Rock, Water, and Wire

The AI Race Is Not a Software Competition. It Is a Geology Competition.

By Nazem Alkudsi, CFA

The mine was closing and the geologist was laughing.

Not bitterly. Not yet. It was a social gathering in Park City, Utah—late January, the kind of weekend where skiing is the excuse but the conversations are the real event. Overcast sky. The town smelling of woodsmoke and cold pine. I found myself in a long exchange with the head of the Department of Geology and Geological Engineering at the Colorado School of Mines—the oldest earth sciences program in the country, founded in 1874 in Golden, Colorado, back when what lay inside the mountain was the only thing that mattered.

He told me something I have carried ever since.

At the height of the dot-com fever—right at the millennium’s turn, just before the whole thing came apart—his school had cratered to its lowest enrollment in living memory. Faculty leaving. Students flooding into computer science, finance, anything that didn’t require thinking about rock. The world had made up its mind. The future was digital. Virtual. Weightless. The earth was merely a platform. Not a partner.

The numbers told the story plainly. Mines enrolled roughly 3,787 students by 2002—a trough before a decade of slow, grinding recovery. Nationally, geoscience enrollment tracked the price of oil, and oil was cheap, and software was king. Seven percent of American high schoolers were taking an earth science class. Geology departments shuttering across the country. When students were surveyed about career paths, geology ranked dead last.

That conversation did something to me. I was a sovereign fund professional—capital allocation was my world, not mineral extraction. But I couldn’t shake what he’d said. So that year, knowing almost nothing about the subject, I got interested in precious metals mining. Arranged visits to a handful of silver mines in northern Idaho—the Coeur d’Alene mining district, the Silver Valley, where fortunes had once been pulled from the Bitterroot Mountains and whole towns had grown up around the shafts.

What I found was not a sector in decline.

It was a world being erased.

One mine shutting down. Another weeks from the same fate. The towns that had grown up around them—places like Kellogg and Wallace, towns that existed for no reason except that the mountain gave them one—going bankrupt. Main streets half-shuttered. You could see it on the faces in the diners, in the hardware stores that had nothing left to supply. Not anger. Something quieter. The look of someone watching the only life they’ve ever known fold up and disappear, knowing nobody outside their valley has noticed. Knowing nobody particularly cares.

Silver was trading below five dollars an ounce. The world treated it as a relic—a monetary metal from another century, of interest to coin collectors and no one else. Nobody thinking about silver as an industrial metal. Nobody asking what the most electrically conductive element on the periodic table might be worth in a future built on circuits, sensors, and servers.

I remember standing in one of those towns on a grey afternoon, looking up at the mountains, thinking: the world has decided that what comes out of the ground no longer matters. That the future belongs to code, to networks, to things you cannot hold in your hand.

The world had stopped looking at the ground.

Twenty-four years later, those memories return with force. The conversation in Park City. The silence inside the closing mine. The faces in the Idaho towns. Because every AI lab being built in the Gulf, every data center humming in Virginia, every sovereign fund pouring capital into the digital future—all of it depends on three things no algorithm can conjure: rare earth minerals locked inside mountains, fresh water to cool the machines, and copper wire to carry the current.

Rock, water, and wire.

The school in Golden now enrolls over 8,200 students. Some of those Idaho mines have reopened, their silver and cobalt suddenly strategic again. Silver? It trades near eighty dollars an ounce today—nearly twenty times what it fetched when the world decided it didn’t matter.

The world remembered. But only after it had to.

Everyone frames the AI race in software terms: US versus China, open-source versus closed, Nvidia versus AMD. Model architecture. Parameter counts. Benchmark scores. These are the metrics that fill the front pages and the pitch decks. Meta, Amazon, Alphabet, and Microsoft have collectively committed to spend nearly $400 billion in 2025 alone. BlackRock and Microsoft are assembling a $100 billion AI infrastructure partnership. Three sovereign wealth funds just co-signed what may be the largest AI infrastructure check in history—but the real story is not the headline number. It is the $50 billion in physical infrastructure commitments and the electricity guarantees that came with it.

The capital is extraordinary.

But these are software framings backed by financial engineering. And the long-arc view says the real competition is not financial at all.

It is geological.

The question is not who builds the smartest model. The question is who controls what the model is built on.

Rock

The Vedic tradition treats the Earth herself—Prithvi—as a goddess whose body bears the weight of civilization. The Atharva Veda’s Hymn to the Earth calls her “mother” and asks that what is drawn from her be quickly replenished—a prayer that sounds less like devotion and more like a term sheet between humanity and the ground it stands on.

The global critical minerals market hit $328 billion in 2024. Projected to reach $586 billion by 2032. The International Energy Agency says meeting this rising demand will require $500 billion in new mining investment by 2040. Copper faces an implied supply deficit of thirty percent by 2035. Lithium, forty percent. These are not figures from activists or venture capitalists talking their book. They are from the IEA—an organization not known for alarmism.

And then there is silver—the metal the world forgot and is now scrambling to remember.

Silver possesses the highest electrical conductivity of any element and the highest thermal conductivity of any metal. Irreplaceable in the AI buildout: threaded through the high-performance chips, the GPU packaging, the thermal interface materials that keep processors from burning out, the silver-plated connectors and switchgear that distribute power through data centers, the solder that bonds semiconductor to server. A single 500-megawatt solar array—enough to power one hyperscale data center—requires roughly 300 metric tons of silver.

Oxford Economics, in a report commissioned by the Silver Institute, recently designated silver a “next-generation metal,” essential across every high-growth sector from AI to electric vehicles to clean energy. The silver market has been in structural supply deficit for five consecutive years, with the world consuming 160 to 200 million ounces more each year than the mines can pull from the ground. At five dollars an ounce in 2002, the market was telling you silver was a relic. At eighty dollars today, after a 148 percent gain last year alone, it is telling you something very different.

Any serious portfolio positioned for the physical infrastructure age and still without meaningful silver exposure is, in my view, incomplete.

The geography of control has barely shifted. China still refines over eighty percent of the world’s battery-grade graphite and rare earth elements—not because it has the most deposits, but because it built the processing infrastructure during the decades when the rest of the world was busy building software platforms and pretending the periodic table was someone else’s problem. The United States just took a ten percent equity stake in USA Rare Earth as part of a $1.6 billion deal. Canada launched a C$2 billion Critical Minerals Sovereign Fund. The Trump administration is exploring a dedicated sovereign wealth fund for domestic mining.

Call them trade policies if you like. They are acts of geological sovereignty, undertaken with the quiet urgency of governments that have realized, very late, that they outsourced something they cannot afford to have outsourced.

The pace of this reckoning is accelerating faster than most allocators grasp. Fifty-four countries convened for the first-ever Critical Minerals Ministerial—a gathering that, had it occurred in the oil markets, would have been front-page news for a month. The PIF—Saudi Arabia’s Public Investment Fund—has begun pivoting toward minerals, signaling that the Gulf’s sovereign capital is no longer content to finance the digital layer alone. It wants the geological layer beneath it.

What we are witnessing is not supply-chain diversification. It is the emergence of a parallel pricing system. Friend-shored minerals will trade at a premium. Those outside the trusted network face stranded assets and shrinking markets. This is the Bretton Woods of geology—and the sovereign funds that see it early will co-invest on terms that did not exist six months ago.

Here is the part that should trouble us most: mining investment rose just five percent in 2024, down from fourteen percent the prior year; adjusted for cost inflation, real growth was a mere two percent. The capital pouring into AI models dwarfs the capital going into the ground those models depend on by an order of magnitude.

This is not a market inefficiency. It is a civilizational blind spot—and not merely an intellectual one. A moral one. We are building the future on a foundation we have collectively decided is not worth investing in, while the communities that live above that foundation—the miners and the engineers and the geologists and the small towns in Idaho and Montana and the Congo—bear the consequences of our negligence.

The sovereign fund that secures offtake agreements on critical mineral refining capacity today holds the equivalent of what upstream oil concessions were in the 1970s.

The allocator who sees this is not early. They are merely not late.

Water

The Quran says: “Do you not see that Allah sends down rain from the sky and makes it flow as springs in the earth?” The image is not metaphor. It is hydrology—and theology—in a single sentence: water is given, not generated. And the giving has conditions.

Jordan just committed $6 billion to a single desalination plant. Forty percent of a nation’s drinking water, from one facility.

Sit with that.

Think about what it means for a family in Amman when they turn on the tap—the knowledge, conscious or not, that their water comes from a single point of engineered vulnerability, that the margin between normal life and crisis is exactly as wide as one plant’s operational continuity. This is what sovereignty looks like when water is scarce. It looks like a single point of failure dressed up as infrastructure.

A large AI data center consumes up to five million gallons of water per day—equivalent to the daily needs of a town of 50,000 people. Google alone used over six billion gallons across its data centers in 2023, with nearly a third drawn from watersheds already classified as stressed. AI workloads consume ten to fifty times more cooling water than traditional servers. In Texas alone, data centers are forecast to consume 399 billion gallons annually by 2030—up from 49 billion this year. The United Nations recently declared the world heading toward “global water bankruptcy.”

Institutional finance has no water beta. No water-adjusted discount rate. We have priced carbon. We have priced volatility. We have priced credit risk down to the basis point. But the water that makes the digital revolution physically possible—the one resource without which the cooling systems fail and the chips overheat and the data centers go dark—has no price.

The spreadsheet has no line for it.

Think about what we are saying when we leave that line blank. We are saying that the thing without which none of this works is worth, for modeling purposes, nothing. Some will point to closed-loop cooling systems and corporate pledges—Microsoft, for instance, has committed to replenishing more water than it consumes. But such commitments remain the exception, not the standard, and even recycled water creates concentrated thermal and hydrological stress on local watersheds that dispersed usage does not. The problem is not solved by recirculating it. It is relocated.

The Buddha asked: “Do you have the patience to wait until your mud settles and the water is clear?” The question was about the mind. But it might as well be about the markets—and the resource they have refused to see.

Wire

The hottest debate among institutional investors right now—the one happening not in published research but in group chats and allocation committees—is about voltage transformers. Lead times of two and a half to three years. Grid expansion cannot keep pace with data center demand.

The scale of the mismatch is breathtaking.

The big five tech companies are expected to spend over $600 billion in capital expenditure in 2026 alone. Data center construction has surged fivefold in two years, now surpassing spending on all other commercial buildings combined. Global data center electricity consumption jumped from roughly 240 TWh in 2022 to 415 TWh in 2024—nearly doubling in two years. US data centers today consume 75.8 gigawatts. By 2030, that figure reaches 134.4 GW. Goldman Sachs projects data center power demand will grow 175 percent by the end of this decade. Every one of those figures describes commitments already made, contracts already signed, ground already broken.

And what carries all that power? Copper. A single hyperscale AI data center can require up to 50,000 tons of it. Nvidia’s DGX GB200 NVL72 rack alone contains over two miles of copper NVLink cables. Yet chips are being warehoused across the country because there is no power to run them. Grid connection lead times run one to two years.

The AI buildout is running into a wall that no amount of capital can scale: the electrical grid itself.

The hyperscalers know this. They are not waiting for the grid to catch up—they are building around it. Meta is betting on nuclear through partnerships with Oklo and TerraPower. Data centers are deploying repurposed aircraft engines as on-site power. Bloom Energy fuel cells proliferating across campuses that cannot secure grid connections fast enough. Strip the press releases away and what you see is an admission, made with billions of dollars, that the existing power infrastructure cannot support the future these companies have already sold to shareholders.

And it creates a second-order competition: not just who has grid access, but who can generate power independently of the grid entirely.

There is a constraint that no amount of engineering can bypass: political will. Grid reliability costs have spiked. Virginia is now forcing data centers to pay eighty-five percent of transmission infrastructure costs. Moratoriums on new data center construction are emerging in communities from northwest Indiana to rural Virginia, backed by coalitions that cross every political line—united in the simple proposition that ordinary families should not subsidize the electrical appetite of trillion-dollar companies.

The geology competition may ultimately be won not just by the nation with the most rock, the most water, and the most wire—but by the nation with the political will to build.

Here is the detail that should keep allocators awake: the corridors where sovereign-backed data centers are being built fastest—Texas, Virginia, Arizona—are the same corridors where the ground is sinking. Aquifer depletion, land subsidence, and grid congestion are converging in the exact geographies where the AI buildout is most concentrated. We are building the most advanced computational infrastructure in human history on land that is physically subsiding beneath it.

The infrastructure is being priced on assumptions the geology no longer supports.

Genesis records the first mandate given to humanity: to work the earth and take care of it. The instruction was not to extract or exhaust, but to tend—a word that implies obligation, not ownership. The Psalmist added: “The earth is the Lord’s, and everything in it.” We are tenants. And tenants who destroy the property eventually lose the lease.

The irony is almost too perfect: the most advanced technology humanity has ever built depends entirely on the most basic infrastructure humanity has ever struggled with. Consider the convergence now underway—the United States becoming Intel’s largest shareholder in the same season that fifty-four countries convene to form a critical-minerals trading bloc. These are not separate stories. Semiconductor sovereignty and mineral sovereignty are the same story—the physical layer asserting itself against a world that had been pretending the physical layer was optional.

Every tradition understood what the spreadsheet does not: the physical world is not an input to be optimized. It is a partner to be respected.

And partners have terms.

We forgot this once—in Golden and in Idaho, where the mines went quiet and the spreadsheets had no line for what was lost.

The ground did not forget.

What Could Prove This Wrong

Any honest thesis must name the forces that could undo it.

Technology substitution is real: the solar industry is reducing silver loading per cell, copper recycling is improving, and small modular reactors could begin to relieve the grid bottleneck within the decade. An AI winter would weaken the timeline for every projection cited here. These are possibilities any serious allocator must weigh.

But here is why I believe the structural case survives them.

Substitution reduces intensity per unit—but the number of units is growing exponentially. Solar installations are multiplying faster than silver loading is declining; data centers proliferating faster than cooling efficiency is improving. Recycling helps at the margin but cannot close a deficit measured in hundreds of millions of ounces when above-ground inventories are already depleting. As for nuclear: even the most optimistic SMR timelines put meaningful grid contribution in the early 2030s, and the permitting, construction, and interconnection challenges are formidable.

The grid constraint is not a five-year problem waiting for a five-year solution. It is a five-year problem facing a ten-year solution.

An AI winter, while possible, would not eliminate the electrification megatrend, the clean energy transition, or the defense and industrial applications that drive mineral demand independently of AI. The AI buildout is the accelerant. Not the sole engine.

What the bear case actually reveals, when you press on it, is the same thing the bull case says: the physical layer is the binding constraint. Whether demand grows at the pace the optimists expect or at half that pace, the supply side is structurally unprepared. Mining projects take seven to fifteen years from discovery to production. Refining capacity takes nearly as long. These are not timelines that can be compressed by enthusiasm or capital alone.

The bear case slows the clock. It does not change the physics.

One more counterargument worth engaging, because it is the most seductive: that AI itself will solve the geology problem. Companies like Earth AI are already using machine learning to discover mineral deposits at a fraction of traditional cost—$2 million per site versus $100 million through conventional exploration. The technology is real. It is impressive. But finding is not having. Faster discovery does not solve the refining bottleneck, the permitting timeline, or the seven-to-fifteen-year development cycle. AI can find the deposit. It cannot will the mine, the smelter, or the political consent into existence.

The very technology accelerating demand for minerals is also accelerating the discovery of how much we lack—without accelerating the capacity to produce them.

This is the paradox of acceleration: the faster AI advances, the more clearly it illuminates the constraints it cannot overcome.

The allocators who built positions in upstream oil in the early 1970s, before the embargo, before the repricing, before the consensus caught up—they did not do so because they predicted a specific catalyst. They did so because they understood the structural mismatch between demand and supply, and they understood that such mismatches do not resolve gently.

The trigger is unknowable. The direction is not.

The consensus says AI winners will be determined by model architecture, capital, and talent.

The Long Arc view: AI winners will be determined by who controls the rock, who secures the water, and who owns the wire. The software layer is a distraction. The real allocation question for the next decade is physical—and most institutions are still underweight it. A $586 billion critical minerals market by 2032. A thirty percent copper supply deficit by 2035. A silver market in structural deficit for five straight years with no substitute for its conductivity. Data center power demand growing 175 percent by 2030. Nearly half of all data center facilities facing high water stress by mid-century.

These are not tail risks. They are the base case.

I wonder if you’ve noticed what I’ve noticed. That the conversations about AI have become almost entirely abstract—models, parameters, benchmarks, valuations—while the physical reality underneath grows more fragile, more contested, and more consequential by the month.

The Gulf states understand this better than most. They sit at the intersection of all three constraints—investing billions in AI infrastructure while managing acute water scarcity and building desalination capacity that doubles as strategic insurance. When the UAE launched its sovereign AI model in January 2026, it was not merely a technology announcement. It was the declaration of a chain of custody that begins in the mine and ends in the model.

The Gulf states have turned desalination into a civilizational achievement—making life, agriculture, and industry possible in one of the most water-scarce regions on earth. But that achievement carries a cost the region’s own scientists are increasingly confronting. The Arabian Gulf is now roughly twenty-five percent saltier than typical seawater, with hotspots running double or triple normal salinity. The GCC nations produce fifty-five percent of the world’s desalination brine—142 million cubic meters of hypersaline concentrate discharged daily into coastal waters, with the Gulf receiving the largest share. The ecological effects are measurable: elevated seafloor salinity, stress on coral systems, and an energy footprint that makes desalination one of the region’s largest sources of emissions.

The Gulf governments know this. Several are investing in next-generation brine management, zero-liquid-discharge systems, and renewable-powered plants. The nations that solve the brine equation will hold a strategic advantage as consequential as the nations that control the minerals—and the Gulf, precisely because it depends on desalination more than anyone, has more incentive to lead that transition.

Water sovereignty and marine stewardship are not competing priorities. They are, for the Gulf, the same priority.

The Buddhist tradition teaches pratityasamutpada—dependent origination—the principle that nothing arises independently, that every phenomenon exists only in relation to the conditions that produce it. The most advanced neural network is no exception. It depends on the mountain, the aquifer, and the copper seam.

Sever the conditions, and the phenomenon ceases.

This is not philosophy. It is physics.

The future is not being built in the cloud. It is being pulled from the earth, carried by the wire, and cooled by the water.

And the earth, as always, will have the final word.

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