Google DeepMind’s achievement of a Nobel Prize in Chemistry for AlphaFold marks a turning point in the history of artificial intelligence and scientific discovery. The award, shared by Demis Hassabis and John Jumper, recognizes the transformative impact of AlphaFold—a deep learning system that accurately predicts the 3D structures of proteins from their amino acid sequences. This breakthrough, long considered one of the most complex challenges in biology, has accelerated research in drug development, disease understanding, and molecular biology. Hassabis, who co-founded DeepMind in 2010 with a vision to build a world-class scientific AI lab within industry, has consistently pushed the boundaries of what AI can achieve. His early ambitions were rooted in applying the scientific method to AI development, ensuring ethical progress and long-term safety. The company’s acquisition by Google in 2014 came with a commitment to responsible AI, including the creation of an internal ethics board. Since AlphaFold’s debut in 2018, DeepMind has expanded its focus to other high-impact scientific domains. The firm is now applying its AI expertise to problems like weather forecasting, nuclear fusion, and materials science. One such project, AlphaGenome, launched in June, aims to decode the function of non-coding regions of the human genome—areas previously poorly understood but critical to gene regulation and disease. Another, GNoME, predicted over 400,000 new materials in 2023, showcasing AI’s potential to design substances with specific properties like superconductivity or magnetism. These efforts reflect a broader strategy: targeting “root node” problems—those whose solutions unlock vast downstream applications. However, replicating AlphaFold’s success is far from guaranteed. Unlike protein folding, which had a well-defined dataset and evaluation metric, many scientific challenges lack clear benchmarks or sufficient data, making AI training more difficult. The rise of large language models (LLMs) after ChatGPT’s 2022 release has fundamentally altered the AI landscape. What was once a research-driven pursuit has become a race for commercialization. DeepMind now releases new versions of its Gemini LLMs almost weekly, balancing scientific ambition with product development. This shift has created internal tension, with some former employees expressing concern over a more commercialized culture and reduced focus on long-term, high-risk research. Despite these challenges, DeepMind maintains a strong commitment to responsible AI. A dedicated committee assesses safety and societal impact at every stage of development, and external experts are consulted to anticipate risks—from bioweapons to algorithmic bias. The firm also runs an “impact accelerator” to identify areas where AI can deliver meaningful societal benefit. DeepMind is no longer alone in this mission. Competitors like OpenAI and Mistral have launched dedicated scientific AI teams, signaling a broader industry shift toward using AI for fundamental discovery. Yet DeepMind’s unique combination of deep scientific rigor, interdisciplinary talent, and long-term vision gives it a distinct advantage. As the race to solve humanity’s greatest challenges intensifies, the question remains: can DeepMind replicate AlphaFold’s success in other fields? The answer may depend not just on technology, but on the company’s ability to stay true to its original mission—using AI not just to build smarter systems, but to make the world a better place.