AI’s Hunger for Power & What to Do About It

While artificial intelligence is expected to positively impact our lives by driving intelligent automation across different use cases, its soaring energy demand, expected to increase by 5% annually, can lead to significant environmental issues.

What comes to mind when you hear the words ‘Artificial Intelligence‘ these days? Intelligent business transformation and its limitless possibilities, right? But there’s another side to this revolutionary technology that not many talk about. It’s AI’s hunger for power.

Behind the scenes, AI’s demand for energy is growing at an alarming rate, consuming massive amounts of energy and resources. If left unchecked, it will pose serious threats to the environment, where the consequences of its insatiable appetite could be catastrophic.

Data centers and AI systems’ rapidly increasing carbon footprint and energy demands are expected to grow by 10-30% over the next five years. This poses a significant challenge for utility and technology enterprises globally.

In this in-depth write-up, we will analyze AI’s overall environmental impact and outline concrete steps that technology companies must take to ensure AI innovation happens sustainably.

The AI Boom and Its Insatiable “Power Appetite”

AI’s astounding growth over the past decade has disrupted the global tech landscape, opening doors to a multitude of opportunities for business leaders to capitalize on. We can now access well-trained AI models and algorithms that allow for seamless, coherent text generation, translation, and the creation of photorealistic images and clips from text prompts.

But what exactly fueled AI’s exponential growth? Here are two critical factors highlighted by Cubix’s experts:

• Access to massive computing power

Apart from AI’s hunger for power, it’s also important to mention how data-intensive this technology is. The more quality data they are trained on, the better they perform. The widespread availability of user data across the internet has provided the raw material for models to improve continually.

• Availability of vast datasets

Training complex AI models requires an enormous amount of computing capacity. Fortunately, cost-effective cloud computing has made it easier for companies big and small to meet the required computing capacity without upscaling their hardware capabilities.

While its growth further catalyzes global tech disruption, companies in different sectors demanding custom AI solutions to streamline their operations, AI’s environmental impact does not receive enough discussion.

Measuring AI’s Energy and Carbon Footprint

OpenAI’s upcoming GPT-5 model is expected to be trained on over 2-5 trillion parameters. In comparison, GPT-4 was trained on around 1.76 trillion parameters, causing an estimated emission of over 15 metric tons of CO2e. Therefore, we expect GPT-5’s carbon footprint to be almost double that of GPT-4.

Accurate estimates of AI’s overall carbon footprint are challenging to obtain due to discrepancies in measurement approaches and the lack of transparency from AI-first tech enterprises. Still, Cubix’s experts have pointed out some key challenges:

• Runtime factors like speed, power, and the number of processors used significantly impact energy consumption but are not commonly reported.
• The energy efficiency of data centers and approaches toward power generation collectively influence the carbon footprint but are not standardized across AI research and development organizations.
• Technology enterprises don’t usually reveal details regarding the data centers where AI models are trained, their power sources, or the renewable energy generation approaches.

The overall environmental impact of all major AI models, including Gemini, GPT, and Claude, is quite ambiguous at the moment. However, if tech-forward business leaders are looking forward to extensively implementing AI across all sectors and multiple use cases, they must devise actionable solutions to ensure environmental sustainability through AI innovation.

The Alarming Resource Crisis in AI

The ongoing AI-driven global tech disruption is nothing short of spectacular. But this innovation comes at a hidden cost. 

Developing and running complex AI models requires a staggering amount of computing resources. Training computing has increased exponentially – by a factor of 10 every year since 2012 – to enable cutting-edge AI capabilities.

Beyond emissions, AI’s water footprint also raises alarming concerns. Training a large language model can require thousands of times more water than an average household annually!

The Undiscovered Truth of Generative AI

While GenAI can drive the future of intelligent automation, popular models like Stable Diffusion are found to be rather power and resource-intensive. Specialized chips are built to train complex AI algorithms on masses of data and parameters, relying on massive physical IT infrastructures to function.

Other investments may include custom storage systems, robust web servers, and building accessible end-user interfaces and platforms. 

Our analysis reveals that carbon emissions largely stem from three key sources:

1. Energy for Running Inference

Over 60% of computing happened on power-hungry GPUs when generating images from the trained model. So their electricity consumption, while running billions of inferences, creates a significant climate impact.

2. Manufacturing Equipment

While less intuitive, IT hardware manufacturing operations emissions accounted for almost 30% of the total carbon footprint. Manufacturing millions of graphics cards, CPUs, and memory units takes an enormous toll on the planet before anyone can train or use models!

Beyond emissions, Stable Diffusion also worsens resource scarcity – its estimated annual usage has a metal footprint of over 5,600 smartphones.

3. Training & Testing

Researchers revealed that running trained Stable Diffusion models for inference produced far more emissions than the initial resource-heavy training process.

However, ignoring the emissions from training and testing would be short-sighted as AI capabilities continue to advance exponentially. More technically capable, intelligent, and powerful successors of Stable Diffusion are already in the works, demanding 2X-10X times more parameters and data.

With climate scientists calling for curbing emissions beyond cars and factories, can we afford to ignore the mammoth environmental footprint of developing AI models that go beyond the scale and capabilities of current models?

Strategic Steps to Curb AI’s Hunger for Power

The AI revolution is inevitable and will be one of the primary drivers of the global economy. To make sure this transition takes place with minimal environmental risks, here are five ways to realize AI sustainability:

1. Opt for Pre-Trained Models Over Custom Development

Platforms like Google’s BERT and HuggingFace let you equip your digital products with generalized capabilities in language, vision, and other areas, which you can customize for numerous use cases.

Partnering with companies like Cubix, which prioritize responsible AI implementation and building AI-powered apps and software solutions on these pre-trained models, tremendously reduces computational requirements compared to training custom models from scratch.

2. Train Models on High-Quality Data

Bigger models trained on billions or even trillions of parameters are not necessarily better for specific business needs. An AI model trained on curated, high-quality, and relevant data will likely generate more accurate and satisfactory responses than a model trained on low-quality, irrelevant data despite being much larger in scale. 

3. Restrict Usage of Specialized Hardware

Leading tech enterprises like Google, Apple, and Samsung build their own proprietary AI chips and hardware components, including GPUs, TPUs, and IPUs, to further customize their AI solutions for better speed and efficiency. 

However, specialized hardware makes upgrading and keeping up with the latest innovations more expensive and challenging. While leading enterprises can afford to make such investments, building specialized chips and hardware components is not an option that SMBs should consider.

4. Optimize Cloud Solutions for Environmental Sustainability

While cloud computing provides companies with convenient, secure, and accessible storage solutions, it also relies on constantly running and power-intensive remote servers, which have environmental downsides. A hybrid approach combining on-site infrastructure and selective cloud utilization can optimize AI systems for better sustainability.

5. Foster Collaborative AI Innovation

Navigating the AI transition solely can be incredibly challenging, expensive, and complex and can also increase AI’s environmental impact. Partnering with an enterprise-grade, AI-driven digital transformation company like Cubix allows you to leverage future-ready AI models and solutions without upgrading computational capabilities. 

For businesses looking to jump on the AI bandwagon without spending excessively on hiring in-house AI development teams, Cubix offers a win-win solution. Our AI outsourcing services enable you to enhance your model’s performance and data efficiency by fostering open-source collaboration with our AI experts and developers.

The Road Ahead

AI-enhanced automation has become a necessity. While the debate often focuses on the prospect of AI superseding specific jobs in the near future, the overall environmental impact of this technology is a much more pressing issue. We must address this broader concern as AI continues to shape industries.

Technology leaders and policymakers must collaborate to assess AI’s resource usage reliably. They need to create strategies that allow for the continuous, exponential scaling of AI. At the same time, these strategies must respect ecological boundaries and promote sustainability.

Cubix adheres to AI ethics and sustainability standards to create solutions that foster environmentally friendly and responsible AI adoption. Contact our representatives to discuss your ambitions for AI-powered digital transformation.