DARPA Backs Project to Measure AI Energy Use at Every Stage of the Workflow
Chevron Profits Nearly Triple to $9.9 Billion Heidelberg Materials’ Edmonton project had… Haley Zaremba is an energy journalist and researcher with more than a decade of professional experience covering global energy systems, land and natural resources, and… The artificial intelligence boom is creating an energy monster.
The indiscriminate integration of machine learning into everything from from our energy grids to our electric toothbrushes – no,really– has created a global race to build up data center capacity as quickly as humanly possible, causing energy demand projections to skyrocket and placing unprecedented strain on the world’s energy grids.
By 2030, data center energy consumption isexpected to more than doublefrom its 2024 consumption level, which stood at about 420 terawatt-hours (TWh).
Proponents of artificial intelligence argue that, eventually, AI will save more energy than it consumes as its integration into nearly every economic sector imaginable makes all kinds of processes more efficient.
“Finding new materials, catalysts or processes that can produce stuff more efficiently is the sort of ‘’needle in a haystack’ problem that AI is ideally suited to,” the Financial Times reported last year in an article musing about “How AI might save more energy than it soaks up.” But in the meantime, the sector’s water and energy use is continuing to skyrocket at a time when humanity simply cannot afford it.
Even though the world is racing to add clean energy capacity faster than at any previous time in human history, clean energy production growth is simply no match for overall energy demand growth.
Artificial intelligence is already causing amassive buildout of natural gas-powered plantsand is set toextend the dominance of fossil fuelsin the global energy mix.
As a result of this demand boom, paired with decades of delayed climate action, surpassing the emissions goals set in the Paris agreement isnow an inevitability.
So instead of waiting for AI to make the world more efficient, why not make AI itself more efficient? A team of researchers at the University of Michigan Engineering has proposed a new system for designing more efficient AI software.
“While computer chips are getting faster, misalignments between software and hardware waste massive amounts of electricity—up to 30% during AI model training alone,”reportsMichigan Engineering.
“More energy-efficient AI brings down electricity demand, reducing the cost of AI and extending battery life for AI-enabled devices.” As such, researchers from University of Michigan Engineering are partnering with colleagues at the University of Illinois Urbana-Champaign (UIUC) and defense contractor BAE Systems to develop a new efficiency-focused software system with better energy tracking capabilities.
One of the biggest challenges facing the AI energy problem today is the extreme opacity of the sector and insufficient measuring and reporting of resource use.
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