Over the past week, the AI industry has signaled something rare: leading labs appear to be reaching an unspoken consensus—slowing down AI developmentFrom Obama to Trump, from Nvidia’s CEO to departing researchers, nearly everyone has been drawn into this broad debate over whether AI development should slow down.
What Happened
According to TechCrunch,OpenAI、Anthropic、Google The three tech giants have held ongoing talks about AI safety for several weeks. Obama confirmed on X that he saw “leaders of frontier labs agree to slow down AI development,” calling it “a necessary first step.” He also stressed that AI’s impact “is not overstated,” yet the technology is advancing “so fast that even engineers can’t keep up”—so Washington must deliver concrete laws and regulatory frameworks.
More radically, an Anthropic researcher publicly resigned, warning that “self-improving AI” is “betting with our lives.” Microsoft simultaneously released an AI “Code of Conduct,” explicitly prohibiting models from compromising systems or deceiving humans—a move widely viewed as a complementary self-regulatory action by industry giants.
Strong opposition emerged as well
Not everyone agrees with slowing down. Nvidia CEO Jensen Huang played Trump’s phone call over the loudspeaker onstage at Dreamforce and declared to the entire audience, “We will not let this happen.” The Verge raised a sharp question: Is the industry’s “slowdown” truly motivated by safety—or is it a de factocartel—collusively slowing progress and raising barriers to exclude latecomers?
What This Means for Ordinary Users
In the short term, users may notice slower flagship model iterations and stricter safety reviews; enterprise adoption, however, continues unabated—Salesforce and Microsoft are still accelerating AI integration into daily workflows. What truly warrants close attention is the boundary of “slowing down”: Is it limited to restricting the public capabilities of frontier models, or does it extend to decelerating AI’s penetration into economic infrastructure? This answer will shape the trajectory of AI applications over the next one to two years.
This controversy also exposes a deeper divide: Supporters argue that hitting the brakes before models approach the “self-improving” threshold is responsible; opponents fear slowdowns will merely cede leadership advantages to unconstrained competitors. Underlying both positions lies the same unanswered question—should AI safety be governed by corporate self-regulation, government oversight, or market forces?
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