India’s Innovation Mirage

Syllabus: GS III/ Science & Technology – Indigenisation and R&D

In News / Context

• The India AI Impact Summit 2026 witnessed large participation and high enthusiasm. However, controversy arose after allegedly imported technology products were showcased as indigenous innovations, raising concerns about the quality and authenticity of India’s innovation ecosystem.

India’s Innovation Aspirations

• India’s aspirations are anchored in becoming a $5 trillion economy and a global high-tech hub.

• IndiaAI Mission which focuses on computer infrastructure, datasets and AI startups.
• Digital India that led to creation of digital public infrastructure (DPI) like UPI, Aadhaar and ONDC.
• Startup India is now the world’s third-largest startup ecosystem with over 1 lakh recognized startups.
• Atmanirbhar Bharat which emphasis on domestic manufacturing and indigenisation.
• India climbed to 38th in the Global Innovation Index (GII) 2025, a massive leap from 81st in 2015.
• India now ranks 4th in trademarks and 6th in patent filings globally.
• Also, the government has operationalized the ₹1 lakh crore Research, Development and Innovation (RDI) Fund and the Anusandhan National Research Foundation (ANRF) to foster a culture of “Viksit Bharat 2047.”

Structured Flaws in India’s Innovation Ecosystem

• The “Filing-to-Grant” Success Gap: While IITs and NITs maintain a grant success rate of 40–65%, high-volume private universities often see rates below 3%. For example, institutions like LPU and Galgotias outpace the combined filings of all IITs, yet secure negligible grants.
• Ranking Arbitrage: The National Institutional Ranking Framework (NIRF) weights the quantity of patent filings (30% weight for Research & Professional Practice). Educational institutions receive an 80% reduction in filing fees. This creates a loop like filing cheap patents then boosting NIRF Rank and then attracting more students/revenue.
• Weak Commercialisation: Even where patents are granted there are still limited industry transfer, poor startup spin-offs & low revenue from technology licensing.
• Low R&D Incentives: India’s R&D expenditure remains around 0.64– 0.7% of GDP, compared to the USA (3-4%), Israel (5.7%) & South Korea (4.8%). Low private sector participation further weakens deep-tech innovation.
• Showcase Culture over Substance: Public summits and events often highlight prototypes and announcements. Without rigorous validation and market testing, innovation risks becoming symbolic rather than transformative.

Implications for India’s AI Ambition

• If innovation remains procedural rather than problem-solving, India’s AI goals face significant risks:
  • Dependence on Foreign Hardware: As seen in the “robodog” case, India risks being a “software skin” over foreign-made “hardware bones.”
  • Credential Inflation: A surplus of frivolous patents dilutes the brand of “Indian Innovation,” making it harder for genuine deep-tech startups to attract global venture capital.
  • Erosion of Trust: Misrepresentation at international summits damages India’s credibility as a reliable partner in the global AI supply chain.
• However, India possesses strong fundamentals like a large AI talent pool, expanding digital infrastructure & huge domestic data scale.  

Lessons from China and the World

• The Chinese Model: Two decades ago, “Made in China” was synonymous with cheap imitations. However, through relentless iteration and a focus on process innovation, China moved from imitation to dominance in 6G, EVs, and high-speed rail. They focused on “disciplined progress” rather than “premature proclamation.”
• The South Korean Model: A focus on Translational Research Centres (TRCs) that bridge the gap between labs and markets, supported by one of the world’s highest R&D-to-GDP ratios.
• Silicon Valley Model: Focus on more venture capital ecosystem, deep university-industry linkages & high tolerance for failure but strict market validation.

Comprehensive Reforms Needed

• Outcome-Linked Incentives: Shift government reimbursements from the filing stage to the grant and commercialization stages.
• NIRF Overhaul: Transition from counting “Patents Filed” to “Patents Granted” and, more importantly, “Revenue from IP Licensing.”
• Auditing and Accountability: Establish a regulatory mechanism to audit institutions with abnormal filing-to-grant ratios to deter frivolous IPR activity.
• Increase Private R&D Investment: Enhanced tax credits & risk capital for deep-tech sectors (AI hardware, semiconductors, biotech).

Conclusion

• To truly become an AI powerhouse, India must shift from volumetric compliance to substantive excellence, ensuring that the “Made in India” label represents rigorous research rather than just “patriotic lighting.”

Source: BL