By Jorge Cárdenas

Artificial intelligence is reshaping not just digital industries, but the foundations of the physical world. The arrival of large-scale computation and LLMs is triggering a new infrastructure cycle—one defined as much by where we build and invest as by how we value and operate what already exists.

At Vallorii, we see two intertwined forces driving this change. The first is a demand shock: AI’s extraordinary appetite for power, land, and grid infrastructure is reordering capital flows while traditional sectors—from transport to water—are adapting to compete for investment. The second is an analytical shock: AI’s ability to quantify risk, forecast demand, and integrate financial logic with real-world operations is transforming the way infrastructure is valued and financed.

Together, these forces are repricing infrastructure—shifting capital towards assets that are not only essential, but measurable, transparent, and intelligent.

The Demand Shock – AI’s Physical Footprint

Every digital model sits on a physical foundation. Training and running AI Agents and systems requires energy-hungry data centres, resilient grids, cooling infrastructure, and high-capacity fibre.

The International Energy Agency (IEA) projects that global data-centre electricity consumption will almost double by 2030, reaching around 950 terawatt-hours, roughly equal to Japan’s total power use. In the United States, the Energy Information Administration expects AI and data workloads to drive nearly half of new electricity demand growth through 2030.

In Europe and the UK, data-centre load is on a similar trajectory. Britain’s National Energy System Operator (NESO) forecasts power demand from data infrastructure rising from around 5 TWh today to up to 35 TWh by 2030, while S&P Global expects total European capacity to nearly double in that time.

These pressures are redrawing investment priorities. Capital is clustering around the physical enablers of intelligence—generation, transmission, energy storage, and digital real estate. Investors once focused on transport, water, or social projects are shifting toward assets tied to compute and power. McKinsey estimates that $7 trillion of new global capital will flow into data infrastructure by 2030.

The result is a subtle but fundamental repricing. Assets with access to cheap, clean energy and rapid grid connections now command a premium. Those with slower permitting or weaker resilience face a higher cost of equity. Infrastructure finance is migrating toward assets that can keep pace with the data economy.

The Analytical Shock – From historical analysis to future foresight

While AI is fuelling new demand for infrastructure, it is also revolutionising how infrastructure is analysed, valued, and managed.

Traditional valuation has long relied on static spreadsheets and expert judgement—models that look backwards rather than forwards. AI replaces this with data-driven foresight. Machine-learning systems can process thousands of signals—weather patterns, demand curves, cost trajectories—to generate probabilistic forecasts and dynamic valuations.

This shift matters. It turns uncertainty into something measurable. Investors can simulate thousands of futures rather than relying on a single forecast. They can calibrate discount rates to risk distributions rather than opinion. EY’s 2024 study found that applying AI-based forecasting and cost-estimation tools could reduce infrastructure project costs by 10–15%, saving the UK alone £150 billion by 2040. But the biggest problem is the existing financing gap: the same study estimates that the United Kingdom alone faces a £700 billion infrastructure funding gap by 2040, with over £1.6 trillion in projects currently without identified financing. It becomes clear that only the projects that can show the usage of AI and data will be the ones being prioritized when capital is scares.

How can this happen? AI does not just transform valuation—it closes the historic gap between finance and operations.

Today, most infrastructure assets are run operationally by engineers and valued financially by analysts, with limited feedback between them. As AI models integrate live operational data—on utilisation, efficiency, maintenance, and failure probabilities—they connect both worlds. When an asset performs better than expected, its cost of capital should fall. When operational risks emerge, financing terms should adjust. AI makes this feedback loop real.

At Vallorii, this is central to how we work. We combine data, economics, and AI to create forward-looking valuation systems that stay in sync with real-world performance. Specifically, we focus on three areas where AI closes critical gaps:

1.        Forward-Looking Valuation and Risk. Our models use AI powered probabilistic forecasting—moving the valuation from historic averages to future drivers: demand volatility, energy costs, technology learning rates, and policy shifts. This produces transparent, defensible valuations investors can trust and enhances understanding of how external variables—such as compute intensity or power availability—affect future revenue and cost profiles.

2.        Probabilistic risk modelling. Our Monte Carlo analysis, calibrated with AI-derived distributions, helps quantify uncertainty rather than mask it. Instead of single-point estimates, investors can view full probability curves for outcomes like cost overruns or demand volatility, improving comparability across projects, understanding of P10, P50 and P90 scenarios while allowing benchmarking of distributions. This is the future of investability analysis.

3.        Operational Feedback and Intelligence. We connect financial models to live asset data. This ensures that every valuation evolves with the asset’s actual performance—aligning operational realities with financial expectations and making risk-taking visible, measurable, and manageable. This provides a more responsive basis for setting discount rates and adjusting portfolio exposure over time, e.g. how sector-specific risk premia evolve.

The result is a closed loop between operations and finance—a long-standing missing piece in infrastructure management. It means the next era of infrastructure economics will not just be built smarter; it will be valued smarter too.

The New Logic of Capital

AI’s twin shocks are reshaping how capital is deployed and priced.

• Dynamic cost of capital. The traditional Weighted Average Cost of Capital (WACC) is becoming dynamic. As AI reduces uncertainty, lenders can increase leverage, and equity investors can accept lower premiums. Projects that quantify their risks precisely will finance more cheaply than those that cannot.

• Speed and sequencing. In a constrained world—of grids, materials, and labour—timing has real economic value. AI-enhanced planning tools can identify which projects yield the highest value of time, allowing investors to prioritise builds that unlock the greatest capacity first.

• Capital efficiency. The integration of AI into valuation and operations means less waste, fewer overruns, and more predictable outcomes. By reducing volatility and improving transparency, AI effectively expands the investable universe—enabling more projects to reach financial close.

These shifts redefine what “investable infrastructure” means. In the coming decade, the premium will belong to assets and portfolios that demonstrate intelligence: measurable performance, real-time data, and adaptive valuation frameworks.

Implications Beyond the Balance Sheet

The repricing of infrastructure has ripple effects that extend beyond finance.

• Energy systems must scale faster to power AI’s expansion, even as decarbonisation targets tighten.

• Investment geography will matter: locations with flexible grids, skilled labour, and clear planning will attract capital first.

• Social equity and affordability come into play: as private money flows to AI-linked assets, governments will need smarter financing mechanisms to fund transport, water, and housing.

• Sustainability will be a test of credibility—AI’s power intensity must be offset by the efficiencies it brings elsewhere.

These questions are not just academic. They shape how societies fund growth in an intelligent age.

A New Discipline: Intelligent Capital

Infrastructure is entering a new age—one where value depends not just on concrete and steel, but on data, transparency, and feedback. AI is both the cause of disruption and the tool to manage it.

At Vallorii, we believe the future of infrastructure finance will hinge on how well investors connect intelligence with capital. That means pricing uncertainty rather than ignoring it, linking financial models with operational data, and building valuation frameworks that learn, adapt automatically and integrate newly available data in seconds.

The repricing of infrastructure is already underway. Those who adapt—by making AI part of their capital discipline—will allocate more efficiently, finance more confidently, and build faster. Those who do not risk owning yesterday’s assets at tomorrow’s prices.

Key References

·       International Energy Agency (2025) – Energy and AI Outlook

·       U.S. Energy Information Administration (2025) – Short-Term Energy Outlook

·       NESO (2025) – Future Energy Scenarios

·       S&P Global Market Intelligence (2025) – European Data Centre Outlook

·       McKinsey & Company (2025) – The Cost of Compute

·       EY-Parthenon (2024) – Bridging the UK Infrastructure Gap

·       European Parliament (2025) – Artificial Intelligence and the Energy Sector