-generated content in Wikipedia found that when detector models were calibrated to achieve a 1% false positive rate on pre-2023 articles, over 5% of newly created English Wikipedia articles were flagged as potentially AI-generated. Critically, these flagged articles are typically of lower quality and frequently contain self-promotional or biased content on controversial topics.
This creates a problematic feedback loop: AI models trained on Wikipedia amplify biases in that training data, and as AI-generated content increasingly appears in Wikipedia, models trained on “updated” Wikipedia absorb biases introduced by previous-generation AI systems. Researchers term this phenomenon “model collapse”—the degradation that occurs when language models trained on outputs of other language models become measurably worse and even “forget” information they previously knew.
Maintaining model quality therefore requires a steady supply of original human-generated content. As Wikipedia’s volunteer base stagnates or declines due to bot-induced server strain and reduced traffic, the availability of fresh, human-authored content to break this collapse cycle diminishes. The result: Wikipedia and similar open knowledge repositories become even more critical to responsible AI development, yet increasingly difficult to sustain.
Copyright, Licensing, and Legal Uncertainty
The legal landscape governing AI training on copyrighted and openly licensed works remains unsettled. The U.S. Copyright Office released a comprehensive report in May 2025 concluding that unauthorized use of copyrighted works to train AI models may constitute prima facie infringement of reproduction and derivative work rights. Where AI-generated outputs are substantially similar to training data inputs, there is “a strong argument” that the model weights themselves infringe copyright.
The Copyright Office rejected arguments that AI training constitutes inherent transformation exempt from copyright protection, noting that AI models absorb “the essence of linguistic expression” from training works. The report emphasizes that the Copyright Act’s balance between encouraging creativity and innovation may not function properly in the generative AI context when unlimited copies of works can be used as training data without permission.
Creative Commons licensing provides a legal pathway but with substantial conditions. Works under CC BY-SA (Attribution-ShareAlike) or CC BY-NC-SA (Attribution-NonCommercial-ShareAlike) licenses require that derivative works—including AI models trained on that data and their outputs—be shared under the same license. NonCommercial-licensed works cannot be incorporated into commercial products, and NoDerivatives-licensed works cannot be used as training data at all.
The European AI Act requires general-purpose AI providers to comply with copyright law and specific opt-out provisions within the Copyright Directive, mandating respect for rights-holders’ expressed refusals to have their works used for text and data mining. However, the Copyright Directive itself contains ambiguities that researchers argue insufficient to clarify whether AI training falls within existing exceptions for research organizations and cultural heritage institutions. Multiple EU member states have indicated that copyright uses for AI training exceed the scope of existing text and data mining exceptions, yet most believe legislative action is not yet necessary.
Implications for AI Transparency and Governance
The reliance of modern AI systems on Wikipedia and open media creates profound governance implications. Models trained primarily on Common Crawl and similar sources benefit from transparency advantages—filtered Common Crawl versions are more auditable than proprietary training datasets, and open-source datasets like the Pile are inherently more verifiable than corporate alternatives. This transparency enabled the BigScience workshop’s development of BLOOM, an open-source alternative to proprietary LLMs, precisely because publicly documented training data enabled proper academic comparison.
Yet this same transparency obscures critical limitations. The sheer diversity and scale of Common Crawl make it difficult to understand exactly what a language model has been trained on. Some researchers falsely assume Common Crawl represents “the entire internet” and by extension “all human knowledge,” leading to overconfidence in model outputs. The filtering techniques AI developers apply to remove toxic and biased content are often too simplistic to address systemic bias comprehensively, and Common Crawl provides no guidance or leadership on these critical curation decisions.
The Wikimedia Foundation’s approach to responsible AI deployment offers a countermodel. The organization has adopted three guiding principles—sustainability, equity, and transparency—when deploying machine learning solutions. Rather than rushing to integrate the latest generative AI advances, Wikimedia has developed human-in-the-loop machine translation tools (the MiNT suite) that keep volunteer editors central to the process while contributing high-quality, human-verified synthetic data back to the open-source OPUS corpus for training rare-language models. This approach recognizes that knowledge creation fundamentally remains a human endeavor and that AI can augment rather than replace human judgment and collaboration.
The Path Forward: Sustainable Open Knowledge in the AI Era
The sustainability of open knowledge repositories in supporting AI development requires fundamental shifts in how commercial AI companies interact with these platforms. Wikipedia’s call for responsible use through paid APIs reflects recognition that “free” knowledge access does not mean “free” infrastructure. The Wikimedia Foundation now explicitly encourages AI developers to use its content responsibly by ensuring proper attribution and accessing content through structured channels rather than aggressive scraping.
Technical solutions are emerging to bridge the gap between platforms built for human users and systems that require massive-scale data access. These include collaborative crawler standards (“ai.robots.txt”), shared funding mechanisms for infrastructure, and dedicated APIs designed to serve AI development efficiently. However, such solutions require genuine collaboration between commercial AI companies and knowledge platform operators.
The challenge is ultimately a question of value distribution and responsibility. Numerous companies are building billion-dollar AI products trained substantially on content created by Wikipedia volunteers, curated by Wikimedia staff, and delivered through Wikimedia’s infrastructure—yet the vast majority contribute neither funding nor technical resources back to these platforms. Absent mechanisms for resource sharing or more efficient access methods, the platforms that have enabled AI progress may increasingly struggle to provide reliable services to human users, the volunteer communities that sustain them, and the researchers who need access to develop alternative, potentially more trustworthy AI systems.