Fission vs Fusion: Nuclear Power Fuels AI’s Future

AI energy demand is soaring as advanced models consume massive power. Training one large AI model can use as much electricity as 100 U.S. homes per year. By 2030, global data center power needs could jump 165%, driven by AI energy growth. This surge highlights the need for clean and reliable nuclear power. Current plants use fission to split uranium atoms. Fission is proven, cost-effective, and offers stable baseload power. Challenges include safety concerns, radioactive waste, and strict regulations. Fusion promises even more energy by merging hydrogen atoms. It produces minimal long-lived waste and avoids meltdown risks. However, fusion reactors require extreme temperatures above 100 million °C and remain experimental. Small Modular Reactors (SMRs) can bridge today’s AI energy gap with faster deployment and flexible output. In parallel, China has expanded its cyber capabilities. The “Salt Typhoon” campaign shows China can pre-position digital weapons in U.S. infrastructure. Experts warn that the U.S. needs AI-powered “digital twins” for defense and must invest in offensive cyber-capabilities. Finally, China’s brain-computer interface (BCI) industry is set to reach $777 million by 2027. Backed by strong policy and patents, China aims for global BCI leadership by 2030. These trends in technology and security underline the strategic importance of energy, cyber, and human–machine integration.