Alongside the astonishing progress of Artificial Intelligence (AI), which is capable of performing complex calculations and analyzing data at speeds beyond human capacity, a serious and hidden challenge has emerged: the vast difference in energy consumption between the biological brain and silicon supercomputers.1
A comparison of their efficiency, even for a simple task like "solving a standard equation," reveals not just a quantitative difference but a profound qualitative one in processing architecture that directly calls into question the long-term sustainability of this technology.
The 20-Watt Paradox: The Unrivaled Efficiency of the Human Brain
The human brain, the most efficient known system in the world, benefits from an extraordinary biological architecture.
Network Size: Approximately 86 billion neurons.2
Energy Requirement: Requires only about 20 watts of energy to manage all cognitive processes, from abstract thinking to memory and problem-solving.3
This figure constitutes about 20% of the body's total energy consumption, demonstrating unparalleled energy efficiency.4
- Processing Method: When the brain solves a simple equation, it activates its neural network in parallel with minimal energy waste, an integrated process where learning and memory are inseparably fused.
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NRMC Oct 28, 2025 • 5 min read
The Gigawatt Gap: The Insatiable Appetite of Silicon vs. Biological Simplicity
On the other side of this duel are AI systems, particularly Large Language Models (LLMs) like GPT-3 and other generative models, which rely on massive data centers to function.5
- These centers not only require thousands of Graphics Processing Units (GPUs), but their energy consumption is measured on the gigawatt scale (billions of watts).
- This represents a billion-fold difference in computational power consumption compared to the human brain.
- This high consumption is due to their different architecture, which performs computations in a non-decentralized structure reliant on power-hungry hardware.
Energy Consumption by Stage
This level of consumption stems from two key stages:
| Stage | Description | Estimated Energy Use (Example: GPT-3) | Environmental Impact |
|---|---|---|---|
| Training | The process of creating a large model requires enormous amounts of data and computation. | Approximately 1,300 MWh of electricity | Produced 552 tons of carbon dioxide, equivalent to a year's worth of emissions from 123 gasoline-powered cars. |
| Inference (Usage) | Extensive computations are required in a data center to generate a simple response in a chatbot. | ChatGPT's daily responses consume approximately 564 MWh of electricity. | Generating a single short response can emit 2 to 10 grams of carbon per use. |
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NRMC Jul 20, 2025 • 5 min read
Finding the Solution in Nature: Super-Turing AI
The staggering gap between the brain's 20 watts and the data centers' gigawatts poses a serious sustainability challenge for the tech industry. For this reason, researchers are turning to the most efficient model available: the human brain.
A new generation of AI called "Super-Turing AI" is being developed, which attempts to emulate the brain's integrated approach to managing memory and learning.
- Goal: Instead of separating and transferring vast amounts of data between different hardware components, these models integrate processes to significantly reduce energy consumption.
- Objective: To not only achieve high computational power but also to attain an energy efficiency that can free AI's long-term progress from the looming energy crisis.
This vast difference in efficiency serves as an important reminder that biological intelligence, despite its speed limitations, remains an unattained model for engineering and computer science in the dimensions of sustainability and energy consumption.
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