Integrating AI Assistants and Agents: Competition Policy in Dynamic Markets
Abstract
Rapid technological change poses a continuing challenge for competition policy. Just as the emergence of digital platforms and ecosystems prompted departures from traditional antitrust enforcement and a recent wave of regulatory intervention, the rise of AI is again reshaping the competitive landscape, raising questions about the adequacy of existing analytical approaches and regulatory tools. Examining the expansion and role of AI-enabled assistants and agents, this paper analyzes strategies for integrating AI applications. It argues that AI’s disruptive potential requires a context-sensitive approach, rather than a mechanical replication of the Big Tech–centric framework that has characterized recent competition policy in digital markets.
I. Introduction
Artificial intelligence (AI) is rapidly becoming 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, organize production, and interact with users. At the downstream level, foundation models enable new product categories and add functionality to existing products across digital markets.
Much of this change now centers on AI assistants and agents. These systems increasingly mediate users’ online activity and reconfigure the traditional web-browsing experience around AI-centered interaction.[2]
AI assistants—e.g., OpenAI ChatGPT, Google Gemini, Microsoft Copilot, Anthropic Claude, Meta AI Assistant, and Mistral Le Chat—are largely 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 AI assistants, early agentic systems—e.g., OpenAI Operator, Anthropic Claude’s Computer Use, Perplexity’s Buy with Pro, and Google DeepMind’s Project Mariner—operate differently. These systems initiate actions, interact with external software environments, and adjust behavior to pursue specified objectives. They can execute multi-step workflows and sustain continuous processes that extend beyond simple response generation. The difference is functional rather than semantic: assistants answer questions, while agents perform tasks.
The expansion of generative AI has rapidly increased both the number and diversity of such 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 integrating these capabilities through two primary strategies. Some partner with third-party AI developers; others build proprietary systems and embed them into existing ecosystems. This strategic divergence tracks 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 scrutinize these developments. Integration may improve quality and lower transaction costs, but regulators fear it may also reinforce market power. Agencies have raised concerns about tying, foreclosure, and self-preferencing, particularly where AI tools are embedded into widely used services.[5] Jurisdictions that adopted ex ante digital-market regulations are already reassessing whether those frameworks adequately address AI-enabled conduct.[6]
Early disputes illustrate the emerging terrain. Amazon has sued Perplexity, alleging 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 favor 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]
This paper analyzes 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 flexible enough 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 the familiar anti–Big Tech framework risks misdiagnosing competitive dynamics in a still-fluid environment.
The same caution applies to digital-platform regulation. A Big Tech-centric regulatory approach produces 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 growth or competitive significance. The rise of agentic systems exposes the difficulty regulators face in anticipating technological trajectories.[12]
Policy therefore confronts two opposing risks: delayed intervention that permits anticompetitive conduct to entrench, and premature intervention that distorts competition in evolving markets.
The paper proceeds 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 analyzes 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 path of earlier digital platforms. Section v concludes.
II. Competition and Distribution in AI Assistant Markets
AI assistants and agents have become a central competitive frontier in online markets. A growing share of users now rely on these tools to interact with digital systems, retrieve and synthesize information, automate tasks, and delegate bounded autonomous actions across platforms and services. The rapid cadence of model releases and the continuing 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 displace or mediate traditional general-purpose search and other gateway services,[13] new entrants pursue rapid diffusion through embedding across multiple platforms. Incumbent firms instead confront 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 can create strategic dependence on external providers and their commercial success.[14] Internal development preserves control but requires significant investment and technological uncertainty.
Observed market behavior reflects these incentives. Most 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 initial 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 follow 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 in kind. Google has partnered with firms including Shopify, Etsy, Wayfair, Target, and Walmart to develop an open standard for agentic commerce. The system enables 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 scrutinize 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 specialized computing capacity. Authorities nonetheless worry that partnerships may neutralize 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 create foreclosure risks. Regulators point to tying and self-preferencing theories of harm: platforms may condition access to core services on 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, skepticism 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 largely 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: A Tying Case in AI Markets
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 emphasized that Meta AI appeared in a prominent interface position and was integrated into the WhatsApp search bar, allowing users to interact with the assistant without opening a separate chat.[25]
The ICA also noted limited user control over the feature’s visibility. Users could access competing AI services by initiating separate chats, but they could not remove the Meta AI interface elements.[26] The authority further identified uncertainty regarding training data. User interactions with Meta AI appeared to contribute to model training, except for private messages and instances in which users explicitly opted out in specific chats.[27]
The investigation therefore centers on alleged anticompetitive tying. In the ICA’s view, preinstallation and preferential placement may give Meta a competitive advantage in the AI-chatbot market by leveraging its position in consumer messaging services.[28] The authority’s concern is that Meta can steer WhatsApp’s large user base into the emerging AI market not through competition on the merits, but through product integration.[29]
The ICA also emphasized the potential interaction between distribution and data. If Meta trains its model using interactions generated by a dominant messaging service, user-base leverage and data accumulation may reinforce each other. This feedback loop could produce lock-in effects and reduce reliance on competing assistants.[30]
Despite the technological novelty of AI, the theory of harm is conventional. The case reflects a familiar vertical-integration framework rather than 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 now 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 in the Facebook interface ensured universal visibility,[36] and 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.
That does not mean the outcome will mirror prior cases. As discussed below, the distinctive feature of AI markets is competitive uncertainty. Even if Meta attempts to leverage messaging dominance into AI services, success cannot be presumed. The key question is whether preinstallation grants an advantage that rivals cannot offset through alternative distribution, product quality, or innovation.
The investigation has also expanded. The ICA 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 the 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 this paper focuses on the integration of Meta AI into WhatsApp, the parallel investigations offer 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 Digital-Market Regulation
If AI complicates antitrust analysis, it poses an even sharper challenge for recent digital-market regulation. The same technological change that motivates regulatory intervention may also undermine it. Rapid advances in AI risk rendering newly enacted regulatory frameworks incomplete or outdated.
The 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, defined as 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 inside the DMA’s scope. The primary question becomes whether existing obligations can adapt to new technologies. The 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 based on 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]
Regulatory expansion, 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 characterized 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 could distort innovation incentives and consumer outcomes. The regulatory problem is thus symmetrical: waiting too long may allow harmful conduct to emerge, but acting too early may suppress competitive experimentation.
IV. Rethinking the Big Tech-Centric Framework
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 recognize that AI has stimulated innovation and entry. At the same time, they worry that markets for foundation models may follow the early trajectory of platform markets.[49] In this view, economies of scale and scope, network effects, data feedback loops, and limited multi-homing could generate “winner-takes-most” outcomes and eventual market tipping.[50] The concern is therefore 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 illustrates this reasoning. The authority emphasizes that AI development requires substantial computing power, high-quality data, specialized labor, and investment capital.[51] Because large platforms control many of these inputs and operate vertically integrated ecosystems, regulators fear they can both resist disruption and extend market power into adjacent markets.[52]
Sound policy analysis, however, must compare AI systems with prior digital platforms, rather than assume they are equivalent.[53] The literature suggests important differences.[54]
Network effects appear weaker for foundation models than for traditional platforms because individual users do not gain meaningful value from additional users. Data feedback loops exist, but the strategic importance of proprietary data may be declining as datasets become more abundant and synthetic data more common. Meanwhile, investment patterns show persistent entry: new AI firms have attracted substantial funding across successive rounds,[55] indicating both investor confidence and expectations of future competition.
In practice, many predicted entry barriers have proven less significant than anticipated. The diversity of downstream uses also makes universal tipping unlikely. Instead of a single dominant platform, AI services increasingly specialize across applications. Rapid market expansion and continued entry therefore challenge assumptions underlying current policy debates and suggest ongoing competitive pressure.
Some scholars further note that static measures 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, show meaningful opportunities for entry and expansion.[57] The emergence of firms such as OpenAI and Anthropic is difficult to reconcile with a theory 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 actual market shares.
Against this background, a presumption against integration strategies by large technology firms risks analytical error. The concern resembles longstanding skepticism toward vertical integration. Yet vertical integration can generate efficiencies, eliminate double marginalization, reduce transaction costs, and improve product quality and coordination.
Applying traditional anti–Big Tech reasoning to AI disregards the current competitive environment. The market exhibits entry, diverse business models, rapid innovation, and abundant capital. Large platforms do not appear to hold a decisive advantage.
The Meta AI case illustrates the point. It is uncertain that integrating Meta AI into WhatsApp would materially harm competition in AI assistants, especially given the success of rivals such as ChatGPT. ChatGPT achieved rapid adoption through cross-platform integration and partnerships enabling 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% during April 2024–March 2025[67] and below 1% in January 2026[68]—and developer adoption was low.[69]
Industry practice further undermines 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 favoring 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]
Accordingly, extending traditional anti–Big Tech assumptions to AI markets risks counterproductive enforcement. Restricting integration strategies could weaken competitive pressure on leading AI firms and produce the opposite of the intended effect. The risk is especially pronounced where digital-platform conduct already faces significant constraints under DMA-type regulation.
V. Conclusion
Artificial intelligence is widely described as a new technological paradigm capable of reshaping market competition. Yet competition policy toward AI remains largely framed by a Big Tech–centric narrative inherited from earlier digital-market debates. Policymakers often assume that AI will reproduce the trajectory of platform markets, in which dominant firms leveraged control over gateways and data to entrench market power. The central concern is therefore under-enforcement: that intervention will arrive “too little, too late.”
This assumption explains the skepticism toward integrating AI applications into the core services of large digital ecosystems. Authorities frequently view preinstallation, default placement, and preferential integration as extensions of earlier self-preferencing concerns. The Meta AI investigation exemplifies this approach, treating the embedding of an AI assistant into a messaging platform as a conventional tying problem.
The analysis developed in this paper suggests that this analogy is incomplete. AI markets differ from earlier platform markets in economically relevant ways. Entry remains frequent, investment levels are high, and competitive leadership changes rapidly. Network effects appear weaker than in traditional platforms, data advantages are less durable, and new firms continue to attract funding and users. The success of companies such as OpenAI and Anthropic demonstrates that incumbents have not seamlessly transferred their dominance into AI markets. Even leading positions remain unstable as competing models quickly improve.
These conditions matter for policy. Where competitive dynamics are fluid and contestable, aggressive intervention may misdiagnose the source of competitive pressure. The strategies under scrutiny—partnerships, ecosystem integration, and the internal deployment of AI tools—are not uniquely exclusionary. They are standard competitive responses in a market defined by technological uncertainty and rapid innovation. Restricting such conduct may therefore reduce, rather than enhance, competitive rivalry by weakening an important constraint on leading AI firms.
A context-sensitive framework is essential in AI markets. Authorities should continue to apply established antitrust doctrine to demonstrably exclusionary conduct, but they should avoid presuming harm from integration alone. Vertical integration and product embedding often generate efficiencies, improve coordination among complementary services, and accelerate deployment of new technologies. The Meta AI case illustrates this point: the relevant question is not whether integration occurs, but whether it actually forecloses rivals that retain alternative distribution channels and the capacity to innovate.
The regulatory implications point in a different direction. Existing digital-market regimes already impose significant obligations on designated gatekeepers when AI functionality is integrated into core platform services. By contrast, standalone AI assistants and agents may fall outside these frameworks altogether, regardless of their competitive significance. The resulting asymmetry suggests that the greater risk may not be insufficient control over incumbents, but regulatory mismatch—rules aimed at yesterday’s intermediaries, rather than today’s forms of competition.
Competition policy therefore faces two symmetrical risks. Delayed intervention may allow harmful conduct to develop, but premature intervention may suppress experimentation in a rapidly evolving market. An approach focused exclusively on preventing the first risk overlooks the second.
AI markets remain uncertain, dynamic, and highly innovative. In such conditions, competition policy should prioritize evidence over analogy. Rather than assuming that AI will replicate the history of digital platforms, enforcement should recognize that vigorous competition may already be occurring—and that poorly calibrated intervention could impede it.
[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 Feb. 9, 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 measurably 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 expansion of 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 organizations’ most salient technological development 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] See, e.g., Autorité de la Concurrence, supra note 4; 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 (Nov. 4, 2025), https://www.bloomberg.com/news/articles/2025-11-04/amazon-demands-perplexity-stop-ai-agent-from-making-purchases (reporting that Amazon claims a third-party AI shopping agent failed to disclose when it purchased on behalf of users, allegedly threatening platform integrity and merchant choice); see also Perplexity Team, Bullying Is Not Innovation, Perplexity (Nov. 4, 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 (Aug. 12, 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 (July 30, 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 Sept. 14, 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% of global search queries by early 2026).
[14] The same risk may also materialize where a partnership involves two large incumbents: see, e.g., Press Release, Google, Joint Statement from Google and Apple, Google Blog (Jan.12, 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 personalized 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 (Oct. 6, 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 (Oct. 14, 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 (Jan. 11, 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 Policy 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. In the literature, see, 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 (July 22, 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 and 6.
[26] Id., ¶ 7.
[27] Id., ¶¶ 8 and 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 (Feb. 25, 2025); Case C-48/22 P, Google LLC & Alphabet Inc. v. Eur. Comm’n (Google Shopping), EU:C:2024:726 (Sept. 10, 2024); Case C-252/21, Meta Platforms Inc. v. Bundeskartellamt, EU:C:2023:537 (July 4, 2023).
[32] Case T-201/04, Microsoft Corp. v. Eur. Comm’n, EU:T:2007:289 (Gen. Ct. Sept. 17, 2007).
[33] Id., ¶ 1036.
[34] Eur. Comm’n, Case AT.40099, Google Android (July 18, 2018), confirmed by Case T-604/18, Google LLC v. Eur. Comm’n, EU:T:2022:541 (Gen. Ct. Sept. 14, 2022).
[35] Eur. Comm’n, Case AT.40684, Facebook Marketplace (Nov. 14, 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. Oct. 7, 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 (Dec. 22, 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. (Jan. 7, 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. (Feb. 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 the 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 (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% 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 Sept. 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 November 27, 2025 (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 (2024), https://www.statista.com/statistics/1483838/ai-tools-usage-among-developers-use-worldwide (reporting GitHub Copilot usage at 44%, Google Gemini at 22%, Bing AI at 14%, and Visual Studio IntelliCode at 13.7%, with lower adoption for Claude (7.6%) and Perplexity AI (4.9%)).
[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%), Grok (3.4%), Perplexity (2.0%), Claude (2.0%), and Copilot (1.1%)).
[65] Bradley Olson, The Week Anthropic Tanked the Market and Pulled Ahead of Its Rivals, Wall St. J. (Feb. 5, 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 (Feb. 6, 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.