Artificial intelligence is reshaping our world. Its transformative power fuels innovation across all sectors, bringing new value to organizations and consumers. As the proliferation of AI accelerates, people are starting to ask important questions: What is the impact of AI on the environment? And furthermore, how can we continue to progress without leaving a heavy carbon footprint on the planet?
The ecological impact of AI
Artificial intelligence software operates in data centers that consume large amounts of energy and often generate significant carbon emissions. According to Bloombergthere are more than 7,000 data centers worldwide. Collectively, they can consume as much electricity each year as the entire electricity production of Australia or Italy. The growing use of AI will further increase this already significant energy consumption of data centers.
The use of AI can be divided into two main tasks: training and inference. During training, AI models learn from large amounts of data, which can take months depending on the complexity and volume of the data. Once an AI model has been trained, it consumes energy every time it generates a new response or “inference.” The International Energy Agency (IEA) reported a ChatGPT query requires up to 10 times more energy than a Google search to answer a typical query. This energy consumption adds up and can quickly exceed the energy used for training.
The World Economic Forum estimates that training accounts for approximately 20% of an AI model’s overall energy consumption over its lifetime, while inference accounts for the remaining 80%. The overall environmental impact of AI depends on the model size, complexity, query volume, and energy source, although data on the energy consumption of algorithms remains limited.
Development of conscious models
As organizations develop AI, understanding the factors that influence its environmental footprint can help address environmental challenges. Specifically, strategic planning during the design phase of an AI can minimize environmental impact throughout its lifespan. Organizations looking to develop energy-efficient AI models should consider:
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A model’s platform architecture determines how efficiently it will use the underlying hardware resources. This also influences the overall resilience of the model and its long-term maintenance. Organizations decide where processing will physically take place and which processors will perform the work. Opting for energy-efficient architectures can help protect businesses from rising AI-related energy costs and growing future energy demands for their solutions, which incur environmental costs even when relying on renewable energy .
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Application design also impacts power requirements. Choosing a base model, instead of training a new one, avoids much of the energy required for development and spreads the energy used throughout the life of the model. Techniques such as quantization (compressing models to reduce parameter memory usage) and dimensional reduction (transforming data from high-dimensional space to low-dimensional space) streamline processing and can further improve the effectiveness of the model. In some cases, AI applications may also be designed for batch processing rather than real-time processing, which tends to be more power intensive.
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Architects of solutions optimizing energy efficiency should aim to build the smallest and most efficient AI models needed to achieve the desired results. Smaller language models work faster and require less time and energy to process tasks. “Right sized” building designs reduce energy requirements without sacrificing performance.
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How often a model is trained and retrained should also be considered. Companies can choose energy-efficient model training methods, such as scavenged augmented generation (RAG). RAG connects an AI neural network to a new knowledge base (like a new technical document or image database) without retraining.
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Designing models with longevity in mind can reduce their environmental impact by avoiding the need for recycling and redeployment. A generative AI model can produce millions or even billions of inferences over its lifetime. The number of processors supporting the model, as well as their speed and power consumption, influence the energy required to produce each inference. A model that sees more traffic will generally require more energy than a less active model.
The economics of greener AI
AI is typically deployed in the cloud, where software-as-a-service (SaaS) providers rely on public cloud platforms to deliver AI-based solutions. The different stakeholders in this ecosystem (SaaS providers, cloud platforms and customers) each have economic reasons to prioritize more environmentally friendly AI practices.
For SaaS companies, the cost of public cloud platform services and resources (such as compute, storage, and networking capacity) directly affects margins. The more efficiently their AI models operate, the lower their resource consumption, reducing costs and environmental impact.
Since AI models can be resource-intensive, it becomes essential to minimize their usage through careful model development, both for cost-effectiveness and sustainability. Public cloud platforms share similar incentives. Their profitability depends on optimizing the provisioning and operation of their data centers. Reducing energy consumption in computing and storage capacities leads to higher efficiency and better margins.
However, as the use of AI increases, the demand for public cloud resources will increase, leading to a significant increase in energy consumption, even with optimized deployment. Therefore, using renewable energy to power public cloud platforms will be crucial to further reducing carbon emissions caused by AI and other cloud software.
This highlights the role of customers who have increasing influence on greener AI practices. With sustainability initiatives, regulatory pressures and consumer demands for transparency, many companies are now prioritizing suppliers who demonstrate environmental responsibility. These organizations have the purchasing power to demand AI solutions that minimize energy consumption, thereby pushing cloud providers toward greener operations, such as the use of renewable energy.
Ultimately, as more companies demand eco-friendly AI, this will lead to broader adoption of greener practices across the entire tech ecosystem.
The path to sustainable AI
By adopting energy-efficient architectures, optimizing the performance of AI models, and pushing cloud providers to adopt renewable energy, companies can help reduce the carbon footprint of their AI solutions. Sustainable AI is not just about protecting the planet, it is also a smart business initiative that can reduce costs and meet the growing demand for responsible technology from regulators and consumers. The future of AI is bright, but only if we ensure it is green.