Why We Invested in ExploreTech: Rebuilding Mining’s Risk Model with AI

The next decade will be defined by our ability to secure the critical minerals that power the modern economy. Electrification, national security, supply chain resilience, and the compute to power the AI revolution all hinge on a reliable, domestic pipeline of metals like copper, nickel, and rare earth elements. And yet, mineral exploration—the first step in that supply chain—is still painfully slow, inefficient, and risk-laden.

Today, discovering a new mine can take over a decade and hundreds of millions of dollars. Even then, the odds of success are brutally low: fewer than 1 in 1,000 greenfield exploration projects ever become productive mines. Despite the enormous capital flowing into energy transition, the mineral discovery process has barely changed in 50 years.

That’s why we invested in ExploreTech.

ExploreTech is building the first exploration platform designed for speed, precision, and repeatability—using AI not to find “treasure maps,” but to fundamentally change how the subsurface is understood and de-risked.

From Venture to Veins: A Shared Risk Curve

Mining, in many ways, mirrors the venture capital lifecycle. Exploration-stage projects are like pre-seed startups—high risk, uncertain potential, and heavily reliant on talented founders and conviction-based capital. As drill data accumulates and the resource potential is validated, these projects raise more capital and trade at higher valuations, eventually graduating into development-stage assets (akin to growth rounds) and then into producing mines (the IPO equivalent).

But unlike startups, where early-stage investing has evolved into a mature asset class, early-stage mining investment remains a cottage industry. Exploration companies are often microcaps listed on the TSXV, have been largely capital-destructive over the last decade, and are financed in piecemeal rounds with minimal technological edge. There is no Y Combinator of mining—and no Sequoia.

What exists on the other end of the mining lifecycle, however, shows what’s possible when risk is well understood. Royalty and streaming companies like Franco-Nevada and Wheaton Precious Metals have built some of the most capital-efficient businesses in the world. Think of these as the IPO investors of mining. Franco-Nevada, with a market cap of over $32 billion, has just over 50 employees. Wheaton, valued at around $38 billion, operates with a similarly lean team. These companies don’t operate mines—they finance them. In exchange for upfront capital to help construct or expand a mine, they receive royalties (a percentage of the mine’s revenue) or streams (the right to buy a portion of the mined metal at a fixed, discounted price). This model gives them commodity arbitrage upside without capex intensity or operational exposure. They are also providing capital to projects that are over 10 years old with already discovered and quantified deposits, which means their underwriting is based on a very well understood portion of the geologic subsurface and is far less risky than an early stage exploration project. Their success underscores a powerful idea: when the subsurface is well-understood, the capital markets work. The challenge is bringing that kind of clarity to the start of the process.

The challenge—and opportunity—is to enable the same underwriting rigor at exploration, the front end of the risk curve.

AI as the Risk Engine

This is where ExploreTech comes in.

They offer a radical improvement in how early-stage fieldwork is conducted and interpreted. The team’s AI models—trained on geophysics, geology, and historical outcomes—guide field teams toward the most statistically promising places to drill, based on real-time data collection and a proprietary “geologic Monte Carlo model.”

This doesn’t produce a magic answer. But it does reduce the number of holes needed, the capital spent, and the time wasted to discover and quantify a deposit. Instead of drilling fifty holes over ten to find out if a resource exists, ExploreTech aims to get there in five holes and two years.

The results so far are compelling to say the least. ExploreTech has made seven drill recommendations to date—all seven have hit mineralization at the predicted location and depth. In one recent case, it predicted copper would be found 600–650 feet below the surface and 200 meters away from any previous drilling. Giant Mining, their partner, found it exactly where the model said it would be.

This kind of accuracy can change the fundamental risk-return profile of early-stage mining. It’s the equivalent of knowing, with high probability, which startups will graduate from seed to Series A—and which should never have raised at all.

Why Now

The timing for a company like ExploreTech couldn’t be better:

• Copper demand is expected to double by 2045 if net-zero targets are to be met (IEA).
• National security concerns around rare earths and battery metals are pushing governments to re-shore supply chains.
• Billions of public dollars are being deployed to catalyze domestic mineral production.
• GPUs and cloud compute have made subsurface modeling at scale newly possible.

In other words: we need to discover more minerals, faster—and we finally have the tools to do so.

Why We Backed ExploreTech

We invested in ExploreTech not just because it has promising technology and a great team (though it does—Stanford Mineral-X alums with field experience at Rio Tinto, Freeport, and Glencore). We invested because we believe in their vision to remake exploration as a rigorous, data-driven discipline.

If successful, the ExploreTech team won’t just improve discovery timelines—they’ll make early-stage mining investable in a way it hasn’t been before. Their approach could open the door for a new class of institutional capital to back the assets that unlock the energy transition.

We’re proud to be early partners to Tyler, Alex, and the ExploreTech team. This is about more than finding the next mine. It’s about rebuilding the discovery engine itself.