Artificial Intelligence (AI) is revolutionizing industries and daily life, but its rapid growth comes with significant environmental costs and poses questions about future costs. From soaring energy demands to increased water consumption, the infrastructure supporting AI is straining our planet's resources. Recent policy shifts, such as President Donald Trump's push to bolster coal-powered data centers, highlight the complex interplay between technological advancement and environmental sustainability.
The Environmental Toll of AI
Energy Consumption
AI models require substantial computational power, leading to high energy consumption. For instance, training a single large AI model can consume thousands of megawatt-hours of electricity and emit hundreds of tons of carbon dioxide. Data centers now account for more than 4% of total U.S. electricity consumption, with over half of this energy derived from fossil fuels.
Water Usage
Cooling the servers in data centers necessitates vast amounts of water. As AI adoption grows, data centers’ water usage is projected to reach up to 6.6 billion cubic meters by 2027, surpassing the annual water consumption of countries like Denmark.
Coal and AI Infrastructure
In response to the escalating energy demands of AI, President Donald Trump announced plans to fast-track approvals for new power plants adjacent to data centers, emphasizing the use of “clean coal” as a backup energy source. This approach aims to bypass the aging U.S. power grid by creating off-grid, fossil-fuel-powered facilities.
Critics argue that this strategy could exacerbate environmental degradation. The Sierra Club warns that such policies may increase utility costs for American families and hinder progress toward sustainable clean energy solutions.
The Dark Side of AI in the Circular Economy
The heavy resource footprint of AI runs counter to the goals of a circular economy, which seeks to minimize environmental impact and reduce resource extraction. AI-driven optimization in production and logistics can paradoxically increase consumption. When businesses use AI to lower costs and streamline operations, the resulting affordability and efficiency can lead to overproduction and overconsumption—creating more waste, not less.
This strategy, known as “planned obsolescence,” undermines the circular economy by promoting a throwaway culture. For example, some tech companies use AI to time product failures just after warranty periods, compelling consumers to purchase new items instead of repairing existing ones.
Lastly, AI’s role in waste sorting and recycling is often seen as a silver bullet, but over-reliance on automation can discourage systemic changes, such as redesigning products for longevity or reducing material use altogether. While AI-powered waste sorting systems can enhance recycling efficiency, an overdependence on these technologies may lead to complacency in product design and consumption habits. Relying solely on AI for waste management can divert attention from the need to design products for durability and recyclability, core tenets of the circular economy.
AI has potential in a circular economy, but without ethical oversight and thoughtful design, it can just as easily reinforce linear, extractive habits under the guise of innovation.
Blind Spots: Cultural Context and Ancestral Wisdom
AI may excel at pattern recognition and optimization, but it fundamentally lacks contextual intelligence—particularly when it comes to cultural values, indigenous knowledge, and ancestral practices that have long embodied circular principles.
Many traditional societies have practiced forms of circular living for generations: repairing rather than replacing, sharing resources communally, and respecting natural limits. These approaches often emerge from deep, place-based wisdom—something that no algorithm, however advanced, can replicate or fully comprehend.
When AI systems are developed using global, often Western-centric data, they risk reinforcing homogenized solutions that overlook local realities and community-led practices. For example, an AI tool optimized for supply chain efficiency might ignore informal circular economies—like community reuse networks or barter systems—because they don’t fit neatly into its datasets or logic models.
Furthermore, as AI increasingly shapes decision-making in policy and design, there’s a danger that it will marginalize these non-digital knowledge systems, pushing out low-tech but sustainable solutions in favor of high-tech fixes that are more extractive, expensive, or dependent on global infrastructure.
Ancestral Intelligence vs. Artificial Intelligence
While AI systems process vast amounts of data rapidly, they lack the nuanced understanding and adaptability inherent in human intelligence. The human brain operates on approximately 20 watts of power, a stark contrast to the megawatts consumed by data centers. Moreover, human cognition encompasses emotional and cultural intelligence, ethical reasoning, and consciousness—facets that AI has yet to replicate. This comparison underscores the importance of valuing and preserving human intellect and ancestral memory, which have evolved over millennia without the environmental costs associated with modern AI systems.
Be on the lookout for the next blog post, which dives deeper into issues related to human creativity and the ethical and legal complexities surrounding AI’s use of creative works.
Sources:
Andrew B. Chow, How AI Is Fueling a Boom in Data Centers and Energy Demand, Time Magazine (June 12, 2024), https://time.com/6987773/ai-data-centers-energy-usage-climate-change/
Cindy Gordon, AI Is Accelerating the Loss of Our Scarcest Natural Resource: Water, Forbes (Feb 25, 2024), https://www.forbes.com/sites/cindygordon/2024/02/25/ai-is-accelerating-the-loss-of-our-scarcest-natural-resource-water/
Ganes Kesari, Turning Trash Into Treasure: How AI Is Revolutionizing Waste Sorting, Forbes (May 31, 2024), https://www.forbes.com/sites/ganeskesari/2024/05/31/turning-trash-into-treasure-how-ai-is-revolutionizing-waste-sorting/?utm_source=chatgpt.com.
Angrej Singh, Trump pledges fast-track for AI data center power plants, Axios (Jan. 23, 2025), https://www.axios.com/2025/01/23/trump-ai-power-plants-data-centers.
Trump signs executive orders to boost coal, a reliable but polluting energy source, AP News (April 8, 2025), https://apnews.com/article/trump-coal-ai-data-centers-energy-dominance-693e2604785c07ff790d9afd2e06d543.
Sierra Club, Trump’s Data Center Announcement Will Increase Emissions & Utility Bills for American Families (Jan. 7, 2025), https://www.sierraclub.org/press-releases/2025/01/trump-s-data-center-announcement-will-increase-emissions-utility-bills.
