Artificial intelligence (AI) is often celebrated for its transformative potential—helping businesses streamline operations, improving decision-making, and even addressing global challenges like climate change. For grant writers, AI tools can accelerate research, improve proposal writing, and identify funding opportunities. However, behind AI’s bright promise lies a less-discussed reality: its environmental costs. From the electricity it consumes to the water it uses and the waste it generates, AI places significant strain on the planet. Understanding these impacts is essential to ensure we use AI responsibly in grant writing and beyond.
AI’s Dependence on Electricity
AI’s immense computational power enables tools like ChatGPT to assist grant writers in drafting proposals, generating action verbs, or organizing ideas. Yet, this convenience comes at a cost: massive energy use. Data centers—the physical facilities that store, process, and manage all the data these tools rely on—consume enormous amounts of electricity. These facilities account for about 1% of the world’s total electricity use, which is roughly the same as the energy consumption of countries like Argentina (International Energy Agency, 2021).
Training large AI models, like GPT-3, uses enough electricity to power more than 100 average U.S. homes for a year (Brown, 2020). To put this in perspective, imagine running a grant-writing retreat where 100 participants leave their lights, computers, and appliances running 24/7 for an entire year. That’s the level of energy required just to train one model. Most of this electricity comes from burning fossil fuels such as coal and natural gas, which release carbon dioxide (CO₂) into the atmosphere, contributing to climate change.
For many regions, electricity grids are still heavily dependent on fossil fuels. This means that every time we use an AI tool to draft a grant narrative or research funding sources, we indirectly add to carbon emissions. While AI can save time and effort, it’s important to balance its benefits with an awareness of its environmental costs.
AI’s Heavy Use of Water
Electricity isn’t the only resource that keeps AI running; water plays a critical role as well. Just as a grant writer might need coffee to stay sharp during a late-night writing session, data centers need water to keep their systems cool and operational. These facilities generate immense heat as they process and store data, and millions of gallons of water are used annually to regulate their temperature (Miller et al., 2023).
In drought-prone regions like Arizona or California, where every drop of water is precious, the demand from data centers creates additional challenges. Imagine if a nonprofit working on water conservation in these areas discovered that its AI-driven grant writing tools were indirectly straining local water supplies. This dilemma underscores the importance of exploring more water-efficient cooling technologies to reduce the strain on resources.
The Waste and Resource Costs of AI Hardware
The physical infrastructure that powers AI—servers, GPUs, and storage devices—comes with its own environmental challenges. Just as a grant writer might need specialized tools like templates or software to streamline their work, AI requires specialized hardware made from rare earth metals and other finite resources. Mining these materials often destroys ecosystems and pollutes local waterways (Rüdiger et al., 2019).
Once this hardware becomes obsolete, it contributes to the growing global problem of electronic waste. In 2019, the world generated 53.6 million metric tons of e-waste, much of it discarded improperly (Forti et al., 2020). To put this into grant-writing terms, imagine a foundation rejecting a proposal simply because the narrative is disorganized, resulting in wasted time and effort. Similarly, improperly discarded hardware wastes valuable resources and creates lasting harm to the environment.
For grant writers who rely on AI tools, this raises a critical question: how can we advocate for sustainability while using tools that may contribute to environmental harm? Supporting initiatives to refurbish or recycle old hardware and choosing companies with sustainable practices can help address these concerns.
Managing Heat in AI Operations
AI operations also produce significant heat, and cooling data centers is a major challenge. Data centers are like massive ovens, generating more heat than a room full of laptops at a grant-writing workshop. Traditional cooling methods, such as air conditioning, use additional electricity, compounding the energy demands of these facilities (International Energy Agency, 2021).
Innovative solutions, like liquid cooling or underwater data centers, show promise. However, these technologies are expensive, much like trying to overhaul a grant-writing program without sufficient funding. Until these solutions become more affordable, the energy and cooling demands of AI tools will continue to create environmental challenges.
What Can Grant Writers Do?
While AI offers incredible tools for grant writers, we have a responsibility to consider its environmental impact. Here are some steps to use AI more sustainably in grant writing:
Support Renewable Energy: Choose AI tools and platforms powered by clean energy sources like wind or solar. Many tech companies now disclose their renewable energy usage.
Use AI Efficiently: Instead of relying on AI for every part of the grant writing process, focus on using it for specific tasks like generating action verbs or organizing ideas. This reduces the computational power needed.
Promote Responsible Practices: Advocate for sustainable practices within the organizations you support, such as recycling outdated hardware.
Educate Funders: When drafting grant proposals, consider highlighting the environmental impact of AI and proposing projects that address sustainability challenges.
Conclusion: Balancing AI’s Promise with Sustainability
For grant writers, AI holds tremendous potential to save time, improve proposals, and increase funding success. However, these benefits come with hidden environmental costs, from electricity and water usage to e-waste and resource depletion. By using AI responsibly and supporting sustainable practices, we can ensure that the tools transforming our work do not come at the expense of our planet.
As grant writers, we often tell the story of how nonprofits make an impact. Let’s ensure that our own use of AI aligns with the values of stewardship and sustainability we so often advocate for.
What are your thoughts? Have you considered how AI’s environmental impact ties into your grant-writing work? Share your ideas and solutions in the comments below!
References
Brown, T. B. (2020). Training GPT-3: The carbon cost of AI. OpenAI Blog. Retrieved from https://www.openai.com
Forti, V., Baldé, C. P., Kuehr, R., & Bel, G. (2020). The global e-waste monitor 2020. United Nations University.
International Energy Agency. (2021). Data centers and energy consumption: 2021 report. IEA Publications. Retrieved from https://www.iea.org
Miller, J., Xu, T., & Lee, H. (2023). Water use in tech: Balancing demand in arid regions. Environmental Science Journal, 45(3), 233–245.
Rüdiger, K., Fisher, E., & Naskar, A. (2019). Rare earth metals and their environmental impact. Journal of Sustainable Mining, 18(2), 97–110.