Major technology companies including Google, Meta, and Amazon are investing billions of dollars in artificial intelligence infrastructure. However, a significant and often overlooked financial risk is emerging from how these massive investments are accounted for, potentially impacting future profitability and stock valuations.
Analysts are now pointing to a growing gap between Wall Street's profit expectations and the true cost of the AI hardware boom. The issue centers on depreciation expenses, which are set to accelerate much faster than the revenue generated from new AI products and services.
Key Takeaways
- Major tech firms face rising depreciation costs from their massive AI infrastructure investments.
- Analysts project these costs will grow significantly faster than AI-related revenue, squeezing profit margins.
- The useful lifespan of expensive AI hardware may be shorter than anticipated, forcing companies to account for costs more quickly.
- Wall Street consensus estimates may not fully account for the scale of these future expenses, creating a potential risk for investors.
The Hidden Cost of AI Infrastructure
When companies like Alphabet (Google's parent company), Meta, and Amazon purchase long-term assets such as servers and advanced graphics processing units (GPUs), they do not expense the full cost immediately. Instead, the cost is spread out over the asset's 'useful life' through a process called depreciation and amortization.
This standard accounting practice allows companies to match expenses with the revenue an asset helps generate over several years. However, the rapid pace of AI development is creating a unique challenge. The hardware purchased today may become obsolete much faster than previous generations of equipment, which could force companies to shorten these 'useful life' timelines and recognize costs more quickly.
Understanding Depreciation
Depreciation is an accounting method used to allocate the cost of a tangible asset over its useful life. It represents how much of an asset's value has been used up. For tech companies, this applies to the vast server farms and specialized computer chips that power AI services. Higher depreciation directly reduces a company's reported operating income.
A Widening Gap in Financial Projections
Concerns are growing that many financial models are underestimating the future impact of these depreciation costs. According to analysis from Bank of America, there is a significant disparity between its projections and the consensus estimates on Wall Street.
The bank's forecasts for depreciation expenses are consistently higher than the market average for Google, Meta, and Amazon. This suggests that the broader market may be underappreciating the future pressure on these companies' earnings.
Projected Discrepancy by 2027
Bank of America projects a substantial gap between its depreciation estimates and Wall Street consensus for 2027:
- Alphabet (Google): $7 billion gap
- Amazon: $5.9 billion gap
- Meta: $3.5 billion gap
These figures indicate that billions of dollars in future expenses may not be fully priced into current stock valuations, potentially making these companies appear more profitable than they will be in the coming years.
Costs Outpacing Revenue Growth
The core of the issue is the speed at which these costs are expected to grow compared to revenue. While AI is expected to create new revenue streams, the associated depreciation expenses are projected to climb at a much steeper rate. This creates a challenging financial dynamic where massive investment is required long before substantial monetization is achieved.
"The Street is still playing catch-up on growing depreciation expense," wrote Bank of America analyst Justin Post, highlighting that consensus estimates have not yet absorbed the full impact of the current AI capital expenditure cycle.
According to Bank of America estimates, the combined revenue for Alphabet, Meta, and Amazon is expected to grow at a rate of 13% in 2026 and 12% in 2027. In stark contrast, their combined depreciation expenses are forecast to surge by 33% in 2026 and another 30% in 2027.
This mismatch means that even with healthy revenue growth, profitability could be significantly eroded by the escalating costs of maintaining a competitive edge in AI.
The Shrinking Lifespan of AI Hardware
For years, many large tech companies extended the useful life of their servers and networking equipment in their accounting, a move that allowed them to spread costs over a longer period and boost near-term profits. A typical lifespan was often revised from four or five years to six years or more.
However, the intense demands and rapid innovation in AI are reversing this trend. The technology is evolving so quickly that state-of-the-art hardware can become outdated in a much shorter timeframe.
In a sign of this shift, Amazon recently changed the useful life of some of its server and networking assets from six years back down to five years. This single-year adjustment increases the annual depreciation expense for those assets, directly impacting the company's bottom line. If other companies follow suit, it could trigger a wave of revised earnings forecasts across the sector.
The Race to Monetize AI
The immense spending on AI infrastructure, which includes billions on GPUs from companies like Nvidia, underscores a high-stakes bet on future returns. Bank of America estimates that the combined capital expenditures for Google, Meta, and Amazon will climb 22% to reach $333 billion in 2026.
With costs accelerating, the pressure to generate meaningful revenue from AI is immense. While advertising and cloud computing are early areas of monetization, the revenue generated so far is not keeping pace with the growth in depreciation expenses.
There is also the risk of overbuilding. If the supply of AI computing power outpaces demand, it could lead to a price war among cloud providers. This scenario would commoditize AI services, further squeezing profitability and making it even harder to recoup the massive upfront infrastructure costs. Ultimately, the clock is ticking for Big Tech to prove that its historic investment in AI can deliver a proportional financial return.
