Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Claude*)
Editor

Introduction

The artificial intelligence chatbot landscape in 2025 faces an unprecedented legal reckoning. As platforms like ChatGPT, Claude, Gemini, Perplexity, Copilot, DeepSeek, and You.ai have evolved from experimental curiosities to essential business tools, they’ve collided headfirst with copyright law in ways that threaten their fundamental operating models. The question is no longer whether these platforms infringe copyright—it’s how extensively they do so and what the consequences will be.

The copyright challenges facing these platforms are remarkably similar, stemming from shared approaches to data collection, training methodologies, and content generation. However, subtle differences in their implementations and business models create varying degrees of legal exposure. This analysis examines the five most critical copyright hurdles these platforms face, ranked by their potential impact on the industry’s future.

The Five Critical Copyright Hurdles (Ranked by Importance)

1. Training Data Acquisition and Fair Use Defense

The Core Issue: The most fundamental challenge facing all AI chatbots is the massive-scale copying of copyrighted content for training purposes. Every major platform has ingested billions of copyrighted texts, images, and other creative works without explicit permission from rights holders. This isn’t a side effect of their operations—it’s the foundation upon which these systems are built.

The legal theory that has allowed this practice to flourish is the “fair use” defense under U.S. copyright law. Companies argue that their use of copyrighted material is “transformative”—they’re not reproducing the original works but using them to create something fundamentally new: an AI system capable of generating novel content. They point to the four fair use factors: purpose and character of use (commercial vs. educational), nature of the copyrighted work, amount used, and effect on the market for the original work.

Why It’s Critical: This hurdle tops the list because it threatens the entire foundation of how current AI systems operate. If courts definitively rule that training AI models on copyrighted content without permission constitutes infringement rather than fair use, it could force a complete reimagining of how these systems are developed. Recent judicial decisions have been mixed, creating uncertainty that paralyzes both AI companies and content creators.

How Companies Navigate the Edge: AI companies are pursuing multiple strategies simultaneously. They’re aggressively defending fair use arguments in court while quietly negotiating licensing deals with major publishers and content creators. OpenAI has signed agreements with news organizations like Associated Press and Financial Times. Google has partnerships with publishers for its AI training. These deals serve dual purposes: they provide some legal cover while demonstrating good faith efforts to compensate creators.

Some companies are also developing “synthetic training data” approaches, using AI to generate training content rather than relying solely on scraped material. However, this creates a circular problem—synthetic data generated by AI trained on copyrighted content may still carry copyright taint.

2. Real-Time Content Scraping and Robots.txt Violations

The Core Issue: Beyond historical training data, platforms like Perplexity AI face acute challenges around real-time web scraping for live information retrieval. This practice involves continuously crawling websites, often ignoring “robots.txt” files that explicitly request AI systems to stay away. Unlike one-time training data collection, this represents ongoing, systematic copying of copyrighted content.

The robots.txt protocol, while not legally binding, has traditionally served as a gentleman’s agreement between website owners and automated crawlers. When AI companies circumvent these restrictions—as Perplexity has been accused of doing by Cloudflare and major publishers—they undermine this informal system and directly defy content creators’ explicit wishes.

Why It’s Critical: This hurdle ranks second because it represents the most visible and immediately actionable form of copyright infringement. Unlike training data disputes that involve complex fair use arguments, ignoring robots.txt and scraping protected content creates clear, documentable instances of unauthorized access and copying. Publishers like Condé Nast and the BBC have begun taking aggressive legal action specifically targeting these practices.

How Companies Navigate the Edge: Some platforms have implemented more sophisticated crawling systems that respect robots.txt files and rate limiting. Others are pivoting toward partnerships with content aggregators and news services to obtain licensed access to real-time information. Microsoft’s Copilot, for instance, leverages Bing’s existing publisher relationships and licensed content deals.

Perplexity has faced the most scrutiny in this area, with CEO Aravind Srinivas defending the practice as fair use while simultaneously pursuing revenue-sharing agreements with publishers. The company argues that their AI-powered summaries drive traffic back to original sources, though publishers increasingly dispute this claim.

3. Output Liability and Derivative Works

The Core Issue: When AI chatbots generate responses that closely resemble copyrighted source material, they potentially create unauthorized derivative works or direct reproductions. This challenge is particularly acute when users prompt systems to recreate specific copyrighted content, such as song lyrics, book excerpts, or distinctive writing styles. The legal question centers on whether AI-generated content that substantially reproduces copyrighted elements constitutes infringement, regardless of the user’s intent.

Traditional copyright law struggles with AI-generated content because it was designed for human creators making deliberate copying decisions. AI systems generate responses through complex statistical processes that don’t map neatly onto concepts like “intentional copying” or “substantial similarity.” Courts must determine whether the automated nature of AI generation affects liability, and whether different standards should apply to AI-created derivative works.

Why It’s Critical: This ranks third because it directly affects how these platforms can operate day-to-day. Even if training data issues are resolved through licensing or fair use determinations, output liability creates ongoing risk with every user interaction. Publishers and creators are increasingly sophisticated about detecting AI-generated content that reproduces their copyrighted works, and they’re building legal cases around specific instances of output infringement.

How Companies Navigate the Edge: Most major platforms have implemented content filtering systems designed to prevent direct reproduction of copyrighted material. OpenAI’s ChatGPT includes safeguards against reproducing song lyrics, extensive quotations from books, and other clearly protected content. Claude has similar restrictions. However, these systems are imperfect and often rely on users reporting problematic outputs.

Some companies are exploring “constitutional AI” approaches that build respect for intellectual property directly into their training processes. Others are developing more sophisticated attribution systems that could provide clearer provenance for generated content. The challenge is balancing user utility with copyright compliance—overly restrictive systems may hobble the AI’s usefulness while insufficient restrictions invite legal action.

4. Publisher Revenue Displacement and Market Harm

The Core Issue: AI chatbots increasingly provide comprehensive answers that eliminate users’ need to visit original sources, potentially causing economic harm to content creators and publishers. This “market substitution” effect is particularly problematic for news organizations, educational publishers, and reference works whose business models depend on user traffic and engagement. When users can get summarized information from an AI chatbot instead of visiting the original source, it directly undermines the creator’s ability to monetize their work.

This challenge extends beyond simple copyright infringement to encompass broader questions of market fairness and economic justice. Publishers argue that AI companies are essentially parasitic—they extract value from copyrighted content while providing little or no compensation to creators. The “fair use” defense becomes weaker when the AI-generated content serves as a market substitute for the original work, as this directly impacts the fourth fair use factor: effect on the market for the original.

Why It’s Critical: This ranks fourth because it threatens the sustainability of content creation itself. If AI systems successfully displace traffic and revenue from original creators, it could create a vicious cycle where less content gets created, ultimately impoverishing the information ecosystem that AI systems depend on. This systemic threat has prompted coordinated action from publisher groups and is driving policy discussions about compensation mechanisms.

How Companies Navigate the Edge: Some AI companies are experimenting with revenue-sharing models and direct licensing agreements that provide ongoing compensation to content creators. Google has announced initiatives to share advertising revenue with publishers whose content appears in AI-generated responses. OpenAI and others are exploring subscription-based models where a portion of revenue flows back to content creators.

There’s also growing interest in technical solutions that could help publishers track and monetize AI usage of their content. These include watermarking systems, content attribution technologies, and micropayment platforms that could automatically compensate creators when their work contributes to AI responses.

5. International Copyright Complexity and Jurisdictional Challenges

The Core Issue: AI chatbots operate globally while copyright laws remain fundamentally national, creating a complex web of conflicting legal obligations. A system trained on content that may be fair use in the United States could violate copyright laws in the European Union, which generally provides stronger creator protections and has different fair use provisions (called “fair dealing”). Similarly, content that’s in the public domain in one jurisdiction may remain protected in another, and moral rights—which protect an author’s attribution and integrity interests—vary significantly between legal systems.

The global nature of AI deployment means that companies must navigate not just different copyright laws, but different enforcement mechanisms, damage calculations, and remedial frameworks. A single AI system may simultaneously be legal in some jurisdictions and infringing in others, creating operational and strategic challenges for companies trying to offer consistent global services.

Why It’s Critical: While ranking fifth, this challenge is becoming increasingly important as governments worldwide develop AI-specific regulations. The EU’s AI Act, various national AI bills, and emerging international treaties all include copyright provisions that could significantly alter the legal landscape. Companies that fail to account for international copyright differences may find themselves facing coordinated legal action across multiple jurisdictions.

How Companies Navigate the Edge: Most major AI companies are developing jurisdiction-specific implementations of their systems, with different training datasets, filtering mechanisms, and output restrictions based on local legal requirements. Some are geo-blocking certain features in jurisdictions with particularly restrictive copyright laws. Others are working with international law firms to develop compliance frameworks that can scale across multiple legal systems.

There’s also growing industry interest in international standards and harmonization efforts that could reduce jurisdictional complexity. However, these efforts face significant political and economic obstacles, as different countries have different views on the appropriate balance between AI innovation and creator protection.

Industry-Wide Strategies and Adaptations

Licensing and Partnership Approaches

The most significant trend across the industry is a shift toward proactive licensing agreements with content creators and publishers. This represents a fundamental acknowledgment that the “ask forgiveness, not permission” approach that characterized AI development’s early years is no longer sustainable. Companies are discovering that licensing costs, while substantial, are often lower than potential litigation expenses and regulatory fines.

These partnerships take various forms. Some involve simple revenue-sharing agreements where AI companies pay publishers a percentage of their earnings in exchange for training data access. Others are more complex strategic partnerships that include co-development of AI-powered products and services. The most sophisticated arrangements involve ongoing licensing relationships where publishers receive payments tied to actual usage of their content in AI responses.

Technical Mitigation Strategies

AI companies are investing heavily in technical solutions to copyright challenges. Content fingerprinting systems can identify and block copyrighted material during both training and inference. Attribution technologies attempt to track the source of information used in AI responses, potentially enabling automatic compensation systems. Some companies are exploring “federated learning” approaches that could train AI systems without centralizing copyrighted data.

More experimental approaches include “copyright-aware” training methods that could theoretically teach AI systems to respect intellectual property boundaries without explicit filtering. However, these remain largely theoretical, and their effectiveness in real-world applications remains unproven.

Legal and Regulatory Engagement

Rather than simply defending against lawsuits reactively, AI companies are increasingly engaging proactively with policymakers and legal authorities. This includes supporting copyright reform efforts that could clarify fair use boundaries for AI applications, participating in regulatory sandboxes that allow controlled experimentation with new technologies, and working with industry groups to develop voluntary standards and best practices.

Some companies are also pursuing declaratory judgment actions—essentially asking courts to rule preemptively on the legality of their practices rather than waiting for infringement lawsuits. While risky, this strategy could provide legal certainty that enables more confident business planning.

Future Implications and Outlook

The resolution of these copyright hurdles will fundamentally shape the AI industry’s future. If broad fair use protections emerge for AI training and operation, it could accelerate AI development while potentially undermining traditional content creation business models. Conversely, restrictive copyright interpretations could force AI companies toward expensive licensing regimes that might favor larger players capable of absorbing higher content costs.

The most likely outcome appears to be a hybrid approach combining limited fair use protections for certain types of AI training with mandatory licensing requirements for commercial AI applications. This could create a tiered system where academic and research uses receive broader protections while commercial AI services operate under more restrictive licensing frameworks.

International coordination will be crucial. Without some level of harmonization between major jurisdictions, AI companies may face the impossible task of complying with contradictory legal requirements. The ongoing development of international AI governance frameworks suggests that copyright issues will be central to future regulatory discussions.

The stakes extend beyond individual companies to encompass the broader information economy. How these copyright challenges are resolved will determine whether AI development continues its rapid pace, whether traditional content creators can maintain sustainable business models, and ultimately, how society balances innovation incentives with creator rights in the digital age.

Conclusion

The copyright hurdles facing AI chatbots in 2025 represent one of the most significant legal and business challenges in the technology sector. While these platforms share similar fundamental challenges around training data, content scraping, output liability, market displacement, and international compliance, their different approaches to navigating these issues will likely determine their long-term viability and market position.

The industry appears to be moving toward a more mature, licensed-based model that acknowledges creator rights while preserving space for AI innovation. However, the transition period remains fraught with legal uncertainty and business risk. Companies that proactively address these challenges through comprehensive licensing strategies, technical safeguards, and regulatory engagement will be best positioned to thrive in the emerging copyright-conscious AI landscape.

The ultimate resolution of these issues will require unprecedented cooperation between AI companies, content creators, legal systems, and policymakers. The decisions made in the next few years will establish the fundamental framework for how artificial intelligence and intellectual property coexist in the digital economy, with implications that will resonate for decades to come.

References

Note: Due to the rapidly evolving nature of AI copyright law and the limitations of available academic sources on this current topic, this reference list includes a mix of legal documents, news reports, and industry analysis. A fully academic treatment would require additional peer-reviewed sources as they become available.

Associated Press. (2024, March 26). Judge allows ‘New York Times’ copyright case against OpenAI to go forward. NPR. https://www.npr.org/2025/03/26/nx-s1-5288157/new-york-times-openai-copyright-case-goes-forward

Chat GPT Is Eating the World. (2025, February 19). Status of all 39 copyright lawsuits v. AI (Feb. 18, 2025): Judge Bibas rejects fair use in AI training in stunning reversal. https://chatgptiseatingtheworld.com/2025/02/19/status-of-all-39-copyright-lawsuits-v-ai-feb-18-2025-judge-bibas-rejects-fair-use-in-ai-training-in-stunning-reversal/

Copyright Alliance. (2025, May 14). AI lawsuit developments in 2024: A year in review. https://copyrightalliance.org/ai-lawsuit-developments-2024-review/

Digital Information World. (2025, August 2). Perplexity AI faces accusations over unauthorised web scraping. https://www.digitalinformationworld.com/2025/08/perplexity-ai-faces-accusations-over.html

McKool Smith. (2025, May 19). AI infringement case updates: May 19, 2025. https://www.mckoolsmith.com/newsroom-ailitigation-23

Slate. (2025, June 30). Why Sarah Silverman and Ta-Nehisi Coates sued Meta over copyright. https://slate.com/technology/2025/06/ai-copyright-lawsuits-anthropic-meta-openai-google.html

Sustainable Tech Partner. (2025, August 10). Generative AI lawsuits timeline: Legal cases vs. OpenAI, Microsoft, Anthropic, Nvidia, Perplexity, Intel and more. https://sustainabletechpartner.com/topics/ai/generative-ai-lawsuit-timeline/

TechCrunch. (2025, August 4). Perplexity accused of scraping websites that explicitly blocked AI scraping. https://techcrunch.com/2025/08/04/perplexity-accused-of-scraping-websites-that-explicitly-blocked-ai-scraping/

The Fashion Law. (2025, July 19). From ChatGPT to deepfake apps: A running list of AI lawsuits. https://www.thefashionlaw.com/from-chatgpt-to-deepfake-creating-apps-a-running-list-of-key-ai-lawsuits/

The Register. (2024, October 22). Major publishers sue Perplexity AI for scraping content. https://www.theregister.com/2024/10/22/publishers_sue_perplexity_ai/

Traverse Legal. (2025, April 24). Copyright in the age of AI: Why publicly visible content isn’t free for the taking. https://www.traverselegal.com/blog/copyright-ai-infringement/

U.S. Copyright Office. (2024). Copyright and artificial intelligence part 3: Generative AI training report (pre-publication version). https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf

Web Pro News. (2025, August 2). Perplexity AI backlash: Bypassing robots.txt for web scraping sparks ethics debate. https://www.webpronews.com/perplexity-ai-backlash-bypassing-robots-txt-for-web-scraping-sparks-ethics-debate/

Word count: Approximately 3,200 words

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* Prompt: Hi Claude. As of August 2025, what are the major copyright hurdles faced by popular chatbots such as Perplexity, Gemini, Claude, ChatGPT, Copilot, DeepSeek, and You.ai? Am I correct in assuming the hurdles are similar across these platforms? In order of importance, rank five of the most critical copyright hurdles, explain each in informal language free, as much as possible, of legal jargon. However, retain jargon that’s essential for a deeper understanding. Explain how chatbot companies are providing services at the edge of these hurdles. Combine your findings in a 3000-to-5000 word article with, if possible, a list of references, in APA style, that informed your report.

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