AI Trust and Safety: Wikipedia's Challenge
Wikipedia, the largest free encyclopedia, faces unprecedented challenges in maintaining its foundational values amidst political and technological pressures. As AI technologies rapidly evolve, the safety, trust, and privacy of data are at risk. Here's a deep dive into how AI impacts Wikipedia and the importance of safeguarding human-curated knowledge.
Why Wikipedia Feels Under Threat
Wikipedia, turning 25, is at the crossroads of political pressures and AI-driven challenges. As accusations of misinformation fly, Wikipedia's AI trust and safety measures are under scrutiny. AI governance becomes critical as new threats emerge from server strain due to AI scraping.
Political Pressure and Misinformation Accusations
Wikipedia faces scrutiny from various political actors. Republican lawmakers in the U.S. Congress have investigated alleged "manipulation efforts" in Wikipedia's editing process, while critics have raised concerns about potential bias and neutral points of view on the platform.
Server Strain and AI Scraping
The encyclopedia has become a key resource for AI training, leading to strained servers due to excessive scraping. In 2024, outgoing bandwidth increased by 50% due to scraper traffic, with at least 65% of the most expensive database requests performed by bots. Web crawlers and AI bots produced more than 88 billion views in 2025 alone.
Volunteer Decline and Cultural Drift
With the graying volunteer base, Wikipedia's reliance on human contributions for maintaining quality is under threat. Lower motivation among volunteers risks content stagnation as human traffic to the site continues to decline.
How AI Scraping and Training Put Open Knowledge at Risk
AI data security and AI data privacy are now at the forefront as AI scraping threatens to overwhelm Wikipedia's resources.
What 'Scraping' Means for Bandwidth and Control
When AI systems scrape Wikipedia, they impose substantial bandwidth demands. Historically, access to the site via scraping and bots has caused multiple infrastructure issues for the Wikimedia Foundation. Recent changes in traffic behavior and volume due to growth of large language models have caused an increase in incidents, challenging Wikipedia's control over its content and infrastructure sustainability.
Training on Human Content vs. Model Collapse
AI models relying on Wikipedia sometimes train recursively on their outputs, risking inaccuracies known as model collapse. Large language models aren't always reliable for generating quality reference material, often regurgitating Wikipedia content in rambling ways, particularly for obscure topics.
Examples: Bots, Bandwidth Spikes, and Scraped Datasets
Bots that scrape data lead to significant spikes in server demand, affecting Wikipedia's infrastructure. Site Reliability Engineers have had to enforce rate limiting or banning of crawlers repeatedly to protect infrastructure from scraper traffic and related site-stability incidents.
Regulatory and Censorship Pressures on Open Platforms
Navigating AI governance becomes a balancing act as Wikipedia aligns with various international laws and addresses regulatory challenges.
National Laws and Platform Compliance
The Wikimedia Foundation must navigate complex international regulations while maintaining its free knowledge mission. The rise of AI-driven content and data collection methods has raised questions about who ultimately bears the costs of the artificial intelligence boom.
Accountability vs. Political Weaponization
There's a thin line between defending openness and succumbing to political pressure. The Wikimedia Foundation aims to work with AI companies while ensuring they contribute fairly to infrastructure costs, balancing sustainability with openness.
The Human Cost: Editors, Credibility, and Relevance
Human contributions are irreplaceable, even with advanced AI systems. Trust and safety guidelines are required as AI risk management strategies are implemented. According to the Wikimedia Foundation's 2025-2026 Annual Plan, assessing AI's impact on trust and safety aspects of Wikimedia's ecosystem is a critical priority.
Graying Volunteer Base and Editorial Incentives
Maintaining an incentivized volunteer base is essential to preserving Wikipedia's credibility. With declining human pageviews (an 8% year-over-year drop reported in 2025, and a 25% decline in direct traffic between 2022 and 2025), volunteer motivation faces pressure as fewer visitors see the foundation's calls for funding.
Why Human-Vetted Sources Still Matter
Human oversight ensures AI systems receive high-quality training data, crucial for accuracy. Wikipedia's volunteer-driven model and commitment to neutrality and source quality remain foundational to its value as a knowledge resource.
Technical and Policy Responses Platforms Can Use
Encorp.ai offers solutions such as private AI solutions, on-premise AI, and trusted AI deployment.
Rate Limiting, API Access Controls, and Bot Management
These technical tools mitigate scraping risks, preserving bandwidth and reliability. The Wikimedia Foundation is developing nuanced approaches to identifying and responding to bot traffic trends while maintaining service availability.
Private/On-Prem Deployment and Controlled Datasets
Secure deployments protect Wikipedia's data assets while managing infrastructure demands and supporting the platform's sustainability.
Governance Frameworks and Trust-and-Safety Teams
By implementing strong governance frameworks, the Wikimedia Foundation aims to maximize efficacy and accountability in AI usage while upholding Wikipedia's editorial integrity and free knowledge mission.
What This Means for the Future of Public Knowledge
The ongoing challenges of AI scraping, server strain, and political pressure test the openness and sustainability of platforms like Wikipedia. Meanwhile, the Wikimedia Foundation is exploring new licensing deals with AI companies to create sustainable revenue streams while maintaining its commitment to free knowledge. Balancing AI trust and safety with human involvement ensures continued relevance and security.
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Martin Kuvandzhiev
CEO and Founder of Encorp.io with expertise in AI and business transformation