AI Data Centers Lose Value Faster Than Expected, Forcing a Rethink of Infrastructure Financing

Written by:

Rudi Davis

Date:

24 Jan 2026

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The surge in AI infrastructure investment has brought in record amounts of capital. Still, operators warn that the economics behind these projects are not keeping pace with the rapid obsolescence of the technology itself. The core issue is not a lack of money or technical skill, but that the financial models built around long-term infrastructure cannot keep pace with the rate at which AI hardware and architectures become outdated.

George Teodorescu, COO of InfraPartners, describes a growing crisis for both operators and investors. He notes that while data centers were once expected to remain valuable for decades, their worth now declines sharply within just a few years as AI chipsets and computing architectures evolve. “The model doesn’t hold the way it used to,” Teodorescu says.

The core of the problem lies in how these projects are financed. Historically, data centers were treated much like real estate, funded with long-term loans and designed to generate value over 15, 20, or even 25 years. Teodorescu uses a mortgage analogy: building a house with a 30-year loan assumes you’ll get decades of use and value, with the option to renovate along the way. This logic carried over to data centers, where long productive lives justified massive upfront investments.

But with AI infrastructure, this calculation no longer works. Conventional data centers could operate for decades without significant upgrades. In contrast, AI-driven facilities see their core systems become obsolete in as little as 3 to 7 years. When the underlying technology depreciates this quickly, spreading costs over decades becomes unsustainable. “Suddenly, only in the last few years, these data centers are literally out of date in a small number of years,” Teodorescu says.

This disconnect between technological reality and financial planning is forcing operators to reconsider how they manage data center assets. The problem is not that upgrades are impossible, but that the financial mechanisms to recover value from those upgrades have not yet been developed.

InfraPartners has responded by trademarking the “upgradeable data center” concept. Teodorescu explains that this approach treats data center components as modular and reusable, rather than fixed installations. The company’s white paper outlines how structural steel, cooling systems, and electrical equipment can be salvaged and repurposed rather than discarded.

The process involves assembling data centers from prefabricated modules—essentially “like Lego bricks”—that can be taken apart, refurbished, and redeployed. Older modules can be moved to locations where less advanced technology is still useful, extending the life and value of these assets. Teodorescu points out that this model is new to the industry: “The financial model around all that doesn’t exist because this was never a thing before the last year, plus.”

The broader significance extends well beyond InfraPartners. Without new financial models that recognize the short useful life of AI infrastructure, operators and investors risk being left with stranded assets—facilities costing hundreds of millions that can no longer generate returns once their technology falls behind. Teodorescu warns that, without change, the industry faces a growing inventory of expensive but underperforming properties.

This challenge is not limited to one company or region. The entire sector is confronting a mismatch between the way infrastructure has traditionally been financed and the pace of change in AI computing. As Teodorescu explains, financial leaders are now being forced to engage with the core economics of the business. “Once the hype was over, it became genuine,” he says.

Whether the upgradeable data center model or similar approaches become industry standards will depend on how quickly financial structures adapt to treat rapid technological obsolescence as a regular part of the business rather than a rare exception.

The industry’s next phase will likely be defined by how well it can reconcile the speed of AI innovation with the need for sustainable investment. Operators, lenders, and investors will need to develop new financial tools and asset management strategies that reflect the reality that AI infrastructure depreciates far more quickly than traditional models anticipated. Those who adapt first may avoid the worst losses—and set the pace for the rest of the industry.