ICLE Comments to Autorité de la Concurrence on Conversational Agents
I. Introduction
The International Center for Law & Economics (ICLE) submits these comments in response to the Autorité de la Concurrence’s public consultation on the competitive situation in the conversational agents sector. ICLE is a non-profit research centre that applies economic analysis to legal and regulatory questions, with a particular focus on competition policy and market institutions.
These comments assess the competitive implications of integrating AI applications into digital ecosystems under both competition law and sector-specific regulation. They address several issues raised in the consultation, particularly the integration of conversational agents into the existing services of vertically integrated firms (Section I.B of the consultation). The analysis also engages with partnerships in this area (Section I.D), the evolving role of conversational agents as platforms (Section I.E), and the adequacy of the current legislative and regulatory framework (Section III, Question 25).
Artificial intelligence (AI) is emerging as a general-purpose technology with immediate implications for competition policy.[1] As deployment spreads across search, commerce, productivity software, and communications services, AI tools are beginning to reshape how firms compete, organise production, and interact with users. At the downstream level, foundation models enable new product categories and add new functionality to existing digital services.
Much of this transformation centres on AI assistants and agents. These systems increasingly mediate users’ online activity and shift the traditional web-browsing experience toward AI-centred interaction.[2]
AI assistants—e.g., OpenAI ChatGPT, Google Gemini, Microsoft Copilot, Anthropic Claude, Meta AI Assistant, and Mistral Le Chat—are primarily reactive, language-based systems. They interpret natural-language prompts and generate responses using probabilistic reasoning and, in some cases, tool-augmented capabilities. Their operation remains prompt-driven: users initiate the task and define its boundaries.
Alongside these systems, early agentic tools—e.g., OpenAI Operator, Anthropic Claude’s Computer Use, Perplexity’s Buy with Pro, and Google DeepMind’s Project Mariner—operate differently. They initiate actions, interact with external software environments, and adjust behaviour to pursue specified objectives. They can execute multi-step workflows and sustain continuous processes that extend beyond simple response generation. The distinction is functional rather than semantic: assistants answer questions, while agents perform tasks.
The rapid expansion of generative AI has increased both the number and diversity of these systems.[3] AI assistants and agents increasingly function as access points to digital services, rather than standalone applications.[4] Through a single interface, users can search, shop, book services, retrieve documents, or interact with third-party tools without leaving the conversational environment. AI-enabled browsers, including OpenAI’s ChatGPT Atlas and Perplexity’s Comet, reinforce this shift.
Firms are deploying these capabilities through two primary strategies. Some partner with third-party AI developers, while others build proprietary systems and embed them into existing ecosystems. This divergence reflects the competitive relationship between incumbent platforms and AI entrants. New entrants seek distribution through widespread embedding across platforms, while incumbents deploy vertically integrated AI to retain users within their ecosystems.
Competition authorities have begun to scrutinise these developments. Integration may improve quality and reduce transaction costs, but regulators have expressed concerns that it may also reinforce market power. Agencies have raised issues relating to tying, foreclosure, and self-preferencing, particularly where AI tools are embedded in widely used services.[5] Jurisdictions that adopted ex ante digital-market regulation are already reassessing whether those frameworks adequately address AI-enabled conduct.[6]
Early disputes illustrate the emerging terrain. Amazon has sued Perplexity, alleging that its agent accessed user accounts and masked automated activity as human browsing.[7] Elon Musk has threatened litigation accusing Apple of manipulating App Store rankings to favour OpenAI’s ChatGPT after Apple integrated the service into iOS devices.[8] Meanwhile, the Italian competition authority opened proceedings regarding Meta’s preinstallation of Meta AI within WhatsApp.[9]
These comments analyse the competitive implications of integrating AI applications into digital ecosystems under both antitrust law and sector-specific regulation.
From an antitrust perspective, the core issues are not new. They resemble longstanding debates over vertical integration and self-preferencing in digital markets.[10] Existing competition law remains sufficiently flexible to address exclusionary conduct. The real novelty lies elsewhere: AI markets currently exhibit rapid entry, experimentation, and technological uncertainty. The speed with which new services and firms have emerged complicates predictions about durable market power. Replicating familiar anti–Big Tech analytical frameworks risks misdiagnosing competitive dynamics in a still-fluid environment.
The same caution applies to digital-platform regulation. A Big Tech-centric regulatory approach creates asymmetric coverage. The European Union’s (EU) Digital Markets Act (DMA), for example, captures AI functionality integrated into designated core platform services,[11] yet standalone AI applications may fall outside its scope regardless of their growth or competitive significance. The rise of agentic systems highlights the difficulty regulators face in anticipating technological trajectories.[12]
Policy therefore confronts two opposing risks: delayed intervention that allows anticompetitive conduct to entrench, and premature intervention that distorts competition in evolving markets.
The comments proceed as follows. Section II examines business strategies for deploying assistive AI services and uses the Meta AI investigation to illustrate potential antitrust concerns arising from integration into core ecosystems. Section III analyses the challenges AI technologies pose for recently enacted digital-market regulations. Section IV evaluates the limits of a Big Tech-centric framework and questions the assumption that AI markets will follow the historical trajectory of earlier digital platforms. Section v concludes.
II. Competitive Strategies for Deploying AI Assistants and Agents
AI assistants and agents have become a central competitive frontier in online markets. A growing share of users rely on these tools to interact with digital systems, retrieve and synthesise information, automate tasks, and delegate bounded autonomous actions across platforms and services. The rapid cadence of model releases and the ongoing race for performance leadership reflect both the importance of these applications and the intensity of rivalry in this sector.
Competition between incumbent technology firms and AI entrants increasingly turns on how these systems are adopted and distributed. Because AI assistants may mediate—or potentially displace—traditional general-purpose search and other gateway services,[13] new entrants pursue rapid diffusion through embedding across multiple platforms. Incumbent firms instead face a familiar make-or-buy decision: partner with external AI developers or build proprietary systems.
Each option presents tradeoffs. Partnerships allow incumbents to reach competitive parity quickly and reduce disruption risk by aligning with potential rivals. Over time, however, reliance on third-party technology may create strategic dependence on external providers and their commercial success.[14] Internal development preserves control but requires substantial investment and entails technological uncertainty.
Observed market behaviour reflects these incentives. Many incumbents have integrated proprietary AI functionality into existing ecosystems. Google has incorporated generative-AI features into Gmail, Google Docs, and Search. Meta has embedded AI assistants into WhatsApp, Messenger, and Instagram. Microsoft integrated Copilot into the Office suite, although its early capabilities relied heavily on its partnership with OpenAI. Microsoft has since diversified Copilot’s architecture by combining internal and external models to reduce reliance on OpenAI.
Many partnerships instead focus on upstream AI inputs—cloud infrastructure, computing chips, training data, and technical expertise—or take the form of financial investment, rather than distribution agreements.[15]
New entrants typically pursue a different strategy. OpenAI’s launch of ‘apps in ChatGPT’ allows users to run third-party applications inside conversations, supported by an open developer toolkit (the Apps SDK).[16] Early partners include Booking.com, Canva, Coursera, Expedia, Figma, Spotify, and Zillow, with additional participants such as OpenTable, Peloton, Target, Tripadvisor, and Uber expected to join.
OpenAI has also pursued commerce integration. Companies including Etsy, Shopify, and Walmart allow users to browse and purchase products directly through ChatGPT.[17] A partnership with PayPal enables instant checkout through PayPal’s digital wallet, while PayPal processes merchant payments.[18]
Incumbents have responded with similar initiatives. Google has partnered with firms including Shopify, Etsy, Wayfair, Target, and Walmart to develop an open standard for agentic commerce. The system allows users to complete purchases through Search or the Gemini application without switching between apps or webpages.[19]
Competition authorities have expressed concern about both types of strategies.
First, regulators scrutinise partnerships between large platforms and AI developers.[20] These arrangements may generate pro-competitive benefits by giving entrants access to capital, distribution, and essential inputs such as specialised computing capacity. Authorities nonetheless worry that partnerships may neutralise emerging competitors—for example, through contractual restrictions that limit downstream competition—and thereby reinforce incumbent positions across the AI value chain.[21]
Second, agencies have focused on the integration of AI into core platform services.[22] Ecosystem integration can improve product quality and reduce transaction costs, but authorities warn it may also create foreclosure risks. Regulators frequently invoke tying and self-preferencing theories of harm. Platforms may condition access to core services on the use of proprietary AI or advantage their own tools through preinstallation, exclusive integration, or interoperability limits. Such practices could restrict user choice and raise barriers to rival applications.
Taken together, scepticism toward both partnerships and internal development reflects a broader concern that AI may strengthen existing digital ecosystems, rather than disrupt them.[23] Current policy debates therefore often adopt a Big Tech–centric perspective.
The ongoing Meta AI investigation provides a useful case study for evaluating these issues and the competitive implications of foundation-model assistants embedded within established digital ecosystems.
A. Meta AI and WhatsApp: Assessing Tying Risks in AI Integration
In July 2025, the Italian competition authority (ICA) opened an antitrust investigation into Meta’s decision to preinstall Meta AI within WhatsApp, combining its messaging service with its proprietary AI assistant.[24] The authority emphasised that Meta AI appears in a prominent interface position and is integrated into the WhatsApp search bar, allowing users to interact with the assistant without opening a separate chat.[25]
The ICA also identified limits on user control. Users can access competing AI services by initiating separate chats, but they cannot remove the Meta AI interface elements.[26] The authority further noted uncertainty regarding training data. Interactions with Meta AI appear to contribute to model training, except for private messages and instances in which users explicitly opt out in specific chats.[27]
The investigation therefore centres on an alleged tying practice. In the ICA’s view, preinstallation and preferential placement may give Meta an advantage in the AI-chatbot market by leveraging its position in consumer messaging services.[28] The concern is that Meta may steer WhatsApp’s large user base into the emerging AI market not through competition on the merits, but through product integration.[29]
The authority also emphasised the potential interaction between distribution and data. If Meta trains its model using interactions generated through a dominant messaging service, user-base leverage and data accumulation may reinforce one another. This feedback loop could create lock-in effects and reduce reliance on competing assistants.[30]
Despite the technological novelty of AI, the theory of harm is familiar. The case fits within a well-established vertical-integration framework, rather than introducing a new antitrust doctrine. Competition law has repeatedly addressed similar conduct in digital markets,[31] and existing rules do not require structural revision simply because the product incorporates AI.
European precedent illustrates the point. In Microsoft, the General Court held that the ubiquity of a dominant operating system could foreclose competition in the tied software market.[32] Bundling software with a preinstalled operating system allowed the tied product ‘to benefit from the ubiquity of that operating system … which cannot be counterbalanced by other methods of distributing media players’.[33]
A decade later, in Google Android, the European Commission found that Google preserved and strengthened its dominance in general search by requiring device manufacturers to preinstall Google Search and Chrome as a condition for licensing the Play Store and by imposing contractual restrictions that locked Android into a Google-controlled ecosystem.[34] The Commission concluded that preinstallation created a status quo bias that reduced both manufacturers’ incentives to preinstall rival applications and users’ incentives to download them.
More recently, in Facebook Marketplace, the Commission determined that tying Facebook Marketplace to Facebook abused Meta’s dominant position because integration provided a distribution advantage that rival platforms could not replicate.[35] Embedding Marketplace within the Facebook interface ensured universal visibility.[36] Although users could adjust certain settings, doing so required multiple complex steps that limited practical effectiveness.[37]
U.S. law reflects similar concerns. The U.S. District Court for the District of Columbia held that Google secured default-search status through anticompetitive distribution agreements with browser developers, device manufacturers, and carriers.[38] The U.S. District Court for the Northern District of California likewise found that Google unlawfully tied access to the Play Store to the use of Google Play Billing.[39] The court barred Google from requiring Play Billing for distributed applications and from imposing contractual restrictions that conditioned payment, distribution, or access to the Play Store on exclusive or preferential treatment.
Against this background, the ICA can rely on a well-developed tying framework. The authority must establish that: (i) the tying and tied products are distinct; (ii) Meta holds a dominant position in the tying market; (iii) users lack a genuine choice to obtain the tying product independently of the tied product; and (iv) the conduct is capable of producing exclusionary effects.
The outcome, however, need not replicate prior cases. AI markets remain highly dynamic and characterised by considerable competitive uncertainty. Even if Meta seeks to leverage messaging dominance into AI services, success cannot be presumed. The relevant question is whether preinstallation confers an advantage that rivals cannot offset through alternative distribution, product quality, or innovation.
The investigation has since expanded. The ICA also examined Meta’s October 2025 business terms, which prohibit providers from using the WhatsApp Business Solution when AI assistants constitute the primary service offered.[40] AI tools remain permitted for ancillary functions, such as automated customer support. The ICA adopted interim measures,[41] and both the European Commission and the Brazilian competition authority opened parallel inquiries.[42]
Authorities worry that the WhatsApp policy may restrict output, market access, or technical development by preventing rival assistants from reaching users through WhatsApp. Meta responds that general-purpose chatbots fall outside WhatsApp’s intended function as a communication tool between businesses and users, and that supporting such systems would require substantial operational resources.[43]
Although these comments focus on the integration of Meta AI into WhatsApp, the parallel investigations provide additional insight into market conditions. The ICA’s interim measures suggest a market with multiple entrants attempting to reach users.[44] Some are smaller providers without proprietary distribution channels—e.g., Ira, Luzia, Poke, Puch AI, and Zapia—while others, including ChatGPT, Copilot, and Perplexity, already possess alternative distribution pathways. The dispute therefore turns not only on dominance, but also on whether messaging platforms function as essential gateways to user access.
III. AI and the Limits of Digital-Market Regulation
If AI complicates antitrust analysis, it poses an even sharper challenge for recently enacted digital-market regulation. The same technological change that motivates regulatory intervention may also undermine it. Rapid advances in AI risk rendering new regulatory frameworks incomplete or outdated.
The Digital Markets Act (DMA) illustrates the problem. As part of the Act’s first review, the European Commission is consulting stakeholders on whether the framework adequately addresses AI-enabled services and whether the list of core platform services and related obligations requires revision.[45] The DMA—like other digital-market regimes—was not designed with AI in mind. Legislators instead targeted large online platforms designated as gatekeepers: firms that serve as gateways for business users to reach consumers and can leverage advantages—particularly access to data—across markets.
AI services may fall within DMA-type regulation through two main channels. First, an AI provider could itself offer a core platform service and qualify as a gatekeeper. Second, AI functionality embedded within an already designated platform service becomes subject to the obligations governing that service. The result is a differentiated regulatory landscape that treats incumbent platforms and AI entrants differently.
For incumbents, integration brings AI features within the DMA’s scope. The primary question then becomes whether existing obligations can adapt to new technologies. The European Commission’s ongoing specification proceeding concerning Google illustrates this dynamic. The proceeding seeks to ensure that Google provides third-party AI services access to Android operating-system features comparable to those available to its own services, consistent with the vertical-interoperability requirement of Article 6(7) of the DMA.[46]
Standalone AI providers face the opposite situation. The DMA may not apply at all, regardless of market significance, because standalone AI applications do not clearly fit within any enumerated core platform service. Unless regulators reinterpret existing categories—e.g., search engines, browsers, or virtual assistants—AI developers cannot be designated as gatekeepers solely on the basis of AI offerings.[47] This structural gap is particularly salient for agentic systems, which may reshape market intermediation while remaining formally outside the regulation’s scope.[48]
Expanding regulation, however, carries its own risks. AI markets remain unsettled in technology, business models, and market structure. Extending ex ante obligations to AI services may constrain experimentation in product design, functionality, and platform architecture. Policymakers would need to predict both the direction and speed of technological change in an environment characterised by rapid innovation and uncertainty.
Premature regulation therefore risks misjudging market power. Authorities may underestimate future competition or overestimate the durability of concentration. Either error may distort innovation incentives and consumer outcomes. The regulatory problem is therefore symmetrical: waiting too long may allow harmful conduct to emerge, while acting too early may suppress competitive experimentation.
IV. Rethinking Big Tech-Centric Assumptions in AI Markets
Both the Meta AI investigation and the ongoing debate over revising the Digital Markets Act (DMA) reflect a similar analytical pattern. Assessments of AI competition remain anchored in the same Big Tech–centric framework that has shaped digital-market policy for two decades.
Competition authorities recognise that AI has stimulated innovation and entry. At the same time, many worry that markets for foundation models may follow the early trajectory of digital platforms.[49] In this view, economies of scale and scope, network effects, data feedback loops, and limited multi-homing could produce ‘winner-takes-most’ outcomes and eventual market tipping.[50] The concern is prospective: large technology firms might shape AI markets in ways that reduce future competition by leveraging existing advantages across layers of the AI stack.
The ICA’s Meta AI investigation reflects this reasoning. The authority emphasises that AI development requires substantial computing capacity, high-quality data, specialised labour, and investment capital.[51] Because large platforms control many of these inputs and operate vertically integrated ecosystems, regulators worry that they can resist disruption and extend market power into adjacent markets.[52]
Sound policy analysis, however, should compare AI systems with earlier digital platforms rather than assume they are equivalent.[53] The emerging literature highlights several differences.[54]
Network effects appear weaker for foundation models than for traditional platforms because individual users gain little value from the presence of additional users. Data feedback loops exist, but the strategic importance of proprietary data may be declining as datasets expand and synthetic data becomes more common. Investment patterns also show persistent entry. New AI firms continue to attract substantial funding across successive rounds,[55] indicating both investor confidence and expectations of continued competition.
In practice, many predicted entry barriers have proved less significant than anticipated. The diversity of downstream applications also makes universal tipping unlikely. Rather than converging on a single dominant platform, AI services increasingly specialise across different applications. Rapid market expansion and continued entry therefore challenge key assumptions underlying current policy debates and point to ongoing competitive pressure.
Some scholars further argue that static indicators of market power—such as market shares and margins—may understate competition in innovative industries.[56] Dynamic indicators, including investment levels, innovation rates, and the ability of smaller firms to attract capital, provide a more informative picture.[57] The emergence of firms such as OpenAI and Anthropic is difficult to reconcile with the claim that incumbents can seamlessly extend dominance into AI markets.[58]
Empirical evidence also complicates the narrative that data advantages determine competitive success. To date, large platforms have not translated existing data holdings into decisive superiority over AI startups. OpenAI’s ChatGPT, for example, has become the most widely used chatbot. By February 2025, it exceeded 400 million monthly active users[59] and accounted for roughly 86% of global chatbot traffic between April 2024 and March 2025.[60] By September 2025, it ranked among the world’s most visited websites,[61] and by November 2025 it was the most downloaded generative-AI mobile application.[62] Developer adoption likewise remains high.[63]
These figures nonetheless remain fluid. As of January 2026, ChatGPT’s traffic share had declined to roughly 65% amid growing competition from Gemini.[64] Anthropic’s Claude has also expanded rapidly and is projected to reach break-even earlier than OpenAI.[65] The relevant point is not which firm leads, but how quickly leadership can change.
Rapid turnover weakens predictions of durable concentration. Even unsuccessful entry can discipline incumbents in contestable markets.[66] The threat of displacement may therefore matter as much as observed market shares.
Against this background, a presumption against integration strategies by large technology firms risks analytical error. The concern resembles longstanding scepticism toward vertical integration. Yet vertical integration can generate efficiencies, eliminate double marginalisation, reduce transaction costs, and improve product quality and coordination.
Applying traditional anti–Big Tech reasoning to AI disregards current market conditions. The sector shows sustained entry, diverse business models, rapid innovation, and abundant capital. Large platforms do not appear to hold a decisive structural advantage.
The Meta AI case illustrates the point. It is uncertain that integrating Meta AI into WhatsApp would materially harm competition in AI assistants, particularly given the success of rivals such as ChatGPT. ChatGPT achieved rapid adoption through cross-platform integration and partnerships that allow users to shop, book services, and perform other tasks within a single interface. By contrast, Meta AI’s market share remained minimal—about 0.2% between April 2024 and March 2025[67] and below 1% in January 2026[68]—and developer adoption remained limited.[69]
Industry practice further weakens a categorical rule against integration. Firms routinely use their own services as distribution channels for AI tools. In recent U.S. litigation, Judge Amit Mehta rejected a proposed remedy that would have broadly prohibited Google from favouring its Gemini system within Chrome.[70] He warned that such a restriction would impair competition:
Such a restriction would set Google apart from its competitors. … The court will not hobble Google’s competitiveness by prohibiting self-preferencing of its own GenAI technologies, when that is precisely how the emerging—and highly competitive—GenAI marketplace operates.[71]
Extending traditional anti–Big Tech assumptions to AI markets therefore risks counterproductive enforcement. Restrictions on integration strategies could weaken competitive pressure on leading AI firms and produce the opposite of the intended effect. The risk is particularly pronounced where digital-platform conduct already faces substantial constraints under DMA-type regulation.
V. Conclusion
ICLE welcomes the Autorité’s decision to launch this public consultation and appreciates the opportunity to contribute to its analysis of competition in the conversational agents sector. In light of the issues examined above, we highlight several considerations relevant to the themes identified in the consultation.
With respect to the integration of conversational agents into the existing services of vertically integrated firms (Section I.B of the consultation), the Autorité should avoid presuming that integration is inherently anticompetitive. Vertical integration and product embedding often generate efficiencies, improve coordination among complementary services, and accelerate the deployment of new technologies. As the Meta AI investigation illustrates, the relevant question is not whether integration occurs, but whether it forecloses rivals that retain alternative distribution channels and the capacity to innovate. Authorities should continue to apply established antitrust doctrine to demonstrably exclusionary conduct, while avoiding the assumption that preinstallation, default placement, or preferential integration automatically extend earlier self-preferencing concerns.
Regarding partnerships among publishers of conversational agents (Section I.D), the analysis in these comments suggests that partnerships, ecosystem integration, and internal AI development represent standard competitive responses in a market defined by technological uncertainty and rapid innovation. AI markets currently exhibit frequent entry, high investment levels, and rapidly shifting competitive leadership. Network effects appear weaker than in traditional platforms, data advantages are less durable, and new firms continue to attract funding and users. The emergence of firms such as OpenAI and Anthropic demonstrates that incumbents have not seamlessly extended dominance into AI markets. Restricting the strategies under scrutiny could therefore reduce, rather than enhance, competitive rivalry by weakening an important constraint on leading AI firms.
On the question of whether conversational agents are evolving into platforms (Section I.E), the competitive landscape remains highly fluid. AI markets differ from earlier platform markets in economically relevant ways. Even leading positions remain unstable as competing models improve quickly. The Autorité should exercise caution before projecting the trajectory of earlier platform markets onto conversational agents. Rather than assuming that AI will replicate the dynamics of digital platforms, analysis should recognise that vigorous competition may already be occurring—and that premature classification of conversational agents as platforms risks triggering regulatory frameworks before market structures stabilise.
Finally, regarding the adequacy of the legislative and regulatory framework (Section III, Question 25), these comments identify a structural asymmetry in existing digital-market regimes. Frameworks such as the EU’s Digital Markets Act already impose substantial obligations on designated gatekeepers when AI functionality is integrated into core platform services. By contrast, standalone AI assistants and agents may fall outside these regimes altogether, regardless of their competitive significance. This mismatch suggests that the greater risk may not be insufficient oversight of incumbents, but rules designed for earlier intermediaries, rather than today’s forms of competition. ICLE therefore encourages the Autorité to consider this regulatory asymmetry and to avoid layering additional obligations that could reinforce, rather than correct, the imbalance.
In sum, competition policy in the conversational agents sector faces two symmetrical risks: delayed intervention that allows anticompetitive conduct to entrench, and premature intervention that distorts competition in evolving markets. Focusing exclusively on the first risk overlooks the second. AI markets remain uncertain, dynamic, and highly innovative. In these conditions, ICLE encourages the Autorité to prioritise evidence over analogy and to adopt a context-sensitive framework that reflects the distinctive competitive dynamics of these markets.
[1] See, e.g., Statista, Number of AI Tool Users Worldwide from 2020 to 2031 (in Millions) (2025), https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide (last visited 9 Feb. 2026) (showing steady growth in global AI-tool users—from about 116 million in 2020 to roughly 350 million in 2025, with projections exceeding 1 billion by 2031).
[2] See, e.g., Org. for Econ. Co-operation & Dev. (OECD), Artificial Intelligence and Competitive Dynamics in Downstream Markets (2025), https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/11/artificial-intelligence-and-competitive-dynamics-in-downstream-markets_c6e81d0e/ccf0624a-en.pdf; see also Amit Zac & Michal S. Gal, The Price of Advice: Experimental Evidence on the Effects of AI Recommenders (2025), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5637090 (finding that consumer-facing AI recommender systems influence purchasing decisions).
[3] See, e.g., CB Insights, Global Number of Deals and Funding for Agentic Artificial Intelligence (AI) Startups from 2020 to 2024 (2025), https://www.statista.com/statistics/1607697/global-agentic-ai-startup-dealsand-funding (showing rapid growth in the agentic-AI startup sector, with funding rising from about $24 million across eight deals in 2020 to roughly $3.8 billion across 162 deals in 2024); see also Deloitte, Interest in Generative Artificial Intelligence (AI) Developments in Organizations Worldwide in 2024 (2025), https://www.statista.com/statistics/1603062/interest-in-future-genai-related-developments (reporting that agentic AI ranked among organisations’ most salient technological developments in 2024).
[4] See, e.g., Press Release, Autorité de la Concurrence, Conversational Agents: the Autorité Starts Inquiries Ex Officio with a View to Issuing an Opinion (2026), https://www.autoritedelaconcurrence.fr/en/press-release/conversational-agents-autorite-starts-inquiries-ex-officio-view-issuing-opinion.
[5] Id.; Austl. Competition & Consumer Comm’n (ACCC), Digital Platform Services Inquiry—Final Report (2025), https://www.accc.gov.au/about-us/publications/serial-publications/digital-platform-services-inquiry-2020-25-reports/digital-platform-services-inquiry-final-report-march-2025; Autorité de la Concurrence, Opinion on the Competitive Functioning of the Generative Artificial Intelligence Sector (2024), https://www.autoritedelaconcurrence.fr/en/opinion/competitive-functioning-generative-artificial-intelligence-sector; U.K. Competition & Mkts. Auth. (CMA), AI Foundation Models—Updated Paper (2024), https://www.gov.uk/government/publications/ai-foundation-models-update-paper; Autoridade da Concorrência, Competition and Generative Artificial Intelligence (2023), https://www.concorrencia.pt/sites/default/files/documentos/Issues%20Paper%20-%20Competition%20and%20Generative%20Artificial%20Intelligence.pdf.
[6] See, e.g., Eur. Comm’n, Consultation on the First Review of the Digital Markets Act (2025), https://digital-markets-act.ec.europa.eu/consultation-first-review-digital-markets-act_en; Eur. Comm’n, Review of the Digital Markets Act—Call for Evidence, Ares(2025)6881572 (2025), https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=intcom:Ares(2025)6881572.
[7] Shirin Ghaffary & Matt Day, Amazon Sues to Stop Perplexity from Using AI Tool to Buy Stuff, Bloomberg (4 November 2025), https://www.bloomberg.com/news/articles/2025-11-04/amazon-demands-perplexity-stop-ai-agent-from-making-purchases (reporting that Amazon alleges a third-party AI shopping agent failed to disclose when it purchased on users’ behalf, threatening platform integrity and merchant choice); see also Perplexity Team, Bullying Is Not Innovation, Perplexity (4 November 2025), https://www.perplexity.ai/hub/blog/bullying-is-not-innovation.
[8] Surbhi Misra, Musk Says xAI to Take Legal Action Against Apple over App Store Rankings, Reuters (12 August 2025), https://www.reuters.com/sustainability/boards-policy-regulation/musk-says-xai-take-legal-action-against-apple-over-app-store-rankings-2025-08-12.
[9] See, e.g., Press Release, Autorità Garante della Concorrenza e del Mercato (AGCM), The Italian Competition Authority Launches Investigation into Meta over Abuse of Dominant Position (30 July 2025), https://en.agcm.it/en/media/press-releases/2025/7/A576.
[10] See, e.g., Giuseppe Colangelo, Antitrust Unchained: The EU’s Case Against Self-Preferencing, 72 GRUR Int’l 538 (2023); Pablo Ibáñez Colomo, Self-Preferencing: Yet Another Epithet in Need of Limiting Principles, 43 World Competition 417 (2020).
[11] Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on Contestable and Fair Markets in the Digital Sector and Amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act), 2022 O.J. (L 265) 1.
[12] See, e.g., Friso Bostoen & Jan Kramer, Is the DMA Ready for Agentic AI?, Centre on Regulation in Europe (CERRE) (2025), https://cerre.eu/publications/is-the-dma-ready-for-agentic-ai; Jan-Frederick Gohsl, Future Proofing the DMA for Agentic AI: Lessons from the AI Act, 48 World Competition 315 (2025).
[13] See, e.g., TokenRing AI, The Search Wars of 2026: ChatGPT’s Conversational Surge Challenges Google’s Decades-Long Hegemony, WRAL (2026), https://markets.financialcontent.com/wral/article/tokenring-2026-1-2-the-search-wars-of-2026-chatgpts-conversational-surge-challenges-googles-decades-long-hegemony (reporting that ChatGPT Search captured roughly 17–18 per cent of global search queries by early 2026).
[14] The same risk may also arise where a partnership involves two large incumbents. See, e.g., Press Release, Google, Joint Statement from Google and Apple, Google Blog (12 January 2026), https://blog.google/company-news/inside-google/company-announcements/joint-statement-google-apple (announcing a multiyear collaboration under which Apple will build next-generation foundation models on Google’s Gemini models to power future Apple Intelligence features, including a more personalised Siri).
[15] See, e.g., Org. for Econ. Co-operation & Dev. (OECD), Competition in Artificial Intelligence Infrastructure (2025), https://www.oecd.org/en/publications/competition-in-artificial-intelligence-infrastructure_623d1874-en.html.
[16] OpenAI, Introducing Apps in ChatGPT and the New Apps SDK (6 October 2025), https://openai.com/index/introducing-apps-in-chatgpt?utm_source=chatgpt.com.
[17] Jaewon Kang, Walmart Partners With OpenAI to Offer Shopping on ChatGPT, Bloomberg (14 October 2025), https://www.bloomberg.com/news/articles/2025-10-14/walmart-partners-with-openai-to-offer-shopping-on-chatgpt.
[18] See, e.g., Press Release, PayPal, OpenAI and PayPal Team Up to Power Instant Checkout and Agentic Commerce in ChatGPT (2025), https://newsroom.paypal-corp.com/2025-10-28-OpenAI-and-PayPal-Team-Up-to-Power-Instant-Checkout-and-Agentic-Commerce-in-ChatGPT.
[19] See, e.g., Vidhya Srinivasan, New Tech and Tools for Retailers to Succeed in an Agentic Shopping Era, Google (11 January 2026), https://blog.google/products/ads-commerce/agentic-commerce-ai-tools-protocol-retailers-platforms.
[20] See, e.g., Press Release, Conselho Administrativo de Defesa Econômica (CADE), CADE to Investigate Big Techs’ Acquisitions of AI Startups (2024), https://www.gov.br/cade/en/matters/news/cade-to-investigate-big-techs2019-acquisitions-of-ai-startups; Press Release, Eur. Comm’n, Commission Launches Calls for Contributions on Competition in Virtual Worlds and Generative AI (2024), https://ec.europa.eu/commission/presscorner/detail/en/IP_24_85; Press Release, Eur. Comm’n, U.K. Competition & Mkts. Auth. (CMA), U.S. Dep’t of Just., & Fed. Trade Comm’n, Joint Statement on Competition in Generative AI Foundation Models and AI Products (2024), https://competition-policy.ec.europa.eu/about/news/joint-statement-competition-generative-ai-foundation-models-and-ai-products-2024-07-23_en; Press Release, U.K. Competition & Mkts. Auth., CMA Seeks Views on AI Partnerships and Other Arrangements (2024), https://www.gov.uk/government/news/cma-seeks-views-on-ai-partnerships-and-other-arrangements; Press Release, Fed. Trade Comm’n, FTC Launches Inquiry into Generative AI Investments and Partnerships (2024), https://www.ftc.gov/news-events/news/press-releases/2024/01/ftc-launches-inquiry-generative-ai-investments-partnerships.
[21] See, e.g., Austl. Competition & Consumer Comm’n (ACCC), supra note 5; Autorité de la Concurrence, supra note 5; Klaus Kowalski, Cristina Volpin & Zsolt Zombori, Competition in Generative AI and Virtual Worlds, Eur. Comm’n, Competition Pol’y Brief No. 3 (2024), https://op.europa.eu/en/publication-detail/-/publication/5530c8ca-7a1f-11ef-bbbe-01aa75ed71a1/language-en; U.K. Competition & Mkts. Auth. (CMA), supra note 5. See also, e.g., Dirk Auer & Mario Zúñiga, AI Partnerships and Competition: Damned if You Buy, Damned if You Don’t, Int’l Ctr. for L. & Econ. (2025), https://laweconcenter.org/resources/ai-partnerships-and-competition-damned-if-you-buydamned-if-you-dont; Josef Drexl & Daria Kim, AI Innovation Competition as a Discovery Procedure: The Role and Limits of Competition Law (2025), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5439660.
[22] See, e.g., Autorité de la Concurrence, supra note 4; Austl. Competition & Consumer Comm’n (ACCC), supra note 5; Autorité de la Concurrence, Opinion on the Competitive Functioning of the Generative Artificial Intelligence Sector, supra note 5; U.K. Competition & Mkts. Auth. (CMA), AI Foundation Models: Initial Report (2023), https://www.gov.uk/government/publications/ai-foundation-models-initial-report; Autoridade da Concorrência, supra note 5; Body of Eur. Regulators for Elec. Commc’ns (BEREC), BEREC High-Level Position on Artificial Intelligence and Virtual Worlds, BOR (24) 68 (2024).
[23] See, e.g., Margrethe Vestager, Making Artificial Intelligence Available to All—How to Avoid Big Tech’s Monopoly on AI?, Eur. Comm’n (2024), https://ec.europa.eu/commission/presscorner/detail/en/speech_24_931.
[24] Autorità Garante della Concorrenza e del Mercato (AGCM), Decision No. 31634, Case A576, Meta AI (22 July 2025) (noting that Meta later offered the service as a standalone product at meta.ai and, at least in the United States and Canada, through a dedicated iOS and Android app).
[25] Id. ¶¶ 4, 6.
[26] Id., ¶ 7.
[27] Id., ¶¶ 8, 10.
[28] Id., ¶ 42.
[29] Id., ¶ 43.
[30] Id., ¶ 45.
[31] See, e.g., Case C-233/23, Alphabet Inc. & Others v. Autorità Garante della Concorrenza e del Mercato (Android Auto), EU:C:2025:110 (25 February 2025); Case C-48/22 P, Google LLC & Alphabet Inc. v. Eur. Comm’n (Google Shopping), EU:C:2024:726 (10 September 2024); Case C-252/21, Meta Platforms Inc. v. Bundeskartellamt, EU:C:2023:537 (4 July 2023).
[32] Case T-201/04, Microsoft Corp. v. Eur. Comm’n, EU:T:2007:289 (Gen. Ct. 17 September 2007).
[33] Id., ¶ 1036.
[34] Eur. Comm’n, Case AT.40099, Google Android (18 July 2018), confirmed by Case T-604/18, Google LLC v. Eur. Comm’n, EU:T:2022:541 (Gen. Ct. 14 September 2022).
[35] Eur. Comm’n, Case AT.40684, Facebook Marketplace (14 November 2024).
[36] Id., ¶ 820.
[37] Id., ¶ 821.
[38] United States et al. v. Google LLC, 747 F. Supp. 3d 1 (D.D.C. 2024).
[39] In re Google Play Store Antitrust Litig., No. 20-CV-05671-JD, 2024 WL 4438249 (N.D. Cal. 7 October 2024), aff’d, 147 F.4th 917 (9th Cir. 2025), modified, 152 F.4th 1078 (9th Cir. 2025).
[40] See, e.g., Press Release, Autorità Garante della Concorrenza e del Mercato (AGCM), The Italian Competition Authority Opens Procedure for the Adoption of Interim Measures Against Meta over Abuse of a Dominant Position (2025), https://en.agcm.it/en/media/press-releases/2025/11/A576.
[41] See, e.g., Press Release, Autorità Garante della Concorrenza e del Mercato (AGCM), The Italian Competition Authority Orders Meta to Suspend the Terms Excluding Competing AI Chatbots from WhatsApp (2025), https://en.agcm.it/en/media/press-releases/2025/12/A576.
[42] See, e.g., Conselho Administrativo de Defesa Econômica (CADE), Cade Abre Inquérito Contra Meta e Aplica Medida Preventiva Suspendendo Novos Termos do WhatsApp sobre IA (2026), https://www.gov.br/cade/pt-br/assuntos/noticias/cade-abre-inquerito-contra-meta-e-aplica-medida-preventiva-suspendendo-novos-termos-do-whatsapp-sobre-ia; Press Release, Eur. Comm’n, Commission Notifies Meta of Possible Interim Measures to Reverse Exclusion of Third-Party AI Assistants from WhatsApp (2026), https://ec.europa.eu/commission/presscorner/detail/en/ip_26_310.
[43] See Autorità Garante della Concorrenza e del Mercato (AGCM), Decision No. 31775, Case A576, Meta AI ¶ 75 (22 December 2025).
[44] Id., ¶¶ 31-32.
[45] See Eur. Comm’n, supra note 6.
[46] See, e.g., Press Release, Eur. Comm’n, Commission Opens Proceedings to Assist Google in Complying with Interoperability and Online Search Data Sharing Obligations under the Digital Markets Act (2026), https://ec.europa.eu/commission/presscorner/detail/en/ip_26_202.
[47] See, e.g., Bostoen & Kramer, supra note 12.
[48] See, e.g., OECD, supra note 2; Gohsl, supra note 12.
[49] See, e.g., Eur. Comm’n, U.K. Competition & Mkts. Auth., U.S. Dep’t of Just., & Fed. Trade Comm’n, supra note 20.
[50] See, e.g., Eur. Comm’n, supra note 20; Kowalski, Volpin & Zombori, supra note 21.
[51] Autorità Garante della Concorrenza e del Mercato (AGCM), supra note 24, ¶ 36.
[52] Id.
[53] See, e.g., Anton Korinek & Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, 40 Econ. Pol’y 227 (2025); Catherine Tucker, How Does Competition Policy Need to Change in a World of Artificial Intelligence?, 40 Oxford Rev. Econ. Pol’y 834 (2024).
[54] See, e.g., Andrei Hagiu & Julian Wright, Artificial Intelligence and Competition Policy, 103 Int’l J. Indus. Org. 103134 (2025); Korinek & Vipra, supra note 53; Zach Meyers & Marc Bourreau, A Competition Policy for Cloud and AI, Centre on Regulation in Europe (CERRE) (2025), https://cerre.eu/publications/acompetition-policy-for-cloud-and-ai; Thibault Schrepel & Alex “Sandy” Pentland, Competition Between AI Foundation Models: Dynamics and Policy Recommendations, 34 Indus. & Corp. Change 1085 (2025). See also Austl. Competition & Consumer Comm’n (ACCC), supra note 5, at 292; Autorité de la Concurrence, supra note 5, at 5.
[55] See, e.g., Kate Clark, Anthropic Raising $10 Billion at $350 Billion Value, Wall St. J. (7 January 2026), https://www.wsj.com/tech/ai/anthropic-raising-10-billion-at-350-billion-value-62af49f4.
[56] Meyers & Bourreau, supra note 54.
[57] Id.
[58] Geoffrey A. Manne & Dirk Auer, From Data Myths to Data Reality: What Generative AI Can Tell Us About Competition Policy (and Vice Versa), CPI Antitrust Chron. (February 2024); see, e.g., CB Insights, Artificial Intelligence (AI) Unicorns Worldwide in 2nd Quarter 2025, by Valuation (2025), https://www.statista.com/statistics/1621613/artificial-intelligence-unicorns-worldwide (showing an AI-startup ecosystem led by ByteDance and OpenAI at roughly $300 billion valuations, followed by Stripe ($70 billion), Databricks and Anthropic ($62 billion each), and xAI ($50 billion)).
[59] See Roland Berger, Most Popular Artificial Intelligence (AI) Applications Worldwide in February 2025, by Monthly Active Users, Statista (2025), https://www.statista.com/statistics/1609163/top-ai-applications-mau-worldwide (reporting that the ByteDance-owned chatbot Doubao reached about 82 million monthly active users, with ChatGPT’s Nova Assistant at roughly 63 million and DeepSeek at about 62 million; a second group—Remini, Talkie AI, Character AI, ChatOn, Genius, and Gemini—each recorded about 28–33 million users); see also Similarweb, 2025 Generative AI Landscape: The State of Gen AI (2025), https://www.similarweb.com/corp/2025-generative-ai-landscape (finding that ChatGPT leads U.S. usage with more than 41 million monthly active users and a 33 per cent stickiness rate, while rivals such as Perplexity, Copilot, and Gemini have smaller user bases and lower engagement).
[60] Semrush, Artificial Intelligence (AI) Chatbots Worldwide Market Share from April 2024 to March 2025 (2025), https://www.statista.com/statistics/1618020/ai-chatbots-traffic-share-ww.
[61] See Similarweb, supra note 59 (reporting that in September 2025 Google received about 82 billion monthly visits worldwide, followed by YouTube at roughly 29 billion, Facebook at about 11 billion, Instagram at approximately 6.5 billion, and ChatGPT at around 6 billion).
[62] See AppMagic, Most Downloaded Generative AI Mobile Apps Worldwide as of 27 November 2025, Statista (2025), https://www.statista.com/statistics/1554189/top-gen-ai-apps-by-downloads (showing Google Gemini with about 392 million downloads, followed by Cici (169 million), DeepSeek (158 million), Perplexity (95 million), and Grok (82 million)).
[63] See Stack Overflow, Most Used Artificial Intelligence (AI) Search and Developer Tools Among Developers Worldwide as of 2024, Statista (2024), https://www.statista.com/statistics/1483838/ai-tools-usage-among-developers-use-worldwide (reporting GitHub Copilot usage at 44 per cent, Google Gemini at 22 per cent, Bing AI at 14 per cent, and Visual Studio IntelliCode at 13.7 per cent, with lower adoption for Claude (7.6 per cent) and Perplexity AI (4.9 per cent)).
[64] See Similarweb, AI Global—Global Sector Trends on Generative AI (2026), https://www.similarweb.com/corp/wp-content/uploads/2026/01/attachment-Global-AI-Tracker-6.pdf?utm_medium=social&utm_source=twit (reporting lower market shares for rivals, including DeepSeek (3.7 per cent), Grok (3.4 per cent), Perplexity (2.0 per cent), Claude (2.0 per cent), and Copilot (1.1 per cent)).
[65] Bradley Olson, The Week Anthropic Tanked the Market and Pulled Ahead of Its Rivals, Wall St. J. (5 February 2026), https://www.wsj.com/tech/ai/the-week-anthropic-tanked-the-market-and-pulled-ahead-of-its-rivals-ef59dff1; George Hammond, Anthropic’s Breakout Moment: How Claude Won Business and Shook Markets, Fin. Times (6 February 2026), https://www.ft.com/content/a75555a6-24c3-4468-aba9-7fe12b5def31.
[66] U.K. Competition & Mkts. Auth. (CMA), supra note 22, ¶ 4.17.
[67] Semrush, supra note 60.
[68] Similarweb, supra note 64.
[69] Stack Overflow, supra note 63.
[70] United States et al. v. Google LLC, No. 20-cv-3010 (APM) (D.D.C. 2025).
[71] Id.