generalization across geological settings, and validation methodologies. At the project scale, significant potential exists to extract additional information from geophysical data by applying AI directly to raw or minimally processed measurements, rather than relying primarily on derived or interpreted products as inputs. More broadly, extending AI-driven frameworks to additional exploration problems—such as survey design, adaptive data acquisition, and integrated economic decision-making—represents a promising avenue for further impact. Continued progress across these fronts will be critical to transitioning AI from promising pilot applications to a scalable, trusted component of modern exploration practice. ACKNOWLEDGEMENTS The author would like to thank the complete team at Terra AI, advisors and past collaborators at Stanford University, and our entire set of industry partners for providing insights and experiences inspiring this work. REFERENCES Caers, J. (2025). The future of AI in critical mineral exploration. arXiv. https://arxiv.org/pdf/2512.02879.pdf Mineral-X. (2025). Scientific research. Mineral-X. https://mineralx.stanford.edu/scientificresearch VRIFY. (2025). Mineral exploration tech report 2025. VRIFY. https://vrify.com/resources/mineral-exploration-tech-report Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., … Colton, S. (2012). A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1–43. https://doi.org/10.1109/TCIAIG.2012.2186810 Carranza, E. J. M., & Laborte, A. G. (2015). Data-driven predictive mapping of mineral prospectivity using machine learning methods. Ore Geology Reviews, 71, 804–818. https://doi.org/10.1016/j.oregeorev.2014.11.014 Cracknell, M. J., & Reading, A. M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms. Computers & Geosciences, 63, 22–33. https://doi.org/10.1016/j.cageo.2013.10.008 Frazier, P., Powell, W., & Dayanik, S. (2008). A knowledge-gradient policy for sequential information collection. SIAM Journal on Control and Optimization, 47(5), 2410–2439. https://doi.org/10.1137/070693424
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