40 regulations, environmental impact—should be quantified alongside economic metrics in portfolio-level analysis. 6.2. Knowledge and Education The arrival of large language models has accelerated a long-overdue transformation in how domain knowledge is created, accessed, and taught. Of concern is the declining enrollment in economic geology and mining engineering in Western countries, the risk of losing century-old knowledge stored in aging reports, and the absence of integrated ‘Mineral Exploration’ curricula that combine geoscience with decision science and data science. Priority recommendations include digitizing and open-sourcing national geological databases, creating web-accessible platforms for historical exploration data, and redesigning curricula around problemsolving rather than discipline-only training. Building a shared language between geoscientists and data scientists is identified as essential. 7. SUMMARY The exploration industry’s declining discovery rate is primarily a methodological problem, not a resource scarcity problem. The proposed fix—Popper-Bayes exploration enabled by AI—replaces singlemodel determinism with multi-hypothesis uncertainty quantification, and replaces ad hoc drilling with sequentially optimal data acquisition. Several components of this framework already exist in research-grade implementations. The bottleneck is integration: assembling them into a usable platform, combined with cultural change in the industry, new financial instruments, and updated regulatory standards. This change will not happen overnight, and that both the industrial and academic communities must move together for it to take hold. REFERENCES Caers, J., 2025. The future of AI in critical mineral exploration. arXiv preprint arXiv:2512.02879.
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