Financing the Physical Backbone of Artificial Intelligence

Key Takeaways:

• AI's biggest financing opportunity may be infrastructure, not software. Data centers, GPUs, power systems, cooling equipment, and networking hardware are driving unprecedented capital investment and creating significant demand for equipment financing.
• Banks are becoming more selective, creating opportunities for non-bank lenders. Regulatory capital requirements and relationship-based profitability models are causing many banks to reduce participation in technology infrastructure financing, opening the door for private credit and independent finance companies.
• AI infrastructure requires specialized financing strategies. Assets within a data center have vastly different useful lives, depreciation schedules, and residual value profiles, making tailored financing structures critical for managing risk.
• Flexible leasing is gaining traction as technology cycles accelerate. Fair market value (FMV) leases can help organizations keep pace with rapid hardware upgrades and refresh cycles while preserving capital and operational flexibility.
• Success in AI infrastructure finance depends on collateral expertise and valuation discipline. Understanding asset lifecycles, replacement cycles, secondary market values, and documentation requirements is essential as demand for AI-related equipment continues to grow.

Artificial intelligence may be grabbing headlines for its impact on software, productivity, and business operations, but behind every AI application sits a massive physical infrastructure buildout that requires financing. Data centers, power systems, cooling equipment, networking hardware, and increasingly expensive graphics processing units (GPUs) are becoming critical assets in the modern economy. According to recent data, combined capital expenditures among the world’s nine largest cloud service providers are projected to reach approximately $830 billion in 2026, representing nearly 79% year-over-year growth. 

While much of the public conversation focuses on AI models and applications, commercial finance professionals should be paying equal attention to the infrastructure supporting them. The scale of investment required to build, expand, and refresh these environments is creating financing demand that extends well beyond traditional technology lenders. 

At the same time, many of the institutions that historically financed technology infrastructure are becoming more selective. That shift is creating new opportunities for non-bank lenders willing to understand the collateral, structures, and operational realities behind these transactions. 

Why Banks Are Stepping Back 

For years, banks played a significant role in equipment finance, but regulatory and economic realities have altered the equation. Evolving capital requirements have increased the amount capital banks must hold against certain lending activities, making some equipment transactions less attractive than they once were. Despite periodic discussions about deregulation, many corporate borrowers and lenders are making decisions based on the regulatory environment that exists today, not the possibility of future policy changes. 

Just as important, many banks evaluate profitability based on the total value of a client relationship rather than the merits of an individual transaction. Equipment finance may be profitable on its own, but when compared against higher-yield offerings such as treasury management, wealth management services, commercial deposits, or revolving credit facilities, it often falls lower on the priority list. 

That does not mean technology infrastructure financing is becoming less attractive. In many cases, it means the opposite. The deals still work economically, they just simply no longer fit as easily within the capital allocation and relationship yield models many banks use today. 

This dynamic is creating a meaningful opening for non-bank lenders and private credit providers. Unlike banks, many nonbank finance companies evaluate opportunities on a transaction-by-transaction basis. Deals that no longer fit a bank’s internal economics can still generate attractive risk-adjusted returns. As AI infrastructure spending continues to accelerate, the pool of opportunities available to these lenders is likely to expand alongside it. 

Financing for a Faster Technology Lifecycle 

The growth of AI infrastructure is also influencing how transactions are structured. Historically, many technology acquisitions were financed through loans designed to support long-term ownership. Increasingly, however, borrowers are gravitating toward fair market value (FMV) lease structures. 

The reason is that technology is evolving faster. Organizations investing in AI infrastructure know that some of today’s hardware may need to be upgraded or replaced well before the equipment reaches the end of its physical life. Financing structures that provide flexibility are becoming increasingly valuable. 

Many companies are also becoming more deliberate about how they manage equipment throughout its lifecycle. Rather than supporting every conceivable contract structure, organizations are often standardizing around financing approaches that make future upgrades easier to manage. FMV leases can offer flexibility that traditional ownership-focused structures often lack, particularly when technology refreshes become necessary. 

The trend reflects a broader shift in mindset. For many borrowers, the financing discussion is becoming less about ownership and more about maintaining access to the most effective technology over time.

Understanding the Collateral 

One of the most important lessons for lenders entering this market is that AI infrastructure should not be viewed as a single asset class. 

A data center may contain assets with dramatically different useful lives, depreciation schedules, and residual value profiles. The facility itself may retain value for decades. Power and cooling systems often have useful lives measured in 10 to 15 years, while GPUs and other high-performance computing equipment may have economically relevant lives of only three to seven years before technological advances diminish their value. 

Yet some borrowers and even experienced executives continue to approach these projects as though all assets should be financed the same way. Doing so may simplify documentation, but it can also create unnecessary risk. 

A useful comparison is the distinction between a mortgage and a home equity line of credit. Different assets require different financing structures because they behave differently over time. The same principle applies within AI infrastructure environments. Matching financing terms to the expected life of the underlying asset is critical to protecting both borrowers and lenders. 

For commercial finance professionals, this means collateral analysis becomes especially important. Understanding what is being financed, how quickly it is likely to depreciate and what replacement cycles may look like can significantly influence transaction performance. 

Where Deals Go Wrong 

Despite strong demand, many of the challenges associated with AI infrastructure financing have little to do with technology itself. 

One common issue is speed. Founders and operators often work under aggressive deployment timelines and intense competitive pressure. In their rush to bring computing capacity online, financing considerations can become secondary. Borrowers may commit to delivery dates, customer contracts, or infrastructure plans before capital arrangements are fully finalized. 

Legal complexity creates another layer of friction. Landlord waivers, for example, can be particularly difficult to obtain when equipment is housed inside third-party data centers. Security protocols, operational restrictions, and facility ownership structures often make negotiations more complicated than those associated with traditional equipment transactions. 

Technology updates can also create challenges when they are not anticipated upfront. Many financing agreements do not automatically accommodate mid-term equipment replacements. If borrowers expect hardware upgrades during the financing term, those possibilities should be addressed during structuring rather than after the need arises. Flexible lease structures, vendor support, and clearly defined replacement provisions can help avoid costly amendments later. 

Valuation discipline may be the most important consideration of all. Demand for AI-related equipment has created situations where borrowers pay substantial premiums for hardware. For lenders, that can quickly become a collateral problem. Recovery values are based on what an asset can reasonably command in the marketplace, not what someone happened to pay during a period of heightened demand. In some cases, an overvalued asset may present greater risk than an asset with little secondary market value at all.

What Commercial Finance Should Be Watching 

Commercial finance has historically evolved alongside major economic transformations. Railroads, manufacturing, telecommunications, and renewable energy all created new asset classes that required specialized financing expertise. AI infrastructure appears poised to follow a similar path. 

The combination of growing demand, changing financing structures, and reduced participation from some traditional lenders is creating opportunities for commercial finance professionals willing to develop expertise in the space. At the same time, success will require a clear understanding of asset lifecycles, collateral management, documentation requirements, and valuation fundamentals. 

The AI boom may be driven by software innovation, but its foundation is physical infrastructure. Those facilities and assets all require capital. For commercial finance professionals, that reality presents a growing opportunity, not simply to participate in the AI economy, but to help build the infrastructure that makes it possible. 

Riley Thompson is VP and head of Direct Sales at Mitsubishi HC Capital America, one of the nation’s largest non-bank commercial equipment finance companies. With nearly 30 years in equipment finance and more than $2.5 billion in funded loans and leases over his career, he oversees the company's direct sales operation across the US.