Skip to main content

The Hidden Environmental Cost of AI: Data Centres' Surging Energy and Water Consumption

 In recent years, artificial intelligence (AI) has become an integral part of our daily lives, powering everything from smart assistants to complex data analysis. However, as AI technologies continue to advance and proliferate, a concerning trend has emerged: the rapidly increasing energy and water consumption of data centres that support these systems.

The Power Hunger of AI

According to the International Energy Agency (IEA), global data centre electricity demand is projected to more than double between 2022 and 2026, largely due to the growth of AI. In 2022, data centres consumed approximately 460 terawatt-hours (TWh) globally, and this figure is expected to exceed 1,000 TWh by 2026. To put this into perspective, that's equivalent to the entire electricity consumption of Japan.

The energy intensity of AI-related queries is particularly striking. While a typical Google search uses about 0.3 watt-hours (Wh), a query using ChatGPT requires around 2.9 Wh - nearly ten times more energy. This difference highlights the substantial energy demands of AI systems compared to traditional computing tasks.

In the United States, the impact is even more pronounced. The Electric Power Research Institute (EPRI) estimates that data centres could consume between 4.6% and 9.1% of US electricity by 2030, up from the current 4%. This significant increase raises concerns about the strain on power grids and the potential need for expanded infrastructure.

Thirsty Data Centres

Energy isn't the only resource being consumed at an alarming rate. Data centres also require substantial amounts of water for cooling systems. In Goodyear, Arizona, for example, Microsoft's data centres are projected to consume over 50 million gallons of drinking water annually. This heavy water usage is particularly concerning in regions already facing water scarcity issues.

Environmental and Social Consequences

The surge in data centre resource consumption has far-reaching implications:

  1. Climate Goals: Despite pledges to run on carbon-free energy, tech giants like Google and Microsoft have seen increased greenhouse gas emissions due to their AI pursuits. This trend poses challenges for companies striving to meet their climate commitments.
  2. Local Impact: Communities are beginning to push back against data centre expansion. In Dublin, Ireland, there is a de facto moratorium on new data centres due to their significant energy consumption.
  3. Infrastructure Strain: The growing demand for data centre capacity is putting pressure on local power grids and water supplies, potentially affecting access for other users.
  4. Economic Implications: The need for increased power infrastructure will lead to financial burdens on taxpayers for future power lines and grid upgrades. Of course there is no guarantee that the increased productivity, should it accrue, from the AI deployments will be reflected in wage growth, on the contrary it is far more likely to increase inequalities.  
Further reading:
https://www.tandfonline.com/doi/abs/10.1080/00213624.2024.2344434
https://www.aeaweb.org/conference/2024/program/paper/5NaKAYsd
https://www.nber.org/system/files/chapters/c14030/c14030.pdf
https://www.sciencedirect.com/topics/economics-econometrics-and-finance/heterodox-economics
https://www.chathamhouse.org/2018/06/artificial-intelligence-and-international-affairs/4-economic-implications-artificial
https://jacobin.com/2024/06/ai-data-center-energy-usage-environment
Labels:

Comments

Post a Comment

Popular posts from this blog

OpenAI's NSA Appointment Raises Alarming Surveillance Concerns

  The recent appointment of General Paul Nakasone, former head of the National Security Agency (NSA), to OpenAI's board of directors has sparked widespread outrage and concern among privacy advocates and tech enthusiasts alike. Nakasone, who led the NSA from 2018 to 2023, will join OpenAI's Safety and Security Committee, tasked with enhancing AI's role in cybersecurity. However, this move has raised significant red flags, particularly given the NSA's history of mass surveillance and data collection without warrants. Critics, including Edward Snowden, have voiced their concerns that OpenAI's AI capabilities could be leveraged to strengthen the NSA's snooping network, further eroding individual privacy. Snowden has gone so far as to label the appointment a "willful, calculated betrayal of the rights of every person on Earth." The tech community is rightly alarmed, with many drawing parallels to dystopian fiction. The move has also raised questions about ...
Post a Comment
Read more

What is happening inside of the black box?

  Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties.  Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in ...
Post a Comment
Read more

Prompt Engineering: Expert Tips for a variety of Platforms

  Prompt engineering has become a crucial aspect of harnessing the full potential of AI language models. Both Google and Anthropic have recently released comprehensive guides to help users optimise their prompts for better interactions with their AI tools. What follows is a quick overview of tips drawn from these documents. And to think just a year ago there were countless YouTube videos that were promoting 'Prompt Engineering' as a job that could earn megabucks... The main providers of these 'chatbots' will hopefully get rid of this problem, soon. Currently their interfaces are akin to 1970's command lines, we've seen a regression in UI. Constructing complex prompts should be relegated to Linux lovers. Just a word of caution, even excellent prompts don't stop LLM 'hallucinations'. They can be mitigated against by supplementing a LLM with a RAG, and perhaps by 'Memory Tuning ' as suggested by Lamini (I've not tested this approach yet).  ...
Post a Comment
Read more