AI Evaluation Leaderboard Control and Model Ranking Power

1. Introduction

AI evaluation leaderboard control refers to the ability of an entity to influence, administer, or control the benchmarks, testing protocols, ranking methodologies, scoring systems, datasets, or publication mechanisms through which AI models are compared.

In AI markets, leaderboards can function as more than informational tools. They may influence:

• which models developers adopt;
• which systems receive enterprise contracts;
• investor and media perceptions;
• model visibility and discoverability;
• access to downstream ecosystems;
• reputational standing;
• benchmark-oriented research and development;
• procurement decisions.

Consequently, control over an important AI evaluation system can potentially create a form of market power over model reputation and competitive access, particularly where the evaluator is independent of the firms being evaluated, controls a uniquely important benchmark, or combines evaluation authority with a competing AI platform.

The principal competition-law concern is not simply that a leaderboard ranks models. The concern arises when control over ranking infrastructure becomes a mechanism for exclusion, discrimination, self-preferencing, foreclosure, manipulation of competitive parameters, or reinforcement of an incumbent's ecosystem position.

2. Meaning of AI Evaluation Leaderboard Control

An AI leaderboard may involve several interconnected components:

1. Benchmark design – deciding which tasks models must perform.
2. Dataset control – controlling evaluation datasets and test questions.
3. Scoring methodology – deciding how accuracy, safety, latency, cost, reasoning or other metrics are weighted.
4. Model eligibility – deciding which models may participate.
5. Submission rules – determining testing conditions and technical requirements.
6. Ranking algorithm – converting scores into competitive rankings.
7. Publication control – determining how results are displayed.
8. Verification authority – deciding whether submitted results are genuine.
9. Update frequency – controlling when rankings change.
10. Commercial certification – potentially converting rankings into procurement or compliance credentials.

Thus, leaderboard power can exist even without traditional ownership of a marketplace.

3. Why Model Rankings Can Have Competitive Significance

AI markets have unusually strong information asymmetries.

A customer ordinarily cannot independently determine whether:

• Model A is better at reasoning than Model B;
• Model B is safer than Model C;
• Model C performs better on specialised tasks;
• a model's benchmark result generalises to real-world applications.

Consequently, third-party evaluation can reduce information costs.

A widely recognised leaderboard may therefore become a competitive information intermediary.

If customers routinely use the leaderboard to choose models, the ranking system may influence demand.

Example

Suppose:

Evaluation Platform X controls the most influential enterprise AI benchmark.

Three competing models receive:

• Model A – Rank 1
• Model B – Rank 2
• Model C – Rank 7

If enterprise customers routinely treat the ranking as a procurement signal, moving Model C from Rank 7 to Rank 2 may materially affect its commercial opportunities.

The ranking therefore has economic significance even though the evaluator does not sell the underlying AI models.

4. Competition Problems Created by Leaderboard Control

A. Self-preferencing

The most obvious concern arises where the evaluator also operates an AI model.

For example:

Company X operates a leading AI leaderboard and also owns Model X.

If Company X systematically designs evaluation criteria that favour its own model, the leaderboard can become an instrument of self-preferencing.

Potential mechanisms include:

• selecting tasks disproportionately suited to its architecture;
• weighting metrics favourable to its model;
• excluding competitors' capabilities;
• giving its model preferential testing conditions;
• publishing its own results more prominently;
• delaying competitors' results;
• changing benchmark methodology following competitors' improvements.

The relevant competition question is whether the conduct distorts competition rather than merely reflecting legitimate methodological choices.

5. Discriminatory Access to Evaluation

A leaderboard may also become problematic if access is selectively controlled.

Possible practices include:

• charging competitors substantially different evaluation fees;
• denying testing access;
• delaying evaluation of selected competitors;
• imposing technically unnecessary submission requirements;
• requiring proprietary APIs;
• limiting the number of submissions;
• imposing discriminatory verification procedures.

If the evaluation platform is sufficiently important, such conduct may resemble discriminatory access to an important competitive input.

6. Benchmark Design as a Competitive Parameter

Benchmark design is particularly important because the choice of what to measure can determine who wins.

Consider two competing AI models:

If the leaderboard heavily weights coding and mathematics, Model A ranks first.

If long-context performance receives much greater weight, Model B may rank first.

Thus:

Benchmark methodology itself can become a competitive parameter.

Where a dominant evaluator changes methodology selectively or without transparent justification, competition authorities may examine whether the change constitutes exclusionary conduct.

7. Ranking Power and Network Effects

Leaderboards can generate powerful feedback loops:

High ranking → greater visibility → more users → more developers → more data → more optimisation → stronger model → higher ranking

This can produce evaluation network effects.

A new benchmark may initially have little influence. But once:

• developers optimise for it;
• investors cite it;
• enterprises use it;
• media report it;
• procurement departments rely on it;

the benchmark can become increasingly difficult to displace.

This raises a potential contestability problem.

A dominant leaderboard may therefore acquire a gatekeeping role without technically being a traditional marketplace.

8. Ranking Manipulation and Algorithmic Discrimination

AI leaderboards frequently involve algorithmic scoring.

Potential problems include:

8.1 Hidden weights

The evaluator may not disclose the relative importance of different metrics.

8.2 Dynamic methodology

The methodology may change frequently, making comparisons difficult.

8.3 Selective benchmark updates

New tests may be introduced when particular models perform well or poorly.

8.4 Data contamination

Models may have encountered benchmark material during training.

8.5 Submission asymmetry

Different companies may receive different testing environments.

8.6 Statistical manipulation

Small differences may be presented as meaningful rankings despite insignificant underlying performance differences.

Competition law becomes relevant where such practices are used strategically to distort competitive conditions.

9. Essential-Facility Analogy

A highly influential AI evaluation platform could theoretically raise an essential-facility-type question.

The classical essential-facility inquiry generally concerns infrastructure that competitors cannot reasonably duplicate and access to which is necessary for effective competition.

Applied cautiously to AI evaluation:

Is the benchmark genuinely indispensable for competing in a relevant market, or are alternative evaluation mechanisms reasonably available?

This distinction is crucial.

A benchmark being popular does not automatically make it an essential facility.

Authorities would ordinarily need to examine:

• availability of alternative benchmarks;
• switching possibilities;
• costs of developing alternatives;
• customer dependence;
• market coverage;
• interoperability;
• technical substitutability;
• duration of the alleged dependence.

10. Tying Leaderboard Access to Other Services

An AI evaluation provider might possess power in evaluation services while also offering:

• cloud computing;
• model hosting;
• API access;
• AI safety certification;
• enterprise procurement services;
• model marketplaces.

Potential competition concerns could arise if it conditions leaderboard participation upon purchasing another service.

For example:

"A model can participate in the leading benchmark only if it uses our cloud inference infrastructure."

Such conduct could potentially combine evaluation power with infrastructure power.

11. Exclusive Evaluation Arrangements

Exclusive arrangements can also create foreclosure concerns.

Suppose a major AI developer enters an exclusive arrangement requiring it to:

use only Platform X for independent model evaluation.

If several leading model developers enter similar arrangements, competing evaluators may be deprived of the scale needed to develop credible alternatives.

The competition analysis would depend upon:

• duration;
• market coverage;
• exclusivity;
• availability of alternatives;
• switching costs;
• foreclosure percentage;
• efficiencies.

12. Certification and Procurement Effects

Leaderboard rankings can evolve into quasi-certification mechanisms.

For example:

Government procurement requires an AI system to achieve a specified ranking on Benchmark X.

This can transform a private ranking into a market-access requirement.

Potential concerns become greater where the evaluator controls both:

1. the benchmark; and
2. the certification necessary for commercial participation.

A ranking then ceases to be merely informational and may become an economic gatekeeping mechanism.

13. Relevant Competition-Law Doctrines

Several established doctrines can provide analytical frameworks.

13.1 Abuse of dominance

A dominant undertaking may face scrutiny where its control over evaluation infrastructure is used to exclude rivals.

13.2 Refusal to deal

Selective denial of access may become relevant where the evaluator occupies a particularly important competitive position.

13.3 Discriminatory treatment

Unequal evaluation conditions can potentially distort competition.

13.4 Self-preferencing

Preferential treatment of an affiliated AI model may constitute an important theory of harm.

13.5 Tying and bundling

Evaluation access could potentially be tied to cloud, API, or infrastructure services.

13.6 Exclusive dealing

Long-term exclusive evaluation agreements may foreclose competing evaluation providers.

13.7 Leveraging

Market power in AI evaluation could potentially be leveraged into model distribution, cloud computing, or enterprise procurement.

13.8 Essential facilities

Where access is genuinely indispensable and alternatives are unavailable, essential-facility principles may become relevant.

14. Case Laws

Because AI-specific reported competition cases concerning leaderboard control remain limited, the following cases provide analogical legal frameworks for analysing AI evaluation and ranking power.

Case 1: United States v. Microsoft Corp., 253 F.3d 34 (D.C. Cir. 2001)

The Microsoft litigation concerned Microsoft's use of its operating-system position to disadvantage competing technologies and preserve its broader platform position.

Relevance to AI leaderboards

The case demonstrates how control over an important technological platform can be used to reinforce power in adjacent markets.

An AI evaluator controlling:

• benchmark access;
• model discovery;
• APIs;
• developer tools;

could theoretically use one layer of technological control to reinforce another.

Principle

Platform power can have competitive consequences in adjacent technological markets when control is used to disadvantage rivals.

Case 2: Google LLC v. Competition Commission of India, 2023 SCC OnLine SC 1246

The Indian Supreme Court dealt with Google's conduct concerning Android and associated digital-market practices.

The broader competition-law significance includes analysis of ecosystem power, tying, leveraging and conduct across interconnected digital markets.

Relevance to AI

AI ecosystems similarly combine:

• operating systems;
• cloud;
• APIs;
• app distribution;
• model marketplaces;
• developer tools;
• evaluation systems.

A leaderboard controlled by a major ecosystem operator could therefore potentially be analysed as one component of a broader ecosystem strategy.

15. Bronner v. Mediaprint, Case C-7/97

The European Court of Justice considered refusal to provide access to a newspaper-delivery system.

The Court applied a demanding test concerning when access to infrastructure could be required.

Relevance to AI evaluation

The case is important because it prevents the essential-facilities doctrine from being applied merely because an input is commercially useful.

For an AI leaderboard:

Commercial importance ≠ automatic indispensability.

The claimant would need to demonstrate circumstances approaching genuine necessity and lack of viable alternatives.

16. IMS Health GmbH & Co. KG v. NDC Health GmbH, Case C-418/01

The European Court considered refusal to license a protected structure used in pharmaceutical market data.

The case developed important principles concerning compulsory access and indispensability.

Relevance to AI leaderboards

An AI benchmark could potentially become comparable to a critical information infrastructure if:

• virtually all customers rely upon it;
• competitors cannot realistically reproduce it;
• alternative benchmarks lack comparable market recognition;
• access is indispensable for effective competition.

However, the demanding requirements of the doctrine remain important.

17. Microsoft Corp. v. Commission, Case T-201/04

The European Union's Microsoft decision concerned Microsoft's refusal to provide interoperability information and the competitive effects of restricting interoperability.

Relevance to AI

AI evaluation systems increasingly depend upon interoperability between:

• models;
• APIs;
• datasets;
• inference infrastructure;
• testing environments;
• developer platforms.

A dominant evaluator that deliberately prevents competing models from functioning properly within its evaluation infrastructure could potentially raise an analogous interoperability concern.

18. Google Shopping, Case AT.39740

The European Commission's Google Shopping decision concerned the treatment of Google's comparison-shopping service within Google's general search results.

The important conceptual issue was the use of a powerful digital intermediary to give preferential treatment to its own related service.

Relevance to AI leaderboards

This provides a particularly useful analogy for AI leaderboard self-preferencing.

Suppose:

Company X operates the dominant model-evaluation portal and also owns Model X.

If Model X is systematically given preferential ranking visibility, placement, methodology or presentation, the conduct could raise a comparable self-preferencing theory, subject to the relevant market and dominance analysis.

19. Android, Case AT.40099

The European Commission's Android decision examined Google's practices involving Android, including tying and leveraging across interconnected digital markets.

Relevance to AI

The case demonstrates the importance of analysing digital ecosystems rather than isolated products.

An AI company may simultaneously control:

• foundation models;
• cloud infrastructure;
• developer tools;
• model stores;
• evaluation platforms.

Leaderboard control could therefore reinforce market power elsewhere in the ecosystem.

20. Qualcomm, Case AT.39711

The European Commission's Qualcomm litigation concerned exclusionary payments and the competitive significance of arrangements involving a powerful technology supplier.

Relevance to AI

The broader lesson concerns the competitive significance of contractual arrangements involving important technology inputs.

An AI evaluation provider could potentially create foreclosure concerns through:

• exclusivity;
• conditional access;
• rebates;
• preferential commercial terms;
• discriminatory evaluation pricing.

The actual legal assessment would depend upon market definition, dominance and effects.

21. United States v. Google LLC – Search and Search Advertising

The U.S. Google search litigation provides a broader modern example of how control over a major digital distribution and information intermediary can affect competitive access.

Relevance to AI

AI evaluation platforms similarly determine what information users see about competing models.

The analogy is:

Search intermediary

→ controls visibility of competing services

AI evaluation intermediary

→ controls visibility of competing models.

The two are not legally identical, but the economic mechanism—control over a crucial information gateway—is relevant.

22. Market Definition

Competition analysis requires defining the relevant market.

Possible markets include:

A. AI benchmark services

Services providing comparative testing of AI systems.

B. AI model discovery

Platforms through which users discover and compare models.

C. Enterprise AI evaluation

Specialised benchmarking for enterprise procurement.

D. AI safety evaluation

Testing focused on safety, reliability and alignment.

E. AI certification

Formal or quasi-formal certification of model performance.

F. Model ranking and reputation services

Platforms whose principal function is comparative ranking.

These markets may overlap but should not automatically be treated as identical.

23. Sources of Market Power

An AI evaluator could acquire market power through:

Network effects

More users make the ranking more valuable.

Reputation effects

Customers trust a recognised evaluator.

Data advantages

Historical evaluation data improve benchmarking.

Switching costs

Developers optimise specifically for one benchmark.

Brand recognition

Customers may rely upon a familiar ranking.

Procurement integration

Enterprise buyers may incorporate rankings into purchasing rules.

Technical complexity

Independent replication becomes expensive.

Benchmark uniqueness

A proprietary dataset may be difficult to reproduce.

24. The Feedback Loop of Ranking Power

A particularly important AI-specific concern is:

Benchmark control

↓

Model optimisation toward benchmark

↓

Improved leaderboard performance

↓

Customer adoption

↓

More developers optimise for benchmark

↓

Benchmark becomes industry standard

↓

Alternative benchmarks become less commercially relevant

This can create a benchmark lock-in effect.

Competition law may therefore need to distinguish between legitimate standardisation and strategic exclusion.

25. AI-Specific Benchmark Gaming

Leaderboard competition can produce Goodhart-type effects:

When a metric becomes the target, optimisation may shift toward the metric rather than the underlying objective.

Models may be tuned specifically for benchmark performance.

Potential consequences include:

• benchmark overfitting;
• contamination;
• prompt-specific optimisation;
• hidden test-set leakage;
• synthetic benchmark gaming;
• selective model configurations;
• cherry-picked inference settings.

A dominant evaluator can potentially influence which forms of optimisation become economically valuable.

26. Transparency as a Competition Remedy

Possible remedies include requiring disclosure of:

• benchmark methodology;
• scoring weights;
• evaluation conditions;
• model versions;
• testing dates;
• statistical confidence;
• submission rules;
• conflicts of interest.

Transparency does not necessarily require disclosure of proprietary datasets.

A regulator could instead require enough information to permit competitors and customers to understand whether rankings are objectively comparable.

27. Structural Separation

Where conflicts of interest are severe, regulators could consider separation between:

AI model provider

and

AI evaluation provider

This could reduce incentives to manipulate rankings.

However, structural separation would be a substantial intervention and would require evidence that behavioural safeguards are insufficient.

28. Interoperability and Portability Remedies

Possible remedies include:

• standardised evaluation APIs;
• common testing protocols;
• portable benchmark formats;
• independent audit mechanisms;
• model-neutral testing environments;
• interoperability between competing evaluation systems.

Such measures can reduce dependence on one ranking provider.

29. Multi-Leaderboard Competition

Competition can also be preserved by supporting multiple independent evaluation systems.

For example:

The existence of credible alternatives reduces the risk that any one leaderboard becomes an unavoidable gatekeeper.

30. Economic Effects

Potential anticompetitive effects

• exclusion of competing AI developers;
• artificial inflation of affiliated model rankings;
• increased entry barriers;
• reduced innovation;
• benchmark dependency;
• higher evaluation costs;
• reduced transparency;
• foreclosure of competing evaluators;
• distortion of enterprise procurement.

Potential procompetitive effects

Leaderboards can also generate substantial benefits:

• lower information costs;
• easier comparison;
• greater transparency;
• improved model quality;
• incentives for innovation;
• independent performance verification;
• reduced customer search costs.

Therefore, the existence of leaderboard control is not itself evidence of an infringement.

31. Key Legal Questions for Regulators

A competition authority examining AI leaderboard control would likely need to investigate:

1. Who controls the benchmark?
2. Does the operator compete with evaluated models?
3. How important is the leaderboard to customers?
4. Are credible alternatives available?
5. Is participation open on equal terms?
6. Are evaluation conditions identical?
7. Is the methodology transparent?
8. Does the operator change scoring rules selectively?
9. Does it favour affiliated models?
10. Are rankings commercially determinative?
11. Are exclusive arrangements involved?
12. Is leaderboard access tied to other services?
13. Does the evaluator possess dominance?
14. What actual or likely foreclosure effects exist?
15. Are there objective technical justifications?
16. Are there efficiencies that benefit consumers?

32. Competition-Law Framework

A useful analytical sequence is:

Market Definition

↓

Market Power / Dominance

↓

Control of Evaluation Infrastructure

↓

Access Conditions

↓

Benchmark Methodology

↓

Self-Preferencing / Discrimination

↓

Foreclosure Effects

↓

Consumer and Innovation Effects

↓

Objective Justification / Efficiencies

↓

Proportionate Remedy

33. Distinction Between Legitimate Ranking and Anticompetitive Ranking

The distinction ultimately depends upon evidence and market circumstances.

34. Conclusion

AI evaluation leaderboards may become strategically important competitive infrastructure because rankings influence model visibility, customer trust, procurement, investment and developer behaviour.

The central competition-law issue is therefore not whether a leaderboard ranks models, but whether control over ranking infrastructure is used to distort competitive conditions.

The strongest potential theories involve:

• self-preferencing;
• discriminatory evaluation access;
• refusal to provide access;
• exclusive evaluation arrangements;
• tying and bundling;
• leveraging;
• interoperability restrictions;
• manipulation of benchmark methodology;
• foreclosure of competing evaluators.

The cases involving Microsoft, Google Shopping, Google Android, Bronner, IMS Health and Qualcomm do not establish AI-specific liability. Rather, they supply established competition-law principles that can be adapted to the emerging problem of AI evaluation and ranking power.

The key future regulatory challenge will be maintaining the benefits of independent AI benchmarking while preventing a sufficiently powerful evaluator from converting epistemic authority over model quality into commercial gatekeeping power over the AI market.