Competition Law and Machine-Generated Exclusivity Arrangements

1. Introduction

Machine-generated exclusivity arrangements are contractual, commercial, or platform arrangements in which an algorithm, artificial-intelligence system, automated contracting system, or machine-learning model creates, recommends, negotiates, enforces, or dynamically modifies an arrangement that restricts a business from dealing with competing suppliers, distributors, platforms, products, or services.

Traditional exclusivity is usually negotiated by humans. Machine-generated exclusivity is different because software may automatically decide:

which suppliers receive exclusive contracts;

which sellers are promoted or restricted;

which distributors are allocated a territory;

which customers receive exclusive offers;

which suppliers are prevented from using rival platforms;

how long exclusivity should continue;

what prices or incentives are attached to exclusivity;

whether competing products should be removed from a marketplace.

Under Indian competition law, the fact that an arrangement was generated by a machine does not by itself make it lawful or unlawful. The Competition Act, 2002 focuses on the competitive effect and the conduct of enterprises. Section 3(4) expressly covers vertical restraints including exclusive supply and exclusive distribution arrangements. (Competition Commission of India)

2. Meaning of Machine-Generated Exclusivity

A machine-generated exclusivity arrangement can be defined as:

An arrangement in which software, an algorithm, AI system, or automated decision-making mechanism creates, recommends, implements, or enforces restrictions that limit an enterprise's ability or incentive to deal with competing businesses or products.

Example

Suppose an online marketplace uses an AI system that automatically tells a major seller:

“You will receive preferred placement, lower commission and additional visibility only if you do not sell the same products through competing marketplaces.”

The algorithm may automatically generate and enforce that condition.

The competition-law question is not simply “Was AI involved?”

The important questions are:

What is the relevant market?

Who has market power?

What exactly is restricted?

How much of the market is foreclosed?

How long does exclusivity last?

Are competitors prevented from obtaining important inputs or customers?

Does the arrangement create efficiencies?

Does the conduct result in an appreciable adverse effect on competition (AAEC)?

3. Indian Legal Framework

3.1 Section 3

Section 3 prohibits agreements that cause or are likely to cause an appreciable adverse effect on competition.

Section 3(4) is particularly important because it covers vertical agreements.

The principal vertical restraints include:

tie-in arrangements;

exclusive supply agreements;

exclusive distribution agreements;

refusal to deal;

resale price maintenance.

CCI itself identifies exclusive supply and exclusive distribution as forms of vertical restraints under Section 3(4). (Competition Commission of India)

4. Machine-Generated Exclusive Supply

An exclusive supply agreement restricts a purchaser from acquiring or dealing with goods or services other than those supplied by a particular supplier.

A machine may automatically determine:

which distributor receives exclusive supply;

minimum purchase requirements;

exclusivity periods;

automatic renewal;

penalties for dealing with competitors;

discounts conditional upon exclusivity.

Competition concern

If the algorithm systematically makes important distributors unavailable to competing suppliers, competitors may be unable to obtain sufficient distribution channels.

This can create foreclosure.

5. Machine-Generated Exclusive Distribution

Exclusive distribution occurs when a supplier restricts distribution of products to particular distributors, territories, or channels.

An algorithm could automatically allocate:

Territory A → Distributor X
Territory B → Distributor Y

and prevent other distributors from selling the product.

The arrangement may be commercially efficient, but if a powerful enterprise uses it to partition the market or exclude rivals, Section 3(4) concerns can arise.

6. Algorithmic Exclusivity and Market Foreclosure

The central competition-law concern is often foreclosure.

Foreclosure occurs when competitors are denied meaningful access to:

suppliers;

distributors;

customers;

platforms;

infrastructure;

data;

technology;

important inputs.

Machine learning can make foreclosure more sophisticated because an algorithm can continuously identify commercially important counterparties.

For example:

Traditional exclusivity

Company identifies 100 important distributors manually.

Machine-generated exclusivity

AI analyses millions of transactions and identifies the 2,000 distributors whose exclusivity would impose the greatest competitive disadvantage on rivals.

The second situation may potentially produce substantially greater foreclosure.

7. Dynamic Exclusivity

One important feature of machine-generated arrangements is dynamic exclusivity.

Instead of signing one five-year contract, an algorithm could continually modify contractual conditions.

For example:

Month 1: 10% discount for exclusivity.

Month 2: 15%.

Month 3: exclusivity automatically extended.

Month 4: competitor access reduced.

Month 5: penalty increased.

The machine therefore becomes an instrument for continuous market management.

Competition authorities may need to examine the entire sequence rather than isolated transactions.

8. Algorithmic Loyalty Arrangements

Machine-generated exclusivity can also appear as a loyalty scheme.

For example:

“A seller receives higher search ranking if 90% of its online sales occur through Platform X.”

Such a system may not expressly say “you must not use competitors.”

Nevertheless, the economic effect could be similar to exclusivity.

The analysis therefore should examine substance and economic effect rather than merely contractual wording.

9. Machine-Generated Exclusivity and Section 4

Section 4 becomes particularly relevant where the enterprise using the algorithm is dominant.

Dominance itself is not prohibited.

The problem arises when dominance is abused.

Relevant forms of abuse may include:

denial of market access;

unfair conditions;

limiting markets;

limiting technical development;

leveraging dominance into another market;

imposing contractual conditions unrelated to the subject matter of the contract.

CCI describes denial of market access and leveraging dominance into another relevant market among the forms of conduct examined under Section 4. (Competition Commission of India)

10. Algorithmic Self-Preferencing

A dominant platform could use an algorithm to give its own products:

better search ranking;

lower commission;

greater visibility;

preferred placement;

faster delivery;

better recommendations.

It might simultaneously impose exclusivity conditions on competing sellers.

This creates a combination of:

self-preferencing + exclusivity + platform power.

Such conduct may be particularly significant in digital markets because sellers may have few practical alternatives to the platform.

11. Machine-Generated Exclusivity and Data

Data can make algorithmic exclusivity considerably more powerful.

A platform may know:

seller revenues;

customer behaviour;

conversion rates;

competitor dependence;

transaction volumes;

switching patterns;

product demand;

geographic concentration.

The algorithm can use this information to identify the businesses most vulnerable to exclusive contracts.

Thus, data advantage + algorithmic optimisation + exclusivity can create a powerful competitive strategy.

12. Network Effects

Digital platforms frequently benefit from network effects.

The value of a platform can increase as:

more consumers join;

more sellers join;

more transactions occur;

more data is generated.

If a machine-generated exclusivity arrangement brings important sellers exclusively onto one platform, the platform may attract additional customers.

More customers then attract more sellers.

This produces a feedback loop:

Exclusivity → more sellers → more consumers → more transactions → more data → stronger platform → greater bargaining power → more exclusivity.

Competition law therefore has to consider both immediate and longer-term effects.

13. Machine-Generated Territorial Allocation

Algorithms can automatically allocate territories.

For example:

Supplier A receives Northern India.

Supplier B receives Southern India.

Supplier C receives Western India.

If competitors independently accept these restrictions, territorial arrangements may raise serious competition concerns.

Where competing enterprises coordinate through a common algorithm, the issue can move beyond vertical exclusivity toward horizontal coordination or market allocation.

Section 3(3) treats market allocation among competitors as a particularly serious category of horizontal conduct. (Competition Commission of India)

14. Common Algorithm and Coordinated Exclusivity

Suppose five competing manufacturers use the same AI contracting platform.

The platform recommends:

“Each manufacturer should give exclusive rights to a different distributor.”

If competitors knowingly use the system to coordinate their conduct, the legal issue is no longer merely vertical exclusivity.

It could potentially involve:

market allocation;

coordinated conduct;

exchange of competitively sensitive information;

hub-and-spoke coordination.

The relevant legal question is whether there is an agreement, arrangement, understanding or concerted action satisfying the statutory requirements.

15. Hub-and-Spoke Risk

A technology provider may become the hub, while competing businesses become the spokes.

Example:

Supplier A
↓
Common AI platform
↓
Supplier B
↓
Supplier C

If the platform facilitates common restrictions among competitors, competition authorities may examine whether the technology is being used as a mechanism for coordination.

The mere use of the same software, however, should not automatically establish an infringement. Evidence of communication, knowledge, adoption or concerted conduct remains important.

16. Automatic Renewal

Machine-generated contracts can contain automatic renewal provisions.

For example:

Exclusivity automatically renews for another 12 months unless the distributor gives 90 days' notice.

Where switching is difficult, automatic renewal may substantially increase the duration of foreclosure.

Factors relevant to assessment include:

contract duration;

renewal frequency;

termination costs;

switching costs;

market share;

availability of alternative suppliers.

17. Switching Costs

Algorithms can also increase switching costs.

A platform may provide:

exclusive APIs;

proprietary software;

customer-data integration;

loyalty benefits;

automated inventory systems.

A seller that leaves the platform may lose access to these systems.

Therefore, an exclusivity arrangement may be formally terminable but economically difficult to escape.

18. Exclusivity and Entry Barriers

Machine-generated exclusivity can increase entry barriers.

A new competitor may require:

suppliers;

retailers;

distributors;

users;

data;

payment infrastructure.

If an established enterprise has algorithmically locked up the most valuable participants, a new entrant may be unable to achieve sufficient scale.

This is particularly important in:

e-commerce;

digital advertising;

cloud computing;

app ecosystems;

payment systems;

logistics platforms;

digital financial services.

19. Machine-Generated Predatory Exclusivity

A dominant enterprise could potentially combine exclusivity with aggressive pricing.

For example:

AI identifies vulnerable distributors.

Platform offers extremely large discounts.

Discounts are conditional upon exclusivity.

Rival suppliers lose distribution.

After rivals weaken, discounts disappear.

The competition analysis would need to distinguish legitimate promotional competition from exclusionary conduct.

20. Machine-Generated Rebates

AI may determine the exact rebate required to persuade a business to remain exclusive.

For example:

Distributor A receives 5%.

Distributor B receives 12%.

Distributor C receives 25%.

The algorithm may use real-time data to calculate the minimum incentive necessary to prevent switching.

This can make exclusionary strategies more targeted and less visible.

21. Personalised Exclusivity

Algorithms can create different exclusivity conditions for different counterparties.

For example:

The legal assessment should consider whether these arrangements collectively create significant foreclosure.

22. Machine-Generated Exclusivity in E-Commerce

E-commerce is one of the most obvious contexts.

A marketplace could algorithmically determine:

which sellers receive preferred status;

which products receive visibility;

which sellers receive advertising subsidies;

which brands receive platform exclusivity;

which sellers can access particular fulfilment systems.

Indian CCI proceedings involving Amazon and Flipkart have specifically examined allegations concerning vertical arrangements and preferred sellers. CCI's investigation orders concerned allegations that such arrangements could foreclose non-preferred sellers. (Competition Commission of India)

23. Delhi Vyapar Mahasangh v. Flipkart

Case: Delhi Vyapar Mahasangh v. Flipkart Internet Private Limited & Ors., CCI Case No. 40/2019.

The CCI initiated examination concerning alleged preferential arrangements and practices in e-commerce. (Competition Commission of India)

Relevance

The case is important for understanding:

platform power;

preferred sellers;

vertical arrangements;

preferential treatment;

marketplace foreclosure.

Application to machine-generated exclusivity

If an algorithm automatically identifies “preferred sellers” and gives them exclusive or preferential access to important platform facilities, the same competition principles may become relevant.

24. Amazon Seller Services Litigation

The Amazon and Flipkart proceedings also demonstrate the importance of the investigation stage.

The Karnataka High Court upheld CCI's decision to direct an investigation under Section 26(1), relying on the Supreme Court's approach in CCI v. SAIL. (Competition Commission of India)

The case illustrates that competition authorities can investigate complex platform arrangements where there is a prima facie basis for examining potentially exclusionary vertical relationships.

25. Amazon Private-Label Proceedings

Case: In Re: Allegations pertaining to private label brands related to Amazon sold on Amazon India marketplace, Suo Motu Case No. 04/2021.

The CCI recorded proceedings concerning allegations involving private-label brands and Amazon's marketplace. (Competition Commission of India)

Relevance

The case is useful for analysing the intersection of:

marketplace operation;

private labels;

access to seller information;

preferential treatment;

platform neutrality.

A machine-generated exclusivity system could intensify these concerns if the marketplace's algorithm systematically restricts rival sellers while favouring the platform's own brands.

26. Umar Javeed v. Google

Case: Umar Javeed & Others v. Google LLC & Another, CCI Case No. 39/2018.

The case concerned Google's Android ecosystem and arrangements involving manufacturers and Google's proprietary services. CCI's proceedings examined several interconnected digital markets and contractual arrangements. (Competition Commission of India)

Relevance

It demonstrates how competition law can examine:

ecosystem restrictions;

contractual conditions;

platform leverage;

tying and related restrictions;

competitive effects across connected digital markets.

Machine-generated exclusivity could create similar concerns when an algorithm automatically conditions access to one service upon restricted dealings with competing services.

27. Matrimony.com v. Google

Case: Matrimony.com Ltd. v. Google LLC & Others, CCI Case Nos. 07/2012 and 30/2012.

CCI examined Google's search-related conduct and allegations concerning preferential treatment. (Competition Commission of India)

Relevance

The broader principle is important for machine-generated markets:

An algorithmic ranking mechanism can itself become a competition-law instrument when controlled by an enterprise possessing significant market power.

Thus, an AI-generated exclusivity arrangement should not be analysed only as a traditional contract.

28. All India Online Vendors Association v. Flipkart

Case: All India Online Vendors Association v. Flipkart India Private Limited & Others, CCI Case No. 20/2018.

CCI considered allegations concerning Flipkart's marketplace conduct. (Competition Commission of India)

Relevance

The case provides an important background for analysing:

marketplace access;

seller relationships;

preferential treatment;

platform competition;

foreclosure.

These concepts can be applied to algorithmically generated exclusivity arrangements.

29. CCI v. SAIL

Case: Competition Commission of India v. Steel Authority of India Ltd., (2010) 10 SCC 744.

This is a foundational Supreme Court decision concerning CCI's investigation process.

Relevance

The Supreme Court recognised the nature of the Commission's initial investigative function under Section 26.

For machine-generated exclusivity, this matters because complicated algorithms may require investigation into:

source code;

contractual databases;

algorithmic instructions;

internal communications;

business rules;

data inputs;

automated decisions.

The Commission may need to establish whether there is sufficient basis to proceed to investigation.

30. Excel Crop Care Ltd. v. CCI

Case: Excel Crop Care Ltd. v. Competition Commission of India, (2017) 8 SCC 47.

The Supreme Court considered competition-law infringement and penalty principles.

Relevance

The case is useful because algorithmic arrangements must ultimately be assessed through the statutory competition framework rather than through technological terminology alone.

The use of sophisticated technology does not remove an enterprise from competition-law scrutiny.

31. CCI v. Bharti Airtel

Case: Competition Commission of India v. Bharti Airtel Ltd., (2019) 2 SCC 521.

The Supreme Court examined the relationship between sectoral regulation and competition jurisdiction.

Relevance

Machine-generated exclusivity may occur in regulated sectors such as:

telecommunications;

banking;

payments;

insurance;

financial technology.

The case illustrates the importance of considering the competence of sectoral regulators alongside competition law.

32. International Analogy: United States v. Microsoft

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

Microsoft was scrutinised for conduct involving exclusionary arrangements and restrictions concerning competing technologies.

Relevance

The case is particularly useful for understanding how contractual and technological restrictions can be used together to protect an established technological ecosystem.

Machine-generated exclusivity could similarly combine:

contract + technology + platform control.

33. International Analogy: Aspen Skiing

Case: Aspen Skiing Co. v. Aspen Highlands Skiing Corp., 472 U.S. 585 (1985).

The US Supreme Court examined refusal-to-deal conduct involving ski operators.

Relevance

It is useful when machine-generated exclusivity involves:

termination of previously beneficial relationships;

exclusion of a rival;

refusal to continue supplying access;

deliberate restriction of interoperability.

The case is an important historical authority, although US refusal-to-deal doctrine is context-specific.

34. International Analogy: Eturas

Case: Eturas UAB and Others v. Lietuvos Respublikos konkurencijos taryba, Case C-74/14.

The European Court of Justice considered competition issues arising through a common computerised booking system.

Importance for AI

This case is particularly relevant to algorithmic competition because the technological system itself became relevant to the alleged coordination.

It illustrates why competition authorities may need to investigate:

software communications;

algorithmic instructions;

system notifications;

automated restrictions;

users' knowledge and participation.

35. Is Every Machine-Generated Exclusivity Arrangement Illegal?

No.

This is one of the most important points.

Exclusive arrangements can generate legitimate economic benefits.

CCI's own training material recognises that exclusive arrangements can sometimes provide benefits such as stable supply, lower distribution costs, market entry, better after-sales service and protection against certain commercial risks. (Competition Commission of India)

Therefore:

Machine generation ≠ automatic illegality.

The competition assessment depends upon the statutory framework and market effects.

36. Potential Pro-Competitive Benefits

Machine-generated exclusivity may:

reduce transaction costs;

improve supply reliability;

encourage investment;

support new-product launches;

protect distributor investments;

improve logistics;

prevent free-riding;

facilitate quality control;

reduce distribution costs;

provide predictable demand;

support entry into new markets.

For example, a manufacturer may provide exclusive rights to a distributor because that distributor must invest heavily in:

warehouses;

technical support;

marketing;

service infrastructure.

The exclusivity may protect that investment.

37. Potential Anti-Competitive Effects

On the other hand, machine-generated exclusivity can:

foreclose competitors;

increase entry barriers;

reduce distributor choice;

raise switching costs;

facilitate market partitioning;

reinforce dominance;

disadvantage innovative competitors;

restrict access to essential channels;

strengthen network effects;

facilitate ecosystem lock-in.

38. Relevant Market

The CCI must ordinarily define the relevant market before assessing dominance under Section 4.

Possible relevant markets might involve:

online marketplace services;

app distribution;

digital advertising;

cloud services;

payment services;

logistics services;

software distribution;

particular physical goods.

The algorithm itself is not necessarily the relevant market.

The competition authority must determine where competitive constraints actually operate.

39. Market Share

Market share remains relevant, but digital markets require broader analysis.

Factors may include:

market share;

network effects;

financial strength;

data advantage;

switching costs;

entry barriers;

consumer dependence;

technological advantages;

vertical integration.

A machine-generated exclusivity arrangement is more likely to raise serious concerns where the enterprise already possesses substantial market power.

40. Duration of Exclusivity

Duration is critical.

Compare:

Short-term exclusivity

3-month promotional arrangement.

Long-term exclusivity

10-year arrangement covering most major distributors.

The second arrangement has substantially greater potential to foreclose competitors.

Algorithms may make this issue less obvious because exclusivity may renew automatically.

41. Market Coverage

Authorities should examine the percentage of the market affected.

For example:

These figures are illustrative, not legal thresholds. The actual analysis depends on the relevant market and circumstances.

42. Algorithmic Exclusivity and SMEs

Small businesses may be particularly vulnerable.

A dominant platform could offer:

“Exclusive participation gives you 30% lower commission.”

A small seller may have little bargaining power and may accept the restriction because it depends heavily on the platform.

The result could be:

Platform dependence → acceptance of exclusivity → rival platform loses sellers → reduced competition.

43. Machine-Generated Exclusivity and Innovation

Exclusivity can affect innovation.

A new entrant may have a technologically superior product but cannot obtain:

distribution;

suppliers;

app access;

platform visibility;

payment access.

Therefore, short-term price effects may not reveal the entire competitive harm.

Competition authorities may also examine innovation competition.

44. AI and Competition Policy in India

The CCI's 2025 Market Study on Artificial Intelligence and Competition recognised that AI is reshaping competitive dynamics and identified emerging competition issues requiring attention. The CCI also indicated the need for stronger technical capabilities and AI-related competition compliance. (Competition Commission of India)

This is important for machine-generated exclusivity because algorithms can change:

speed of contracting;

scale of exclusion;

precision of targeting;

complexity of decision-making;

difficulty of detecting restrictive arrangements.

45. Human Responsibility for Machine Decisions

An enterprise should not be able to escape competition liability simply by saying:

“The algorithm made the decision.”

If management:

designed the system;

selected the objective;

supplied the data;

imposed commercial constraints;

approved the algorithm;

knowingly adopted its recommendations,

the role of the enterprise remains central.

Competition law generally focuses on the conduct of enterprises and the economic arrangement, not merely the identity of the person who clicked the final button.

46. Algorithmic Audit

Businesses using AI for exclusivity should maintain:

algorithm documentation;

decision logs;

contractual records;

approval records;

data sources;

model versions;

compliance reviews;

exception records;

human oversight mechanisms.

This is particularly important because algorithmic decisions can otherwise be difficult to reconstruct.

47. Competition Compliance Framework

Enterprises should consider the following safeguards:

Step 1 — Identify exclusivity

Determine whether the algorithm creates any exclusive condition.

Step 2 — Identify market power

Assess the enterprise's position.

Step 3 — Identify affected competitors

Determine who may be foreclosed.

Step 4 — Measure coverage

Calculate the proportion of distribution or supply affected.

Step 5 — Review duration

Examine contract duration and automatic renewal.

Step 6 — Review switching costs

Determine whether counterparties can realistically switch.

Step 7 — Evaluate efficiencies

Document legitimate business justification.

Step 8 — Human review

Require competition-law review for high-risk algorithmic decisions.

Step 9 — Monitor outcomes

Check whether the system is producing discriminatory or exclusionary results.

Step 10 — Correct the model

Modify algorithmic rules producing unjustified foreclosure.

48. Key Difference: Traditional vs Machine-Generated Exclusivity

49. Major Legal Issues

The principal legal issues can therefore be summarised as:

A. Exclusive supply

Does the algorithm restrict purchases from competitors?

B. Exclusive distribution

Does it restrict distribution channels?

C. Refusal to deal

Does automated decision-making deny access?

D. Market allocation

Does it divide customers or territories?

E. Dominance

Is the enterprise dominant?

F. Foreclosure

Are competitors substantially excluded?

G. Network effects

Does exclusivity reinforce platform power?

H. Data advantage

Does data allow more effective exclusion?

I. Self-preferencing

Does the platform favour its own products?

J. Coordination

Does common software facilitate competitors' coordination?

50. Important Case-Law Summary

The Indian CCI record confirms that e-commerce and digital-platform arrangements have already generated competition investigations involving preferred sellers, vertical arrangements and potential foreclosure. (Competition Commission of India)

51. Key Legal Principles

AI does not create a separate immunity from competition law.

Machine-generated exclusivity must be assessed under the Competition Act, 2002.

Section 3(4) is particularly relevant to exclusive supply and exclusive distribution.

Section 4 becomes important when a dominant enterprise uses exclusivity in an abusive manner.

Foreclosure is a central economic concern.

Market power and duration matter.

Market coverage matters.

Switching costs can increase exclusionary effects.

Network effects can amplify exclusivity.

Data can make algorithmic exclusivity more powerful.

Common algorithms can create coordination risks.

Not every exclusive arrangement is anti-competitive.

Legitimate efficiency justifications must be considered.

Human responsibility cannot necessarily be avoided by delegating decisions to AI.

Algorithmic auditability and documentation are increasingly important.

Digital-platform cases provide useful analogies even where there is not yet a case directly involving AI-generated exclusivity.

52. Quick Revision

Machine-generated exclusivity = AI/algorithm + exclusive commercial restriction.

Main provisions:

Section 3(1) — AAEC prohibition

Section 3(4) — vertical restraints

Section 4 — abuse of dominant position

Section 19 — inquiry/investigation framework

Section 26 — investigation process

Main risks:

Algorithm → Exclusivity → Foreclosure → Entry barriers → Reduced competition

Important cases:

CCI v. SAIL

Excel Crop Care v. CCI

CCI v. Bharti Airtel

Umar Javeed v. Google

Delhi Vyapar Mahasangh v. Flipkart

AIOVA v. Flipkart

Matrimony.com v. Google

Amazon Private Label proceedings

Eturas

Microsoft

Conclusion

Machine-generated exclusivity arrangements represent a modern form of a traditional competition-law problem. The important issue is not whether a human or machine created the restriction, but whether the arrangement substantially restricts competitive opportunities.

An AI system can nevertheless make exclusivity more significant because it can identify vulnerable counterparties, personalise incentives, continuously modify restrictions, exploit large datasets and operate at enormous scale. The CCI's recent work on AI and competition confirms that AI is changing competitive dynamics and requires greater technical and compliance capabilities. (Competition Commission of India)

Accordingly, Indian competition law should examine machine-generated exclusivity through market power, foreclosure, duration, coverage, switching costs, network effects, efficiencies, innovation effects and the possibility of coordination, rather than treating algorithmic decision-making as either automatically lawful or automatically unlawful.