① NVIDIA CEO Jensen Huang stated that AI technology is reshaping the logic of societal operations and has entered a phase akin to the early days of the internet or automobile adoption; ② Huang believes that the United States’ greatest weakness in the AI race lies in its power and energy supply capacity, as large-scale data centers are rapidly driving up electricity demand, which could become a critical constraint on AI expansion in the future.
Caixin Global, June 17 (Editor: Niu Zhanlin) — As the AI boom continues to drive up valuations in global technology and capital markets, $NVIDIA (NVDA.US)$ NVIDIA CEO Jensen Huang said on Tuesday that the rapid proliferation of AI technology is reshaping the logic of societal operations, and that broader adoption of AI could improve people's lives.
As one of the key figures driving AI development, Huang has long maintained an optimistic outlook on AI’s potential. He noted that AI has now reached a stage similar to the early adoption phases of the internet or automobiles, with impacts extending beyond productivity tools to directly disrupt employment structures, education systems, and societal trust mechanisms. Against this backdrop, he emphasized that “society needs to establish new norms.”
“I advocate for everyone to use AI,” Huang said, arguing that only when society as a whole understands and uses AI can it collectively define new boundaries of usage and risk awareness, rather than relying solely on a single regulatory framework for constraints.
AI has become a contentious issue in U.S. politics. On one hand, opposition to data center construction is growing; on the other, there are concerns that rapid AI adoption could trigger mass layoffs, while many workers lack sufficient social safety nets as a buffer.
AI is narrowing the digital divide
Huang stated that AI can already help users design websites, analyze complex documents, assist in advanced scientific research, and even plan kitchen renovations—thereby helping to narrow the long-standing digital divide in American society.
He pointed out that people today no longer need to learn programming or software development to accomplish complex tasks that previously required specialized technical expertise, thanks to AI assistance.
At the same time, he acknowledged that AI still requires a degree of government regulation and safety standards, stressing that national security should be a top priority in AI development.
As AI becomes central to technological competition, the policy environment is tightening rapidly. The U.S. government has recently stepped up interventions in areas such as export controls and model review mechanisms, particularly targeting high-performance models and computing infrastructure.
In Jensen Huang's view, the essence of AI competition lies not in isolated technological advantages, but in systemic capabilities encompassing chips, models, energy, and developer ecosystems.
Reserved about government ownership stakes in AI companies
Regarding discussions emerging in the United States on whether the government should hold equity in AI companies to share in their gains, Jensen Huang explicitly expressed reservations.
He noted that American society is already broadly engaged in the growth of technology companies through capital markets, the tax system, and employment chains. “I’m not entirely sure what they’re trying to achieve—I haven’t had in-depth discussions with them on this yet.”
Jensen Huang emphasized that these AI companies are inherently American, and their success has already benefited U.S. society across multiple dimensions: generating returns for American investors holding related assets, creating numerous jobs, and driving growth in sectors such as energy, construction, and hardware technology.
Energy is the critical bottleneck for AI development in the U.S.
Compared to debates over policy and capital, Jensen Huang’s most industry-focused insight centered on energy constraints. He stated plainly that the U.S.’s biggest shortcoming in AI competition is not chips or models, but its capacity to generate and supply electricity and energy. “We’ve fallen behind in energy production for far too long.”
He explained that large-scale data centers are rapidly increasing electricity demand, which could become a key limiting factor for AI expansion in the future.
Against the backdrop of exponentially growing demand for AI training and inference, data centers have evolved from “IT infrastructure” into “energy-intensive industrial systems.” Electricity prices, transmission and distribution capacity, and energy mix will all directly affect the pace of AI industry expansion.
Jensen Huang also noted that next-generation optical interconnect and chip communication technologies could reduce system energy consumption by approximately 50%, but this improvement would not fundamentally offset overall demand growth.
Editor/KOKO