Recent studies published in Physical Review E suggest that the learning processes in artificial intelligence (AI) models, such as ChatGPT, Claude, and Gemini, can be understood as physical phenomena akin to water turning into vapor. Physicists John Hopfield and Geoffrey Hinton, Nobel laureates for their 1980s work on neural networks as systems governed by thermodynamics, laid the groundwork for this idea. Two new papers, one by Francesco Mori and Francesca Mignacco from the University of Oxford and Princeton University focusing on the "dropout" technique, and another by Abdulkadir Canatar and SueYeon Chung from the Flatiron Institute and New York University examining "tolerance," demonstrate that these AI engineering methods are rooted in physical principles. Both studies utilized a teacher-student framework to illustrate how AI networks undergo "phase transitions" during learning, moving from an unspecialized state to a specialized one, mirroring physical phase changes.