WikiHow's OpenAI Lawsuit and What It Means for Publishers

Key Points

• WikiHow sued OpenAI in federal court, alleging the company scraped more than 11,000 articles without permission to train ChatGPT.
• The lawsuit argues ChatGPT now reproduces WikiHow-style content on demand, directly displacing WikiHow's traffic and revenue.
• This case joins a growing wave of publisher copyright lawsuits against AI companies over training data use.
• OpenAI's standard defense is fair use. Courts haven't definitively settled that question yet.
• Publishers watching from the sidelines need to decide now whether to protect their content, monetize what traffic remains, or both.

What Happened

Reuters reports that WikiHow filed suit against OpenAI in Manhattan federal court, alleging the company scraped more than 11,000 of its how-to articles to train ChatGPT's large language models. The complaint alleges infringement of at least 1,200 registered copyrights, and WikiHow is seeking monetary damages plus a court order blocking further infringement.

WikiHow's complaint states that OpenAI's models "now produce competing how-to content on the same subjects, at a fraction of the time, effort, and cost of researching, writing, and editing a wikiHow article." The lawsuit calls this displacement a threat to WikiHow's revenue and, eventually, its reason to keep producing content at all.

OpenAI's response was the predictable one: its models "are trained on publicly available data and grounded in fair use."

Why This Case Matters to Publishers

WikiHow is not a fringe player filing a long-shot lawsuit. It's a scaled content operation with thousands of indexed articles across a massive range of instructional topics. If OpenAI can scrape and replicate that content at will, the same logic applies to every publisher running informational content at scale.

The substitution argument at the center of the complaint is interesting. WikiHow isn't just saying its content was used without permission. It's saying the output of that training directly competes with its product. That framing targets the economic harm more precisely than many earlier AI copyright cases, and it may get more traction in court as a result.

This is also the latest case in what Reuters describes as "a wave of cases brought by publishers, authors and other copyright holders against AI companies." Publishers with registered copyrights and documented traffic loss are building cases that courts are finding increasingly hard to dismiss as speculative.

Essential Background Reading:

• AI Crawler Resource Center for Publishers: Everything you need to know about AI crawlers, blocking strategies, and protecting your content from unauthorized scraping.
• Should Publishers Let AI Bots Crawl Their Sites: A practical breakdown of the tradeoffs between blocking AI crawlers and staying visible in AI-generated responses.
• AI Bot Traffic Is Now 40% of the Web: Why the scale of AI crawler activity on the open web demands publisher attention right now.
• Generative AI and Publishers: An overview of how generative AI is reshaping the publisher landscape and what it means for your ad revenue strategy.

What the Fair Use Defense Means

OpenAI's fair use argument has been consistent across every lawsuit it faces. Fair use is a legal doctrine that permits limited use of copyrighted material without permission under specific conditions, including the purpose of use, the nature of the work, the amount used, and the effect on the market for the original.

That last factor is exactly what WikiHow is targeting. The complaint argues that ChatGPT's ability to generate competing how-to content at scale constitutes real market harm. Courts weigh all four factors together, and no federal court has yet issued a definitive ruling on whether training large language models on scraped web content qualifies as fair use.

The outcome of cases like this one will shape the legal ground every publisher is standing on. It's not resolved, and publishers shouldn't treat it as resolved.

Related Content:

• Anthropic's $1.5B Copyright Settlement: What the Anthropic settlement signals about where AI copyright liability is heading and what publishers should take from it.
• Reuters' AI Licensing Strategy: How one major news publisher turned AI training data into a licensing revenue stream rather than just a legal dispute.
• AI Licensing Deals: A New Revenue Stream for Publishers: A look at how publishers are structuring AI content licensing agreements and what terms actually matter.
• Cloudflare's AI Bot Controls: A technical walkthrough of the controls available to publishers who want to manage AI crawler access at the infrastructure level.
• Google's Voice AI Lawsuit: Why the legal disputes piling up around AI and content rights affect every publisher, not just the ones actively suing.

What Publishers Should Do Right Now

The legal process will take years. Publishers need to act on shorter timelines. Here's what makes sense now, regardless of how the courts eventually rule.

• Audit your robots.txt coverage: Check which AI crawlers you're currently blocking and which ones still have access. Our AI Crawler Protection Grader gives you a fast, free assessment of where your defenses stand.
• Register your copyrights: WikiHow's ability to allege infringement of 1,200 specific registered copyrights gave this lawsuit teeth. Unregistered content has significantly weaker legal standing. If you haven't registered your primary content, start.
• Document your traffic baselines: Before you need to prove market harm in court, you need a record of what normal traffic looked like. Pull historical session and revenue data now.
• Consider your blocking strategy deliberately: Blocking all AI crawlers protects your content from further scraping but removes any chance of being cited in AI-generated responses. That's a real tradeoff worth thinking through based on your traffic mix and revenue model.
• Maximize revenue from every session that does land: If AI tools are eating into your organic traffic, your remaining visitors are worth more. Session-level yield optimization becomes more important as total visit volume faces pressure.

Next Steps:

• AI and Publishers Resource Center: The full Playwire hub for publisher guidance on AI traffic, content protection, and revenue strategy in the AI era.
• AI Citation Ranking Factors: If you're allowing some AI crawlers through, here's what actually determines whether your content gets cited in AI responses.
• Publisher Ad Revenue Maturity Model: Assess where your monetization strategy stands and identify the highest-leverage improvements for your current traffic reality.
• Yield Experiment Playbook: A practical guide to running yield optimization tests that protect revenue while you navigate changing traffic conditions.
• Agentic AI Promises Are Not a Revenue Strategy Yet: Why publishers should be skeptical of AI revenue narratives that aren't backed by actual monetization mechanics.

The Revenue Equation Publishers Can't Ignore

WikiHow's complaint puts a sharp point on something every ad-supported publisher should already know: if AI tools generate substitute content, fewer users visit the original source. Fewer visits means fewer ad impressions. Fewer impressions means less revenue.

The publishers who will weather this period are the ones treating their existing traffic as the asset it is. Tighter yield management, better monetization of high-intent pages, and making sure every session is working as hard as it can.

Litigation against OpenAI may eventually produce compensation or behavioral changes. It may not. Either way, publishers can't afford to wait on a court schedule to protect their revenue.

See It In Action:

• Our Publishers Are Partners, Not Just Customers: How Playwire's publisher-first approach plays out in practice, from ad quality decisions to revenue protection.
• News Publishers Ad Revenue Resource Center: Resources built specifically for news and editorial publishers managing revenue pressure from search and AI disruption.
• Publisher Earnings Index: Real earnings data across the Playwire ecosystem so you can benchmark your own performance against what publishers are actually making.

How We Think About This

We work with publishers across gaming, education, sports, and entertainment. AI-driven traffic erosion isn't theoretical for most of them. It's showing up in dashboards.

Our position is consistent: publishers deserve to understand exactly what's happening to their inventory and to have the tools to respond. That's why we built the AI crawler resource center: the technical decisions around blocking, licensing, and optimization shouldn't require a legal degree to navigate.

Whatever WikiHow's lawsuit ultimately produces in court, it raises a question every publisher should answer for their own situation: what is your content worth, and who's getting paid for it?