UPSC Editorial Analysis: Public Domain vs. Generative AI

*General Studies-3; Topic: Awareness in the fields of IT, Space, Computers, robotics, Nano-technology, bio-technology and issues relating to intellectual property rights.*

Understanding the Public Domain

• The public domain refers to the “cultural commons”—the vast body of creative works that are not protected by intellectual property laws.

• The Concept of Balance: Copyright law is built on a “quid pro quo” (something for something). Society gives creators a temporary monopoly over their work (income and control) in exchange for the work eventually becoming free for everyone to use.

• The 2026 Milestone: This year is significant globally. In the US, Disney’s 1930 film Mickey Mouse from The Chain Gang and the first Nancy Drew books have entered the public domain.

• This year is significant globally. In the US, Disney’s 1930 film Mickey Mouse from The Chain Gang and the first Nancy Drew books have entered the public domain.

• The Indian Context: Under the Indian Copyright Act, 1957, protection generally lasts for the life of the author plus 60 years. In 2026, the works of the legendary Carnatic musician G. N. Balasubramaniam (who passed away in 1965) have officially entered the public domain, allowing free adaptation and digital archiving of his compositions.

• Under the Indian Copyright Act, 1957, protection generally lasts for the life of the author plus 60 years. In 2026, the works of the legendary Carnatic musician G. N. Balasubramaniam (who passed away in 1965) have officially entered the public domain, allowing free adaptation and digital archiving of his compositions.

About Public Domain vs. Generative AI

• The public domain allows free creativity after copyright expires. Generative AI disrupts this by training on protected works earlier. India’s 2026 policy proposes statutory licensing to balance creator royalties and innovation.

The Generative AI Challenge

The traditional cycle of “Creation -> Protection -> Public Domain” is being disrupted by Generative AI.

• Massive Speed vs. Human Learning: While humans take decades to learn a style, AI models (like GPT-5 or Stable Diffusion) can “ingest” millions of works in days.

• While humans take decades to learn a style, AI models (like GPT-5 or Stable Diffusion) can “ingest” millions of works in days.

• The “Ghibli” Paradox: In late 2025, social media was flooded with AI-generated art mimicking Studio Ghibli. While AI “learned” from copyrighted images, the resulting work wasn’t a direct copy, yet it directly threatened the market value of the original studio’s style.

• In late 2025, social media was flooded with AI-generated art mimicking Studio Ghibli. While AI “learned” from copyrighted images, the resulting work wasn’t a direct copy, yet it directly threatened the market value of the original studio’s style.

• Erosion of Future Value: If AI can perfectly mimic an artist’s style before their copyright expires, the “protection period” becomes economically meaningless for the human creator.

• If AI can perfectly mimic an artist’s style before their copyright expires, the “protection period” becomes economically meaningless for the human creator.

India’s Policy Response: The 2026 DPIIT White Paper

The Department for Promotion of Industry and Internal Trade (DPIIT) recently released a white paper suggesting a “middle path” for India.

• Statutory Blanket Licensing: Instead of AI companies negotiating with every single artist, the government suggests a “blanket licence.” AI platforms can train on copyrighted data by paying a fixed fee into a central fund.

• Instead of AI companies negotiating with every single artist, the government suggests a “blanket licence.” AI platforms can train on copyrighted data by paying a fixed fee into a central fund.

• Royalty Distribution: A statutory body would distribute these royalties to creators based on how much their work was used for training.

• A statutory body would distribute these royalties to creators based on how much their work was used for training.

• Legal Standing: This attempts to resolve the “fair use” debate. Rather than deciding if AI training is “theft” or “learning,” India is opting to treat it as a paid utility.

• This attempts to resolve the “fair use” debate. Rather than deciding if AI training is “theft” or “learning,” India is opting to treat it as a paid utility.

Multi-Dimensional Impact Analysis

• Legal Dimension

• Moral Rights: Even after a work enters the public domain, the “Moral Rights” (paternity and integrity) of the creator remain. AI cannot “mutilate” a work in a way that hurts the creator’s reputation. Doctrine of Fair Dealing: Indian law (Section 52) allows the use of copyrighted works for research and education. The debate in 2026 is whether commercial AI training counts as “research” or “commercial exploitation.”

• Doctrine of Fair Dealing: Indian law (Section 52) allows the use of copyrighted works for research and education. The debate in 2026 is whether commercial AI training counts as “research” or “commercial exploitation.”

• Economic Dimension

• Market Substitution: If AI-generated “Nancy Drew-style” books are cheaper and faster to produce, the economic incentive for new human authors to create original characters diminishes. The “Paywall” Effect: To prevent AI from “scraping” their work for free, many publishers are moving content behind high paywalls. This hurts the public and students more than it hurts AI companies, who can afford the fees.

• Market Substitution: If AI-generated “Nancy Drew-style” books are cheaper and faster to produce, the economic incentive for new human authors to create original characters diminishes.

• The “Paywall” Effect: To prevent AI from “scraping” their work for free, many publishers are moving content behind high paywalls. This hurts the public and students more than it hurts AI companies, who can afford the fees.

• Ethical & Cultural Dimension

• Knowledge Enclosure: There is a fear of “Digital Enclosure,” where human culture—once free to browse—becomes a proprietary dataset for large tech corporations. Cultural Appropriation: AI training on indigenous art or traditional music (like GNB’s works) without proper context can lead to the commercialization of sacred or traditional knowledge without benefiting the community.

• Knowledge Enclosure: There is a fear of “Digital Enclosure,” where human culture—once free to browse—becomes a proprietary dataset for large tech corporations.

• Cultural Appropriation: AI training on indigenous art or traditional music (like GNB’s works) without proper context can lead to the commercialization of sacred or traditional knowledge without benefiting the community.

The Future of the Public Domain

The public domain is supposed to be the “nursery” of future creativity. However, if AI captures the value of works before they enter the public domain, the incentive to wait for the protection period to end disappears.

• The Risk: We might enter a “feedback loop” where AI only trains on other AI-generated content, leading to cultural stagnation or “model collapse.”

• We might enter a “feedback loop” where AI only trains on other AI-generated content, leading to cultural stagnation or “model collapse.”

• The Solution: Policy must ensure that “Public Accessibility” remains a priority. Paying for AI training should not mean locking the doors for human learners.

• Policy must ensure that “Public Accessibility” remains a priority. Paying for AI training should not mean locking the doors for human learners.

Way Forward

To ensure a sustainable balance between technology and creativity, India must adopt a multi-pronged strategy:

• Transparency in Datasets: Mandate that AI companies disclose what datasets they used. Without transparency, creators cannot know if their work was used to train a model.

• Mandate that AI companies disclose what datasets they used. Without transparency, creators cannot know if their work was used to train a model.

• Explicit “Opt-Out” Rights: While India leans toward blanket licensing, creators should have the right to opt-out of AI training for sensitive or highly personal works.

• While India leans toward blanket licensing, creators should have the right to opt-out of AI training for sensitive or highly personal works.

• Tiered Royalty Structures: Small AI startups should pay lower fees compared to trillion-dollar tech giants to ensure a level playing field.

• Small AI startups should pay lower fees compared to trillion-dollar tech giants to ensure a level playing field.

• Strengthening Moral Rights: Law must ensure AI doesn’t “mutilate” a creator’s work or use their likeness (Deepfakes) even after the work enters the public domain.

• Law must ensure AI doesn’t “mutilate” a creator’s work or use their likeness (Deepfakes) even after the work enters the public domain.

• Global Harmonization: India should align its policies with the EU AI Act and WIPO treaties to ensure Indian creators are protected when their data is used by foreign AI models.

• India should align its policies with the EU AI Act and WIPO treaties to ensure Indian creators are protected when their data is used by foreign AI models.

Conclusion

• The public domain is the soul of human culture, and it must not be sacrificed at the altar of efficiency. While India’s “One Nation, One Licence” model is a bold attempt at technological neutrality, the ultimate goal must be a “Pro-Human AI Policy.”

• AI should supplement human creativity, not replace it, and the public domain must remain a truly “public” space, free from the walls of digital monopolies.

https://www.insightsonindia.com/2026/01/23/examine-the-role-of-generative-ai-in-addressing-capacity-constraints-in-healthcare-systems-discuss-its-limitations-as-a-clinical-support-tool/