Nvidia CEO Jensen Huang reveals the two simple reasons (energy and regulation) why China has almost won the AI ​​race.

The AI ​​Paradox: Why the West is falling behind despite having the best technology

Energy and regulation as key factors in the global AI competition: The underestimated dimension of the technological power struggle

Nvidia CEO Jensen Huang's provocative assertion that China will win the artificial intelligence race has caused a stir in the West. But behind the headline lies an uncomfortable truth that goes far beyond the sheer power of chips. The global race for AI dominance will not be decided solely by algorithms and computing power, but by two fundamentally underestimated physical factors: energy availability and the effectiveness of government regulation. While the West indulges in an illusion of technological superiority, China has recognized the true bottlenecks and is acting with strategic ruthlessness.

The first dimension is the seemingly insatiable energy hunger of AI. Data centers will double their electricity consumption by 2030 – an increase equivalent to Japan's entire annual consumption. While in the US technological development is hampered by the limitations of an inadequate power grid, China is pursuing a ruthless but effective strategy: massive subsidies for electricity, the construction of dozens of new nuclear and coal-fired power plants, and an unprecedented expansion of renewable energies.

The second dimension is the regulatory paradox. Although the US preaches deregulation at the federal level, a chaotic patchwork of contradictory laws at the state level stifles any rapid development. China, on the other hand, uses its centralized system to create clear, strategic frameworks that channel innovation in an orderly fashion and provide companies with planning certainty.

This analysis shows how China's pragmatic, state-directed approach—a combination of massive infrastructure investment and strategic industrial policy—creates a decisive competitive advantage. While the West remains mired in debates about perfect regulation, China is creating facts on the ground. The race for the future of AI is thus less a sprint for the best algorithm and more a marathon for the most robust infrastructure—a race the West risks losing before it has even grasped the true rules of the game.

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The provocation behind the truth: Why the US is already losing the AI ​​race before it has even properly begun.

Jensen Huang, CEO of chip designer Nvidia, declared that China would win the artificial intelligence race, quickly made headlines in Western media. But behind this provocative statement lies a fundamental insight that the Western technological establishment is reluctant to hear: the AI ​​race will not be decided primarily by chip design or software sophistication, but by two mundane yet crucial economic factors whose importance is systematically underestimated. These two factors are the available energy infrastructure and the regulatory flexibility for its expansion. Huang speaks of a kind of cynicism that paralyzes the West, while China acts pragmatically.

While the US under Trump is committed to deregulation and has recognized that innovation should not be stifled by regulation, it is simultaneously failing at the second part of the equation: providing the physical infrastructure that makes AI systems function in the first place. This is not an abstract technical question, but a stark economic reality that will determine success or failure in the global AI race.

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The energy dimension of the AI ​​race: Why electricity is the new oil

To understand the criticality of the energy issue, one must first consider the sheer amount of electricity that AI systems require. According to forecasts by the International Energy Agency, global electricity consumption by data centers will more than double by 2030, from approximately 415 terawatt-hours in 2024 to around 945 terawatt-hours. This is roughly equivalent to Japan's current total annual electricity consumption. This exponential increase is driven almost entirely by AI applications. A single modern, AI-optimized data center consumes, on average, as much electricity as about 100,000 households. The largest of these facilities, currently under construction, can consume twenty times that amount.

According to current calculations, the US will account for almost half of this global increase in electricity consumption, underscoring the absolute dependence of American technology companies on energy availability. China will experience an even stronger growth rate of around 170 percent, highlighting the urgent need to create new capacity. Europe lags behind with growth of approximately 70 percent.

Herein lies the central economic problem: While the US possesses a modernized energy infrastructure, this infrastructure is not adequately sized to meet the anticipated electricity demands of the AI ​​industry. While the Trump administration is pushing forward an unprecedented deregulation agenda with its AI Action Plan to expedite permitting processes for data centers and power plants, America is failing to actually expand these facilities. Although the Secretary of Energy has announced that AI infrastructure will ultimately lead to cheaper electricity, this is a medium-term hope, not a current reality.

China, on the other hand, has pursued a completely different strategy. The country has massively increased its energy subsidies, resulting in a reduction of up to 50 percent in electricity costs for large data centers. This investment is neither random nor short-term. It is part of a systematic industrial policy aimed at protecting and promoting the domestic AI industry. While Nvidia CEO Huang is forced to argue to the US government that energy costs could be virtually free because the infrastructure is already in place, China is acting accordingly, deploying massive state resources to actually drive those costs down.

The economic significance of this energy subsidy is enormous. A data center that can reduce its electricity costs by 50 percent increases its profitability or can offer its services at roughly half the price that competitors from countries with higher energy costs have to charge. This is a classic example of state-manipulated competitive conditions, which in global trade policy are usually met with accusations of dumping. Yet in the field of AI, this is considered legitimate national security policy.

China's energy strategy for AI data centers is multifaceted. The country is building new coal-fired power plants on a large scale, which is ecologically problematic but pragmatic from an energy policy perspective. At the same time, China is investing in more than two dozen new nuclear power plants and undertaking unprecedented efforts to expand wind, hydro, and solar energy. The difference lies in the speed and focus: While vague plans for nuclear expansion circulate in America, and the reality is one of delays, China is building concretely.

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The regulatory paradox: Why fewer rules don't automatically lead to greater competitiveness

The Trump administration enacted a deregulation agenda of unprecedented scale. The AI ​​Action Plan comprises over 90 measures aimed at removing obstacles to AI development. Government departments are instructed to identify and amend rules that could hinder AI. The Federal Trade Commission is to interpret antitrust law in a business-friendly manner. Permitting processes for data centers and energy generation are to be expedited. All of this sounds excellent on paper and, from a purely free-market perspective, makes perfect sense.

But Huang argues that this deregulation isn't enough. The reason lies in what could be called the American regulatory patchwork problem. While the government in Washington preaches deregulation, individual states have already enacted their own AI laws. California, Colorado, Utah, and Texas have passed specific AI regulations. Around 15 other states are considering similar regulations. In addition, there are numerous data protection and data security laws that indirectly affect AI. Huang speaks of roughly 50 new regulations that could result from this federal system and warns of this regulatory labyrinth, which stifles innovation.

This is a classic example of an economic phenomenon known in the literature as regulatory fragmentation. Companies operating nationally must contend with a patchwork of local regulations, leading to compliance costs, delays, and ultimately, competitive disadvantages. China does not face this problem due to its centralized authority system. While regional differences also exist, they are integrated into a unified national strategy. The AI ​​industry knows its position and what it needs to do.

The paradox is this: Huang argues that the West is hampered by regulation precisely because this regulation is fragmented, contradictory, and constantly reinterpreted. A unified European regulatory system could provide clarity, even if it were restrictive. The American system, on the other hand, represents the worst of both worlds: regulation exists, but it is locally fragmented, ineffective, and unnecessarily costly.

The US therefore has a deregulation problem that is actually a hidden regulation problem. This raises a fundamental question: Is it really regulation that is holding America back, or is it rather the flawed implementation of regulation?

The Chinese approach: Central planning meets strategic pragmatism

While the US is fragmenting its efforts across individual states, China is pursuing an integrated, centrally planned approach. The country understands that AI is not just a technical problem, but also an economic and geopolitical one. Accordingly, a massive investment framework has been established. According to estimates by Bank of America, China plans to increase its AI investments to as much as 700 billion yuan (approximately $98 billion) by 2025. This represents a year-on-year increase of about 48 percent. This unprecedented level of investment demonstrates that the Chinese political system treats AI as a strategic priority.

These investments are by no means haphazardly distributed. They follow a clear strategy. In its AI+ Action Programme published in 2025, China outlined three phases. By 2027, AI technologies are to be integrated into six core areas: science, industry, consumption, general prosperity, administration, and global cooperation. This is not the rhetoric of an innovative startup ecosystem, but rather the language of a centralized superpower that uses AI as a tool in its comprehensive industrial policy.

The public sector is investing directly and substantially. A sovereign wealth fund for the AI ​​industry, established in 2025, comprises 60.06 billion RMB (approximately 7.2 billion euros) with a term of 13 years. State-owned banks and financial institutions are participating. In addition to this national fund, there are other specialized funds for AI clusters: the Shanghai Pioneer AI Fund with approximately 2.7 billion euros, the Shenzhen AI and Robotics Fund with approximately 1.2 billion euros, and eight other regional industry funds in Beijing, each with at least 1.2 billion euros.

This is the institutional framework for China's AI offensive. The country is under no illusions about the challenges. China's supply gap for AI chips is estimated to exceed ten billion dollars by 2025. Domestic alternatives like Huawei's Ascend 910B still lag behind in performance for training large language models. Utilization rates for Chinese AI data centers range between 20 and 30 percent, meaning significant capacity remains unused and profitability is at risk. This is addressed by China's strategic capacity for massive investment, while the West must assess the profitability of each individual project.

The domestic chip industry as an economic sphere of influence

A key reason for China's energy subsidies is the targeted promotion of its domestic chip industry. This cannot be understood without considering the interplay between Nvidia and Chinese chip manufacturers like Huawei and Cambricon.

The US has imposed a strict embargo on the export of Nvidia's most powerful chips to China. This is a classic technological embargo, which historically tends to be ineffective, as it forces countries to develop their own solutions. Huang himself has warned the government that this embargo is counterproductive. An export ban forces countries like China to invest in alternative solutions.

Cambricon is a case of particular interest here. The company suffered a collapse when Huawei, its main customer, decided to develop its own AI chips through HiSilicon. 98 percent of Cambricon's revenue vanished overnight. But in the new situation, where Nvidia is virtually non-existent in the Chinese market, Cambricon has emerged as a star of the Chinese AI industry.

Between 2020 and 2024, the company invested a total of 5.6 billion RMB in research and development, equivalent to approximately 780 million euros. The focus was on software, particularly interfaces that allow models trained on Nvidia GPUs to run on Cambrico's Siyuan chips. This software compatibility is considered a crucial advantage over Huawei's Ascend series, which is difficult to integrate into existing systems due to software issues.

In the first half of 2025, Cambricon achieved a profit of 1 billion renminbi, approximately 140 million dollars. Its market capitalization doubled within a few weeks to around 580 billion RMB. Analysts at Goldman Sachs expect Cambricon's revenue to rise to 13.8 billion RMB by 2026, and its market share to grow from approximately 3 percent today to 11 percent in 2028. This is happening with the direct support of major Chinese companies such as Alibaba, Tencent, and Baidu, which have a strong interest in building a competitor to Huawei.

Energy subsidies have direct economic effects on this development. If electricity costs for data centers using Chinese AI chips are reduced by 50 percent, the use of these chips becomes more economically attractive. This is a classic example of industrial promotion through subsidizing inputs rather than outputs.

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The efficiency revolution: Why DeepSeek and Chinese AI startups are shifting the technological paradigm

Much of the Western confusion surrounding China's AI capabilities stems from the spectacular emergence of a company called DeepSeek. Based in Hangzhou, the company caused a global sensation in 2025 with its open-source AI models V3 and R1. What was revolutionary about DeepSeek wasn't primarily the quality of the models, but rather the incredible cost-efficiency of their development.

DeepSeek claimed to have developed its advanced language model, DeepSeek-V3, for just $5.6 million. This sent shockwaves through the global technology and investment markets because it fundamentally challenged the Western understanding of the cost of AI development. OpenAI and other Western companies have spent billions on comparable models. Here was a Chinese startup that appeared to be creating a comparable model for a tiny fraction of that cost.

The reality is more complex. Experts at Semianalysis estimate that the hardware costs for DeepSeek's GPU fleet alone are likely around $1.6 billion. Added to this are estimated operating costs of approximately $944 million. These figures stand in stark contrast to the officially communicated $5.6 million. This is therefore a classic case of misleading information, where only the direct training costs of the final model are reported, while the entire infrastructure, research, and development are ignored.

At the same time, the fact that DeepSeek was able to raise these massive infrastructure costs is a testament to the financial resources behind it. A private startup could not make these investments without the support of a major source of funding. The close connection to state or state-affiliated investors in China is often discussed speculatively, but is not clearly documented.

Regardless of the exact funding structure, the technical result is real. DeepSeek has proven that intelligent architecture and algorithms can massively improve the efficiency of AI training. The company used a technique called Mixture of Experts Architecture, along with a Sparse Attention method that processes only the relevant parts of the context. This enabled a model with impressive performance and significantly lower energy consumption.

The economic impact of this efficiency revolution is considerable. DeepSeek later reduced its API prices by 50 to 75 percent, massively increasing the pressure on Western providers. A company wanting to use AI services can now choose between expensive Western models or opt for a cheaper Chinese alternative. This is a classic economic mechanism: when a competitor lowers prices through efficiency, the market share of Western providers erodes, and profit margins are compressed.

This clearly illustrates the interaction between energy costs and technological efficiency. China can experiment with cheaper energy and iterate more quickly. An inefficient model costs less in China than in the West. This enables faster learning cycles and faster innovation. DeepSeek is the result of hundreds of trials, the cumulative cost of which would be economically prohibitive in the West, but which are subsidized in China by cheap energy.

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The technological illusion of the West: Why the superiority of Nvidia chips is a fiction

Huang argues that the latest US AI models are not far ahead of their Chinese competitors. This is an inconvenient truth that undermines Western confidence in technological superiority. The West has become accustomed to believing that Nvidia chips and Western AI models are simply better, more advanced, more elegant. Trump himself claims that the new Blackwell chip is ten years ahead of any other chip on the planet.

This is an exaggeration, possibly based on a confusion between performance and market saturation. The Blackwell chip is indeed impressive, but it is not ten years ahead. A large part of Western technological superiority stems from two factors: first, proprietary datasets where Western companies have an advantage; and second, decades of experience in optimizing hardware and software.

However, Chinese companies have rapidly caught up in both areas. DeepSeek's models are not inferior to their Western competitors, but in some specific domains, they are superior. Huawei's Ascend chips, while not as advanced as Nvidia's, are good enough to handle many practical applications. The West's perfectionism, the notion that only the best solution is good enough, puts it at an economic disadvantage compared to China's pragmatic, satisfactory approach, which accepts "good enough."

This is also an example of what could be called the over-optimization trap. The West optimizes its chips and models to perfection, which is expensive and time-consuming. China builds faster and iteratively, leading to faster market penetration, even if the solutions aren't perfect. An imperfect chip that is available is better than a perfect chip that isn't.

China's regulatory strategy: Central planning with sandboxes

China is pursuing an interesting middle ground between centralized control and local experimentation. The country has established over 20 national AI innovation pilot zones, which function as regulatory sandboxes. These are places where companies can test AI technologies with a degree of regulatory freedom. This is a smart mechanism because it allows innovation while remaining within a central framework.

This contrasts sharply with the American system, where states compete to create their own rules, leading to fragmentation. While fragmentation also exists in China, it is organized within a unified national AI strategy framework. This allows for faster iteration at the national level without each state having to reinvent its own rules.

At the same time, China has a clear regulatory strategy for AI content and its use. The Chinese government retains control over the content, meaning that AI models available online are monitored and must adhere to Chinese standards. This is outrageous to Western liberals, but it also has the economic advantage that companies know exactly where their development is headed. There is no regulatory uncertainty.

At the same time, China is actively promoting open-source AI models, especially for developing countries. This is a geopolitical strategy to break the Western monopoly on AI and draw emerging economies into the Chinese technological sphere. If DeepSeek's models become widespread in Africa, South America, and Southeast Asia, it means these regions will be dependent not on OpenAI or other Western AI providers, but on China.

Western optimism as a cultural inhibition

Huang speaks of what he calls Western cynicism. This is a surprisingly insightful cultural diagnosis of technological competition. What he means is that the West has a mentality problem. The West constantly says that regulation stifles innovation, that major problems aren't solved quickly enough, that government is incompetent. This is constant complaining without action.

China, on the other hand, says that big problems can be solved quickly, and then builds. The US says we need nuclear power plants, and then maybe builds one. China says we need two dozen nuclear power plants, and builds two dozen. This is not primarily a question of technology, but a question of cultural conviction and institutional capacity.

The optimism Huang calls for is not naive. It is an optimism based on the understanding that major infrastructure challenges can be solved if only the political will is there. Historically, the US has had this. The railroads, electrification, the freeway, the space program, the internet itself—all of these were made possible by massive public investment and deregulation. But in the current era, Western optimism seems to have run dry.

The energy policy dimension: Why the energy transition and AI are in competition

A deeper question remains hidden here. The massive energy demands of AI data centers compete with the green energy transition. Governments and companies have set themselves the goal of becoming emission-free by 2050 or 2045. This requires massive investments in renewable energies and nuclear power. At the same time, they want to build AI infrastructure on an unprecedented scale.

China has found that these two goals don't have to conflict if priorities are set. On the one hand, the country is expanding coal-fired power generation, which is ecologically problematic, but on the other hand, it is also concentrating massive resources on renewables and nuclear power. Its energy mix is ​​pragmatic, not idealistic.

The West, by contrast, has tried to combine the energy transition and economic growth through purely green means, leading to a kind of paralysis. They want nuclear power, but it takes decades to build a power plant. They want renewable energies, but these are variable. They want AI data centers, but they also want to solve the climate crisis. In China, this tension is accepted pragmatically and not resolved through moral considerations.

Microsoft CEO Satya Nadella recently explained in a podcast that Microsoft has millions of AI chips sitting unused in warehouses because the power supply infrastructure is lacking. This is the opposite of progress. It's a situation where the capital is there, but the physical infrastructure is missing. This is a classic failure of infrastructure policy.

Huang's appeal as a wake-up call: The economic implications

Huang's statement that China will win the AI ​​race is therefore not a pessimistic prediction, but an appeal to economic rationality. He is not saying that China is technologically superior or more innovative. He is saying that China is creating the infrastructural prerequisites for AI to function, while the West is blocking this path.

This has immediate implications for the profitability of AI companies. A data center in China that obtains electricity at 50 percent lower costs can either be more profitable or offer services more cheaply. This puts price pressure on Western AI providers. If OpenAI offers an AI model for $100 per training run, but a Chinese company offers the same service for $50, who will win?

The economic answer is simple: The cheaper company will dominate the market. This is especially true for markets where price is crucial, such as emerging economies, and markets that require unlimited computing power, i.e., the training of even larger models.

At the same time, there is a psychological effect for Western companies. If Chinese competitors are faster and cheaper, investors become more skeptical about the profitability of Western AI startups. This could lead to a tightening of credit, which in turn stifles innovation. This is a kind of self-fulfilling prophecy: Pessimism about Western competitiveness leads to worse investment conditions, which in turn worsens competitiveness.

The geopolitical dimensions: AI as a power

Behind all these economic factors lies a deeper geopolitical reality. AI is no longer seen as a scientific achievement or economic innovation, but as an instrument of power. A country that is a leader in AI has not only economic, but also military and political advantages.

The Trump administration understands this. Hence the strict export restrictions on Nvidia chips to China. Hence the announcement that the most advanced chips will not be exported. Trump says that the most advanced technologies will not be available outside the US. This is a kind of digital embargo, similar to the embargoes on oil or other critical commodities in earlier phases of geopolitics.

China's answer is pragmatic: if Western technology isn't available, we develop our own. This is a classic pattern in international economics. Countries cut off from technology devote massive resources to developing it themselves. The Soviet Union did this with rocket technology and nuclear power. China did it with semiconductors and AI.

The illusion of Western control

A key irony lies hidden here: The US believes it can control China through export restrictions. In reality, this only leads to China developing autonomous solutions more quickly. DeepSeek is partly a product of these restrictions. If Nvidia chips were freely available, Chinese companies might have less incentive to develop their own architectures.

Huang has repeatedly told the US government this: an open market where Nvidia is dominant is better for the US than a fragmented market where China develops its own solutions. This is a classic case of the boomerang effect, where attempts to control another country lead to unintended consequences.

At the same time, there is also an element of economic rationality at play for the US government. The blacklists and export embargoes are not primarily intended to control China, but rather to cement the US-dominated global order. This is a question of hegemony. The US not only wants to be a leader in AI itself, but also to make all other countries dependent on the best AI chips.

But this assumes that the US itself has sufficient capacity to meet this requirement. Nvidia cannot produce enough chips to satisfy global demand. Let alone does the US have the energy infrastructure to supply AI to the entire world. If, on the other hand, America denies other countries access to the best AI, it will force those countries to find alternative solutions.

The economic outcome: Who will dominate AI?

According to estimates by the market research firm CCID Consulting, China's AI market will reach a volume of 1.73 trillion yuan by 2035, representing approximately 30.6 percent of the global total volume. This would be a massive market share, considering that China started with about 15-20 percent of the global AI market in 2024.

The US will, of course, remain a huge AI market. But its relative share will shrink if China continues with the strategies described. This is the economic logic behind Huang's statement. It's not that China will become technologically superior. It's that China will lower the price of AI through infrastructure and energy subsidies, thereby capturing the market.

One point often overlooked in Western debates is that dominance doesn't mean a country always has the best technology. It means a country dominates the market. IBM had the best computer technology in the 1980s, but lost the PC market to faster and cheaper competitors like Compaq and later to Asian manufacturers.

The parallel to AI is relevant. The West might still have better models. But if Chinese AI is cheaper, faster, and just good enough, the market will gravitate toward China. This isn't a question of technological superiority, but a question of economic efficiency.

The analysis shows that while the US is pushing forward with a deregulation agenda, it forgets that deregulation alone is not enough. It must also provide the physical infrastructure on which this deregulation can take effect. China has recognized that energy, not regulation, is the bottleneck and is therefore massively subsidizing electricity costs. This creates economic advantages that translate into lower prices and faster innovation. The Western belief that technological superiority automatically leads to market dominance is an illusion refuted by an economic reality in which price and availability are more important than theoretical performance. Huang's prediction is thus not pessimistic, but rational.

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