Prime Minister Narendra Modi has inaugurated the AI Impact Summit in the national capital, Delhi. Leading figures from India and abroad, including heads of state, ministers, and prominent industrialists, are participating in the summit. This event is being held at a time when the world is rapidly embracing artificial intelligence (AI). The government aims to establish India as a credible voice in the global AI landscape. Currently, the AI domain is dominated by the United States and China, with nearly 90% of AI patents coming from the U.S., China, and Europe.
This summit will set the framework for how quickly India can race alongside the U.S. and China in this field. Prominent participants include Sundar Pichai, CEO of Google; Sam Altman of OpenAI; Brad Smith, President of Microsoft; Demis Hassabis of DeepMind; Cristiano Amon, CEO of Qualcomm; among others. Being the first and largest AI conference in the Global South, the summit will discuss global AI impacts, such as employment, road safety, AI applications for safe mobility, driver training, and technology-driven solutions for public welfare.
Special sessions will focus on topics such as “From Research to Solutions,” exploring how AI can support sustainable, efficient, and climate-sensitive agriculture. Similar sessions will be held in Paris, Berlin, New York, Geneva, Oslo, Bangkok, and Tokyo. Broad adoption of AI will require large-scale skills development, for which detailed guidelines will be prepared. India has the strength to advance significantly in the AI sector. With one of the largest consumer markets for AI tools globally, numerous companies are rapidly entering the Indian market.
Amazon has pledged investments exceeding $35 billion by 2030, Microsoft announced $17.5 billion over four years, and Google plans $15 billion. This will be Google’s largest AI and data center hub outside the U.S. India’s digital public infrastructure, low-cost data, and the Digital Personal Data Protection Act have strengthened investor confidence through regulatory stability. India’s comprehensive strategy balances investment in sovereign computing and domestic large language models with expensive frontier model development and localized use cases.
A recent economic review highlighted disparities between frontier model development and application-based use, noting that bridging the frontier gap can be costly. Therefore, either frontier-level models are pursued, or limited resources are directed toward domestic, region-specific AI systems aligned with national priorities. Structural pillars include expanding compute access through 38,000 Graphics Processing Units (GPUs), creating AI-KOS datasets, establishing an AI security institute, and maintaining a database of AI incidents.
These are critical institutional steps, but enforceable accountability, strong data governance, and clear remediation mechanisms are equally essential. Incentives should not push companies toward opacity, as voluntary compliance alone may be insufficient. Challenges in the AI sector are significant. AI can assist companies, industries, and courts in case management, and virtual courts can expedite case resolution. However, growing misuse of AI poses risks related to privacy, security, national and individual interests, and fairness.
The use of AI to create deepfakes, disinformation, and lethal weapons poses threats to human safety. Training AI systems requires vast amounts of data, which can compromise personal information. Ethical and societal issues must be handled carefully to avoid harm. Cybersecurity has become crucial in today’s era, and financing AI in countries like India is a significant challenge.
Currently, a paradox is emerging in the AI landscape: cutting-edge models are becoming both extremely expensive and widely commoditized. Governments must consider entirely new forms of infrastructure, just as the Global South has pioneered lightweight, low-cost physical infrastructure, such as Bus Rapid Transit systems. Similarly, there are opportunities to rethink foundational AI infrastructure. General-purpose translation modules are an example. Initiatives like Bhashini in India and Masakhane in Africa are not end-user applications but shared layers providing reusable linguistic capabilities.
Other initiatives include open speech-to-text systems for low-resource languages or evaluation benchmarks, enabling context-sensitive, locally aligned models. Investing in AI infrastructure provides high-level benefits and fosters innovation, though targeted public funding may sometimes be more suitable for specific applications. Governments must take concrete steps to establish AI infrastructure in the private sector and develop roadmaps to encourage private investment.
Despite these challenges, the summit is expected to generate solutions to mitigate AI’s negative impacts through global dialogue. AI has the potential to reshape the world, and this conference may be a significant step toward influencing its direction and standards.
