The underestimated factor: Why China's electricity surplus could wipe out the US chip advantage

Energy supply as a crucial weapon in the global technology race

Nvidia chips without a power outlet: Hundreds of millions of dollars invested, but no power in sight.

The global debate surrounding dominance in artificial intelligence (AI) has thus far been conducted almost exclusively as a technological arms race, dominated by discussions about semiconductor technology, algorithms, and export restrictions. However, a thorough analysis of the current geopolitical situation reveals that the decisive battleground has shifted: away from pure computing power and toward the physical availability of electrical energy.

While the United States leads technologically with companies like Nvidia and OpenAI, it is increasingly hitting the harsh limits of its decades-neglected energy infrastructure. The paradox is glaring: State-of-the-art data centers worth hundreds of millions of dollars stand empty because local utilities cannot provide connections, and tech giants are forced to build their own power plants in a kind of “energy Wild West.”

In stark contrast, the People's Republic of China has created a situation of strategic asymmetry. Through massive state investments in excess energy capacity and targeted subsidies, Beijing is compensating for its technological lag in chip development. The logic is as simple as it is effective: what Chinese chips lack in raw power, they make up for in sheer mass and virtually free energy. This development is not only forcing the West to radically reassess its industrial policy priorities, but is also driving the US population into a dilemma of rising electricity prices and unstable grids, while China is consistently deploying its energy policy as a geopolitical weapon.

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How China's strategic electricity overcapacities and American grid bottlenecks are redefining the balance of power in artificial intelligence

The development of artificial intelligence has evolved into an economic and geopolitical competition between the United States and the People's Republic of China, the outcome of which depends not primarily on technological innovation or scientific excellence, but on a far more fundamental factor of production: the availability of electrical energy. This has emerged as a critical resource that determines the success or failure of national AI development strategies. While American technology companies, despite superior semiconductor technology, are hampered by the physical limitations of their energy infrastructure, China, through decades of strategic planning, has achieved a position where surplus electricity generation capacity can be strategically deployed to promote its domestic chip industry and accelerate the development of artificial intelligence. This asymmetric starting position represents a fundamental economic paradox that fundamentally challenges assumptions about technological supremacy and competitive advantages in the digital age.

The economic dimensions of data center expansion

The global investment wave in artificial intelligence data centers is reaching historically unprecedented dimensions and transforming fundamental patterns of capital flow and industrial development. Goldman Sachs estimates that American technology companies will invest $737 billion in new data center infrastructure by the end of 2026—a sum comparable to national investment programs and determining the dynamics of entire economic sectors. This capital accumulation is concentrated in a specific type of infrastructure whose value creation stems not from physical production, but from the processing of information by highly specialized semiconductor chips. The economic significance of this development is manifested in the fact that individual data centers are currently considered the most valuable buildings in the world, not because of their architectural design or size, but because of the technology they house.

The energy intensity of this new infrastructure surpasses all historical benchmarks for industrial plants. Analyses by the Wall Street Journal predict an electricity demand of 80 gigawatts for the American data centers planned by the end of next year, a figure exceeding the peak consumption of the entire German economy. This magnitude illustrates the fundamental transformation of the demand structure in electricity markets. While data center electricity consumption remained almost constant between 2010 and 2018 despite the exponential growth of digitalization, as efficiency gains offset the increase in demand, the introduction of large language models and generative artificial intelligence has abruptly reversed this trend. The International Energy Agency documents that data centers already accounted for four percent of global electricity consumption in 2024, with projections predicting an increase to as much as twelve percent of American electricity demand by 2028.

This surge in demand is occurring at a time when the American energy infrastructure had been accustomed to decades of stagnant demand patterns. The U.S. Energy Information Administration recorded an increase in electricity consumption of roughly 1,000 billion kilowatt-hours between 1991 and 2007, reaching approximately 3,900 billion kilowatt-hours, a level that remained largely stable until 2021. The sudden return of substantial demand increases driven by data centers, the electrification of mobility, and the reshoring of industrial production is impacting a system whose planning and investment cycles were geared toward stagnation. Goldman Sachs Research forecasts a 165 percent increase in global power consumption for data centers by 2030, from one to two percent of global electricity consumption in 2023 to three to four percent by the end of the decade. This development requires an estimated $720 billion in investment in transmission grids alone, with the realization of these projects entailing multi-year permitting processes and lengthy construction times.

Microeconomic disruptions in regional electricity markets

The spatial concentration of data centers is creating significant distortions in local electricity markets, whose pricing mechanisms are responding to a fundamentally altered demand structure. Bloomberg has documented price increases of up to 267 percent over five years in regions with high data center density. This development does not primarily reflect rising generation costs, but rather the scarcity of existing transmission capacity and the cost distribution for necessary infrastructure expansions. In Virginia, the largest regional market for data centers in the United States, electricity prices for residential properties rose by 13 percent, in Illinois by 16 percent, and in Ohio by 12 percent. Analyses show that households in Ohio will spend at least an additional $15 per month on electricity starting in June 2025, a direct consequence of data center growth.

This price dynamic raises fundamental questions about distributive justice and efficient resource allocation. Private households and traditional businesses are effectively subsidizing infrastructure expansion for data centers, whose services are marketed globally and whose owners are among the most capital-intensive companies in world history. The regulatory structures of American electricity markets, in which utilities finance infrastructure investments through general tariff increases, lead to a socialization of costs while simultaneously privatizing revenues. Utilities like American Electric Power report demand projections of 24 gigawatts by 2030, a fivefold increase in current system size, yet data center operators are increasingly held accountable by regulatory measures such as the requirement to purchase at least 85 percent of subscribed capacity.

The situation in Santa Clara, California, Nvidia's hometown, illustrates the systemic bottlenecks of American energy infrastructure with particular clarity. Bloomberg reports that two completed data centers by developers Digital Realty and Stack Infrastructure, with a combined capacity of nearly 100 megawatts, are sitting idle because the local utility, Silicon Valley Power, cannot provide the necessary grid connections until 2028. The city is investing $450 million in grid modernization, but the construction of new transmission lines and substations requires three-year permitting processes. This delay between the completion of the physical infrastructure and its commissioning represents a glaring dysfunction in capital allocation. Digital Realty invests an average of $13.3 million per megawatt in fully equipped data centers, with the structural framework alone accounting for 20 to 25 percent of the total cost. A 48-megawatt project like the one in Santa Clara thus represents capital investments of several hundred million dollars that will generate no return for years.

China's strategic energy overcapacity as an industrial policy instrument

Through systematic overinvestment in power generation capacity, the People's Republic of China has created a position of strategic flexibility that serves as a key competitive advantage in the development of artificial intelligence. While Western energy systems traditionally aim for reserve capacity of 15 to 20 percent, China operates with overcapacity of 80 to 100 percent, as reported by Fortune magazine, citing American energy experts. This deliberate overprovision represents a fundamental departure from market-based efficiency criteria, but it is proving to be a strategic asset in a context of rapid technological transformation. The Chinese leadership views data centers not as a threat to grid stability, but as a welcome opportunity to absorb excess generation capacity.

The scale of these investments far surpasses international benchmarks. In 2024 alone, China installed 356 gigawatts of renewable energy capacity, exceeding the combined investments of the United States, the European Union, and India. Total installed renewable energy capacity reached 1,878 gigawatts by the end of 2024, with China achieving its 2030 target of 1,200 gigawatts of combined wind and solar capacity six years ahead of schedule. This overachievement of its own targets does not reflect inefficient planning, but rather a deliberate strategy of creating capacity in anticipation of future demand. While American energy providers react to existing demand, resulting in multi-year delays, China builds capacity in anticipation of technological developments that will ultimately generate demand.

This strategy is particularly evident in the targeted development of remote provinces as data center locations. Gansu, Guizhou, and Inner Mongolia, historically considered economically underdeveloped regions, have been transformed into centers of digital infrastructure through massive investments in wind and solar farms, as well as hydropower. The Eastern Data Western Computing program, initiated in 2022, coordinates the relocation of data centers to these energy-rich regions, with documented investments of 45.5 billion yuan. This spatial reallocation addresses several objectives simultaneously: absorbing surplus electricity production in remote areas, reducing energy costs for technology companies, and promoting regional development in previously neglected territories. Implementation proves complex, as reports of unused capacity and actual dependence on conventional power plants persist, but the fundamental availability of energy as a production factor remains undisputed.

Subsidy policy as a vehicle for technological independence

The Chinese government has implemented a system of energy subsidies that forces the adoption of domestic semiconductor technology through financial incentives, linking strategic industrial policy with short-term competitiveness. Local governments in Gansu, Guizhou, and Inner Mongolia grant electricity cost reductions of up to 50 percent to data centers using domestic chips from Huawei or Cambricon. The Financial Times reports that some of these subsidies are enough to run data centers for nearly a year free of charge, an intervention whose economic dimension reaches several billion dollars. This measure addresses a fundamental challenge of Chinese semiconductor technology: lower energy efficiency compared to American products. Huawei's CloudMatrix 384 system consumes more energy than Nvidia's NVL72 system because Chinese manufacturers compensate for the performance deficits of individual chips by aggregating larger quantities of chips.

The strategic logic of this subsidy policy follows an industrial policy pattern that China has already successfully implemented in other sectors. Similar approaches in the solar, telecommunications, and electric vehicle industries have led to China achieving global leadership in these fields. Subsidizing energy instead of providing direct product subsidies circumvents international trade restrictions and subsidy prohibitions, as it can be declared as general infrastructure policy. At the same time, conditioning subsidies on the use of domestically produced chips creates a closed market, enabling Chinese semiconductor manufacturers to achieve economies of scale that lead to product improvements through data collection and iterative development.

This policy reflects a fundamental difference in the conception of state-led economic management. While American industrial policy primarily operates through tax credits and research subsidies, the effects of which are delayed and indirect, China implements direct price intervention that induces immediate behavioral changes. Companies like ByteDance, Alibaba, and Tencent, which have substantial infrastructure investment budgets, are effectively forced by energy subsidies to use domestically produced chips, even if these are technologically inferior. Goldman Sachs China Research projects capital expenditures by Chinese internet companies of over $70 billion in 2025, with a substantial portion allocated to data centers. The electricity subsidies reduce operating costs so significantly that they offset the higher hardware costs and lower efficiency, keeping Chinese companies competitive in the global market.

The technological asymmetry in semiconductors and its economic implications

The American lead in semiconductor manufacturing represents the United States' most significant technological advantage in the artificial intelligence race, but its long-term importance is diminished by energy shortages and alternative Chinese development paths. Industry experts estimate that China lags behind the leading producers in high-end chip manufacturing by about ten years. The CEO of ASML, the Dutch monopolist for extreme ultraviolet lithography systems, puts the technological gap at ten to fifteen years due to the export ban on this key technology to China. This time lag manifests itself in lower production yields and higher energy consumption of Chinese chips. SMIC, the leading Chinese semiconductor manufacturer, achieves yields of only 20 percent with 7-nanometer processes, while TSMC achieves yields of over 90 percent with equivalent technologies.

This technological inferiority translates directly into longer training times for artificial intelligence models, putting Chinese companies at a competitive disadvantage. Developing large language models requires massive parallel computations over periods of weeks or months, with faster chips substantially reducing time-to-market. American companies with access to Nvidia's H100 or H200 chips can train models in a fraction of the time required by Chinese competitors using Huawei Ascend or Cambricon chips. This speed difference impacts not only direct development costs but also the ability to respond to market changes and implement iterative improvements.

Nevertheless, recent developments show that technological lag can be compensated for by alternative innovation pathways. DeepSeek's release of the R1 model in January 2025 demonstrated that algorithmic efficiency can offset hardware deficiencies. The model achieves performance levels comparable to OpenAI's advanced systems at one-tenth of the training costs through innovative approaches such as mixture-of-experts architectures and selective activation of subnetworks. This development illustrates a fundamental principle of technological competition: constraints induce innovation along alternative dimensions. While American companies can pursue computationally intensive approaches due to access to superior hardware, Chinese resource scarcity forces the development of more efficient algorithms that ultimately offer advantages even when better hardware becomes available.

Regulatory fragmentation as a systemic obstacle to American infrastructure development

The decentralized structure of American energy markets and the complexity of the permitting process create friction that fundamentally limits the speed of response to changing demand patterns. Developing new transmission lines in the United States takes an average of seven to ten years from initial planning to commissioning, requiring the coordination of permitting processes at the federal, state, and local levels. This time lag between identifying demand and providing capacity creates structural inefficiencies that can only be partially addressed by accelerated permitting processes. The Trump administration initiated measures to expedite data center permitting processes through executive orders and directives to the Federal Energy Regulatory Commission, setting targets of 60 days for connection permits—a radical reduction from current processes that take several years.

These regulatory initiatives, however, are encountering fundamental capacity constraints. Even accelerated permitting processes do not address the physical limitations of production capacity for critical components such as transformers, switchgear, and gas turbines. Analysts identify these supply-side constraints as a significant limitation on infrastructure development. The North American Electric Reliability Corporation documents that electricity demand for the winter of 2024/25 increased by 20 gigawatts compared to the previous year, while generation capacity expansion was insufficient. This increases the risk of supply shortages during extreme weather conditions, with regions in the southeastern United States and parts of the West, including Washington and Oregon, identified as being particularly vulnerable.

The fragmentation of American electricity markets into Regional Transmission Organizations with differing rule sets and tariff systems creates additional complexity. While China can develop transmission capacity in a coordinated manner through centralized planning, American projects must navigate multiple jurisdictions and resolve conflicts of interest between utilities, regulators, and data center providers. American Electric Power reported a decrease in grid connection requests from over 30 gigawatts to 13 gigawatts after the introduction of new tariff structures in Ohio that require data centers to take delivery of at least 85 percent of their subscribed capacity. This measure aims to reduce speculative requests and prevent capacity reservation without actual use, but illustrates the difficulty of creating incentive structures that both encourage infrastructure investment and discourage opportunistic behavior.

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Temporary self-sufficiency as a transitional strategy of American technology companies

The inability of the American power grid to keep pace with the speed of data center development has prompted technology companies to implement on-site power generation, a development the Wall Street Journal has characterized as an energy Wild West. OpenAI's $500 billion Stargate project in West Texas, Elon Musk's xAI Colossus data centers in Memphis, and over a dozen other facilities use on-site gas-fired power plants or fuel cells for power. This "bring your own power" strategy represents a fundamental departure from traditional business models in which data centers operated as mere consumers of grid electricity.

The economic logic behind these efforts toward energy self-sufficiency reflects the opportunity costs of delayed commissioning, which justify the investment in on-site generation facilities. When a data center represents hundreds of millions of dollars in installed hardware, the value of which is continuously eroded by rapid technological advancements, the cost of waiting several years for grid connections outweighs the investment in temporary on-site generation. Bloom Energy, a fuel cell technology provider, reports rapidly increasing demand from data center operators who historically took grid connections for granted. ICF, a consulting firm, estimates that the U.S. needs to add 80 gigawatts of new generation capacity annually to keep pace with demand from artificial intelligence, cloud computing, cryptocurrencies, and electrification, but is actually only realizing 65 gigawatts.

This 15-gigawatt capacity gap is equivalent to the electricity demand of two Manhattan boroughs during peak summer demand and illustrates the scale of the undersupply. However, decentralized on-site generation using gas-fired power plants is not a sustainable solution, but rather a temporary bridging strategy. Most technology companies aim for grid connections in the long term, as decentralized generation incurs higher operating costs and emissions. Nevertheless, a hybrid model is emerging in which data centers act as both grid feed-in and consumption, with excess on-site generation being fed into the grid during periods of low computing load. GE Vernova, a leading manufacturer of gas turbines, reports record sales and plans to invest $700 to $800 million in American manufacturing facilities and hire 1,800 additional workers.

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Nuclear energy as a potential system solution and its implementation

The limitations of renewable energies regarding baseload capability and political resistance to fossil fuels have established nuclear power as the preferred long-term solution for data center power. Google announced partnerships with Kairos Power and the Tennessee Valley Authority to utilize advanced Small Modular Reactors, with the Hermes 2 project expected to deliver up to 50 megawatts. Amazon, together with X-energy, Korea Hydro & Nuclear Power, and Doosan, is investing up to $50 billion in the development and deployment of Xe-100 SMR technology, with target capacities exceeding five gigawatts. These partnerships signal a fundamental shift in the energy strategy of American technology companies, which historically favored renewable energies.

The economic appeal of nuclear power for data centers stems from several factors. First, nuclear power provides continuous baseload power without the intermittency of solar or wind energy, thus eliminating the need for expensive storage systems. Second, small modular reactors (SMRs) allow for modular scaling and faster implementation than traditional large reactors, with projected construction times of four to five years. Third, nuclear power meets sustainability goals without carbon emissions, addressing both economic and political requirements. Google and NextEra Energy plan to reactivate the Duane Arnold Energy Center in Iowa by 2029, while Blue Energy and Crusoe are developing a nuclear-powered AI factory in Texas, with the intention of gradually replacing existing gas infrastructure with nuclear power.

These developments reflect a remarkable irony: While the Trump administration systematically hindered wind and solar projects and eliminated subsidies, the demand from data centers is effectively forcing an energy transition, as conventional fossil fuel power plants cannot be built at a sufficient pace. Jefferies Investment Bank characterizes the situation as a golden age for renewable energy in the US, despite political resistance. The Federal Energy Regulatory Commission documents that 91 percent of the 15 gigawatts of new generation capacity added between January and May 2025 came from renewable sources, with solar dominating at 11.5 gigawatts. Projections show that of the 133 gigawatts of planned capacity by 2028, 84 percent will come from solar and wind, while gas will account for only 15 percent.

China's coal-fired power plant paradox and the persistence of fossil fuel infrastructure

Despite massive investments in renewable energy, China is paradoxically pursuing a parallel strategy of massive coal-fired power plant expansion, illustrating the complexity of its energy transition. In 2024, Chinese authorities approved 67 gigawatts of new coal-fired power plant capacity, with 95 gigawatts already under construction—the highest rate since 2015. This seemingly contradictory policy reflects the function of coal as insurance against the volatility of renewable energy and as a policy instrument to ensure energy security. While wind and solar capacity fluctuates with the weather, coal-fired power plants offer dispatchable capacity that can be activated on demand. The Centre for Research on Energy and Clean Air argues that this overcapacity of conventional power plants effectively crowds out renewable energy, as long-term coal-fired power contracts create economic incentives to use this capacity even when renewable alternatives are available.

The economic logic of this dual strategy is determined by the structure of Chinese electricity markets, where coal-fired power plants are compensated through capacity payments regardless of actual electricity production. Analyses show that 100 to 200 gigawatts of coal-fired reserve capacity will be needed by 2050 as backup for renewable energy, requiring capacity payments of 400 to 700 billion yuan. These payment flows incentivize the maintenance of coal capacity, even as its use declines. Planning reserve margins for Chinese regional grids averaged 28 percent in 2014, nearly double the 15 percent typical in the US, with some regions, such as the Northeastern Grid, exhibiting reserve margins as high as 64 percent.

This overcapacity reflects systemic perverse incentives in China's energy sector, where local governments use power plant projects as instruments of regional economic development, and coal producers secure their markets through vertical integration into power generation. Over three-quarters of new coal-fired power permits went to companies with mining operations, thus creating demand for their own product. This structure generates the political and economic persistence of fossil fuel infrastructure, despite official emissions reduction targets and President Xi Jinping's pledge to reduce coal consumption from 2026 onward. Thermal power generation grew by only two percent in 2024, while renewable capacities exploded, yet the existence of massive coal backup capacity limits the actual integration of renewable energy.

Geopolitical dimensions of technological competitiveness

The race for dominance in artificial intelligence transcends economic competition and manifests itself as a geostrategic conflict for technological hegemony with far-reaching implications for global power structures. Nvidia CEO Jensen Huang explicitly warns that China will win the AI ​​race, an assessment particularly noteworthy coming from the head of America's most valuable company, whose products are primarily sold to American customers. Huang's argument focuses on the structural advantages of Chinese companies: free or heavily subsidized energy, fewer regulatory restrictions on AI applications, and the ability to experiment with new products more quickly. His statement that electricity is practically free in China may be hyperbolic, but it reflects the actual subsidy practices that reduce operating costs so significantly that they become virtually negligible.

The American Edge Project, a coalition of American organizations, published a report in November 2025 warning that despite initial leadership, the United States is not positioned for long-term AI dominance. The report identifies a decade of underinvestment in power generation and transmission grids, combined with a talent shortage and slow AI adoption, as structural weaknesses that China is exploiting. OpenAI communicated to the White House that China's commitment to expanding power generation gives the country an advantage in the AI ​​race, treating capacity provision as the foundation of industrial competitiveness. This assessment converges with observations by American experts who, after trips to China, conclude that American grid infrastructure is so weak that the race may already be over.

The geopolitical significance of artificial intelligence stems from its applicability across virtually all economic sectors and its potential use for military purposes. Anthropic documented the first confirmed case of fully AI-orchestrated cyber espionage, in which a China-affiliated group automated 80 to 90 percent of its attack process, including reconnaissance, exploit validation, credential harvesting, and data extraction. This development demonstrates that AI capabilities have direct security implications, with the nation gaining asymmetric advantages in cyber warfare and intelligence gathering with more advanced systems. The Trump administration responded with executive orders to expedite data center approvals and directives to the Department of Energy that explicitly link national security and economic dominance to AI infrastructure.

Distributional effects and social implications of infrastructure development

The cost distribution of data center development generates significant distributional effects, with geographically concentrated benefits accruing to globally distributed actors, while costs are borne by local communities. Data centers are globally networked via the internet and serve worldwide user bases, yet consume energy locally at their physical locations. This spatial discrepancy between beneficiaries and cost bearers creates fundamental problems of equity. Residents of Virginia, Illinois, or Ohio subsidize global AI services through increased electricity prices, services from which they do not necessarily benefit, while technology companies privatize profits and socialize costs.

The regulatory structure of American electricity markets exacerbates this asymmetry. Utilities finance grid expansion through tariff increases for all customers, and while data centers consume significant amounts of energy, they often receive more favorable rates than residential customers due to economies of scale and bargaining power. Georgetown Law Review documents that residential customers effectively subsidize the energy costs of data centers, even though these are owned by for-profit companies that are among the most well-capitalized in the world. In Santa Clara, data center consumption already accounts for 60 percent of total electricity sales, with the city levying a five percent utility tax that provides at least partial compensation for infrastructure costs.

These distributional effects are complemented by labor market implications. Data centers generate relatively few direct jobs after commissioning because they operate in a highly automated manner. While construction phases create temporary employment and specialized technical positions emerge, the ratio of capital investment to job creation remains extremely low compared to traditional industries. Municipalities that attract data centers receive tax revenue and indirect economic benefits, but also bear infrastructure costs and environmental burdens from increased energy consumption. The mismatch between local costs and global profits generates political resistance to further data center development in some regions, with municipalities implementing moratoria or more restrictive permitting practices.

Innovation dynamics under asymmetric resource constraints

The differing resource constraints faced by American and Chinese AI developers are driving divergent innovation paths with potentially surprising long-term consequences. American companies, with access to superior Nvidia chips, focus on computationally intensive approaches that maximize hardware advantages but can be inefficient in terms of energy consumption. Chinese developers, limited to less powerful hardware by export restrictions, must prioritize algorithmic efficiency, leading to innovations that offer advantages even when better hardware becomes available. DeepSeek's R1 model exemplifies this pattern: through a mixture-of-experts architecture and selective activation of subnetworks, it achieves comparable performance at one-tenth the cost.

This dynamic illustrates a fundamental principle of technological evolution: scarcity stimulates innovation along alternative dimensions. While resource abundance encourages incremental improvements along established paths, scarcity forces fundamental redesigns. The release of DeepSeek R1 under the MIT license as an open-source model amplifies this effect, as global developers can build upon these advancements. This open-source strategy reflects China's understanding of the logic of AI competition: every improvement made by one player feeds into the next global development cycle, even if competitors benefit. This dynamic favors players with vibrant entrepreneurial ecosystems, top-tier research labs, and strong venture capital networks—structural strengths that remain primarily concentrated in the US.

However, the efficiency innovations of Chinese developers do not address all limitations. While training costs are reduced, inference—the generation of text or images by trained models—remains a computationally intensive process. This could limit China's ability to scale AI services globally, especially under tightened chip sanctions. Nevertheless, the DeepSeek example demonstrates that export controls do not eliminate innovation, but can merely delay and redirect it. The development speed of Chinese AI models has accelerated dramatically: while previous generations took years to catch up with American models, DeepSeek completed an initial version of R1 within months of OpenAI's release. This acceleration reflects both accumulated expertise and intensified government support and industrial investment.

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Long-term system stability and transformation risks

The rapid transformation of global energy systems to accommodate AI infrastructure poses significant risks to grid stability and long-term system resilience. The North American Electric Reliability Corporation identifies increased blackout risks for the winter of 2024/25 due to data center demand exceeding generation capacity by 20 gigawatts. Regions in the American Southeast, as well as Washington and Oregon, are particularly vulnerable, where a combination of increased demand, reduced solar generation in winter, and potential gas pipeline restrictions during extreme weather risks supply shortages. This situation reflects systemic underinvestment in resilience and redundancy over decades of stagnant demand growth.

The long-term sustainability of current development paths is questionable. While both nations are making massive data center investments, it remains unclear whether AI applications will generate value that justifies these investments. Goldman Sachs expresses heightened alert regarding potential market weakness, with risks including AI monetization failing or innovations commoditizing and drastically reducing the cost of model development. In the latter scenario, massive infrastructure investments would be rendered unnecessary before they generate returns. The volatility of Nvidia shares following DeepSeek's announcement, which wiped out $600 billion in market capitalization, illustrates investor uncertainty regarding the persistence of current business models.

Environmental implications of rising energy demand further complicate transformation pathways. While technology companies articulate commitments to carbon-free energy, the International Energy Agency forecasts that gas-fired power generation for data centers will more than double from 120 terawatt-hours in 2024 to 293 terawatt-hours in 2035, primarily in the US. Goldman Sachs estimates that 60 percent of the additional data center demand will be met by natural gas, resulting in 215 to 220 million tons of additional greenhouse gas emissions by 2030, equivalent to 0.6 percent of global energy emissions. This development undermines national climate targets and exacerbates political conflicts between economic development and environmental protection. China faces similar dilemmas, with massive coal-fired power plant expansion, despite renewable energy investments, jeopardizing emissions reduction targets and casting doubt on achieving peak emissions before 2030.

The global dimension of these developments transcends bilateral US-Chinese competition and affects energy systems worldwide. The International Energy Agency projects that by 2035, data centers will consume over four percent of global electricity, making them the fourth-largest electricity consumer as a standalone nation, after China, the US, and India. This surge in demand coincides with the electrification of transportation, industrial reshoring, and economic development in emerging economies, with cumulative increases in demand potentially overwhelming generation capacity and grid infrastructure. The resulting competition for limited energy resources has the potential to create international tensions, with energy surplus nations accumulating strategic advantages while energy-import-dependent economies become vulnerable.

Resolving these multiple tensions requires coordinated industrial policy interventions, massive infrastructure investments, and potentially fundamental revisions of current artificial intelligence development models. Whether through technological innovation that enables efficiency gains, regulatory reforms that accelerate approval processes, or demand management that limits wasteful applications, balancing AI development, energy availability, and environmental goals requires a systemic redesign of established structures. The coming years will determine whether this transformation process unfolds in an orderly fashion or whether resource scarcity, grid instability, and geopolitical conflicts force chaotic adjustments. Current developments suggest that China has accumulated structural advantages through strategic foresight and centralized coordination, while American strengths in innovation and entrepreneurial dynamism are counterbalanced by infrastructural deficits, with the ultimate outcome of this competition depending on both systems' ability to address their respective weaknesses.

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