Last October, Google DeepMind won bragging rights that its competitors in the field of AI could only dream of. A share of the 2024 Nobel Prize in Chemistry was awarded to Demis Hassabis, its CEO, and John Jumper, an on-staff scientist, for the creation of AlphaFold: an AI system that can predict the 3D structure of any protein. The award vindicated DeepMind’s decision to focus, at least in part, on solving tricky problems in the hard sciences. Tech companies often brag that future AIs will be able to cure all human diseases. But of the so-called “frontier” AI companies, DeepMind—which Hassabis founded in 2010 and Google parent Alphabet acquired in 2014—is the only one that focuses on building tools like AlphaFold today. The free-to-use AI system has already helped scientists model protein structures in hours rather than years—accelerating work on malaria vaccines, human longevity, and cancer research. To be sure, DeepMind is also competing intently in the field du jour of AI research: large language models. Its latest model, Gemini 2.5 Pro, currently tops a popular (though constantly-changing) crowdsourced leaderboard judging models’ intelligence. In an April interview with TIME, Hassabis said that he was working hard on leveraging this prowess to build a so-called “universal digital assistant”—essentially a supercharged version of Siri or Alexa. That tech won’t just pad Google’s bottom line, he said. It will also enable future AIs that can carry out their own scientific research, potentially unlocking more miracle cures. “I identify myself as a scientist first and foremost,” Hassabis said. “The whole reason I’m doing everything I’ve done in my life is in the pursuit of knowledge and trying to understand the world around us.”
