# The Critical Technology in Finding Critical Materials  ## Episode metadata - Episode title: The Critical Technology in Finding Critical Materials - Show: The a16z Show - Owner / Host: Andreessen Horowitz - Guests: [Mvkay Makai](https://share.snipd.com/person/a73ec04c-002d-4db1-92ca-5f18b3f8fea7), [George Gilchrist](https://share.snipd.com/person/3e271fc4-7d95-4439-88dc-b3cc0673213e), [Tom Hunt](https://share.snipd.com/person/6fa707b9-526c-49f3-9094-983d3d740d1d) - Episode publish date: 2025-02-11 - Episode AI description: Tom Hunt, VP of Technology at KoBold Metals, focuses on AI in mineral exploration. Mvkay Makai and George Gilchrist bring their geoscience expertise to the table. They delve into the critical role of metals like lithium and copper in the energy transition. The discussion highlights advanced exploration techniques such as AI, hyperspectral imaging, and data analysis, transforming how we locate vital resources. They also explore the collaboration between geoscientists and data scientists to boost mining efficiency while addressing environmental concerns. - Duration: 42:38 - Episode URL: [Open in Snipd](https://share.snipd.com/episode/55d3f86e-c47c-4515-9f9e-955b11af9631) - Show URL: [Open in Snipd](https://share.snipd.com/show/e4874dcd-789e-493a-858b-a9ec77e81cad) - Export date: 2026-02-11T20:06:35 ## Snips ### [The Importance of Critical Materials](https://share.snipd.com/snip/9f890bb3-41f5-43cf-aec2-16b4bffe0aa9) 🎧 00:00 - 02:06 (02:05) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/c9c95a88-b70c-4f8f-94c8-8819c826d9b0" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Critical materials like copper and lithium are essential for modern technology and the green revolution. - The increasing demand for electric vehicles and data centers necessitates a significant increase in mining these metals. #### 💬 Quote > if you want to build the future, so many of the new technologies that we're talking about and dreaming about are going to require more metals. > — Connie Chan Connie Chan on the rising demand for metals in new technologies. #### 📚 Transcript **Connie Chan:** There is a phrase that's common in the mining world, which goes something like, if you can't grow it, you must mine it. That's so central to understand that so much of our physical world around us, our homes, our cars, everything on our tabletops, these things require materials that must be mined from the Earth's surface and below. That is Connie Chan, A16Z General Partner. Connie has led investments into all kinds of companies, from live shopping to religious super apps to AI leasing agents. But one of Connie's most important bets isn't your standard technology story. It's in a mineral exploration company, one that uses artificial intelligence, but also human intelligence to find critical materials across five continents. Now, for many of you, this is a topic that **Connie Chan:** is just starting to bubble up because if you want to build the future, so many of the new technologies that we're talking about and dreaming about are going to require more metals. One very clear example is for electric vehicles. EVs require massive batteries, and those batteries need more copper, more lithium, more nickel. And there's very clearly a big supply gap that's coming in a couple decades. EV cars right now globally account for already 14% of car sales. In China, it was the majority of car sales in 2024. And these EVs require 4x amount of copper as a normal gas vehicle. So if we want to power this green revolution, we definitely need more mining. And it's not just electric vehicles. If you think about data centers, BHP estimates by 2050, we're going to have six to seven percent of the world's copper going directly to data centers. Now you might say, well, 2050 sounds so far away, the reality is it can take years to find the mine, years to find the deposit, years to then estimate the size of the deposit, figure out how to extract it best, then years to build out the mine, and then decades to actually extract all of that metal, which means if we need more metal in one, two, three decades, we have to find that metal today. --- ### [Irreplaceable Metals](https://share.snipd.com/snip/d2a9fb60-49e8-4b09-a41d-f274f98d3658) 🎧 03:40 - 04:47 (01:06) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/25d27e44-be87-4a71-9d76-b168354c0427" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - There's no substitute for copper and lithium due to their unique properties. - They are irreplaceable in the global supply chain, especially for the energy transition. #### 💬 Quote > So there is no substitute for copper or lithium. > — Tom Hunt Tom Hunt on the lack of alternatives for key metals. #### 📚 Transcript **Connie Chan:** To kick off, I'd like to talk about why are these metals so critical? For **Tom Hunt:** the energy transition, we will need to build about 2 billion electric vehicles, which means that we actually have to discover about 1,000 new mines in order to provide the lithium, nickel, copper, and cobalt that's going to go into those vehicles. After we've built those vehicles, we can recycle the batteries, but we need to put the batteries into those cars to begin with. So there is no substitute for copper or lithium. Copper is the second most conductive metal after silver, and unless we find an enormous pile of silver, we're going to be using copper long into the future. Lithium is both the lightest element and the most electronegative element, and having worked on next generation batteries, there's really no substitute for the energy density you can get out of lithium. So these metals are irreplaceable in the global supply chain for the energy transition. And they're also critical for the build out of solar, of utility scale batteries, of all kinds of new power generation, as well as the next generation of data centers. --- ### [Metal Concentration](https://share.snipd.com/snip/17335b8a-3b51-4383-99d1-f577a080861e) 🎧 04:52 - 06:49 (01:57) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/7f5fdb66-7272-4234-8f5d-585d2e73adef" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Metal deposits are not rare, but finding concentrated, mineable sources is challenging. - Geological processes concentrate these metals, making some locations, like the Central African Copper Belt, prime targets. #### 💬 Quote > There's plenty of metal in the Earth's crust. And the question is, how do we find places where the history of the Earth has concentrated these metal deposits to the point where we can extract them both cost effectively and with minimal environmental impact? > — Tom Hunt Tom Hunt discussing the challenge of finding concentrated metal deposits. #### 📚 Transcript **Connie Chan:** is the main problem that we're trying to solve, though? Is there a shortage of supply? Are these metals rare? Are they very difficult to find? **Tom Hunt:** There's plenty of metal in the Earth's crust. And the question is, how do we find places where the history of the Earth has concentrated these metal deposits to the point where we can extract them both cost effectively and with minimal environmental impact? So the more concentrated a metal deposit is, the less rock you have to process in order to extract a certain amount of metal. You **Connie Chan:** talk about how these metals are not that rare. Does that mean we can find them in our backyard? Should we be searching for it locally, domestically? Where do these metal deposits usually live? **Tom Hunt:** Yeah, I think my **George Gilchrist:** kids and my neighbor's dog certainly try to look in my backyard for metals, but they don't concentrate in very many places. a very unique combination of factors that are required to concentrate any of the metals into a small mineable target. And these will vary depending on what you're looking for. So one of the tricks in exploration is to really become super familiar with the deposit style that you're targeting to understand where these controls are coming together. So if you're looking for copper, it might be in a very different place to where you might be looking for lithium. The techniques that you use are different. Even within copper, there will be different types of deposits that will have very different characteristics and require different tools, different approaches. You have to be really flexible. There's no formulated approach to making a discovery. The question now is, can we do it faster or more effectively than we have been? Can we use more of the data that we have at our disposal to guide those decisions and to really help identify where the most prospective areas are? cobalt goes around the world to find the best rocks. --- ### [Changing Exploration](https://share.snipd.com/snip/e771ddfc-cc70-4b3f-ab21-6e99f9e95a25) 🎧 08:10 - 09:08 (00:57) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/4521c578-8bbc-460f-8c7f-2aededfdd6b6" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Historically, surface-level deposits with visible signs, like green staining for copper, were easier to find. - Now, deeper exploration requires advanced tools and data analysis. #### 💬 Quote > So copper, if it gets to surface, it will oxidize and form minerals that look green or look blue. And so in the copper belt, for instance, there will be hills that had green staining on them. > — George Gilchrist George Gilchrist on historical exploration techniques. #### 📚 Transcript **Connie Chan:** Right. George, a lot of mines today are being expanded as opposed to new mines being discovered. Why is that? **George Gilchrist:** It's really hard to find new mines. So 40, 50, 60 years ago, a lot of the earth hadn't been tested. A lot of the deposits were at surface. They might have had an expression at surface. Meaning **Connie Chan:** I can see it with my eyes on the ground or just digging with a shovel? Yes. **George Gilchrist:** So copper, if it gets to surface, it will oxidize and form minerals that look green or look blue. And so in the copper belt, for instance, there will be hills that had green staining on them. So it wasn't difficult to discover those deposits, but those have largely been found. I haven't stumbled into a green hill that's just waiting to be mined. So now we've got to start looking underneath the surface and we need a lot more tools, a lot more data. And so it's easier to expand your existing mind than spend the money to try and make a discovery elsewhere. --- ### [Data Explosion](https://share.snipd.com/snip/dcd8bf3f-9568-4ba6-9c49-c37345520bed) 🎧 09:36 - 11:05 (01:28) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/3f00735e-4830-461c-a2d0-7f19ce019cd3" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Traditional exploration relies on ground mapping, soil sampling, and geophysical methods. - Advances in technology allow airborne surveys and generate vast datasets, creating a new challenge in data processing. #### 💬 Quote > What has changed is the level of technology in terms of the geophysical methods that are available. So the ability to measure the properties of the rock. > — George Gilchrist George Gilchrist on advancements in exploration technology. #### 📚 Transcript **Connie Chan:** maybe before we jump into how technology is changing mining, maybe can you share with us how has mining exploration changed over the decades? One of the stories I've always loved in mining was there was a story of how someone made a huge gold deposit discovery based off the cover of a National Geographic magazine. Because just like you said, it was being expressed on the surface. So one, two decades ago, what did exploration look like then? And then we can contrast that to today. A **George Gilchrist:** lot of the work we're doing today is still the same groundwork. We're still moving onto ground. We're still mapping the geology. We're taking samples of the soil. That's been done for a long time. It's very effective. What has changed is the level of technology in terms of the geophysical methods that are available. So the ability to measure the properties of the rock. Is it magnetic? Is one rock denser than another rock? We can measure that through what we call gravity measurements. We can use seismic surveys to try and identify the shapes of deposits deep underground. And those technologies have advanced dramatically. They've become airborne so we can fly over deposits so we don't need to build roads and bridges. We can now access the ground easily. They've become better in their resolution, the quality of the data. But with that has come significant volumes of data. And so the ability to process and extract the value out of that data is now the challenge. --- ### [Data Digitization](https://share.snipd.com/snip/ae9ba173-f812-4013-b109-68bd6564d399) 🎧 13:18 - 15:01 (01:43) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/105a1caa-2ed4-4919-bfd1-3576ec0be6cc" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Kobold utilizes diverse data sources, from handwritten records to digital maps, which require extensive processing. - Data digitization includes translating specialized geological terms, like those from Finnish records. #### 💬 Quote > So very talented geologists have been walking the surface of the Earth for over 100 years and collecting sometimes handwritten records, sometimes physical samples that are analyzed, sometimes hand-drawn maps, sometimes digital maps. > — Tom Hunt Tom Hunt on Kobold's data sources and processing challenges. #### 📚 Transcript **Connie Chan:** of the data is so important. And you mentioned a lot of the data that COBOL uses previously was very unstructured. Give us a taste of just how difficult it was to digitize the data to begin with and then put it in a usable format. So **Tom Hunt:** very talented geologists have been walking the surface of the Earth for over 100 years and collecting sometimes handwritten records, sometimes physical samples that are analyzed, sometimes hand-drawn maps, sometimes digital maps. So there are all this very diverse set of unstructured data. And these are then sometimes in government databases, either paper or digital archives, or they are with potential joint venture partners who might have a huge pile of papers that go to date back 50 or 100 years. And there's an incredible wealth of information there, but information that hasn't been exploited because it's been locked up in these paper records. So yeah, we've had teams out scanning some of these paper records. The job isn't done once you have a record scanned. We really want to get as much information out of this raw data as we can. And so, for example, in Finland, there's a wealth of historic data, but it's in Finnish. And so we need to be able to both translate that. And many of the words that people use to describe rocks are highly specialized. And so we need special translation modules to make sure that those come through correctly. And then extract the structured data of these unstructured reports. So for example, somebody may describe a certain rock type and a certain grain size on that rock and a certain chemistry of that rock. And we would like to take that into a table that our algorithms can then use. --- ### [Teamwork and Innovation](https://share.snipd.com/snip/ad8a7646-d467-477c-8767-5b41beceba03) 🎧 15:13 - 18:16 (03:02) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/0c1d56fd-7f7c-45a0-bf75-808c49773714" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Kobold's data science and geoscience teams collaborate closely, using cloud-based systems to share information globally. - They use innovative hardware, like 360-degree core imaging during drilling, to streamline data acquisition. #### 💬 Quote > We're actually fortunate that we live in the world of technology and somebody sitting in Silicon Valley is working with someone sitting here in Central Africa or in Australia. > — Mvkay Makai Mvkay Makai on Kobold's global collaboration. #### 📚 Transcript **Connie Chan:** We're **Mvkay Makai:** actually fortunate that we live in the world of technology and somebody sitting in Silicon Valley is working with someone sitting here in Central Africa or in Australia. And we've been very deliberate on making sure that our data science, software engineering, and our geoscience teams are highly collaborative and are paired in being able to extract the rock that we have drilled, upload it into the cloud, and anyone around the world within Cobalt can look with the same precision at the same information as if they were standing in front on the ground here in South Central Africa. We also have the abilities to actually look at the images through our different models that we're building to analyze our core. And that's something that we build within the company through the tech stack that we have. **Connie Chan:** Even just the languages, the words, how do you teach each other about your craft? If you are a scientist, a biochemist, a geologist, you're bringing together people of all different backgrounds. How do you get everyone on the same page? One **Mvkay Makai:** of the things we kind of do when you first join Cobalt, we have some sort of nomenclature session of what the basics mean in terms of geology and rocks, in terms of also the tech and marrying those together, like creating our own sort of internal glossary that is easy for people from backgrounds who have never been on a mine or never been around exploration and vice versa, people who have never understood the different AI models that are used, say, in the US. that starts quite early when you're inducting a new team member. If you talk about some of the local staff we have down to community members, we've had indigenous words for COPPA, like the word mukuba, and we help teach our North American or South African colleagues, like, this word mukuba is COPPA in our local language. It's literally one language in a way. We also have a Zambian data scientist on the team and they come and spend many, many weeks on site. They get to go to a drill rig. They get to suggest many new ideas with drilling companies, drilling contractors, who've kind of worked in a certain way over industry for many, many years. So we look at the rig and say, how can we be more efficient in extracting the score and collecting information in a manner that reduces the time to process the information. So we've been fortunate to work with contractors that allow us trial our hardware that Tom and his team build, ship it out to Zambia, put it next to a driller, do some basic training on how do we capture imaging of the core, like 360 imaging of the core, as it's coming out of the ground, which is really revolutionary. Whereas the standard was you have to wait a couple of days, take an image, take it right, stitch it together. And a data scientist working with a geoscientist is also getting training from a geoscientist on just the type of lithology that they see in the rock, what we're looking for, and how do we make better interpretations and better predictions in a meaningful way through either a mixture of AI and HI through all the experience of the brilliant geoscientists who've worked in industry for many, many years. --- ### [Data Insights](https://share.snipd.com/snip/91a7c7e6-7df3-4899-ae9a-a3acf13593f9) 🎧 19:57 - 22:06 (02:09) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/1f828fd1-f2d2-4c87-ba17-e1943227c988" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Data analysis reveals relationships between elements, allowing for estimations of target element grades even with incomplete datasets. - Digitized maps enable quick searches for specific terms, enhancing data interrogation efficiency. #### 💬 Quote > And now we might be looking for a very specific element and it's only available in a small portion of that data set. Normally you think, oh, it's such a pity that we have this huge data set, but we can only use a small part of it. Whereas the data scientists will say, well, that's okay. > — George Gilchrist George Gilchrist explains how data scientists use relationships between elements to overcome data limitations. #### 📚 Transcript **Connie Chan:** great. And George, given that you've worked at more traditional exploration and mining companies before, maybe share some examples of how this technology or the data has surprised you. Yeah, **George Gilchrist:** there have been a number of examples. Some of the countries have big data sets. And these are data sets that have been accumulated over the years from explorers in different parts of the country. Each of those explorers was looking for something specific. And so they didn't assay necessarily for everything. They didn't measure every element in every sample. They were just targeting a few. And now we might be looking for a very specific element and it's only available in a small portion of that data set. Normally you think, oh, it's such a pity that we have this huge data set, but we can only use a small part of it. Whereas the data scientists will say, well, that's okay. That element that we're interested in has relationships to other elements. And we can have a really good idea of what the grade of that element would be, given the grade of all the other elements that we know at each of the sample points. So we can test it by taking out the examples where we do have that grade. We can then estimate what that grade would be and we compare it. And that's remarkably close because we're not just comparing it to one element or two elements. We'll be looking at relationships from numerous elements. And so suddenly, this huge data set that looked like at a trunk, we're actually able to use the full value and the spread of that data. And that allows us to move into areas where other people wouldn't be. That's one way. Another is Tom spoke about data that you digitize, maps. If I scan a map, I can look at it on a computer screen, but I have to look at it really carefully. Now I can just look for a search term and the 12 maps that have that term will pop up and it'll show me where that term is on the map straight away. And I'm able to interrogate, maybe it's the name of a drill hole, or it's a certain element that I'm looking for in a report or on a series of maps. And suddenly, all of this information is just so much quicker to interrogate. I can spend my time applying my geological training and my experience rather than spend my time just opening and closing things. --- ### [Prioritizing Data](https://share.snipd.com/snip/e4139618-5f2c-4890-be46-644bbc1dcf18) 🎧 23:25 - 24:27 (01:01) <iframe src="https://share.snipd.com/embed/obsidian-player/snip/0ccc3ec3-402b-43f1-97d9-d150d5db4595" width="100%" height="100" style="border: none; border-radius: 12px;" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-clipboard-write" ></iframe> - Data prioritization involves collaboration between geoscientists and data scientists, tailoring the approach to specific environments. - This collaboration allows for targeted drilling, maximizing information gain. #### 💬 Quote > In a world that is rich in data, that becomes the challenge is how do you know what to actually focus on? Some of that will come from our experience with working with data from other projects. > — George Gilchrist George Gilchrist explaining how data is prioritized. #### 📚 Transcript **Connie Chan:** In **George Gilchrist:** a world that is rich in data, that becomes the challenge is how do you know what to actually focus on? Some of that will come from our experience with working with data from other projects. So the geoscientists will be interacting with the data scientists saying, in this environment, we know that these factors are really critical. And this is where the collaboration between the geoscientists and the data scientists becomes so important that it's not two separate entities. It's very collaborative and specific. And so there's no off-the option. We're not developing a tool that we can sell to an exploration company that will help them discover in any environment. Everything we're doing tailored to the areas that we're working. And **Tom Hunt:** one key aspect is also modeling the uncertainty of what's underground. And there's incredible uncertainty of what's just under the surface. And so by being able to map out the regions of high uncertainty or low uncertainty, that can also allow us to optimize where we collect the next data point. --- Created with [Snipd](https://www.snipd.com) | Highlight & Take Notes from Podcasts