TL;DR

AI GPU depreciation refers to how companies spread the cost of expensive AI hardware over time in their financial statements.
Major tech firms have extended the useful life of AI GPUs from 3 years to as long as 6 years, lowering annual depreciation expenses.
This change boosts reported profits but raises investor concerns about whether the numbers fairly reflect true economic reality.
Critics argue that rapid advances in AI chip technology make long depreciation periods unrealistic.
Auditors and regulators are watching closely, as depreciation policy has major implications for investor trust and valuation.

What Is GPU Depreciation

AI GPU depreciation is the process of allocating the cost of AI-related computing hardware—especially graphics processing units (GPUs)—over their estimated useful life. This accounting treatment determines how much of the cost is recognized as an expense each year. The longer the useful life, the smaller the annual expense and the higher the company’s reported profits.

Traditionally, server hardware was depreciated over about three years. But as AI infrastructure costs surged, major tech firms such as Microsoft, Google, and Meta began extending depreciation schedules for AI GPUs to five or even six years.

Graphics Processing Unit (GPU): A Simple Definition

A graphics processing unit (GPU) is a specialized computer chip designed to handle large amounts of visual and mathematical information very quickly. Originally built to display graphics—like images, videos, and video games—GPUs are now widely used for tasks that require fast, repeated calculations, including artificial intelligence, scientific research, and data analytics.

How a GPU works (in simple terms):

While a regular computer processor (CPU) is good at handling a few tasks at a time, a GPU can handle thousands of small tasks at once. This makes it ideal for anything involving big batches of data—whether that’s rendering a 3D scene in a video game or training an AI model.

GPUs make modern visuals smoother, AI tools smarter, and data processing much faster. They’re one of the key pieces of hardware powering today’s technology—from gaming PCs to self-driving cars to cloud computing.

Why It Matters

Depreciation policy directly affects a company’s bottom line. When a firm extends the useful life of its equipment, it lowers its yearly depreciation expense. This accounting choice can significantly inflate short-term profits without changing actual cash flow.

For example, Meta disclosed that extending the useful life of its AI servers to 5.5 years would reduce 2025 depreciation charges by $2.9 billion. While this boosts near-term earnings, it also delays expense recognition into future years, potentially masking the true cost of keeping up with rapid AI innovation.

Key Concepts

Depreciation: A non-cash accounting expense that spreads an asset’s cost over its estimated useful life.
Useful life estimate: The number of years a company expects hardware to remain productive and economically valuable.
Impact on profits: Longer useful life → smaller annual depreciation → higher reported profits.
Technological obsolescence: The risk that rapid hardware innovation renders AI GPUs outdated long before their accounting life ends.
Auditing: Firms must justify useful life assumptions with data such as performance metrics, utilization rates, and engineering analysis.

Examples

Meta Platforms: Increased GPU lifespan from 4 to 5.5 years, reducing expenses by billions annually.
Microsoft & Google: Extended depreciation schedules to around 6 years for AI servers, aligning with internal studies on system durability and usage.
Investor scrutiny: Investors such as Michael Burry have warned that aggressive depreciation policies could inflate tech earnings much like pre-2008 accounting optimism in housing and finance.

Limitations and Risks

Obsolescence risk: AI hardware evolves rapidly, and new generations of GPUs can outperform old ones by multiples, limiting their true economic lifespan.
Profit distortion: Longer depreciation can make companies appear more profitable than they are in cash terms.
Regulatory and audit risk: If auditors find that assumptions are unrealistic, companies may face restatements or investor backlash.
Capital intensity: AI infrastructure requires enormous upfront investment, and small shifts in accounting assumptions can materially affect earnings and valuations.

FAQ

Why are companies extending GPU lifespans? They argue that modern GPUs are more durable and can still be productively reused for lower-priority tasks even after newer models arrive.
Why are investors worried? Because longer depreciation schedules can artificially boost earnings, giving a rosier financial picture than reality.
Does this affect cash flow? No—depreciation is non-cash. But it changes reported net income and, by extension, valuation metrics like P/E ratios.
How do auditors verify useful life? They review technical and operational data from companies to confirm that the equipment truly remains productive over the stated period.
Could regulators intervene? Possibly, if depreciation policies are deemed misleading or inconsistent across the industry.