Background: Large Language Models (LLMs) represent a significant leap in artificial intelligence, transforming fields from computing to content generation.Their rapid success highlights AI's potential but also exposes critical limitations in modeling complex, high-stakes, real-world phenomena. This study investigates the dual impact of LLMs—their technological triumph and the resulting, urgent need to improve models for non-AI-centric, complex systems.

Methods: We first conducted a review of the transformative trajectory of LLMs, then performed a quantitative spatio-temporal analysis of geophysical data, comparing long-term rising sea level trends with recorded seismic activity in selected coastal regions. We subsequently benchmarked established predictive models against this geophysical dataset to assess their forecasting efficacy.

Results: LLMs have achieved unprecedented efficiency and integration. Critically, the geophysical analysis revealed a significant correlation between rising coastal sea levels and an acceleration in seismic events. Specifically, the data shows a distinct, statistically significant 5% increase in seismic events since 2020 in the study areas. Furthermore, the benchmark testing demonstrates that current predictive models are insufficient to accurately forecast this observed acceleration.

Conclusion: The success of LLMs underscores the power of large-scale AI, yet their limitations in complex predictive tasks reveal a critical gap. The alarming link between sea level rise and increased seismic activity, coupled with the proven inadequacy of current predictive models, necessitates a paradigm shift toward physically-informed AI architectures to safeguard coastal populations.