Competition Law and Intelligent Value Ecosystems and Dominance

1. Introduction

An intelligent value ecosystem is a network of interconnected products, platforms, data resources, algorithms, infrastructure, applications, suppliers, distributors and users in which value is created through continuous interaction among the different components.

Examples include:

• AI platforms connected with cloud infrastructure and application stores;
• digital payment ecosystems;
• e-commerce marketplaces linked with logistics, advertising and financial services;
• smartphone ecosystems connecting operating systems, app stores, browsers and search engines;
• social-media ecosystems integrating messaging, advertising, data and content;
• intelligent transport ecosystems involving mobility platforms, payment systems, mapping and data;
• smart-energy ecosystems involving grids, storage, software and demand-management systems.

Competition law becomes particularly important where an undertaking uses control over one part of the ecosystem to obtain or preserve market power in adjacent markets.

The central question is therefore not simply:

“Is the undertaking large?”

It is:

Does the structure and conduct of the ecosystem enable the undertaking to exclude competitors, exploit users or business partners, foreclose adjacent markets, or entrench dominance through data, algorithms, interoperability restrictions, network effects and switching costs?

Modern competition authorities increasingly examine ecosystems as interconnected competitive structures rather than treating every product or service in isolation. The EU's current market-definition framework expressly recognises digital ecosystems involving interconnected products and platforms.

2. Meaning of an Intelligent Value Ecosystem

An intelligent value ecosystem normally has several interconnected layers.

A. Infrastructure layer

This includes:

• cloud computing;
• telecommunications;
• operating systems;
• data centres;
• payment infrastructure;
• APIs;
• digital identity;
• AI computing infrastructure.

B. Platform layer

The platform connects different groups, such as:

• consumers;
• sellers;
• advertisers;
• developers;
• service providers;
• manufacturers.

C. Data layer

The undertaking may collect:

• behavioural data;
• transaction data;
• location data;
• search data;
• purchasing history;
• advertising data;
• device data;
• interaction data.

D. Intelligence layer

Algorithms and AI may be used for:

• recommendation;
• pricing;
• ranking;
• advertising;
• fraud detection;
• credit scoring;
• product matching;
• demand forecasting.

E. Complementary-service layer

The ecosystem may extend into:

• payments;
• logistics;
• advertising;
• finance;
• cloud services;
• insurance;
• entertainment;
• communications.

The combination can produce ecosystem power that is greater than the competitive significance of any individual product.

3. Why Ecosystems Can Create Dominance

3.1 Network effects

The value of a platform can increase as more users participate.

For example:

More users → more sellers → more transactions → more data → better algorithms → better service → more users.

This creates a feedback loop.

3.2 Data advantages

A dominant ecosystem may obtain data from multiple connected services.

For example:

Search + shopping + payments + location + advertising + browser data

may produce a much larger information advantage than a competitor operating only one service.

The German Facebook case illustrates the competition significance of combining data from Facebook, Instagram, WhatsApp and third-party websites.

3.3 Switching costs

Consumers may face substantial costs when leaving an ecosystem because they would lose:

• applications;
• stored data;
• contacts;
• transaction history;
• subscriptions;
• loyalty benefits;
• personalised recommendations;
• interoperability;
• accumulated reputation.

Consequently, a nominally “free” service may nevertheless possess substantial competitive power.

3.4 Economies of scope

An ecosystem operator can use infrastructure developed for one market in another market.

For example:

Cloud infrastructure → AI services → enterprise software → advertising → financial services.

This can make entry more difficult for firms attempting to compete with only one component.

3.5 Self-reinforcing intelligence

AI creates another layer of ecosystem advantage.

A simplified model is:

Users → Data → AI training → Better predictions → Better service → More users → More data.

Competition concerns arise if the incumbent prevents rivals from obtaining the data, interoperability or access necessary to compete.

4. Dominance in an Intelligent Ecosystem

Dominance should be analysed using traditional competition-law principles together with ecosystem-specific factors.

Traditional factors

• market share;
• barriers to entry;
• financial strength;
• customer dependence;
• competitors' ability to expand;
• countervailing buyer power.

Ecosystem factors

• network effects;
• multi-homing;
• switching costs;
• data accumulation;
• interoperability;
• ecosystem breadth;
• vertical integration;
• control over essential interfaces;
• algorithmic advantages;
• access to AI computing resources;
• cross-market leveraging.

The important point is that ecosystem size alone does not automatically establish unlawful dominance.

Competition law normally distinguishes between:

1. legitimate success through innovation; and
2. unlawful conduct that uses market power to exclude or exploit competitors.

5. Major Competition Concerns

5.1 Self-preferencing

A platform may rank or display its own products more favourably than competing products.

Example:

Marketplace → controls ranking → promotes its own products → rivals receive less traffic.

This may become particularly problematic where the platform is also an unavoidable intermediary.

5.2 Tying and bundling

An ecosystem owner may make access to one service conditional upon accepting another.

Examples:

• operating system + search engine;
• smartphone + app store;
• cloud + AI service;
• payment wallet + marketplace;
• hardware + software.

The competitive concern is stronger when the undertaking possesses substantial power in the tying market.

5.3 Exclusive dealing

The platform may require sellers, developers or suppliers to deal exclusively with it.

This can reduce multi-homing and prevent competitors from obtaining scale.

5.4 Data leveraging

A dominant undertaking may combine datasets obtained from different services and thereby obtain a competitive advantage unavailable to rivals.

This was central to the German Facebook/Meta proceedings.

5.5 Interoperability restrictions

A dominant ecosystem may restrict:

• APIs;
• technical interfaces;
• data portability;
• interoperability;
• compatibility with rival products.

Such restrictions can increase switching costs and preserve ecosystem control.

5.6 Algorithmic discrimination

An ecosystem may use algorithms to:

• downgrade rivals;
• increase commissions selectively;
• manipulate rankings;
• restrict visibility;
• impose discriminatory access conditions.

China's Platform Economy Anti-Monopoly Guidelines specifically recognise mechanisms such as search demotion, traffic restrictions, technical barriers and deposits when assessing restrictive “choose-one-of-two” conduct.

6. Important Case Laws

Case 1 — Google Android

Google LLC and Alphabet Inc. v European Commission, T-604/18

This is one of the most important cases for understanding ecosystem dominance.

The European Commission's case concerned Google's Android ecosystem involving:

• Android operating systems;
• Google Play Store;
• Google Search;
• Chrome;
• device manufacturers;
• mobile network operators.

The General Court described the dispute in terms of a multi-sided platform and ecosystem, and examined product bundling, exclusivity payments and anti-fragmentation obligations.

Competition-law significance

The case demonstrates how dominance can operate across interconnected markets.

The relevant theory was not simply that Google was large in mobile operating systems. Rather, the conduct was assessed according to its ability to reinforce Google's position in related services.

Ecosystem lesson

An undertaking controlling an ecosystem component may potentially use that position to reinforce its position elsewhere.

The case therefore provides a framework for analysing:

Operating system → app store → search → browser → advertising

as interconnected competitive markets.

Case 2 — Google Shopping

Google Search (Shopping), Case AT.39740

The Google Shopping case concerned Google's treatment of comparison-shopping services within its general search results.

The European Commission found that Google had favoured its own comparison-shopping service in search results and had disadvantaged competing comparison-shopping services.

The EU's current market-definition materials continue to identify Google Shopping as an important ecosystem-related precedent.

Competition-law significance

The case illustrates leveraging of dominance:

Dominant general search → control over traffic → preferential treatment of own adjacent service.

Ecosystem lesson

A platform may possess competitive power because it controls an important gateway rather than merely because it directly sells the competing product.

Case 3 — Intel v Commission

Intel Corporation v European Commission

Intel concerned rebates and exclusivity-related conduct in the market for x86 central processing units.

The case became particularly important for the analysis of exclusionary rebates and the assessment of whether conduct by a dominant undertaking can foreclose an equally efficient competitor.

Competition-law significance

The case demonstrates that competition analysis cannot simply stop at the existence of a dominant position.

Authorities must examine:

• the nature of the conduct;
• competitive conditions;
• foreclosure effects;
• economic context;
• potential efficiencies and objective justification where relevant.

Ecosystem lesson

In an intelligent ecosystem, preferential incentives may appear in many forms:

• discounts;
• preferential API access;
• cloud credits;
• advertising subsidies;
• developer incentives;
• infrastructure rebates.

Competition analysis must determine whether such arrangements merely reward efficiency or substantially restrict competitive access.

Case 4 — Facebook/Meta Data Combination Case

Bundeskartellamt Facebook Decision, 2019

The German Bundeskartellamt prohibited Facebook from combining user data obtained from different sources without voluntary consent.

The authority considered Facebook's position in the social-networking market and the competitive significance of combining information from Facebook, Instagram, WhatsApp and third-party websites/apps.

Competition-law significance

The case is particularly important for data-driven ecosystem dominance.

The theory can be represented as:

Multiple services → multiple datasets → combined user profiles → improved advertising capability → stronger ecosystem → more users/data.

Ecosystem lesson

Data can operate as a competitive asset.

Accordingly, competition authorities may examine whether a dominant undertaking's contractual or technological control over data contributes to:

• market foreclosure;
• exploitation;
• increased entry barriers;
• strengthening of dominance.

Case 5 — Meta Platforms and Section 19a GWB

Bundeskartellamt's Meta determination, 2022

The German authority determined that Meta possessed paramount significance for competition across markets under Section 19a of the German Competition Act.

The authority expressly referred to Meta's extensive digital ecosystem, including Facebook, Instagram, WhatsApp and related services.

Competition-law significance

This is important because it shows a move from analysing one isolated market toward examining the overall competitive significance of a digital ecosystem.

Relevant ecosystem characteristics included:

• very large user base;
• extensive user data;
• multiple interconnected services;
• social-media position;
• advertising;
• expansion into hardware and software.

Ecosystem lesson

Large digital firms can possess competitive significance across multiple markets simultaneously, making conventional single-market analysis potentially incomplete.

Case 6 — Alibaba

Alibaba Group – SAMR, 2021

China's State Administration for Market Regulation found that Alibaba held a dominant position in China's online retail platform service market.

SAMR concluded that Alibaba had required platform merchants to choose between Alibaba and competing platforms and had used mechanisms involving market power, platform rules, data and algorithms to implement the restriction.

The conduct was treated as an abuse involving restrictions on counterparties' ability to transact with competing platforms.

Competition-law significance

The case demonstrates how ecosystem power can be reinforced through:

• platform rules;
• data;
• algorithms;
• contractual restrictions;
• incentives and penalties.

Ecosystem lesson

A platform can potentially transform its marketplace position into control over merchants' participation in competing ecosystems.

Case 7 — Meituan

SAMR Meituan Food-Delivery Platform Case, 2021

SAMR investigated Meituan's conduct in China's online food-delivery platform services market.

According to SAMR, Meituan used differentiated rates, delayed merchant onboarding, exclusive cooperation arrangements, deposits and data/algorithmic measures to implement its “choose-one-of-two” restrictions.

Competition-law significance

This case is highly relevant to intelligent ecosystems because the conduct involved both:

• conventional contractual restrictions; and
• data and algorithmic mechanisms.

Ecosystem lesson

Modern exclusion need not take the form of an express contractual prohibition.

It can be implemented through:

Algorithm + ranking + traffic allocation + commissions + technical measures.

Case 8 — Microsoft

Microsoft Corp. v Commission / Microsoft tying jurisprudence

Microsoft's European competition proceedings concerning Windows and related software are foundational for analysing technological tying and interoperability.

The case demonstrates how control over a dominant technological platform can influence adjacent markets.

Competition-law significance

A dominant undertaking may have strong incentives to integrate complementary products.

Integration itself is not necessarily unlawful. The competition concern arises where integration or restrictions on interoperability substantially impede competing products.

Ecosystem lesson

The Microsoft line of jurisprudence is particularly relevant to:

• AI operating environments;
• cloud ecosystems;
• enterprise software;
• cybersecurity platforms;
• interoperable AI agents;
• API ecosystems.

7. Ecosystem-Specific Forms of Abuse

8. Intelligent Ecosystems and Network Effects

The traditional competition model often assumes relatively independent markets.

Intelligent ecosystems create feedback loops.

Positive feedback loop

More users
↓
More transactions
↓
More data
↓
Better AI/algorithms
↓
Better recommendations/pricing/service
↓
More users

This can create a powerful form of endogenous market expansion.

The competition problem arises where an incumbent deliberately strengthens the loop by excluding rivals.

9. Data as a Source of Ecosystem Dominance

Data can function as:

Input

Data trains algorithms and AI models.

Product

Data may itself be commercially valuable.

Competitive advantage

Large datasets can improve:

• recommendations;
• fraud detection;
• advertising;
• search;
• pricing;
• personalisation.

Entry barrier

A new entrant may lack historical data necessary to reproduce the incumbent's performance.

Therefore, competition authorities may need to examine:

Who controls the data, who can access it, whether data can be ported, and whether competitors can realistically reproduce the same informational advantage.

10. AI and Intelligent Value Ecosystems

AI increases the importance of ecosystem competition because AI systems depend upon several complementary inputs:

Compute + Cloud + Data + Models + APIs + Applications + Users

A company controlling several of these layers may obtain significant ecosystem advantages.

For example:

Cloud provider → AI infrastructure → foundation model → enterprise software → distribution platform

can create substantial vertical and horizontal relationships.

Current EU digital-market developments illustrate the increasing focus on ecosystem and cloud power: in June 2026, the European Commission announced a preliminary view that Amazon Web Services and Microsoft Azure should be designated as DMA gatekeepers for cloud services, citing entrenched user bases, switching costs, large ecosystems and the growing role of AI tools and partnerships in cloud procurement.

11. Essential-Facility Dimension

An ecosystem component may become strategically indispensable.

Examples could include:

• dominant app store;
• payment infrastructure;
• cloud infrastructure;
• operating system;
• interoperability interface;
• major digital identity infrastructure;
• critical marketplace access.

A refusal to provide access may raise competition concerns where the applicable legal requirements for an access obligation are satisfied.

However, not every successful platform is an essential facility.

The legal test must remain carefully tied to:

• indispensability;
• feasibility of duplication;
• exclusionary effects;
• objective justification;
• proportionality;
• impact on competition.

12. Multi-Homing and Ecosystem Competition

A crucial question is whether users can use competing ecosystems simultaneously.

Strong multi-homing

A user can easily use:

Google + Bing + ChatGPT + another AI assistant.

Competition may remain relatively fluid.

Weak multi-homing

If users face:

• high switching costs;
• incompatible formats;
• proprietary data;
• exclusive contracts;
• technical restrictions;

the ecosystem may become more entrenched.

Thus, competition authorities should examine actual switching behaviour, not merely theoretical alternatives.

13. Ecosystem Expansion and Conglomerate Effects

A dominant undertaking may expand from one market into another.

For example:

Search → advertising → browser → mobile OS → app store → payments → AI

or:

Marketplace → logistics → payments → lending → advertising → cloud

The concern is not diversification itself.

The question is whether dominance in one component is being used to foreclose competition in another component.

14. Merger Control and Intelligent Ecosystems

Ecosystem competition also affects merger analysis.

Traditional merger analysis may focus on:

“What are the parties' market shares?”

Ecosystem analysis additionally asks:

• Does the target provide complementary technology?
• Does the acquisition remove a potential competitor?
• Does the target possess strategically important data?
• Will interoperability be restricted?
• Will the acquirer gain control over a critical input?
• Will the transaction increase switching costs?
• Will the transaction strengthen network effects?
• Will competitors lose access to users or data?

This is particularly relevant to acquisitions involving:

• AI startups;
• cloud companies;
• data providers;
• cybersecurity firms;
• digital payment firms;
• app developers;
• recommendation technology.

15. Remedies

Competition authorities may use several remedies.

Structural remedies

• divestiture;
• separation of business units;
• restrictions on acquisitions.

Behavioural remedies

• prohibition of self-preferencing;
• non-discrimination obligations;
• interoperability;
• data portability;
• access obligations;
• transparency requirements.

Algorithmic remedies

Authorities may require:

• independent auditing;
• explanation of ranking criteria;
• monitoring of discriminatory effects;
• restrictions on manipulation of rankings.

Data remedies

Potential measures include:

• data portability;
• restrictions on combining datasets;
• interoperability;
• user consent mechanisms;
• data-access obligations.

16. EU Digital Markets Act Dimension

The Digital Markets Act complements traditional Article 102 TFEU enforcement by imposing ex ante obligations on designated gatekeepers.

The EU currently lists gatekeepers including Alphabet, Amazon, Apple, Booking, ByteDance, Meta and Microsoft, with numerous designated core platform services across search, app stores, operating systems, marketplaces, advertising, social networking and related services.

This is important for intelligent ecosystems because the regulatory model increasingly recognises that competition may need to be protected before ecosystem power becomes irreversible.

17. China: Platform-Ecosystem Approach

China's platform-economy competition framework is particularly relevant to intelligent value ecosystems.

The Platform Economy Anti-Monopoly Guidelines specifically address:

• “choose one of two” arrangements;
• big-data price discrimination;
• platform rules;
• algorithmic mechanisms;
• technical restrictions;
• traffic limitations;
• exclusive arrangements.

SAMR has stated that “choose-one-of-two” restrictions can constitute abuse of dominance where the relevant dominance and exclusionary conditions are established.

The Alibaba and Meituan decisions therefore provide important examples of competition enforcement involving platform rules combined with data and algorithms.

18. Indian Competition-Law Relevance

Under India's Competition Act, 2002, intelligent ecosystem cases can potentially engage:

Section 4 — Abuse of dominant position

Relevant forms may include:

• unfair or discriminatory conditions;
• denial of market access;
• limiting technical or scientific development;
• leveraging dominance in one market into another;
• discriminatory access to infrastructure or data.

Sections 5 and 6 — Combinations

Ecosystem acquisitions may raise concerns where transactions strengthen:

• network effects;
• data concentration;
• vertical integration;
• foreclosure;
• potential-competitor elimination.

Section 3

Algorithmic coordination, information exchange and other concerted conduct may potentially fall within the prohibition against anti-competitive agreements where the statutory requirements are satisfied.

19. Key Legal Test for Intelligent Value Ecosystems

A useful analytical framework is:

Step 1 — Identify the ecosystem

What products, platforms, infrastructure and services are interconnected?

Step 2 — Define relevant markets

Identify:

• product markets;
• geographic markets;
• multi-sided markets;
• complementary markets.

Step 3 — Establish market power

Examine:

• market shares;
• network effects;
• data;
• switching costs;
• entry barriers;
• multi-homing;
• ecosystem breadth.

Step 4 — Identify the conduct

Determine whether the undertaking is engaging in:

• tying;
• bundling;
• self-preferencing;
• exclusivity;
• discriminatory access;
• data combination;
• interoperability restrictions;
• algorithmic foreclosure.

Step 5 — Examine foreclosure

Ask:

Does the conduct materially impair competitors' ability to compete?

Step 6 — Examine consumer effects

Consider:

• price;
• quality;
• innovation;
• privacy;
• choice;
• service quality;
• technological development.

Step 7 — Consider justification

Assess:

• legitimate business reasons;
• efficiencies;
• security;
• technical necessity;
• product improvement;
• proportionality.

Step 8 — Select remedy

Possible responses include:

• behavioural commitments;
• interoperability;
• non-discrimination;
• data remedies;
• access obligations;
• structural separation.

20. Comparative Case-Law Matrix

21. Core Principles Emerging from the Cases

Principle 1

Large ecosystems are not automatically unlawful monopolies.

Competition law focuses on market power and conduct rather than size alone.

Principle 2

Control of a gateway can confer power over adjacent markets.

Search engines, operating systems, app stores, marketplaces and cloud platforms can operate as strategic gateways.

Principle 3

Data may reinforce market power.

The Facebook/Meta proceedings demonstrate the possibility of competition-law intervention where data practices reinforce an existing dominant position.

Principle 4

Algorithms can be instruments of exclusion.

Algorithmic ranking, traffic allocation and technical restrictions may form part of an exclusionary strategy, as illustrated particularly by the Alibaba and Meituan cases.

Principle 5

Ecosystem analysis should examine multiple markets simultaneously.

The Meta Section 19a approach expressly recognises the competitive significance of a company operating across interconnected markets.

Principle 6

Interoperability is increasingly important.

Where switching costs and network effects are substantial, interoperability can materially affect whether competition remains contestable.

22. Conclusion

Intelligent value ecosystems represent a significant evolution in competition-law analysis. Traditional dominance analysis focuses primarily on a firm's position within a defined relevant market. Ecosystem competition requires an additional examination of the relationships between markets, platforms, data, algorithms, infrastructure and complementary services.

The principal competition risks are:

1. leveraging dominance across adjacent markets;
2. self-preferencing;
3. tying and bundling;
4. exclusive dealing;
5. data accumulation and combination;
6. algorithmic discrimination;
7. interoperability restrictions;
8. increased switching costs;
9. network-effect-driven foreclosure;
10. acquisition of emerging competitors; and
11. control over essential ecosystem infrastructure.

The Google Android, Google Shopping, Intel, Facebook/Meta, Alibaba and Meituan cases, together with Microsoft's technological-ecosystem jurisprudence, demonstrate the transition from conventional single-market analysis toward a more sophisticated examination of gateway control, data, algorithms, interoperability, network effects and cross-market leveraging.