Competition Law and Adaptive Governance of Intelligent Commercial Ecosystems

1. Introduction

Adaptive governance of intelligent commercial ecosystems refers to a modern approach to competition regulation in which competition authorities continuously adjust legal, regulatory, and enforcement mechanisms to deal with businesses operating across interconnected digital markets.

An intelligent commercial ecosystem is more than a single product or market. It may combine:

• artificial intelligence;
• cloud computing;
• digital platforms;
• app stores;
• operating systems;
• online marketplaces;
• digital advertising;
• payment systems;
• data analytics;
• search engines;
• recommendation algorithms;
• connected devices;
• consumer services; and
• business-to-business infrastructure.

Competition problems can therefore move from one market to another. A company may use control over one important infrastructure—such as an operating system, app store, marketplace, search engine, cloud platform, or advertising network—to strengthen its position elsewhere.

Traditional competition law generally examines a specific market and a particular form of conduct. Intelligent ecosystems create a need for continuous monitoring, cross-market analysis and flexible remedies.

The EU's Digital Markets Act and Germany's Section 19a GWB illustrate this movement toward earlier and more structural intervention. Germany, for example, introduced Section 19a to permit earlier intervention against companies having "paramount significance for competition across markets."

2. Meaning of an Intelligent Commercial Ecosystem

An intelligent commercial ecosystem can be understood as a network in which several products, services, users, suppliers and technologies interact under the control or influence of one or more central firms.

For example:

Operating System → App Store → Developers → Consumers → Payments → Advertising → Data → AI Services

Each component can reinforce the others.

This creates several competition-law characteristics:

A. Multi-sided markets

A platform may simultaneously serve:

• consumers;
• advertisers;
• application developers;
• merchants;
• content providers; and
• business customers.

A competition authority therefore cannot always analyse the platform from only one side.

B. Network effects

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

For example:

More users → more developers → more applications → more users.

This can create self-reinforcing market structures.

C. Data advantages

Large ecosystems may generate enormous quantities of behavioural, commercial and technical data.

That data can improve:

• search;
• recommendations;
• advertising;
• AI models;
• pricing;
• fraud detection; and
• customer targeting.

The resulting feedback loop can make entry by smaller competitors more difficult.

D. Switching costs

Consumers and businesses may become dependent upon:

• stored data;
• applications;
• subscriptions;
• technical integrations;
• customer reviews;
• cloud infrastructure;
• payment systems; or
• business relationships.

Consequently, a competitor may technically be available but practically difficult to adopt.

E. Ecosystem leverage

A firm with market power in one service can potentially use that position to influence another market.

This is particularly important in:

• tying;
• bundling;
• self-preferencing;
• exclusive arrangements;
• interoperability restrictions;
• data access;
• default settings; and
• platform rules.

3. What Is Adaptive Competition Governance?

Adaptive governance means that competition regulation does not remain fixed while technology changes.

It involves:

1. Continuous market monitoring

Authorities monitor changes in:

• technology;
• market structure;
• acquisitions;
• algorithms;
• business models;
• consumer behaviour;
• interoperability; and
• emerging competitors.

2. Early intervention

Instead of waiting until competition has already disappeared, authorities can sometimes intervene when structural risks become apparent.

Germany's Section 19a framework is an important example. It permits the Bundeskartellamt to identify companies with paramount significance across markets and subsequently examine specific practices.

3. Behavioural monitoring

Authorities may examine:

• ranking algorithms;
• recommendation systems;
• default settings;
• access conditions;
• pricing rules;
• platform terms;
• data practices; and
• interoperability.

4. Structural analysis

The question becomes not merely:

"Did the company harm competition in one transaction?"

but also:

"How does the company's position across connected markets affect competitive conditions throughout the ecosystem?"

5. Flexible remedies

Possible remedies include:

• access obligations;
• interoperability;
• data portability;
• removal of discriminatory conditions;
• restrictions on self-preferencing;
• modification of contractual terms;
• transparency requirements;
• non-discrimination obligations; and
• structural remedies in exceptional circumstances.

4. Main Competition-Law Issues

A. Abuse of Dominance

Article 102 TFEU and comparable national laws can apply when a dominant company uses its market power in an abusive manner.

In an intelligent ecosystem, abuse may occur through:

• tying;
• bundling;
• discriminatory access;
• exclusionary rebates;
• self-preferencing;
• refusal to supply;
• exploitative contractual conditions;
• interoperability restrictions.

The difficulty is that dominance may exist in one part of an ecosystem while the alleged competitive harm appears elsewhere.

5. Self-Preferencing

Self-preferencing occurs when a platform gives preferential treatment to its own products or services.

For example:

Platform controls marketplace + platform sells its own products

The competition question becomes whether the platform uses its intermediary position to disadvantage independent sellers.

The issue is especially significant where algorithms determine:

• ranking;
• search visibility;
• recommendations;
• product placement;
• advertising exposure.

6. Algorithmic Competition Problems

AI and algorithms create new competition concerns.

An algorithm may determine:

• prices;
• ranking;
• recommendations;
• advertising;
• search results;
• access to customers.

The important legal issue is not simply that an algorithm is used. Algorithms are normal business tools.

The competition question is whether the algorithm facilitates or implements conduct that restricts competition.

Potential concerns include:

• algorithmic coordination;
• discriminatory ranking;
• exclusion of competitors;
• personalized exploitation;
• automated retaliation;
• preferential treatment;
• discriminatory access.

7. Data as a Competitive Resource

Data can function as an important competitive input.

An ecosystem may possess:

• consumer data;
• transaction data;
• search data;
• location data;
• advertising data;
• merchant data;
• technical data.

A dominant firm may potentially use commercially sensitive information obtained from business users to compete against those same businesses.

This creates a conflict:

Platform provider → receives business-user data → analyses data → competes with business users

Competition authorities therefore increasingly examine data access and data use as part of ecosystem governance.

8. Interoperability

Interoperability means allowing different systems or services to work together.

Examples include:

• messaging interoperability;
• payment interoperability;
• cloud interoperability;
• operating-system interoperability;
• app interoperability;
• data portability.

An ecosystem owner may have incentives to limit interoperability because compatibility can make switching easier.

However, competition law must balance interoperability against legitimate concerns such as:

• security;
• privacy;
• intellectual property;
• technical integrity;
• cybersecurity.

9. Network Effects and Entry Barriers

Network effects can make an ecosystem increasingly difficult to challenge.

A simplified cycle is:

More users
↓
More data
↓
Better services/AI
↓
More businesses and developers
↓
More users
↓
Greater ecosystem power

This is sometimes called a data-network-feedback loop.

Competition law therefore needs to consider not only current market shares but also:

• switching costs;
• entry conditions;
• access to data;
• interoperability;
• ecosystem expansion;
• acquisition of emerging competitors.

10. Relevant Case Laws

Case 1 — Google Shopping

Google Search (Shopping) — Case T-612/17

The European Commission found that Google had abused its dominant position in general search by favouring its own comparison-shopping service in search results and disadvantaging competing comparison-shopping services.

The General Court upheld the essential finding of abuse in 2021, while examining the specific competitive effects and legal reasoning.

Importance

The case is important for intelligent ecosystems because Google was not simply competing as a shopping service.

It simultaneously operated:

Search infrastructure + ranking system + shopping service

The case demonstrates how control over a gateway can influence competition in an adjacent commercial service.

Governance lesson

Competition authorities may need to examine:

• ranking;
• visibility;
• algorithmic placement;
• platform neutrality;
• vertical integration.

The case helped demonstrate why traditional competition law must be capable of addressing platform-based ecosystems.

Case 2 — Google Android

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

The Android case concerned Google's conduct involving the Android operating system, Google Search, Chrome and the Play Store.

The General Court's judgment addressed:

• multi-sided platforms;
• ecosystem structure;
• operating systems;
• app stores;
• product bundling;
• exclusivity payments;
• anti-fragmentation obligations.

The court recognized the importance of examining the interconnected nature of Google's mobile ecosystem.

Importance

The case illustrates ecosystem leverage:

Android → device manufacturers → Play Store → apps → Search → Chrome → users

A restriction imposed at one level can affect competition at several other levels.

Governance lesson

Competition analysis should examine the overall commercial architecture, rather than considering every contractual practice completely in isolation.

Case 3 — Microsoft

Microsoft Corp. v Commission, Case T-201/04

The Microsoft case involved Microsoft's Windows operating-system dominance and conduct involving:

• interoperability information;
• Windows Media Player;
• software distribution;
• competing technologies.

The European courts upheld important findings concerning Microsoft's abuse of dominance.

Importance

Microsoft demonstrated that an operating system can become an important technological gateway.

Control over that gateway can affect:

• software developers;
• competing applications;
• hardware manufacturers;
• consumers.

Governance lesson

An intelligent ecosystem should not be analysed solely according to the immediate product being sold.

The authority should examine:

Who controls the gateway, who depends upon it, and what competitive opportunities exist outside the gateway?

The U.S. Microsoft litigation similarly remains an important illustration of how tying and platform control can affect adjacent software markets.

Case 4 — Amazon Marketplace

Bundeskartellamt proceedings concerning Amazon

The German competition authority examined Amazon's treatment of sellers on its marketplace.

Among the issues examined were Amazon's former price-parity provisions and contractual conditions affecting marketplace sellers.

Amazon ultimately removed the price-parity provision examined in the 2013 proceeding. In the 2018–2019 proceedings, Amazon also made significant changes to its terms of business for sellers.

The Bundeskartellamt subsequently determined in 2022 that Amazon had paramount significance for competition across markets under Section 19a GWB. Germany's Federal Court of Justice upheld that determination in 2024.

Importance

Amazon illustrates the ecosystem model particularly clearly:

Marketplace + retailer + advertising + logistics + cloud + streaming

A marketplace can simultaneously function as:

• intermediary;
• competitor;
• data collector;
• advertising provider;
• infrastructure provider.

Governance lesson

Adaptive governance can examine the overall power of an ecosystem, rather than waiting for traditional dominance analysis in every individual market.

Case 5 — Meta / Facebook

FTC v. Facebook, Inc. / Meta Platforms

The U.S. Federal Trade Commission brought an antitrust case alleging that Facebook maintained monopoly power in personal social networking through conduct including acquisitions of Instagram and WhatsApp and certain restrictions affecting developers.

The case illustrates the competition concern surrounding acquisitions of potentially important future competitors.

The FTC's case remained subject to litigation, and the FTC announced in January 2026 that it was appealing a November 2025 district-court ruling in Meta's favour.

Importance

The case demonstrates the relevance of ecosystem expansion through acquisitions.

A digital ecosystem can grow not only organically but also by purchasing:

• emerging competitors;
• complementary services;
• new technologies;
• user networks.

Governance lesson

Adaptive competition policy therefore needs effective merger surveillance.

The FTC's earlier review of acquisitions by major technology companies also showed why authorities may examine transactions that were not initially subject to ordinary mandatory reporting requirements.

Case 6 — Intel

Intel Corp. v European Commission, Case C-413/14 P

The Intel litigation concerned rebates provided by Intel to major computer manufacturers and a retailer.

The Court of Justice required the Commission to properly examine, where raised and relevant, whether the rebates were capable of producing exclusionary effects.

Importance for intelligent ecosystems

The case demonstrates an important principle:

Competition analysis cannot simply assume that a particular contractual mechanism is harmful without properly examining its competitive effects where the legal framework requires such analysis.

Governance lesson

Adaptive governance should be:

• flexible;
• evidence-based;
• technologically informed;
• economically rigorous.

It should not become automatic regulation of every successful business practice.

Case 7 — Apple App Store / Epic Games

Epic Games, Inc. v Apple Inc.

The U.S. litigation concerning Apple's App Store examined Apple's rules governing distribution and payments for apps.

The dispute involved questions concerning:

• app distribution;
• payment systems;
• platform rules;
• developer access;
• restrictions on alternative payment mechanisms.

Importance

It illustrates the central ecosystem problem:

Operating system → App Store → developers → consumers → payment infrastructure

A platform may control the technical gateway through which businesses reach customers.

Governance lesson

Competition authorities increasingly need to consider whether platform rules:

• restrict entry;
• increase switching costs;
• prevent alternative distribution;
• prevent alternative payment systems;
• favour platform-controlled services.

11. Why These Cases Matter Together

The cases reveal a gradual evolution of competition-law thinking.

12. EU Digital Markets Act and Adaptive Governance

The Digital Markets Act (DMA) represents one of the clearest examples of a shift toward ex-ante digital-market governance.

The European Commission initially designated six gatekeepers in September 2023:

• Alphabet;
• Amazon;
• Apple;
• ByteDance;
• Meta; and
• Microsoft.

Booking was subsequently designated as well, and the Commission's current gatekeeper framework covers multiple core platform services.

The DMA addresses issues including:

• interoperability;
• data access;
• data portability;
• anti-self-preferencing;
• switching;
• platform access;
• advertising transparency.

Germany's competition authority similarly describes the DMA as applying to designated gatekeepers providing services such as online intermediation, search engines and social networking.

13. Section 19a GWB — German Adaptive Model

Germany provides another important model.

Section 19a GWB creates a two-stage mechanism.

Stage 1

The Bundeskartellamt determines whether a company has:

paramount significance for competition across markets.

This determination has been made in relation to companies including:

• Meta/Facebook;
• Alphabet/Google;
• Amazon;
• Apple; and
• Microsoft.

Stage 2

The authority can then examine particular conduct that may threaten competition.

This is significant because the authority does not have to treat each market as completely isolated.

It can consider the company's cross-market ecosystem power.

14. AI and Intelligent Commercial Ecosystems

Artificial intelligence makes adaptive governance even more important.

AI can strengthen ecosystems through:

A. Data advantages

More data can improve AI models.

B. Compute advantages

Large cloud providers may control critical computing infrastructure.

C. Distribution advantages

An ecosystem can place its AI assistant directly into:

• operating systems;
• search engines;
• smartphones;
• cloud services;
• productivity software.

D. Bundling

AI services can be combined with:

• cloud subscriptions;
• enterprise software;
• advertising;
• operating systems;
• productivity suites.

E. Vertical integration

One company may simultaneously control:

chips → cloud → AI models → applications → distribution

This can create new competition-law questions.

The EU's 2026 preliminary position concerning AWS and Microsoft Azure illustrates how cloud infrastructure is increasingly being examined as a potential gateway within broader digital ecosystems, particularly given the relationship between cloud services, AI tools and switching costs.

15. Adaptive Governance of AI Ecosystems

A modern competition authority may therefore need to monitor:

1. Access to computing resources

Whether competitors can obtain essential computing capacity on reasonable terms.

2. Data access

Whether important datasets are accessible or effectively controlled by one ecosystem.

3. Model distribution

Whether a dominant platform gives its own AI model preferential distribution.

4. Cloud-AI tying

Whether cloud infrastructure is tied to proprietary AI services.

5. Default settings

Whether AI assistants are installed or selected by default.

6. Interoperability

Whether competing AI services can function effectively across dominant platforms.

7. Acquisitions

Whether major platforms acquire emerging AI competitors or critical technologies.

16. Role of Merger Control

Merger control becomes particularly important because digital ecosystems can grow through acquisition.

Traditional merger thresholds may sometimes fail to capture transactions involving:

• startups;
• AI developers;
• data-rich companies;
• emerging platforms;
• nascent competitors.

The competition concern is not necessarily the current revenue of the target.

The target may possess:

• technology;
• data;
• intellectual property;
• user networks;
• engineering talent;
• future competitive potential.

Therefore, adaptive merger governance may examine potential competition, not merely current market share.

17. Pro-Competitive Benefits Must Also Be Considered

Adaptive governance should not assume that ecosystem integration is automatically anti-competitive.

Integration can produce legitimate benefits such as:

• lower costs;
• improved security;
• better interoperability;
• faster innovation;
• integrated customer experience;
• improved fraud prevention;
• better AI performance;
• reduced transaction costs.

For example, technical integration between an operating system and an application may genuinely improve security or functionality.

Therefore, the competition-law question is generally:

Does the conduct represent competition on the merits, or does it use ecosystem power to exclude or disadvantage competitors without sufficient legitimate justification?

18. Proportionality of Remedies

An important principle of adaptive governance is proportionality.

Authorities should match the remedy to the competition problem.

Possible remedies

Behavioural remedies

• remove discriminatory terms;
• modify contracts;
• provide access;
• stop self-preferencing;
• change ranking practices.

Technical remedies

• interoperability;
• APIs;
• data portability;
• switching tools.

Structural remedies

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

Structural remedies are generally more intrusive and therefore require particularly strong legal and factual justification.

19. Institutional Cooperation

Intelligent ecosystems often cross borders.

Therefore, effective governance may require cooperation between:

• national competition authorities;
• the European Commission;
• courts;
• consumer authorities;
• data-protection regulators;
• telecommunications regulators;
• digital-market regulators.

This is important because the same platform may simultaneously create:

competition issue + privacy issue + consumer-protection issue + cybersecurity issue.

No single regulatory perspective necessarily captures the entire ecosystem.

20. Challenges for Competition Authorities

A. Rapid technological change

AI and digital markets can change faster than litigation.

B. Market definition

Traditional market boundaries may become difficult to establish.

C. Algorithmic opacity

Authorities may not easily understand complex ranking or pricing algorithms.

D. Cross-market leverage

Power can move from one market to another.

E. Potential competition

A small company may represent a significant future competitive threat despite limited current revenue.

F. Global ecosystems

A company may operate infrastructure across multiple jurisdictions.

G. Innovation dilemma

Excessive regulation could potentially reduce legitimate innovation, while insufficient regulation could allow entrenched ecosystem power to become difficult to challenge.

21. Key Principles of an Adaptive Competition Framework

A strong framework can be organized around eight principles:

1. Ecosystem-based analysis

Study interconnected markets rather than isolated products.

2. Dynamic assessment

Consider how market power may develop over time.

3. Continuous monitoring

Track technological and commercial developments.

4. Data-aware enforcement

Treat control and use of data as potentially relevant competitive factors.

5. Interoperability

Examine whether technical restrictions unnecessarily prevent competition.

6. Early intervention

Address structural risks before they become irreversible where legislation permits.

7. Evidence-based remedies

Use remedies specifically connected to the identified competitive harm.

8. Regulatory coordination

Coordinate competition law with other relevant regulatory regimes.

22. Conclusion

Competition Law and Adaptive Governance of Intelligent Commercial Ecosystems represents the transition from a largely static view of competition toward a more dynamic understanding of digital commercial power.

The central issue is no longer simply:

"Who has the largest market share?"

It increasingly involves questions such as:

• Who controls the gateway?
• Who controls access to consumers?
• Who controls important data?
• Who controls infrastructure?
• Can businesses switch platforms?
• Can competitors interoperate?
• Does an ecosystem favour its own services?
• Can a dominant platform transfer power between markets?
• Can emerging competitors realistically challenge the ecosystem?
• Are mergers eliminating future competition?

The Google Shopping, Google Android, Microsoft, Amazon, Meta and Intel cases demonstrate different parts of this evolution. Modern instruments such as the EU DMA and Germany's Section 19a GWB go further by permitting more structural and anticipatory approaches to major digital ecosystems.

Ultimately, adaptive competition governance seeks to preserve contestability, innovation, consumer choice, access and fair competitive opportunities while allowing legitimate technological integration and business innovation to continue.