The Agentic Revolution

AI Won’t Kill Software — It Will Change Who’s in Charge

From Chatbots to Autonomous Systems

For a brief moment, global markets seemed gripped by a new kind of technological anxiety. Not the familiar fear that artificial intelligence would replace workers, but a deeper concern: what if the next generation of AI makes entire categories of software obsolete? If autonomous systems can plan, decide and act independently, why would companies still need dozens — sometimes hundreds — of specialized applications to run their operations?

This question has become unavoidable as AI moves beyond conversational interfaces into what technologists now call agentic systems: software that does not merely respond but executes. Instead of drafting emails or summarizing documents, these systems can initiate workflows, coordinate between platforms and complete multi-step objectives with minimal human supervision.

Yet the premise that AI will replace software may misunderstand the nature of the shift. Autonomous systems cannot operate in the real economy without access to the digital machinery that already runs it. They need payment rails to move money, logistics platforms to ship goods, CRM systems to manage customers and compliance tools to navigate regulation. In that sense, the rise of agents may increase dependence on software rather than reduce it.

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The Physicalization of AI Finance

Why Wall Street Is Repricing Intelligence as Infrastructure

From Bits to Atoms

For nearly three years, artificial intelligence was valued as software. Scalable. Asset-light. Margin-rich. Infinite in replication and near-zero in marginal cost. The narrative was familiar: AI would behave like SaaS on steroids. A handful of dominant platforms would capture network effects, compress labor costs and expand digital rents.

Capital followed that logic. Multiples expanded. Venture funding exploded. Public markets priced AI exposure as exponential optionality.

But a structural misclassification was embedded in that enthusiasm. Artificial intelligence may scale mathematically. It operates thermodynamically. And thermodynamics obeys scarcity.

The great repricing now underway in global capital markets is not about better models. It is about physical constraint. The shift from bits to atoms is quietly redefining valuation logic, infrastructure strategy and sovereign power.

This is the physicalization of AI finance.

“The cost of AI will converge to the cost of energy.”
— Sam Altman, CEO, OpenAI (Congressional testimony, June 2025)

Altman’s remark, delivered in a policy setting rather than a developer conference, reframed the entire industry. If the marginal cost of intelligence converges toward the marginal cost of electricity, AI ceases to be a purely digital business. It becomes an energy business.

The implication is profound: in the long run, intelligence is priced in kilowatts.

From Eyeballs to Volts

The dot-com era revolved around attention. Eyeballs determined valuation. Growth was measured in users and engagement. The AI era revolves around power density.

Training clusters consume megawatts. Inference at scale strains regional grids. Data centers increasingly resemble industrial plants rather than server rooms. Hyperscalers negotiate long-term power purchase agreements before launching new model generations.

“AI will run out of electricity and transformers… You need transformers to run transformers.”
— Elon Musk, CEO, Tesla & xAI (Bosch Connected World, March 2024)

The irony is structural. Digital “transformers”—the architecture behind large language models—depend on physical transformers in substations. And those physical components were never built for exponential compute demand.

The scarcity is no longer GPUs alone. It is copper, steel, cooling capacity, transmission lines and permitting timelines. The market is beginning to price that reality.

The Capital Repricing

For a decade, valuation models in technology were anchored in revenue growth and software margins. Price-to-earnings multiples assumed asset-light scalability. Capital intensity was minimal. Expansion meant hiring engineers, not building substations.

AI disrupts that template.

Capital expenditures are front-loaded. Revenue realization is back-loaded. Energy contracts stretch decades. Land acquisition and grid interconnection are regulatory processes measured in years.

“Infrastructure is just at the beginning of a golden age… AI builders are leveraging up: investment is front-loaded while revenues are back-loaded.”
— Larry Fink, Chairman & CEO, BlackRock (Energy & Innovation Summit, July 2025)

Fink’s framing reveals a shift in capital logic. AI is no longer a pure growth narrative. It is an infrastructure cycle. Investment resembles utilities or pipelines more than cloud software.

The metric transition follows:

Old logic:
• P/E expansion driven by user growth
• Gross margin scalability
• Minimal physical assets

Emerging logic:
• EV/EBITDA tied to hard infrastructure
• Regulated-return characteristics
• Scarcity premiums on grid access

AI firms begin to resemble energy refiners: massive CAPEX upfront, stable cash flows later. Scarcity replaces scalability as the dominant valuation axis.

The Model Is the Commodity; the Grid Is the Moat

This is where the Abel Doctrine becomes structural proof rather than anecdote.

Greg Abel’s Berkshire Hathaway is not racing to build models. It owns railroads, utilities, insurance float and transmission assets. In a world where AI consumes power like heavy industry, the decisive asset is not the model that answers questions—it is the grid that allows any model to operate.

The historical parallel is instructive. Railroads did not invent industrial goods. They transported them. Yet those who owned the rails controlled the economy’s throughput.

Compute is becoming baseload. Transmission becomes strategic.

The model is the commodity; the grid is the moat.

“There are certain really major investment situations where we have capital like nobody else… In the whole generation and transmission arena.”
— Warren Buffett, Berkshire Hathaway Annual Meeting (May 2025)

Buffett’s statement, delivered during his final year as CEO, reads differently in hindsight. Generation and transmission are no longer peripheral to digital growth. They are foundational to it.

Where the software era rewarded intellectual property, the AI-industrial era rewards energy logistics.

Kilowatt Sovereignty

The physicalization of AI reshapes geopolitics. In the 20th century, oil reserves defined strategic leverage. In the 21st, surplus kilowatts may play a similar role.

Regions with energy abundance and regulatory efficiency—Canada, Iceland, the Gulf states—are emerging as AI safe havens. Not because they have the best programmers, but because they can guarantee power at scale.

Capital flows accordingly.

Permitting timelines for high-voltage lines now matter as much as research grants. The shortest approval cycle for a substation may attract more AI investment than the largest AI conference.

This is kilowatt sovereignty.

National power is no longer measured solely by chip design capacity or software talent. It is measured by integrated infrastructure: generation, transmission, cooling, fiber and political stability.

AI policy becomes energy policy.

Trust as Physical Infrastructure

The physicalization thesis extends beyond energy. It reaches into financial architecture itself.

As billions in capital are allocated toward AI infrastructure, the informational layer supporting those allocations must become equally hardened. Deepfakes, synthetic earnings calls and manipulated signals introduce systemic fragility into an already capital-intensive cycle.

If AI becomes industrial, verification must become infrastructural. The market cannot allocate trillions based on synthetic signals untethered from physical origin.

The emerging requirement is “proof of origin”—cryptographic verification embedded into financial communication. Authenticity becomes compliance, not courtesy.

In a capital-intensive environment, misinformation is not reputational risk. It is misallocation risk.

The Industrial Turn

Jensen Huang framed generative AI as the “New Industrial Revolution”. The phrase is more literal than metaphorical.

“Generative AI is the ‘New Industrial Revolution’… a once-in-a-generation opportunity to fuse industrial capability with AI.”
— Jensen Huang, CEO, NVIDIA (World Economic Forum, January 2025)

Industrial revolutions are not software upgrades. They are reorganizations of physical production.

Steam required rail. Electricity required grid build-out. The internet required fiber and server farms. AI requires megawatt-scale compute clusters integrated into energy networks.

The speculative excess of generative hype is giving way to industrial sobriety. Copper shortages replace cloud demos as strategic headlines. Transformer supply chains become market catalysts.

The intoxication phase ends. The engineering phase begins.

From Weightless to Heavy Capitalism

For decades, finance drifted toward intangibility. Platform economics. Network effects. Zero marginal cost. AI reverses that drift.

Heavy industry re-enters the center of capital allocation:

• High-voltage transmission
• Cooling technologies
• Grid modernization
• Land banks near substations
• Long-duration storage

Balance sheets regain importance. Cash reserves regain strategic weight. Sovereign funds regain leverage.

The most conservative capital—long dismissed as slow—finds itself structurally aligned with the new AI economy.

Thermodynamic capitalism rewards mass.

The Great Sobering

The physicalization of AI finance is not a collapse of the technology thesis. It is its maturation.

After the rhetorical crescendo of generative breakthroughs, markets are rediscovering physics. Compute is not ethereal. It is bounded by heat dissipation, copper availability and transformer capacity.

Scarcity has returned. And scarcity reintroduces discipline.

Wall Street’s discovery is not that AI is overhyped. It is that AI is heavier than assumed.

The industrial cycle has begun. In that cycle, advantage belongs not to the loudest innovator, but to the entity that can finance, power and stabilize intelligence at scale.

Artificial intelligence may be coded in silicon.
But it is powered in steel.

The future of AI will not be decided solely by who writes the best model. It will be decided by who owns the grid.

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Photo credit:
AI-generated illustration (DALL·E / OpenAI)

Caption:
Conceptual visualization of the “physicalization” of artificial intelligence — digital bits converging with industrial atoms in red, white and blue tones, symbolizing the shift from scalable software narratives to energy- and infrastructure-driven capital markets.

When AI Attacks Trust

The Synthetic Voice of Capital and the Fragility of Financial Authority

For centuries, finance has rested on an invisible architecture. Markets move on numbers, yes—but they stabilize on trust. The credibility of a central banker. The voice of a legendary investor. The authority of a CEO. The subtle signal embedded in tone, posture, timing. In capital markets, reputation is not decoration. It is infrastructure. Artificial intelligence has now learned to imitate that infrastructure.

Deepfakes are often discussed as consumer fraud, election interference or social media manipulation. But when synthetic voices begin to replicate financial authority, the threat shifts categories. It moves from nuisance to systemic risk. When capital markets cannot reliably distinguish between authentic signal and synthetic noise, the core mechanism of price discovery becomes unstable.

This is no longer hypothetical.

“I saw one [deepfake] recently and I was even a little bit confused myself. It’s a huge force for potential harm. When you think about the potential for scamming people… if I were interested in investing in fraud, it would be the growth industry of all time.”
— Warren Buffett, Chairman & CEO, Berkshire Hathaway (Annual Meeting, 2024)

When Warren Buffett publicly acknowledges that he himself could momentarily be fooled by a synthetic version of his own voice, something profound has shifted. Authority—long tied to presence and identity—has become replicable.

In finance, replication of authority is not a minor technical problem. It is an existential one.

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The Abel Doctrine

Berkshire’s Quiet Entry into the AI Age

For more than half a century, Berkshire Hathaway has functioned less as a company than as a gravitational field. When it acquires, entire sectors reprice. When it accumulates cash, markets speculate about what cycle may be turning. Its capital does not chase volatility; it absorbs it.

The official transition from Warren Buffett to Greg Abel in January 2026 did not resemble a revolution. There was no strategic manifesto, no dramatic portfolio purge. Continuity was the message. Stability was the tone. And yet, beneath that continuity, a structural repositioning has begun.

This is not a pivot toward Silicon Valley exuberance. It is something quieter and heavier. Where The Material Turn described Wall Street’s revaluation of artificial intelligence as infrastructure, the Abel era represents the execution of that thesis by the most conservative capital bloc on Earth.

“I can’t imagine how much more he can get accomplished in a week than I can in a month. Greg understands many of our businesses and personnel far better than I now do.”
— Warren Buffett, Chairman, Berkshire Hathaway (CNBC interview and shareholder letter, Nov 2025 / Jan 2026)

Buffett’s endorsement was framed as operational praise. In retrospect, it reads as a strategic signal. The era of selecting value may be giving way to the era of engineering efficiency.

The $5 Billion Signal — Compute as Utility

When Berkshire disclosed a roughly $5 billion position in Alphabet Inc., markets interpreted it as a late, cautious entry into artificial intelligence. That reading is too shallow.

Alphabet is not simply an AI laboratory. It is a cloud operator, a data center builder, a network owner and a vertically integrated silicon designer. It controls substantial portions of the compute stack — from custom TPUs to global fiber backbones. It is closer to a digital utility than a speculative software venture.

As Peter Garnry observed in an analysis of Berkshire’s repositioning:

“Trimming Apple while adding Alphabet suggests a preference for AI embedded in cloud and software ecosystems rather than tied to hardware cycles. Alphabet sits where AI ambition meets old-fashioned cash generation.”
— Peter Garnry, Chief Equity Strategist, Saxo Bank

The comparison is instructive. In the nineteenth century, railroads did not compete on storytelling; they owned the tracks. In the AI age, compute is becoming baseload infrastructure. You do not bet on the fastest algorithm; you own the platforms through which they must run.

Abel appears to recognize that in an economy increasingly dependent on artificial intelligence, cloud infrastructure resembles regulated utilities more than venture capital gambles. Compute is not optional. It is structural.

GEICO and the Operational Siege — Margin Expansion Through Intelligence

If Buffett perfected the art of acquiring companies with economic moats, Abel is beginning to deepen those moats through operational intelligence.

At GEICO, AI is not a marketing narrative but a margin instrument. Insurance is a data business at industrial scale — pricing risk, processing claims, modeling probability. Under Abel’s oversight, artificial intelligence becomes an engine of margin expansion through intelligence.

“AI will be a game changer. It will change the way Berkshire evaluates, prices and sells risk, as well as how it pays claims. We are not great at being the first mover; our approach is to wait for the opportunity to crystallize.”
— Ajit Jain, Vice Chairman of Insurance Operations, Berkshire Hathaway (Shareholder Meeting, May 2025)

The remark is revealing. Berkshire does not aim to invent the future. It waits until the technology stabilizes — and then integrates it into vast, cash-generating systems.

The same logic extends beyond insurance. In rail operations at BNSF, predictive analytics optimize freight flows. In energy subsidiaries, grid data refines load management. AI is not a product sold to consumers; it is an efficiency multiplier embedded in heavy industry.

Buffett bought moats. Abel reinforces them with algorithms.

The Cashberg — Stored Strategic Potential

As of early 2026, Berkshire sits on approximately $380 billion in cash and short-term equivalents. The size of this reserve has prompted speculation that the conglomerate is overly cautious or waiting for a market correction.

A more structural reading sees something else: stored strategic potential.

“The size of Berkshire’s cash pile is what makes this transition so consequential. A decision that commits tens of billions of dollars will give the first glimpse of Abel’s capital allocation approach in the post-Buffett era.”
— Nasdaq Analyst Report (January 2026)

Artificial intelligence at scale is colliding with aging power grids, transmission bottlenecks and escalating capital expenditures. Data centers demand baseload electricity at unprecedented densities. When AI enthusiasm encounters infrastructure scarcity, financing becomes decisive.

In that context, the “Cashberg” resembles a reservoir behind a dam. It is potential energy. Abel does not need to predict which AI application will dominate; he can finance the physical systems that all of them require.

Few entities possess the balance sheet to absorb the capital intensity of grid modernization, transmission upgrades or large-scale generation projects. Berkshire is one of them.

Berkshire Hathaway Energy — The Taproot of the AI Economy

The often-overlooked pillar of this story is Berkshire Hathaway Energy. Utilities and transmission assets lack the glamour of cloud computing, but they form the thermodynamic base layer of artificial intelligence.

Market analysts have begun to notice this asymmetry.

“Rather than competing in crowded fields of software or hardware, Abel can position Berkshire as the indispensable backbone of the AI revolution: the provider of energy and infrastructure without which no AI system can operate.”
— Market Analyst, Barchart.com (June 2025)

The demand trajectory is visible across the sector.

“Demand from data center and high-tech customers is forecast to continue at a 10% compounded annual growth rate through 2030. We are executing contracts that build on a track record of strong industrial demand driven by AI infrastructure.”
— Portland General Electric, Financial Results Statement (February 2026)

AI cannot escape thermodynamics. For every bit processed, energy is consumed. For every model trained, heat must be dissipated. The digital age does not abolish physics; it intensifies its relevance.

In that environment, owning generation and transmission assets is not peripheral to AI — it is foundational.

Thermodynamic Capitalism — The Anchor of the Cycle

Here lies the intellectual core of the Abel Doctrine.

Artificial intelligence may be coded in silicon and optimized through mathematics, but it remains bound to physical constraints. Intelligence scales through energy. Data centers resemble industrial plants more than software startups.

Berkshire’s unique position is that it owns large segments of the physical substrate — rail, insurance float, utilities, capital reserves. It behaves like thermal mass in an overheated system. Where venture capital accelerates volatility, Berkshire absorbs and stabilizes it.

An online investor summarized the asymmetry succinctly:

“BHE is like a very boring Nvidia—selling energy (a physical product) to fuel AI instead of chip designs. They only benefit when a data center wants to connect to their network, and they are the only ones who can provide that scale.”
— Market Observer, r/BerkshireHathaway (January 2026)

The comparison is informal but analytically sharp. Nvidia designs the processors. Berkshire can own the power lines.

If AI represents a new industrial cycle, then Berkshire functions as its anchor — the capital bloc that ties digital exuberance to physical reality.

The Abel Doctrine (n.)

The recognition that digital intelligence is wholly dependent on physical infrastructure.
The reallocation of capital from speculative software growth toward defensible operational efficiency.
The aggregation of energy assets as the ultimate strategic moat in a digitized economy.

The transition from Buffett to Abel does not announce a departure from value investing. It extends it into a new domain. Value, in the AI age, is not confined to brands or consumer franchises. It resides in grids, rails, risk models and balance sheets.

Berkshire is not racing to build the smartest model. It is positioning itself to own the systems that make all models possible.

When the most conservative capital on Earth begins aligning with the thermodynamic realities of artificial intelligence, the signal is unmistakable: the AI boom is no longer merely technological. It is industrial.

And in industrial cycles, the decisive advantage belongs not to the loudest innovator, but to the heaviest balance sheet. Industrial cycles are not only economic phenomena; they reorder geopolitical leverage.

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Photo credit:
AI-generated illustration (DALL·E / OpenAI)

Caption:
Illustrative composition of industrial infrastructure, energy grids and digital intelligence beneath the American flag — symbolizing Berkshire Hathaway’s quiet repositioning from value investor to infrastructural anchor in the emerging AI economy.

The Material Turn

Why Wall Street Is Pricing AI as Infrastructure

As the generative hype cools, a new industrial reality emerges. From BlackRock’s energy bets to Berkshire’s quiet pivot, Wall Street is remapping AI from a Silicon Valley software story to a multi-trillion-dollar infrastructure cycle defined by megawatts and physical constraints.

For much of the past decade, artificial intelligence was narrated as a triumph of software — an exponential curve of smarter models, larger datasets and frictionless digital scale. Intelligence appeared to detach from geography, material limits and even from the industrial logic that governed earlier technological revolutions. The cloud, in this telling, was weightless.

Early 2026 is dissolving that illusion. Warehouses of processors draw as much electricity as small cities. Cooling systems consume rivers of water. Transmission bottlenecks delay deployments more effectively than any shortage of code. The constraint on artificial intelligence is no longer conceptual sophistication but physical capacity — how much power can be generated, moved and dissipated without destabilising the systems that support it.

Markets are beginning to reflect this reality. Valuations once driven by narrative momentum are being recalibrated around capital expenditure, grid access, thermal efficiency and land availability. Intelligence, it turns out, scales physically, not digitally.

“We believe there will be trillions of dollars of investment needed in infrastructure related to power grids and AI. Infrastructure is at the beginning of a golden age.”
— Larry Fink, Chairman & CEO, BlackRock

Fink’s assessment is less a prediction than a recognition of a structural shift already underway. The world’s largest asset manager is not repositioning toward electricity infrastructure because it is fashionable; it is doing so because electricity has become the binding constraint on the most celebrated technology of the era.

From Models to Megawatts

The first phase of the AI boom was defined by logical efficiency — improvements in algorithms, architectures and training techniques that made models more capable per unit of computation. The emerging phase is dominated by thermal efficiency: how effectively physical systems convert electricity into computation without overheating, failure or prohibitive operating costs.

This distinction is not academic. Logical efficiency can be improved through research; thermal efficiency requires steel, copper, cooling towers, substations and permitting processes. It transforms AI from a software industry into a heavy industrial ecosystem.

Utilities, transformer manufacturers, cooling specialists and grid operators — historically stable, low-growth sectors — now sit at the centre of strategic capital allocation. The speed at which new generation capacity can be brought online increasingly determines the pace at which AI capabilities can expand.

“The bottleneck for scaling AI has shifted from model capacity to speed to power. Intelligence is becoming a utility.”
— Strategic report, The Physical Bottleneck, Financial Content Markets (Feb 2026)

The phrase “speed to power” captures a new metric of competitiveness. In the early cloud era, speed to market mattered. In the AI infrastructure era, speed to electricity may matter more.

The Repricing of the Picks and Shovels

Technological revolutions rarely reward only the companies that produce the most visible innovations. Railroads enriched steel producers, land developers and financiers as much as locomotive manufacturers. Electrification transformed copper mining and utilities before it transformed consumer appliances.

AI appears to be following the same pattern. Investors are rotating from companies selling applications to those controlling the physical layer: semiconductors, data-centre construction, power equipment and energy supply chains. The market is rediscovering the logic of “picks and shovels” — the suppliers of essential inputs to a boom rather than its most glamorous participants.

This shift also reflects a deeper reassessment of risk. Software valuations depend heavily on expectations of future adoption and pricing power. Infrastructure assets, by contrast, generate predictable cash flows once operational, often backed by long-term contracts or regulated returns. In an environment of uncertainty, predictability itself becomes a premium asset.

“Investors are no longer willing to reward all AI exposure equally. Capital is rotating toward the physical layer where operational profit growth is visible.”
— Ryan Hammond, Senior Analyst, Goldman Sachs Research

Such repricing does not signal the end of AI innovation. It signals the maturation of the market’s understanding of where value will ultimately accumulate.

Capital at Scale: The Berkshire Signal

If BlackRock represents the world’s largest allocator of capital, Berkshire Hathaway represents something different: the archetype of patient, conservative capital. Its decisions are often interpreted less as tactical moves than as indicators of long-term structural confidence.

Greg Abel’s succession to Warren Buffett in January 2026 marks the beginning of a new era for the conglomerate. Early signals suggest not a departure from Berkshire’s cautious ethos but a recalibration of where durable value is likely to reside. A multibillion-dollar position in Alphabet reflects exposure not merely to advertising revenue but to cloud infrastructure, data centres and AI platforms embedded in the physical economy.

Within Berkshire’s operating companies, AI is being deployed less as a product than as an efficiency multiplier for large-scale systems — insurance underwriting, energy distribution and freight logistics.

“There is no question that AI will be a game-changer. It will affect how we manage risk and pay claims.”
— Ajit Jain, Vice Chairman, Insurance Operations, Berkshire Hathaway

Even more revealing is Berkshire’s exposure to rail and energy infrastructure. Optimising train networks through predictive analytics does not create a new digital service; it moves physical goods more efficiently across a continent. Algorithms become tools for improving throughput in systems built from steel and fuel.

Abel himself has emphasised the scale of investment required to support rising demand from data centres.

“The challenge for utilities is the enormous capital investment needed to modernize the grid and meet demand.”
— Greg Abel, CEO, Berkshire Hathaway

When capital that has historically favoured stability begins preparing for massive infrastructure spending, it suggests that the AI boom is entering a phase defined less by experimentation and more by construction.

Volatility and the End of Pure Hype

Financial markets in early 2026 exhibit a distinctive pattern: enthusiasm for AI remains strong, but tolerance for disappointment has evaporated. Companies that exceed expectations are rewarded disproportionately, while those that fall even slightly short experience sharp corrections.

This asymmetry reflects a transition from narrative-driven valuation to evidence-driven valuation. During the initial hype cycle, the mere association with AI could sustain elevated prices. As capital expenditures mount and timelines extend, investors demand proof of profitability.

At the macro level, some institutions remain bullish, arguing that AI-driven productivity gains could justify historically high market multiples. Yet even optimistic forecasts increasingly assume massive investment in energy systems, manufacturing capacity and supply chains — conditions more typical of industrial expansions than software booms.

AI, Trust and Systemic Risk

Beyond economics, AI introduces new vulnerabilities into financial systems themselves. The capacity to generate convincing synthetic voices, images and documents challenges the mechanisms through which markets verify information.

Warnings from major institutions about impersonation and fraud underscore that AI can erode trust as easily as it can create efficiency. In markets built on confidence, uncertainty about authenticity can propagate rapidly, amplifying volatility.

The issue is not merely criminal misuse but epistemic instability — difficulty determining what is real. In such an environment, authority figures and trusted institutions become targets for manipulation precisely because their credibility carries economic weight.

The Industrialization of Intelligence

Taken together, these developments point to a fundamental reframing of artificial intelligence. Rather than a discrete sector, AI is becoming an infrastructure layer embedded across the economy — comparable to electricity, rail networks or telecommunications.

This transformation has geopolitical implications. Regions with abundant, reliable energy supplies and favourable regulatory environments are positioned to attract data centres and the capital that accompanies them. Jurisdictions constrained by power shortages or permitting delays risk technological marginalisation regardless of their software talent.

AI capacity may thus evolve into a measure of national power, analogous to oil reserves in the twentieth century. The competition is not only between companies but between energy systems.

“AI investments will continue, but more capital will flow into energy infrastructure than into AI companies themselves.”
— BlackRock Investment Directions 2026

The concept of “sovereign compute” — the ability of a nation or region to host and sustain large-scale AI operations — is emerging as a strategic priority. In this context, megawatts become as consequential as microchips.

Conclusion: Intelligence Meets Physics

The narrative of artificial intelligence as an ethereal software phenomenon is giving way to a more prosaic but more consequential reality. Intelligence at scale requires enormous physical support systems: power plants, transmission lines, cooling infrastructure, specialised facilities and vast quantities of capital.

Wall Street’s recalibration reflects a recognition that technological revolutions ultimately obey the laws of physics and economics. The companies and regions that can supply energy reliably and afford sustained investment may capture disproportionate benefits, regardless of who produces the most elegant algorithms.

The decisive question of the AI age may therefore not be who designs the smartest systems, but who can finance, power and maintain them.

In the end, intelligence is not escaping the material world. It is becoming one of its most energy-intensive expressions — constrained by thermodynamics, enabled by infrastructure and priced by capital markets accordingly.

Photo credit:
AI-generated illustration (DALL·E / OpenAI)

Caption:
Stylized depiction of Wall Street’s iconic Charging Bull in the colors of the American flag — symbolizing the fusion of finance, national power and industrial-scale capital in the emerging AI infrastructure era.