Artificial Intelligence (AI) systems need to be powered. They require compute resources, comprising hardware and infrastructure components – primarily Central Processing Units (CPUs), Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Neural Processing Units (NPUs), memory, storage, and networking – that provide the processing power, data handling, and parallel-computation capabilities required to train, run, and scale AI models and other intensive workloads.
These compute resources reside in energy-hungry data centres and AI factories.
A data centre is a physical facility that houses IT infrastructure, such as servers and storage, to manage, process, and store data.
An AI Factory (which leverages data centres) is designed to develop, train, and deploy AI models at scale, focusing on computational resources and AI workflows.
The power requirements of data centres and AI factories are at the gigawatt level.
For example, a typical AI factory’s energy requirement is 2 GW (2000 MW), equivalent to that of a city like San Francisco or a country like Zimbabwe.
Furthermore, there is a demand for large volumes of fresh water for cooling (to prevent hardware overheating), which puts pressure on the available water supply for human consumption, agriculture, and other industrial uses.
Hence, the large carbon footprint of AI systems primarily stems from the significant compute resources required for energy-intensive tasks such as training and deploying Large Language Models (LLMs).
These tasks require high-performance hardware, such as GPUs and TPUs, which consume substantial electricity.
For example, training a single LLM can emit hundreds of tonnes of carbon dioxide equivalent, comparable to the emissions of several cars over their lifetime.
Furthermore, as explained earlier, these models are trained and run in energy-hungry data centres, which must also be cooled.
Larger AI models require exponentially more energy.
A model with billions of parameters demands far more computational power than a smaller one, resulting in a significantly larger carbon footprint.
Once trained, AI models continue to consume energy during deployment, particularly for real-time or large-scale applications.
Common examples include AI in content recommendation systems, search engines, and real-time translation, which require continuous processing power.
The iterative nature of AI research, where models are trained and retrained to optimise performance, means that significant energy is expended in the development phase before a final model is deployed.
Hence, a country’s participation in driving the AI revolution (through data centres and AI factories) carries high environmental costs.
According to the International Energy Agency, total global electricity consumption by data centres could reach the level of Japan’s energy intake by 2026.
Another projection is that, in 2030, if all data centres worldwide were considered as one country, their overall energy demand would rank only fourth, behind China, the United States, and India.
AI companies in highly industrialised economies are even exploring the establishment of private nuclear power plants to meet their energy requirements.
Some countries are ramping up their fossil-fuel-driven power supplies to meet AI energy demands – a direct reversal of clean energy transition commitments.
US President Donald Trump revealed on 23 January 2025, while addressing the World Economic Forum, that the United States would have to double its annual electricity production to lead and drive the AI revolution.
That is the extent of the enormous energy demand exerted by the technology.
President Trump intends to use the Executive Order (which he signed on 20 January 2025) declaring a national energy emergency to address this challenge.
This legal instrument directs US agencies to utilise their statutory emergency powers to speed up the development and authorisation of energy projects.
Unfortunately, with his slogan – “drill, baby, drill” – Trump’s AI energy plan will be anchored by boosting fossil fuel production to the detriment of global climate policies and regulations.
While many data centres are increasingly powered by renewable energy, non-renewable sources such as coal, natural gas, derived gas, crude oil, and petroleum products are still often used, contributing to emissions.
Clearly, AI poses challenges to the global supply of adequate energy, threatens freshwater supplies, and has the potential to worsen the climate change crisis.
Applications of AI in Decarbonisation, Energy Generation and Management
AI systems can be deployed to optimise energy generation, enhance grid management, increase renewable energy adoption, improve energy efficiency, and expand energy access.
The technology can play a transformative role in making clean energy more accessible and sustainable.
Thus, AI systems can drive innovation in energy management, grid optimisation, renewable energy forecasting, smart energy consumption, global decarbonisation, and the expansion of access to clean energy in underserved regions.
This transformative role of AI supports the achievement of UN SDG 7 (Affordable and Clean Energy), which seeks to ensure universal access to affordable, reliable, and modern energy services, increase the share of renewable energy in the global energy mix, and improve energy efficiency.
Optimising Energy Generation and Distribution
Efficient energy generation and distribution are essential for achieving universal access to clean, reliable energy.
AI has the potential to enhance the performance of both traditional and renewable energy sources while reducing waste and improving reliability.
Indeed, AI can enhance the efficacy of various clean energy sources such as solar, wind, geothermal, biofuels, and hydroelectric power.
In traditional power plants, AI-driven predictive maintenance systems monitor equipment conditions in real time, identifying potential issues before they lead to failures.
By analysing data from sensor-embedded turbines, generators, and other machinery, AI algorithms can detect signs of wear and tear, enabling proactive maintenance.
Improving Renewable Energy Forecasting
Renewable energy sources like solar and wind are essential for reducing greenhouse gas emissions, but their intermittency presents challenges for a consistent energy supply.
AI-driven forecasting models have the potential to mitigate these challenges by accurately predicting energy generation from renewable sources, enabling operators to integrate them more effectively into the grid.
AI-based forecasting models use Machine Learning algorithms to analyse historical weather data, satellite imagery, and sensor readings.
These models can predict energy generation levels hours, days, or even weeks in advance by identifying patterns in temperature, sunlight, wind speed, and other factors.
This capability enables grid operators to plan for variability in renewable energy supply, ensuring a stable and reliable flow of electricity.
Energy Efficiency in Buildings and Industries
Energy efficiency is essential because reducing energy consumption minimises environmental impact and conserves resources.
AI offers significant opportunities to improve energy efficiency in buildings, industrial facilities, and transportation systems by enabling real-time monitoring, predictive analytics, and automated energy management.
In buildings, AI-powered energy management systems monitor and adjust heating, ventilation, and air conditioning (HVAC) systems based on occupancy patterns, weather conditions, and user preferences.
These AI systems help reduce electricity bills, lower greenhouse gas emissions, and promote sustainable building practices by optimising energy use.
In industrial facilities, AI enhances energy efficiency by monitoring machinery performance, optimising production processes, and reducing resource use.
AI also optimises energy use in transport, particularly in electric vehicles (EVs) and public transport systems.
Access to Clean Energy in Underserved Regions
AI offers innovative solutions to expand clean energy access in remote and underserved areas by enabling decentralised energy systems, optimising microgrids, and supporting off-grid renewable energy projects.
AI-driven microgrids (localised energy grids that operate independently from the central grid) provide a sustainable solution for remote communities with limited access to conventional energy infrastructure.
AI algorithms optimise microgrid performance by balancing energy generation, storage, and consumption based on real-time data.
By ensuring efficient energy distribution, AI-powered microgrids provide a reliable, affordable energy source for communities without access to traditional energy grids.
Smart Energy Consumption and Demand Response
Smart energy consumption and demand response are essential for balancing supply and demand in energy systems, especially as more renewable energy sources are integrated into the grid.
AI supports these processes by enabling real-time monitoring, automated control, and predictive analytics that help manage energy consumption more effectively.
In demand response programmes, AI algorithms analyse data on electricity prices, grid load, and consumer behaviour to encourage users to shift energy consumption to off-peak hours.
AI also supports smart metering systems, which provide consumers with real-time information about their energy consumption patterns.
Way Forward: Is AI a Friend or Foe?
It is instructive to acknowledge the complex interplay among AI, energy availability, and decarbonisation.
While the AI revolution demands enormous energy resources and has a large carbon footprint, the technology can also help create more efficient, sustainable, and equitable energy systems globally.
Indeed, in the context of global energy supply and climate change mitigation, AI is a double-edged sword.
The technology is both a problem and a solution!
However, creative and intentional deployment of AI, such as WEF’s Net-Positive AI Energy Framework, and revolutionary innovations, such as building AI data centres in space, will make AI more of a friend than a foe.
Excerpt from the upcoming book:
Written by Professor Arthur Guseni Oliver Mutambara who is a Zimbabwean academic, roboticist, author, and former Deputy Prime Minister of Zimbabwe (2009–2013) under the Global Political Agreement. He is currently the Director and Full Professor of the Institute for the Future of Knowledge (IFK) at the University of Johannesburg (UJ) in South Africa.
