The surging demand for AI is on a collision course with environmental sustainability, with experts stating that coal power might stick around to keep up electricity demands.
Countries worldwide are aiming to transition to net zero, investing in green energy and cutting fossil fuel consumption.
This is at odds with the immense electricity demand created by AI, particularly generative AI, which serves tens of millions of users worldwide.
That’s a definitive point – even a few years ago, AI models were comparatively small and confined to localized uses.
Today, you, me, and tens of millions of others have no less than experimented with AI. Some 40% of adults within the US and Europe, by some estimates, and 75% of under-18s.
AI firms see a future where their products are embedded into every little thing we do and each device we use, but AI isn’t powered by thin air. Like all technologies, it requires energy.
A recent paper found the BLOOM model used up 433 MWh to coach, and GPT-3 needed a whopping 1287 MWh, which is comparable to a small nation.
OpenAI’s ChatGPT needs an estimated 564 MWh each day to compute answers to user prompts. Each individual output represents a calculation performed across OpenAI’s sprawling neural networks, each requiring energy.
Let’s put that into perspective: 1287 MWh could power 43,000 to 128,700 average households for a day, assuming a median each day use of 10 to 30 kWh per household.
It could also power over 200,000,000 LED light bulbs for an hour or drive an electrical vehicle for roughly 4 to five million kilometers.
While this study and others have their limitations, public data from open-source AI firms like HuggingFace corroborates the size of those figures.
Data centers currently use over 1% of the world’s electricity consumption. Source: Shutterstock.
The environmental implications of AI extend beyond mere energy consumption. Water usage at Microsoft’s data centers underscores the resource-intensive nature of AI operations. A 15-megawatt data center can eat as much as 360,000 gallons of water each day.
The International Energy Agency (IEA) has warned of the broader impact of information centers, which already account for greater than 1.3% of world electricity consumption. This figure is poised to rise as AI and data processing demands escalate, further stressing the worldwide energy infrastructure and amplifying the decision for more sustainable practices inside the AI industry.
The Boston Consulting Group estimates that by the top of this decade, electricity consumption at US data centers could triple from 2022 levels to as much as 390 terawatt hours, accounting for about 7.5% of the country’s projected electricity demand.
The EU has also said data center energy demands will double by 2026. In the US or China alone, data centers could eat equal to the annual output of about 80 to 130 coal power plants by around 2030.
In a worst-case scenario, the below graph shows that data centers might eat some 8,000 TWh of electricity by 2030, which is 30% of the world’s electricity consumption today. That’s double what the US consumes annually.
We’d hasten to not sensationalize – let’s watch out to recollect that is an upper-bound estimate by some margin and that data centers are used for a lot of other things besides AI – however it’s still quite shocking, even on the lower bounds of 1,100 TWh.
Data center electricity is rocketing. Source: Research Gate.
Speaking on the World Economic Forum, Sam Altman, CEO of OpenAI himself said, “We do need far more energy on the earth than we thought we wanted before. We still don’t appreciate the energy needs of this technology.”
“AI will eat vastly more power than people expected,” he contiued, suggesting that energy sources like nuclear fusion or cheaper solar energy are vital for AI’s progress.
Data centers strain the energy grid
In the center of northern Virginia, a region now famously often called “data center alley,” the rapid growth of generative AI is pushing the boundaries of electricity generation.
Local power providers even needed to halt connections to recent data centers at one point in 2022, because the demand was just too high. Due to community resistance, proposals to make use of diesel generators during power shortages were shelved.
Bloomberg reports that within the Kansas City area, the development of an information center and an electric-vehicle battery factory required a lot power that plans to decommission a coal plant were postponed.
Ari Peskoe, from the Electricity Law Initiative at Harvard Law School, warned of the potential dire consequences if utilities fail to adapt: “New loads are delayed, factories can’t come online, our economic growth potential is diminished,” he says.
“The worst-case scenario is utilities don’t adapt and keep old fossil-fuel capability online and so they don’t evolve past that.”
Rob Gramlich of Grid Strategies echoed these concerns, highlighting to Bloomberg the chance of rolling blackouts if infrastructure improvements lag behind.
The utility sector’s challenges aren’t limited to data centers. Recent laws and incentives are spurring the development of semiconductor, EV, and battery factories, which can be contributing to the soaring demand for electricity.
For instance, Evergy, serving the Kansas City area, delayed retiring a Nineteen Sixties coal plant to deal with the demand from recent developments, including a Meta Platforms data center and a Panasonic EV battery factory.
Despite the preference of many tech firms and clean tech manufacturers for renewable energy, the truth says in another way. It’s difficult to envisage quite how this energy usage will be offset.
The situation shouldn’t be unique to the US. Globally, China, India, the UK, and the EU have all issued warnings about AI’s rising electricity demands.
How will we pay?
As AI technologies change into ubiquitous, their ecological footprint clashes with global ambitions for a net-zero future. Even putting lofty goals for obtaining net zero aside, power grids simply can’t sustain the industry’s current trajectory.
Is an “AI winter” coming when AI undergoes an extended means of becoming more refined and efficient before becoming more intelligent? Or will breakthroughs and industry guarantees keep development afloat?
Bio-inspired AI, for instance, is a promising frontier that seeks to harmonize the efficiency of natural systems with computational intelligence. Earth is inhabited by billions of extremely advanced organisms ‘powered’ by natural sources like food and the Sun – can this be a blueprint for AI?
The answer is a tentative yes, with neuromorphic AI chips based on synaptic functions becoming increasingly viable. AI speech recognition has even been performed using biological cells formed into ‘organoids’ – essentially ‘mini-brains.’
The “Brainoware” system is a brain organoid (a lab-grown mini-brain) connected to a tool that records and stimulates its electrical activity to perform AI functions. Source: Nature Electronics.
Other methods of mitigating the AI industry’s resource drain include an “AI tax.”
Usually posited as a technique of mitigating AI-related job losses, an AI tax could see entities benefiting from AI advancements contribute to mitigating their environmental impacts.
In the top, it’s difficult to predict how the industry will handle these demands and to what extent people may have the shoulder the burden.
