With the larger adoption of artificial intelligence (AI), businesses seem to have automated most of their functions. This eventually is expected to threaten the stability of the energy industry, economy, and climate due to its high energy consumption. Experts suggest that the tech sector’s decarbonization goals are challenged by AI’s power demands.
As reported by Google, there has been a 48 percent increase in carbon emissions over the last five years. As AI picks up speed, the rapid increase in power consumption can pose a direct threat to the tech sector’s ability to follow its decarbonization plans.
The threatening market of AI
AI’s energy demand is growing at an alarming rate, between 26% and 36% annually, as reported by the World Economic Forum. By 2028, this could push AI’s energy consumption beyond countries like Iceland. In addition to this, each AI-powered internet query can consume about ten times more energy than traditional internet searches.
It looks like the constant rise of AI can be a double-edged sword for today’s digitalised world. While it unlocks incredible potential of automating tasks and other advancements, its energy footprint can be a cause for concern. A recent Goldman Sachs report predicts a 160% increase in data centre power demand by 2030, with AI being a key driver. This means that data centres are potentially consuming 8% of US power by 2030, compared to just 3% in 2022.
The dark side of AI
The variability and unpredictability of renewable energy pose significant challenges for AI applications. For example, solar and wind power generation are inherently intermittent and dependent on weather conditions. AI algorithms need to sync with the dynamic nature of these energy sources to optimize production and distribution effectively. However, developing accurate predictive models accountable for fluctuations in renewable energy generation remains a formidable challenge. This drawback eventually impacts the reliability and stability of energy grids.
The energy industry already seems to face several challenges when it comes to the endorsement of rapid digitalization. It is believed that the energy sector’s software architecture is much older than that of other sectors such as finance. The energy industry also needs to ensure that any form of change is compatible with ‘on-the-ground infrastructure’ located across several places. This makes the implementation of modern technology more costly and difficult, especially for smaller companies. Moreover, combined with the complexity of training models and limitations in accessing adequate computational power, the cost-benefit analysis of energy AI requires further investigation.
Looking from an economical perspective, automation with AI and increased connectivity has the potential to make the energy sector vulnerable to cyber threats. For example, aging and unprotected points in the electric grid can be exploited to gain access to the entire ecosystem. The energy sector can be vulnerable to cyberattacks, with each average 2020 attack costing about $6.4 million in damages, according to a study by MIT.
According to a report by the BBC, AI-powered services can involve considerably more computer power and electricity, in comparison to standard online activity. Another recent study by scientists at Cornell University suggested that generative AI modules such as ChatGPT can use up to 33 times more energy than computers running task-specific software.
Industry reacts
Industry experts suggest that the widespread adoption of AI and its utilization in the field of energy and other domains may result in significant job displacement. As AI systems become more advanced and widespread, their energy consumption skyrockets. This rise in energy consumption can eventually strain power grids and increase reliance on fossil fuels. Furthermore, “This surge in demand exacerbates greenhouse gas emissions, undermining global climate goals. Economically, the escalating costs of energy required to support AI infrastructure could drive up operational expenses for businesses, potentially slowing innovation and growth,” Jaspreet Bindra, founder, Tech Whisperer, explained.
Additionally, “A significant amount of heat is generated due to data centers and cooling equipment needed for the running of AI. This could be potentially contributing to climate change. AI could create socio economic imbalances, as fossil fuels have been the primary source of power for centuries,” Sirajuddin Ali , founder and CEO, Malitra India, said.
Despite their ambitious sustainability commitments, big tech companies such as Microsoft, have seen their carbon emissions continue to rise partly due to AI’s energy requirements. Moreover, “Big tech companies’ expansion of data centers to support AI has contributed to increased infrastructure emissions. This highlights the complex nature of achieving net-zero goals while scaling AI capabilities,” Agam Chaudhary, CEO, Two99, highlighted.
Moreover, “ The rapid growth of AI presents a complex mix of challenges and opportunities for climate action by way of improving efficiency in the process of sustainable disposal. It can be a path breaking intervention through continuous improvement of AI models. The key is responsible implementation and collaboration between industry leaders and policymakers to align AI development with sustainability goals and economic stability” Prashant Singh, co-founder and CEO, Blue Planet Environmental Solutions, concluded.
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