Competition Law and Self-Executing Commercial Ecosystems

1. Introduction

A self-executing commercial ecosystem is a commercial environment in which transactions, access conditions, pricing, ranking, licensing, settlement, compliance, or other business rules are implemented automatically through software, smart contracts, APIs, algorithms, blockchain protocols, platform rules, or machine-to-machine systems, with limited need for human intervention.

Examples include:

• blockchain-based marketplaces;
• smart-contract supply chains;
• automated payment networks;
• app stores and digital ecosystems;
• cloud/API ecosystems;
• automated procurement platforms;
• autonomous trading and pricing systems;
• connected-device ecosystems;
• AI-agent commerce;
• tokenised commercial networks.

The competition-law difficulty is that the commercial rule may be embedded in code rather than expressly negotiated in a conventional contract. Nevertheless, automation does not by itself remove the conduct from competition law.

Modern competition analysis increasingly recognises that digital ecosystems can create power through network effects, interoperability restrictions, data advantages, switching costs and control over bottlenecks, rather than merely through conventional market shares.

2. Meaning of a Self-Executing Commercial Ecosystem

A self-executing ecosystem generally contains five elements:

A. Core platform

A central or technically important platform provides the infrastructure.

Examples:

• app store;
• blockchain;
• payment network;
• cloud platform;
• operating system;
• marketplace;
• API gateway.

B. Complementors

Third parties build products or services around the infrastructure.

Examples include:

• developers;
• merchants;
• suppliers;
• advertisers;
• fintech companies;
• AI developers.

C. Embedded rules

The commercial rules are encoded into:

• smart contracts;
• APIs;
• protocols;
• ranking algorithms;
• access controls;
• software-development kits;
• automated pricing systems.

D. Automatic execution

Once predetermined conditions are satisfied, the system automatically:

• executes payment;
• changes access;
• allocates resources;
• ranks products;
• imposes fees;
• triggers contractual rights;
• blocks transactions.

E. Network effects

The ecosystem becomes more valuable as more users, sellers, developers or complementary services participate.

This can create a self-reinforcing competitive structure:

more users → more complementors → greater functionality → more users → greater data → stronger ecosystem → higher switching costs.

3. Why Self-Execution Creates Competition-Law Problems

The fundamental issue is:

Can an automated technical rule produce an anticompetitive effect even though no employee is manually making the exclusionary decision?

The answer can be yes.

Competition law generally focuses on conduct and its competitive effects, not merely on whether a human being personally presses the button.

A smart contract that automatically excludes rival suppliers can raise substantially the same competition questions as a manually enforced exclusivity agreement.

4. Principal Competition Concerns

A. Self-executing exclusion

A platform may encode a rule preventing participants from dealing with competitors.

For example:

Seller joins Platform A → smart contract automatically prevents seller from supplying Platform B.

Potential issues include:

• exclusive dealing;
• foreclosure;
• market partitioning;
• raising rivals' costs;
• restriction of multi-homing.

The greater the market power of the ecosystem operator, the greater the competition concern.

5. B. Automated Self-Preferencing

An ecosystem may automatically favour its own products.

For example:

Search algorithm → ranks ecosystem-owned service above competing services.

This is particularly important where the platform controls access to customers.

The Google Shopping litigation is a major reference point. In Case T-612/17, the EU General Court considered Google's preferential positioning of its own comparison-shopping service in general search results and upheld the finding of an abuse of dominance.

Application to self-executing ecosystems

A blockchain marketplace, app store or AI marketplace could potentially encode:

• preferred ranking;
• preferential API access;
• lower transaction fees for affiliated services;
• faster execution;
• privileged data access.

The automated nature of the mechanism would not necessarily eliminate Article 102-type concerns.

6. C. Interoperability Restrictions

Self-executing ecosystems frequently depend upon technical compatibility.

A dominant ecosystem may control:

• APIs;
• operating-system functions;
• authentication;
• data formats;
• payment interfaces;
• hardware functionality;
• software-development tools.

If the ecosystem automatically denies interoperability to competing services, competition may be weakened.

Microsoft v Commission

In Microsoft Corp. v Commission, Case T-201/04, the EU General Court dealt with Microsoft's refusal to provide interoperability information concerning work-group server operating systems, together with other conduct involving its Windows ecosystem. The case demonstrates the importance of interoperability where a dominant platform controls a technically important interface.

Modern relevance

The same principle has increasing significance for:

• cloud ecosystems;
• AI assistants;
• connected devices;
• smart-home systems;
• digital wallets;
• automotive software.

The EU's current Digital Markets Act framework expressly addresses interoperability with gatekeeper ecosystems. In 2026, the European Commission adopted measures concerning interoperability between competing AI services and Android capabilities.

7. D. Lock-In and Switching Costs

A self-executing ecosystem may make exit technically difficult.

Examples:

• smart contracts tied to one blockchain;
• proprietary APIs;
• non-portable data;
• incompatible digital identities;
• proprietary tokens;
• ecosystem-specific loyalty points;
• hardware-software integration.

This can produce technological lock-in.

Competition authorities may therefore examine whether the ecosystem's architecture makes it artificially difficult for users or suppliers to migrate to competitors.

8. E. Network Effects and Tipping

Self-executing ecosystems can exhibit powerful network effects.

For example:

Users increase → developers increase → applications increase → users increase.

This can eventually produce a tipping process in which one ecosystem becomes difficult to challenge.

The significance of network effects was expressly recognised in Ohio v American Express Co.

The U.S. Supreme Court treated American Express as a two-sided transaction platform in which merchants and cardholders were interconnected and the value of participation on one side depended upon participation on the other.

Application

For self-executing ecosystems, competition analysis may need to consider:

• both sides of the platform;
• indirect network effects;
• user acquisition;
• developer participation;
• transaction volume;
• switching costs.

9. F. Automated Pricing and Algorithmic Collusion

One of the most difficult issues is algorithmic coordination.

Suppose competing platforms independently use algorithms programmed to:

• monitor rivals;
• adjust prices;
• respond instantly to competitors;
• maintain a target margin.

The result might be parallel pricing without conventional communication.

The legal question becomes whether the outcome is:

1. legitimate independent adaptation;
2. tacit coordination;
3. concerted practice;
4. algorithm-assisted collusion; or
5. unilateral conduct.

The absence of a traditional written cartel agreement does not automatically resolve the competition-law issue.

10. G. Smart Contracts and Cartels

Smart contracts can potentially make cartel arrangements unusually stable.

Competitors could theoretically use automated mechanisms to:

• monitor prices;
• enforce agreed prices;
• punish deviations;
• automatically exchange information;
• allocate customers;
• restrict output.

This creates a significant distinction between:

Traditional cartel

Human beings agree → employees implement agreement.

Self-executing cartel

Agreement/rule → code implements arrangement automatically.

The enforcement mechanism may therefore become more reliable and less dependent on human compliance.

Competition authorities would still need to establish the legally relevant elements of the infringement under the applicable jurisdiction.

11. H. Data Accumulation

Self-executing ecosystems can continuously collect:

• transaction data;
• consumer preferences;
• prices;
• search behaviour;
• supplier information;
• payment information;
• performance data.

Data can then be fed automatically into the ecosystem.

This produces a potentially powerful feedback loop:

transactions → data → better algorithm → better service → more transactions → more data.

The resulting data advantage may strengthen barriers to entry.

12. I. Data Combination and Competition

The Meta/Facebook proceedings provide an important illustration of the relationship between data and competition.

The German Bundeskartellamt's proceedings concerned Meta's combination of user data from different sources. The CJEU subsequently confirmed that competition authorities could take data-protection rules into consideration when assessing competition-law issues.

For self-executing ecosystems, this is important because data collection and combination can become automatic and continuous rather than involving individual human decisions.

13. J. Refusal of Access

An ecosystem may automatically refuse access to:

• API interfaces;
• payment systems;
• databases;
• interoperability protocols;
• authentication systems;
• essential technical infrastructure.

Where the ecosystem operator possesses substantial market power, refusal of access may raise issues similar to traditional essential-facility/interoperability cases.

However, competition law does not generally impose a universal obligation to share every asset. The legal threshold depends upon the applicable jurisdiction and the particular circumstances.

14. K. Tying and Bundling

Self-executing ecosystems can automatically condition one product upon another.

Example:

Access to cloud infrastructure → automatically conditional upon using the provider's payment system.

Or:

Access to operating-system functionality → automatically conditional upon installing the platform's own application.

Such arrangements can raise tying or bundling concerns where the legal requirements are satisfied.

15. L. Platform Governance as a Competition-Law Issue

In conventional commerce, governance is frequently found in:

• contracts;
• corporate policies;
• employee instructions.

In self-executing ecosystems, governance may instead be embedded in:

• source code;
• protocol rules;
• token economics;
• consensus mechanisms;
• automated dispute resolution.

This means that technical governance becomes commercially significant.

The competition-law question is therefore not merely:

“What does the contract say?”

but also:

“What does the system automatically permit, prevent, reward or penalise?”

16. Important Case Laws

1. Microsoft Corp. v Commission — Case T-201/04

Principle

Microsoft involved interoperability and the use of control over an important operating-system environment.

The case concerned refusal to supply interoperability information and other conduct associated with Microsoft's dominant Windows ecosystem.

Relevance

It demonstrates how control over a technological interface can become a competition-law issue.

Application

A self-executing ecosystem that automatically denies competing services access to necessary technical interfaces could face analogous scrutiny.

2. Google and Alphabet v Commission — Google Shopping, Case T-612/17

Principle

The General Court examined Google's preferential treatment of its own comparison-shopping service within its general search results.

Relevance

The case is highly relevant to algorithmic ecosystems because ranking and visibility can be determined automatically.

Application

An ecosystem could potentially use an algorithm to:

• rank its own services first;
• demote competitors;
• privilege affiliated suppliers;
• manipulate access to customers.

Automation would not itself convert such conduct into legitimate competition.

3. Google Android — Case T-604/18

Principle

The General Court's Android judgment concerned Google's ecosystem involving:

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

The Court considered product bundling, exclusivity payments and anti-fragmentation obligations as part of the wider ecosystem conduct.

Relevance

This is especially important because it expressly addresses the ecosystem dimension of competition law.

Application

A self-executing ecosystem may similarly use technical dependencies to extend power from one layer into another.

4. Ohio v. American Express Co., 585 U.S. ___ (2018)

Principle

The U.S. Supreme Court recognised the special characteristics of two-sided transaction platforms and held that both sides of the platform had to be considered in the relevant-market analysis in that case.

Relevance

Self-executing ecosystems are often multi-sided.

Application

Competition analysis may need to examine:

• consumers;
• merchants;
• developers;
• advertisers;
• suppliers;
• platform operators.

A restriction affecting one group may generate effects elsewhere in the ecosystem.

5. United States v. Microsoft Corp., 253 F.3d 34 (D.C. Cir. 2001)

Principle

The Microsoft litigation examined Microsoft's conduct involving the Windows operating-system platform and competing technologies, including browser-related conduct.

Relevance

It illustrates how a dominant technological platform can use control over a platform environment to influence adjacent markets.

Application

The same reasoning is relevant where self-executing architecture makes access to an ecosystem conditional upon compliance with platform rules.

6. Meta Platforms / Facebook Bundeskartellamt Proceedings

Principle

The German competition authority's Facebook case concerned the combination of data from different sources and the relationship between data protection and competition law. The CJEU confirmed that data-protection considerations could be relevant in competition-law enforcement.

Relevance

Self-executing ecosystems can automatically collect and combine information across multiple services.

Application

Automatic data integration can potentially strengthen:

• entry barriers;
• network effects;
• personalization advantages;
• advertising power;
• ecosystem lock-in.

7. FTC v. Amazon.com, Inc.

The FTC and state authorities alleged that Amazon used interconnected strategies affecting sellers and rival platforms, including anti-discounting practices, marketplace conditions, fulfilment requirements and search-related conduct. The litigation illustrates how several practices can operate together as an ecosystem strategy, rather than being assessed entirely in isolation.

Relevance

The ecosystem can become the competitive unit through which multiple restraints reinforce one another.

Application

A self-executing Amazon-type ecosystem could theoretically automate:

• seller ranking;
• fulfilment eligibility;
• advertising access;
• price-related penalties;
• marketplace visibility.

The competitive effects could consequently arise from the interaction of multiple automated rules.

17. Competition-Law Framework

A. Agreements and concerted practices

Relevant where:

• competitors jointly design the protocol;
• competitors agree on smart-contract rules;
• common algorithms are used pursuant to an agreement;
• automated systems implement an unlawful arrangement.

Key questions:

1. Was there an agreement?
2. Was there concerted conduct?
3. Was the restriction by object or effect?
4. What is the relevant market?
5. Is there a legitimate justification?

18. Abuse of Dominance

For a dominant ecosystem, authorities may examine:

Exclusionary conduct

• self-preferencing;
• discriminatory access;
• tying;
• bundling;
• refusal to interoperate;
• exclusive dealing;
• discriminatory APIs;
• data foreclosure.

Exploitative conduct

Depending on the jurisdiction:

• excessive charges;
• discriminatory conditions;
• unfair contractual terms;
• exploitative data practices.

19. Merger Control

Self-executing ecosystems also create merger concerns.

A transaction may involve:

platform + payment system + data provider + AI service + cloud infrastructure.

Even if each individual company operates in a seemingly separate market, their combination may produce:

• ecosystem foreclosure;
• data advantages;
• interoperability advantages;
• increased switching costs;
• elimination of potential competitors.

20. Relevant-Market Definition

Traditional market definition can become difficult.

A self-executing ecosystem may operate across several connected markets:

The Google Android litigation demonstrates why ecosystem relationships can matter alongside conventional market definitions.

21. The Role of Interoperability

Interoperability is one of the most important safeguards against ecosystem foreclosure.

Competition may be enhanced by:

• open APIs;
• data portability;
• common standards;
• cross-platform functionality;
• non-discriminatory technical access;
• interoperability obligations.

The EU's DMA has expressly developed interoperability requirements for gatekeepers, including measures concerning Android and competing AI services.

22. Self-Executing Agreements: Key Legal Question

A crucial distinction should be maintained:

Automation ≠ immunity

A company cannot necessarily avoid competition law merely because:

“The computer executed it automatically.”

The relevant inquiry remains whether the underlying design, agreement, algorithm, rule or conduct produces an unlawful competitive restriction.

23. Human Responsibility vs Algorithmic Execution

A useful analytical model is:

Human design

↓

Commercial rule

↓

Code / algorithm

↓

Automatic execution

↓

Market effect

Competition authorities can potentially examine each layer.

The most important evidentiary question may therefore be:

Who designed the rule, who controlled it, and what competitive consequences did the system predictably produce?

24. Defences and Legitimate Justifications

Automation is not inherently anticompetitive.

A self-executing restriction may have legitimate purposes such as:

• cybersecurity;
• fraud prevention;
• transaction integrity;
• consumer protection;
• privacy;
• technical compatibility;
• prevention of malicious code;
• blockchain security;
• reduction of transaction costs.

Therefore, competition analysis must distinguish legitimate technical design from strategic exclusion.

25. Remedies

Potential remedies include:

Structural remedies

• divestiture;
• separation of business units;
• prohibition of certain acquisitions.

Behavioural remedies

• non-discriminatory access;
• interoperability;
• API access;
• data portability;
• prohibition of self-preferencing;
• modification of ranking algorithms;
• termination of exclusivity.

Technical remedies

Particularly important for self-executing ecosystems:

• changing protocol rules;
• disabling discriminatory code;
• opening APIs;
• permitting third-party interoperability;
• independent algorithm auditing;
• modifying smart-contract conditions.

26. Compliance Framework for Businesses

Businesses developing self-executing commercial systems should undertake a competition-by-design assessment.

Before deployment

1. Define relevant markets.
2. Identify ecosystem bottlenecks.
3. Test market power.
4. Examine exclusivity provisions.
5. Review API restrictions.
6. Test self-preferencing.
7. Examine data combination.
8. assess interoperability.
9. examine algorithmic pricing.
10. document legitimate technical justifications.

After deployment

Continuous monitoring should examine:

• foreclosure;
• discriminatory access;
• algorithmic coordination;
• switching costs;
• pricing;
• ranking;
• data accumulation;
• complaints from complementors.

27. Special Problem of Immutable Smart Contracts

Blockchain ecosystems create an additional difficulty.

If a smart contract is immutable, an unlawful commercial restriction may continue automatically even after the business recognises the problem.

Therefore:

immutability can become a competition-compliance risk.

Businesses should consider:

• upgrade mechanisms;
• emergency controls;
• governance procedures;
• competition-law review before deployment;
• mechanisms for changing discriminatory rules.

28. Emerging AI-Agent Ecosystems

A new category is AI-mediated commerce.

An AI agent may automatically:

1. search products;
2. compare prices;
3. select suppliers;
4. negotiate;
5. place orders;
6. execute payment.

If the agent is controlled by a dominant ecosystem, competition questions may arise concerning:

• preferred suppliers;
• ranking;
• access to transaction data;
• steering;
• interoperability;
• exclusion of rival agents.

The EU's 2026 Android interoperability measures demonstrate that regulators are already addressing competition and interoperability issues involving AI services within dominant digital ecosystems.

29. Distinction Between Traditional and Self-Executing Commercial Ecosystems

The legal principles may remain familiar, but the evidentiary and economic analysis becomes substantially more technological.

30. Six Core Competition Risks

For examination purposes, the subject can be condensed into six major risks:

1. Foreclosure

Automatic exclusion of rival businesses.

2. Self-preferencing

Automatic prioritisation of ecosystem-owned products.

3. Interoperability discrimination

Technical denial or degradation of access.

4. Lock-in

Automatic contractual or technological barriers to switching.

5. Algorithmic coordination

Automated pricing or conduct facilitating coordination.

6. Data concentration

Continuous accumulation and combination of commercially valuable data.

31. Conclusion

Self-executing commercial ecosystems represent a shift from contract-based commerce toward code-based commercial governance. Competition law therefore increasingly has to examine not only traditional agreements and prices but also algorithms, APIs, smart contracts, interoperability, data flows, network effects and ecosystem architecture.

The central legal principle is that automation does not itself make commercial conduct lawful or unlawful. The critical questions remain whether the ecosystem possesses market power, what rules have been embedded into the system, whether competitors can access the ecosystem on fair terms, whether the rules facilitate coordination or exclusion, and what actual or potential effects they have on competition.

The leading authorities—Microsoft, Google Shopping, Go