Using data science to solve exploration's search problem

2024 was a landmark year for the energy transition. With record-setting investments in climate infrastructure, we saw the price of renewables out-compete just about every electricity source worldwide, we saw advancements in industrial decarbonization (which we’ve featured prominently on this show), and we saw a breakout year for next generation energy storage just to name a few. While momentum is definitely on our side, with the electrification of everything, our industry will face new hurdles in the coming years, including unprecedented demand for critical minerals.  

From solar panels, to batteries, to EVs, critical minerals are needed to advance the energy transition. Specifically: copper, lithium, nickel, and cobalt, to name a few. According to our guest today, the amount of newly discovered minerals needed to produce the anticipated number of EVs by midcentury will cost more than $10 trillion dollars. 

We’ve been extracting minerals for centuries, so you might assume we have it figured out. However, not only are there some serious ethical and environmental concerns, but mineral exploration has, in some ways, actually gotten worse, slower, and more expensive over time. What if there was a way to make critical mineral exploration drastically more efficient? Our guest today, Kurt House, Co-Founder and CEO of KoBold Metals wants to show the world how scientific computing can turn that idea into a reality.  

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The $1 Billion Microsoft Climate Innovation Fund is investing in innovative technologies that have the potential for meaningful, measurable climate impact by 2030. To date, Microsoft has allocated more than $800M into a global portfolio of over 50 investments including sustainable solutions in energy, industrial, and natural systems. Visit https://www.microsoft.com/en-us/corporate-responsibility/ to learn more about Microsoft’s progress toward their impact commitments. 

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