Competition Law and Strategic Competition Policy for the Next Digital Era

Introduction

The next digital era is likely to be characterised by artificial intelligence, generative AI, autonomous agents, cloud computing, digital platforms, data ecosystems, digital payments, extended reality, blockchain, quantum technologies, 5G/6G and increasingly automated decision-making.

These technologies can generate substantial efficiencies and consumer benefits. At the same time, they may create new forms of market power because successful digital firms can accumulate:

• enormous datasets;
• computing resources;
• algorithms and AI models;
• intellectual property;
• network effects;
• ecosystem advantages;
• user attention;
• technical standards;
• specialised infrastructure.

Strategic competition policy therefore involves using competition law in a forward-looking manner to preserve contestability, innovation, interoperability and competitive access while allowing firms to obtain legitimate rewards for innovation.

1. Meaning of Strategic Competition Policy

Traditional competition law generally examines:

• anti-competitive agreements;
• abuse of dominance;
• mergers and acquisitions;
• foreclosure;
• collusion;
• exclusionary conduct.

Strategic competition policy goes further by examining how future technological structures may affect competition.

It asks:

Will today's technological arrangements permit meaningful competition tomorrow?

Thus, competition authorities increasingly need to consider:

Current market power + technological advantages + network effects + future innovation = future competitive structure

2. Characteristics of the Next Digital Era

A. AI-driven markets

AI may become embedded in:

• search;
• education;
• healthcare;
• finance;
• advertising;
• software;
• logistics;
• autonomous vehicles.

Competitive advantage may depend on access to:

• training data;
• GPUs;
• cloud computing;
• foundation models;
• specialised talent.

B. Autonomous digital agents

Future AI agents may independently:

• search for products;
• negotiate prices;
• execute transactions;
• choose financial products;
• manage logistics.

This could fundamentally change traditional market structures.

Competition authorities may need to examine whether dominant AI agents:

• favour affiliated suppliers;
• restrict competing services;
• manipulate rankings;
• discriminate between suppliers.

3. Data as a Source of Market Power

Data can produce a competitive feedback loop:

More users

↓

More data

↓

Better algorithms

↓

Better service

↓

More users

This can create substantial entry barriers.

However, data ownership itself is not automatically anti-competitive.

The relevant questions are:

• Is the data genuinely difficult to replicate?
• Does it confer substantial competitive advantage?
• Is access being strategically restricted?
• Is data being combined across markets?
• Does the conduct exclude competitors?

4. Network Effects

Digital markets often have strong network effects.

For example:

More buyers → more sellers → greater product variety → more buyers.

This may cause a market to "tip" toward one or a few platforms.

Network effects can produce efficiencies, but they may also make entry increasingly difficult once a platform reaches substantial scale.

5. Ecosystem Competition

The next digital era will increasingly involve ecosystem competition.

A company may control:

Operating system + app store + payment system + browser + cloud + advertising + AI assistant.

Competition may therefore occur between entire ecosystems rather than individual products.

This creates possible concerns involving:

• tying;
• bundling;
• self-preferencing;
• interoperability restrictions;
• discriminatory access;
• leveraging.

6. Case Law 1 — United States v. Microsoft Corp.

253 F.3d 34 (D.C. Cir. 2001)

Facts

Microsoft possessed substantial power in PC operating systems. It was accused of using its operating-system position to disadvantage competing technologies, particularly Netscape's browser and Java-related technologies.

Principle

A dominant technological undertaking cannot necessarily use exclusionary methods to protect its market position against emerging technological competition.

Relevance

The case provides a foundation for analysing future:

• AI platforms;
• cloud ecosystems;
• operating systems;
• app stores;
• digital assistants.

The central distinction is between competing through superior technology and using technological control to exclude rivals.

7. Case Law 2 — Google Search (Shopping)

Facts

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

Principle

A dominant platform controlling an important digital gateway may raise competition concerns if it uses that position to favour its own downstream service.

Relevance

The principle is particularly important for:

• AI search;
• AI marketplaces;
• digital advertising;
• application platforms;
• recommendation systems.

A future AI platform could potentially become both a gateway and a competitor to businesses using that gateway.

8. Case Law 3 — Google Android

Facts

The European Commission examined Google's contractual arrangements concerning Android devices, including practices relating to Google Search, the Play Store and other applications.

Principle

A dominant company may raise competition concerns when it uses contractual or technical arrangements to extend its power from one market into connected markets.

Next-digital-era relevance

A future ecosystem could look like:

AI operating system → AI assistant → search → payments → advertising → cloud

Competition policy must consider whether dominance in one layer is being used to restrict competition in another.

9. Case Law 4 — Bundeskartellamt v Facebook/Meta

The German competition authority examined Facebook's market position together with its collection and combination of user data from different sources.

Principle

Data practices can become relevant to competition law when they interact with substantial market power.

Significance

This case demonstrates the growing importance of data concentration.

Data can strengthen market power through:

Users → data → improved targeting/service → more users.

This issue will become particularly important for AI-driven platforms.

10. Case Law 5 — FTC v. Facebook, Inc.

Facts

The United States Federal Trade Commission challenged Facebook's alleged maintenance of monopoly power in personal social networking, including through acquisitions and platform conduct.

Principle

Competition analysis may need to account for potential competition, not simply current market shares.

A startup with modest revenue can nevertheless be competitively significant if it possesses:

• disruptive technology;
• valuable users;
• innovative capabilities;
• a realistic path toward expansion.

Next digital era

This principle is relevant to acquisitions involving:

• AI startups;
• robotics companies;
• digital-payment innovators;
• cybersecurity firms;
• emerging platforms.

11. Case Law 6 — Intel Corp. v Commission

Case C-413/14 P

Facts

Intel was investigated concerning conditional rebates offered to major computer manufacturers and a retailer.

Principle

Conditional rebates by a dominant firm require analysis of their potential foreclosure effects.

Digital relevance

Similar economic mechanisms may appear in:

• cloud credits;
• AI-computing subsidies;
• developer incentives;
• advertising credits;
• platform discounts.

The key question is whether the arrangement promotes legitimate competition or materially forecloses competing suppliers.

12. Case Law 7 — Qualcomm v European Commission

Facts

The European Commission investigated payments made by Qualcomm to Apple concerning baseband chipsets.

Principle

Conditional payments involving important customers may create competition concerns when they foreclose competing suppliers.

Relevance

The case is particularly relevant to:

• semiconductor markets;
• AI chips;
• telecommunications;
• autonomous vehicles;
• connected devices.

The next digital era will depend heavily on specialised technological inputs.

13. Case Law 8 — United Brands v Commission

Case 27/76

Principle

United Brands remains a foundational authority concerning dominance.

A dominant position involves economic strength that enables an undertaking to behave to an appreciable extent independently of competitors, customers and consumers.

Digital relevance

Modern dominance may derive from:

• data;
• algorithms;
• network effects;
• ecosystems;
• cloud infrastructure;
• intellectual property.

The legal concept remains relevant even though the economic sources of power have changed.

14. Case Law 9 — Bronner v Mediaprint

Case C-7/97

Principle

Bronner provides important guidance concerning refusal to deal and access to infrastructure.

Digital significance

Similar questions can arise regarding:

• app stores;
• payment networks;
• cloud infrastructure;
• digital identity systems;
• telecommunications;
• AI computing infrastructure.

However, an important digital infrastructure does not automatically qualify as an essential facility. The established legal requirements must still be satisfied.

15. AI Platforms and Competition

AI creates a potentially layered market:

Layer 1 — Chips

GPUs and specialised processors.

Layer 2 — Computing

Cloud and data-centre infrastructure.

Layer 3 — Models

Foundation and specialised AI models.

Layer 4 — Applications

AI-powered products.

Layer 5 — Distribution

Search engines, operating systems, app stores and digital assistants.

Control across multiple layers can create opportunities for vertical leveraging.

16. Generative AI and Competition

Generative AI creates several possible competition concerns.

A. Training-data access

Large datasets can create advantages that new entrants cannot easily replicate.

B. Compute access

Training sophisticated models can require enormous computational resources.

C. Distribution

An established digital platform can favour its own AI service.

D. Integration

A company controlling cloud infrastructure may favour its affiliated AI model.

E. Acquisitions

Large technology firms may acquire emerging AI competitors.

17. Algorithmic Collusion

Algorithms can make markets more efficient by:

• reducing search costs;
• adjusting prices;
• improving inventory management.

But they can also make coordination easier.

For example:

Algorithm A observes competitor price → Algorithm B responds automatically → competitors' prices converge.

Competition authorities must distinguish:

• independent algorithmic decision-making;
• intentional coordination;
• algorithmically facilitated collusion.

18. Self-Preferencing

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

Possible future examples include:

• an AI assistant recommending its owner's products;
• an app store favouring affiliated applications;
• a search engine favouring affiliated AI services;
• a cloud marketplace prioritising its own applications.

The competition analysis depends on market power, conduct and actual or likely competitive effects.

19. Interoperability

Interoperability may be crucial for maintaining competitive pressure.

Examples include:

• messaging interoperability;
• payment interoperability;
• cloud portability;
• AI-agent interoperability;
• data portability.

Where users can easily switch between systems, network effects and lock-in may be reduced.

But mandatory interoperability can also create:

• security risks;
• intellectual-property concerns;
• free-riding;
• innovation disincentives.

Therefore, interoperability remedies should be carefully designed.

20. Digital Gatekeepers

A gatekeeper controls an important route through which businesses reach consumers.

Examples can include:

• search engines;
• app stores;
• operating systems;
• online marketplaces;
• payment systems;
• digital advertising infrastructure.

Strategic competition policy increasingly focuses on whether gatekeepers can use their position to:

• impose unfair conditions;
• restrict access;
• favour their own services;
• collect competitors' data;
• prevent switching.

21. Digital Merger Control

Traditional merger analysis may understate the significance of acquisitions in digital markets.

A target may have:

• little revenue;
• few employees;
• limited market share,

but possess:

• a breakthrough AI model;
• important technology;
• valuable data;
• talented researchers;
• potential to become a platform.

Therefore, strategic merger assessment may consider innovation competition and potential competition.

22. Competition and Intellectual Property

Digital innovation depends heavily upon:

• patents;
• copyrights;
• software;
• trade secrets;
• proprietary algorithms.

Intellectual-property rights are designed to reward innovation.

Competition problems may arise where IP is used to:

• prevent interoperability;
• exclude competing technologies;
• impose discriminatory licensing;
• extend dominance into neighbouring markets.

The mere possession of IP is not normally sufficient to establish an antitrust violation.

23. Standards and Digital Competition

Future digital systems will depend upon standards for:

• 5G/6G;
• IoT;
• cybersecurity;
• AI interoperability;
• connected vehicles;
• digital identity.

Standards can increase competition by allowing products from different manufacturers to work together.

But control over standard-essential technology may create:

• licensing power;
• exclusion risks;
• royalty disputes;
• interoperability bottlenecks.

Competition law therefore intersects with standard-essential patent licensing and FRAND principles.

24. Cloud Computing

Cloud markets may become a critical infrastructure layer for the digital economy.

Competition concerns may involve:

• high switching costs;
• restrictive contracts;
• data portability;
• interoperability;
• tying;
• preferential treatment;
• exclusive arrangements.

The strategic objective is to ensure that cloud customers can change providers without facing unnecessary technological or contractual barriers.

25. Digital Payments and Fintech

The next digital era may involve increasing integration of:

• banking;
• digital wallets;
• payment networks;
• AI;
• embedded finance.

Competition concerns may arise through:

• discriminatory access to payment networks;
• tying financial services;
• exclusionary interoperability restrictions;
• preferential treatment of affiliated financial products.

Because payment systems can operate as infrastructure, access conditions may have significant competitive consequences.

26. Indian Competition Law Framework

The principal Indian legislation is the Competition Act, 2002.

Section 3 — Anti-competitive agreements

Potential digital applications include:

• algorithmic collusion;
• technology licensing restrictions;
• information exchange;
• exclusive arrangements.

Section 4 — Abuse of dominant position

Potential concerns include:

• discriminatory access;
• denial of market access;
• tying;
• leveraging;
• unfair conditions;
• exclusionary conduct.

Sections 5 and 6 — Combinations

These provisions are relevant to:

• technology acquisitions;
• digital-platform mergers;
• AI acquisitions;
• data-driven combinations;
• ecosystem consolidation.

27. Strategic Competition Policy in India

For the next digital era, competition policy can focus on:

1. Contestability

Can new firms realistically enter?

2. Interoperability

Can different technological systems communicate?

3. Switching

Can users change providers without excessive costs?

4. Data access

Can competitors obtain competitively relevant data where legally justified?

5. Innovation

Does conduct reduce incentives for technological development?

6. Potential competition

Could a startup become an important competitor?

28. Competition Policy Versus Industrial Policy

Governments may legitimately seek to promote strategic technologies through:

• subsidies;
• public procurement;
• R&D programmes;
• infrastructure investment;
• tax incentives.

However, strategic industrial policy should not unnecessarily eliminate competition.

The principle of competitive neutrality is therefore important where government-supported firms compete with private undertakings.

29. Remedies for the Next Digital Era

Competition authorities may employ several remedies.

Behavioural remedies

• non-discrimination;
• transparency;
• access obligations;
• interoperability;
• restrictions on self-preferencing.

Structural remedies

In exceptional circumstances:

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

Merger remedies

• licensing;
• access commitments;
• technology separation;
• preservation of competing innovation pipelines.

The remedy should correspond to the identified competitive harm.

30. Strategic Analytical Framework

A useful framework is:

Technology

↓

Strategic Asset

↓

Market Structure

↓

Network Effects / Switching Costs

↓

Market Power

↓

Conduct

↓

Competitive Effects

↓

Innovation Effects

↓

Remedy

For example:

AI model + exclusive data + dominant platform + self-preferencing

may require considerably closer examination than:

AI model + strong market position + competition entirely on product quality.

31. Key Competition Risks

32. Core Principles

Principle 1 — Innovation is not itself anti-competitive

Competition law should not punish successful technological innovation.

Principle 2 — Dominance is not automatically unlawful

The central issue is generally the exercise of dominance through prohibited conduct.

Principle 3 — Future competition matters

Potential competitors and innovation pipelines can be competitively important.

Principle 4 — Data can reinforce market power

But possession of data alone does not establish an antitrust violation.

Principle 5 — Ecosystems require cross-market analysis

Market power can potentially be leveraged from one technological layer into another.

Principle 6 — Interoperability can preserve contestability

But interoperability obligations should be proportionate.

Principle 7 — Merger control must consider innovation

Current revenue may not reflect future competitive significance.

Conclusion

Competition law for the next digital era must become increasingly forward-looking without abandoning established principles of economic and legal analysis.

The central competitive assets of the future may not be factories or physical distribution networks. They may instead be:

data + AI models + computing power + algorithms + intellectual property + ecosystems + digital infrastructure.

The cases of Microsoft, Google Shopping, Google Android, Facebook/Meta, FTC v Facebook, Intel, Qualcomm, United Brands and Bronner demonstrate how established doctrines concerning dominance, leveraging, tying, exclusion, potential competition and access can be adapted to these new environments.

The fundamental objective of strategic competition policy should therefore be:

To ensure that innovation creates competitive opportunities rather than allowing technological control, data accumulation, network effects and ecosystem integration to become permanent barriers to entry and innovation.

In the next digital era, the most important competition-policy question will increasingly be not merely “Who has market power today?”, but “What technological structures are determining who will be able to compete tomorrow?”