The United States is struggling to win the AI race because its rollout of AI technology lacks public support, in contrast to China which is years ahead in deploying AI at scale and integrating it into everyday infrastructure. Western leaders often mistakenly frame the competition as a sprint for bigger, smarter AI models, rather than recognizing that the real determinant of power is the ability to deploy AI broadly and gain public trust. This deployment advantage in China is bolstered by top-down industrial policy and deep integration across various sectors, leading to a public that is far more accepting of AI than in Western countries.
Western societies view AI with suspicion. A Gallup survey indicated that 71% of Americans oppose new data centers for AI, and 76% believe AI needs regulation. The 2025 Edelman Trust Barometer shows that only 32% of people in the U.S. and other Western democracies trust AI, compared to 72% in China. This lack of trust is a significant barrier to the widespread adoption of AI; for example, only 40% of firms in the U.S. and Europe have integrated AI into their workflows, and an MIT report found that 95% of U.S. AI deployments yielded no measurable impact on profit.
Key industry leaders acknowledge this issue. Anthropic CEO Dario Amodei described negative public perception as a "crisis of trust," citing public suspicion that tech companies are exploiting them. The financial industry is taking notice, with over $265 million amassed by groups advocating for or against AI regulation, including a political action committee called Leading the Future, backed by Trump donors, OpenAI co-founder Greg Brockman, and Andreessen Horowitz, which has raised over $125 million to oppose state-level regulations. Meanwhile, Anthropic has provided $20 million to Public First Action, an organization advocating for increased AI scrutiny, demonstrating a divide even within the tech industry on regulatory approaches.
To catch up, the West must shift from an "innovation-only" mindset to a "deployment-first" strategy centered on public trust. This requires rigorous third-party evaluation, extensive red-teaming, transparent governance frameworks, and credible plans to address labor market disruption. Without these safeguards and public reassurance, the deployment of AI will stall due to resistance from the public, workforce, and institutions, hindering the realization of AI's economic potential in democratic societies.
