New AI methods attract capital to mining sector
Here is an unavoidable truth. Resource extraction is hard physical work. Even in today’s tech-happy world, there’s no app for that. And perhaps this is the very reason modern investors have wandered away from mining—whether or not it’s to their benefit. New AI methods may change that.
Just like society, many investors today are overlooking the connection between the products we use and the source of the materials to make them. And yet it’s still true that “if you cannot grow it, you have to mine it.” Comically, we see generous valuations for end products but outright hostility toward the companies providing the basic materials to create the products. New flashy sectors like digital currencies, online commerce and cannabis are grabbing the spotlight while the basic source of materials for many everyday needs falls into the shadows.
Yes, some of this shunning may be deserved. The mining industry for the most part is still trying to use a classical economics appeal to attract investors. They trot out supply and demand fundamentals, and costs of production, hoping the market will make rational investment choices based on the facts. But as much as investors claim to be rational, there is a significant chunk of sentiment associated with attracting capital to any sector.
It will take more than just facts to win back investors. Some may hold a negative impression or position on the sector, one that has been reinforced with poor performance in recent years. And so funding for mining exploration is declining and, not surprisingly, so are discoveries.
But here is another unavoidable truth. Demand for resource materials increases alongside the development of society.
The next generation of ideas
The industry needs to attract capital with new ideas to streamline the process of value creation. To illustrate, let’s look at GoldSpot Discoveries Corp. (TSXV: SPOT). GoldSpot is a technology company striving to be a disruptor in the industry of mining exploration and investment. Its proprietary technology uses AI and machine learning to interpret underused data and to spot sites with the potential to host a mineral deposit.
Mining a mountain of data
The simple idea behind the big-data idea is to exploit the abundance of data collected in the world’s mining camps. In the past, there was not enough time, talent and skill to evaluate it all, even as still more data was being created. GoldSpot uses AI and machine learning and other tools to organize and analyze the data. The geologist spends less time collecting the data and more time interpreting the results—potentially making new discoveries.
GoldSpot is confident in its abilities to use machine learning to identify good exploration projects and the company wants to participate in their development and success through investments, royalties and consulting work. Their list of clients already includes significant names from the industry: Hochschild Mining, McEwen Mining, Sprott Mining and Yamana Gold.
Recently GoldSpot announced that Gran Colombia Gold Corp. used their methods at their Segovia project. The preliminary targets were confirmed through drill results at near mine and regional targets. This truly is a new idea: a client company that gives confirmation and relates success to the service provider. In the mining sector, new technology often faces challenges in development, promotion and acceptance. The sector does not change its methods quickly.
Fast on the heels of this success came the announcement of a $15 million strategic investment by Eric Sprott and plans for a 70,000 m drilling program on the Segovia project to further test these targets. This too validates GoldSpot’s AI and machine learning methods for doing exploration differently.
Now GoldSpot is working on another significant opportunity to showcase their method and further develop their tools. Through a deal with Metal Earth project and Laurentian University, GoldSpot will sort through this significant and large dataset to identify the characteristics of metal endowment in Canada’s mining camps.
The AI effect on the market
Impressively, quantitative analysis of the markets is driving up to 65% of returns during the past 20 years. And more investment funds are using this method for stock selection. On the other hand, these methods are also partially responsible for the drop in investment in the mining sector, particularly in exploration. And yet GoldSpot is using these same methods to analyze exploration results and market data to identify the best targets for investing in this opportunity gap.
For the tech-minded investor or mining company, AI and machine learning methods are revealing opportunities that can change minds and grow investments for exploration.