People often object to AI's energy use simply because it represents a *new* source of emissions. This psychological bias distracts from the fact that these new emissions are minuscule compared to massive, existing sources like personal transportation.

Although 90% of an AI server's financial cost is the upfront hardware purchase, the vast majority (~95%) of its lifetime carbon footprint comes from the electricity used to run it, not from its manufacturing.

The International Energy Agency projects global data center electricity use will reach 945 TWH by 2030. This staggering figure is almost twice the current annual consumption of an industrialized nation like Germany, highlighting an unprecedented energy demand from a single tech sector and making energy the primary bottleneck for AI growth.

Despite staggering announcements for new AI data centers, a primary limiting factor will be the availability of electrical power. The current growth curve of the power infrastructure cannot support all the announced plans, creating a physical bottleneck that will likely lead to project failures and investment "carnage."

Public perception of nuclear power is skewed by highly visible but rare disasters. A data-driven risk analysis reveals it is one of the safest energy sources. Fossil fuels, through constant air pollution, cause millions of deaths annually, making them orders of magnitude more dangerous.

While semiconductor access is a critical choke point, the long-term constraint on U.S. AI dominance is energy. Building massive data centers requires vast, stable power, but the U.S. faces supply chain issues for energy hardware and lacks a unified grid. China, in contrast, is strategically building out its energy infrastructure to support its AI ambitions.

The projected 80-gigawatt power requirement for the full AI infrastructure buildout, while enormous, translates to a manageable 1-2% increase in global energy demand—less than the expected growth from general economic development over the same period.

Most of the world's energy capacity build-out over the next decade was planned using old models, completely omitting the exponential power demands of AI. This creates a looming, unpriced-in bottleneck for AI infrastructure development that will require significant new investment and planning.

The primary factor for siting new AI hubs has shifted from network routes and cheap land to the availability of stable, large-scale electricity. This creates "strategic electricity advantages" where regions with reliable grids and generation capacity are becoming the new epicenters for AI infrastructure, regardless of their prior tech hub status.