The value of the investment can go up and down, but investors might not receive the amount invested.

Any data can be made intelligent with generative AI, which, in turn, can be organised and analysed to support decision making. This is because the mining sector generates lots of data, and there are many applications of AI in mining. “Almost every modern plant, mine, or farm has years of data residing in sensor historians, and databases for failure modes and effects analysis, engineering reports, work orders and maintenance logs that describe daily operations,” consultant McKinsey said in a recent report. Simply put, for resource exploration and extraction, countless terabytes of electromagnetic and seismic measurements are taken … This dominance of structured and unstructured data is ripe for exploration and analysis by gen AI.”

In what ways does generative AI add value to the mining sector?

AI can assist advanced mining techniques in several ways. It can give a processing boost, with companies extracting more, and more efficiently, from their mines. And it can increase precision and focus for drilling and exploration, which helps the mining industry grow. It may likewise assist in maintaining costly machinery and equipment. A much wider spectrum of sensor and data networks – the ‘internet of things’ – have emerged that can pick up on small issues and solve them before they escalate, and extend equipment life. It is also through digitising supply chains that early warning systems can be introduced for when problems arise, which in turn can curb supply disruptions. It also helps produce more products overall in the mines.

The prospectus gains could be notable for the mineral companies. McKinsey calculates as much as $390 billion to $550 billion of additional value that could be generated throughout the agricultural, chemical, energy, and materials industries as they pursue creative strategies to AI implementation.

The few reasons AI has to invest in natural resources

Mining and AI are also symbiotic because you need natural resources to help power AI. AI is energy-intensive. Digesting and analysing data takes a huge amount of processing power. It is then stored and interrogated to create AI insights. This occurs in data centres all around the planet.

According to the International Energy Agency (IEA), data centres around the world are consuming more and more electricity. Burned through 460 terawatt hours of electricity in 2022, and the IEA expects consumption to double within four years. In Ireland, data centre electricity consumption has increased by 400% since 2015 and accounted for approximately one-fifth of all electricity consumed in the country in 2022. The AI industry could use as much energy as a country the size of the Netherlands by 2027, a recent study warned.

These extra energy needs will surely require investments in natural resources. Mined materials are also needed for the construction of data centres: they demand lithium and cobalt, among others, to manufacture components like processor chips and batteries. There is also pressure on the AI industry to lower its energy footprint, which is also helping to drive demand for renewable energy options. We have previously examined the link between the mining industry and the energy transition in previous articles.

That’s what makes mining and AI feel like far-flung industries, yet, in reality, they are interdependent. Each one also helps the other become more effective and efficient. In the BlackRock World Mining Trust, we’re considering the areas that will benefit from AI solutions and where mining companies, for that matter, could benefit.