Google and the Publishers' AI Lawsuits: What Changes for Content

The timeline: from OpenAI to Google, a expanding legal front

On July 14, 2026, TechCrunch reported a new class action against Google. The plaintiffs include heavyweight names in academic and commercial publishing: Hachette, Cengage, Elsevier and other major international publishers. The accusation is direct: Google allegedly used copyrighted works to train its artificial intelligence models without obtaining the necessary licenses.

This is not an isolated case. Over the past two years, the legal landscape around generative AI has grown dense very quickly. OpenAI has faced lawsuits from the New York Times and other publishers. Stability AI was sued by Getty Images. Likewise, Meta and other big tech companies are handling similar proceedings in several jurisdictions. The lawsuit against Google is part of a structural pattern, not an exceptional episode.

What sets this case apart is the caliber of the plaintiffs. Elsevier, in particular, is one of the world's leading distributors of scientific and academic content. Cengage operates in the educational publishing market. Hachette is one of the big five in global trade publishing. As a result, the economic and symbolic value of the works involved is considerable.

The legal crux: fair use or systematic appropriation?

The heart of the dispute is the fair use doctrine, a cornerstone of US copyright law. Google, like other AI developers, argues that training on publicly accessible data counts as legitimate use. The publishers counter that mass ingestion of protected works to build commercial products cannot qualify as fair use.

The question is far from settled. Still, some recent rulings are starting to outline legal trends. The Verge has documented how the NYT vs OpenAI case is setting important precedents. In addition, the European Union has already written training data transparency obligations into the AI Act, with direct implications for models distributed in Europe.

So for companies operating in Italy, the relevant legal framework is not just the American one. If anything, European rules tend to be stricter on intellectual property and data processing. The outcome of these proceedings will therefore have a real impact on the AI tools adopted in the Italian market.

Winners, losers and those watching from the sidelines

A strategic reading of the case means identifying who wins and who loses in this scenario. Publishers, of course, aim to obtain financial compensation and, above all, to establish a licensing model for the use of their content in AI training. In that sense, they could be the long-term winners if the courts side with them.

Google, for its part, has the legal and financial resources to hold out for a long time. Still, losing in court could force it to renegotiate access to training data for Gemini and the other models in the Google AI family. As a result, the cost of developing and maintaining these systems could rise significantly.

Watching with interest are alternative providers of AI-ready content, licensing platforms and ethically curated data marketplaces. Opportunities are also emerging for those building models trained on datasets with explicit licenses, a segment that could grow quickly over the next 18-24 months. Finally, end users, meaning the companies that integrate AI into their marketing workflows, find themselves in a forced waiting position.

SHM Studio's take: three implications for Italian martech

At SHM Studio, we work every day with marketing managers and digital leads at Italian SMEs and mid-market companies. We have identified three concrete implications of this case for anyone managing a martech stack and content strategy.

First implication: the provenance of training data becomes a vendor selection criterion. Until yesterday, few marketing leaders cared about what data the AI model they used had been trained on. After these legal developments, that question will become part of technology procurement. Just as already happens with GDPR for personal data, expect growing attention to data provenance in contracts with AI vendors.

Second implication: AI-generated content could be subject to policy changes. If courts rule that certain models infringed copyright, platforms could be forced to modify or withdraw features. Anyone who has built content production workflows that depend entirely on a single AI tool would be exposed. Diversifying your tools and keeping editorial skills in-house therefore remains a solid strategic choice.

Third implication: original content gains value. Paradoxically, this wave of lawsuits strengthens the position of those who produce authentic content built on proprietary expertise. Search engines, Google included, are already signaling a preference for content with strong signals of authority and originality. Investing in strategic copywriting and in SEO built around E-E-A-T is not just good practice: it is a form of competitive resilience.

The impact on digital marketing platforms

The fallout from these lawsuits doesn't concern only those who produce content. It also affects the platforms through which content is distributed and amplified. Google Ads, for example, increasingly integrates generative AI features, from automatic asset creation to Performance Max campaigns. Likewise, LinkedIn has introduced AI tools for generating ad copy.

If the models behind these features became subject to legal restrictions, platforms could be forced to limit or change them. Teams managing Google Ads campaigns or LinkedIn campaigns should therefore watch closely for official communications from the respective providers.

On top of that, the issue intersects with the management of AI services built into digital marketing workflows. It makes sense to start documenting which AI tools are used, for what purposes and how often, also with a view to future compliance audits.

What nobody is saying: the content problem