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Artificial intelligence investments reduce risks to critical mineral supply

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Abstract

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and nontechnical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium†. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself through shortening the duration of mining projects and the required rate of investment through reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

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  • Vespignani, Joaquin & Smyth, Russell, 2024. "Artificial intelligence investments reduce risks to critical mineral supply," Working Papers 2024-02, University of Tasmania, Tasmanian School of Business and Economics.
    • Handle: RePEc:tas:wpaper:25814770
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    Cited by:

    1. Saadaoui, Jamel & Smyth, Russell & Vespignani, Joaquin, 2025. "Ensuring the security of the clean energy transition: Examining the impact of geopolitical risk on the price of critical minerals," Energy Economics, Elsevier, vol. 142(C).
    2. Lee, Chien-Chiang & Zou, Jinyang & Chen, Pei-Fen, 2025. "The impact of artificial intelligence on the energy consumption of corporations: The role of human capital," Energy Economics, Elsevier, vol. 143(C).
    3. Kang, Wilson & Smyth, Russell & Vespignani, Joaquin, 2025. "The Macroeconomic Fragility of Critical Mineral Markets," Working Papers 2025-01, University of Tasmania, Tasmanian School of Business and Economics.
    4. Barson, Zynobia & Ahadzie, Richard Mawulawoe & Daugaard, Dan & Vespignani, Joaquin, 2025. "A Systematic Literature Review of Asset Pricing: Insights from AI and Big Data," Working Papers 2025-03, University of Tasmania, Tasmanian School of Business and Economics.
    5. Joaquin Vespignani & Russell Smyth & Jamel Saadaoui, 2025. "Strategic Stockpiling Reduces the Geopolitical Risk to the Supply Chain of Copper and Lithium," CAMA Working Papers 2025-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Wang, Yitian & Vespignani, Joaquin & Smyth, Russell, 2025. "Stockpiling or Recycling? Country-Specific Strategies for EV Battery Mineral Security," MPRA Paper 127186, University Library of Munich, Germany.
    7. Zhou, Xiaoyong & Li, Gaochao & Wang, Qunwei & Li, Yangganxuan & Zhou, Dequn, 2025. "Artificial intelligence, corporate information governance and ESG performance: Quasi-experimental evidence from China," International Review of Financial Analysis, Elsevier, vol. 102(C).
    8. Zhenghao Meng & Han Sun & Simeng Song & Yannan Ding & Jinhua Cheng & Chenxi Liu & Lu Chen, 2025. "A study on the impact of technological innovation on the sustainability of critical mineral supply from a multidimensional perspective: a case study of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(3), pages 493-511, September.

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    JEL classification:

    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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