Closing the loop on AI and renewables: a promising investment landscape
Powering the growth of Artificial Intelligence by supporting the energy transition can create a virtuous financial path towards a more sustainable future.
Artificial intelligence is playing an increasingly important role in our lives. Over the last years it has significantly improved sectors such as healthcare, logistics or transportation, with the rise of programs that are constantly optimizing operations towards more efficient services. But AI’s greatest impact is undoubtedly in our everyday lives. The number of queries submitted by users to generative Ais chatbots exploded over the past year and is now counted in billions, as around 250 million people worldwide use these programs every day1. But the AI boom is not without consequences for our environment. While it represents a revolution that could well transform many aspects of our lives, it also comes with a major energy challenge.
An increasing power demand
AI needs to constantly improve in order to meet our demands ever more precisely. To do so, it relies on Learning Language Models (LLMs), programs designed to understand and generate text with the same accuracy and precision as a human would. “Those models are really complicated mathematical calculation, and they rely on a huge amount of data to learn the function” says Shaolei Ren, an Associate Professor of Electrical and Computer Engineering at the University of California. Configuration and execution of LLMs require extensive computing power, provided by small electronic chips called Graphic Processing Units (GPUs). Initially created to optimize video games graphics, these chips are now widely used to train and run AI, thanks to a computing capacity that far exceeds that of the traditional Central Processing Units (CPUs) that run our computers.
Stacked in thousands of servers in over 8,000 data centers around the world, GPUs are so power-hungry that an average research on an AI chatbot requires almost 10 times more electricity than a regular search engine2. At its peak, the consumption of a single server roughly matches that of 10 American households combined3. As more and more data centers are specifically dedicated to AI – a demand that is expected to raise at a 33% rate by 20304, reaching around 10% of the US national electricity consumption5 – a problem arises: these centers consume so much power that in certain areas, the local electrical grids can no longer keep up, putting power infrastructures at strain and causing frequent shortage6.
Closing the loop on AI and renewable energies
As a result, AI firms have been investing into renewable energies such as solar power and wind turbines, a vital alternative for supporting the rapid growth of the sector7. But their dependence on weather conditions, such as wind speed and sun exposure, makes these solutions unstable. Luckily enough, AI itself can play a part in their optimization, by helping better anticipate their performance and meet energy demands more effectively. This is what companies like Fluence, dedicated to renewable energy storage, are working on. Thanks to a large-scale battery system, it’s able to store energy when renewables produce unused power, and release it when the demand surges or the grid faces congestion. “Traditional energy infrastructure takes years to deploy, but data centers need power fast” says SVP and Chief Product Officer Rebecca Boll. “Fluence’s battery storage solutions can deliver massive amounts of power quickly and reliably, enabling 24/7 operation for AI-dependent facilities.” Moreover, the company operates within a virtuous circle in which AI improves energy storage efficiency by analyzing deployment data, building operational models, and extending battery life. Furthermore, the company enables renewable asset owners to bid their energy into merchant markets, through a custom trading platform also powered by AI. “It’s a financial win-win: AI enhances energy storage efficiency, extends the lifespan of assets and reduces operational costs.”
A compelling investment landscape
At BNP Paribas Asset Management, Edward Lees and Ulrik Fugmann have spent the last 20 years observing and investing in energy transition technologies. They founded the Environmental Strategies Group in 2019, to take an active and ownership approach to how the company invests in these solutions. “Availability and affordability of clean power is becoming a key bottleneck for economic growth” says Ulrik Fugmann. “There’s an excess demand today for clean power, and there is a lack of availability of clean energy technologies. This creates a really interesting flywheel whereby an increase in wholesale power prices make clean power and technologies even more attractive.” While the broader economy plays a crucial role in adopting these technologies, energy transition companies such as Fluence become both enablers for a brighter future, and powerful financial levers for investors. “We need more vehicles in the public space, to pull more public money at scale to solve these problems and, hopefully, create tomorrow’s green unicorns” says Edward Lees. Like a ripple effect, a small push in energy transition can reverse the course of things and help AI pursue its learning journey, while offering a compelling investment landscape.
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Disclaimer
Please note that articles may contain technical language. For this reason, they may not be suitable for readers without professional investment experience. Any views expressed here are those of the author as of the date of publication, are based on available information, and are subject to change without notice. Individual portfolio management teams may hold different views and may take different investment decisions for different clients. This document does not constitute investment advice. The value of investments and the income they generate may go down as well as up and it is possible that investors will not recover their initial outlay. Past performance is no guarantee for future returns. Investing in emerging markets, or specialised or restricted sectors is likely to be subject to a higher-than-average volatility due to a high degree of concentration, greater uncertainty because less information is available, there is less liquidity or due to greater sensitivity to changes in market conditions (social, political and economic conditions). Some emerging markets offer less security than the majority of international developed markets. For this reason, services for portfolio transactions, liquidation and conservation on behalf of funds invested in emerging markets may carry greater risk.