AI and sustainability: How Dow innovation is shaping the AI ecosystem for a more sustainable future

These efforts exemplify how targeted innovation can address the environmental impact of AI, paving the way for a responsible and sustainable digital transformation.

• AI and ML are transforming industries, but their rapid expansion is creating substantial environmental impact and that footprint is growing.
• A major portion of data center energy consumption—up to 40%—is dedicated to cooling systems. Efficient thermal management is critical to reducing GHG emissions and operational costs.
• Organizations are adopting sustainable AI practices, such as designing energy-efficient networks, using renewable energy sources and incorporating recycled materials into hardware.
• Companies are also exploring advanced nuclear technologies, a clean, reliable and nearly net-zero emission source, to help meet increasing energy demand.
• Dow’s advanced materials—like thermal management solutions for consumer devices, data center solutions for cooling systems and silicone-based adhesives, sealants and interface materials for telecom infrastructure—play a crucial role in enabling a sustainable AI ecosystem.

The environmental impacts of AI extend across the entire lifecycle of AI systems, encompassing aspects like energy and water consumption and waste generation.

Data centers that power AI operations consume substantial amounts of electricity. Their global electricity consumption combined is expected to be close to 1,050 terawatt-hours by 2026. To put that into perspecive, if data centers were a country, they would be the 5th largest energy consumer globally—less than Russia but more than Japan.¹ This energy demand can result in higher GHG emissions if the energy is primarily sourced from fossil fuels.

In addition to energy, AI systems also consume water, primarily to cool data center operations. Projections for data centers’ global combined annual water use could reach 4.2 to 6.6 billion cubic meters by 2027.² It would take the entire city of London over 4 years to use that much water based on the city’s current consumption.³

The rapid pace of technological advancement can also produce more electronic waste when devices and components become obsolete and are replaced to run more sophisticated AI models. The hardware and other equipment that is used to train generative AI models and run AI applications could produce up to 5 million tons of electronic waste by 2030.⁴

Addressing these challenges is vital for ensuring that the benefits of AI are not undermined by unsustainable practices—guiding both industry leaders and policymakers toward sustainable approaches.

AI and sustainability are increasingly intertwined as the demand for advanced digital solutions grows alongside the potential environmental impact.

The vast computational power required by expansive artificial intelligence models drives significant energy requirements for powering the computational and cooling equipment, which in turn can place strain on electrical grids and contribute to GHG emissions if not managed responsibly. The sourcing and manufacturing of key components, such as semiconductors and high-performance processors, depend on resource-intensive mining and processing operations, raising issues around habitat disruption, water use and other environmental impacts.

As technological trends continue, it is crucial to address the entire lifecycle of AI systems, from design and materials to system deployment and end-of-life management.

Enhancing the sustainability of AI innovation through materials science and responsible resource strategies is vital for realizing a future where artificial intelligence aligns with global environmental and sustainability practices.

Innovative materials play a critical role in enabling the sustainable evolution of AI networks and infrastructure, particularly as next-generation smart connectivity becomes more widely adopted.

For example, Dow’s advanced thermal management materials help ensure that high-performance computing systems work efficiently, optimizing heat dissipation across densely packed electronic components, thereby reducing energy waste and the risk of overheating. Silicone adhesives and sealants further contribute to system reliability by providing robust protection against moisture, dust and mechanical stresses, which can extend the operational lifespan of devices and minimize maintenance needs.

Liquid silicone rubbers are employed to improve critical components found in edge devices and data center equipment. They provide sealing solutions for data centers in the form of O-rings and gaskets due to their combination of thermal performance and low volume change in a variety of fluids. In compact, high-performance applications, liquid silicone rubbers enable flexibility and the miniaturization of electronic products through attributes like thermal stability and electrical insulation and the ability to be applied through precision molding.

The innovations in the segments below collectively help enable AI-powered networks to meet the demands for faster, more secure smart connectivity solutions while mitigating the environmental impact of those endeavors.

Sustainable AI practices are increasingly emerging as organizations seek to address environmental impact while leveraging AI innovation to meet the world’s biggest challenges.

One notable example involves the design of energy-efficient networks, which use optimized algorithms and hardware to significantly reduce energy consumption. Additionally, companies are adopting renewable energy sources—such as wind and solar power—to sustainably run their data centers and reduce the GHG emissions associated with the energy demands of extensive computational workloads. Some enterprises are also incorporating recycled materials in the assembly of hardware that powers AI, supporting circular economy initiatives and promoting responsible resource use.

Lifecycle analysis and transparent supply chain monitoring have become integral strategies for identifying and mitigating environmental impacts, ensuring that sustainable AI can thrive across industries.

These approaches collectively underscore the importance of integrating the best environmental practices into AI development, fostering a more resilient and ethical technological future.

Meeting AI energy demands with nuclear power

Major tech companies are investing in advanced small modular nuclear reactors to power their growing data center operations.⁶

These tech giants are turning to nuclear energy to meet surging electricity demands driven by AI in locations where renewable energy like solar and wind alone may not suffice. Small modular reactors enable access to a clean and reliable energy source that is also nearly net-zero GHG emissions.

Dow has engaged in a similar endeavor to potentially deploy advanced nuclear technology to provide industrial steam and electricity at Seadrift Operations manufacturing site on the U.S. Gulf Coast. Together with X-energy, a leading developer of advanced small modular nuclear reactors and fuel technology, we aim to reduce the site’s GHG emissions when the technology becomes fully operational.

Integrating sustainable practices into the growing AI ecosystem is essential for ensuring the longevity and ethical impact of technological advancements. Organizations are taking significant steps, such as creating energy-efficient networks, using renewable or clean energy sources, and employing innovative materials, to minimize environmental impact and promote responsible resource use.

As AI becomes more prevalent in various sectors—from consumer electronics to telecom infrastructure and cloud computing—the need for sustainable strategies is critical. By using advanced materials and thoughtful design, companies not only enhance the performance and reliability of AI-powered systems but also contribute to a sustainable technological future.

These efforts highlight the vital role of sustainability in the evolution of AI, paving the way for innovations that can meet both the needs of the planet and the demands of a rapidly digitizing world.

Sources

1. Zewe, A. (2025, January 17). Explained: Generative AI’s environmental impact. MIT News.
2. Gupta, J. (2024, May). AI’s excessive water consumption threatens to drown out its environmental contributions. UN Sustainability Development Goals.
3. Water resources. (2025). Greater London Authority.
4. Crownhart, C. (2024, October 28). AI will add to the e-waste problem. Here’s what we can do about it. MIT Technology Review.
5. Offutt, M. C. and Zhu, L. (2025, August 26). Data Centers and Their Energy Consumption: Frequently Asked Questions. Library of Congress.
6. St. John, A. and McDermott, J. (2024, October 16). Amazon, Google make dueling nuclear investments to power data centers with clean energy. AP.