By Lauren Hyland
Artificial intelligence (AI) is revolutionizing industries, enhancing productivity and transforming how we interact with technology. However, behind its powerful capabilities lies a significant environmental cost. Generative AI models like ChatGPT consume disproportionately more energy than traditional search engines like Google. While both AI and search engines rely on vast data centers, AI’s unique demands make it considerably more harmful to the environment.
Energy consumption
Google Search is designed for efficiency, retrieving information from databases with minimal computational effort. A single Google search consumes roughly 0.3 watt-hours (Wh) of energy, generating about 0.2 grams of CO₂ emissions.
Given that Google processes over 8.5 billion searches per day, this results in a substantial environmental footprint contributing to global warming, ocean acidification and many more environmental issues. However, AI-powered models require exponentially more processing power.
A query to an AI model like ChatGPT involves multiple layers of deep learning computations. Research suggests that AI-driven searches consume up to 10 times more energy than a traditional Google search. Some estimates indicate that a single interaction with an AI model can use between 2 to 10 Wh per query, significantly increasing carbon emissions.
Training AI models
The environmental harm caused by generative AI extends beyond just daily usage. The training of large AI models is one of the most resource-intensive processes in modern computing. A 2019 study by the University of Massachusetts Amherst found that training a single large AI model can emit over 626,000 pounds of CO₂—equivalent to five cars’ lifetime emissions.
For comparison, Google’s indexing system is already optimized, meaning its infrastructure does not require frequent retraining on the same scale. While Google’s data centers consume significant energy, they have invested heavily in renewable energy sources and efficiency improvements, making them less harmful than AI systems that require continuous retraining and updating.
Water consumption and cooling systems
Both AI data centers and search engine servers require extensive cooling systems to prevent overheating. However, AI workloads run for longer periods and at higher intensities, demanding greater cooling efforts.
Recent reports indicate that training large AI models can require millions of liters of water to keep data centers cool. This immense amount of water usage contributes to climate change issues and drought concerns around the world.
E-waste and hardware demand
AI models also accelerate the need for high-performance graphics processing units and specialized chips that have shorter lifespans due to their intensive use. The rapid turnover of hardware contributes to electronic waste (e-waste), a growing environmental crisis.
Google’s traditional search infrastructure, by contrast, requires fewer hardware upgrades per year, making it a more sustainable option in the long run.
Reducing AI’s Environmental Harm
Some ways to decrease the impact AI has on the environment would be for large tech companies to invest in renewable energy — AI companies must follow Google’s lead in using solar and wind energy to power data centers. Reducing the computational requirements for AI queries and improving model training methods can also lower energy consumption.
However, those changes are only able to be made by large companies creating and maintaining AI sources, some changes the public can make include users relying on AI only when necessary while using traditional search engines for simple queries can limit unnecessary energy use.
Conclusion
AI’s environmental footprint is significantly larger than that of traditional search engines like Google, primarily due to its high energy consumption, water usage and hardware demand. Although Google Search has its own sustainability challenges, it remains far more efficient per query than AI-powered models.
If AI companies do not take proactive measures to improve sustainability, the widespread adoption of AI could accelerate climate change at an alarming rate. Balancing AI advancements with environmental responsibility is crucial to ensuring a sustainable future for technology.
