In July 2026, the global capital markets were simultaneously shaped by actions from two major tech giants.
On one side, Samsung Electronics delivered a quarterly performance that will go down in history—operating profit surged 1,810% year-over-year to 89.4 trillion KRW (approximately $58.4 billion), marking the third consecutive quarter of record-breaking results. On the other, social media giant Meta was reported to be planning to sell idle AI computing power and launch a new cloud business called "Meta Compute."
Though these events seem unrelated, they point in the same direction: expectations for AI hardware investment are undergoing a collective reassessment. The most profitable companies are facing sell-offs, while those with the most computing power are starting to sell their "excess" capacity. The market’s pricing logic is experiencing subtle yet profound changes.
How the Storage Supercycle Powered Samsung’s Historic Results
Samsung’s explosive performance this quarter was driven almost entirely by its memory chip business. According to preliminary financials, the company’s Q2 2026 sales reached 171 trillion KRW, up 129% year-over-year; operating profit hit 89.4 trillion KRW, more than 19 times last year’s 4.7 trillion KRW. This figure even surpasses Samsung Electronics’ cumulative profit from 2023 to 2025.
The core driver behind these results is persistent supply tightness in memory chips. AI data centers’ strong demand for high-bandwidth memory (HBM) has pushed manufacturers to shift capacity toward high-end products, leading to shortages in traditional DRAM and NAND chips and driving prices higher across the board. According to HSBC, average DRAM prices rose over 40% quarter-on-quarter from April to June, while NAND prices jumped more than 50%.
Samsung’s progress in HBM is especially critical. The sixth-generation high-bandwidth memory, HBM4, began mass production in February 2026, significantly boosting profitability in the storage segment. Analysts expect memory chip shortages to persist at least through 2027.
However, there’s another side to these results worth noting. While the memory business contributed around 112 trillion KRW in profit, Samsung’s system semiconductor and foundry business posted losses exceeding 2 trillion KRW, and the smartphone division is expected to lose about 1 trillion KRW. Nearly 90 trillion KRW in quarterly profit came almost entirely from a single business line—this "one-legged" structural imbalance is key to understanding the market’s subsequent reaction.
The Better the Results, the Sharper the Drop: A Classic Case of "Buy the Rumor, Sell the News"
The capital market’s response sharply contrasted with the fundamentals.
After the Q2 earnings release on July 7, Samsung Electronics’ stock opened with a steep drop, at one point falling below the 300,000 KRW mark, down nearly 10%. By the close, shares were at 296,000 KRW, down 6.92%. This dragged the KOSPI index down over 8% intraday, triggering its sixth circuit breaker of the year.
This counterintuitive "the better the results, the harder the fall" pattern stems from the market’s "pricing ahead of time." Samsung’s stock had already rallied significantly before the earnings announcement—shares more than doubled year-to-date. On July 3, Samsung surged 8.22% in a single day, closing at 309,500 KRW. The market had already priced in "better-than-expected" results.
Deeper concerns focus on the sustainability of AI demand. Petra Capital Management Managing Partner Albert Yong noted, "What investors really worry about is the sustainability of this AI frenzy, and whether US tech giants will slow capital spending on AI infrastructure." eToro market analyst Zavier Wong echoed this, saying, "The stock price has been priced for a ‘historic quarter’ for months. Once the data confirms expectations but doesn’t far exceed them, there’s nothing left to reward new investors."
When "perfection" is priced in ahead of time, any "confirmation" that fails to exceed expectations becomes a reason to sell.
Why Meta’s Plan to Rent Out Computing Power Triggered a Chain Reaction
Just a week before Samsung’s stock plunge, another headline sent ripples through global tech stocks.
On July 1, media reported that Meta was preparing a cloud infrastructure business plan, aiming to sell AI computing power and model access to external clients. After the news broke, Meta’s stock jumped 8.8%, but the AI hardware supply chain came under pressure—CoreWeave tumbled 13.92%, and memory chip stocks like Micron and SanDisk dropped over 10%. The sentiment quickly spread to Asian markets, with Samsung and SK Hynix falling 9.06% and 14.57%, respectively, on July 2.
The logic behind the market panic is straightforward: if even one of the world’s largest GPU buyers—Meta—is seeking to sell "excess" computing power, does it mean AI infrastructure buildout has reached a turning point? The "computing power shortage" narrative that fueled AI hardware stocks for the past two years is now facing fundamental skepticism.
Meta’s situation itself offers insight. The company expects capital expenditures in 2026 to reach $125–145 billion, nearly double 2025’s figure. Such massive investment needs a path to returns—aside from advertising, cloud services are almost the only way to monetize idle capacity. Notably, media reports say Meta CEO Mark Zuckerberg told employees that AI agent development over the past four months "hasn’t accelerated as we expected." When the pace of AI application deployment falls short of expectations, overbuilding on the hardware side becomes more apparent.
Is "Computing Power Surplus" a Real or False Problem?
Concerns about a "computing power surplus" are facing pushback from various angles.
Nomura Securities analysts recently stated that worries about "computing power surplus" may be overstated. Korean chip investments are hard to quickly convert into capacity, and AI demand is causing memory chip shortages. Citigroup’s research also argues that "computing power surplus" concerns are exaggerated—AI demand still outpaces supply.
CITIC Securities points out that Meta’s move is mainly about revitalizing legacy computing assets; renting out computing power doesn’t conflict with continued investment. Tianfeng Securities’ overseas tech team says the market shouldn’t simply interpret this as "peak AI computing demand." More accurately, Meta is trying to turn AI infrastructure from a cost center into an asset that can be rented, charged for, and platformized.
"Structural mismatch" rather than "overall surplus" is closer to reality. Industry insiders cite data showing that current intelligent computing clusters have an average utilization rate below 20%, yet there’s a roughly 40% shortage of high-end computing power for large model training. Low-end capacity is idle, but high-end capacity remains in short supply—this structural divergence is a detail easily overlooked amid market panic.
Research from SemiAnalysis offers another perspective: in early 2026, on-demand GPU rental capacity in tracked markets was nearly sold out. If computing power were truly in surplus, the rental market wouldn’t see such supply-demand tension.
When "Expectations" Outpace "Reality": What’s Happening to AI Hardware Valuations
Samsung’s stock plunge and Meta’s computing power sale plan may seem like separate events, but they share the same underlying logic: AI hardware asset valuations have been inflated by overly optimistic expectations.
Samsung’s case is especially illustrative. Operating profit of 89.4 trillion KRW set a new record, but the stock had already reflected this expectation before earnings. When actual numbers merely "meet" rather than "greatly exceed" the market’s most optimistic forecasts, "all good news priced in" becomes the rational choice for capital. Statistics show that out of Samsung’s last eight preliminary earnings releases, the stock fell on the day or day after four times—"buy the rumor, sell the news" isn’t a coincidence, but a recurring pattern.
Meta’s computing power sale can also be seen through the lens of valuation. When a company invests over $120 billion annually in AI infrastructure and the market doubts its return prospects, monetizing idle computing power becomes inevitable. This isn’t the end of AI investment, but a sign that capital expenditures are moving from "cost is no object" to "return matters."
From a broader perspective, Alphabet, Amazon, Microsoft, and Meta are expected to invest about $650 billion in expanding AI infrastructure in 2026, up nearly 60% from around $410 billion in 2025. Such massive capital spending inevitably brings strict scrutiny of return cycles. When investments reach this scale, the market becomes much more sensitive to the efficiency of every dollar spent.
From "Computing Power Reigns" to "Efficiency Reigns": The Shift in AI Infrastructure Investment Logic
The shared lesson from Samsung and Meta is clear: AI hardware investment is moving from a "scale race" to an "efficiency validation" phase.
Over the past two years, tech giants’ capital spending logic was simple—whoever had the most computing power had the strongest AI competitiveness. GPU, HBM, data centers—everything aimed for "bigger, faster, more." This logic drove valuations for hardware suppliers like Nvidia, Samsung, and SK Hynix sky-high.
But as capital spending reaches hundreds of billions, and as major buyers like Meta start considering renting out idle computing power, the focus naturally shifts from "how much was invested" to "what was produced." The speed of AI application deployment, the return cycle of capital spending, and the ability to absorb hardware capacity—these previously overlooked issues are becoming new pricing focal points.
Samsung’s own results provide evidence. The rise in memory chip prices isn’t driven solely by demand; the shift in capacity from traditional DRAM to HBM also played a key role. This supply discipline-driven price increase is essentially a fragile balance—any sign of demand slowdown could quickly reshape the pricing system.
For investors, this means the analytical framework for AI hardware investment needs an update. The simple "computing power shortage" narrative is no longer enough to support valuations. Instead, sustained scrutiny of factors like demand sustainability, capacity absorption, and monetization pace at the application layer will take center stage.
Conclusion
Samsung Electronics’ historic Q2 2026 operating profit of 89.4 trillion KRW, up 1,810% year-over-year, and the 6.92% single-day stock plunge together form the year’s most representative "performance paradox." Meanwhile, news of Meta’s plan to sell idle AI computing power sparked a global tech stock panic over "computing power surplus."
These events don’t signal the end of AI hardware, but rather a restructuring of expectation management. When the market prices in "perfection" months ahead and hundred-billion-dollar capital expenditures face return scrutiny, AI infrastructure investment is transitioning from a "scale race" to an "efficiency validation" phase. Structural shortages in memory chips coexist with structural mismatches in computing power—high-end capacity remains in short supply, but the market’s demand for returns on every investment is stricter than ever.
For investors, this is both a risk warning and an opportunity to reassess the logic of AI hardware investment—as the tide turns from "cost is no object" to "efficiency matters," truly sustainable assets will emerge amid volatility.
FAQ
Q: What was Samsung Electronics’ Q2 2026 operating profit?
According to Samsung’s preliminary results released July 7, Q2 2026 operating profit was 89.4 trillion KRW (about $58.4 billion), up 1,810% year-over-year. This is a preliminary estimate; the final report will be released on July 30.
Q: With such strong results, why did Samsung’s stock plunge?
The main reason is "buy the rumor, sell the news"—the market had already priced in strong results before the earnings release, with Samsung’s stock more than doubling this year. When actual results met expectations but didn’t greatly exceed them, investors took profits. Additionally, concerns about the sustainability of AI demand and the possibility of slowing capital expenditure are weighing on the stock.
Q: Does Meta’s sale of computing power mean AI computing is already in surplus?
Most institutions believe this is a market misinterpretation. Meta’s sale is more about monetizing legacy assets and optimizing capital expenditure returns, not a signal that AI demand has peaked. The real issue is "structural mismatch"—low-end computing power is idle, but high-end capacity for large model training remains in short supply.
Q: How long will the trend of rising memory chip prices last?
Analysts expect memory chip shortages to persist at least through 2027. AI data centers’ demand for HBM, the shift in capacity from traditional DRAM and NAND to high-end products, and other factors continue to push prices higher. However, market disagreements over future price trends are growing.
Q: What lessons do these events offer for AI hardware investment?
AI hardware investment is moving from a "scale race" to an "efficiency validation" phase. The market focus is shifting from "how much computing power was invested" to "how much real value it generates." For investors, this means paying closer attention to demand sustainability, capacity absorption, and monetization pace at the application layer.
