The Indian AI market has grown from USD 3.2 billion in 2020 to over USD 6 billion in 2024, and is expected to reach nearly USD 32 billion by 2031.

Artificial Intelligence and Competition in India – Insights from the CCI Market Study

by ChatGPT-4o

Artificial Intelligence (AI) is rapidly redefining the competitive dynamics of global and national economies. India, poised to become one of the largest AI markets, is witnessing exponential growth in AI adoption across industries, supported by increasing state investment and a burgeoning startup ecosystem. The Competition Commission of India (CCI), in collaboration with the Management Development Institute (MDI), Gurgaon, has undertaken a seminal market study titled Artificial Intelligence and Competition, with the dual goals of examining the AI ecosystem and evaluating emerging competition concerns arising from its deployment. This essay synthesizes the key insights from the report and offers broader reflections on the implications for regulators, enterprises, and consumers.

1. India’s Expanding AI Landscape

The Indian AI market has grown from USD 3.2 billion in 2020 to over USD 6 billion in 2024, and is expected to reach nearly USD 32 billion by 2031. This expansion is fueled by rising demand for automation, personalized services, and decision-support tools across industries such as BFSI, healthcare, logistics, retail, and marketing. Startups dominate the downstream layers of the AI stack—particularly the application development layer—where 67% of Indian AI startups are concentrated. These startups rely heavily on open-source tools, with over 75% using open-source models and libraries, underscoring the democratizing force of public codebases in early innovation cycles.

2. The AI Stack and Market Structure

The CCI study proposes a layered model of the AI value chain—from upstream data and compute infrastructure to downstream fine-tuning and user interfaces. This “AI Stack” includes:

• Data Layer: Dominated by global providers like Appen, AWS, Google, and Microsoft Azure.

• Compute Layer: Reliant on cloud giants and chipmakers such as NVIDIA, Intel, and AMD.

• Development Layer: Where AI models are trained using ML, NLP, CV, and increasingly, Generative AI (LLMs).

• Foundation Models Layer: Largely shaped by hyperscalers like OpenAI, Google, and Meta.

• Application Layer: Focused on adapting AI models for specific sectoral needs.

• Deployment, Interaction, and Governance Layers: Overseeing real-world application and ensuring orchestration, compliance, and user interaction.

India’s startup activity is largely downstream, but its dependence on upstream infrastructure—especially foreign-owned compute, data, and models—raises systemic concerns around sovereignty, market concentration, and long-term competitiveness.

3. Adoption Patterns and Competitive Impacts

AI adoption is widespread in India’s digital-first sectors. For example, 90% of surveyed firms use AI to monitor customer behavior, and nearly 70% deploy AI for demand forecasting. However, AI adoption also creates an unequal playing field:

• Advantages for Adopters: Improved efficiency, cost reduction, and customer personalization.

• Disadvantages for Non-Adopters: Competitive irrelevance, lower productivity, and reduced customer retention.

This “AI divide” risks reinforcing market power in favor of early movers and resource-rich incumbents, particularly those with access to proprietary data and advanced models.

4. Emerging Competition Risks

While AI offers pro-competitive benefits, the report identifies several anti-competitive risks:

• Algorithmic Collusion: 37% of startups flagged risks of tacit collusion, including hub-and-spoke models where third-party platforms align pricing across competitors.

• Price Discrimination: Enabled by personalization algorithms, 32% of respondents saw AI being used for targeted pricing, potentially harming vulnerable consumers.

• Predatory Pricing: AI can selectively undercut prices for switch-prone consumers while charging others more—a concern cited by 22% of respondents.

• Barriers to Entry: Data hoarding, expensive compute, and talent scarcity favor incumbents. Only 3% of Indian firms are building foundational models, illustrating steep entry barriers.

• Ecosystem Lock-in: Exclusive partnerships and vertical integration across the AI stack could cement dominant positions, particularly by global hyperscalers.

The opacity of AI systems (especially black-box models) further complicates detection and enforcement, raising the need for algorithmic transparency and auditability.

5. Regulatory Frameworks: Global Comparisons and Indian Context

The report benchmarks legal approaches from the U.S., EU, UK, China, Japan, and Canada, observing a global shift toward AI governance that emphasizes transparency, fairness, and accountability. India’s regulatory response includes:

• The Competition (Amendment) Act, 2023, which strengthens CCI’s oversight capabilities (e.g., hub-and-spoke cartels, deal value thresholds).

• The Digital Personal Data Protection Act (DPDPA), 2023, which focuses on data privacy and protection.

• The IndiaAI Mission, a major government initiative investing ₹10,300 crore to promote compute infrastructure, skills, datasets, and responsible AI.

India is also launching an AI Safety Institute and collaborating with OpenAI on educational initiatives—important steps in building a safe and innovation-friendly AI ecosystem.

6. Recommendations and Action Plan

The report proposes several measures to ensure a competitive and fair AI landscape in India:

• Self-Audit Frameworks: Enterprises should implement internal audits to detect AI-driven anti-competitive behaviors.

• Transparency & Information Symmetry: Encouraging explainability in AI systems and sharing clear communication with consumers and regulators.

• Removing Entry Barriers: Investments in compute infrastructure, data commons, and AI talent are crucial to levelling the playing field.

• Regulatory Capacity Building: CCI plans to organize conferences, advocacy workshops, and inter-agency coordination efforts to improve oversight.

• International Cooperation: Collaborating with global competition authorities and multilateral platforms to align norms and enforcement practices.

Conclusion: A Forward-Looking Framework for Responsible AI Competition

The CCI’s market study is a timely and comprehensive effort to map the terrain of AI and its impact on competition in India. It offers a clear-eyed view of both the opportunities and risks posed by AI technologies—particularly as adoption accelerates and global incumbents entrench their positions. For India, the dual challenge is to foster indigenous AI capacity while ensuring that the benefits of automation and intelligence are not captured by a few monopolistic actors.

Robust regulatory interventions—grounded in transparency, fairness, and interoperability—will be key to avoiding an AI-driven replay of platform monopolies seen in Web2. The inclusion of startup voices, emphasis on open-source tools, and focus on self-audit and transparency frameworks are welcome. However, the need for enforceable safeguards, real-time audit tools, and legal remedies for algorithmic harms remains urgent.

India stands at a pivotal moment. With strategic public investment, progressive regulation, and global engagement, it can shape an AI economy that is innovative, inclusive, and truly competitive.