What is the environmental impact of AI?

​Introduction

Most of us don't think much about the electricity usage and water usage of our digital devices and the work we do on them, but every keystroke, internet search, or interaction with ai data centers consumes power. Until now, most tasks we accomplish with our phones and computers have been quite economical in terms of power consumption. However, that is changing with the introduction of the AI boom, deepening reliance on artificial intelligence algorithms, and the continued rise of AI hardware.

Generative AI, which is covered more in depth in this article, is a relatively new kind of software application that can create digital content such as text and imagery. To create these powerful systems requires a lot of electricity, and using them is also more energy-intensive than more traditional forms of computing, contributing to increasing environmental impact of AI.

Key Concepts‍

• Electricity Consumption of AI Models: Understand how training and operating AI models and data center energy use require significant amounts of electricity and how this energy demand impacts the environment.
• Carbon Footprint of Data Centers: Explore how the new data centers that host AI models contribute to significant carbon footprints and what measures can be taken to mitigate this impact.
• Evaluating AI vs. Traditional Computing in the Workplace: Learn how to assess when to use generative AI models versus traditional computing methods to optimize energy use and reduce overall electricity consumption in a professional setting.

How much electricity does generative AI use?

The first step in creating sophisticated software systems is training. During this process, large quantities of data are analyzed to improve the deep learning models' ability to detect and predict patterns. The latest models require data sets as large as significant portions of the internet. Processing this much information needs thousands of the world's most powerful chips working together in huge data centers. It is estimated that training the AI model for the first version of ChatGPT, launched in November 2022, used roughly the same amount of electricity as 130 average U.S. households consume in a year, highlighting the significant power demand.

To push these systems to improve, companies are increasing both the size of the training datasets and the amount of computation used to process the data, leading to a related carbon footprint. Each new model is much larger and requires exponentially more computing power to create, increasing the environmental cost.

Moreover, the energy consumption doesn't stop at the training phase. Once deployed, these models continue to consume significant amounts of electricity to generate responses, perform tasks, or maintain availability for user queries. The operational energy usage of these AI systems can be substantial, especially as they scale to meet growing demands.

‍For example, a typical search engine query requires about .3 watt-hours of electricity, while a generative AI prompt requires roughly ten times as much power to produce an output. To put this into perspective, consider a common household appliance like a refrigerator. A typical refrigerator uses about 1 to 2 kilowatt-hours (kWh) of electricity per day. Let's take an average value of 1.5 kWh for our calculations.‍

Online Searches

‍An online search uses about 0.3 watt-hours (Wh) of electricity.

1. Convert 1.5 kWh to Wh:
   • 1.5 kWh = 1500 Wh

• Calculate the number of searches equivalent to 1.5 kWh:
  • 1500 Wh / 0.3 Wh/search = ~5000 searches

AI Prompts

‍A generative AI prompt uses about 3 Wh of electricity.

1. Calculate the number of AI prompts equivalent to 1.5 kWh:
   • 1500 Wh / 3 Wh/prompt = ~500 AI prompts

Therefore, a day of refrigerator use is equivalent to approximately 5000 online searches or 500 generative AI prompts.‍

While a single generative AI prompt's electricity use seems minuscule, when multiplied by billions of users and countless daily interactions, the cumulative effect becomes significant. For instance, if a billion people each made just one generative AI query per day, the total electricity consumption would be equivalent to running over 2 million refrigerators continuously, indicating the enormous amounts of energy involved.

What is AI’s carbon footprint?

One way to approach this question is to look at the energy consumption and environmental impact of AI and data centers, massive warehouses containing the actual hardware that runs our modern digital infrastructure. A typical data center is a large facility designed to house a vast array of computer servers, storage devices, and networking equipment. These centers are engineered to provide a controlled environment that ensures optimal performance and reliability of the hardware. Key features of data centers include:

• Climate Control: To maintain the necessary temperature and humidity levels, data centers are equipped with advanced cooling systems. This is essential to prevent overheating, which can cause equipment failures.
• Power Supply: Data centers require substantial and consistent power supplies. They often have multiple power sources, including backup generators and uninterruptible power supplies (UPS), to ensure continuous operation even during power outages.
• Security Measures: To protect sensitive data and infrastructure, data centers implement robust security protocols. These may include physical security (e.g., biometric access controls, surveillance cameras) and cybersecurity measures (e.g., firewalls, encryption).
• Redundancy: To ensure high availability and minimize downtime, data centers often incorporate redundancy at various levels such as duplicate network connections, power supplies, and storage systems.
• Scalability: Data centers are designed to accommodate growth. This means they can scale up their capacity by adding more servers or expanding their physical footprint as demand increases.

Data centers are critical hubs of our digital world, consuming vast amounts of electricity to support the ever-growing demands of AI products and services. As of now, there are thousands of data centers spread across the globe, with major hubs in the United States, Europe, and Asia. These data centers range from small server rooms to massive facilities covering millions of square feet.

According to industry reports, the number of data centers is expected to grow substantially in the coming years. This growth is driven by the escalating demand for cloud services, data storage, and computational power required to support emerging technologies like generative AI applications. Estimates suggest that the global data center market could expand by several hundred new facilities annually, each consuming large amounts of electricity and contributing to the overall carbon footprint.

In 2021, data centers accounted for between 0.9% and 1.3% of global energy consumption, a number expected to grow to 1.86% by 2030. Many leading data center operators have committed to powering their operations entirely from renewable sources within a few years. This is an important effort given our rapidly increasing reliance on them as a critical part of our energy infrastructure.

While generative AI systems currently represent only a small fraction of data center workloads, they require significant computing power and electricity. Given the likelihood that generative AI features will be integrated into many common digital resources, it is probable that future data center creation and energy consumption will largely be driven by the development and deployment of increasingly large and powerful generative AI systems.

Supply chains and their environmental impacts

Understanding the environmental impact of AI technologies requires examining the supply chains for the raw materials used in manufacturing the hardware essential for AI systems. Key components like semiconductors and batteries rely on metals such as lithium, cobalt, and rare earth elements. Mining and processing these materials can lead to significant environmental degradation, including deforestation, soil erosion, and water contamination.

• Mining Impact: Extracting raw materials damages ecosystems and emits greenhouse gases. The mining process often involves harmful chemicals that contaminate local water supplies.
• Energy-Intensive Processing: Refining raw materials requires substantial energy, contributing further to carbon emissions.

Public health implications

The environmental impact of raw material extraction extends to public health concerns in communities near mining operations. Pollutants in air and water can lead to respiratory issues, waterborne diseases, and other health problems.

• Toxic Exposure: Communities near mines may be exposed to toxic chemicals like mercury and lead, affecting overall health.
• Resource Strain: Intensive mining depletes water resources crucial for local populations, exacerbating scarcity issues.

Transparency challenges

The complexity of global supply chains presents significant obstacles to achieving transparency. Many companies struggle to trace the origins of their materials, making it difficult to ensure ethical and sustainable practices.

• Fragmented Supply Chains: Multiple layers of suppliers can obscure the true environmental and social costs of production.
• Lack of Regulation: Inconsistent global regulations hinder efforts to ensure responsible sourcing and production.

Implementing comprehensive strategies for monitoring and improving supply chain transparency is essential to mitigate the environmental and public health impacts associated with AI technologies. Emphasizing responsible sourcing and promoting international cooperation can make a significant difference in reducing these issues.

Is using generative AI bad for the environment?

Like many things in life — it depends. Imagine a teacher needs to create a multiple-choice exam for a chapter her students just covered. Using a generative AI tool, the teacher can create the quiz in seconds and then spend a few minutes reviewing it to ensure everything is accurate and appropriate.

Using AI is an excellent choice in this situation because the teacher can verify the accuracy of the information. The quiz format is simple for the AI to generate, and the teacher saves time by not having to come up with questions and incorrect answers. What might have taken several hours now takes just a few minutes.

Despite generative AI being an energy-intensive form of computing, using it in the right situations can be quite beneficial. According to a study in Nature, using generative AI to create text or images can produce 130 to 2,900 times less CO2 than humans doing the same tasks. If that same teacher were to sit in the office for hours writing a quiz with the lights and air conditioner running the whole time, it could be argued that using AI is a better and more environmentally sound option.

The inverse is also possible. Let’s say you need to write a thank you note and you spend several minutes asking an AI model for dozens of different versions, none of which you end up using. In this case, a significant amount of resources were expended for little value, and it may have been more effective and environmentally friendly to simply sit down with a pen, think for a moment, and write something from the heart.

Just as there's no definite rule for deciding whether to walk or drive somewhere, there's no clear way to know when to use generative AI. The most important thing is to consider the energy these tools consume, including their potential impact on global temperatures and extreme weather events, and to develop a sense of when using them is both efficient and effective.

Positive environmental AI projects and energy efficiency potential

As AI technology advances, it opens opportunities to contribute positively to the environment and improve energy efficiency. Here are some examples and potential benefits:

Positive Environmental Impact of AI Projects

1. Climate Prediction Models  
   AI enhances climate models that predict weather patterns, helping to improve disaster preparedness and resource management. More accurate models can guide policy decisions and infrastructure planning.
2. Wildlife Conservation  
   AI-powered drones and cameras monitor wildlife populations, track poaching activities, and assess habitat health, significantly aiding conservation efforts.
3. Sustainable Agriculture  
   AI optimizes irrigation, fertilization, and pest control, reducing waste and promoting sustainable farming practices. These technologies can increase crop yields while conserving resources.
4. Energy Management Systems  
   AI systems manage energy consumption in buildings and industrial settings by predicting usage patterns and optimizing energy distribution, reducing overall consumption.

Energy Efficiency Potential

• Algorithm Optimization  
  As AI models become more refined, they require less computational power to achieve the same results, reducing the energy needed for training and deployment.
• Hardware Innovations  
  Advancements in AI hardware, such as specialized chips designed for efficient processing, can significantly decrease the power requirements of AI operations.
• Data Center Innovations  
  Implementing AI to manage data center operations can optimize cooling systems and energy consumption, leading to reduced carbon footprints.
• Renewable Energy Integration  
  AI systems can optimize the integration and management of renewable energy sources, ensuring better allocation and reducing reliance on fossil fuels.

By focusing on these areas, AI not only reduces its environmental impact but also plays a crucial role in fostering sustainable practices across various sectors. As AI technology becomes more efficient, its potential to contribute positively to environmental goals will continue to grow.

Conclusion

While modern automation offers remarkable capabilities and efficiencies, it also comes with substantial environmental impact of AI. The electricity and water usage required to train and operate these advanced models is significant, leading to increased carbon emissions and energy consumption. Data centers, which are the backbone of modern AI infrastructure, contribute to a growing carbon footprint that necessitates the adoption of more sustainable practices. As generative AI continues to integrate into various digital resources, it is crucial to balance its benefits with its environmental impact.

‍However, the potential for generative AI to optimize tasks and reduce human labor cannot be overlooked. When used judiciously, generative AI can be a powerful tool for saving time and resources. The key lies in discerning the appropriate contexts for its use, ensuring that its deployment maximizes efficiency and minimizes unnecessary energy expenditure. As we move forward, it will be essential to develop best practices for utilizing generative AI in ways that are both environmentally and operationally sustainable, possibly contributing toward a net carbon-negative AI ecosystem if planned emissions reductions are successfully implemented.