Artificial intelligence investments reduce risks to critical mineral supply
Joaquin Vespignani1,2, Russell Smyth3
1Tasmanian School of Business and Economics, University of Tasmania, Sandy Bay, TAS, Australia. Joaquin.Vespignani@utas.edu.au.
Nature Communications
|August 24, 2024
Summary
Critical mineral projects face "back-ended risk premium," increasing capital costs and potentially hindering investment. Artificial intelligence (AI) can mitigate this risk, supporting the energy transition.
Area of Science:
- Earth science
- Economics
- Financial risk analysis
Background:
- Critical mineral projects, essential for the energy transition, face significant financial risks.
- Unaddressed technical and non-technical barriers in projects (e.g., lithium, cobalt) create unique investment challenges.
Purpose of the Study:
- To present an economic theory of critical minerals incorporating earth science insights on project development risk.
- To introduce and define the
- back-ended risk premium
- for critical mineral projects.
- To analyze the impact of this premium on investment and the potential role of artificial intelligence (AI).
Main Methods:
- Economic theory development.
- Analysis of financial risk in project development.
- Assessment of the impact of technical and non-technical barriers on investment.
Main Results:
- A novel economic theory of critical minerals is proposed.
- The "back-ended risk premium" is identified as a key factor increasing the cost of capital for critical mineral projects.
- This premium may reduce investment and productivity gains from AI in mining.
Conclusions:
- Government investment in AI mining technologies and research is crucial.
- AI advancements can potentially reduce the "back-ended risk premium" by shortening project durations and lowering investment risk.
- Strategic investment in AI is essential to lower energy transition costs.
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