Traditional energy models incorrectly started with climate supply targets. A more accurate approach models fundamental demand drivers first (population, GDP), revealing a massive, underestimated need for all energy types to meet future growth, challenging supply-centric narratives.
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The rapid construction of AI data centers is creating a huge surge in electricity demand. This strains existing power grids, leading to higher energy prices for consumers and businesses, which represents a significant and underappreciated inflationary pressure.
Incremental increases in material production won't significantly move the needle on energy consumption. The next 10x in per capita energy use will be driven by two main factors: expanding aviation to billions of people and the explosive growth of AI compute, which acts as a 'per capita' increase in intelligence.
Contrary to common assumptions, China's future natural gas demand growth will be led by the industrial sector, not power generation. Policy support for manufacturing and lower global LNG prices are expected to drive significant coal-to-gas switching in industrial processes, while gas in the power sector remains a secondary source to balance renewables.
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.
Contrary to the renewables-focused narrative, the massive, stable energy needs of AI data centers are increasing reliance on natural gas. Underinvestment in grid infrastructure makes gas a critical balancing fuel, now expected to meet a fifth of the world's new power demand (excluding China).
The energy trilemma (clean, stable, abundant) has been reordered. Previously, 'clean' was the top priority. Now, driven by massive demand and geopolitical instability, the market and policymakers prioritize securing 'more' energy that is 'stable,' even if it means delaying decarbonization goals.
The restructuring of the U.S. electricity sector wasn't purely ideological. It was a direct response to regulated utilities making massive, incorrect bets on demand growth, building unneeded power plants, and causing prices to skyrocket for captive customers. Competition was introduced to shift this investment risk from consumers to private investors.
Despite the narrative of a transition to clean energy, renewables like wind and solar are supplementing, not replacing, traditional sources. Hydrocarbons' share of global energy has barely decreased, challenging the feasibility of net-zero goals and highlighting the sheer scale of global energy demand.
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.
As hyperscalers build massive new data centers for AI, the critical constraint is shifting from semiconductor supply to energy availability. The core challenge becomes sourcing enough power, raising new geopolitical and environmental questions that will define the next phase of the AI race.
