The Billion-Dollar Demand of AI: How New Data Centers Are Being Funded

The race for artificial intelligence is prompting a shift in the narrative that can no longer be explained solely with GPUs, racks, or megawatts: now the uncomfortable question is who’s funding it and under what rules. Amid the fever for computing capacity, data centers have stopped being just an “IT” chapter to become critical infrastructure, with a financial logic increasingly similar to that of toll roads, airports, or renewables.

In this context, analysts and investment banks have started putting numbers to the phenomenon: the tech sector faces a near one-trillion-dollar investment barrier to sustain capacity expansion related to AI, especially in the United States, where most of the effort is concentrated.

From Wall Street to Tomelloso: the map of data centers is ruralizing

The most visible collateral effect is geographic. Where once we spoke of classic “hubs,” now projects appear in locations that until recently were outside the radar of the digital market. In Spain, for example, data center development operations involve international corporate structures aiming to prepare land, permits, and electrical connections in order to later sell the ready-to-build asset or attract the major tenant who unlocks financing.

This nuance is key: the business is no longer just “building a data center,” but turning a project into a financing asset, which requires a very specific recipe.

The three main paths in the “playbook” of financing

In practice, the industry relies on three major routes that often combine at various project stages:

1) Corporate debt: Big Tech leverages its balance sheet to buy time (and megawatts)

Hyperscalers and large tech companies have a clear advantage: balance sheets, credit ratings, and market access. They issue debt to secure long-term capacity, commit orders, sign energy contracts, and speed up deployments without waiting for each project to “mature” financially.

This trend is evident in the increase of planned issuances by instrument type (especially in the U.S.), where investment-grade bonds become the main driver when the issuer is a Big Tech or a financially robust player.

2) Project finance and private credit: when the data center is financed “as an asset”

The second route resembles traditional infrastructure financing: special purpose vehicles (SPVs) are created, debt is structured based on project cash flows, and risk is shared among promoters, banks, funds, and sometimes industrial partners.

But here, a key condition dominates: without critical permits and long-term lease/capacity agreements, cheap financing is unavailable. Banks and funds are not financing “dreams,” but reasonably secured cash flows.

An example heavily circulated in the market involves financing structures with financial partners to develop large campuses, with billions in infrastructure projects in the U.S.

3) Securitizations and refinancings: moving to “asset-backed” when the asset is operational

The third path occurs once the data center is up and running (or highly stabilized): assets and cash flows are bundled for refinancing at lower costs, freeing capital for further construction.

This approach, common in real estate and mature infrastructure, is gaining traction in digital infrastructure. In Europe, there have already been securitization operations linked to data center portfolios, with significant amounts and a focus on assets in Germany.

The “step-by-step” process that determines whether a project secures funding (or gets stuck)

In the financial world, data centers are now evaluated as infrastructure projects with clear milestones:

1. Land and industrial use license (securing location and urban feasibility).
2. Water and electricity (and often, negotiating power is the real bottleneck).
3. Operational permits and construction-ready design.
4. Tenants/contracts: without long-term capacity agreements, debt becomes expensive or unavailable.
5. Refinancing: when the asset generates stable cash flow, the door opens to bonds, securitizations, or cheaper debt.

This sequence explains why the sector is shifting toward “asset-backed” models: capital seeks certainty, and in data centers, that certainty comes from energy + permits + contracts.

Table: how the “wave” of debt associated with digital infrastructure is growing

Based on market estimates (in billion dollars), the pattern is clear: volumes are rising, and financing is diversifying among public debt, private credit, and project finance.

More “asset-backed” than corporate finance? Probably yes… but with nuances

The market’s driving logic is almost unavoidable: if digital infrastructure is going to absorb massive investment figures, capital will seek mechanisms to recycle balance sheets, lower average costs, and continue building. That’s where securitizations and refinancings make sense.

But corporate debt won’t disappear: Big Tech will continue using direct debt because it offers speed and strategic control. The real battleground is in the middle layer: operators, developers, and funds that need to scale without turning every expansion into an endless equity round.

In other words: AI is not only reindustrializing computing. It’s also reindustrializing the finance that supports it, with data centers increasingly treated as regulated infrastructure assets backed by the market’s toughest duo: available power and signed contracts.

Frequently Asked Questions

What’s the difference between “corporate debt” and “project finance” for a data center?
Corporate debt is backed by the company’s (Big Tech or operator) balance sheet. In project finance, the debt relies mainly on the project itself (SPV) and its future cash flows, with milestones and covenants tied to the asset.

Why are long-term contracts so important for accessing cheap financing?
Because they reduce revenue risk. A data center without tenants is a gamble; with capacity agreements over several years, it becomes an asset with predictable cash flows, allowing for lower-cost financing.

What is securitization applied to data centers?
It involves bundling assets and cash flows (leases/contracts) into a financial structure to issue debt backed by those revenues. Usually used to refinance, free up capital, and continue investing.

What’s the biggest bottleneck today for financing new data centers?
Beyond the money, the main limitations are energy (power), permits, and connection timelines. Without those, the project can’t “close,” neither technically nor financially.