May 11, 2026
Data Scraping
By
Tendem Team
Web Scraping for AI Training Data: Legal & Practical Guide
Seventy percent of all generative AI models are trained primarily on scraped web data (Actowiz 2026). Every major large language model – GPT, Claude, Gemini, Llama – was built on datasets assembled by crawling billions of web pages. Web scraping is not just adjacent to the AI revolution. It is the foundation the entire edifice stands on.
And that foundation is cracking under legal pressure. The New York Times sued OpenAI and Microsoft in December 2023 for copyright infringement. Anthropic settled a copyright class action for $1.5 billion in September 2025 (Thunderbit 2026). Reddit sued both Anthropic and Perplexity AI in 2025 under multiple legal theories. YouTube content creators filed class actions against Nvidia, Snap, and Meta for scraping training data in early 2026. The AI Accountability for Publishers Act, introduced in February 2026, would require AI companies to get permission and pay publishers before scraping their content.
For businesses building AI products, fine-tuning models, or assembling training datasets, this article covers what you can legally scrape for AI training, what has changed in 2025–2026, the practical approaches to building compliant datasets, and where human oversight is essential for both data quality and legal defensibility.
Why AI Needs Scraped Data
AI models learn from examples. The more diverse, high-quality examples they see during training, the better they perform. Web scraping is the primary method for assembling these example datasets because the internet contains the largest, most diverse corpus of human-generated text, images, code, and structured data in existence.
AI Application | Training Data Needed | Typical Scraped Sources |
|---|---|---|
Large language models (LLMs) | Trillions of tokens of text across domains | Websites, forums, books, academic papers, code repositories |
Retrieval-Augmented Generation (RAG) | Domain-specific, current information for grounding | Industry websites, news, documentation, knowledge bases |
Sentiment analysis models | Labeled reviews, social media posts, comments | Amazon, Yelp, Reddit, X/Twitter, product review sites |
Price prediction models | Historical and current pricing across markets | E-commerce sites, marketplaces, competitor product pages |
Computer vision models | Labeled images across categories | Image hosting sites, product catalogs, real estate listings |
Recommendation engines | User behavior patterns, product attributes, preferences | E-commerce catalogs, streaming platforms, review aggregators |
The distinction between scraping for direct business use (competitive intelligence, lead generation) and scraping for AI training is critical in 2026 – because the legal landscape treats them very differently.
The Legal Landscape in 2026: A Seismic Shift
The Copyright Battleground
The central legal question is whether using scraped content to train AI models constitutes fair use under US copyright law. The answer is still unresolved, but the trajectory is clear: content creators and publishers are fighting back, and courts are increasingly sympathetic.
The key test is transformativeness – does the AI model transform the input data into something new, or does it reproduce it? If you are scraping product data for a competitive pricing tool, the use case is clearly transformative (data becomes intelligence). If you are scraping articles to train an LLM that can generate similar articles, the transformativeness argument is much weaker (PromptCloud 2026).
The DMCA Section 1201 Theory
Platforms are increasingly arguing that their rate limiting, CAPTCHAs, and anti-bot systems constitute "technological protection measures" under DMCA Section 1201. Reddit’s October 2025 lawsuit against Perplexity AI used this theory. YouTube creators sued Nvidia, Snap, and Meta under the same framework in early 2026. Google sued SerpApi with similar claims (ZwillGen 2026). If courts accept this argument, circumventing any anti-bot measure while scraping could carry statutory damages – regardless of whether the underlying data was public.
The EU AI Act
The EU AI Act enters full enforcement by August 2026 and requires AI developers to disclose training data sources, respect copyright opt-outs, and document data provenance. The text and data mining exception under EU copyright law allows scraping for research purposes, but commercial AI training does not automatically qualify (Startup House 2026). Companies deploying AI in the EU must demonstrate that their training data was collected lawfully – creating a compliance burden that retroactive documentation cannot fully address.
State Privacy Laws
CCPA 2026 updates added new rules for automated decision-making technology and data broker obligations. Indiana, Kentucky, and Rhode Island enacted comprehensive privacy laws effective in 2026. For AI training datasets that contain personal data – names, emails, locations, behavioral data – the compliance requirements are multiplying across jurisdictions (Thunderbit 2026).
What You Can and Cannot Scrape for AI Training
Data Type | Risk Level | Key Consideration |
|---|---|---|
Government and public records | Low | Explicitly public data; check for reproduction restrictions |
Open-license content (Creative Commons, MIT, etc.) | Low | Respect license terms; some licenses prohibit commercial use |
Public business data (prices, product specs, company info) | Low–Moderate | Generally safe for non-reproductive uses; check site ToS |
User-generated content (reviews, forum posts, social media) | Moderate–High | Consent mismatch – content was shared for human audiences, not AI training |
News articles and published content | High | Active litigation; publishers increasingly adding AI opt-out clauses |
Copyrighted creative works (books, music, art) | Very High | $1.5B Anthropic settlement signals enormous financial exposure |
Personal data (names, emails, locations) | Very High | GDPR, CCPA, and state privacy laws apply regardless of public visibility |
Practical Approaches to Building Compliant AI Training Datasets
1. Source Audit and Documentation
Before scraping begins, document every source: what site, what data, what license or ToS applies, and what the intended use is. This documentation is not optional under the EU AI Act – it is a legal requirement for AI systems deployed in Europe. Even outside the EU, documentation provides legal defensibility if your data sourcing is challenged.
2. Respect Opt-Out Signals
Check robots.txt for AI-specific directives (many publishers now include GPTBot, CCBot, and other AI-crawler blocks). Check site terms of service for AI training clauses – Reddit, Getty Images, and many news publishers have added explicit prohibitions (PromptCloud 2026). Respect these signals even when they are technically optional – ignoring them creates evidence of willful disregard that strengthens any future legal claim against you.
3. Prioritize Transformative Use
The stronger your case for transformativeness, the lower your legal risk. Scraping product data to build a pricing intelligence model is highly transformative. Scraping articles to build a summarization tool is moderately transformative. Scraping content to train a model that generates similar content is weakly transformative – and carries the highest risk.
4. Minimize Personal Data
If your training data does not need personal information, strip it before ingestion. Names, email addresses, locations, and other identifiers create GDPR/CCPA obligations that add compliance cost and legal risk. Anonymization and pseudonymization at the point of collection – not after the model is trained – is the safest approach.
5. Use Licensed and Open Data Where Possible
The emerging "permission economy of web data" (PromptCloud 2026) means that more platforms are offering licensed data access for AI training. These licensed feeds are more expensive than scraping, but they eliminate copyright risk entirely. For high-risk data categories (news content, creative works, user-generated content), licensed access is increasingly the only defensible option.
Where Human Oversight Is Essential
AI training data pipelines require human judgment at several critical stages that automation cannot handle reliably.
Source evaluation requires legal and ethical assessment of each data source – not just whether the data is technically accessible, but whether collecting it creates legal exposure. As the California Law Review noted in 2025, "scraping violates nearly all of the key principles of privacy laws, including fairness, individual rights and control, transparency, consent, and purpose specification" (Solove & Hartzog 2025). Human legal review determines which sources fall within acceptable risk for your specific use case.
Data quality review is especially important for AI training because model quality depends directly on training data quality. Biased, incomplete, or inaccurate training data produces biased, incomplete, or inaccurate models. Human reviewers sample training datasets to identify quality issues, demographic bias, content gaps, and systematic errors that automated quality checks miss.
Copyright and consent assessment requires human judgment on a per-source basis. Is this content licensed for AI training? Does the site’s ToS prohibit it? Was the content created with the expectation that it might be used for commercial AI? These questions cannot be answered algorithmically.
Build your AI training datasets with Tendem’s AI agent – we handle the extraction at scale while human co-pilots ensure data quality and compliance at every stage.
The Shift from Scraping to Data Partnerships
The trajectory of the industry is clear: between 2025 and 2030, AI data scraping will evolve from largely unregulated bulk collection to more controlled, contract- and standards-based access (Startup House 2026). TollBit and similar services are already pushing bots to pay for content access. Major publishers are licensing their archives to AI companies directly. And the EU AI Act creates a regulatory framework that makes undocumented scraping increasingly untenable for commercial AI.
For businesses building AI products, the strategic implication is to invest in data sourcing infrastructure that can withstand regulatory scrutiny. Document everything. License where possible. Scrape public, non-personal, non-copyrighted data where licensing is not available. And build human oversight into every stage of the pipeline – because demonstrating that a human reviewed the data sourcing, quality, and compliance decisions is becoming a legal requirement, not just a best practice.
Conclusion
Web scraping remains the foundation of AI training data – but the ground rules are changing fast. The combination of copyright lawsuits, new DMCA theories, the EU AI Act, and expanding state privacy laws is creating a compliance landscape that demands careful navigation. The era of scraping first and asking questions later is ending.
The businesses that will thrive in this new environment are those that document their data sources, respect opt-out signals, prioritize transformative uses, minimize personal data, license high-risk content where possible, and build human oversight into every stage of the data pipeline. This approach costs more than indiscriminate scraping – but it costs far less than a $1.5 billion settlement.
Need compliant training data? Describe your requirements to Tendem’s AI agent – AI-powered collection with human compliance review built in.
Related Resources
Understand the legal framework in our web scraping legal compliance guide.
See the AI + human model in our hybrid scraping guide.
Learn about the future of the industry in our future of web scraping article.
Ensure data quality with our data quality checklist.
Explore Tendem’s data scraping services.
