1. Introduction

The future trajectory of antitrust enforcement in Saudi Arabia under artificial intelligence (AI) economy transformation represents a major evolution in the Kingdom's competition policy. As Saudi Arabia advances its Vision 2030 objectives, digitalisation, AI development, fintech, cloud computing, autonomous systems, data-driven services and digital platforms are becoming increasingly important components of its economic structure.

Traditional competition enforcement primarily focuses on price-fixing, market allocation, monopolisation, abuse of dominance, restrictive agreements and mergers. However, AI-driven markets introduce more complex competition problems, including algorithmic collusion, data concentration, platform dependency, automated discriminatory pricing, self-preferencing, exclusionary AI ecosystems and acquisitions of emerging competitors.

Saudi Arabia's central competition regulator, the General Authority for Competition (GAC), will therefore have an increasingly important role in ensuring that technological transformation does not create artificial barriers to market entry or concentrate economic power in a small number of technology companies.

The principal legal foundation is the Saudi Competition Law issued under Royal Decree No. M/75 of 29 June 1440H (7 March 2019), together with its Implementing Regulations.

The central challenge is to achieve two objectives simultaneously:

Encourage AI investment, innovation, technological development and digital entrepreneurship.

Prevent dominant enterprises from using AI, proprietary datasets, computing infrastructure and algorithmic control to eliminate competition.

The future of Saudi antitrust enforcement will increasingly depend on the ability to regulate not merely traditional commercial conduct but also the technological architecture through which market power is created and exercised.

2. Legal and Institutional Framework

A. Saudi Competition Law

The Competition Law provides the foundation for regulating economic conduct affecting competition within Saudi Arabia.

Its principal objectives include:

Protecting and encouraging fair competition.

Preventing monopolistic practices.

Protecting competitive market structures.

Promoting economic development.

Preventing conduct that harms consumers and market participants.

Article 3 extends the legislation to entities operating within the Kingdom and certain conduct occurring outside the Kingdom where it adversely affects competition in Saudi Arabia.

This territorial principle will become increasingly important where foreign AI companies supply cloud computing, algorithmic services, digital advertising, financial technology and automated decision-making services to Saudi customers.

B. General Authority for Competition

The GAC is responsible for enforcing the Competition Law.

Its future responsibilities in AI-related markets are likely to include:

Investigating algorithmic price coordination.

Examining exclusionary platform arrangements.

Reviewing acquisitions involving AI startups.

Investigating discriminatory access to essential digital infrastructure.

Examining data-related competitive restrictions.

Monitoring digital ecosystem concentration.

Evaluating the competitive effects of automated commercial decisions.

C. Other relevant institutions

AI competition enforcement also requires institutional cooperation.

The division of institutional responsibilities is important because an AI practice may simultaneously create competition, privacy, cybersecurity and consumer-protection concerns.

D. Principal legal provisions

The precise application of these provisions to AI conduct will depend on the facts, the implementing rules and the applicable legal interpretation.

3. Major Areas of Future Antitrust Enforcement Under AI Transformation

A. Algorithmic collusion and automated price-fixing

One of the most significant future challenges will be the use of artificial intelligence to coordinate prices between competing businesses.

AI pricing systems can independently monitor competitors, analyse market conditions, predict consumer demand and modify prices automatically.

Although algorithmic pricing can improve efficiency, it can also facilitate collusive outcomes.

Potential competition violations include:

Competitors using the same pricing algorithm to coordinate prices.

AI systems exchanging commercially sensitive information.

Automated price adjustments designed to maintain supracompetitive prices.

Algorithms identifying and punishing businesses that discount prices.

Competing companies using a common AI provider to coordinate commercial strategies.

For example, competing Saudi airlines could employ interconnected AI pricing systems that continuously monitor each other's fares and systematically avoid meaningful price reductions.

The GAC may investigate whether such arrangements constitute prohibited agreements under Article 5.

An important distinction is that parallel algorithmic pricing alone does not necessarily establish an infringement. Authorities must establish the relevant agreement, coordination or abusive conduct under the applicable legal test.

B. AI-driven abuse of dominant position

Article 6 will become increasingly important as AI businesses develop substantial market power.

A company may achieve dominance through control over:

Proprietary training datasets.

Advanced computing infrastructure.

Foundation models.

Cloud services.

AI application marketplaces.

Digital distribution channels.

Exclusive technological partnerships.

The future enforcement concern is not simply that a company becomes technologically successful. It is whether that company uses its market power to restrict competition.

Examples include refusing competitors access to essential infrastructure without objective justification, discriminatory access conditions, exclusionary bundling and predatory strategies.

C. Data concentration and competitive foreclosure

Data is becoming a strategically important economic resource.

AI companies require substantial quantities of high-quality data for model training, testing, refinement and commercial deployment.

Where one enterprise controls an important dataset, it may obtain advantages that competitors cannot reasonably reproduce.

Potential antitrust concerns include:

Exclusive data acquisition agreements.

Unjustified refusal to provide access to important datasets.

Data portability restrictions.

Discriminatory licensing arrangements.

Combining data from different markets to reinforce dominance.

Acquisition of data-rich competitors to eliminate emerging competitive threats.

Saudi enforcement is likely to examine whether data accumulation creates durable barriers to entry and whether particular access restrictions have exclusionary effects.

D. Competition in foundation models and generative AI

Foundation models can support numerous downstream services, including:

Legal technology.

Financial analysis.

Healthcare administration.

Education platforms.

Government services.

Business automation.

Industrial robotics.

If a small number of companies control advanced foundation models, computing resources and distribution infrastructure, competition may become dependent on access to their technological ecosystems.

Future GAC investigations may examine:

Exclusive licensing of foundation models.

Preferential access to advanced AI chips.

Restrictive model-hosting agreements.

Bundling of AI services with cloud infrastructure.

Discriminatory API pricing.

Restrictions on model interoperability.

Acquisitions of promising AI developers.

A particularly important issue will be whether a dominant AI provider can use control over an upstream model to disadvantage independent downstream application developers.

E. Cloud computing and AI infrastructure concentration

AI development requires substantial computing resources, specialised chips, storage infrastructure and high-speed connectivity.

Cloud computing providers may therefore become strategically important gatekeepers.

Future enforcement may focus on:

Long-term cloud exclusivity agreements.

Excessive switching costs.

Restrictive cloud licensing.

Technical barriers to interoperability.

Bundling of computing capacity with proprietary AI services.

Discriminatory access to GPU infrastructure.

Preferential treatment of affiliated AI developers.

The GAC may need to examine whether infrastructure contracts prevent competing AI providers from entering or expanding in Saudi Arabia.

F. AI acquisitions and killer acquisitions

AI markets are characterised by rapid innovation and uncertain future competition.

An established technology company may acquire a smaller AI developer before the target becomes a substantial competitor.

Such acquisitions may involve:

AI startups.

Machine-learning developers.

Proprietary datasets.

Robotics businesses.

AI research teams.

Specialised model developers.

Companies possessing valuable patents or algorithms.

Merger analysis should therefore consider not only existing market shares but also innovation potential, future competition and access to strategically important assets.

A transaction involving a relatively small startup may still eliminate an important potential competitive constraint.

G. Self-preferencing in AI-powered platforms

Self-preferencing occurs when a vertically integrated platform favours its own products or services over those of independent competitors.

For example, an AI-powered marketplace may rank its affiliated products above competing products, even where independent suppliers offer better commercial terms.

Future investigations may examine:

Biased AI search rankings.

Preferential recommendation systems.

Discriminatory visibility for independent sellers.

Exclusive access to platform-generated customer data.

Preferential treatment of affiliated AI applications.

Algorithmic restrictions on rival service providers.

The competitive assessment must establish whether the conduct produces exclusionary effects rather than treating every form of product integration as unlawful.

H. Dynamic and personalised pricing

AI can analyse customer behaviour, purchasing history, geographical information and demand patterns to determine individual prices.

Personalised pricing may improve efficiency, but it may also create competition concerns where dominant businesses use sophisticated data to exploit market power or discriminate between similarly situated commercial partners.

The GAC may increasingly examine whether automated pricing systems:

Facilitate exclusionary pricing.

Discriminate between similarly situated trading partners.

Conceal coordinated pricing.

Use proprietary data to disadvantage rivals.

Create artificial barriers to switching.

Personalised pricing is not automatically unlawful under competition law. Its legality depends on the relevant market position, conduct and competitive effects.

4. Important Saudi and Comparative Case Laws

The following decisions provide a foundation for understanding how Saudi antitrust enforcement may develop in the AI economy.

Legal qualification: Saudi Arabia's published competition record includes administrative enforcement decisions, merger-control decisions and judicially upheld committee decisions. These are not all conventional appellate precedents. The Saudi examples below are distinguished from comparative judicial authorities, which provide persuasive analytical guidance rather than binding Saudi law.

Case 1. Uber/Careem acquisition – Digital data concentration

Highly relevant Saudi digital-market decision

Parties: Uber and Careem.

Subject: Acquisition of control over Careem by Uber.

Competition issues:

The GAC examined the competitive implications of combining two major ride-hailing platforms.

The assessment considered:

Market concentration.

Customer and driver data.

Indirect network effects.

Barriers to entry.

Switching costs.

Potential price increases.

Reduction in competitive incentives.

The GAC recognised that larger platforms accumulate more data, improve service quality and attract additional users. This creates a self-reinforcing competitive advantage.

Outcome: The transaction was approved subject to conditions, including maintaining separate brands, pricing constraints and restrictions on exclusive driver contracts.

Relevance to AI:

This decision is particularly important because AI market power frequently develops through the combination of data, network effects and automated optimisation.

It provides a foundation for examining future acquisitions involving AI platforms, digital financial services and data-intensive businesses.

Case 2. Delivery Hero / The Chefz – Digital platform merger control

Parties: Delivery Hero and The Chefz.

Subject: Proposed acquisition of a Saudi food-delivery platform.

Competition issues:

The transaction raised questions concerning merger notification and the regulatory treatment of digital platform acquisitions.

The GAC prohibited the transaction in December 2021 on procedural grounds relating to merger-control requirements.

Legal significance:

The decision demonstrates that digital businesses are not exempt from merger notification obligations simply because they operate through online platforms.

Relevance to AI:

AI startups, automated delivery businesses and platform operators must assess merger-control requirements before completing acquisitions.

The decision also illustrates the importance of procedural compliance in technology-sector transactions.

Case 3. GAC gas-sector vertical merger prohibition – Access to essential inputs

Subject: Proposed vertical merger in the Saudi gas sector, 2022.

Competition issues:

The GAC prohibited a proposed vertical transaction on substantive competition grounds.

The decision reflected concerns about the potential effects of vertical integration, including the possibility of restricting competitors' access to important inputs.

Access to data was also identified as an important consideration in the GAC's competitive assessment.

Legal significance:

The decision demonstrated that competition authorities can scrutinise vertical transactions rather than focusing exclusively on mergers between direct competitors.

Relevance to AI:

AI infrastructure markets are also characterised by vertical relationships.

For example, a cloud provider that acquires an important AI application developer could potentially use its infrastructure position to disadvantage competing developers.

The gas-sector decision therefore offers an important analytical foundation for future AI infrastructure merger reviews.

Case 4. Panda Retail / Atabet Elbab – Digital acquisition notification

Parties: Panda Retail Company and Atabet Elbab for Telecommunications and IT.

Subject: Acquisition involving a business providing delivery services through electronic platforms.

Competition issue:

The GAC determined that the parties had failed to notify a transaction that was subject to merger-control requirements.

Outcome: The Authority imposed fines of SAR 400,000 on each party, totalling SAR 800,000.

Legal significance:

The decision demonstrated that both the acquiring business and the target may bear responsibility for merger notification.

Relevance to AI:

AI-related acquisitions cannot avoid merger scrutiny merely because the target is a technology startup, has a relatively small market share or operates through a digital business model.

Case 5. Cement companies price-fixing decision – Coordinated commercial conduct

Parties: Fourteen cement-trading companies.

Subject: Coordinated pricing and market allocation.

Competition issue:

The GAC investigated companies that had agreed to increase prices simultaneously and allocate markets.

The Administrative Appeal Court in Riyadh upheld the GAC's penalty decision on 10 April 2023.

Legal significance:

The decision reinforces the principle that competing businesses cannot use coordination to replace independent commercial decision-making.

Relevance to AI:

Similar competitive harm may arise when competing enterprises use algorithmic pricing tools to facilitate coordinated pricing or market allocation.

However, an AI system producing parallel prices does not, by itself, establish the necessary coordination.

Case 6. PepsiCo and Saudi bottling plant – Failure to notify economic concentration

Parties: PepsiCo Services LLC and a Saudi bottling plant.

Subject: Failure to notify a transaction subject to merger-control requirements.

Competition issue:

The GAC imposed fines of SAR 20 million on the companies for failure to notify the relevant economic concentration.

The penalties were subsequently reduced on appeal to SAR 5 million each.

Legal significance:

The decision illustrates the seriousness of procedural obligations in Saudi merger control.

Relevance to AI:

Technology businesses should not treat regulatory notification as a formality. Acquisitions involving AI models, data assets and computing infrastructure require careful assessment of applicable notification rules.

Case 7. Medical-waste services dominance decision – Exclusionary abuse

Subject: Abuse of dominant position in medical-waste services.

Competition issues:

The GAC examined a company with an estimated market share of 93.09%.

The investigation concerned substantial differences between its pricing in competitive and non-competitive areas, alongside evidence indicating strategies directed against competitors.

The enforcement decision resulted in a SAR 10 million fine.

Legal significance:

The case illustrates the importance of examining how a dominant enterprise uses its market position and pricing strategies.

Relevance to AI:

A dominant AI provider may similarly use selective discounts, below-cost pricing or targeted exclusionary strategies to prevent smaller AI businesses from competing effectively.

Case 8. Telecommunications interconnection abuse – Discriminatory access

Subject: Abuse of dominance through telecommunications interconnection conditions.

Competition issue:

The GAC addressed interconnection terms that placed a new entrant at a competitive disadvantage.

The decision illustrates the importance of fair access to infrastructure controlled by a dominant enterprise.

Legal significance:

A sector regulator's separate treatment of particular conduct does not necessarily eliminate the competition authority's jurisdiction under competition law.

Relevance to AI:

The principle is important for cloud infrastructure, AI computing networks, telecommunications systems and digital distribution platforms.

A dominant provider's discriminatory access conditions may prevent competitors from entering downstream markets.

The Saudi decisions above should be understood according to their respective procedural status. In particular, merger prohibitions, administrative penalties and judicially upheld decisions are distinct forms of enforcement.

Additional comparative judicial authorities

The following international cases are not Saudi judgments. They are useful for understanding the direction in which AI competition enforcement may develop.

Case 9. United States v. Google LLC (2024) – Search monopoly

Court: United States District Court for the District of Columbia.

Issue: Google maintained monopoly power in general search services through exclusionary distribution agreements.

Principle: Control over distribution channels and default positioning can reinforce market dominance.

AI relevance: AI-powered search and answer engines may become new distribution gatekeepers. Authorities may examine whether dominant AI assistants favour affiliated products or exclude rival services.

Case 10. European Commission v. Google – Google Shopping (2021)

Court: Court of Justice of the European Union, Case C-48/22 P, judgment of 10 September 2024.

Issue: Self-preferencing by a dominant search platform.

Principle: A dominant digital platform's preferential treatment of its own comparison-shopping service can constitute abuse of dominance in the circumstances established.

AI relevance: This reasoning is important for AI-powered recommendation engines, model marketplaces and digital platforms that rank their own services above independent competitors.

Case 11. Google Android – European Commission (2018)

Authority: European Commission; General Court judgment in Case T-604/18 (2022).

Issue: Restrictions associated with Google's Android mobile operating system, including tying and contractual restrictions.

Principle: Dominant businesses cannot use restrictive arrangements to reinforce their position in related markets in violation of competition law.

AI relevance: AI companies may face similar scrutiny where they bundle foundation models, operating systems, cloud services and application distribution.

Case 12. Microsoft v. Commission – Microsoft Tying (2007)

Court: Court of First Instance of the European Communities, Case T-201/04.

Issue: Microsoft's tying of Windows Media Player with its Windows operating system and refusal to provide certain interoperability information.

Principle: A dominant company may not unlawfully leverage its market position through tying or exclusionary interoperability restrictions.

AI relevance: This is relevant to future investigations involving closed AI ecosystems, restrictive APIs and interoperability barriers.

5. Major Developments Shaping the Future of Saudi Antitrust Enforcement

A. Development of specialised AI competition guidelines

Saudi Arabia is likely to move towards more specialised guidance for AI-related competition problems.

An important development is the GAC's 2026 Guidelines to Enhance Competition in the Food Delivery Platform Sector.

These guidelines identify several platform practices requiring scrutiny:

Predatory pricing.

Price-parity obligations.

Discriminatory pricing between similarly situated sellers.

Unfair exclusive trading arrangements.

Self-preferencing.

Misuse of restaurant transaction data.

Data-driven barriers to market entry.

Importantly, the guidelines recognise that a platform may possess substantial competitive influence even below the general 40% dominance threshold, with a 25% share or even less potentially being relevant in particular circumstances.

This indicates a more sophisticated approach to measuring digital market power.

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B. Creation of technology-assisted enforcement mechanisms

Future enforcement may increasingly employ AI-based investigative systems.

Possible applications include:

For example, the GAC could analyse electronic procurement data to identify suspiciously similar bids submitted by supposedly independent companies.

Nevertheless, automated indicators should initiate investigation rather than automatically establish legal liability.

C. Expansion of merger-control scrutiny

AI acquisitions will become increasingly important in Saudi merger control.

The GAC may examine acquisitions according to:

Market concentration.

Potential competition.

Access to valuable datasets.

Innovation capabilities.

Computing infrastructure.

Network effects.

Control over distribution channels.

Future technological development.

The future challenge is to identify acquisitions that eliminate potential competition before the acquired business becomes commercially significant.

D. Structural and behavioural remedies

Saudi merger enforcement is likely to develop more sophisticated remedies.

Structural remedies may include:

Divestiture of overlapping businesses.

Separation of competing commercial units.

Disposal of strategically important assets.

Removal of problematic ownership links.

Behavioural remedies may include:

Non-discriminatory access obligations.

Restrictions on exclusive contracts.

Data-access commitments.

Interoperability requirements.

Transparent ranking practices.

Restrictions on tying and bundling.

Periodic compliance reporting.

An important indication of this development is the GAC's first structural remedy in the 2025 Schlumberger–ChampionX transaction, which required the divestiture of a US subsidiary. The same year's merger-control activity involved 406 applications and two conditional approvals.

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E. Stronger international cooperation

AI businesses frequently operate across national boundaries.

A company may develop its model in one country, train it using internationally sourced data, host it through foreign cloud infrastructure and sell its services in Saudi Arabia.

This creates jurisdictional and evidentiary challenges.

The GAC may increasingly cooperate with:

International competition authorities.

OECD competition committees.

Gulf Cooperation Council regulators.

Foreign digital-market regulators.

Cross-border data-governance institutions.

Such cooperation will become particularly important for international AI mergers, algorithmic pricing arrangements and digital platform investigations.

6. Future Sector-Specific Antitrust Enforcement in Saudi Arabia

7. Principal Challenges Facing Saudi Antitrust Enforcement

A. Difficulty establishing algorithmic collusion

Traditional cartel investigations often rely on communications, agreements and evidence of coordination.

AI systems can produce similar commercial outcomes without direct human communication.

The GAC will need to distinguish between:

Independent algorithmic adaptation.

Conscious parallel conduct.

Information exchange.

Algorithmic facilitation of an agreement.

Deliberate coordination through a common service provider.

The legal challenge will be establishing the necessary elements of an infringement.

B. Lack of algorithmic transparency

Advanced AI systems may operate through complex machine-learning models.

Even their developers may encounter difficulties explaining individual outputs.

Competition investigations may therefore require:

Access to relevant model documentation.

Examination of pricing inputs.

Training-data analysis.

Audit logs.

Algorithmic testing.

Independent technical expertise.

C. Balancing innovation and competition

Excessive regulatory intervention could discourage investment in AI research and development.

Conversely, insufficient enforcement could allow dominant companies to eliminate smaller innovators.

The GAC must distinguish between legitimate technological integration and exclusionary commercial conduct.

D. Defining relevant markets

Traditional market definition methods may not adequately capture AI ecosystems.

For example, a single foundation model may compete in several downstream markets, including education, finance, healthcare and enterprise software.

Future market analysis may require consideration of:

Substitution between AI models.

Switching costs.

Multi-homing.

Data advantages.

Computing constraints.

Network effects.

Innovation competition.

Ecosystem dependency.

E. Institutional coordination

Competition law, data protection, cybersecurity and consumer protection overlap in AI markets.

A restriction on data access might raise privacy concerns while simultaneously creating a competition barrier.

The GAC must therefore coordinate with relevant authorities without confusing their respective statutory mandates.

Saudi Arabia's 2026 contribution to the OECD confirms that it is developing this coordinated approach through specialised institutional committees and dedicated digital-market research capabilities.

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8. Future Institutional and Regulatory Reforms

The following reforms could strengthen the Saudi competition framework.

These are recommendations for future development, not a statement that every proposed mechanism has already been adopted.

9. Economic Analysis of Future AI Antitrust Enforcement

The GAC will increasingly need to move beyond conventional market-share analysis.

A. Network effects

AI platforms may become more valuable as additional users contribute data and commercial activity.

This creates a feedback loop:

Larger user base

More data generated

Improved AI performance

Greater consumer attraction

Reinforced market position

The GAC must assess whether these effects reflect legitimate competition on the merits or create exclusionary barriers.

B. Innovation competition

A company may have a relatively small current market share but possess technology capable of challenging an established competitor.

Competition assessment should therefore consider future innovation, research expenditure, intellectual property, technical capabilities and potential entry.

C. Switching costs

AI ecosystems may create significant switching costs through:

Proprietary file formats.

Model-specific applications.

Data migration difficulties.

API dependencies.

Employee training costs.

Cloud infrastructure commitments.

These factors can reduce competitive pressure even where alternative services technically exist.

D. Consumer welfare and dynamic efficiency

AI competition enforcement should consider more than immediate price reductions.

Relevant factors include:

Product quality.

Innovation.

Privacy.

Consumer choice.

Technological accessibility.

Service reliability.

Long-term market contestability.

A competitive AI economy should encourage firms to improve their products rather than rely on artificial exclusion of rivals.

10. Compliance Responsibilities of Saudi Businesses

Businesses operating in Saudi Arabia should develop competition compliance programmes that account for AI-related commercial risks.

Recommended corporate safeguards:

Conduct periodic competition-risk assessments of AI systems.

Review agreements with common AI pricing providers.

Avoid exchanges of competitively sensitive information.

Review exclusivity and interoperability clauses.

Assess merger notification requirements before completing acquisitions.

Audit algorithmic ranking and recommendation systems.

Maintain records of important automated commercial decisions.

Review discriminatory pricing practices.

Establish procedures for reporting suspected algorithmic coordination.

Train directors, management and technology teams on competition law.

Boards should also ensure that AI procurement and deployment decisions receive appropriate legal and economic scrutiny.

11. Future Trajectory: 2026–2035

The following is a prospective assessment rather than an officially adopted Saudi government timetable.

2026–2027: Institutional development

Expansion of digital-market studies.

Development of platform-specific competition guidance.

Improved coordination between GAC and digital regulators.

Increased scrutiny of data-driven platform conduct.

2028–2030: Technology-focused enforcement

More sophisticated algorithmic investigations.

Greater attention to AI mergers.

Examination of cloud and foundation-model concentration.

Increased use of digital evidence and economic modelling.

2031–2033: Advanced AI competition governance

More developed algorithmic auditing.

Increased attention to autonomous commercial decision-making.

Stronger interoperability and data-access analysis.

More sophisticated monitoring of digital ecosystems.

2034–2035: Integrated competition governance

Greater integration of AI, economic and legal expertise.

More advanced cross-border enforcement cooperation.

Dynamic assessment of technological market power.

Greater emphasis on preserving innovation and market contestability.

12. Critical Evaluation

The future of Saudi antitrust enforcement will depend on whether existing competition principles can effectively address the economic realities of AI markets.

Three major considerations will determine its success.

First: Technological neutrality.

Competition law should apply to commercial conduct irrespective of whether it is performed by humans, traditional software or autonomous AI systems.

Second: Evidence-based enforcement.

The GAC should distinguish legitimate algorithmic efficiency from conduct that restricts competition. Technical sophistication should not automatically create immunity from legal scrutiny.

Third: Innovation-oriented regulation.

Competition enforcement should not punish successful innovation or prevent legitimate commercial integration. It should preserve opportunities for competing innovators to enter, expand and challenge established businesses.

A particularly important development is the GAC's 2026–2030 strategy, which identifies digitalisation and innovation as institutional priorities alongside fair competition, compliance and cooperation. Its establishment of a dedicated digital-market studies department demonstrates that digital competition is becoming a more specialised area of enforcement.

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13. Conclusion

The future trajectory of antitrust enforcement in Saudi Arabia under AI economy transformation will involve a transition from conventional competition enforcement towards more sophisticated digital-market governance.

The GAC will increasingly need to examine algorithmic pricing, AI-based dominance, data concentration, cloud infrastructure, foundation-model licensing, digital acquisitions, platform self-preferencing and restrictions on technological interoperability.

The Uber/Careem transaction, Delivery Hero/The Chefz merger decision, gas-sector vertical merger prohibition and other Saudi enforcement decisions already demonstrate the importance of examining digital concentration, market access, procedural compliance and exclusionary commercial practices.

International decisions involving Google and Microsoft provide additional analytical guidance for dealing with technological gatekeepers.

Ultimately, Saudi Arabia's objective should not be to restrict the development of powerful AI companies. It should be to ensure that technological success does not become a mechanism for eliminating competition.

The central principle for the future is that AI innovation must create competitive opportunities rather than permanent technological monopolies.

14. Important Legal Principles for Examination and Research

Overall research conclusion: Saudi Arabia is moving towards a more proactive, data-conscious and technology-oriented competition enforcement model. The direction is increasingly visible in its 2026 digital-market policy contribution, platform guidelines and institutional strategy. The next major challenge will be translating these developments into consistent enforcement against genuinely exclusionary AI-related conduct.