15. December 2025
Indias Groundbreaking Ai Copyright Plan Faces Crunch Time As Lawsuits And Technical Hurdles Loom
The Department for Promotion of Industry and Internal Trade’s (DPIIT) working paper on copyright use in generative AI has sparked a heated debate. The government body proposes a mandatory blanket licence for AI training, seeking to strike a balance between granting AI companies access to content while enforcing copyright laws. This framework aims to ensure that content creators receive statutory remuneration through a single-licence, single-payment mechanism.
The Ministry of Electronics and Information Technology has endorsed DPIIT’s proposed framework, but the technology industry is pushing back. Industry experts argue that the framework misunderstands the mechanics of AI training and risks burdening an emerging ecosystem still taking shape. This could lead to a stifling of innovation and hinder the development of new technologies.
The rapid growth of the Artificial Intelligence (AI) sector has triggered debates around fostering innovation while protecting businesses and consumers. One of the subjects drawing attention is the regulation of synthetic data. Synthetic data, created through algorithms and models, can democratise AI by providing startups with data that they might otherwise struggle to access. However, if misused and unregulated, it could lead to the proliferation of fake data, causing significant concerns regarding data integrity and privacy.
Without proper supervision, the unregulated generation of synthetic data presents risks. The Competition Commission of India’s market study on AI suggested that this could lead to a loss of trust in AI-driven systems, resulting in potential economic and social consequences. As such, there is an urgent need for policymakers to establish clear guidelines and regulations for the use of synthetic data.
The global IT services sector is undergoing a profound structural transformation, driven by a dual mandate from clients: vendor consolidation for reduced complexity and cost-optimisation increasingly fueled by Artificial Intelligence (AI) expectations. This confluence of factors is leading to a shift toward mega-deals—contracts of a larger size, longer duration, and higher Total Contract Value (TCV)—even as the overall volume of contracts remains relatively flat.
For instance, Infosys recently secured a ₹14,000 crore ($1.6 billion) 15-year mega-deal with NHS for workforce management, a contract that integrates AI-driven tools to streamline operations. Similarly, Tata Consultancy Services (TCS) bagged a $640 million, seven-year contract with Danish insurer, HUFSA.
India’s IT services industry is facing slowing growth and layoffs as its business model collides with automation. AI was supposed to be the next big growth engine, but so far, it has been noise. The sector’s dependence on the time-and-materials model—where revenue scales with billable hours—is now its biggest drag. Large language models and automation are cutting those very hours.
The sector’s reliance on this model has stalled hiring for new skills, while layoffs continue for those who haven’t upskilled. Neeti Sharma, CEO of TeamLease Digital, described the situation as “the lowest of lows.” Hiring for new skills has stagnated, while enterprises are struggling to adapt to the changing landscape.
India’s AI economy is entering a high-stakes phase. Despite the government’s efforts to boost investment in AI, startup founders are facing a different reality. Funding rounds have slowed, enterprise pilots have stretched longer, and translating policy and capital into product-market traction has just begun.
“The $20 billion investment commitment is fundamentally dual-natured,” Peeyoosh Pandey, CEO of Hoonartek, explained. “On one side, there’s structural conviction at the infrastructure layer, long-term capex by global hyperscalers and Indian conglomerates on compute, data and digital infrastructure. On the other side, this investment is also dependent on startups being able to navigate the policy and regulatory landscape effectively.”
Recent industry reports suggest that AI initiatives are yielding mixed results. Average ROI from AI initiatives hovers around 5-7%, but certain sectors are beginning to show double-digit gains. Experts attribute this gap to how organisations embed AI, rather than the technology itself.
“Most enterprises have moved from AI adoption for optics to AI adoption for outcomes,” said Anirudh Bhardwaj, chief technology officer at Recur Club, a fintech platform that uses AI to streamline revenue-based financing. “ROI today is measured not just in terms of cost savings but also in the impact it has on business outcomes.”
The unveiling of the India AI Governance Guidelines has prompted reactions from policy experts, legal commentators, and AI governance specialists. While the framework’s intent received praise, experts sought clarity on how its principles will translate into operational safeguards.
The Ministry of Electronics and Information Technology (MeitY) announced the guidelines as a national framework to ensure the safe, inclusive, and responsible adoption of artificial intelligence across sectors. Launched in November, the document outlines India’s most coordinated effort yet to guide AI development at scale ahead of the India AI Impact Summit 2026.
At its core, the guidelines aim to create a comprehensive regulatory framework that balances innovation with accountability. By doing so, policymakers hope to establish trust and confidence in AI-driven systems, which are increasingly being used across various sectors. The government body’s proposed approach to copyright law reform is seen as a major step towards ensuring that the benefits of AI are shared fairly among stakeholders.
However, critics argue that the framework is overly broad and may stifle innovation. Some industry experts have expressed concerns that the guidelines may not adequately address the nuances of AI development and deployment in specific sectors.
Ultimately, the success of India’s AI copyright plan will depend on its ability to strike a balance between regulatory certainty and technological innovation. As policymakers continue to grapple with the complexities of AI governance, it remains to be seen whether this framework will prove successful in driving growth while ensuring accountability and fairness for all stakeholders involved.