Competition Law and Algorithmic Ecosystem Governance

1. Introduction

Algorithmic ecosystem governance describes the way digital platforms use algorithms, technical rules, data, APIs, ranking systems, operating systems, app stores, advertising systems, and automated decision-making to govern an entire network of businesses and users.

Traditional competition law often examined a relatively simple relationship: one firm competing against another in a particular market. Digital ecosystems are more complicated. A single undertaking may simultaneously control an operating system, app marketplace, search engine, payment mechanism, advertising infrastructure, browser, data resources, and interfaces through which third-party businesses reach customers.

Competition law therefore asks whether the firm controlling an ecosystem is merely designing its products efficiently or is using algorithmic governance to exclude competitors, favour its own services, increase switching costs, restrict interoperability, control valuable data, or extend market power from one service into another.

This issue has become particularly important in the European Union. The Digital Markets Act (DMA) complements conventional competition rules by imposing specific obligations on designated digital gatekeepers. Current designated services include platforms operated by Alphabet, Apple, Amazon, Meta, Microsoft, ByteDance and Booking.

2. Meaning of an Algorithmic Ecosystem

An algorithmic ecosystem is broader than an individual algorithm.

Consider a mobile ecosystem. It may contain:

Operating system → app store → developers → payment system → advertising network → browser → search engine → AI assistant → consumers.

Algorithms may determine:

• which applications appear prominently;
• which seller or service receives the highest ranking;
• which advertisements win auctions;
• which apps obtain access to operating-system functions;
• what data third parties can access;
• which default service consumers encounter;
• how recommendations are generated;
• whether competing applications can communicate with other services;
• what fees developers or sellers must pay.

Consequently, control over algorithms can become a form of ecosystem governance power.

3. Competition-Law Concerns

Competition law normally does not prohibit a company merely because its ecosystem is large or technologically sophisticated. The concern arises when market power is combined with conduct capable of restricting the competitive process.

Important theories include abuse of dominance or monopolization, tying, exclusive dealing, discriminatory access, self-preferencing, foreclosure, restrictions on interoperability and data access, and anticompetitive agreements.

Self-preferencing

A vertically integrated platform may operate the infrastructure while simultaneously competing with businesses using that infrastructure.

For example:

Platform → ranking algorithm → platform's own product + competing products.

If the platform systematically gives its own downstream product advantageous placement or treatment, authorities may investigate whether competitors are being disadvantaged for reasons unrelated to competitive merit.

The EU's Google Shopping litigation is the leading example.

4. Case Law

Case 1 — Google Shopping

Google and Alphabet v Commission, Case C-48/22 P

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

Google operated its general search engine while also offering its own comparison-shopping service. The European Commission concluded that Google gave its comparison-shopping service more favourable positioning and display in general search results while rival comparison-shopping services were subjected to Google's general ranking mechanisms.

The General Court substantially upheld the Commission's decision, and in September 2024 the Court of Justice dismissed Google and Alphabet's appeal. The approximately €2.4 billion fine therefore remained in place.

Importance

The case demonstrates that algorithmic ranking cannot necessarily be treated as an entirely internal product-design matter where a dominant undertaking uses the ranking architecture to favour an adjacent service.

The central ecosystem issue can be represented as:

Control of search → control of visibility → advantage for own vertical service → possible foreclosure of competing services.

It therefore provides an important foundation for competition-law scrutiny of algorithmic self-preferencing.

Case 2 — Google Android

Google Android, European Commission decision; subsequent EU litigation

Android created a broader ecosystem issue involving the mobile operating system, Google Play, Google Search and related applications.

The Commission's case concerned contractual and technical arrangements associated with Google's Android ecosystem. Among other matters, the Commission considered requirements relating to pre-installation of Google Search and Chrome and restrictions associated with competing versions of Android.

The case illustrates the importance of ecosystem leverage.

A powerful position at one level of an ecosystem can potentially influence competition at another level:

Mobile OS → app distribution → default/pre-installed services → search traffic → data → stronger search position.

From an algorithmic-governance perspective, the significance lies in recognizing that competition may depend not simply on whether rival software technically exists, but on its practical ability to obtain distribution and compete inside the ecosystem.

Case 3 — United States v Google — Search

United States et al. v Google LLC

The U.S. Department of Justice challenged Google's arrangements concerning distribution of its search engine.

In August 2024, the U.S. District Court for the District of Columbia concluded that Google had unlawfully maintained monopolies in general search services and general search text advertising. The litigation subsequently moved into remedies proceedings.

In September 2025, the court imposed remedies including restrictions concerning certain exclusive distribution contracts and requirements involving access to specified search and user-interaction data and search syndication.

Ecosystem significance

Search quality can involve a feedback mechanism:

more users → more query/interactions data → improvement of search → stronger product → more users.

Distribution agreements can therefore matter beyond immediate access to consumers. They can potentially influence the scale of data available for improving algorithms.

This case demonstrates why competition authorities increasingly consider defaults, distribution, data and algorithmic improvement together rather than examining each component separately.

Case 4 — United States v Google — Advertising Technology

United States et al. v Google LLC, E.D. Virginia

This case concerns another form of algorithmic ecosystem: digital advertising infrastructure.

Google participates at several levels of the advertising technology supply chain. The U.S. government challenged conduct involving publisher ad servers and advertising exchanges.

In April 2025, the federal district court held that Google had unlawfully monopolized certain open-web digital advertising technology markets.

The government's case had alleged practices including acquisitions, restrictions connecting Google's advertising tools and advantages in auction mechanisms.

Importance

The case demonstrates how control of interconnected algorithmic infrastructure can matter.

The relevant ecosystem can involve:

advertisers → buying tools → automated auctions → exchanges → publisher tools → websites.

An undertaking operating multiple layers may have both information and architectural advantages. Competition law therefore examines whether integration generates legitimate efficiencies or whether control across layers is being used to disadvantage independent competitors.

Case 5 — Microsoft

Commission v Microsoft, Case T-201/04

Although this litigation predates today's AI-driven ecosystems, Microsoft remains foundational for ecosystem competition law.

The case concerned Microsoft's position in PC operating systems and, among other issues, interoperability information and the tying of Windows Media Player.

The General Court largely upheld the Commission's decision.

Modern relevance

The case established important principles concerning interoperability and leveraging market power across complementary technological products.

Its ecosystem logic remains highly relevant:

dominant platform → control over technical interfaces → third-party compatibility → ability of complementary products to compete.

Modern APIs, mobile operating-system functions, cloud interfaces and AI integrations raise structurally similar questions.

The case therefore provides an important doctrinal ancestor of modern ecosystem-governance disputes.

Case 6 — Microsoft/Internet Explorer

United States v Microsoft Corp.

The U.S. Microsoft litigation is another foundational ecosystem case.

Microsoft controlled the Windows operating-system environment and faced allegations concerning conduct directed at preserving its operating-system monopoly against threats associated with browsers and related technologies.

The litigation became especially significant for the concept of platform threats.

A complementary product can eventually weaken the platform owner's market power. Consequently, an incumbent may have incentives to restrict technologies capable of developing into alternative platforms.

Algorithmic relevance

The same logic can apply today to:

• alternative app stores;
• independent AI assistants;
• competing browsers;
• alternative payment systems;
• cloud platforms;
• super-apps;
• independent search engines.

Competition law therefore needs to consider not only existing competitors but also technologies that might reduce dependence on the incumbent ecosystem.

Case 7 — Intel v Commission

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

Intel concerned rebates offered by a dominant processor manufacturer.

Although it was not an algorithm case, it is highly relevant to ecosystem analysis because the litigation emphasized careful consideration of the capacity of allegedly exclusionary conduct to foreclose competitors.

Its broader lesson is that authorities should not automatically assume that conduct by a dominant technological firm is anticompetitive merely because competitors are disadvantaged.

Economic context matters.

Relevant questions include:

• market coverage;
• duration;
• conditions attached to incentives;
• competitive alternatives;
• ability of an efficient competitor to compete;
• actual economic context.

This principle is important when evaluating algorithmic governance because algorithmic differentiation can have legitimate explanations, including quality, security, fraud prevention and technical efficiency.

Case 8 — Slovak Telekom

Slovak Telekom v Commission, Case C-165/19 P

This case involved access to telecommunications infrastructure and alleged exclusionary practices.

Its importance for digital ecosystems lies in the broader distinction between circumstances involving a straightforward refusal to provide access and situations where an undertaking already provides access but imposes allegedly unfair or exclusionary conditions.

That distinction can become relevant to modern digital ecosystems involving:

APIs, operating-system functionality, platform interfaces, datasets and interoperability mechanisms.

Competition law must therefore identify precisely what the ecosystem controller has done rather than treating every limitation on access as legally identical.

5. Interoperability as an Ecosystem-Governance Issue

Modern competition increasingly depends on whether third-party products can interact effectively with a dominant platform.

Suppose an operating system gives its owner's AI assistant access to:

• messaging functions;
• camera functions;
• contacts;
• application controls;
• notifications;

while independent AI assistants receive substantially weaker technical access.

The independent AI product might therefore struggle even if its underlying model is competitive.

This issue is now especially concrete under the EU DMA. In July 2026, the Commission issued specification measures concerning Google's Android ecosystem aimed at giving competing AI services access to relevant Android capabilities, alongside measures concerning sharing of Google Search data with eligible competing search services.

This represents a significant development in algorithmic ecosystem governance: regulation can target the interfaces surrounding an algorithm, not merely the algorithm itself.

6. Data as Ecosystem Infrastructure

Data can reinforce ecosystem power through feedback effects.

A simplified mechanism is:

large user base
↓
more behavioural/query data
↓
better algorithms
↓
better service
↓
more users
↓
more data

This does not mean that data automatically creates an unlawful monopoly.

Competition concerns become stronger when competitors cannot realistically reproduce an important dataset and the incumbent combines its data advantage with exclusionary conduct.

The EU's current approach illustrates this point. Under Article 6(11) DMA, Google must provide eligible competing search engines access to specified anonymised search data on fair, reasonable and non-discriminatory terms. In July 2026, the Commission adopted measures specifying implementation of this obligation, including its relevance to eligible AI chatbots offering search functionality.

7. Algorithmic Ranking and Self-Preferencing

Ranking is one of the most important governance mechanisms.

A platform can determine:

Who appears first?
Who receives recommendations?
Who receives traffic?
Which products are effectively invisible?

Ranking itself is not inherently anticompetitive. Algorithms legitimately rank services according to relevance, price, quality and many other factors.

Competition concerns arise where a dominant ecosystem controller manipulates the rules to favour itself or systematically disadvantages rivals without an adequate competitive justification.

The development from Google Shopping to the Digital Markets Act demonstrates this shift.

In July 2026, the Commission found Google non-compliant with DMA requirements concerning self-preferencing in Google Search and separately concerning restrictions on steering users to alternative channels through Google Play, imposing fines of €460 million and €430 million respectively.

8. Multi-Homing and Switching

Another important question is whether businesses and consumers can participate in several ecosystems simultaneously.

This is called multi-homing.

For example, an app developer might distribute through several app stores.

Competition problems may become more serious where an ecosystem uses technical or contractual rules that make multi-homing difficult.

Potential restrictions include:

• exclusivity;
• restrictive APIs;
• incompatible standards;
• data portability barriers;
• anti-steering provisions;
• default settings;
• discriminatory access;
• contractual penalties for using competing services.

High switching costs can reinforce ecosystem power even without an explicit prohibition on switching.

9. Algorithmic Tying

Algorithmic ecosystems can also create modern forms of tying.

Suppose access to Service A effectively requires adoption of Service B.

Examples could involve:

Operating system + app store

Advertising exchange + publisher technology

Cloud infrastructure + proprietary AI service

Marketplace + payment system

Competition law examines whether these products constitute separate products or markets, whether sufficient market power exists, and whether the arrangement is capable of producing anticompetitive foreclosure.

Microsoft and Google's ad-tech litigation demonstrate why this theory remains important in technology markets.

10. Ecosystem Effects and Network Effects

Digital platforms frequently benefit from network effects.

Direct network effect

More users can make a communications platform more useful.

Indirect network effect

More consumers attract developers, while more developers attract consumers.

For example:

more smartphone users → more developers → more apps → more attractive OS → more users.

Algorithmic systems can intensify this process because increased activity can generate additional information for recommendation, matching or search algorithms.

Strong network effects are not themselves illegal. The competition question is whether firms are competing on the merits or using exclusionary practices to prevent competitive constraints from developing.

11. Ecosystem Governance Under the Digital Markets Act

Traditional competition enforcement is largely case-specific and frequently requires substantial investigation of market power and competitive effects.

The DMA takes a complementary approach by establishing obligations for designated gatekeepers.

Relevant obligations address matters such as:

• interoperability;
• data portability;
• access to certain data;
• self-preferencing;
• steering;
• defaults and user choice;
• interactions between gatekeeper and third-party services.

The Commission specifically describes interoperability and data portability as important mechanisms for improving contestability in mobile ecosystems.

Thus, modern European digital competition policy effectively contains two layers:

Competition law: investigates particular anticompetitive agreements or abuses.

DMA: establishes specified ex ante obligations for designated gatekeepers.

12. AI and the Next Generation of Ecosystem Governance

AI adds another layer.

An AI assistant may increasingly operate as an interface connecting consumers with:

search + shopping + travel + communication + entertainment + payments + applications.

This raises a potentially important competition question:

Who controls the algorithm that decides which underlying service receives the user's request?

If an ecosystem owner operates both the AI assistant and downstream services, authorities may need to examine whether competing providers receive equivalent opportunities to interact with users.

The Commission's 2026 Android interoperability measures illustrate that this issue has already moved beyond theory: they specifically address access by competing AI assistants to Android capabilities.

13. Main Legal Test

A useful framework for analysing algorithmic ecosystem governance is:

Step 1 — Define the relevant market or ecosystem relationships.

Identify operating systems, app stores, search, advertising, marketplaces, AI services or other relevant layers.

Step 2 — Establish market power or dominance where the applicable law requires it.

A large ecosystem alone does not establish unlawful conduct.

Step 3 — Identify the governance mechanism.

This could involve ranking algorithms, APIs, defaults, data-access rules, auction algorithms, interoperability requirements or contractual restrictions.

Step 4 — Determine the exclusionary mechanism.

Ask whether rivals face foreclosure, discriminatory treatment, restricted access, increased switching costs or artificial barriers to entry.

Step 5 — Examine competitive effects.

Relevant effects can include reduced choice, weaker innovation, higher costs, restricted entry or reinforcement of market power.

Step 6 — Consider objective justification and efficiencies.

Security, privacy, product quality, technical integrity and efficiency may provide legitimate explanations for some restrictions.

Step 7 — Design proportionate remedies.

Possible remedies can concern contracts, interoperability, data access, non-discrimination, user choice or, in appropriate cases, structural measures.

14. Key Case-Law Summary

Conclusion

Competition Law and Algorithmic Ecosystem Governance concerns more than whether a particular algorithm produces an unfair result. The central issue is whether control over an interconnected technological environment allows an undertaking to shape the competitive opportunities of businesses that depend upon that environment.

Modern analysis therefore increasingly examines the combination of algorithms + data + defaults + APIs + interoperability + ranking + distribution + network effects + vertical integration.

Cases such as Google Shopping, Google Android, United States v Google, Microsoft, Intel and Slovak Telekom provide the doctrinal foundations. More recent DMA developments extend the focus toward practical ecosystem contestability, including search-data access and interoperability for competing AI services.

The emerging principle is that an ecosystem controller may design and improve its technology, but competition rules can become relevant where control of the ecosystem is used to foreclose rivals, discriminate in favour of affiliated services, restrict effective interoperability, or preserve market power through mechanisms unrelated to competition on the merits.