The United States is leveraging artificial intelligence to revolutionize critical minerals exploration and reduce its heavy dependence on foreign sources, particularly China. As of 2024, the US imports 100% of 12 out of 50 designated critical minerals, including graphite, manganese, and gallium, according to the United States Geological Survey. These raw materials are essential for modern technology—from smartphones and 5G networks to military weapons, electric vehicle batteries, and semiconductors that power AI hardware.
With the global critical minerals market projected to reach nearly $500 billion by 2030, the Trump administration has made domestic manufacturing a national priority, issuing executive orders to boost mining and imposing 50% tariffs on imported metals. However, rebuilding domestic mineral supply faces significant challenges, prompting startups and legacy tech companies to deploy AI-powered solutions to accelerate discovery and secure supply chains.
Earth AI is leading the charge with predictive software and proprietary drilling hardware that identifies mineral deposits with a remarkable 75% success rate—far exceeding the industry average of less than 1%. The company’s algorithms analyze decades of historical data to pinpoint hydrothermal systems rich in valuable deposits. Over the past 12 months, Earth AI has made three discoveries in Australia, including indium, a rare metal crucial for touchscreens and AI semiconductor manufacturing. The company claims it can cut mineral discovery timelines from years to months.
Terra AI takes a similar approach, using AI to process geological data like magnetic field readings and seismic activity to generate thousands of underground maps. CEO John Mern notes that despite decades of investment, “the amount of metal added to the global supply this year is 90% lower than it was in 1990.” Terra’s platform is being piloted on rare earth projects across the US, Americas, Africa, and Europe, with potential to cut the 17-year average mine development timeline in half.
Legacy players are also adopting AI. Exiger, a supply chain management provider, uses AI to create digital twins of products and trace material compositions through a database of 10 billion transaction records. This helps Fortune 500 companies and governments identify supply chain vulnerabilities and reduce geopolitical risks. In one case, Exiger identified methods to extract germanium from coal ash and smelter waste in the US.
Despite promising results, experts caution that AI faces limitations. Mining engineering professor Rajive Ganguli emphasizes that “AI on bad numbers does not result in good answers,” noting that high-quality data remains scarce and expensive. Additionally, the US permitting process can take 10 to 15 years, creating systemic bottlenecks that technology alone cannot solve.
Key Quotes
We think that we can create the most value by drilling into the ground, proving that ‘Yes, there are chemical concentrations of metal there,’
Monte Hackett, CFO of Earth AI, explained the company’s approach to validating AI-discovered mineral deposits. This statement highlights how AI startups are moving beyond theoretical predictions to deliver tangible, verified results that can be sold to larger mining firms.
Despite decades of investment in sensors and data, we’re doing worse every year. The amount of metal added to the global supply this year is 90% lower than it was in 1990.
John Mern, cofounder and CEO of Terra AI, emphasized the urgent need for AI innovation in mineral discovery. This stark statistic underscores why traditional exploration methods are failing to meet growing demand and why AI represents a potential paradigm shift for the industry.
When China restricted exports on rare earths, it exposed customers to price volatility and geopolitical uncertainty. Our platform helps clients navigate that risk with a level of precision previously unattainable.
Brandon Daniels, CEO of Exiger, described how AI-powered supply chain management addresses geopolitical vulnerabilities. This quote illustrates the strategic importance of AI in helping companies and governments anticipate and mitigate risks in critical mineral supply chains.
AI on bad numbers does not result in good answers.
Rajive Ganguli, a mining engineering professor at the University of Utah, provided a critical perspective on AI’s limitations in mineral exploration. His caution reminds us that AI effectiveness depends entirely on data quality, and that high-quality geological data remains scarce and expensive to obtain.
Our Take
This story reveals a fascinating recursive relationship: AI needs critical minerals to exist, and now AI is being deployed to find those very minerals. The 75% success rate claimed by Earth AI, if validated at scale, would represent one of the most dramatic efficiency improvements AI has delivered in any traditional industry. However, the 10-15 year permitting bottleneck in the US exposes a crucial truth—technology alone cannot solve systemic regulatory challenges. The most sophisticated AI models are useless if bureaucratic processes prevent drilling. What’s particularly noteworthy is the investor interest from firms like Founders Factory partnering with Rio Tinto, signaling that even conservative mining giants recognize AI as transformative. Yet Professor Ganguli’s warning about data quality and the continued need for domain experts suggests we’re in an augmentation phase, not replacement. The geopolitical dimension adds urgency: as US-China tensions escalate, AI-driven mineral independence becomes both an economic and national security imperative, making this a story about technological sovereignty as much as innovation.
Why This Matters
This development represents a critical intersection of AI technology and national security, as the US seeks to reduce strategic dependence on China for materials essential to defense, technology, and the AI industry itself. The irony is striking: AI requires semiconductors and hardware built from critical minerals, yet AI is now being deployed to secure the very supply chains that enable its existence.
The 75% success rate achieved by AI-powered exploration companies like Earth AI could fundamentally transform an industry where traditional methods succeed less than 1% of the time. This efficiency gain has profound implications for the speed at which the US can develop domestic mineral production, potentially reshaping global supply chain dynamics and geopolitical power structures.
For the broader AI industry, securing critical mineral supplies is existential. Data centers require copper, AI chips need rare earth elements, and the entire digital infrastructure depends on stable access to these materials. As AI adoption accelerates across industries, demand for these minerals will only intensify, making AI-driven exploration not just an innovation story but a strategic imperative for maintaining technological leadership and economic competitiveness in the AI era.
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