- Future AI growth will increase demand for Australian minerals and energy
- Australian mining a major user of AI to boost productivity and reduce costs
- AI's success in Australia should be based on technology-neutral and evidence-based regulation.
Australian mining is both an advanced user of artificial intelligence and crucial to its growth and ability to reshape our economy by providing the energy and critical minerals required for data centres and other AI infrastructure.
The potential benefits of efficient AI adoption in Australia are large; the Minerals Council of Australia estimates in its submission to the Joint Select Committee on Artificial Intelligence that AI's impact on total factor productivity could increase Australia's GDP by at least $45 billion by 2030.
As demand for AI grows, so too will demand for the minerals and energy required to build and power it.
AI systems depend on data centres, advanced electronics, communications networks, electricity generation and transmission infrastructure.
Expanding this infrastructure will drive significant additional demand for a range of critical minerals of which Australia is already a reliable producer, including:
- Iron ore and metallurgical coal for construction and energy
- Copper for data centres, grids, generation, storage and communications
- Uranium as a potential input to the reliable electricity systems supporting AI-related loads.
The opportunity is substantial: the International Energy Agency projects that data centre electricity consumption will more than double from 2024 to about 945 terawatt hours by 2030, exceeding Japan's total electricity consumption.
Australia's major miners and smaller producers are well placed to benefit. For example, Rio Tinto estimates that it produces up to 70 per cent of the raw materials required for each gigawatt of data centre capacity.
Australian mining is doing more than supplying the foundations of AI: it's also helping to lead its adoption.
The submission outlines current uses of AI in the Australian minerals industry including:
- BHP's Olympic Dam operation uses AI and machine learning models to monitor the condition of haul trucks, crushers, pumps and other critical plant equipment. Sensors collect real-time data on vibration, temperature, and operational parameters, which AI algorithms analyse to predict potential equipment failures before they happen
- CSIRO, in partnership with Rio Tinto, has developed advanced geospatial AI techniques to analyse satellite imagery and geological datasets to identify promising mineral deposits across Western Australia. By integrating AI with remote sensing data, the project has enhanced the accuracy and speed of orebody mapping, reducing exploration costs and environmental impacts.
These practical examples show how Australian mining companies are already using AI to solve real-world challenges in complex, safety-critical environments.
The submission includes recommendations to encourage the use of AI while providing appropriate safeguards such as:
- A technology-neutral and evidence-based approach to AI regulation
- Any additional regulation to be targeted at new identifiable harms while avoiding unnecessary barriers to innovation, investment and productivity growth
- Accelerating opportunities to use AI to improve regulatory performance including AI-enabled approvals systems which have the potential to improve the timeliness and quality of regulatory decision-making while maintaining appropriate human oversight and accountability
- Developing a larger pool of technical and trade skills to support the continued development and adoption of AI across the economy.
