Competition Law and Machine-to-Machine Commercial Networks

1. Introduction

Machine-to-Machine (M2M) commercial networks are commercial ecosystems in which connected devices, software agents, sensors, vehicles, industrial machines, payment systems, or AI-enabled systems communicate and make commercial decisions with limited or no direct human intervention.

Examples include:

• connected vehicles negotiating charging or energy purchases;
• industrial machines automatically selecting suppliers;
• smart-grid devices responding to electricity prices;
• IoT devices automatically purchasing replacement components;
• warehouse robots selecting logistics providers;
• connected agricultural equipment purchasing inputs;
• autonomous procurement systems comparing suppliers;
• software agents automatically changing prices;
• machines exchanging capacity, inventory and demand information;
• connected platforms automatically allocating customers or transactions.

M2M technology can increase competition by reducing transaction costs, improving price discovery and enabling smaller firms to participate. At the same time, it can create new competition-law problems because machines may exchange commercially sensitive information, coordinate prices, exclude rivals, or reinforce the market power of the company controlling the network.

The central legal question is therefore not simply whether machines are making the decision, but whether the underlying human, contractual, technological or platform arrangements produce an anticompetitive effect or facilitate coordination that competition law prohibits.

2. Meaning of an M2M Commercial Network

A typical M2M network may contain five layers:

A. Device layer

Sensors, vehicles, robots, smart meters, industrial equipment and other connected devices generate information.

B. Connectivity layer

The devices communicate through:

• telecommunications networks;
• IoT networks;
• cloud infrastructure;
• 5G;
• APIs;
• industrial communication protocols.

C. Data layer

The network aggregates:

• prices;
• inventory;
• demand;
• capacity;
• customer information;
• production data;
• location information;
• competitor information.

D. Decision layer

Algorithms or AI systems transform the data into commercial decisions such as:

• price;
• supplier selection;
• allocation;
• routing;
• purchasing;
• production;
• inventory;
• capacity utilisation.

E. Transaction layer

The machine may automatically execute the transaction.

For example:

Smart vehicle → searches charging stations → compares prices → selects station → reserves capacity → pays automatically.

The competition-law issue can arise at every stage.

3. Why M2M Networks Create Competition-Law Problems

3.1 Algorithmic coordination

If competing machines receive similar information and optimise against one another, prices may converge.

Parallel pricing alone does not automatically establish an unlawful agreement. However, competition authorities may investigate whether competitors:

• intentionally supplied sensitive information;
• used a common algorithm;
• agreed to follow algorithmic recommendations;
• delegated pricing to a common intermediary;
• knowingly replaced independent decision-making with coordinated decision-making.

The UK CMA has specifically warned that pricing algorithms can be used to facilitate price fixing, while also recognising legitimate efficiency benefits from algorithmic pricing.

4. Exchange of Competitively Sensitive Information

M2M networks can make information exchange almost instantaneous.

A competing machine could automatically receive:

• a rival's price;
• production capacity;
• inventory;
• future demand;
• delivery capacity;
• discount information.

This can produce algorithmic transparency without meaningful human communication.

Competition authorities therefore have to distinguish:

Legitimate information exchange

For example, a logistics network needs capacity information to allocate shipments.

Potentially problematic information exchange

Competing suppliers receive detailed, current and individualised information concerning each other's future prices and commercial strategies.

The more granular, current and strategically sensitive the information, the greater the potential competition concern.

5. Common Algorithm Problem

A particularly important M2M scenario is:

Competitor A + Competitor B + Competitor C → common algorithm → prices determined by common software.

The software provider may therefore become a coordination hub.

The United States authorities have expressly argued in algorithmic-pricing litigation that traditional antitrust principles can apply when competitors share competitively sensitive information with, and rely upon, a common pricing agent.

This is important because the absence of a traditional meeting or telephone conversation does not necessarily eliminate the possibility of an unlawful agreement.

6. Six Major Case Laws and Their Relevance to M2M Networks

Case 1: United States v. Apple Inc. — E-books

Court: U.S. District Court, Southern District of New York / Second Circuit
Subject: Electronic-books price coordination

Apple and major publishers were found to have participated in arrangements concerning e-book pricing.

Principle

Competition law focuses on the substance and economic operation of coordination, rather than requiring competitors to communicate through a particular technological medium.

M2M relevance

Suppose competing connected-device manufacturers use a common software system that automatically implements agreed pricing parameters.

The fact that the final prices are generated automatically would not necessarily protect the arrangement from antitrust scrutiny.

Lesson:
Technology cannot convert an otherwise unlawful coordination arrangement into lawful independent conduct.

7. Case 2: United States v. Topkins

Court: U.S. District Court, Northern District of California
Subject: Algorithmic price fixing in online retail

Topkins and other sellers were involved in an arrangement concerning online prices, with software being used to implement pricing coordination.

M2M relevance

This is particularly significant for M2M commerce because connected commercial systems can automatically:

1. obtain competitors' prices;
2. process the information;
3. adjust prices;
4. maintain the coordinated outcome.

Thus, the algorithm may become the mechanism for implementing an agreement.

Principle

An automated pricing system does not eliminate liability merely because the final pricing decision is executed by software.

8. Case 3: Eturas UAB v. Lietuvos Respublikos Konkurencijos Taryba

Court: Court of Justice of the European Union
Case: C-74/14

This case concerned an online travel-booking system through which a software administrator transmitted a message concerning limitations on discounts available to travel agencies.

Importance

The case is highly relevant to digital and M2M networks because the alleged coordination was facilitated through a common technological platform rather than traditional face-to-face negotiations.

The CJEU considered whether knowledge of the technological mechanism, together with participation in the system, could support an inference concerning participation in concerted conduct.

M2M lesson

A platform can become a mechanism through which independent businesses coordinate commercial behaviour.

Therefore:

"The machine did it" is not necessarily a competition-law defence.

The legal analysis remains concerned with the conduct of the participating undertakings and their knowledge and behaviour.

9. Case 4: RealPage Algorithmic Pricing Litigation

Jurisdiction: United States
Subject: Algorithmic rental pricing

RealPage has been the subject of major U.S. antitrust proceedings concerning algorithmic pricing in rental markets.

The U.S. Department of Justice alleged that competing landlords supplied competitively sensitive information to RealPage and used its algorithmic recommendations to influence rental pricing.

The U.S. agencies have specifically stated that traditional principles concerning price coordination can apply when competitors use a common software pricing agent.

M2M relevance

The case illustrates a particularly important model:

Competitor data → common software → algorithmic recommendation → independent commercial decision

Competition authorities may ask whether the arrangement has effectively reduced independent competitive decision-making.

Legal significance

The central issue is not simply the existence of an algorithm.

It is:

• what data was supplied;
• who supplied it;
• what competitors knew;
• what the algorithm did;
• whether competitors knowingly relied upon it;
• whether the arrangement reduced competitive independence.

10. Case 5: Cornish-Adebiyi v. Caesars Entertainment

Jurisdiction: United States
Subject: Algorithmic hotel pricing

The FTC and DOJ filed a statement of interest concerning allegations involving algorithmic hotel-room pricing.

The agencies emphasised that businesses cannot use an algorithm to engage in conduct that would be unlawful if carried out by people.

M2M relevance

Consider:

Hotel A machine + Hotel B machine + Hotel C machine → common pricing algorithm.

If the system facilitates coordinated pricing, the fact that there was no direct human communication between hotel managers does not necessarily remove the competition issue.

Principle

Competition law can follow the economic substance of automated coordination, rather than the physical identity of the decision-maker.

11. Case 6: CMA — Online Sales of Posters and Frames

Jurisdiction: United Kingdom
Authority: Competition and Markets Authority

The CMA dealt with an online pricing arrangement involving sellers that agreed not to undercut each other's prices on Amazon's UK website. The CMA subsequently used the matter as an example of how algorithms can facilitate unlawful pricing arrangements.

M2M relevance

The case demonstrates a two-stage problem:

Human agreement → automated implementation

The algorithm can therefore become the enforcement mechanism for the underlying commercial arrangement.

Principle

Automation does not neutralise an otherwise unlawful restriction.

12. Case 7: CMA Investigation into Amazon Marketplace

Jurisdiction: United Kingdom
Authority: Competition and Markets Authority

The CMA investigated Amazon's Marketplace concerning:

• use of third-party seller data;
• Buy Box selection;
• Prime-related arrangements;
• treatment of competing sellers.

Amazon subsequently offered commitments addressing the CMA's concerns.

M2M relevance

This is highly relevant to M2M networks because a dominant platform may simultaneously:

1. operate the infrastructure;
2. collect competitors' data;
3. operate its own competing business;
4. control algorithmic ranking;
5. determine which commercial offers receive greater visibility.

This creates the possibility of vertical and horizontal conflicts.

Example

A dominant smart-device platform could:

collect suppliers' machine-generated data → analyse it → operate a competing service → use algorithmic allocation to favour its own service.

That combination raises potential dominance and self-preferencing concerns.

13. Case 8: Booking.com and Expedia — Hotel Online Booking

The UK CMA investigated pricing practices involving online travel agents and commitments concerning parity arrangements.

The arrangements concerned whether hotels could offer different prices, availability or conditions through competing online channels.

M2M relevance

In an M2M environment, similar restrictions could be encoded directly into APIs or procurement software.

For example:

Smart procurement system → checks supplier terms → rejects supplier unless its price matches the dominant platform.

This could make a traditional contractual restriction technologically automatic.

14. Dominance in M2M Networks

M2M competition problems are not limited to cartels.

A company may control a critical network consisting of:

• IoT devices;
• operating systems;
• cloud infrastructure;
• APIs;
• device-management software;
• data;
• authentication systems;
• payment infrastructure.

Such control can create substantial market power.

Potential abuses include:

Refusal of interoperability

A dominant platform refuses to allow competing machines to communicate.

Discriminatory API access

Competitors receive slower, incomplete or inferior access.

Self-preferencing

The network gives its own commercial service priority.

Data foreclosure

Competitors cannot access data necessary to compete.

Tying

Access to one M2M service requires purchasing another product.

Exclusive dealing

Connected devices are technically configured to communicate only with the dominant supplier.

15. Interoperability as a Competition Issue

Interoperability is particularly important because M2M systems derive value from communication.

Suppose:

Manufacturer A's machine → cannot communicate with Manufacturer B's platform.

If the dominant platform controls the technical standard, it may potentially use interoperability restrictions to exclude competing systems.

Competition analysis may therefore consider:

1. whether the platform is dominant;
2. whether interoperability is technically feasible;
3. whether access is objectively necessary;
4. whether competitors have alternative routes to market;
5. whether refusal excludes effective competition;
6. whether legitimate security or technical justifications exist.

16. Data Concentration

M2M systems generate enormous quantities of commercial data.

A dominant operator may possess:

• real-time demand data;
• machine utilisation data;
• inventory data;
• predictive maintenance data;
• customer purchasing patterns;
• competitor performance data.

This creates a possible data-based entry barrier.

The competition concern becomes stronger where:

Data → better algorithm → better service → more users → more data → even better algorithm.

This is a potential data-network-effect feedback loop.

17. Network Effects

M2M markets can exhibit strong network effects.

For example:

More connected vehicles → more charging data → better charging algorithm → more vehicles attracted → more charging data.

A similar pattern can occur with:

• smart grids;
• logistics;
• industrial IoT;
• connected agriculture;
• autonomous vehicles;
• supply-chain platforms.

Network effects are not inherently unlawful. They become relevant when a firm uses them to obtain or maintain market power through exclusionary conduct.

18. Algorithmic Discrimination

An M2M platform may automatically discriminate between participants.

For example:

Competition authorities may investigate whether these differences constitute:

• discriminatory access;
• exclusionary conduct;
• self-preferencing;
• leveraging;
• margin-related foreclosure;
• unfair trading conditions.

19. Merger Control and M2M Networks

M2M markets also raise merger concerns.

A merger could combine:

Device manufacturer + IoT platform + cloud provider + data analytics company

The combined company might control several layers of the commercial ecosystem.

Authorities may therefore consider:

Horizontal effects

Two competing IoT providers merge.

Vertical effects

A device manufacturer acquires a platform.

Conglomerate effects

A company combines devices, cloud, payments and analytics.

Data effects

The merger combines datasets that were previously controlled by separate competitors.

Ecosystem effects

The merged firm gains the ability to make competing systems technically incompatible.

20. Essential-Facility-Type Issues

Some M2M infrastructure may potentially become strategically important infrastructure.

Examples include:

• dominant industrial IoT platforms;
• critical interoperability protocols;
• smart-grid interfaces;
• connected-payment infrastructure;
• autonomous logistics exchanges;
• major IoT cloud infrastructure.

However, not every technologically important facility is legally an "essential facility."

The relevant jurisdiction's legal test must be satisfied, usually involving questions concerning:

• indispensability;
• effective duplication;
• exclusion;
• competition;
• objective justification;
• proportionality.

21. M2M and Consumer Harm

Competition authorities may examine whether M2M systems result in:

• higher prices;
• reduced output;
• reduced innovation;
• poorer quality;
• reduced choice;
• discriminatory access;
• higher switching costs.

But M2M networks can also create substantial consumer benefits:

• lower transaction costs;
• lower search costs;
• improved logistics;
• energy savings;
• better utilisation of capacity;
• faster price adjustments;
• predictive maintenance;
• improved reliability.

Therefore, automation itself should not be treated as anticompetitive.

The competition-law assessment should focus on the actual conduct and its competitive effects.

22. Cybersecurity and Competition

Cybersecurity creates an unusual competition-law tension.

A company may legitimately restrict interoperability because unrestricted access could:

• create security vulnerabilities;
• expose confidential information;
• compromise connected devices;
• threaten network integrity.

However, a dominant undertaking should not automatically be able to invoke "security" as a justification for exclusionary conduct.

Authorities may therefore examine whether:

1. the security concern is genuine;
2. the restriction is necessary;
3. less restrictive alternatives exist;
4. competitors can satisfy equivalent security requirements.

23. M2M Networks and India

For an Indian competition-law analysis, the principal framework would be the Competition Act, 2002, particularly:

Section 3

Prohibits agreements that cause or are likely to cause an appreciable adverse effect on competition.

Relevant M2M arrangements may include:

• algorithmic price fixing;
• information exchange;
• coordinated procurement;
• market allocation;
• restrictions embedded in software.

Section 4

Concerns abuse of dominant position.

Potential M2M examples include:

• discriminatory API access;
• denial of interoperability;
• self-preferencing;
• tying;
• unfair conditions;
• exclusionary technical standards.

Sections 5 and 6

Become relevant where M2M ecosystems are affected by mergers, acquisitions or other combinations.

24. M2M Commercial Network — Competition-Law Risk Matrix

25. Distinguishing Legitimate Automation from Anticompetitive Automation

A useful analytical test is:

Step 1 — Identify the market

What exactly competes?

• devices?
• connectivity?
• cloud services?
• data?
• software?
• transactions?

Step 2 — Identify the decision-maker

Is the decision made by:

• a human;
• an algorithm;
• an autonomous agent;
• a common intermediary?

Step 3 — Identify the data

What information does the machine receive?

Step 4 — Identify the source

Does the data come from:

• public sources;
• the undertaking's own customers;
• competitors;
• a common platform?

Step 5 — Examine competitive independence

Are competitors independently determining their commercial strategy?

Step 6 — Examine market power

Does one undertaking control a critical part of the M2M ecosystem?

Step 7 — Examine foreclosure

Can competitors realistically enter, interoperate and compete?

Step 8 — Examine efficiencies

Does the arrangement generate:

• lower costs;
• better quality;
• innovation;
• improved security;
• faster transactions?

Step 9 — Examine less restrictive alternatives

Could the same efficiency be achieved without excluding competitors?

26. Emerging Doctrine: From Human-to-Human to Machine-to-Machine Coordination

Traditional competition law frequently assumes:

Human A ↔ Human B → agreement → market effect

M2M markets increasingly create:

Machine A ↔ Platform ↔ Machine B → automated interaction → market effect

The legal challenge is determining when technological interaction constitutes:

• independent parallel conduct;
• conscious adaptation;
• information exchange;
• concerted practice;
• agreement;
• unilateral exclusion;
• legitimate automation.

This is likely to become an increasingly important issue in digital competition law.

27. Important Doctrinal Principles from the Cases

The cases discussed above collectively illustrate several principles:

Principle 1 — Automation does not immunise conduct

An unlawful arrangement does not become lawful merely because software implements it.

Principle 2 — Common technological infrastructure can facilitate coordination

A platform may become the mechanism through which competitors coordinate.

Principle 3 — Data can be competitively significant

Real-time competitor information can materially affect competitive independence.

Principle 4 — Algorithms can create new forms of coordination

Traditional cartel concepts may have to be applied to algorithmic environments.

Principle 5 — Dominance can arise from ecosystem control

Control over devices, data, APIs, cloud infrastructure and standards can create significant competitive advantages.

Principle 6 — Interoperability may become a competition parameter

Technical compatibility can be as important as price in an M2M ecosystem.

28. Compliance Requirements for M2M Businesses

Businesses operating M2M networks should consider:

1. Do not allow competitors to exchange unnecessary competitively sensitive information.
2. Establish rules for algorithmic pricing.
3. Audit common pricing and procurement algorithms.
4. Maintain records explaining algorithmic decisions.
5. Avoid programming competitors' systems to coordinate prices.
6. Separate competitively sensitive datasets where appropriate.
7. Establish API-access policies based on objective criteria.
8. Monitor self-preferencing risks.
9. Conduct competition-law reviews before changing interoperability standards.
10. Include competition-law safeguards in AI-agent deployment.
11. Audit automated contracting systems.
12. Conduct competition assessments before M2M acquisitions.

The CMA has similarly emphasised that businesses using pricing algorithms should understand how their systems work and proactively manage competition-law risks.

29. Conclusion

Machine-to-Machine commercial networks do not create a separate exemption from competition law. Their significance lies in the fact that they can transform the mechanism through which market behaviour occurs.

The major competition-law risks are:

• algorithmic collusion;
• automated information exchange;
• common-agent coordination;
• data concentration;
• platform dominance;
• API discrimination;
• interoperability restrictions;
• self-preferencing;
• device and ecosystem lock-in;
• anticompetitive M2M mergers.

The cases involving Apple, Topkins, Eturas, RealPage, Cornish-Adebiyi/Caesars, the CMA posters-and-frames investigation, Amazon Marketplace, and Booking.com/Expedia demonstrate that competition law increasingly examines the technological mechanisms through which commercial decisions are made, rather than focusing exclusively on traditional human-to-human communication.