Risks of a blockade in the Strait of Hormuz have intensified since the U.S.-Iran conflict escalated in March, sparking fears of a repeat of 1970s-style stagflation. With ICE Brent crude prices fluctuating around $100 per barrel and global stock markets under pressure, concerns over an oil crisis triggering stagflation have resurfaced. However, despite the stagflation or recession narrative, gold—often seen as a safe-haven asset—has experienced a significant 16% correction over the past three weeks, while industrial metals, represented by LME copper, have fallen by approximately 11%. Under the assumption of a prolonged U.S.-Iran conflict, stagflation has reemerged as a key variable influencing major asset class performance. This article examines the impact of the current oil price shock and assesses whether it could lead to a repeat of the 1970s stagflation scenario. It also explores shifts in market expectations amid rising oil prices and offers an outlook on major asset price trends.
Why the Current Oil Price Shock Is Unlikely to Repeat 1970s-Style Stagflation The ongoing U.S.-Iran conflict has placed the Strait of Hormuz at the center of global economic pressure. Approximately 20 million barrels of crude oil pass through this channel daily, accounting for about 20% of global petroleum consumption, making it the world’s most critical energy chokepoint. A prolonged blockade would significantly disrupt global energy markets, and markets have already begun pricing in a scenario where the Federal Reserve holds off on interest rate cuts, adding volatility to global equities. However, supply disruption alone does not guarantee a return to stagflation. The 1973 oil embargo and the 1979 Iranian Revolution escalated into years of global stagflation only when three conditions were met simultaneously: high concentration of Middle Eastern oil supply with limited alternative sources, high oil intensity in developed economies, and the absence of domestic supply buffers in the United States. The convergence of these factors amplified regional shocks into systemic crises.
Given the current economic landscape, even a prolonged U.S.-Iran conflict is unlikely to trigger genuine stagflation risks. First, the concentration of Middle Eastern supply has declined, while consumption patterns have become more asymmetric. According to EIA/OPEC data, Iran’s share of global oil output was about 8.5% in 1978, with the broader Middle East accounting for 37.8%. By 2026, Iran’s share has fallen to 3-5%, and the Middle East’s overall contribution is around 30%. Meanwhile, non-Middle Eastern producers such as Russia and the Americas have increased their market share, diversifying global oil supply sources.
On the demand side, Middle Eastern crude primarily flows to European and Asian markets. A supply disruption in the Strait of Hormuz would impact energy security in Japan, South Korea, and parts of Europe far more severely than in the United States. This suggests that the direct macroeconomic transmission of the current geopolitical shock to the U.S. economy differs significantly in scope and magnitude from the 1979 episode.
Second, energy structures have evolved, with renewable alternatives gaining traction. A key measure of an economy’s oil dependence is “oil intensity”—the amount of oil consumed per unit of GDP. In the early 1970s, this figure stood at approximately 0.92 barrels per thousand dollars of GDP; today, it has dropped to 0.32. This means that, for the same level of economic output, current oil consumption is only about one-third of what it was five decades ago, reducing the pass-through effect of oil price increases on overall production costs.
Notably, the adoption of new energy sources has further constrained oil demand growth. The rapid uptake of electric vehicles in China, which historically contributed 60-70% of global oil demand growth, is expected to cause a structural shift around 2024-2025. Europe and Japan already peaked in oil consumption in 2012 and 2019, respectively. Long-term growth drivers for global oil demand are systematically weakening, limiting the sustainability of high oil prices over the medium to long term. According to the International Energy Agency, oil’s share of global energy demand fell below 30% in 2024 for the first time, down from a peak of 46% fifty years ago. In the same year, global electric vehicle sales continued to grow rapidly, accounting for about one-fifth of new car sales worldwide. By 2025, global energy investment is projected to reach $3.3 trillion, with clean energy technologies and infrastructure attracting roughly twice the capital allocated to fossil fuels.
Third, the shale revolution has restored U.S. supply elasticity. The commercialization of hydraulic fracturing technology in 2008 fundamentally transformed U.S. energy supply capabilities. U.S. crude oil production has rebounded from a low of about 5 million barrels per day to between 13 and 14 million barrels per day, making the country the world’s largest producer, surpassing both Russia and Saudi Arabia. Shale oil’s key economic feature is its high supply elasticity. The breakeven price for many small and medium-sized U.S. shale producers is around $58 per barrel. When prices exceed this level, firms have clear incentives to expand production, and drilling cycles are relatively short, enabling faster capacity responses compared to conventional oilfields. With the long-term equilibrium oil price around $70 per barrel, shale production remains within a sustainably expandable range.
Additionally, following the U.S. “Big and Beautiful” legislation, federal policies on land use, offshore leasing, energy development, and regulatory approvals have become more accommodative. This means that oil price increases triggered by a Strait of Hormuz blockade would automatically incentivize supply expansion from shale producers, providing a market-based price cushion—a mechanism absent in the 1970s.
That said, rising oil prices are not without inflationary effects. The more precise question is: given the current structure of the Consumer Price Index (CPI), through which channels and to what extent can oil price shocks influence U.S. inflation data, and could they prompt the Fed to resume rate hikes?
U.S. CPI Breakdown: Why the Current Oil Price Pass-Through Is Limited The Russia-Ukraine conflict in 2022 represented the most severe recent episode of oil price transmission to inflation. At that time, a combination of global supply chain disruptions, excess liquidity from prior quantitative easing, and strong demand recovery created a “reference model” for oil-driven inflation. Following the outbreak of the conflict in February 2022, WTI crude rose 18.4% month-on-month in March, while Brent increased over 20%. The energy CPI surged 10.09% month-on-month, with gasoline prices jumping 16.28%. With energy accounting for about 7% of the CPI basket, the direct contribution of energy to the monthly CPI increase was approximately 0.74 percentage points in March, when the overall CPI rose 1.12%. Even during the sharpest oil price shock, energy explained about two-thirds of the monthly increase.
Year-on-year, the headline CPI climbed from 7.56% in January 2022 to a peak of 8.98% in June. The energy CPI peaked at 41.46% in June 2022, contributing about 3 percentage points to the headline figure. However, it is important to note that even before the conflict, energy CPI was already elevated at 27.2% year-on-year in January 2022 due to pandemic-related supply constraints, contributing 1.9 percentage points to inflation. Thus, the net incremental effect of the oil price surge post-conflict was about 1.1 percentage points.
What truly drove CPI to 8.98% in June 2022 was the persistent rise in sticky components such as housing rents and wages. During the same period, housing (peaking at +7.5% year-on-year), food (+11%), transportation (+23%, driven largely by used cars +41%), and medical care (+6%) all saw significant increases. This reflected a combination of overheated demand, excess liquidity, and sticky inflation—not solely oil prices.
Examining the correlation between oil prices and CPI components reveals that oil-sensitive items are concentrated in the energy chain and parts of the transportation sector. The contemporaneous correlation between gasoline prices and WTI price changes is 0.58, and 0.55 with a one-month lag. The overall energy component has a contemporaneous correlation of 0.56. Airfare shows a one-month lag correlation of 0.18, while the broader transportation category has a lagged correlation of 0.56. These components share the characteristic that oil price changes can quickly and directly pass through to costs.
In contrast, larger-weighted components show almost no correlation with oil prices. Housing (including owners’ equivalent rent, accounting for about 33% of CPI) has a near-zero contemporaneous correlation with oil prices, even slightly negative. Medical care (8% weight) has a correlation of about 0.04. Education and communication show a negative correlation, while food overall has a correlation of -0.03, indicating no direct pass-through. This suggests that oil price shocks have limited impact on the largest CPI components.
From the current CPI structure, the inflationary impact of oil prices is even more constrained than in 2022. First, base effects are a dampening factor. In February 2026, gasoline prices were still in negative territory at -5.62% year-on-year, while overall energy inflation was only +0.4%. Starting from such a low base, even a sharp short-term rise in oil prices would be unlikely to push energy inflation to levels that would force the Fed to act abruptly. Moreover, geopolitical shocks often lead to short-lived price spikes rather than sustained increases, and base effects would exert downward pressure in subsequent months.
Second, the main sources of U.S. CPI pressure currently are housing and services: housing inflation, though down from a peak of 7.47% in 2022, remained at 2.96% in February 2026; food away from home was near 4%; medical care was around 3.36%. These components are largely insensitive to oil price movements, being driven instead by labor costs, rental cycles, and other endogenous factors. Oil price fluctuations have little direct effect on them.
Furthermore, it is important to remember that “inflation is always a monetary phenomenon.” A comparison of Japan’s experience during the 1973 and 1979 oil crises supports this logic: in 1973, the Bank of Japan chose to expand money supply in response to rising oil prices, leading to runaway inflation; in 1979, it refrained from monetary accommodation, and oil price increases did not translate into broad inflation. Beyond the Fed’s inherent reluctance toward quantitative easing under its new chair, Kevin Warsh, the current economic environment differs fundamentally from the loose monetary conditions of the 1970s. High public debt, political pressure from a cooling labor market, and the cumulative economic fatigue from maintaining interest rates at 3.5-3.75% for an extended period all constrain the Fed’s ability to embark on large-scale monetary expansion.
In this context, the current oil price shock is more likely to manifest as a relative price disturbance rather than trigger a broad-based CPI surge amplified by monetary easing. The transmission channels that are truly responsive to oil prices—such as retail gasoline, air transport costs, and logistics expenses for certain industrial goods—collectively account for no more than 13% of the CPI basket. Their impact is more likely to be felt in a rapid rise in the Producer Price Index (PPI).
Thus, the more relevant question is: how will the PPI increase, driven by energy and chemical price pass-through, affect different economic agents?
Coping with “Inflation”: Will Firms Accelerate AI Adoption Amid Oil Price Shocks? How businesses manage cost increases from rising oil prices—specifically, whether they can pass them on to consumers—is key to understanding the inflationary impact of the current shock. In the 1960s and 1970s, the post-war baby boom generation entered its peak consumption years, the middle class expanded steadily, and household incomes grew, supporting strong end-demand. In that environment, firms could pass on higher input costs to downstream customers because demand was robust enough to absorb price increases without significant volume contraction. This demand-side condition was foundational to the stagflation of the 1970s.
Today, global demand conditions have fundamentally changed. End-consumption remains subdued, with real household purchasing power squeezed by both energy costs and employment pressures. Firms have much less room to pass on costs. Faced with rising expenses from oil prices, businesses are more likely to focus on cost reduction—through layoffs, wage cuts, organizational streamlining, or mergers to achieve scale economies—rather than raising prices.
This dynamic may accelerate the adoption of artificial intelligence (AI). AI offers a lower-cost alternative to human labor. For example, OpenAI’s GDPval report released on September 25, 2025, evaluated the performance of advanced AI models on real-world economic tasks (based on outputs from professionals across 44 occupations with an average of 14 years of experience). The key finding: advanced models completed these tasks about 100 times faster than human experts, at roughly 1/100th of the cost.
The direct consequence of such AI substitution is a concentration of technological gains toward capital, with a corresponding contraction in employment opportunities and wage bargaining power for labor. Since the ChatGPT-driven AI wave began in 2023, the S&P 500 has diverged from job openings: corporate profits have hit new highs, yet job demand has contracted. This decoupling reflects the market’s recognition of AI-driven labor substitution and the shifting of technological rents to capital.
Behind this data lies a divergence in economic perception between production-based and expenditure-based GDP measures. From a production and capital returns perspective, the economy appears strong: corporate profits, capital expenditure, and computing investments are expanding. Yet from a household income and consumption standpoint, rising unemployment, weak consumption, and lower income expectations signal economic strain. The same economy feels vastly different depending on one’s position.
The root cause of this divergence is that, in the AI era, human labor is no longer the irreplaceable center of economic production. Capital, through computing power and models, can perform an increasing share of tasks previously reliant on labor. Technological gains are accruing primarily to capital, rather than being distributed to workers via employment expansion.
The wealth distribution logic of the peaceful globalization era rested on three pillars: stable global supply chains, low geopolitical risk premiums, and smooth cross-border capital flows. Under that framework, productivity gains from technological progress were passed on to households through steadily declining consumer prices, making residents key beneficiaries of globalization. In other words, technological gains flowed to households, while geopolitical costs were absorbed by the global system. Now, technological gains are concentrating in capital, while geopolitical costs are increasingly borne by households. This shift is not easily reversible through monetary or short-term fiscal stimulus.
A more direct example is that capital markets now reward firms that increase AI-related capital expenditure with higher valuations.
The “Stagnation” Reflection: Why Are Tech Giants Betting on the Production Side? In 2026, capital markets appear to reward computing investment and layoffs, while penalizing consumer subsidies and user growth. In February 2025, Alibaba announced plans to invest at least 380 billion yuan in cloud computing and AI infrastructure over three years, which was met with positive market reaction and share price strength. However, by March 2026, when the market observed increased e-commerce subsidies and loss-making instant retail operations—without a corresponding boost in demand—the company’s shares fell sharply after earnings. Heavy capital expenditure in computing is rewarded; consumer subsidies are punished. This contrast starkly illustrates the shift in how tech firms are valued.
Currently, market preference is shifting from whether AI-related firms can expand consumer user bases to whether they can establish foundational platform capabilities in business, research, and industrial applications. Once AI penetration reaches the enterprise level, it could rapidly diffuse across industrial, defense, robotics, autonomous driving, and research tool scenarios, unlocking exponential profit growth potential.
It is not just capital markets; tech giants themselves are making similar choices. For instance, at GTC 2026, NVIDIA placed “physical AI” and “AI factories” at the core of its strategy, anchoring future computing demand in productive sectors like industry, robotics, research, and agent-based AI, rather than consumer-facing content generation. In March 2026, OpenAI shut down its viral AI video generator Sora, with CEO Sam Altman pivoting resources toward robotics and world simulation research. The reason: generating a single video with Sora consumed excessive computing resources, and the underlying technology would be more efficiently applied to robotic world modeling. Clearly, AI firms are now all-in on industrial, robotic, and enterprise applications.
This transition will be accompanied by rising capital expenditure in computing and simultaneous cost-cutting through layoffs. As the marginal cost of AI substitution falls, the opportunity cost of retaining human labor rises, changing corporate resource allocation logic from consumer expansion to production efficiency optimization. For households, this means a shift from being served as customers to being optimized as cost centers.
This trend is likely to coincide with a strengthening PPI (due to energy prices and manufacturing expansion), limited CPI increases, rising unemployment, and accelerated AI adoption across industries.
Thus, based on the above logic, the macroeconomic impact of the current oil price shock differs fundamentally from the “stagflation rerun” feared by markets.
Technology in the Stagflation Era: Why Does Adversity Drive Productivity Leaps? As discussed, the current oil price shock may create a divergence where households bear the brunt while the production sector expands. This pattern is not without historical precedent. Between 1971 and 1980, two oil shocks, intensified U.S.-Soviet rivalry, high inflation (reaching 13.5% in 1980), and elevated interest rates (averaging 13.4% in 1980) made the decade a challenging period for asset allocation. The Dow Jones Industrial Index rose only 20% nominally, from around 800 points in 1971 to 964 by end-1980. In contrast, the Nasdaq Index, launched at 100 points in 1971, more than doubled to 202 points by 1980. Defense-driven technology investments, spurred by great-power competition, were a key factor behind the relative outperformance of tech assets.
The reason is straightforward: only under significant external pressure are governments willing to fund long-cycle, low-return, high-uncertainty R&D, and are firms and capital forced out of “quick-profit” comfort zones. Major productivity-enhancing technological revolutions rarely occur during comfortable, peaceful, consumption-led booms. From the Industrial Revolution to electrification, aerospace, and the information revolution, key technologies often saw accelerated investment, breakthroughs, and global diffusion during periods of intense great-power rivalry.
Semiconductors and the internet are prime examples. The early commercialization of transistors and integrated circuits in the 1950s-60s was largely driven by U.S. Department of Defense procurement, which accounted for over 70% of U.S. integrated circuit output in the early 1960s. Similarly, the establishment of NASA and the Apollo program were direct responses to the Soviet launch of Sputnik in 1957. Without such external strategic pressure, the political will and capital concentration required for such endeavors would have been unlikely.
In contrast, the peaceful globalization era, while beneficial for household welfare and consumption, was not a fertile period for technological paradigm shifts. When geopolitical risks are low and capital flows freely to the highest-return assets, rational allocation favors asset speculation, traffic acquisition, and platform building—not foundational science or long-cycle manufacturing. During the peak globalization years around 2015, vast amounts of capital were absorbed by real estate and consumer internet, with no systemic increase in funding for basic research or advanced manufacturing.
This perspective is key to understanding the current situation. The rapid development of AI since the 2020s has occurred against a backdrop of re-intensifying great-power competition. The U.S. CHIPS and Science Act essentially uses state capital to forcibly reallocate resources in response to supply chain security concerns. Similarly, China’s accelerated investments in aerospace, nuclear power, defense electronics, and AI computing are direct outcomes of external strategic pressure.
Therefore, if measured by household consumption, middle-class welfare, or demand for real estate and luxury goods, the current era may appear weak. But if assessed by total factor productivity, strategic capital expenditure, and industrial engineering capability, it may represent a reacceleration for governments and related industries. Households may experience stagflation-like conditions, while the state and corporate sectors resemble a reindustrialization drive—an inherent characteristic of an era defined by rivalry.
Allocation Recommendations Based on the above analysis, the oil price surge triggered by the U.S.-Iran conflict is unlikely to repeat the 1970s-style broad stagflation. The current economy exhibits an atypical structure: PPI is strengthening due to energy prices and manufacturing expansion, while CPI remains subdued by weak household demand, resulting in incomplete pass-through. In an era of rising AI-related capital expenditure, higher PPI may coincide with rising unemployment, as AI substitution continues to pressure labor markets.
Under these conditions, the Fed and other major central banks are unlikely to significantly tighten monetary policy. Maintaining current interest rate levels should not fuel rapid CPI increases. The combined impact of the U.S.-Iran conflict and accelerating AI adoption may lead to a “stagflation-like” environment for households alongside “expansion” in the production sector.
Considering the price drivers of different asset classes in light of the oil price shock:
Gold: Faces short-term headwinds from: 1) forced liquidation of liquid assets under extreme risk aversion, dampening safe-haven demand; and 2) repricing of reduced Fed rate-cut expectations. Both factors were evident in March 2026, explaining why gold did not benefit from the U.S.-Iran conflict. However, short-term liquidity pressures do not alter the medium-term uptrend. Ongoing central bank gold accumulation, a weakening U.S. dollar credit system, and the value of real assets in a competitive geopolitical landscape support gold’s medium-term strength. Investors may look for entry opportunities after liquidity shocks subside.
Global AI Sector: Short-term cautious, long-term positive; correction likely milder than the dot-com bubble. Tech asset valuations are fundamentally tied to the AI theme. Short-term, a stronger U.S. dollar weighs on tech valuations, and risk-off sentiment may trigger outflows, pressuring the Nasdaq. However, any downturn should be far less severe than in 2000, as leading AI firms already generate substantial and growing profits. Capital expenditure by major global tech companies continues to expand, providing fundamental support for valuations. Medium- to long-term, intensifying great-power competition should sustain AI capital expenditure. National AI races and corporate efficiency pressures will drive further investment, with these drivers strengthening rather than weakening amid geopolitical tensions. The upward trend in AI capex remains intact; current adjustments are more about valuation normalization.
A-Shares: Slow-bull pattern unchanged. For the broader A-share market, the recent correction has not broken the slow-bull trend that began in September 2024. The core drivers—sustained inflows from long-term capital, China’s rising strategic importance, and the AI-tech narrative—remain intact, with some reinforced by accelerating geopolitical shifts. The index has corrected over 8% from its peak; historical mid-cycle adjustments suggest limited further downside. At current levels, blue-chips and high-dividend assets offer entry opportunities. After the correction, utilities, energy/chemicals, insurance, and banks show improved valuation appeal. Utilities may benefit from potential price increases, energy/chemicals from oil price-driven earnings elasticity, and blue-chips from a broader recovery.
Within A-share tech, two risks persist: 1) liquidity risks for small- and mid-caps due to high valuations and crowded positioning; and 2) linkage pressure from Nasdaq adjustments for overseas-tech correlated names. U.S. stocks were initially optimistic about a quick resolution; prolonged Strait closures and escalating conflict could pressure the Nasdaq. Until these risks are fully priced, tech allocation should prioritize themes related to “security deficits”—energy security (nuclear, energy storage, new energy equipment, grid), manufacturing and defense expansion (industrial machinery, defense electronics, minor metals). These sectors have lower correlation to overseas valuation swings and are directly catalyzed by geopolitical risks, offering both defensive and offensive characteristics. A-share tech may present buying opportunities after small-cap liquidity risks are released or when conflict intensity peaks and sentiment bottoms.
Hong Kong Stocks: The U.S.-Iran conflict may benefit Hong Kong’s medium-term outlook. After a prolonged correction, Hang Seng Tech valuations are relatively low, providing resilience to external shocks. The high weight of high-dividend, high-yield assets in the Hang Seng Index offers defensive characteristics amid uncertainty, and Hong Kong property rents provide underlying support. Medium- to long-term, Hong Kong could benefit from structural shifts. Erosion of the petrodollar system and progress in RMB settlement, coupled with Gulf states’ security concerns, may drive global capital to increase strategic allocations to Hong Kong assets. China’s manufacturing competitive advantage remains strong in a contested world, yet this is not fully reflected in current valuations. Overall, Hong Kong stocks face limited short-term risks and warrant positive medium-term attention.
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