How Google’s “AI Overviews” Product Exposes a New Frontier in Copyright Infringement and Monopoly Abuse: Lessons from the Chegg Lawsuit

In February 2025, Chegg, Inc.—a Santa Clara education technology company—filed what I think will be a groundbreaking antitrust lawsuit against Google and Alphabet over Google’s use of “retrieval augmented generation” or “RAG.” Chegg alleges that the search monopolist’s new AI-powered search product, AI Overviews, is the latest iteration of its longstanding abuse of monopoly power.

The Chegg case may be the first major legal test of how RAG tools, like those powering Google’s AI search features, can be weaponized to maintain dominance in a core market—while gutting adjacent industries.

What Is at Stake?

Chegg’s case is more than a business dispute over search traffic. It’s a critical turning point in how regulators, courts, and the public understand Google’s dual role as:
– The gatekeeper of the web, and
– The competitor to every content publisher, educator, journalist, or creator whose material feeds its systems.

According to Chegg, Google’s AI Overviews scrapes and repackages publisher content—including Chegg’s proprietary educational explanations—into neatly summarized answers, which are then featured prominently at the top of search results. These AI responses provide zero compensation and little visibility for the original source, effectively diverting traffic and revenue from publishers who are still needed to produce the underlying content. Very Googley.

Chegg alleges it has experienced a 49% drop in non-subscriber traffic from Google searches, directly attributing the collapse to the introduction of AI Overviews. Google, meanwhile, offers its usual “What, Me Worry?” defense and insists its AI summaries enhance the user experience and are simply the next evolution of search—not a monopoly violation. Yeah, right, that’s the ticket.

But the implications go far beyond Chegg’s case.

Monopoly Abuse, Evolved for AI

The Chegg lawsuit revives a familiar pattern from Google’s past:

– In the 2017 Google Shopping case, the EU fined Google €2.42 billion for self-preferencing—boosting its own comparison shopping service in search while demoting rivals.
– In the U.S. DOJ monopoly case (2020–2024), a federal court found that Google illegally maintained its monopoly by locking in default search placement on mobile browsers and devices.

Now with AI Overviews, Google is not just favoring its own product in the search interface—it is repurposing the product of others to power that offering. And unlike traditional links, AI Overviews can satisfy a query without any click-through, undermining both the economic incentive to create content and the infrastructure of the open web.

Critically, publishers who have opted out of AI training via robots.txt or Google’s own tools like Google-Extended find that this does not block RAG-based uses in AI Overviews—highlighting a regulatory gap that Google exploits. This should come as no surprise given Google’s long history of loophole seeking arbitrage.

Implications Under EU Law

The European Union should take note. Article 102 of the Treaty on the Functioning of the European Union (TFEU) prohibits dominant firms from abusing their market position to distort competition. The same principles that justified the €2.42B Google Shopping fine and the 2018 €4.1B Android fine apply here:

– Leveraging dominance in general search to distort competition in education, journalism, and web publishing.
– Self-preferencing and vertical integration via AI systems that cannibalize independent businesses.
– Undermining effective consent mechanisms (like AI training opt-outs) to maintain data advantage.

Chegg’s case may be the canary in the coal mine for what’s to come globally as more AI systems become integrated into dominant platforms. Google’s strategy with AI Overviews represents not just feature innovation, but a structural shift in how monopolies operate: they no longer just exclude rivals—they absorb them.

A Revelatory Regulatory Moment

The Chegg v. Google case matters because it pushes antitrust law into the AI litigation arena. It challenges regulators to treat search-AI hybrids as more than novel tech. They are economic chokepoints that extend monopoly control through invisible algorithms and irresistible user interfaces.

Rights holders, US courts and the European Commission should watch closely: this is not just a copyright fight—it’s a competition law flashpoint.

How RAG Affects Different Media and Web Publishers

Note: RAG systems can use audiovisual content, but typically through textual intermediaries like transcripts, not by directly retrieving and analyzing raw audio/video files. But that could be next.

CategoryExamples of Rights HoldersHow RAG Uses the Content
Film Studios / ScriptwritersParamount, Amazon, DisneySummarizes plots, reviews, and character arcs (e.g., ‘What happens in Oppenheimer?’)
Music Publishers / SongwritersUniversal, Concord, Peer/Taylor Swift/Bob Dylan/Kendrick LamarDisplays lyrics, interpretations, and credits (e.g., ‘Meaning of Anti-Hero by Taylor Swift’)
News OrganizationsCNN, Reuters, BBCGenerates summaries from live news feeds (e.g., ‘What’s happening in Gaza today?’)
Book Publishers / AuthorsHarpersCollins, Hachette, Macmillan Synthesizes themes, summaries, and reviews (e.g., ‘Theme of Beloved by Toni Morrison’)
Gaming Studios / ReviewersGameFAQs, IGN, RedditExplains gameplay strategies using fan walkthroughs (e.g., ‘How to defeat Fire Giant in Elden Ring’)
Visual Artists / PhotojournalistsArtNet, Museum Sites, Personal PortfoliosExplains style and methods from exhibition texts and bios (e.g., ‘How does Banksy create his art?’)
Podcasters / Transcription ServicesPodcast transcripts, show notesPulls quotes and summaries from transcript databases (e.g., ‘What did Ezra Klein say about AI regulation?’)
Educational Publishers / EdTechKhan Academy, Chegg, PearsonDelivers step-by-step solutions and concept explanations (e.g., ‘Explain the Pythagorean Theorem’)
Science and Medical PublishersMayo Clinic, MedlinePlus, PubMedAnswers medical questions with clinical and scientific data (e.g., ‘Symptoms of lupus’)