Competition Law and Strategic Competition Regulation of Intelligent Ecosystems

1. Introduction

Intelligent ecosystems are interconnected economic systems in which artificial intelligence, algorithms, data, cloud computing, digital platforms, autonomous agents, connected devices and physical infrastructure interact to provide products or services.

Examples include:

AI-platform ecosystems;

cloud and AI ecosystems;

autonomous-vehicle ecosystems;

smart-home ecosystems;

digital-payment ecosystems;

robotics ecosystems;

healthcare-AI ecosystems;

intelligent energy systems;

digital advertising ecosystems;

AI-agent marketplaces.

Competition regulation in such ecosystems is more complex than conventional competition law because market power may arise not from a single product, but from control over an entire technological architecture.

The central competition question becomes:

Can an undertaking use control over one technological layer to restrict competition across the wider ecosystem?

2. Meaning of an Intelligent Ecosystem

An intelligent ecosystem can be represented as:

Infrastructure → Data → AI/Algorithm → Platform → Agent → Application → Consumer

For example:

Cloud infrastructure
↓
AI model
↓
AI assistant
↓
Marketplace
↓
Payment system
↓
Consumer

A firm controlling several layers may possess substantial ecosystem power.

However, ecosystem size alone does not establish an infringement. Competition authorities must establish the relevant market, market power, conduct and competitive effects under the applicable legal framework.

3. Why Intelligent Ecosystems Create New Competition Problems

Traditional markets generally involve:

Producer → Product → Consumer.

Intelligent ecosystems involve:

Infrastructure → Platform → Data → Algorithm → Multiple services → Multiple user groups.

This produces several special characteristics.

A. Network effects

More users attract more businesses, which attract more users.

B. Data feedback loops

More users → more data → better algorithms → more users.

C. Economies of scope

The same infrastructure can serve many markets.

D. Switching costs

Users may lose:

data;

preferences;

applications;

contacts;

accumulated reputation;

compatibility.

E. Ecosystem lock-in

A firm can make competing products less attractive by controlling complementary services.

4. Strategic Competition Regulation

Strategic competition regulation combines:

Ex-post competition law

cartel enforcement;

abuse of dominance;

merger control.

with:

Ex-ante digital regulation

interoperability;

data portability;

gatekeeper obligations;

platform neutrality.

and:

Sectoral regulation

telecommunications;

financial services;

energy;

healthcare;

transport.

The objective is to preserve contestability throughout the ecosystem.

5. Relevant Market Definition

Intelligent ecosystems can contain several interconnected markets.

For example:

Layer 1

AI chips.

Layer 2

Cloud computing.

Layer 3

Foundation models.

Layer 4

AI assistants.

Layer 5

AI applications.

Layer 6

AI-mediated commerce.

Authorities must determine whether the relevant market is:

narrow;

broad;

multi-sided;

ecosystem-based;

technology-specific.

Market definition should therefore consider:

demand substitutability;

supply substitutability;

multi-homing;

switching costs;

network effects;

technological constraints.

6. Ecosystem Dominance

Traditional dominance analysis asks whether an undertaking possesses substantial market power in a defined relevant market.

In intelligent ecosystems, authorities should additionally examine:

control over gateways;

network effects;

data advantages;

interoperability;

vertical integration;

ecosystem dependency;

switching costs;

access to complementary products.

A company can potentially have modest shares in several markets but possess substantial strategic ecosystem power because its services are interconnected.

7. Case Law 1 — Microsoft

United States v. Microsoft Corp.

Microsoft remains a foundational case for understanding technology-platform power.

The case concerned Microsoft's position in operating systems and its conduct affecting competing technologies.

Important competition concepts included:

leveraging;

exclusion;

interoperability;

network effects;

technological barriers to entry.

Relevance to intelligent ecosystems

An AI ecosystem may operate similarly:

AI operating system → AI assistant → applications → marketplace.

Control of the central platform may give the operator opportunities to disadvantage competing products.

Principle

Competition analysis should examine how control over a technological platform affects adjacent markets.

8. Case Law 2 — Google Shopping

Google Search (Shopping)

The European Commission found that Google had abused its dominant position in general search by giving preferential treatment to its own comparison-shopping service.

The case is important for:

self-preferencing;

ranking;

platform neutrality;

leveraging;

downstream foreclosure.

Intelligent-ecosystem application

An AI assistant may eventually determine:

Which retailer appears first?

Which financial service is recommended?

Which software is selected?

Which supplier receives an automated purchase order?

If a dominant AI platform systematically favours affiliated services, competition authorities may need to examine whether such conduct unlawfully disadvantages rivals.

9. Case Law 3 — Google Android

Google Android

The European Commission's Android case concerned contractual practices involving Google's mobile ecosystem.

Important issues included:

tying;

defaults;

distribution restrictions;

ecosystem leverage.

Intelligent ecosystem relevance

The future equivalent could involve:

AI model → AI assistant → application marketplace → payment system.

A dominant company might attempt to make its downstream services the default or condition access to one product upon acceptance of another.

Principle

Competition law must examine whether dominance in one technological layer is being used to reinforce power in adjacent markets.

10. Case Law 4 — Eturas

Eturas UAB and Others v Lithuanian Competition Council

Eturas involved an online booking platform that transmitted a technical restriction affecting discounts offered by participating travel agencies.

The case is significant because a digital platform can facilitate coordination among competitors.

Intelligent ecosystem relevance

Consider:

Competitor A's AI

Competitor B's AI

Competitor C's AI
↓
Common platform
↓
Coordinated pricing environment.

Digital architecture does not eliminate traditional competition-law principles concerning concerted practices.

11. Case Law 5 — T-Mobile Netherlands

T-Mobile Netherlands NV and Others

The case addressed coordination and information exchange between competitors.

The Court emphasized the significance of conduct that can reduce strategic uncertainty between competitors.

Intelligent ecosystem relevance

AI systems can process enormous quantities of:

prices;

inventories;

forecasts;

production data;

customer information.

The competition question becomes whether information flows facilitate coordination.

Key principle

Greater technological sophistication does not make information exchange immune from competition law.

12. Case Law 6 — Dole Food

Dole Food and Dole Fresh Fruit Europe v European Commission

The case concerned exchanges of commercially sensitive information between competitors.

It demonstrates how information exchanges can affect competitive uncertainty.

Intelligent ecosystem relevance

AI platforms can create highly transparent markets.

For example:

Every competitor receives real-time information about demand, inventory and prices.

This could potentially facilitate coordination.

Competition authorities therefore need to distinguish:

legitimate market intelligence;

efficiency-enhancing information;

competitively sensitive information;

coordination-facilitating information.

13. Case Law 7 — United States v. Topkins

Topkins is especially important for intelligent ecosystems because it involved algorithmic price coordination.

The case demonstrates that algorithms can be used as instruments for implementing anticompetitive agreements.

Future significance

AI systems may increasingly:

monitor competitors;

change prices;

negotiate;

forecast demand;

allocate inventory.

Therefore, competition law must examine not merely the software but also the purpose, design and control of the software.

14. Case Law 8 — Bronner

Oscar Bronner GmbH & Co. KG v Mediaprint

Bronner established important limits concerning compulsory access to infrastructure controlled by a dominant undertaking.

The case is relevant to the essential-facilities question.

Intelligent ecosystem application

Potential strategic infrastructure includes:

cloud computing;

AI marketplaces;

digital identity;

payment systems;

telecommunications networks;

robotics platforms.

But competition law should not automatically require every successful platform to share every technological asset.

Compulsory access requires the legally established conditions to be satisfied.

15. Case Law 9 — IMS Health

IMS Health GmbH & Co. OHG v NDC Health

IMS Health concerned the relationship between intellectual property rights and competition law.

It is particularly relevant to intelligent ecosystems because ecosystem components may be protected by:

patents;

copyrights;

trade secrets;

proprietary databases;

software rights.

Future application

Competition law may encounter disputes concerning:

AI model access;

APIs;

proprietary datasets;

model interfaces;

interoperability technology.

The central challenge is balancing:

innovation incentives + intellectual property

against

competition + market access.

16. Data as Ecosystem Power

Data can generate a competitive feedback loop:

More users
↓
More data
↓
Better algorithm
↓
Better service
↓
More users

This may produce a reinforcing competitive advantage.

Competition concerns arise where a dominant firm:

denies necessary data access;

restricts portability;

combines data to foreclose rivals;

discriminates against competing users;

uses data obtained from business customers against those customers.

However, data possession alone is not sufficient to establish unlawful conduct.

17. Interoperability

Interoperability is one of the most important strategic regulatory tools.

An ecosystem can become difficult to challenge if competing systems cannot communicate.

Examples:

AI

Model A ↔ Model B

Cloud

Cloud A ↔ Cloud B

Payments

Wallet A ↔ Payment network

Healthcare

Hospital A ↔ Health platform B

Robotics

Robot A ↔ Operating system B

Interoperability can reduce switching costs and make markets more contestable.

18. Self-Preferencing

A dominant ecosystem operator may control the mechanism determining:

search results;

rankings;

recommendations;

marketplace placement;

AI-generated answers.

This creates a potential conflict:

The platform is simultaneously the market operator and a competitor within that market.

Google Shopping provides an important precedent for examining this type of platform behaviour.

19. Tying and Bundling

Intelligent ecosystems naturally encourage integration.

Integration may create legitimate efficiencies.

For example:

AI assistant + cloud + cybersecurity.

But competition concerns may arise if a dominant firm effectively requires customers to purchase an additional service to obtain access to a strategically important product.

The legal analysis should consider:

dominance;

separate products;

coercion;

foreclosure;

consumer benefits;

efficiencies.

Google Android is an important reference point for this analysis.

20. Exclusive Dealing

Intelligent ecosystems may use:

exclusive distribution;

default arrangements;

preferred-provider contracts;

loyalty discounts;

platform exclusivity.

These arrangements may be legitimate in some circumstances but can become problematic where they significantly foreclose rivals.

Relevant factors include:

duration;

market coverage;

entry barriers;

market power;

ability of rivals to reach customers.

21. Algorithmic Coordination

Intelligent ecosystems create several types of coordination.

Type 1 — Human agreement

Competitors explicitly agree.

Type 2 — Algorithmic implementation

Humans agree and software executes the agreement.

Type 3 — Platform facilitation

A common platform facilitates coordination.

Type 4 — Autonomous algorithmic adaptation

Algorithms independently learn similar strategies.

The first three can often be analysed using existing competition-law concepts.

The fourth creates more difficult questions concerning:

attribution;

intent;

foreseeability;

causation;

liability.

22. AI Agents as Market Participants

AI agents may eventually become economic actors capable of:

buying;

selling;

negotiating;

selecting suppliers;

changing prices;

managing logistics.

This creates a shift from:

human-to-human competition

to:

machine-to-machine competition.

Competition regulation must determine how responsibility is allocated between:

AI developer;

platform operator;

business deploying the AI;

user;

autonomous agent.

23. Cloud and Compute Ecosystems

Advanced AI depends heavily on:

data centres;

processors;

specialized AI chips;

cloud infrastructure;

networking;

energy.

Potential competition concerns include:

discriminatory access;

tying;

bundling;

switching costs;

contractual lock-in;

preferential treatment;

vertical foreclosure.

Because compute can be an essential input for AI development, access conditions may become strategically significant.

24. Foundation Models

Foundation models may become a layer of general economic infrastructure.

A single model can be incorporated into:

healthcare;

finance;

education;

transportation;

manufacturing;

legal services;

robotics.

Potential competition concerns include:

model access;

API restrictions;

preferential treatment;

exclusive cloud arrangements;

data advantages;

vertical integration.

25. Intelligent Marketplaces

Future marketplaces may not simply display products.

They may have AI systems that:

select suppliers;

negotiate prices;

rank products;

predict demand;

purchase automatically.

This creates a new form of algorithmic gatekeeping.

The platform may effectively determine which businesses receive economic opportunities.

26. Merger Control

Intelligent ecosystems create difficult merger questions because competitive importance may not correlate with current revenue.

A startup may possess:

revolutionary AI technology;

unique data;

highly skilled researchers;

important patents;

a promising competing architecture.

A large technology firm acquiring such a company may therefore require analysis of potential competition and innovation competition, where legally applicable.

Relevant considerations include:

R&D pipeline;

future products;

technological alternatives;

customer switching;

competing innovation;

ecosystem effects.

27. Killer Acquisitions

The concern is not necessarily that every acquisition of a startup is harmful.

Rather:

Could the acquisition eliminate a future competitive constraint that would otherwise have developed?

Competition authorities may therefore need to consider:

Current competition + potential competition + innovation competition.

28. Ecosystem Lock-In

Lock-in can occur through:

proprietary formats;

incompatible APIs;

accumulated user data;

loyalty programmes;

device compatibility;

software dependencies;

contractual restrictions.

Switching costs may become particularly high when multiple services are interconnected.

For example:

Device → operating system → cloud → AI assistant → payment → data storage.

A consumer may technically have a choice while facing substantial practical costs of switching.

29. Intelligent Physical Ecosystems

Competition regulation must also cover physical systems.

Autonomous vehicles

Vehicle → software → mapping → charging → insurance → data.

Smart homes

Device → operating system → assistant → marketplace.

Robotics

Robot → operating system → cloud → maintenance → data.

Smart energy

Grid → storage → AI management → energy marketplace.

Each may create an interconnected ecosystem in which control of one layer affects competition elsewhere.

30. Regulatory Coordination

Intelligent ecosystems cannot be governed effectively by competition authorities alone.

Relevant institutions may include:

competition authorities;

telecommunications regulators;

financial regulators;

energy regulators;

data-protection authorities;

consumer-protection authorities;

cybersecurity regulators.

The challenge is avoiding both:

Regulatory gaps

No authority addresses the competition problem.

and

Regulatory duplication

Multiple authorities impose inconsistent requirements.

Strategic governance therefore requires institutional coordination.

31. Remedies

A. Interoperability remedies

Require technically feasible interoperability.

B. Data portability

Enable consumers and businesses to move relevant data.

C. Non-discrimination

Prevent discriminatory platform access.

D. Access remedies

Where legally justified, require access to strategically important infrastructure.

E. Behavioural commitments

Restrict self-preferencing, tying or exclusionary practices.

F. Structural remedies

In exceptional cases, separate infrastructure from downstream commercial activities.

G. Merger remedies

Preserve independent innovation and potential competitors.

32. Strategic Regulation Framework

A useful framework is:

Step 1 — Map the ecosystem

Identify:

infrastructure;

data;

models;

platforms;

applications;

users.

↓

Step 2 — Identify bottlenecks

Determine who controls:

compute;

data;

APIs;

marketplaces;

standards;

distribution.

↓

Step 3 — Determine market power

Examine:

shares;

network effects;

switching costs;

entry barriers;

multi-homing.

↓

Step 4 — Identify conduct

Look for:

tying;

self-preferencing;

exclusion;

discriminatory access;

information exchange;

algorithmic coordination;

acquisitions.

↓

Step 5 — Assess competitive effects

Consider:

price;

quality;

innovation;

entry;

consumer choice;

interoperability.

↓

Step 6 — Select remedy

Consider:

interoperability;

portability;

access;

behavioural remedies;

structural remedies.

33. Competition Regulation Matrix

34. Six Core Principles

1. Ecosystem power is not automatically unlawful

Large and integrated ecosystems can generate substantial efficiencies.

2. Market power must be connected to legally relevant conduct

Dominance alone is not an abuse.

3. Interoperability can preserve contestability

Where legally appropriate, interoperability can reduce lock-in.

4. Algorithms remain subject to competition law

Automated conduct does not automatically escape antitrust rules.

5. Innovation competition matters

Competition policy should consider future technological alternatives where legally relevant.

6. Access obligations must be carefully justified

Bronner and IMS Health demonstrate why compulsory access should not become automatic.

35. Key Case-Law Summary

36. Exam-Oriented Answer Structure

For a 20-mark examination answer, the following structure is effective:

Introduction

Define intelligent ecosystems and explain their economic significance.

Legal framework

Discuss:

restrictive agreements;

abuse of dominance;

merger control;

digital regulation.

Structural characteristics

Explain:

network effects;

data advantages;

switching costs;

multi-sided markets;

ecosystem integration.

Competition concerns

Discuss:

self-preferencing;

tying;

exclusion;

interoperability;

algorithmic coordination;

data foreclosure;

killer acquisitions.

Case law

Discuss at least six cases, preferably:

Microsoft

Google Shopping

Google Android

Eturas

T-Mobile Netherlands

Dole Food

Topkins

Bronner

IMS Health

Remedies

Discuss:

interoperability;

portability;

access;

non-discrimination;

behavioural remedies;

structural remedies.

Conclusion

Explain how competition regulation can preserve contestability while allowing technological integration and innovation.

37. Conclusion

Strategic competition regulation of intelligent ecosystems requires competition law to move beyond a narrow firm-versus-firm perspective.

The future competitive structure is increasingly:

Infrastructure → Data → AI → Agents → Platforms → Applications → Consumers

The central regulatory challenge is ensuring that control of one layer does not unnecessarily become control over the entire ecosystem.

The jurisprudence of Microsoft, Google Shopping, Google Android, Eturas, T-Mobile Netherlands, Dole Food, Topkins, Bronner and IMS Health provides important foundations for addressing these problems.

The ultimate objective is not to prevent technological scale or integration. It is to preserve contestability, innovation, interoperability and meaningful choice while allowing intelligent ecosystems to generate legitimate efficiencies.

Thus, future competition regulation should combine:

Antitrust + merger control + digital-platform regulation + interoperability + data governance + infrastructure regulation + technological expertise.