Peter Diamandis and the panel debate calls to slow AI development against the potential for abundance and scientific breakthroughs. They examine data as an entrepreneurial moat, warnings of extinction risk, alignment and governance, collapsing inference costs, compute scarcity, Chinese AI advances, world models, economic disruption, AI-designed medicine, genomic prediction, personhood, suffering, and recursive self-improvement. The conversation ultimately favors steering and preparing for acceleration rather than assuming progress can be stopped.