Autonomous Reasoning Engine Dominance Theories

Introduction

Autonomous Reasoning Engine Dominance refers to a competition-law theory concerning AI systems capable of independently processing information, selecting strategies, generating recommendations, allocating resources, negotiating transactions, or making commercial decisions with limited human intervention.

A reasoning engine may become commercially dominant where control over its model, compute infrastructure, proprietary data, APIs, agentic tools, distribution channels, or feedback loops enables its operator to restrict rivals, foreclose complementary services, exploit dependent users, or extend power from one market into adjacent markets.

The central competition-law question is therefore not simply whether an AI reasoning engine is technologically superior. It is whether the operator possesses substantial and durable market power and uses that power in a manner capable of harming competition.

Because fully autonomous reasoning engines are comparatively new, there is limited case law dealing with them directly. The following established cases provide the principal doctrinal analogies.

I. Meaning of an Autonomous Reasoning Engine

An autonomous reasoning engine is an AI system capable of moving beyond simple prediction or classification and performing functions such as:

• interpreting complex instructions;
• selecting among alternative strategies;
• planning multiple steps;
• invoking external tools;
• negotiating or transacting;
• optimizing prices or quantities;
• allocating resources;
• ranking competing suppliers;
• making procurement decisions;
• controlling workflows;
• generating and testing alternative solutions; and
• continuously learning from market feedback.

Examples include an AI procurement agent that independently selects suppliers, an autonomous trading system, an enterprise reasoning platform that controls software workflows, or an AI intermediary that determines which competing services users see.

The competition concern becomes stronger where the same undertaking controls:

reasoning engine + data + compute + operating environment + distribution + transaction interface.

That combination can create a vertically integrated technological ecosystem.

II. Theories of Autonomous Reasoning Engine Dominance

1. Core-Engine Dominance Theory

The first theory treats the reasoning engine itself as the potentially dominant product.

A particular engine may become indispensable because users depend upon:

• proprietary reasoning capabilities;
• accumulated training data;
• specialized models;
• domain-specific fine-tuning;
• inference infrastructure;
• proprietary APIs;
• accumulated user feedback; and
• compatibility with existing enterprise systems.

If switching costs become substantial, competitors may technically exist but remain unable to discipline the incumbent.

Competition concern

The operator may then:

1. raise API or inference prices;
2. restrict access;
3. degrade interoperability;
4. impose discriminatory conditions;
5. limit portability;
6. bundle the engine with unrelated products; or
7. use the engine's position to enter downstream markets.

III. Data-Feedback Dominance

Autonomous reasoning systems can generate a powerful data-feedback loop.

The mechanism may be:

More users → more interactions → more behavioral data → better reasoning → better performance → more users.

This can produce increasing returns to scale.

A dominant engine may therefore possess an advantage that is difficult for new entrants to replicate.

Competition-law issue

The relevant question is whether the data advantage is merely the result of superior competition or whether the undertaking has used exclusionary conduct to make the advantage durable.

Potential conduct includes:

• exclusive access to data;
• preventing customers from exporting interaction histories;
• discriminatory access to datasets;
• contractual restrictions on data sharing;
• tying data access to another service; and
• acquiring important data sources primarily to eliminate competitive access.

IV. Compute and Infrastructure Dominance

Reasoning engines require substantial computational infrastructure.

Dominance can therefore arise at several layers:

Layer 1 — Compute

GPUs, accelerators and specialized processors.

Layer 2 — Cloud infrastructure

Training and inference infrastructure.

Layer 3 — Model layer

Foundation and reasoning models.

Layer 4 — Agent layer

Autonomous decision-making and tool execution.

Layer 5 — Distribution

Enterprise software, operating systems, search, productivity suites and application stores.

Control over multiple layers can create vertical foreclosure risks.

For example:

Cloud provider → controls compute → operates reasoning engine → supplies enterprise applications → distributes autonomous agents.

A competitor may technically be able to develop another reasoning engine while nevertheless being unable to obtain equivalent access to infrastructure or distribution.

V. API and Interoperability Dominance

An autonomous reasoning engine frequently operates through APIs.

An incumbent could potentially:

• refuse API access;
• provide inferior API functionality to rivals;
• change API terms selectively;
• impose discriminatory latency;
• limit calls;
• prevent interoperability;
• restrict competing agents from accessing proprietary tools; or
• make customers dependent upon proprietary formats.

This creates a possible digital essential-input theory.

However, competition law generally does not transform every commercially important technology into an essential facility. The legal requirements for intervention depend upon the applicable jurisdiction and doctrine.

VI. Autonomous Agent Ecosystem Dominance

A reasoning engine may not merely answer questions.

It may control an ecosystem of autonomous agents:

User → Reasoning Engine → Search → Payment → Procurement → Logistics → Customer Service

If the engine determines which downstream providers an autonomous agent contacts, the operator may acquire substantial intermediation power.

The concern is particularly significant where the AI system itself chooses the supplier rather than presenting a neutral list of alternatives.

The operator could potentially:

• favor its own services;
• rank affiliated businesses first;
• suppress rivals;
• impose commissions;
• condition visibility on participation;
• manipulate recommendations; or
• use proprietary data generated through the intermediary to compete downstream.

VII. Self-Preferencing by Autonomous Reasoning Engines

Self-preferencing occurs where an intermediary gives preferential treatment to its own downstream products or services.

An autonomous reasoning engine could theoretically recommend:

Operator's payment service → operator's cloud → operator's shopping service → operator's logistics provider

even where competing services are available.

The competition-law question is whether the preference reflects legitimate product design or constitutes exclusionary conduct capable of restricting competition.

This issue has important parallels with digital-platform cases.

VIII. Leveraging Theory

An undertaking dominant in one market may attempt to extend its power into another market.

For example:

Dominant reasoning engine → dominant enterprise agent → dominance in procurement

or:

Dominant cloud → dominant AI engine → dominance in enterprise software.

Leveraging can occur through:

• tying;
• bundling;
• exclusive dealing;
• technical restrictions;
• interoperability restrictions;
• discriminatory access; or
• contractual foreclosure.

IX. Tying and Bundling

A reasoning engine may be tied to:

• cloud computing;
• office software;
• search;
• cybersecurity;
• payments;
• CRM software;
• enterprise databases; or
• operating systems.

Suppose customers wanting access to a dominant reasoning engine must also purchase the operator's cloud service.

The analysis would ordinarily examine:

1. whether there are distinct products;
2. whether the undertaking has substantial power in the tying product;
3. whether customers are effectively compelled to obtain the tied product;
4. whether the practice forecloses rivals; and
5. whether objective efficiencies justify the arrangement.

X. Exclusive Access to Reasoning Infrastructure

A dominant undertaking could enter agreements requiring:

• exclusive use of its reasoning engine;
• exclusive cloud deployment;
• minimum-volume commitments;
• restrictions on competing models;
• restrictions on multi-homing; or
• exclusive access to proprietary agent tools.

Such arrangements may increase barriers to entry where customers cannot economically maintain multiple reasoning systems.

XI. Reasoning Engine as an Essential Facility

An especially controversial theory is that a dominant reasoning engine could constitute an essential facility.

A claimant might argue that access is indispensable because:

• users are locked into the dominant ecosystem;
• alternatives are commercially inadequate;
• switching costs are extremely high;
• the engine controls essential data;
• interoperability is technically feasible; and
• denial of access eliminates effective competition.

But courts traditionally apply essential-facility principles cautiously.

The fact that an AI system is technologically important does not automatically mean that its owner must provide access to competitors.

XII. Autonomous Decision-Making and Exclusionary Conduct

An unusual issue arises where the harmful conduct is generated by an autonomous system.

Suppose an AI agent independently determines that:

competing suppliers should receive lower visibility because this maximizes the platform's commercial return.

The competition-law analysis must determine:

• who designed the system;
• who controlled its objectives;
• what constraints were imposed;
• whether the outcome was foreseeable;
• whether the operator monitored the system;
• whether the operator benefited from the conduct; and
• whether the conduct can legally be attributed to the undertaking.

Autonomy therefore does not necessarily eliminate corporate responsibility.

XIII. Six Important Case Laws

1. United States v. Microsoft Corp. (2001)

Principle

The Microsoft litigation established important principles concerning leveraging, tying, exclusionary conduct and maintenance of monopoly power in technology markets.

Microsoft's control over the Windows operating-system platform was considered in relation to its conduct affecting competing browser technology.

Relevance to reasoning engines

The case provides an analogy for a dominant AI platform using control over one technological layer to disadvantage competitors at another layer.

For example:

dominant reasoning engine → preferential access to operating system → disadvantage to rival AI agents.

The critical issue would be whether the conduct protects legitimate technological integration or instead unlawfully maintains monopoly power.

2. Google Search (United States v. Google LLC)

Principle

The Google litigation concerned alleged exclusionary arrangements involving search distribution and access points.

Relevance

Autonomous reasoning engines increasingly function as information intermediaries.

If an AI reasoning engine becomes the primary gateway through which users obtain information, the operator may possess significant control over:

• visibility;
• ranking;
• recommendations;
• referrals; and
• downstream traffic.

The Google litigation therefore provides an important framework for analyzing contractual and distribution-based barriers surrounding AI intermediaries.

3. Google Shopping — Google Search (Shopping), European Commission

Principle

The European Commission found Google liable for giving its comparison-shopping service more favorable positioning and display treatment than competing comparison-shopping services.

The case is particularly relevant to self-preferencing.

Relevance

An autonomous reasoning engine could perform a similar function without conventional search-result pages.

Instead of:

Search result → ranking → click

the system could produce:

User request → AI reasoning → autonomous recommendation → transaction.

If the engine systematically favors the operator's own downstream service, the legal analysis may resemble the broader self-preferencing and leveraging concerns illustrated by Google Shopping.

4. Slovak Telekom v European Commission

Principle

The Court of Justice addressed exclusionary conduct involving access to infrastructure and margin-squeeze issues in telecommunications.

The case illustrates the importance of distinguishing between:

• ordinary commercial competition;
• refusal or restriction of access; and
• conduct that uses control over an important upstream input to exclude downstream competitors.

Relevance

An AI infrastructure operator could occupy an analogous position where competitors depend upon:

• model APIs;
• inference infrastructure;
• specialized compute;
• data access; or
• interoperability interfaces.

The case is therefore useful for analyzing vertical foreclosure involving AI infrastructure.

5. Bronner v Mediaprint

Principle

The Court of Justice established a restrictive framework for refusal-to-supply claims under the essential-facilities doctrine.

Among the important considerations is whether the input is genuinely indispensable and whether refusal would eliminate effective competition.

Relevance

This principle is particularly important for autonomous reasoning engines.

A competitor's assertion that:

"We need access to the incumbent's reasoning engine"

would not, by itself, establish an obligation to supply.

The claimant would need to satisfy the applicable legal requirements concerning indispensability, competition elimination and other elements of the doctrine.

6. IMS Health GmbH & Co. KG v NDC Health

Principle

IMS Health concerned access to a protected information structure and the circumstances under which refusal to license intellectual property may become abusive.

The Court emphasized stringent conditions before imposing compulsory access.

Relevance

The case is relevant where a dominant reasoning engine controls:

• proprietary datasets;
• model interfaces;
• structured knowledge repositories;
• technical standards; or
• protected technological infrastructure.

It demonstrates that competition law must balance innovation incentives and access requirements.

7. Intel Corp. v European Commission

Principle

Intel concerned conditional rebates and exclusionary effects.

The case became particularly important for the analysis of whether loyalty-inducing commercial arrangements can foreclose equally efficient competitors.

Relevance

An AI provider might offer:

discounted inference + cloud credits + preferential API pricing

conditional upon customers committing substantially to the provider's ecosystem.

The relevant competition question would be whether such conditions foreclose competitors rather than merely reflecting legitimate volume efficiencies.

8. AKZO Chemie BV v Commission

Principle

AKZO is a foundational EU competition-law authority concerning predatory pricing and abuse of dominance.

Relevance

A dominant AI provider might temporarily price:

• inference below cost;
• APIs below sustainable levels; or
• enterprise AI services at aggressively subsidized prices.

If designed to eliminate competitors and permit later exploitation, the conduct could raise predatory-pricing concerns.

However, low AI prices are not inherently anticompetitive; the economic and legal context remains crucial.

XIV. Theories Emerging From These Cases

The cases collectively support several doctrinal analogies.

XV. Autonomous Reasoning Engines and Market Definition

Market definition becomes particularly difficult because the same reasoning engine can perform numerous functions.

Possible markets include:

A. Foundation-model market

Providers of general-purpose reasoning models.

B. Enterprise reasoning market

AI systems designed for corporate decision-making.

C. Autonomous-agent market

Systems capable of executing tasks rather than merely generating outputs.

D. AI infrastructure market

Compute, inference and model-hosting services.

E. AI intermediation market

Systems that independently connect consumers with downstream providers.

F. Vertical reasoning markets

Specialized systems for:

• law;
• medicine;
• finance;
• procurement;
• logistics;
• engineering; and
• cybersecurity.

The correct market may therefore depend heavily upon functionality, substitutability and user demand.

XVI. Network Effects

Reasoning engines can generate several forms of network effects.

Direct network effects

More users increase interaction data and potentially improve system performance.

Indirect network effects

More developers create more tools, plugins and integrations.

Data network effects

More transactions generate more behavioral information.

Ecosystem network effects

More enterprises adopt the platform, increasing compatibility incentives.

These effects may reinforce an incumbent's position.

However, network effects do not themselves establish unlawful dominance. They become competition concerns when combined with barriers to entry or exclusionary conduct.

XVII. Switching Costs

Enterprise AI systems may become deeply embedded in:

• databases;
• APIs;
• workflows;
• employee training;
• compliance systems;
• procurement systems;
• customer records;
• automated agents; and
• internal decision architectures.

Consequently, switching from one reasoning engine to another may be expensive.

A dominant undertaking could exploit these switching costs through:

• proprietary formats;
• contractual lock-in;
• technical incompatibility;
• data-export restrictions;
• interoperability limitations; or
• escalating migration costs.

XVIII. Autonomous Reasoning and Consumer Choice

Traditional competition analysis often assumes that consumers ultimately make purchasing decisions.

Autonomous agents complicate this assumption.

An AI agent may decide:

"I will purchase Product A."

The consumer may never inspect Product B.

Consequently, the agent itself becomes an important competitive gatekeeper.

This can shift competition from:

consumer choice → algorithmic selection.

Control over the reasoning engine can therefore translate into control over downstream demand.

XIX. Algorithmic Discrimination

A reasoning engine could potentially discriminate between competitors by:

• giving different API access;
• providing different rankings;
• allocating different computational resources;
• changing recommendation probabilities;
• restricting tool access; or
• imposing individualized commercial conditions.

Competition authorities would need to distinguish legitimate personalization from exclusionary discrimination.

XX. Dominance Through Vertical Integration

The most significant theoretical risk may arise where one undertaking controls the complete technological stack:

Semiconductors

↓

Cloud

↓

Foundation Model

↓

Reasoning Engine

↓

Agent Platform

↓

Operating System

↓

Application Distribution

↓

Transaction Interface

Such integration can create opportunities for vertical foreclosure, but vertical integration itself is not unlawful.

The relevant question is whether the integrated structure enables the undertaking to restrict competition in identifiable markets.

XXI. Efficiency Defences

Autonomous reasoning engines can produce substantial efficiencies.

Potential benefits include:

• lower transaction costs;
• faster procurement;
• improved logistics;
• fraud detection;
• individualized services;
• better resource allocation;
• reduced administrative costs;
• improved scientific research; and
• enhanced productivity.

Therefore, competition analysis should distinguish between:

technological integration that improves the product

and

integration or conduct that unnecessarily excludes competitors.

This distinction is central to modern digital competition analysis.

XXII. Remedies

Where unlawful dominance is established, possible remedies may include:

Structural remedies

• divestiture;
• separation of business units;
• limits on acquisitions.

Behavioral remedies

• non-discriminatory API access;
• interoperability requirements;
• data portability;
• prohibition of exclusive dealing;
• transparency requirements;
• non-preferencing obligations.

Technical remedies

• open APIs;
• interoperability standards;
• portable agent configurations;
• standardized data formats.

Monitoring remedies

• independent compliance monitoring;
• algorithmic auditing;
• reporting requirements;
• access-discrimination monitoring.

XXIII. Autonomous Reasoning Engine Dominance — Analytical Framework

A competition authority could conceptually proceed through the following sequence:

1. Identify the relevant market

↓

2. Determine whether the reasoning engine operator possesses substantial market power

↓

3. Identify the source of that power

• data
• compute
• model capability
• network effects
• switching costs
• distribution

↓

4. Identify the conduct

• tying
• bundling
• self-preferencing
• refusal to supply
• discrimination
• exclusivity
• predatory pricing
• interoperability restrictions

↓

5. Establish foreclosure or exploitative effects

↓

6. Examine efficiencies and objective justification

↓

7. Assess causation and competitive effects

↓

8. Select proportionate remedies

XXIV. Key Legal Questions for Future Litigation

Future autonomous-reasoning cases are likely to raise novel questions:

1. Can an AI reasoning engine constitute a distinct relevant market?
2. Can proprietary reasoning capability itself constitute a barrier to entry?
3. When does refusal to provide model/API access become abusive?
4. Can autonomous recommendations constitute self-preferencing?
5. Who bears responsibility for exclusionary decisions made by an autonomous agent?
6. How should data-feedback loops be incorporated into dominance analysis?
7. Can AI-generated switching costs establish durable market power?
8. When does vertical integration between cloud, model and agent layers become foreclosure?
9. Can an AI intermediary acquire monopsony power over suppliers?
10. What technical interoperability obligations can competition law legitimately impose?

XXV. Conclusion

Autonomous Reasoning Engine Dominance Theory extends traditional abuse-of-dominance concepts into an environment where the competitive bottleneck may no longer be a conventional search engine, operating system or marketplace, but an AI system that independently determines what information, supplier, product, service or transaction a user encounters.

The principal legal theories are:

• engine-level dominance;
• data-feedback dominance;
• compute and infrastructure control;
• API and interoperability foreclosure;
• self-preferencing;
• tying and bundling;
• exclusive dealing;
• leveraging;
• essential-facility/refusal-to-supply theories;
• predatory pricing; and
• autonomous-agent intermediation power.

The established authorities—Microsoft, Google Shopping, Slovak Telekom, Bronner, IMS Health, Intel and AKZO—do not directly decide the legality of autonomous reasoning engines. Rather, they provide doctrinal foundations from which courts and competition authorities can analyze new AI-specific forms of market power and exclusion.