Trump’s ambitious vision for an AI “Manhattan Project” faces serious skepticism from experts who argue that his proposed cuts to science and technology funding could derail the entire initiative. Critics describe the plan as little more than a symbolic gesture—a “Band-Aid on a giant gash”—that ignores the foundational role of sustained federal investment in scientific research. The proposal, which aims to accelerate U.S. leadership in artificial intelligence through aggressive government-backed innovation, hinges on rapid progress in computing, data infrastructure, and advanced research. Yet, critics warn that slashing funding for agencies like the National Science Foundation (NSF), the Department of Energy’s national labs, and the Defense Advanced Research Projects Agency (DARPA) would undermine the very pillars that make such a moonshot possible. “These cuts aren’t just about budgets—they’re about dismantling the ecosystem that produces breakthroughs,” said Dr. Lena Patel, a former senior advisor at the NSF. “You can’t build a moonshot on a foundation of depleted labs, shrinking grants, and brain drain.” The concerns come amid broader worries about the impact of political interference in science. Past administrations have emphasized innovation, but Trump’s repeated criticism of scientific institutions—particularly those perceived as politically aligned—has raised alarms among researchers and tech leaders alike. Moreover, the AI race is no longer just about hardware or algorithms; it’s about talent, data, and long-term research. Cutting funding for university research, STEM education, and basic science would weaken the pipeline of future innovators. As one AI engineer at a major tech firm put it, “You can’t outsource innovation. You can’t just order a breakthrough from a spreadsheet.” The DOGE (Department of Government Efficiency) initiative, which aims to eliminate or reduce federal spending across agencies, has already led to the cancellation of several research programs and the layoff of scientists and engineers. Critics argue that these moves are short-sighted and counterproductive, especially when global competitors like China are increasing their investments in AI and quantum computing. “An AI moonshot isn’t a sprint—it’s a marathon,” said Dr. Rajiv Mehta, a computer scientist at MIT. “You need consistent support, long-term vision, and trust in experts. What we’re seeing now is the opposite.” Without stable funding and institutional trust, even the most ambitious goals risk becoming hollow promises. For Trump’s AI vision to succeed, experts say, it must be backed by real investment—not just rhetoric. Otherwise, the so-called “Manhattan Project” may end up being remembered not as a triumph, but as a cautionary tale of political overreach and scientific neglect.