AI Energy Implications

Extracted Attributes

• Potential Solutions

  AI-driven energy management for optimizing consumption, lowering operational costs, improving sustainability profiles, and reducing greenhouse gas emissions

• Environmental Consequences

  Significant contribution to greenhouse gas emissions, excessive water consumption, higher electricity bills

• Key Infrastructure Impacted

  Data Centers and Energy Infrastructure

• Revived Energy Source Interest

  Nuclear Energy, specifically Small Modular Reactors (SMRs)

• Primary Driver of Energy Demand

  Rapid advancement and widespread adoption of AI, particularly generative AI

• Projected AI-specific Electricity Consumption by 2028

  165 to 326 terawatt-hours per year

• Projected Share of US Electricity for Data Centers by 2028

  Triples from 4.4% to 12%

Timeline

• Artificial intelligence was founded as an academic discipline. (Source: wikipedia)

  1956-XX-XX

• Funding and interest in AI vastly increased after graphics processing units started being used to accelerate neural networks and deep learning outperformed previous AI techniques. (Source: wikipedia)

  2012-XX-XX

• Growth in AI accelerated further with the transformer architecture. (Source: wikipedia)

  2017-XX-XX

• An ongoing period of rapid progress in advanced generative AI became known as the AI boom. (Source: wikipedia)

  2020-XX-XX

• The share of US electricity going to data centers is projected to begin tripling from its current 4.4% to 12% by 2028. (Source: web_search_results)

  2024-XX-XX

• The power going to AI-specific purposes is estimated to rise to between 165 and 326 terawatt-hours per year. (Source: web_search_results)

  2028-XX-XX

Artificial intelligence

Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., language models and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore." Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, perception, and support for robotics. To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields. Some companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI)—AI that can complete virtually any cognitive task at least as well as a human. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest vastly increased after 2012 when graphics processing units started being used to accelerate neural networks and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture. In the 2020s, an ongoing period of rapid progress in advanced generative AI became known as the AI boom. Generative AI's ability to create and modify content has led to several unintended consequences and harms, which has raised ethical concerns about AI's long-term effects and potential existential risks, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

Web Search Results

• Optimize Efficiency With AI-Driven Energy Management

  For business and sustainability leaders, the implications of AI in energy management are profound. It presents an opportunity to significantly lower operational costs, improve sustainability profiles, and contribute to environmental conservation efforts. At the same time, it allows companies to respond to growing consumer and regulatory demands for greater environmental responsibility. [...] For business and sustainability leaders, the implications of AI in energy management are profound. It presents an opportunity to significantly lower operational costs, improve sustainability profiles, and contribute to environmental conservation efforts. At the same time, it allows companies to respond to growing consumer and regulatory demands for greater environmental responsibility. [...] The advancements in AI energy management will significantly impact business sustainability and environmental conservation. Companies will be able to optimize energy consumption more effectively, leading to significant cost reductions and boosting sustainability profiles. For instance, AI can help businesses achieve their Sustainability Development Goals (SDGs) by minimizing greenhouse gas emissions and transitioning towards renewable energy.

• AI has high data center energy costs — but there are solutions

  ## AI’s energy impact AI models — particularly generative AI models like GPT-4 — are becoming exponentially larger, which means that more data center energy is being used to train them and to process data. [...] “As we move from text to video to image, these AI models are growing larger and larger, and so is their energy impact,” said Vijay Gadepally, a senior scientist and principal investigator at MIT Lincoln Laboratory, where he leads the Supercomputing Center’s research initiatives. “This is going to grow into a pretty sizable amount of energy use and a growing contributor to emissions across the world.”

• Energy and AI – Analysis - IEA

  The development and uptake of artificial intelligence (AI) has accelerated in recent years – elevating the question of what widespread deployment of the technology will mean for the energy sector. There is no AI without energy – specifically electricity for data centres. At the same time, AI could transform how the energy industry operates if it is adopted at scale. However, until now, policy makers and other stakeholders have often lacked the tools to analyse both sides of this issue due to a [...] This report from the International Energy Agency (IEA) aims to fill this gap based on new global and regional modelling and datasets, as well as extensive consultation with governments and regulators, the tech sector, the energy industry and international experts. It includes projections for how much electricity AI could consume over the next decade, as well as which energy sources are set to help meet it. It also analyses what the uptake of AI could mean for energy security, emissions,

• We did the math on AI's energy footprint. Here's the story you haven't ...

  This leaves even those whose job it is to predict energy demands forced to assemble a puzzle with countless missing pieces, making it nearly impossible to plan for AI’s future impact on energy grids and emissions. Worse, the deals that utility companies make with the data centers will likely transfer the costs of the AI revolution to the rest of us, in the form of higher electricity bills. [...] By 2028, the researchers estimate, the power going to AI-specific purposes will rise to between 165 and 326 terawatt-hours per year. That’s more than all electricity currently used by US data centers for all purposes; it’s enough to power 22% of US households each year. That could generate the same emissions as driving over 300 billion miles—over 1,600 round trips to the sun from Earth. [...] The researchers were clear that adoption of AI and the accelerated server technologies that power it has been the primary force causing electricity demand from data centers to skyrocket after remaining stagnant for over a decade. Between 2024 and 2028, the share of US electricity going to data centers may triple, from its current 4.4% to 12%.

• Why AI uses so much energy—and what we can do about it

  The environmental impact of AI extends beyond high electricity usage. AI models consume enormous amounts of fossil-fuel-based electricity, significantly contributing to greenhouse gas emissions. The need for advanced cooling systems in AI data centers also leads to excessive water consumption, which can have serious environmental consequences in regions experiencing water scarcity. [...] Additionally, the storage and transfer of massive datasets used in AI training require substantial energy, further increasing AI’s environmental burden. Without proper sustainability measures, the expansion of AI could accelerate ecological harm and worsen climate change. [...] Stacked area and bar chart showing historical and projected U.S. server electricity consumption from 2014 to 2028, broken down by processor type. AI workloads, especially those using 8 GPUs, drive significant growth in projected energy use, with total consumption reaching nearly 400 TWh in high scenarios by 2028. ## What are the key environmental consequences of AI development?