The Nobel committees acknowledged the significant impact of artificial intelligence (AI) in this year's awards, honoring neural network pioneers in physics and developers of computational tools for protein study in chemistry. However, some researchers expressed dissatisfaction post-announcement. Physicists criticized the recognition of Geoffrey Hinton and John Hopfield, arguing that their work, although influential in machine learning, doesn't align with traditional physics. Jonathan Pritchard, an astrophysicist, remarked on social media that such advancements should not be classified as physics discoveries. Sabine Hossenfelder echoed these sentiments, emphasizing that this was a missed opportunity for physics to shine. Conversely, others viewed the interdisciplinary nature of Hopfield and Hinton's work as deserving of recognition. Theoretical physicist Matt Strassler noted that their research bridges physics, mathematics, computer science, and neuroscience.

Science writer Anil Ananthaswamy pointed out that while not pure theoretical physics, their techniques are grounded in physics concepts like energy. The relationship between physics and machine learning has evolved, with physics methods re-emerging to explain complex deep-learning systems, as highlighted by Lenka Zdeborová. Giorgio Parisi, a 2021 Nobel laureate, suggested that the physics Nobel should embrace broader scientific realms. Computer science appeared to further establish its Nobel significance when Demis Hassabis and John Jumper, creators of the AlphaFold AI system for protein-folding predictions, received part of the chemistry Nobel. David Baker, who contributed to protein design without machine learning, shared the award. This prize honors AI's disruptive potential but also recognizes ongoing advancements in structural biology, according to David Jones from University College London. Jumper emphasized that AlphaFold's success stemmed from a thorough research process, integrating existing knowledge about proteins rather than simply applying AI. The availability of extensive protein structure data was crucial to AlphaFold's development, highlighting the collaborative nature of scientific progress.