The extent to which academics are likely to keep up with media attention, money and plaudits from the Nobel Prize committee is a question that irritates Julian Togelius, an associate professor of computer science at the University’s Tandon School of Engineering from New York, who works on AI. “Scientists generally follow a combination of paths that present the least resistance and get the most bang for their buck,” he says. And given the competitive nature of academia, where funding is increasingly scarce and directly linked to researchers’ employment prospects, it seems likely that the combination of a trendy topic that, as of this week, has the potential for high achievers to win a Nobel Prize might be too tempting to resist.

The risk is that this could hinder any new innovative thinking. “Extracting more fundamental data from nature and coming up with new theories that humans can understand are difficult things to do,” says Togelius. But this requires careful consideration. It is much more productive for researchers to run AI-enabled simulations that support existing theories and involve existing data, producing small advances in understanding rather than giant leaps. Togelius predicts that a new generation of scientists will eventually do just that, because it’s easier.

There is also the risk that overconfident computer scientists, who have helped advance the field of AI, will start to see work on AI being awarded Nobel Prizes in unrelated areas of science – in this case , physics and chemistry – and decide to follow in their footsteps, encroaching on the territory of others. “Computer scientists have a well-deserved reputation for poking their noses into areas they don’t know, injecting algorithms and calling it a breakthrough, for better and/or worse,” says Togelius, who admits to having already been tempted to add deep learning to another scientific field and “advance” it, before thinking about it further, because he does not know much about physics, biology or geology.

Hassabis is an example of using AI GOOD in order to advance science. He was a neuroscientist by training, earned a doctorate on the subject in 2009, and credited that experience to the advancement of AI through Google DeepMind. But even he recognized a shift in how the industry is improving efficiency. “Today, [AI] has become heavier on engineering,” he said during his Nobel Prize press conference. “We now have many techniques that we are improving purely on an algorithmic level, without any more reference to the brain.”

It could also impact what type of research is done – and who does it, their level of knowledge of the field, and the incentives for them to pursue it. Rather than researchers having devoted their lives to a specialty, we could see more research carried out by computer scientists, detached from the reality of what they observe.

But that risks taking a backseat to the celebrations for Hassabis, Jumper and the colleagues they both thanked for helping them win the Nobel Prize this week. “We are about to clean up the [AlphaFold3] code to publish it so that the academic community can use it freely,” he said earlier today. “Then we’ll continue to progress from there.”