The energy required to train and run AI models has surged, raising critical concerns about the technology’s long-term sustainability.
AI technology that promises to solve global challenges while simultaneously consuming vast amounts of energy necessitates a shift in our approach.
By leveraging technological convergence, we can ensure that AI’s continued growth is not a burden on the planet.
세계경제포럼, 2025년 8월 8일 게시
David Timis
Global Communications and Public Affairs Manager, Generation: You Employed, Inc.
The explosive growth of AI is transforming every sector, from healthcare to finance, and it’s poised to add trillions to the global economy. Yet, this rapid expansion comes with a significant cost: a burgeoning demand for energy.
As AI models become more complex and computationally intensive, the energy required to train and run them has surged, raising critical concerns about the technology’s long-term sustainability. Data centres, the silent engines powering the AI revolution, are now among the largest drivers of rising global power consumption, with projections indicating that AI’s demand could rival entire countries by 2030.
This paradox — a technology promising to solve global challenges while simultaneously consuming vast amounts of energy — necessitates a fundamental shift in our approach. The key to navigating this complex challenge lies in technological convergence, a powerful synergy of innovations that can transform AI from an energy liability into a force for a greener energy future. By combining advances in AI with breakthroughs in energy generation, storage and grid management, we can accelerate the transition to a sustainable energy system and ensure that AI’s profound benefits are realized without compromising our planet.
Image: International Energy Agency
The innovations driving a sustainable AI future
Several interconnected technological innovations are paving the path to a greener AI energy future. These solutions are making AI more efficient and can help us use AI to reshape how we produce, distribute and consume energy, without compromising environmental integrity.
AI-driven smart grid management and optimization
The electricity grid is the backbone of our energy system, but it’s increasingly strained by rising demand and the intermittency of renewable sources, like solar and wind. This is where the convergence of AI, advanced measurement technologies (such as satellite imagery and LiDAR) and smart grid infrastructure becomes a game-changer for enhancing grid stability and reliability.
AI can analyze vast datasets from smart meters, sensors and weather forecasts to predict energy demand and renewable energy output with unprecedented accuracy. This enables utilities to optimize power distribution, reduce transmission losses and balance baseload requirements in real-time. By dynamically managing the flow of electricity, AI minimizes waste and reduces reliance on fossil fuels.
Managing the variability of wind and solar power is crucial for scaling their adoption. AI can forecast the output of renewable energy sources more effectively, allowing grid operators to better integrate these intermittent sources. When paired with advanced energy storage systems, such as lithium-ion batteries or pumped hydro storage, AI can optimize the charging and discharging of batteries, ensuring a reliable energy supply even during periods of low renewable generation. This capability is vital for achieving a higher penetration of renewables without sacrificing grid stability.
AI-accelerated materials science and advanced manufacturing
The clean energy transition is heavily dependent on new materials and more efficient manufacturing processes for everything from solar panels to batteries and sustainable fuels. AI is a powerful tool for accelerating these breakthroughs, embodying the ‘materials informatics’ convergence highlighted by the World Economic Forum in its Technology Convergence Report.
As demonstrated by KoBold Metals’ use of AI for mineral exploration, AI can dramatically improve the efficiency of discovering and extracting critical battery metals. Beyond exploration, AI can be used to design and discover new, more efficient materials for solar panels (like the perovskite tandem technology), advanced electrolyzers for hydrogen production and cutting-edge battery storage systems.
The convergence of AI with innovative industrial technologies, such as Boston Metal’s molten oxide electrolysis for steel production and Coolbrook’s RotoDynamic Heater for industrial heat, holds immense potential. These technologies use electricity instead of fossil fuels to reduce carbon emissions in hard-to-abate sectors like steel and cement. When coupled with AI, these processes can be optimized in real-time to maximize efficiency, reduce waste and lower energy consumption.
Converged systems for carbon capture, monitoring and mitigation
The mix of AI, satellite technology and advanced data analytics is creating a new set of tools for tackling emissions, enabling real-time environmental oversight.
The Carbon Mapper initiative, a not-for-profit coalition leveraging advanced satellite technology, is a prime example of this convergence. By combining NASA JPL’s cutting-edge imaging spectrometer with AI-powered analytics, this system can detect and track methane and CO₂ ‘superemitters’ worldwide with unprecedented precision. This publicly accessible, facility-level data empowers governments, industries and the public to identify leaks and verify emissions reductions rapidly.
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AI-enhanced nuclear fission and fusion
Nuclear power, with its ability to provide stable, low-carbon baseload electricity, is a crucial part of the energy mix, particularly as AI and data centre demand surges.
Small Modular Reactors (SMRs) are a promising innovation aimed at making nuclear power more affordable, flexible and rapidly deployable. AI can be used to optimize the design and engineering of SMRs, significantly shortening construction timelines and potentially lowering capital expenditures. AI also enables advanced modelling and simulation of reactor performance, enhancing safety and efficiency. The recent agreements by tech giants like Microsoft, Google and Amazon to purchase power from nuclear plants underscore the growing recognition of this technology’s potential to meet the surging, continuous power demands of data centres.
A collaborative and convergent future
The journey to a greener AI energy future requires more than just isolated innovations. It demands a holistic approach and a collaborative ecosystem. Stakeholders from the technology, energy and government sectors must work together, sharing data and expertise to integrate these solutions seamlessly and addressing systemic challenges, like supply chain readiness and labour availability.
Ultimately, the future of AI and energy is not a zero-sum game. By leveraging technological convergence, we can ensure that AI’s continued growth is not a burden on the planet, but a powerful catalyst for a more sustainable, resilient and equitable energy future.
The innovations are here; the next critical step is to embrace their synergy and build the integrated systems that will power a cleaner, smarter world.
