TED Talks Daily - The emerging science of finding critical metals | Mfikeyi Makayi

Episode Date: September 24, 2025

Your smartphone, computer and electric car all depend on one thing — critical minerals buried deep underground. But there’s a catch: the mining industry has gotten dramatically worse at discovering new deposits just when we need them most, says mining innovator Mfikeyi Makayi. She introduces new AI-powered technology that could fix this problem by predicting mineral locations 10,000 times faster than conventional methods and transforming an industry essential to a sustainable future. Interested in learning more about upcoming TED events? Follow these links:TEDNext: ted.com/futureyouTEDSports: ted.com/sportsTEDAI Vienna: ted.com/ai-viennaTEDAI San Francisco: ted.com/ai-sf Hosted on Acast. See acast.com/privacy for more information.

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On one of my first visits to Vancouver, I spent the morning biking around Stanley Park. Just me, the trees, the seawall, and the quiet. I grabbed lunch by the harbor and thought next time I want to come back here with my people. When you're traveling with family or friends, you want more than just a place to sleep. You want a kitchen for

big pancake breakfasts, a living room where everyone can pile in and play games and laugh about the day, and space to stay up late without whispering in the dark. That's why for our next trip, I'm looking at Airbnb. The homes feel personal and thoughtful and with guest favorites,

the most loved homes across Canada. It's easy to find day that's already made someone else's trip special. Now I just need to decide, is it Tefino for beachwalks and seafood dinners or a cozy cabin near Whistler with a view of the mountains? You're listening to TED Talks Daily, where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hugh. Electric vehicles are exploding in popularity and use all over the world, and yet all of these EVs need rare earth minerals in order to work, and those minerals need to be mined, an industry which is not exactly known for its sustainable

practices. In her talk, mining innovator Mfakeyi-Makai shares how her team is looking to build the sustainable mine of the future, using radical new AI-aided technology that maximizes resource recovery while minimizing environmental impact. I was born and raised in Zambia, a country known for its rich copper mining history. Alignment of the stars meant that by birth and by science, I became a miner. Everything we build and use was either grown or mined.

From the walls to the windows, the tables and the chairs, your phones, your computers, the stage, my copper earrings, and maybe. your jury. So today, when we talk about building a circular economy, we mean we need to electrify everything. Our economies will have cars and trucks, robots, drones and aircraft powered by batteries. Our children will need computers in all schools with equal access, and will have data centers full of advanced chips to bring us AI, all sourced by abundant sources of renewable energy. The raw materials will need,

will be recyclable so we can become clean and circular. So that means a lot more lithium, copper, cobalt, nickel, and others. So we need to build more than 400 new mines by 2040 for us to become circular. But before you can build a mine, you have to find the raw materials. The thing is, today's mining industry leaders are doing too little to advance our qualities of life. In other industries that rely on discovery for growth, like pharmaceuticals and technology, for every dollar they return to shareholders,

they spend about a dollar in R&D. In mining, however, for every dollar return to shareholders, less than a penny is spent in exploration. With such underinvestment, it shouldn't surprise you that the technology used in exploration and mining has barely advanced. In fact, we've gotten 10 times worse in the last 30 years

at making ore body discoveries, but there's good news. The vast majority of ore deposits are still out there waiting to be found. They're just harder to find. Of all the past minds we know of, they were easy because they were poking out of the surface and they were near the surface. So we need to look deeper. Controversially, we've been taught that these materials will run out.

We don't lack ore body. deposits. We lack information of where they lie. So if you had a crystal ball, you just look into it and start digging out the rocks that are the best and generate the least waste. But we don't have a crystal ball. So the thing that we should do is make predictions of where these materials lie. My colleagues and I at Cobold are doing what the industry has neglected to do. We aim to predict everything, quantify what we don't know, and collect. information efficiently. So we're all going to try that right now.

I'd want you to predict a thousand meters below your feet what the concentration of copper is right where you're sitting. I want you to predict how hard it is, how fractured it is, what's its density. We aim to predict all these things

and more. We're developing machine learning technologies that helps us predict all of this and rigorously quantify our uncertainties these predictions. So what does this look like in practice? When we're exploring for mines, we often fly aircraft, thousands of kilometers across the Earth, to try collect information, such as the Earth's magnetism, its gravitational field, that tells

us something about the rocks beneath. But there's a problem. For everything that we're looking at, there are going to be an infinite number of possibilities, and that's because we're building three-dimensional models to fit two-dimensional data. So if a body was smaller and closer to the surface or larger and further away, the measurement would be the same. The incumbent industry deals with this problem by ignoring it. They pick one possible answer and act like the other ones don't exist.

And as a result, we design suboptimal mines, make suboptimal decisions, often mining and necessary material. We've invented a different way. We collect all the possibilities consistent with data measured, and we do this by simulating the the physical response of each of the arrangement of rocks. We do this 10,000 times faster by training an AI to learn the relevant physics of the rock beneath in the time it takes the conventional method to test one.

That means we collect better data, we make better predictions of where to look next. So if you had a rock body and a rock body that's denser than material around it, you might draw through the middle of it. But if you have all the hundreds of thousands of possible solutions, The best thing you can do is to collect data where you're the most uncertain and rigorously eliminate as many possibilities as possible. This enables us to maximize the information we get for every dollar we spend.

And we do this repeatedly so we can quantify our uncertainties. Even after we've made an all-body discovery, we still have to contend with this uncertainty. We have to define the size and shape of this old body. Let me illustrate how difficult this is. So now, 1,000 meters below your feet, you drilled, you sampled the rock, and you determined that it has 5% copper. So now you know you've got your data point and your observation. Now I ask you to make a prediction of the concentration of copper of the person sitting

next to you. What would your prediction be, and how confident would you be in your prediction? What about across the room? Think of any person across this room. and try to predict 1,000 meters below them. What about in the next building or the next city? This is the vast challenge that we face. We've only sampled a tiny fraction of rock collected

several football fields apart from each other for which we're trying to make predictions of all the rock properties in between. This technology has helped us move fast in Zambia, where I come from, to design and develop a mind based on our predictions for which we've only sampled a tiny fraction of rock. of rock. Once again, there are many possibilities, all consistent with the data,

some with a lot more metal, some with less, and the difference is a measure of uncertainties. This enables us to know where we should collect information next, where we should drill the next hole, and when we can stop drilling and actually start building a mine. To build the mine of the future, we continue to contend with this uncertainty. The industry designs an entire mine based on a single model. developing cobal mine, a mine design optimization tool that looks at the many possible mine designs against the many possible odd-body geometries that we talked about earlier. This enables the best decisions about how much ore we're going to mine, how much waste we're

going to produce, how much water will use, the cash flows, and so on. This enables the best mine planning decisions about where to put permanent infrastructure like a shaft, where the traffic and the tunnels will be placed so we can make efficient decisions, and also how we can maximize the ore and the metal we get and minimize the waste. This technology will move into mine operations to help guide day-to-day decisions for efficiencies. Better predictions don't just mean profitability. It means a safer mind, knowing where the rocks are weaker. It means an environmentally sustainable mind so we can lessen our impact on the environment. And it also means a resilient mind with cash flows to support local communities and businesses

through different commodity pricing cycles. Our Mingomba project in Zambia will be the mine of the future. It's being designed and developed by amazing talent from around the world, including Zambians and Africans like myself. We face the reality that our need for these materials will continue to grow because our lifestyles are going to advance and they're going to demand for it. So the mining industry must ensure they transform

so we can become responsible miners and build better mines with better minds, better technology. Asante, and thank you. That was Mfakeyi-Makai speaking at the TED Countdown Summit in Nairobi, Kenya in 2025. If you're curious about TED's curation, find out more at TED.com slash curation guidelines. And that's it for today. Ted Talks Daily is part of the TED Audio Collective. This talk was fact-checked by the TED Research team and predict. produced and edited by our team,

Martha Estefanos, Oliver Friedman, Brian Green, Lucy Little, and Tonica, Sung Marnivang. This episode was mixed by Christopher Faisi Bogan.

Additional support from Emma Tobner and Daniela Balerazo. I'm Elise Hugh. I'll be back tomorrow with a fresh idea for your feed. Thanks for listening. Thank you for your patience.

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