The Hidden Cost of the Modern Day Artificial Intelligence Boom

It might be too steep of a price to pay for our future generations.

As artificial intelligence (AI) revolutionizes industries and drives technological advancements, it brings significant environmental challenges alongside its opportunities. The immense computational power required for training and deploying advanced AI models demands vast amounts of energy, resulting in a substantial carbon footprint.

AI’s Environmental Footprint

Data centers serve as the backbone of AI infrastructure, consuming massive amounts of electricity. If powered by non-renewable sources, these operations produce greenhouse gas emissions. Additionally, the rapid evolution of AI technology contributes to electronic waste (e-waste) and necessitates continuous resource extraction for hardware production.

Although large tech companies are aiming to reduce their footprint—such as Google Sustainability’s 2024 Environment Report—is it really enough?

Image created by Microsoft Copilot

Key Environmental Challenges of AI

Energy Consumption

Training large AI models, such as deep neural networks and large language models, requires significant computational power and energy. Engineers often allow these models to train continuously for weeks, leading to substantial electricity consumption. Notably, MIT reports that:

> “…training a single (large) AI model can emit as much energy as five cars over their entire lifetimes.”

The environmental costs of training grow proportionally to model size, and the cost exponentially curves up with additional tuning steps used to increase the model’s accuracy.

Carbon Footprint

The energy used for training AI models frequently comes from non-renewable sources, contributing to greenhouse gas emissions. While some tech companies are transitioning to renewable energy for their data centers, the overall carbon footprint of AI remains a concern, especially with the increasing number of models being trained by small companies and individuals.

Electronic Waste

The rapid advancement of AI technology leads to hardware obsolescence, creating a growing problem of electronic waste. Disposing of or recycling outdated hardware poses environmental risks if not managed properly. The AI breakthroughs have created a massive boom of startups, each rapidly iterating on AI models, which only burdens the already existing e-waste issue.

In 2024 alone, there are an estimated 67,200 AI companies.

Indirect Environmental Impacts

The distributed nature of data centers allows many servers to be placed throughout the globe. This raises the question of how many backups and networks are going unmonitored. Along with the distributed nature of data, AI applications in other growing areas contribute to increased consumption and natural resource depletion if not carefully managed.

Cooling Requirements

Data centers that host AI systems require extensive cooling to prevent overheating, often relying on energy-intensive air conditioning systems. Implementing efficient cooling technologies is crucial for minimizing this energy demand. Many companies go to great lengths to cool their data centers; in an effort to be more green, Microsoft built an underwater data center. But not all of the 67,200 companies can do this, leading to energy waste.

The Greener Side of AI

Despite the environmental challenges, AI has the potential to contribute positively to sustainability. AI-driven innovations, such as electric vehicles and energy-efficient materials, can significantly benefit the environment. For example, Google’s DeepMind has developed over 700 environmentally friendly solutions.

In Life 3.0, a book written by Max Tegmark, he highlights that many scientists believe Artificial General Intelligence, once sentient, will offer solutions on climate change. And the AI revolution will arrive much quicker than the ever-looming climate change overhaul.

Moving Forward

To address these environmental challenges, technologists, policymakers, and industry leaders must collaborate on practices that prioritize sustainability. Companies leading the way need to guide the industry toward a greener future. By fostering innovation that considers ecological footprints, we can harness AI’s potential to drive progress while supporting environmental preservation.

The path to a sustainable AI ecosystem requires continuous improvement and shared responsibility, ensuring that technological advancements align with broader environmental goals.