Artificial Intelligence & Mined Commodities

Mining may seem a world apart from the glamorous and modern artificial intelligence (AI) sector. In reality, mining and AI have a closer relationship than it might initially appear in a changing global economy. AI helps the mining sector become more efficient and productive, while natural resources help power the data centres that enable AI. This mutual dependence may be helpful on both sides.

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Generative AI can add intelligence to any data, which can then be organised and analysed to inform decision making. The mining sector generates vast amounts of data and there are multiple uses for AI in mining. In a recent report, consultant McKinsey said: "Nearly every modern plant, mine, or farm has years of data in sensor historians, as well as databases for failure modes and effects analysis, engineering reports, work orders, and maintenance logs detailing daily operations. Resource exploration and extraction comes with terabytes of electromagnetic and seismic measurements… This preponderance of structured and unstructured data is ripe for exploration and analysis via gen AI".¹

How does generative AI enhance the mining industry?

There are a number of ways AI can help advanced mining techniques. It can provide a processing boost, helping companies extract more from their mines, and with greater efficiency. It can help drilling and exploration to become more precise and targeted, contributing to the development of the mining industry.

It can also help with the maintenance of expensive machinery and equipment. A growing range of sensor and data networks - the 'internet of things' - can detect small problems, allowing them to be fixed quickly and to improve the lifespan of equipment². Digitising supply chains can help introduce early warning systems for problems, which can help minimise supply disruptions. This also contributes to the overall productivity of mines.

The gains for mining companies could be significant. McKinsey estimates that up to additional $390 billion to $550 billion of value could be created across the agricultural, chemical, energy, and materials sectors as they take innovative approaches to the adoption of AI.

Why AI needs investment in natural resources to thrive

There is also a symbiosis between mining and AI because natural resources are required to help power AI. AI is energy-intensive. It requires huge processing power to digest and analyse data. That data needs to be stored and interrogated to create AI insights. This happens in data centres across the world.

The International Energy Agency (IEA) reports that the world's data centres are using ever more electricity. In 2022, burned through 460 terawatt hours of electricity, and the IEA expects this to double in the next four years³. In Ireland, data centre electricity usage has risen by 400% since 2015 and accounted for around one-fifth of all electricity used in the country in 2022⁴. A recent study warned that the AI industry could consume as much energy as a country the size of the Netherlands by 2027⁴.

Investment in natural resources is needed to meet these additional energy requirements. There is also a requirement for mined materials in the building of data centres: they require lithium and cobalt, for example, to make components such as processors and batteries². Also, there is pressure on the AI industry to improve its energy footprint, which is also helping drive demand for renewable energy options. We have explored the relationship between the mining sector and the energy transitionin previous articles.

In this way, mining and AI may seem like very different industries, but, in reality, are mutually dependent. Each helps the other to become more efficient and productive. In the BlackRock World Mining Trust, we are looking at where AI solutions are helping improve productivity and where mining companies may be beneficiaries.

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