The Hidden Accounting Time Bomb Behind the AI Investment Boom: Earnings Cliff Risk and Korea’s HBM Supercycle

📌 The Hidden “Accounting Time Bomb” Behind the AI Investment Boom ― Implications for Global Financial Markets and Korean Companies

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Introduction ― The Shiny Surface of the AI Rally

As of 2025, the single most influential keyword shaping global financial markets is undoubtedly Artificial Intelligence (AI). Just two to three years ago, AI was regarded as one of many emerging technologies, but today it has become the central engine of global capital flows and the foundation of corporate strategy.

Tech giants such as Microsoft, Alphabet (Google), Amazon, and Meta are pouring massive amounts of money into GPU purchases and data center expansion. The scale of these investments goes beyond corporate budgets, exerting such a powerful ripple effect that it even lifts U.S. GDP growth. Investors are cheering this development as “AI is the new growth engine of the global economy.” Yet, behind this glittering success story lies a shadow that is less frequently discussed.

That shadow is the “Accounting Time Bomb” ― the hidden risk of depreciation costs. While the actual technological lifespan of AI infrastructure like GPUs and servers is only about one to three years, corporate balance sheets still record them as five- or even six-year assets. This discrepancy inflates net profits in the short term, but a few years down the road, it could strike back as a devastating Earnings Cliff for the market.

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Part 1. The Age of Massive AI Investment ― Astronomical CAPEX and Its Global Economic Impact

1. Explosive Spending by the Big Four

AI competition has escalated beyond mere technological advancement to become a massive capital boom reshaping global markets.

In Q2 2025, the combined capital expenditures (CAPEX) of Microsoft, Alphabet, Amazon, and Meta reached $96.8 billion, a 65% year-over-year increase. This marks the largest single-quarter private sector investment in history.

Amazon alone spent $32.18 billion on property and equipment in Q2, with its first-half spending totaling a staggering $57.2 billion ― exceeding the annual infrastructure budgets of many entire nations.

Alphabet spent $22.4 billion in the same quarter. Microsoft spent $24.2 billion in its fiscal Q4 and projected over $30 billion in the following quarter.

Meta went further, raising its annual CAPEX forecast to $66–72 billion, effectively declaring an “all-in” AI infrastructure strategy.

Taken together, these moves suggest that global AI infrastructure spending in 2025 alone will surpass $400 billion. No previous industry has ever absorbed such concentrated capital in such a short span of time.

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2. Impact on the U.S. Economy

This colossal investment has not only influenced corporate financials but also reshaped national economic indicators.

Barclays estimated that data center investment contributed roughly 1 percentage point to U.S. GDP growth in the first half of 2025. In other words, at a time when recession fears loomed, AI spending acted as the “relief pitcher” for the U.S. economy.

However, this is a double-edged sword. While AI spending currently lifts GDP, if investment momentum slows or capital returns lag, the vacuum could directly drag down economic growth. This reveals a structural vulnerability: the AI investment cycle has become inseparable from the broader economic cycle.

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3. Physical Constraints ― The Power Crunch

Another variable in AI investment is not technology or money, but physical limitations.

AI data centers run thousands of GPU servers at full capacity 24/7, consuming enormous amounts of electricity. A state-of-the-art data center can use as much power as a small city.

AWS (Amazon Web Services) has admitted bluntly: “The biggest constraint on AI expansion is not money, but power.”

S&P Global has echoed this, noting: “Transmission infrastructure is currently the most severe bottleneck.”

Thus, no matter how much capital is deployed, if power grids and transmission infrastructure cannot keep up, data center expansion becomes impossible. In fact, some regions are already experiencing delays in new data center construction due to power shortages. This means the pace of AI investment could be forcibly slowed by physical infrastructure limits.

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👉 In short, AI investment is currently setting the global economy ablaze. But the sheer scale and speed of this boom also cast shadows in the form of economic, physical, and accounting risks.

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Part 2. The Shadow of Accounting ― Technological Lifespan vs. Accounting Lifespan

The biggest accounting issue in AI infrastructure investment is depreciation. Depreciation refers to spreading the cost of an asset (like GPUs, servers, or data centers) over a set number of years. The problem is that how long this lifespan is set can dramatically alter a company’s reported profits.

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1. The Illusion of Depreciation ― “Long-Lived Assets” on Paper, “Short-Lived Assets” in Reality

The actual technological lifespan of GPUs and servers is at most three years. When new chips are released, older ones are quickly rendered obsolete for high-performance AI training. And because data centers run equipment 24/7 under heavy stress, the usable lifespan can be even shorter.

Yet, corporate accounting records continue to assume a useful life of five to six years. This mismatch makes assets look longer-lasting than they are, lowering annual depreciation expenses and creating the illusion of higher profits.

In 2022, Microsoft and Alphabet extended the useful life of their servers and network equipment from four to six years.

As a result, Microsoft’s FY2023 profits rose by about $3.7 billion simply due to reduced depreciation charges.

Alphabet saw a similar boost, with about $3.9 billion in extra profit.

On paper, this looks like “efficient management,” but in reality it is just inflated profits courtesy of accounting assumptions.

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2. Warning of an “Earnings Cliff” ― 2026 as a Turning Point

This illusion may boomerang back in a few years. Barclays has warned that Wall Street is grossly underestimating future depreciation costs.

Their analysis projects Alphabet’s depreciation expenses will reach $28 billion in 2026, far above the consensus estimate of $22.6 billion ― a $5.4 billion (24%) gap. This difference would directly slash operating income.

Barclays suggested that Meta, Amazon, and Microsoft face similar risks. In other words, today’s optimistic earnings forecasts may be overstated, and by around 2026, markets could face a sharp Earnings Cliff.

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3. A Real-World Case ― The Oracle Shock

This risk is not just theoretical.

Oracle expanded its AI server leasing business with heavy GPU investments, but its average gross margin over the past five quarters was just 16% ― drastically lower than its traditional software business margins of about 70%.

Following this revelation, Oracle’s stock price plunged, cementing the realization that “AI services are not instant money-makers.” The initial costs of GPUs, data center expansion, and electricity were simply too overwhelming to generate high profits in the short term.

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🧾 Summary

The mismatch between technological lifespan (1–3 years) and accounting lifespan (5–6 years) inflates profits now but risks an eventual blowback.

Around 2026, firms like Alphabet, Meta, Amazon, and Microsoft could face an Earnings Cliff.

Oracle’s case already shows that AI is not an immediate profit engine.

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Part 3. Can AI Infrastructure Spending Be Sustained?

1. Cannibalistic Innovation ― When Progress Eats Itself

The AI hardware market is evolving at unprecedented speed, epitomized by NVIDIA’s announcements. CEO Jensen Huang declared a shift to a one-year GPU release cycle:

2024: Blackwell

2025: Blackwell Ultra

Next: Rubin

This means that GPUs worth billions can become outdated in just a year. For instance, Google’s TPU upgrades improved computational carbon intensity (CCI) threefold across generations, rendering previous models far less valuable almost overnight. Running old chips is uneconomical: power costs rise, cooling costs increase, and performance lags.

Thus, rapid innovation risks turning corporate assets into “cannibalistic investments” where progress accelerates depreciation rather than generating durable value.

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2. The Rise of the Secondary GPU Market ― A Safety Valve?

Interestingly, outdated GPUs are not entirely useless. While they cannot handle massive AI training, they are still valuable for inference, fine-tuning, and academic research.

This has given birth to a secondary GPU market. Firms like Prokary and Bitpro specialize in reselling used GPUs from Big Tech companies. This allows corporations to recoup some residual value and reduce depreciation burdens.

However, the scale of this market remains limited. It may act as a partial cushion, but it cannot eliminate the systemic risk of depreciation shocks.

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3. The Korean Angle ― The HBM Supercycle’s Bright and Dark Sides

Among the biggest beneficiaries of this AI investment boom is South Korea.

The market for High Bandwidth Memory (HBM), essential for AI accelerators, is dominated almost exclusively by Samsung Electronics and SK hynix. The AI surge has lifted their performance and solidified their monopoly in this critical segment.

SK hynix has enjoyed soaring demand for HBM sales throughout 2025.

Samsung, as a late mover, is quickly catching up to increase its market share.

But the risks are clear. If global AI investment slows or the Earnings Cliff materializes, Korean semiconductor firms will face a demand cliff as well. Heightened competition could also trigger price declines, threatening the so-called “HBM Supercycle.”

The Korean government is attempting to mitigate these risks by investing heavily itself. Plans call for ₩65 trillion ($47 billion) in joint public-private AI investments by 2027, with R&D spending for 2026 raised to ₩35.3 trillion, a 19.3% increase. This signals Korea’s ambition to position itself not just as a supplier, but also as a proactive consumer and strategic player in AI.

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Conclusion ― Accounting Risks and Investor Implications

AI is undeniably the central growth driver of the global economy in the late 2020s. Yet, while the near-term boom may produce dazzling numbers, ignoring structural risks could prove costly.

Short-term opportunity: Korean semiconductor firms and AI infrastructure supply chains are enjoying peak benefits.

Medium-term risks: Accelerated tech cycles, exploding depreciation costs, and power grid bottlenecks could trigger an Earnings Cliff.

Investor takeaway: Do not be swayed solely by headlines like “AI sales are growing.” Instead, scrutinize profitability, residual asset value, and accounting methods.

AI innovation is real. But given the speed of progress and the cost structure, part of today’s profitability may be an illusion created by “the magic of numbers.”

The market’s focus is shifting beyond top-line growth to a deeper question: “How much actual profit is being made?” The answer to this question will determine whether AI investment proves sustainable or collapses under its own weight.

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✅ This article is written for informational purposes only. It does not constitute a recommendation to buy or sell any specific stock.

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📚 References

1. The Information – Coverage of Oracle’s AI cloud margins and infrastructure costs (Oct 25, 2025)

2. The Economist – “The $4 trillion accounting puzzle at the heart of AI cloud” (2025)

3. Barclays Research – Reports on Big Tech CAPEX and projected depreciation (2025)

4. Reuters – Coverage of Microsoft, Alphabet, Amazon, Meta CAPEX and earnings announcements (July–Oct 2025)

5. Financial Times (FT) – Analysis of Amazon, Microsoft AI investments and asset lifespan (2024–2025)

6. Bloomberg / WSJ – Reports on GPU cycles, power grid constraints, and economic impacts of AI infrastructure (2025)

7. Oracle Earnings Report – Disclosure of Oracle AI Cloud gross margins and financial performance (Q2 2025)

8. The Korea Economic Daily (Hankyung) – “Will Big Tech’s AI Illusion Break? The $4 Trillion Time Bomb” (Oct 24, 2025)

9. South Korea Ministry of Science and ICT / Ministry of Finance Press Releases – AI designated as national strategic technology, ₩65 trillion investment plan by 2027, and R&D budget increase for 2026