The 2024 Nobel Prizes in Physics and Chemistry have brought artificial intelligence (AI) to the center stage of science like never before. In a historic year for the Nobel Foundation, the prizes for both Chemistry and Physics have been essentially awarded for AI achievements.
Geoffrey Hinton and John Hopfield, pioneers in artificial intelligence, were jointly awarded the Nobel Prize in Physics for their work on machine learning and artificial neural networks. The Nobel Committee praised their contributions to modern AI, particularly the development of neural networks that mimic the human brain. These systems enable computers to learn from data, forming the bedrock of everything from facial recognition to autonomous vehicles.
On the chemistry side, half the spotlight was on Demis Hassabis and John Jumper from Google DeepMind. The rest went to David Baker from the University of Washington. Hassabis and Jumper’s work on AlphaFold, an AI system that accurately predicts protein structures, earned them half of the Nobel Prize in Chemistry. Their AI model has revolutionized biology, enabling scientists to predict the structure of proteins — an essential task for drug development — at a speed and scale that were previously unimaginable.
But while the achievements celebrated are groundbreaking, the laureates themselves raise questions about the future of AI — and humanity’s role in a world increasingly shaped by intelligent machines.
Titans in the Field
Geoffrey Hinton and John Hopfield are absolute legends in the field of AI science. Their work started decades ago in the 1980s. Back then, they developed artificial neural networks inspired by the structure of the human brain. Hopfield created a network that stores and retrieves information, while Hinton’s work took these concepts further by allowing AI to identify patterns in complex datasets. Their breakthroughs have become central to AI’s ability to solve problems, recognize images, and even generate new content.
“This year’s two Nobel Laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning,” said the academy in a statement.”Machine learning based on artificial neural networks is currently revolutionizing science, engineering and daily life.”
“The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties,” says Ellen Moons, Chair of the Nobel Committee for Physics.
Not the First Big AI Prize — But the Most Striking
There’s no denying the impact that AI is already having on science and the world. From climate modeling to medical image analysis and material design, AI is already delivering on its revolutionary promise. And these Nobel Prizes further cement that. However, this was not the first major scientific award for AI pioneers.
In 2018, The ACM A.M. Turing Award, often referred to as the “Nobel Prize in Computing,” was awarded to Geoffrey Hinton, Yoshua Bengio, and Yann LeCun. The Association for Computing Machinery (ACM), which grants the prize, made a slightly different selection of laureates but essentially praised the same awards.
It’s not clear why the Nobel and Turing Awards went to (partially) different people, but it could be that the Nobel committee was more focused on AI progress that helped physics specifically, whereas the Turing was more general AI. It could also be a sign of subjectivity from the selection committees.
It wouldn’t be the first time a Nobel prize stirs controversy for its selection of laureates (and probably not the last either). Hinton himself credited his former supervisor, the psychologist David Rumelhart, as the real pioneer of AI. It was Rumelhart who supervised Hinton’s work on neural networks and came up with the “backpropagation algorithm,” but the Nobel Prize is only awarded to living scientists.
Still, the chemistry prize is even more striking.
From Go and Chess to Proteins
It’s the first time an AI-powered scientific breakthrough was recognized with a Nobel prize, though probably not the last.
In biochemistry, AlphaFold promises to be transformative. For decades, predicting how proteins fold—a process essential for understanding their function—was considered one of biology’s greatest challenges. AlphaFold, first introduced by Hassabis and Jumper in 2021, solved this problem using deep learning. It quickly became a cornerstone of biological research, helping scientists understand everything from antibiotic resistance to how proteins degrade plastics.
Remarkably, AlphaFold followed AlphaZero (a chess-playing AI) and AlphaGo (a Go-playing AI); these game-playing AIs served as stepping stones for more significant goals like protein folding.
Their latest model, AlphaFold 3, can predict the structures of DNA, RNA, and key molecules which are essential to drug discovery. DeepMind has also released its results’ source code and database to scientists for free.
“I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people,” said Demis Hassabis. “AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery,” he added.
Quick Time
Yet the striking part here is timing. AlphaFold has barely been out for 3-4 years, which in Nobel time, is very recent. It’s not uncommon for Nobel Laureates to receive the prize for work done 20, 30, or even 40 years ago.
In addition to the sheer volume of outstanding scientific work which can create a backlog, the purpose of the Nobel prizes is to reward the researchers who “have conferred the greatest benefit to humankind”. And you often need time to confirm the impact of scientific work.
Despite the promise to revolutionize the pharma industry, it’s not yet clear just how much impact AlphaFold will have on the world.
Yet even this is not the most interesting story of this year’s Nobel Prizes.
There are several narratives around this year’s Nobels. The over-arching theme is that AI seems to have reached scientific maturity and its impact is confirmed in one way. There’s the discussion around how these achievements are not physics and chemistry per se, but rather tools that help in chemistry and physics. And, notably, there are warnings that the new laureates are giving.
The laureates cautioning against reckless use of AI
While much of the scientific community is firmly aboard the AI train, Hinton is part of a vocal minority of researchers warning against the potential threats of the technology.
Geoffrey Hinton, often called the “Godfather of AI,” resigned from Google in 2023, citing AI fears. Hinton expressed deep concerns about AI systems evolving beyond human control. In interviews following the Nobel announcement, Hinton reiterated his concern that AI could surpass human intelligence, leading to consequences that society is ill-prepared to handle.
“We have no experience of what it’s like to have things smarter than us,” Hinton said over the phone to the Nobel press conference, speaking from a hotel in California. “It’s going to be wonderful in many respects, in areas like healthcare,” Hinton said. “But we also have to worry about a number of possible bad consequences. Particularly the threat of these things getting out of control.”
It’s telling that when acknowledging his students (Hinton mentored some of the most impactful researchers in AI), he took a moment to take a jab at Sam Altman, the CEO of OpenAI, the company behind ChatGPT. Hinton has been a vocal critic of Altman, hinting that Altman is more interested in profits than AI safety.
“I’d also like to acknowledge my students. I was particularly fortunate to have many very clever students, much cleverer than me, who actually make things work. They’ve gone on to do many great things. I’m particularly proud of the fact that one of my students fired Sam Altman,” said Hinton.
John Hopfield, a professor emeritus at Princeton, has echoed Hinton in expressing AI-related concerns.
“One is accustomed to having technologies which are not singularly only good or only bad, but have capabilities in both directions,” he said.
“And as a physicist, I’m very unnerved by something which has no control, something which I don’t understand well enough so that I can understand what are the limits to which one could drive that technology.”
“That’s why I myself, and I think Geoffrey Hinton also, would strongly advocate understanding as an essential need of the field, which is going to develop some abilities that are beyond the abilities you can imagine at present.”
A Nobel shift and a double-edged sword
This year’s Nobel Prizes suggest a growing trend: AI is not only transforming industries but also academia’s highest honors. For a long time, the Nobel Prizes primarily celebrated advances in pure science—discoveries rooted in natural laws and physical phenomena. Now, the lines between science and technology are blurring, with AI researchers recognized for building tools that shape how we approach and solve scientific problems.
The physics and chemistry Nobel Prizes highlight AI’s staggering potential to solve some of humanity’s most intractable problems. Yet AI also poses significant threats, the likes of which our species is not used to facing. Warnings pioneers like Hinton and Hopfield serve as a chilling reminder that unchecked technological advancement can have unintended consequences. AI is a tool, but it is a tool that can surpass us in some ways.
The future of AI, much like the technologies it enables, will require careful stewardship.
