The Looming Depreciation Crisis in AI Infrastructure: A Hidden Risk to Tech Giants and Neoclouds

The artificial intelligence (AI) boom has transformed global tech valuations, with hyperscalers and neoclouds investing billions in GPU-driven infrastructure. Yet beneath the surface of this growth lies a critical vulnerability: the rapid obsolescence of AI hardware and the flawed accounting assumptions used to value it. As companies extend depreciation schedules to mask the true cost of innovation, investors face a growing risk of overstated earnings and asset impairments.

The GPU Lifecycle: Training vs. Inference and the "Value Cascade"

GPUs at the cutting edge of AI training-such as Nvidia's Blackwell series-are rendered obsolete within 1-2 years as newer architectures emerge, according to a MBI Deep Dive. However, these chips retain economic value when repurposed for inference tasks, which prioritize throughput over raw computational power. Hyperscalers like Google and Microsoft employ a "value cascade" model, systematically redeploying older GPUs for less demanding workloads. For instance, Nvidia V100 GPUs have remained in service for up to 7.5 years in Azure's infrastructure, as noted in the MBI Deep Dive. This dynamic extends the useful life of hardware but creates a false sense of stability in depreciation models.

Accounting Assumptions: A Double-Edged Sword

The integration of AI into financial reporting has introduced new complexities. Deloitte's 2025 Technology Industry Accounting Guide highlights how AI-driven automation is reshaping revenue recognition and asset valuation, according to a Deloitte Tech Guide. Yet this same innovation is being weaponized to manipulate depreciation schedules. Hyperscalers like Amazon and Meta have extended server depreciation periods from 3 to 6 years, citing software optimizations that reduce hardware stress, as detailed in a Cernocapital article. While this lowers reported expenses and smooths earnings, it ignores the reality of AI hardware's short economic lifespan. As Michael Burry argues, such practices could inflate Oracle's profits by 27% and Meta's by 21% by 2028, per a Coinotag analysis.

Case Studies: When Assumptions Fail

The risks of flawed depreciation assumptions are already materializing. BigBear.ai, a defense-focused AI firm, reported an 18% revenue decline in Q2 2025 due to disrupted U.S. Army contracts and non-cash adjustments, as reported in a SSBCrack article. Meanwhile, C3.ai's exploration of a potential sale-amid a 19% year-over-year revenue drop-exposes the fragility of AI hardware valuations in competitive markets, according to a TechSavvy report. These cases underscore how geopolitical dependencies and market volatility can accelerate asset write-downs.

Meta's Q3 2025 results further illustrate the problem. Despite a 26% revenue increase, the company faced a $15.93 billion non-cash tax charge from the One Big Beautiful Bill Act, as detailed in a Meta investor release. Its aggressive AI infrastructure spending, now outpacing AI-generated revenue (Bain estimates $40 billion in annual depreciation vs. $15-20 billion in revenue, per a Coinotag analysis), signals a sector-wide imbalance. Investors are beginning to price in these risks: Meta's stock collapsed 12.3% in October 2025 as doubts about monetization strategies took hold, as noted in a Coinotag analysis.

The Investor Dilemma: Short-Term Gains vs. Long-Term Liabilities

Extended depreciation schedules artificially inflate margins, but they also create a deferred liability. If older GPUs lose value faster than assumed-due to technological shifts or market saturation-hyperscalers could face massive impairments. For example, Burry estimates a $176 billion understatement of AI industry depreciation from 2026 to 2028, per a Coinotag analysis. This would not only erode earnings but also strain cash flows as companies scramble to replace aging infrastructure.

Moreover, the rise of "Responsible AI" frameworks adds another layer of risk. EY's 2025 survey found that 90% of large AI adopters incurred financial losses due to compliance failures, biased outputs, or sustainability missteps, as reported in a Reuters article. These costs are rarely factored into depreciation models, further distorting valuations.

Conclusion: A Call for Prudence

The AI infrastructure boom is built on a house of cards: rapid innovation, extended asset lives, and optimistic accounting assumptions. While the value cascade model and strategic redeployment of GPUs mitigate some risks, they cannot fully offset the inherent volatility of hardware depreciation. Investors must scrutinize depreciation schedules with a critical eye, particularly for companies with aggressive AI spending and opaque governance. As the sector enters a "discrimination phase" where only monetizable AI strategies are rewarded, per a Coinotag analysis, the hidden depreciation crisis will likely reshape the tech landscape in 2026 and beyond.