Why America's AI Bosses Want to Slow Down AI
Anthropic CEO Dario Amodei called for slowing work on the most powerful AI systems, warning they could soon bring potentially catastrophic risks for humanity. U.S. President Donald Trump rejected the call, saying the United States is leading China in AI and stating "whoever wins AI wins." Al Jazeera
Amodei warned that in 6-12 months an AI swarm could take over the entire internet with a persistent botnet. A botnet is a large group of hacked computers controlled together. He said the damage could reach hundreds of billions of dollars. He described this as a near-term cybersecurity danger, not a distant story.
His requests to Washington were specific. He urged the government to keep limits on cutting-edge AI chips and chipmaking equipment going to China. Advanced AI chips are like powerful engines. They supply the power to build and run AI. He also called for action against alleged "distillation" of U.S. AI models by Chinese labs. That technique uses outputs from one model to train another.
OpenAI CEO Sam Altman and Elon Musk backed Amodei's warnings on AI risks. The White House said no.
Trump said artificial intelligence will bring "more good than bad" in response to rising AI safety concerns. Fox Business He also said he did not want to cede the U.S. edge in AI development to China. PBS House Speaker Mike Johnson called for meetings, not legislation, in response to calls by AI CEOs to slow AI development.
Beijing pushed back. China's Foreign Ministry spokesperson Guo Jiakun urged an "open, inclusive and benevolent approach to AI." He said fomenting threats and confrontation would disrupt global AI governance. Guo also said China will work with LAC countries and other parties to jointly build a just and equitable global AI governance system.
China's Global Times described Amodei's proposal as a "Cold War playbook" to curb Chinese AI development and preserve U.S. technological dominance. It called the "silent AI Cold War" hypocritical and short-sighted. The paper presents safety arguments as cover for containment.
Stanford University's 2026 AI Index Report reported U.S. companies invested $285.9 billion in AI in 2025, compared with $12.4 billion in China.
The United States and China planned dedicated AI safety talks for mid-September 2026, the first dedicated AI safety talks of Trump's second term. Reuters Washington sought joint monitoring of AI-driven cyberattacks in those talks.
Washington is also pressing third countries to choose. The United States planned to tell partners they must pick sides in the AI race with China. Reuters The United States hoped that pressing countries to choose sides would starve China of resources in the race to make the most sophisticated AI. A U.S. congressional advisory body said China's data dominance gives it an AI advantage. It said China is commercialising and monetising data as a strategic national asset to power AI. Reuters
China will host the 2026 World AI Conference & High-Level Meeting on Global AI Governance in Shanghai in July 2026. China presents it as a place for standards, safety norms and South-South cooperation on AI access.
The broader context here is a clash between two ideas of safety. One idea favors slowing frontier training, securing model weights, policing distillation and denying high-end compute to rivals. The other favors inclusive governance, shared monitoring and broad sharing of benefits, with export controls seen as escalation rather than precaution.
The broader context for diplomacy is whether cyber cooperation can be separated from business competition. Joint monitoring of AI-driven attacks is narrow and technical. Chip controls, partner pressure and data rules are structural. Progress on the first would not settle the second.
In my view, experts should watch three pressure points next. The first is whether Washington defines distillation enforcement in a way allies can follow. The second is whether Beijing can turn governance talk and LAC outreach into clear steps on model safety and cyber restraint. The third is whether the capital-spending lead in the Stanford figures brings lasting capability, or whether data scale and efficient training close the gap faster than export policy expects.
