THE SUPPLY SIDE DILEMMA
The pressure of surging demand is exposing a critical shortcoming: AI’s exponential growth depends on infrastructure built for an entirely different era. Every server rack, cooling system, and algorithmic breakthrough relies on a grid that hasn’t undergone a transformation of this scale since World War II.
Once heralded as a marvel of engineering, today’s grid operates on borrowed time. Substations and transformers, originally built with a 50-year lifespan, are now approaching or exceeding 90 years in some regions. In critical areas like PJM1
, two-thirds of grid assets are
more than 40 years old, creating vulnerabilities that ripple across the energy supply chain. Winter Storm Elliott in 2022 exposed this fragility, as millions lost power and systems like PJM strained to their limits. Similarly, the 2021 Texas winter storm left 4.5 million homes and businesses in the dark, not just from fuel shortages but from cascading equipment failures that also impacted critical data centers. These crises underscore a harsh reality. We are building AI systems for the future on power systems from the past.
This fragility is further compounded by the limitations of renewable energy sources like wind and solar, which, while expanding rapidly, remain intermittent and unreliable to power AI Infrastructure. Renewable energy depends on weather patterns, leaving grids vulnerable during peak demand. In 2020, California faced rolling blackouts during a heatwave as solar generation dropped in the evening, illustrating the Duck Curve2
, a sharp mismatch
between midday energy generation and evening demand surges. Large-scale energy storage solutions, like grid- scale batteries, have shown promise but are not yet scalable enough to smooth out these fluctuations. This intermittency, coupled with the slow adoption of storage technologies, leaves renewables struggling to bridge the imbalance between supply and AI- driven demand.
Other energy sources, such as nuclear and geothermal, offer consistency but come with significant lead times. Building a nuclear power plant, for example, can take decades, while geothermal energy remains constrained by geographic limitations. These technologies hold promise for the long term but cannot address the immediate3 an AI-driven economy.
energy needs of
In the meantime, natural gas has emerged as the go-to fuel to meet demand quickly. Its scalability and reliability make it an attractive option, particularly in the U.S., where it has steadily grown to become the dominant source of electricity generation. However, this reliance comes at a cost. Natural gas is still a fossil fuel, and increasing its use locks the energy mix into a carbon- intensive resource pool. Meanwhile, in countries like China, coal remains the dominant energy source, with the nation prioritizing energy security and technological progress over environmental concerns. This reflects a notable trade-off. Should nations sacrifice sustainability goals in their pursuit of AI leadership?
1 PJM stands for Pennsylvania-New Jersey- Maryland Interconnection, which is a nonprofit organization that manages the electric grid for a region in the eastern United States. PJM is responsible for the safety, security, and reliability of the power transmission system.
2 The duck curve is a graph that shows the difference between electricity demand and solar energy generation over the course of a day. The curve’s shape resembles a duck, with a dip in the middle and a steep rise in the evening.
3 ~5 years in context of power industry. 6 | ADMISI - The Ghost In The Machine | Q1 Edition 2025
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44
