Investigation Report: AT.40983 - Systemic Abuses in the Generative AI Value Chain and the Foreclosure of Digital Markets

Executive Summary

by Gemini 3.0, Deep Research. Warning, LLMs may hallucinate!

The European Commission’s initiation of formal antitrust proceedings against Google(Alphabet Inc.) under case number AT.40983 represents a watershed moment in the regulation of the digital economy. Announced on December 9, 2025, this investigation targets a sophisticated matrix of behaviors that allegedly leverage Google’s entrenched dominance in general search—a market share exceeding 90% across the European Economic Area (EEA)—to monopolize the nascent but critical sector of Generative Artificial Intelligence (GenAI). The investigation focuses on two primary distinct but interrelated pillars of conduct: the uncompensated appropriation of web publisher content to power “AI Overviews” and “AI Mode,” which fundamentally alter the economic incentives of the open web, and the exclusionary reservation of YouTube data for Google’s proprietary models while technically barring competitors.¹

This report provides an exhaustive, expert-level analysis of the evidentiary record, the predicted economic consequences of these practices, and the broader regulatory horizon affecting other “Gatekeeper” platforms. The analysis indicates that the behaviors under scrutiny are not merely product improvements but strategic foreclosures designed to secure a “data moat” that renders competition in Large Language Models (LLMs) and Multimodal Models (LMMs) impossible for rivals. By transitioning the search experience from a “referral engine”—which directs traffic to content creators—to an “answer engine” that retains users within Google’s walled garden, the platform is allegedly imposing unfair trading conditions in violation of Article 102 of the Treaty on the Functioning of the European Union (TFEU).¹

The evidence compiled herein, drawn from publisher complaints, technical traffic analyses, and comparative market studies, suggests that the “Google Zero” phenomenon—where zero clicks result from a search query—has accelerated to crisis levels, threatening the financial viability of independent media and the plurality of the European digital ecosystem.³ Furthermore, the investigation into YouTube’s data practices reveals a potential “essential facilities” abuse, where Google denies access to a critical input (video training data) that is indispensable for the development of competitive AI systems.⁵

As this report will detail, the implications of these findings extend far beyond Google. The regulatory principles established in AT.40983 are currently being applied to parallel investigations into Meta, Microsoft, and Nvidia, signaling a coordinated EU strategy to prevent the monopolization of the AI value chain.

Chapter 1: The Evidentiary Landscape of Investigation AT.40983

The evidentiary basis for the Commission’s investigation is robust, multifaceted, and grounded in a convergence of technical implementations, granular traffic data, and restrictive contractual terms. The conduct in question does not exist in a vacuum but is a continuation of the self-preferencing strategies previously identified in the Google Shopping and Google Android decisions, now adapted for the algorithmic age. The evidence can be categorized into three distinct clusters: the mechanics of traffic displacement via GenAI integration, the coercion of data access through “all-or-nothing” indexing terms, and the exclusionary practices regarding the YouTube training corpus.

1.1 The Mechanics of Displacement: AI Overviews and AI Mode

The investigation distinguishes between two specific GenAI product implementations within Google’s ecosystem. While technically distinct, both utilize third-party content to generate outputs that compete directly with, and often substitute for, the source material.

1.1.1 AI Overviews (formerly SGE)

“AI Overviews” appear at the absolute zenith of the Search Engine Results Page (SERP), pushing organic links—the traditional lifeblood of publisher traffic—below the fold and often off the initial viewport entirely. Technical analysis indicates that these overviews utilize Retrieval-Augmented Generation (RAG) to synthesize information from multiple indexed sources into a coherent, direct answer.⁷ Unlike traditional “featured snippets,” which historically drove high click-through rates (CTR) by enticing users to read more, AI Overviews are engineered to satisfy the user’s informational intent in situ.

The data shows that this feature triggers for a significant percentage of queries, particularly those with high commercial or complex informational value, which are traditionally the most lucrative for publishers. Data from late 2024 and throughout 2025 suggests that AI Overviews appear for approximately 13-15% of all queries.⁴ However, this aggregate figure masks the targeted nature of the deployment; for “Your Money or Your Life” (YMYL) queries—health, finance, and complex how-to guides—the frequency is significantly higher. The interface design effectively creates a “walled garden” where the search engine becomes the destination rather than the conduit, satisfying the query without a click.⁹

1.1.2 AI Mode (Conversational Search)

Distinct from the passive AI Overviews, “AI Mode” represents a paradigmatic shift towards a chatbot-style interface integrated directly into the core Search product. This feature allows for multi-turn conversations, follow-up questions, and multimodal inputs (text, voice, image), leveraging the Gemini model’s advanced reasoning capabilities.¹⁰

The Commission’s concern regarding AI Mode centers on its capacity to act as a perfect substitute for visiting publisher websites. By retaining the context of the conversation and synthesizing answers from real-time web data and the Knowledge Graph, AI Mode eliminates the user’s need to “pogo-stick” between multiple search results to gather information.¹² This feature essentially “re-intermediates” the relationship between the user and the content creator, inserting Google’s AI as the primary and final interface for information consumption.¹¹ The evidence suggests this is not merely an interface evolution but a strategic move to capture the entire value chain of the user session.

Table 1: Technical Distinction Between AI Overviews and AI Mode

1.2 Quantitative Impact: The Acceleration of “Google Zero”

The term “Google Zero,” coined to describe the phenomenon where a search engine satisfies a query without sending traffic to a third-party website, has moved from a theoretical concern to a measurable crisis. The evidence collected by industry bodies such as the European Publishers Council (EPC) and Digital Content Next (DCN) demonstrates a sharp acceleration of this trend following the widespread deployment of AI Overviews in the EEA.

1.2.1 Traffic Attrition Data

Recent longitudinal studies from 2024 and 2025 provide compelling quantitative evidence of the harm inflicted on the publishing ecosystem. Research indicates that zero-click searches have risen to approximately 60% of all queries globally, with mobile zero-click rates reaching an alarming 77%.⁴ This means that for every ten queries performed on a mobile device, fewer than three result in a visit to a non-Google property.

Publishers participating in anonymized studies have reported median referral traffic drops of 10-25% directly correlated with the rollout of AI Overviews.¹⁵ The impact is unevenly distributed, with non-news brands (lifestyle, evergreen content) suffering steeper declines of roughly 14% compared to news brands at 7%, though specific weeks have shown plunges of up to 17% for entertainment brands.¹⁷

The impact is particularly existential for news publishers relying on “breaking news” or “explainer” traffic. A widely cited analysis by the SEO firm Authoritas found that a site previously ranked first in organic search results could lose up to 79% of its traffic for that specific query if an AI Overview is inserted above it.¹⁸ This massive displacement occurs because the AI Overview occupies the screen real estate previously reserved for the top result, and unlike the “ten blue links” era, the user is not compelled to scroll.

1.2.2 Click-Through Rate (CTR) Divergence

Behavioral analysis further corroborates the displacement theory. A Pew Research Center study analyzed user interactions and found that users who encounter an AI summary click on a traditional search result link in only 8% of visits. In stark contrast, when no AI summary is present, the click rate is nearly double at 15%.² Even more damaging to the publisher ecosystem is the finding that users are significantly more likely to end their browsing session entirely after reading an AI summary, indicating that their information need was fully satisfied by the platform’s scraping and synthesis of third-party content.¹⁹

1.2.3 Revenue Implications

The correlation between traffic loss and revenue decline is direct and devastating. The EPC and other trade bodies have submitted confidential evidence to the Commission showing that the loss of referral traffic undermines the advertising-supported business model of the open web.²⁰ The digital advertising market relies on “impressions”—eyes on a page. With programmatic advertising revenue dependent on page views, a 20-30% drop in search referrals can render independent journalism financially unviable.

Industry estimates suggest the publishing sector could lose over $2 billion in annual ad revenue due to these shifts in search behavior.²² Furthermore, the displacement effects threaten subscription funnels; users who do not visit a publisher’s site cannot be converted into subscribers, thereby threatening the primary alternative revenue model that high-quality journalism has pivoted toward in recent years.²³

1.3 The Data Foreclosure Strategy: YouTube and the Training Data Bottleneck

A critical and novel aspect of the Commission’s investigation focuses on the upstream market for AI training data, specifically video and multimodal data hosted on YouTube. This represents a shift in antitrust focus from consumer-facing outputs to the inputs required for innovation.

1.3.1 Asymmetric Access and the Terms of Service

The Commission alleges that Google is engaging in exclusionary conduct by reserving the vast corpus of YouTube content for its own AI training while technically and legally blocking rivals. Google’s Terms of Service force creators to grant a worldwide, royalty-free, sublicensable license for Google to use their content for “machine learning and AI” purposes.²⁴ This clause is mandatory; creators cannot upload content to the world’s dominant video platform without agreeing to train Google’s Gemini models.

Crucially, the Commission notes a “double standard” in how this data is treated. While Google claims an unlimited right to ingest this content for its own models, it simultaneously employs aggressive technical and legal measures to prevent competitors (such as OpenAI, Anthropic, and open-source researchers) from scraping transcripts or video data.⁵ While protecting creators from unauthorized third-party scraping is a legitimate justification on its surface, the Commission argues that Google grants itself privileged access to this data.²⁷ This creates a market distortion where Google utilizes the labor of creators to train models that compete with those same creators (e.g., AI video generation), while denying rival developers access to the same “essential facility.”

1.3.2 The Blocking of Rival Crawlers

Technical evidence underscores this exclusionary strategy. Reports indicate that Google has modified the YouTube infrastructure and robots.txt protocols to differentiate between “good” bots (Google’s internal indexers) and “bad” bots (competitor AI crawlers). Evidence suggests that rival scrapers are systematically blocked at the IP level, while Google’s internal data pipelines bypass these restrictions entirely.²⁸

This conduct aligns with the “Refusal to Supply” theory of harm under Article 102 TFEU. By denying access to the world’s largest video repository—a dataset that is likely impossible to replicate given the network effects of YouTube—Google effectively forecloses the market for multimodal AI development.³⁰ Without access to this data, rival models cannot achieve the same level of video understanding or generation capabilities as Gemini, cementing Google’s dominance in the next generation of AI applications.

1.4 Coercion via “All-or-Nothing” Indexing

The investigation also examines the lack of meaningful choice for web publishers regarding the use of their content. The Commission has found indications that Google imposes unfair trading conditions by presenting publishers with a binary, coercive choice: allow Google to use their content for AI training and summarization, or opt out of Google Search entirely.

1.4.1 The Robots.txt Limitations

Historically, the robots.txt protocol allowed webmasters to control crawling with some granularity. However, Google’s implementation regarding AI has been criticized for being intentionally blunt. While Google introduced Google-Extended to allow opting out of some AI training, testimony and technical analyses suggest that opting out of AI Overviews specifically is often impossible without also de-indexing the content from the core web index.⁷

This means that if a publisher wishes to prevent their work from being scraped and summarized by an AI Overview (which cannibalizes their traffic), they must block the Googlebot entirely. Doing so removes them from the traditional “ten blue links,” which remains the primary discovery mechanism for the internet.

1.4.2 The “Must-Have” Nature of Search

Given Google’s >90% market share in general search across the EU, de-indexing is an economically suicidal option for any commercial publisher. The Commission views this “take it or leave it” dynamic as an abuse of dominance. Publishers are effectively forced to surrender their intellectual property for AI processing as a non-negotiable condition of retaining access to the search market.² This coercion is central to the antitrust complaint filed by the Independent Publishers Alliance and the European Publishers Council.³¹ The regulatory theory posits that Google is leveraging its monopoly in the search market to extract favorable terms (free data) in the separate but related market of AI development.

Chapter 2: Predicted Consequences Based on the Evidence

If the evidentiary trends identified in Chapter 1 continue unchecked, the consequences for the digital economy, media plurality, and the AI development landscape will be profound and potentially irreversible. These predictions are based on the extrapolation of current zero-click trends, the economic incentives of dominant platforms, and the historical precedents of antitrust enforcement in digital markets.

2.1 The “Hollow Web” Scenario and Publisher Collapse

The most immediate and severe consequence of the “Google Zero” trend is the financial destabilization of the content ecosystem. As AI Overviews and AI Mode satisfy a growing proportion of user intent directly on the SERP, the value proposition of creating original content diminishes drastically.

• Feedback Loop of Destruction: If publishers lose the revenue necessary to fund original reporting and content creation, the volume and quality of the “raw material” that Google’s AI relies upon will degrade. This creates a parasitic relationship where the host (the open web) is starved by the parasite (the AI search engine). Long-term, this could lead to a “model collapse” scenario where AI systems are trained on increasingly synthetic, older, or low-quality data, as high-quality journalism retreats behind impenetrable paywalls.²³

• Consolidation of Media: The economic pressure will likely lead to a mass extinction of smaller, independent, and niche publishers who lack diversified revenue streams (such as events or direct separate subscriptions). The market will consolidate around a few massive legacy publishers (e.g., Axel Springer, News Corp) who possess the necessary scale and leverage to negotiate private licensing deals with AI giants.³³This bifurcation will create a two-tier information ecosystem: a paid, high-quality tier for the wealthy, and a generic, AI-summarized tier for the general public.

• Subscription Fatigue: As traffic funnels from search dry up, publishers will be forced to aggressively lock content behind paywalls to survive. However, with consumer budgets limited, “subscription fatigue” will set in, further reducing the availability of free, high-quality information to the public and exacerbating information inequality.²¹

2.2 Entrenchment of the AI Duopoly

The “data moat” created by Google’s exclusive access to YouTube video data and real-time Search interaction data predicts a severe foreclosure of the AI innovation market.

• Insurmountable Barriers to Entry: Training frontier-level multimodal models (text-to-video, video-to-text) requires massive, diverse video datasets. If YouTube remains exclusively accessible to Google for training, no rival can replicate the capabilities of models like Gemini or Veo without incurring prohibitive costs or immense legal risks regarding copyright.³⁵ This solidifies Google’s dominance not just in search, but in the downstream market of Generative AI applications, preventing the emergence of a truly competitive landscape.

• The End of the Open Crawler: The normalization of blocking rival crawlers—as seen with YouTube blocking OpenAI and the general rise of adversarial blocking via robots.txt—will likely spread. We predict a fractured web where data access is determined by bilateral treaties between tech giants and massive content aggregators (e.g., Reddit, Stack Overflow), effectively shutting out open-source AI researchers, academic institutions, and smaller startups who cannot afford these access fees.²⁹

2.3 Regulatory Fragmentation and Jurisdictional Clash

The investigation foreshadows a deepening regulatory divide between the EU and the US/Global market, driven by the differing legal frameworks of the DMA/Article 102 TFEU versus US antitrust law.

• Divergent Product Experiences: To comply with EU competition rules (and avoid fines of up to 10% of global turnover), Google may be forced to disable AI Overviews or offer granular opt-outs specifically in the EEA. This mirrors the “feature delay” strategy employed by Apple regarding Apple Intelligence.³⁸ Consequently, European consumers may receive a “stripped-down” version of Search compared to US counterparts, creating a technological lag.

• Mandatory Licensing Regimes: The logical endpoint of the Commission’s “unfair trading conditions” argument is the imposition of a mandatory licensing framework. The Commission may compel Google to pay for the content used in AI Overviews, similar to the logic behind the Copyright Directive’s neighboring rights but applied via the stronger instrument of antitrust enforcement.³⁹ This would fundamentally alter the economic model of Search, transitioning it from a free indexing model to a paid licensing model, potentially setting a global precedent for the “value exchange” of the internet.

Chapter 3: Other Companies Subject to EU Scrutiny for Similar Behavior

Google is not the sole focus of the European Commission’s aggressive enforcement agenda. The principles applied in investigation AT.40983—specifically regarding data access, self-preferencing, and the leveraging of dominance into adjacent AI markets—are actively being applied to other designated “Gatekeepers” and dominant technology firms. The regulatory environment is shifting from specific product concerns to systemic inquiries about the AI value chain.

3.1 Meta Platforms: Foreclosure in Messaging and “Pay or Consent”

Meta faces parallel scrutiny that mirrors the Google investigation in its focus on leveraging ecosystem dominance to disadvantage AI rivals.

3.1.1 WhatsApp AI Foreclosure

The Commission opened a formal investigation into Meta regarding its October 2025 policy update for the WhatsApp Business API. This policy effectively prohibits third-party AI providers (such as customer service bots powered by rival LLMs like GPT-4 or Claude) from using WhatsApp as a distribution channel, while Meta integrates its own “Meta AI” directly into the user interface.⁴⁰

• Similarities to Google: Like Google using Search to push Gemini (via AI Mode), Meta is accused of using its dominant messaging platform to foreclose the market for conversational AI agents. The timeline for full enforcement of this ban (January 2026) has created urgency for regulators, who fear that once Meta AI becomes the default for 450 million European users, the market will tip irreversibly.⁴²

3.1.2 “Pay or Consent” Model

Meta is also under fire for its “Pay or Consent” model, where users must either pay a subscription or consent to data processing for personalized ads. While primarily a GDPR/DMA issue, it intersects with antitrust as it forces the accumulation of data—essential for AI training—under coercive terms.⁴³ This mirrors the “all-or-nothing” coercion argument used against Google’s indexing terms.

3.2 Microsoft and OpenAI: The Quasi-Merger and Exclusive Access

The relationship between Microsoft and OpenAI is subject to intense scrutiny, often referred to by regulators as a “quasi-merger” designed to bypass traditional merger control while securing exclusive advantages.

3.2.1 Exclusive Cloud and Data Arrangements

The Commission is actively investigating the exclusivity clauses that require OpenAI to use Microsoft Azure services. More pertinently to the Google case, regulators are examining whether Microsoft provides OpenAI with exclusive access to Bing Search data and LinkedIn data for model training.⁴⁵

• The LinkedIn Factor: Microsoft’s recent changes to LinkedIn’s terms, which opt users into AI training by default (with complex opt-out mechanisms), mirror Google’s YouTube strategy. This provides Microsoft/OpenAI with a proprietary dataset of professional data that rivals cannot access, potentially constituting an abuse of dominance in the professional networking market leveraged into AI.⁴⁷ The Federal Trade Commission (FTC) in the US is conducting a parallel inquiry into these lock-in effects.⁴⁹

3.3 Nvidia: Hardware Dominance and CUDA Lock-in

While primarily a hardware vendor, Nvidia is subject to a specific probe by the French competition authority (l’Autorité de la concurrence), which coordinates closely with the European Commission.

• The CUDA Moat: The investigation focuses on Nvidia’s CUDA software ecosystem, which creates high switching costs for AI developers. The allegation is that Nvidia abuses its dominance in GPU hardware to lock the AI development industry into its proprietary software stack, preventing the emergence of alternative hardware providers.⁵⁰ This mirrors the “essential facility” logic applied to Google’s YouTube data; just as Google controls the “software” input (data), Nvidia controls the “hardware” input (compute), and both are accused of using this control to stifle competition.

3.4 Apple: The Interoperability and Privacy Shield Defense

Apple’s delay of “Apple Intelligence” in the EU highlights the defensive stance taken by companies against the Digital Markets Act (DMA).

• DMA vs. Product Integration: Apple argues that the DMA’s interoperability requirements would force it to compromise the privacy and security architecture of its AI features.³⁸ However, the Commission views these delays and “privacy/security” arguments skeptically, investigating whether they are pretexts to avoid opening the iOS ecosystem to rival AI assistants (e.g., allowing Gemini or ChatGPT to replace Siri as the system-level default).⁵³ This investigation focuses on “malicious compliance” and the refusal to interoperate, which is the flip side of the data access issues seen with Google.

3.5 Perplexity AI and X (Twitter): Copyright and GDPR Compliance

Emerging players are also facing scrutiny, primarily regarding the inputs of their AI models and the legality of their data acquisition strategies.

• Perplexity AI: Facing multiple lawsuits and potential EU regulatory action for “parasitic” scraping. Unlike Google, Perplexity does not have a search index monopoly, but its business model of summarizing news without payment is being challenged under the same copyright/fair competition principles. The company faces allegations of bypassing robots.txt protocols and infringing on the rights of major publishers like the New York Times and News Corp.⁵⁴

• X (formerly Twitter): The Irish Data Protection Commission (DPC) has already taken decisive action against X for processing EU user data for its “Grok” AI without valid consent. The DPC forced X to suspend this processing, establishing a precedent that “legitimate interest” is insufficient for training AI on user content—a precedent that directly threatens Google’s justification for using web publisher data.⁵⁶

Table 2: Comparative Analysis of EU Tech Investigations in the AI Sector

Conclusion

Investigation AT.40983 represents a critical juncture in European competition policy. By targeting the intersection of generative AI and the “essential facilities” of the digital age—search indexes and video repositories—the Commission is challenging the fundamental economic model of the AI transition. The evidence presented in this report shows a clear pattern of traffic displacement, data foreclosure, and coercive trading conditions that threatens the sustainability of the open web and the competitive viability of the AI market.

The outcome of this investigation will likely define the parameters of the “value exchange” between content creators and AI platforms for the next decade. It suggests a regulatory willingness to force a transition from an extraction-based economy—where data is taken freely to train proprietary models—to a licensing-based one, where fair compensation and access are mandated by law. As scrutiny widens to encompass Meta, Microsoft, and others, it is evident that the EU intends to systematically dismantle the “data moats” that Big Tech is currently constructing, placing the preservation of a competitive, pluralistic digital market above the unrestricted expansion of AI technologies.

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