Why We Run Two Pools

GZC operates two investment pools: Technology and Commodities. Most clients who understand the Technology pool thesis — AI infrastructure supply chain, semiconductor bottlenecks, power delivery equipment — intuitively grasp why it is constructed the way it is. The Commodities pool is less immediately intuitive. This letter explains the structural rationale.

The Insight That Drives Both Pools

The insight underlying both the Technology and Commodities pools is the same: major capital cycles create forced-spend dynamics that benefit specific supply chain positions, and those dynamics are more durable and more predictable than the earnings growth of individual companies.

In the Technology pool, we apply this insight to the AI infrastructure supply chain. In the Commodities pool, we apply it to the physical inputs — energy, metals, and materials — that every version of technological and industrial progress requires. The insight is the same; the application domain is different.

The Commodities pool is not a commodity ETF. It is not a bet on oil prices, gold prices, or any particular commodity price level. It is a set of concentrated positions in companies with structural supply chain advantages in energy and materials markets — selected using the same BTT analytical framework that drives our Technology portfolio.

The AI-Commodities Connection

The connection between the AI buildout cycle and commodity markets is direct and consequential:

Energy: AI data centers consume extraordinary amounts of power. The energy required to train and serve large AI models ultimately comes from the grid — from natural gas generators, nuclear plants, and renewable sources. Natural gas is the most important marginal power source in the near term, because it is the most flexible dispatchable generation technology available at scale. Companies with natural gas production, pipeline infrastructure, or gas-fired generation capacity benefit directly from AI-driven power demand growth.

Uranium: Nuclear energy is increasingly the preferred clean baseload power source for hyperscalers with carbon commitments. Uranium demand is growing from multiple directions simultaneously: existing reactor refueling requirements, extended reactor life programs, and new reactor construction driven by clean energy policies. The AI infrastructure thesis connects directly to uranium demand through the nuclear power procurement dynamic we have covered in prior research.

Critical minerals: Rare earth elements, lithium, cobalt, and other critical minerals are physical inputs to the technology supply chain — in EV drivetrains, grid storage batteries, and advanced manufacturing processes. The energy transition that AI data center buildout accelerates requires these materials in quantities that current production cannot supply without significant new investment.

Oil and gas: The world does not stop using oil and gas because AI is growing. Global energy demand is increasing, not decreasing, as electrification and AI infrastructure add new demand on top of existing consumption. The oil and gas companies we hold in the Commodities pool are selected for capital discipline, production durability, and shareholder return frameworks — not for sensitivity to short-term commodity price cycles.

Current Positioning

The Commodities pool is currently allocated across four primary categories:

Oil and gas production: We hold positions in two E&P operators with Permian Basin operations and disciplined capital allocation frameworks. These companies have low break-even costs, strong free cash flow generation, and shareholder return programs that include both dividends and buybacks. Our oil and gas holdings are not a macro bet on crude prices — they are positions in well-managed businesses with durable production assets.

Energy infrastructure: We hold one position in a natural gas pipeline operator with North American network scale and contractual revenue visibility. This holding is a defensive, cash-flow-oriented position that benefits from sustained natural gas demand — particularly the gas-fired power generation that data center buildout drives.

Uranium and nuclear: We hold one domestic uranium producer position through our uranium thesis, described in detail in prior GZC research. The forced-spend dynamics in uranium — limited domestic production, growing demand from nuclear utilities, AI-driven clean energy procurement — create the BTT characteristics we require.

Critical minerals: We hold one position in a rare earth and critical mineral producer with domestic US production capacity and processing capabilities. The domestic production thesis is supported by both commercial demand and policy-driven procurement requirements.

What We Watch in the Commodities Pool

The monitoring framework for the Commodities pool differs from the Technology pool: commodity supply chains move on longer timescales, and the relevant data is in production reports, regulatory filings, and contract disclosures rather than technology procurement lead times.

We track: oil and gas production levels and well economics, uranium contract pricing and utility procurement programs, rare earth processing capacity additions and US policy developments, and natural gas storage levels and power generation demand signals.

Position duration in the Commodities pool is typically longer than Technology positions — commodity supply chains take years to adjust, and the forced-spend dynamics that drive our positions (non-discretionary energy demand, constrained mineral production, limited new mine development) resolve on decade-scale timelines rather than year-scale ones.

Why Both Pools Together

The Technology and Commodities pools complement each other in a way that matters for portfolio construction: they respond to different drivers at different times in the economic cycle. When AI infrastructure investment is accelerating and semiconductor supply chains are tight, the Technology pool performs strongly. When commodity markets are tight and energy demand is robust, the Commodities pool contributes. Both are connected to the same mega-trend — the AI-driven transformation of the global economy and its energy and materials requirements — but they access that trend through different supply chain layers.

This is not diversification for its own sake. Both pools are concentrated in BTT-identified forced-spend positions. But the combination provides exposure to the full capital cycle — from the materials in the ground to the AI chips in the data center — in a way that a single-sector fund cannot.