The race to build power infrastructure for AI may lead to an oversupply if adoption follows a sigmoid curve. This excess capacity, much like the post-dot-com broadband glut, could become a positive externality that significantly lowers future energy prices for all consumers.
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While currently straining power grids, AI data centers have the potential to become key stabilizing partners. By coordinating their massive power draw—for example, giving notice before ending a training run—they can help manage grid load and uncertainty, ultimately reducing overall system costs and improving stability in a decentralized energy network.
The narrative of energy being a hard cap on AI's growth is largely overstated. AI labs treat energy as a solvable cost problem, not an insurmountable barrier. They willingly pay significant premiums for faster, non-traditional power solutions because these extra costs are negligible compared to the massive expense of GPUs.
Unlike typical diversified economic growth, the current electricity demand surge is overwhelmingly driven by data centers. This concentration creates a significant risk for utilities: if the AI boom falters after massive grid investments are made, that infrastructure could become stranded, posing a huge financial problem.
Rather than viewing the massive energy demand of AI as just a problem, it's an opportunity. Politician Alex Boris argues governments should require the private capital building data centers to also pay for necessary upgrades to the aging electrical grid, instead of passing those costs on to public ratepayers.
Soaring power consumption from AI is widening the "power spread"—the difference between the cost to generate electricity and its selling price. This projected 15% expansion in profit margins will significantly boost earnings for power generation companies, creating massive value across the supply chain.
The rapid build-out of data centers to power AI is consuming so much energy that it's creating a broad, national increase in electricity costs. This trend is now a noticeable factor contributing to CPI inflation and is expected to persist.
A theory suggests Sam Altman's massive, multi-trillion dollar spending commitments are a strategic play to incentivize a massive overbuild of AI infrastructure. By driving supply far beyond current demand, OpenAI could create a 'glut,' crashing the price of compute and securing a long-term strategic advantage as the primary consumer.
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.
A theory suggests Sam Altman's $1.4T in spending commitments may be a strategic move to trigger a massive overbuild of AI infrastructure. This would create a future "compute glut," driving down prices and ultimately benefiting OpenAI as a primary consumer of that capacity.
