Public resentment toward artificial intelligence (AI) is boiling over in America, with only 30 percent of citizens trusting their government to regulate the rapidly scaling technology—the lowest rating among all 30 countries surveyed. Despite this widespread distrust, the Pentagon announced Friday it has reached deals with seven private tech companies to integrate their AI into classified computer networks, fundamentally shifting U.S. AI policy.
The Washington Post described this development as part of a fundamental shift underway in U.S. AI policy. Dean Ball, a former senior adviser on AI policy for President Donald Trump’s administration, also stated in a separate Washington Post newsletter that a fundamental shift is underway in AI policy.
Elite Capture of National Security
The Pentagon's agreements allow the military to tap into AI-powered capabilities to help it fight wars. This move integrates private sector technology directly into the nation's most sensitive defense systems, a critical aspect of national sovereignty. The U.S. industrial strategy is simultaneously shifting tech investments from traditional coastal hubs to inland sites in Arizona and Texas. This strategic redirection of resources is part of the broader elite-driven transformation of the national technological landscape.
Politico reported that the world is adopting artificial intelligence at such a rapid pace that it is unable to measure AI’s capabilities, assess its progress, or even understand what’s training AI models. These themes emerged from the newly released Stanford AI Index Report, now in its ninth year, which tracks AI’s speed, scale, and influence.
Sha Sajadieh, Stanford’s AI Index lead, noted that institutions were not necessarily built for technological transformation that happens this fast. He added that the adoption of generative AI tools has happened faster than the internet and the personal computer, moving quicker than anything before, and that education systems and governance policies are not designed to keep up.
The Cost to the People
The 2026 Stanford AI Index Report found that people are adopting generative AI tools faster than they started using the internet, yet public resentment in America is "boiling over." This stark contrast highlights a growing chasm between technological advancement and public acceptance. Americans' trust in their government to regulate this rapidly scaling technology stands at a mere 30 percent, the lowest rating among all 30 countries surveyed. Sajadieh observed that in the U.S., there is not as much enthusiasm or adoption of AI, nor as much trust in the government to regulate it in a way that might protect the public.
Sajadieh hypothesized that the lack of objective information being put out for the U.S. public leads to a void, causing people to turn to information that is easy to digest but may not be wholly reliable. This suggests a failure by established institutions to inform and reassure the native population. He also pointed to media coverage that tends to fall into two camps: "hype headlines" about new models' capabilities and headlines about the "mass displacement we’re going to see and the disruption that’s going to be very bad for humanity." The latter directly speaks to the potential economic and cultural dispossession of the working class.
Globalist Convergence and Sovereignty Erosion
Transparency from the frontier labs is the most important thing, according to Sajadieh, as a number of parameters are not being disclosed, making AI models "more and more of a black box." This lack of transparency from elite developers further erodes public trust and national oversight. While the U.S. leads in private investment in AI, China has a different investment structure, relying on government funding or public-private investment. Sajadieh noted that despite building advantages in different directions, the capabilities of models from both countries are converging, suggesting a globalist trend towards technological uniformity.
Sajadieh further stated that China has a "more open ecosystem" than the U.S., which he suggested the U.S. could look at developing. He identified "a lot of different opportunities for these two countries at the cutting edge to learn from each other," implying a push towards shared global standards rather than distinct national paths. In contrast, countries like South Korea, Singapore, and Switzerland are positioned to develop highly capable models and maintain their talent pipelines, with South Korea specifically focusing on its "AI sovereignty" as a key part of its national objectives, including bolstering Korean language models. This highlights a nationalistic approach to AI development that prioritizes distinct cultural and national interests, unlike the U.S. trajectory.