It is clear that very soon, maybe even later this year, we will be making more chips than we can enable – except in China.
Elon Musk, at the 2026 World Economic Forum
When Musk made that remarkhis main argument was that the biggest obstacle to AI progress is not computer hardware, but lack of electricity.
In recent years, the global conversation about artificial intelligence has revolved around one issue: semiconductors. Policymakers around the world have discussed the supremacy of silicon in their planning documents, as if the future of the AI industry depended on it alone. NVIDIA became the first chip company to do so 5 trillion dollars assessment. It has now become the gold standard of the industry.
But nowadays, there seems to be another challenge facing AI companies. The launch of DeepSeek in 2025 has changed the assumption that was becoming conventional wisdom.
Today, nations, companies and investors are realizing that they have less need to stockpile GPUs, fight for chip supply and chase the next hardware breakthrough. DeepSeek shows that algorithmic efficiency can partially compensate for hardware limitations through optimizations.
Unlike other AI models that use the entire model for each query, Deep Seek activates only the relevant parts, improving performance per unit of computation.
Every time an AI model answers a question, generates a word, answer, image or video, it performs billions of calculations. But these calculations are done with electricity. For example, a ChatGPT query used 10 times more resources than a Google search query. Creating an AI image requires as much energy as charging your smartphone.
Now multiply that by hundreds of millions of questions every day, and the math will be terrifying. So the biggest limitation in the AI industry today is not computers, but power. As AI systems grow and become more capable, power supply is emerging as a critical constraint, and the pressure is already showing in the United States.
Washington focused on semiconductors and Beijing on networking
In the US, data centers will account for 38% of electricity demand growth between 2024 and 2030, though only 6% in China, according to Bloomberg NEF. projections. Data centers will command almost 7% of total energy demand in the US by 2030, compared to 2% in China.
By 2035, US data center energy demand is projected to reach 106 gigawatts. To put this in perspective, the United States operates the largest nuclear fleet on the planet. But its total power is 97 gigawatts, which would still not be enough to meet the demands. A data center can be built in just 18 months, while bringing in a new power supply takes three times as long. In 2024, the US built 888 miles of transmission line, when it needs about 5,000 miles each year.
In March 2025 CSIS reported that the supply of electricity had become the biggest obstacle of American companies. This can be confirmed by the fact that AI data centers are driving up electricity costs in the US. In heavy data center states like Virginia, energy prices have risen by as much 267% during the last five years. According to the IEA sermons, global data center demand for electricity may increase from current estimates of 415 terawatt hours in 2024 to approx. 945 TWh by 2030, with AI being the single largest driver of this growth.
In 2024, China generated over 10,000 TWh of electricity, more than twice that of any other country on earth. In the next five years, China could add more than 3.4 terawatts of new generating capacity, far exceeding expected additions in the United States. This is equally important because the deployment of AI ultimately depends on access to reliable and affordable electricity. So today, the AI race has shifted from computational efficiency to energy abundance.
The AI race will have a clean energy advantage
Today, in the West, government and energy companies are still focused on protecting traditional industries like oil and natural gas. Meanwhile, China has invested heavily in new technology such as solar panels, batteries and wind power, not to reduce pollution, but as an industry to create jobs, spur innovation and strengthen the economy. In short, Western leaders saw solar panels, batteries and wind turbines as climate tools; the Chinese, as industrial tools.
Today, China owns approx 430 gigawatts of hydropower, 550-600 GW of wind power and 850-900 GW of solar power, in addition to 1,150 GW of coal-fired generation. These clean energy assets provide China with a sustainable energy foundation capable of supporting any infrastructure challenge of industrial expansion, electrification or artificial intelligence.
The future of the AI race will not be determined by the fastest chips or the best algorithms, but by who has the best power capacity to run those models. China has known this way before the rest of the world. Over the past two decades, the country has invested massively in clean energy and cemented its status as undisputed in the world. clean energy superpower, spending more on renewable energy than the rest of the world combined.
But the United States has tremendous strengths: the best universities, deep capital markets, a culture of innovation, global dominance in the semiconductor industry, entrepreneurial dynamism, and the world’s most influential AI companies. As the landscape evolves, AI is no longer a software competition, but an infrastructure competition. In infrastructure, expertise and scale matter.
The next decade will reveal whether the deciding factor in the AI race will be silicon or electricity. If AI becomes a competition over electricity, then the most important geopolitical story of the next decade may or may not take place in Silicon Valley.
