Is Silicon Valley finally realizing it doesn’t have all the answers? For years, the narrative has been about Western tech giants dictating the future of AI, exporting solutions to the “developing world.” But the flurry of announcements coming out of this week’s Global AI Summit in India, spearheaded by Google DeepMind alongside the Indian government, suggests a significant power shift. The real story here isn't just about deploying AI to India – it’s about building AI with India, and increasingly, letting India lead the conversation.
On February 18, 2026, Demis Hassabis, Lila Ibrahim, and Pushmeet Kohli of Google DeepMind unveiled a sweeping expansion of their “National Partnerships for AI” initiative, with India taking center stage. This isn’t simply a philanthropic gesture; it’s a strategic acknowledgement that India’s unique challenges and vast datasets are crucial for developing truly robust and globally relevant AI. The $30 million Google.org Impact Challenge: AI for Science, an open call for researchers worldwide, is a tangible example, but the deeper commitment lies in collaborative projects with institutions like the Anusandhan National Research Foundation (ANRF). We’ve seen similar partnerships announced with the US and UK, but the scale and ambition in India feel different.
The focus on scientific breakthroughs is particularly telling. India is already the fourth largest adopter of AlphaFold, Google DeepMind’s protein-folding AI, with over 180,000 researchers actively using the tool. Now, they’re gaining access to even more advanced models – AlphaGenome, AI Co-scientist, and Earth AI – alongside dedicated engineering support and mentorship. This isn’t about handing over finished products; it’s about fostering a local ecosystem of AI innovation. Consider the implications: a country with a massive scientific workforce, grappling with unique agricultural and environmental problems, now has access to cutting-edge tools to address them. That’s a force multiplier.
But the impact extends far beyond research labs. Google’s partnership with Atal Tinkering Labs, serving over 10,000 schools and 11 million students, is a calculated move to embed AI literacy into the next generation. The integration of Gemini into teacher workflows and the development of a curriculum-aligned AI assistant aren’t about replacing educators; they’re about augmenting their capabilities. Early data from Fab AI shows students aren’t simply seeking answers with Gemini, but actively using it to deepen their understanding – a crucial distinction often lost in the hype around AI in education. The transformation of two million textbooks into interactive digital journeys via QR codes linked to custom Gemini models is a particularly clever application, addressing the persistent issue of access to quality educational resources.
This piece references the deepmind.google report.
This isn’t happening in a vacuum. A recent Ipsos survey revealed that learning is the top motivation for AI usage globally, and India leads the world in daily Gemini usage by students. This demonstrates a genuine appetite for AI-powered education, but also highlights a potential dependency. The success of these initiatives hinges on ensuring equitable access and avoiding the creation of a digital divide, where only privileged students benefit from these advanced tools. Furthermore, the commitment to incorporating India’s linguistic diversity into AI development, with a $2 million contribution to the Indic Language Technologies Research Hub at IIT Bombay, is a critical step towards building AI that truly serves all citizens. Ignoring local languages and cultural nuances is a recipe for irrelevance, and Google appears to be taking that lesson to heart.
The energy and agriculture sectors are also seeing significant investment. Collaborations with Indian startups and organizations like the Council on Energy, Environment and Water (CEEW) are leveraging Google’s Agri AI models to enhance agricultural resilience and crop productivity. The integration of WeatherNext AI models into India’s electricity grid, showing an 8% accuracy improvement in wind generation forecasts, is a concrete example of AI addressing a critical national priority – achieving 500 GW of renewable energy capacity by 2030. These aren’t theoretical exercises; they’re practical applications with measurable economic and environmental benefits.
However, the narrative of seamless collaboration needs a dose of reality. While Google touts its partnerships, the underlying power dynamic remains. Access to these advanced AI models is still controlled by a single company, and the data generated through these collaborations will inevitably flow back to Google. The question isn’t whether these partnerships are beneficial, but whether they are truly equitable. Will India be able to develop its own independent AI capabilities, or will it remain reliant on Western technology?
Looking ahead, watch closely for the development of India’s own large language models (LLMs). The groundwork is being laid now, with investments in linguistic diversity and AI literacy. But the real test will be whether India can leverage its unique data assets and talent pool to create LLMs that are not simply copies of Western models, but genuinely innovative solutions tailored to its specific needs. By 2028, we’ll see if India has moved beyond being a major user of AI to becoming a major creator of it – and whether that creation is truly independent.
