MIT has also outlined some new, targeted strategies to reduce emissions, from improving algorithm and hardware efficiency to rethinking how and when AI workloads draw power from the grid.
Generative AI’s rise comes with an increasingly steep environmental price tag. Powering the massive data centers behind these models requires enormous amounts of electricity. According to the International Energy Agency, global data center energy use is expected to more than double by 2030, outpacing Japan's entire current energy consumption.
Worryingly, much of that additional demand is expected to be met by fossil fuels, which could add hundreds of millions of tons of carbon emissions each year. This rapid surge places mounting pressure on power grids while accelerating the pace of global greenhouse gas emissions.
MIT researchers argue that addressing AI’s climate footprint requires action across the entire technological pipeline, from how models are trained to where and when computations are run.
Smarter Algorithms, Leaner Hardware
A major focus of MIT’s approach is making AI computation more efficient at both the software and hardware level.
One strategy draws inspiration from energy-saving tactics used in homes: reducing power use without compromising performance. At the MIT Lincoln Laboratory Supercomputing Center, researchers found that scaling GPU power usage down to 30 % of maximum capacity resulted in only minimal performance loss while significantly easing cooling demands. This is similar to dimming lights to save electricity without darkening the room too much.
Another key tactic is using lower-precision, task-specific chips rather than defaulting to high-powered general-purpose processors. Training large-scale models like GPT-5 demands massive resources, but many practical applications can run just as well, and more efficiently, on specialized hardware tuned to the task.
Efficiency gains also come from smarter training methods. Research shows that around half of the electricity used in training models goes toward squeezing out the final 2–3 % of accuracy. For many applications, this extra accuracy has limited practical value. Stopping training earlier can reduce energy use significantly with little loss in performance.
Additionally, MIT teams have developed tools to cut out unnecessary computations. One such tool eliminated 80% of redundant simulations during model selection—slashing energy consumption without sacrificing accuracy. These advances are further supported by ongoing improvements in chip design, where denser transistors continue to boost energy efficiency.
Rethinking How AI Uses Energy
Beyond improving how AI systems compute, the researchers emphasize the importance of managing when and where those computations take place.
Electricity doesn’t carry a fixed carbon cost; it fluctuates based on the energy mix on the grid at any given time. That opens up opportunities to schedule non-urgent AI tasks during periods of high renewable energy availability, such as mid-day solar peaks or overnight wind surges. This kind of “demand shifting” can reduce emissions without sacrificing productivity.
Where data centers are built also matters. Locating them in cooler regions reduces the need for energy-intensive cooling systems. For example, Meta’s facility in Luleå, Sweden, benefits from the Arctic climate to lower cooling costs. Similarly, placing data centers in areas with a high share of renewable energy can further cut emissions.
To support better decision-making, MIT and Princeton researchers have developed GenX, a software tool that models environmental impacts and helps companies choose low-carbon locations for new infrastructure. Looking ahead, integrating long-duration energy storage with data centers could also help match computing demand to clean energy supply.
Rethinking AI’s Role in a Warming World
The challenge now isn’t just technical—it’s strategic.
As generative AI becomes embedded in more industries and daily life, its environmental impact must be treated as a design constraint, not an afterthought. The tools and methods exist to reduce emissions, but deploying them will require policy shifts, transparency from AI developers, and greater public scrutiny of the infrastructure behind digital innovation.
Perhaps most critically, institutions and companies must resist the urge to chase scale at all costs. Efficiency, context, and purpose need to take precedence over raw performance metrics. AI’s role in the climate crisis is no longer a distant or abstract concern; it's a present, measurable force. The question is whether the industry will act fast enough to keep innovation aligned with the planet’s limits.
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.