Not too long ago, the AI buzz was all about chatbots that could hold a decent conversation and tools that could work up emails or poems faster than you could say “generative.” And back then, those really were the wow moments. Just recently, the internet had been caught up in the 'Ghiblification' frenzy, ever since OpenAI opened Pandora’s box when it comes to image generation—specifically, the ability not just to create photos from text-based prompts, but to upload and transform them into a specific style with ease. Within just an hour following the release of its free Ghibli-style image generation feature, OpenAI's ChatGPT added a million plus users. To put some context, at the time of its launch, it took ChatGPT 5 days to reach its first one million users on its platform.
We have already stepped up into a much more profound phase of AI transformation. Across every frontier of science, AI is driving a revolution. From translating brain scans into text, to predicting the 3D shape of proteins, AI is not only reshaping how research is done, it is shaping the very foundations of how we understand science, design drugs, discover materials, solve climate equations, and reimagine industries.
Imagine this. A researcher is trying to understand a rare protein structure to design a treatment for a disease that has baffled scientists for decades. Today AI can not only search the literature, but can simulate molecular interactions at an atomic level, in minutes. With its ability to predict protein structures, Google’s AlphaFold has completely transformed how we discover new drugs. So far, AlphaFold has predicted over 200 million protein structures—which covers nearly every catalogued protein known to science. AlphaFold’s Protein Structure Database already has over two million users across 190 countries . As Kent Walker, President of Global Affairs at Google, put it to The New York Times: “It’s Google Maps for biology.”
Scientists have since used updated versions of AlphaFold to work on pharmaceuticals including a vaccine against malaria, second only to tuberculosis in terms of its devastating global impact.
The next phase of AI disruption will not unfold in our phones or productivity tools. It is playing out in laboratories, observatories, and research institutes across the world, where AI is quietly but decisively reshaping the very structure of scientific inquiry and industrial capability.
Last year, the Nobel Committees in Stockholm announced that the prizes in Physics and Chemistry were awarded to work related to artificial intelligence. The Nobel Prize in 2024 acknowledged a development that would have been inconceivable a decade ago. John J Hopfield and Geoffrey E Hinton were recognized for their pioneering contributions to neural networks—work that has become essential in analysing high-dimensional datasets in cosmology and quantum mechanics. These networks are no longer abstract constructs; they are instruments of observation. AI has become a critical tool in gravitational wave detection, particle physics simulations, and even in refining the imagery of phenomena like black holes.
Not just in physics and biology, for centuries, mathematical discovery has relied on a combination of logic, intuition, and decades of meticulous efforts. Today, AI is accelerating this process, multifold. In 2021, a landmark collaboration between mathematicians and DeepMind’s AI system led to the discovery of previously unknown relationships in knot theory, a subfield of topology. The significance lies not just in the discovery, but in the methodology: AI was used not to compute solutions, but to propose conjectures—hypotheses that were later verified through traditional proof. Similarly, AI is altering the game in Nuclear Physics. At ITER and other experimental fusion facilities, machine learning models are being developed to predict and prevent plasma disruptions in real-time. Moreover, in reactor design and materials testing, AI simulations are replacing what would have taken decades of trial and error. This is especially critical as we stand at the brink of an energy transition.
This is a profound shift. We are now in an era where AI can assist in the generative process of theory itself, not merely its validation. In India, for example, satellite imagery combined with AI models is helping smallholder farmers make informed decisions about irrigation and soil health. The convergence of AI with botanical sciences has also accelerated how we identify and cultivate climate-resilient crop varieties. With climate volatility threatening traditional farming cycles, AI-powered systems are being deployed to predict pest outbreaks, model crop responses to weather patterns, and optimize planting strategies for resilience rather than yield alone.
From +AI to AI+
What does it mean when AI begins to participate in discovery, rather than just automate its margins? As European Commission President Ursula von der Leyen proclaimed at Davos 2024, “AI is already revolutionising healthcare… First movers will be rewarded, and the global race is already on.” That race is not merely geopolitical, it is deeply scientific not just in healthcare but in almost every possible sector imaginable. However, the common thread among all these breakthroughs is speed. The first movers in this phase will be those building AI into the very foundation of scientific innovation.
While the global North often dominates the AI narrative, India is quietly shaping its own distinctive AI story, one that is multilingual, and grounded in problem-solving for real-world complexities. Samsung R&D Institute India, Samsung’s largest R&D centre outside of Korea has been crucial in expanding the language support of Galaxy AI. The entire leadership and the team that created the Galaxy AI innovation resides in India.
The future is not something we enter—it is something we create
If we want to make the most of this defining moment in history, we must move away from a fragmented approach and embrace a more integrated, collaborative, and forward-thinking strategy, with the ability to take greater risk. We must double down on boosting our AI talent and at the same time attract, nurture and retain cutting edge top tier AI research talent to ensure AI innovations for the world to emerge from India. At the same time, we must move beyond the notion of creating an AI strategy in the context of challenging global AI landscape. India’s AI strategy should be truly Indian, trained on our datasets, cultures and problems with solutions built in India, for the world.
Disclaimer
The views, thoughts, and opinions expressed in this blog are solely those of the author and any content provided on this blog is for informational purposes only.
