Cognitive AI systems are approaching reasoning capability, but their energy demands are rising faster than their intelligence. This article examines the architectural limits of modern AI and outlines a path toward sustainable cognition through neuromorphic hardware, energy-aware design, and carbon-aligned compute.
The IEA estimates global data center electricity consumption at 415 terawatt-hours in 2024, projecting growth to 945 TWh by 2030. This paper argues that achieving sustainable cognitive AI requires simultaneous optimization across three interdependent layers: neuromorphic hardware, software algorithms, and governance frameworks.
We are racing toward Cognitive AI systems designed for continual learning, cross-domain transfer, and meta-learning across a lifetime. Yet this ambition collides directly with the energy obstacle. The next wave of breakthroughs will not come from brute-force compute scaling—it will come from smarter, physics-constrained compute.
