CCI cautions against anticompetitive risks posed by Artificial Intelligence India's AI market size is expected to grow from USD 7.84 billion in 2025 to USD 31.94 billion in 2031.

The Competition Commission of India (CCI) has cautioned against partnerships and merger acquisitions in the AI space and their subsequent impact on competition.

"The AI ecosystem is also witnessing partnerships among players across layers in the AI stack. These partnerships can spur growth and innovation by facilitating access to critical inputs, technologies, etc. However, such partnerships in the AI space may also raise competition concerns under certain conditions," the CCI said in a prelude to a full report on the same.

Titled 'Market Study Report on Artificial Intelligence and Competition', the CCI details the adoption and subsequent rise of AI in India, trends across sectors, and most importantly, a regulatory and legal framework analysis.

According to the CCI, worldwide AI increased from USD 93.24 billion in 2020 to USD 186.43 billion in 2024, and the global AI market is poised to grow from USD 244.22 billion in 2025 to USD 1 trillion in 2030. India's AI market size is expected to grow from USD 7.84 billion in 2025 to USD 31.94 billion in 2031.

CCI's conundrum on AI: The good, the bad, and the complicated

The watchdog also noted that some of the AI adopters may be in advantage as compared to the ones who don't have the same.

"AI can significantly enhance operational efficiency and productivity, allowing adopters to reduce Artificial Intelligence and Competition costs and improve service quality. This can lead to a competitive advantage by enabling firms to offer better products or services at lower prices. AI can automate routine processes, thereby reducing the need for manual

labour and improving operational accuracy and speed. This not only lowers costs but also allows firms to scale operations more effectively, enhancing their competitive position in the market," it said.

In the report, the competition watchdog further elaborates on the multifaceted impact, which interestingly also includes both pro-competitive and anti-competitive.

It notes that AI brings significant benefits in terms of efficiency, innovation, and consumer experience. However, at the same time, it brings new challenges for competition in markets.

"Some of the challenges are possible concentration in AI value chain, ecosystem lock-in, risk of algorithmic collusion, price discrimination, exclusive partnerships and opaque nature of algorithms. Literature and case law across the globe raise concerns around algorithmic collusion, AI-driven pricing strategies that may lead to price discrimination," it noted.

Cutting the clutter

There is indeed a lot of weightage in the concerns raised by the CCI in terms of exploiting AI for possible price manipulation and distribution for individual gains. It's plausible that it will be sophisticated and extremely hard for the watchdogs to track at large scale. Let's try to address CCI's concerns point-by-point:

Pricing algorithms

AI systems, particularly those using reinforcement learning or Q-learning, can unintentionally learn to align their pricing strategies with competitors, even without direct communication. This phenomenon, known as algorithmic collusion, occurs when algorithms adjust their pricing based on observed market conditions, leading to coordinated pricing outcomes that resemble price-fixing.

Such behaviour can result in higher prices for consumers and reduced market competition.

The CCI identified this as a significant concern, noting that 37% of surveyed AI start-ups recognized the potential for algorithmic collusion, highlighting the need for proactive measures to prevent such outcomes.

Vishal Maru, Global Processing Head, FSS (Financial Software and Systems), tells Entrepreneur India that in order to mitigate the risk of AI-driven price collusion, companies may enforce a few rules such as ensuring AI systems make pricing decisions without referencing non-public competitor data, introduce small random variations in pricing to prevent algorithms from aligning their strategies with competitors, human intervention in reviewing and approving AI-generated pricing recommendations, and conduct regular audits of AI systems to detect and address any signs of collusive behaviour.

Maru also stressed the need for maintaining clear logs and documentation of AI decision-making processes to ensure accountability and traceability.

"With these measures, companies can help prevent AI systems from inadvertently engaging in collusive pricing behavior, fostering a more competitive and consumer friendly market environment," he explained.

Advantage to firms with deep pockets

Creating a level-playing field has been one of the core philosophies of the Indian market. In the AI space, however, could there be a possibility that startups that have just started out and lack large VC fundings may not have access to the quality and large data as well as computing chips needed for modern AI?

Rahul Rai, Partner at Axiom5 Law Chambers, explains that new and emerging startups and companies can reach central/state governments, perhaps in partnership with the private sector, to create and manage large, anonymized, quality datasets for training AI models. In this regard, the National Data Governance Framework Policy is a step in the right direction. Clear legal and technical frameworks to encourage private enterprises to share non-proprietary data with each other through standardized formats may help startups access data otherwise unavailable to them.

"Central/state Governments should consider offering incentives/subsidies to reduce the cost of compute whereas app developers building atop LLMs can rely on open-source models that could significantly reduce barriers to entry," he said.

And same goes with the large tech firms which may engage in anti-competitive practices. Given the track record and several lawsuits around the world, it's not hard to predict its plausibility.

Sagar Vishnoi, Director at Future Shift Labs, explains that larger tech firms should not be allowed to create a data monopoly, which would enable them to acquire or suppress smaller AI firms, stifling innovation and competition.

"Exclusivity terms should be implemented to prevent blockages for smaller AI firms, ensuring that they have access to the resources and markets they need to compete. By promoting competition and preventing monopolies, we can foster a vibrant and innovative AI ecosystem that benefits consumers and businesses alike," he said.