A recent study published in the Journal of Cosmology and Astroparticle Physics demonstrates that artificial intelligence systems can significantly accelerate the search for new physics in cosmology, though the approach carries inherent risks of misinterpretation. Researchers led by Veena Krishnaraj of Princeton University and Adrian Bayer of the Flatiron Institute and Princeton University investigated transfer learning, a machine learning strategy that allows AI models to reuse knowledge from initial tasks to accelerate performance on subsequent, more complex tasks. Cosmologists rely on high-precision simulations to test theories that extend beyond the standard Lambda Cold Dark Matter model. These alternatives, which include massive neutrinos, modified gravity, and evolving dark energy, typically require enormous computational resources to simulate and analyze. The research team applied transfer learning by first pretraining neural networks on computationally inexpensive baseline simulations before fine-tuning them on more complex models incorporating potential new physics. This phased approach allows the AI to grasp fundamental cosmic structure formation before encountering advanced variations. The results indicate that this method dramatically improves computational efficiency. In several test cases, transfer learning reduced the required number of expensive simulations by more than a factor of ten. By providing the model with a preliminary understanding of standard cosmological parameters, researchers avoided the computational burden of training the AI from scratch on highly complex datasets. However, the study also identified a critical limitation known as negative transfer. When new physical phenomena produce observable effects that closely resemble existing model parameters, the AI tends to default to its pretraining knowledge rather than recognizing genuinely novel signals. The researchers observed this specifically in simulations involving massive neutrinos, where neutrino-induced effects closely mimic variations in the matter clustering parameter, sigma-8. These physical degeneracies cause the pretrained network to misattribute new data to established categories, initially hindering accurate detection. The team noted that this interference is not random but is systematically driven by underlying model similarities. The findings underscore both the potential and the constraints of applying foundation model architectures to fundamental physics. While pretraining enables rapid inference and substantial resource savings, it can also obstruct the discovery of unconventional physical phenomena if not properly calibrated. The researchers emphasize that future implementations must include mitigation strategies to address negative transfer as machine learning models transition from virtual simulations to real-world observational data. This work establishes a viable computational framework for upcoming cosmological surveys, which will generate unprecedented volumes of high-precision astronomical data. As observational capabilities expand, AI-driven analysis methods that balance efficiency with interpretive accuracy will become essential for identifying deviations from current cosmological standards.