AI Challenge for Competition Law

AI Challenge for Competition Law

Why in News?

The Indian government recently announced the selection of Bengaluru-based Sarvam to develop the country’s first indigenous large language model (LLM) within six months. This marks a major step in India’s push for AI development, but it also raises critical concerns around competition law and regulation.

Introduction

AI, particularly large language models (LLMs), is rapidly becoming the cornerstone of the Fourth Industrial Revolution. While AI offers vast benefits such as efficiency, innovation, and data-driven insights, it also brings with it worrying risks—especially when it comes to market competition. Authorities globally are waking up to these challenges and are considering new frameworks to regulate AI’s potential monopolistic tendencies.

Key Issues

1. Market Concentration & Entry Barriers

   • Developing powerful AI models requires massive resources, high-quality datasets, and computing capacity—assets that only a few tech giants possess.

   • This can lead to high levels of concentration, restricting access for smaller competitors.

2. Exclusive Licensing & Self-Preferencing

   • Digital majors may license AI tools exclusively or structure them in ways that block competitors.

   • Self-preferencing (e.g., prioritizing their own AI products) can unfairly disadvantage others in the ecosystem.

3. Opaque AI Systems

   • The functioning of many AI models remains a “black box,” which makes assessing fairness or bias difficult.

   • This lack of transparency may prevent effective competition scrutiny.

4. Data Dominance

   • Ownership and access to quality datasets determine the strength of an AI product.

   • Big players who control data can potentially deny data access to competitors.

Alternative Approaches

• Global Examples:

  • The US, UK, and EU have already started evaluating the competition risks posed by AI.

  • The EU’s AI Act, for example, mandates evaluation of potential monopolistic risks.

• India’s Response:

  • India’s Competition Commission (CCI) is likely to study the AI space soon, learning from global regulators to frame appropriate domestic laws.

Challenges and the Way Forward

Challenges:

• Rapid AI innovation is outpacing legal and regulatory frameworks.

• Absence of clear guidelines on data access and fairness in AI.

• Enforcement is difficult due to technical complexities and lack of transparency.

Way Forward:

• Adopt a balanced regulatory framework that encourages innovation while curbing monopolistic practices.

• Enhance transparency through audits of AI systems.

• Create open-access datasets and tools for startups and smaller players.

• Promote AI literacy among regulators and stakeholders.

Conclusion

As India makes bold strides in developing its own AI capabilities, it must also stay vigilant. Regulation must evolve alongside technology to ensure that AI development does not result in digital monopolies. A fair, competitive ecosystem will drive not just innovation, but inclusive technological growth.

5 Questions and Answers

Q1: What is the recent AI initiative announced by the Indian government?
A: The government has chosen Sarvam, a Bengaluru-based firm, to develop India’s first indigenous large language model (LLM) within six months.

Q2: What are the competition law concerns related to AI development?
A: AI development requires high resources and data, leading to market dominance by large firms and potential exclusion of smaller players. Practices like exclusive licensing and self-preferencing are also concerns.

Q3: How are global authorities addressing AI competition challenges?
A: Regulatory bodies in the US, UK, and EU have conducted studies and introduced frameworks like the EU’s AI Act to evaluate AI’s impact on competition.

Q4: What can be done to prevent monopolistic behavior in AI?
A: Measures include transparency in AI systems, open-access data, fair licensing practices, and regulatory oversight.

Q5: What role should India’s Competition Commission (CCI) play?
A: The CCI should proactively study AI’s competitive landscape and develop guidelines that ensure innovation is not stifled by monopolistic practices.