Artificial Intelligence has become the engine behind almost everything we use online - from chatbots to image recognition to recommendation engines. But like any engine, it runs on energy.
The more we train and use AI models, the more electricity they consume, and that means higher carbon emissions. The good news? The environmental cost of AI can be reduced through smarter engineering, greener infrastructure, and more conscious human choices. Let’s see how it impacts the digital sustainability.
What Is Sustainable AI?
Sustainable AI refers to the practice of designing, developing, and deploying AI systems in ways that minimize their environmental impact.
This means rethinking how we build and run algorithms, how we power the hardware behind them, and how we store and process the data they depend on.
Sustainable AI focuses on three key technical pillars:
- Algorithm efficiency
- Hardware optimization
- Data center sustainability
…and it’s supported by government policy and user behavior.
1. Greening the Algorithms
AI models are often huge - billions of parameters trained on massive datasets. That power comes at a cost. Optimizing algorithms can significantly reduce computational load, training time, and therefore energy use.
Smarter, Smaller Models
- Model compression reduces complexity without sacrificing accuracy. A recent podcast episode of Green IO states that:
- Pruning removes redundant neurons or weights.
- Quantization uses lower-precision data types (like 8-bit instead of 32-bit).
- Knowledge distillation trains a “student” model from a larger “teacher” model, retaining accuracy with fewer resources.
Efficiency Over Obsession with Accuracy
Instead of creating one massive system for everything, narrow AI focuses on specialized tasks using smaller models. Frugal AI takes it further - prioritizing energy efficiency even at the cost of slight precision loss, when it’s not critical.
The Frugal AI approach questions the necessity of seeking the highest possible accuracy. Industry practitioners often prioritize high accuracy (e.g., 95%), which can triple the time needed to provide a result. Frugal AI is used by determining if a lower, but sufficient, precision level (e.g., 75% accuracy) is enough, especially if that level is already much higher than human accuracy for the task
Chasing that extra 1-2% improvement in accuracy can triple energy use.
Do we really need 95% precision when 75% already beats human performance? Sometimes, “good enough” is better — for the planet.
Open Source and Efficient Code
Reusing existing open-source components saves development time and energy. Also, lower-level programming languages are more energy-efficient than high-level ones like Python, though they require early planning and expertise.
2. Optimizing the Hardware
Hardware defines how fast, and how efficiently algorithms run. The hardware powering AI models and digital platforms plays a major role in their overall energy consumption. While the technical details can get complex, the main takeaway is simple: choosing the right infrastructure matters.
Modern data centers and processing units vary widely in how efficiently they use power. Optimizing this setup - whether through better equipment, smarter distribution of computing tasks, or local (edge) processing can significantly reduce emissions during model training and everyday operations.
A few guiding principles:
- Use appropriate hardware for the task. High-performance units aren’t always necessary and can waste energy if underutilized.
- Maximize existing capacity. Distributing workloads effectively prevents servers from sitting idle while consuming power.
- Consider edge computing. Running smaller computations directly on user devices or closer to data sources reduces energy spent on transferring information to and from large data centers.
In short, efficiency isn’t only about algorithms - it’s also about how and where they run. Smarter infrastructure choices lead to measurable reductions in energy use and environmental impact.
Digital Compatibility in the Sense of Sustainability
Another often overlooked aspect of digital sustainability is compatibility.
When we build websites or applications that only work on the newest devices or browsers, we unintentionally push users to upgrade their hardware sooner than necessary, fueling the very overconsumption we’re trying to fight.
By ensuring digital products remain functional on older devices and operating systems, we extend their usability and reduce electronic waste. This principle applies to both web development and AI-powered systems:
- Design with progressive enhancement, so features degrade gracefully on older setups.
- Avoid unnecessary dependencies or heavy frameworks that make products unusable on less powerful hardware.
- Test across a range of devices, not just the latest models.
Sustainable development isn’t only about optimizing code and infrastructure, it’s also about building technology that lasts longer and works for more people.
3. Rethinking Data Centers
Data centers are the physical backbone of AI, and also its biggest energy consumer. Making them sustainable is essential.
Renewable Energy and Location
Hosting AI workloads in data centers powered by renewable energy, and located in low-carbon regions like Norway, drastically cuts emissions.
Heat Recovery
Some facilities reuse waste heat to warm nearby buildings or pools. A creative, circular use of energy.
Dynamic Load Management
Smart systems can adjust server loads and cooling based on demand, reducing unnecessary energy draw.
Modular Infrastructure
Flexible, scalable modules make it easier to expand or relocate centers efficiently.
4. The Role of Governments and Regulation
Governments hold the power to drive large-scale change through incentives, standards, and enforcement.
- Set Roadmaps & Regulations: Establish national guidelines for energy-efficient AI systems and data centers.
- Financial Incentives: Offer tax breaks or rewards for green AI initiatives, or require companies to track “energy per AI use.”
- Promote Open Access: Shared datasets and pre-trained models prevent redundant computation across the industry.
- Mandate Sustainable Code Reviews: When corporations acquire startups, they should be required to evaluate and optimize code sustainability.
- Involve Researchers Early: Academic expertise is key for long-term innovation in green AI practices.
This regulatory layer is also crucial to prevent Jevons Paradox - when greater efficiency leads to higher overall consumption due to easier access and wider adoption.
When we make websites faster, servers more efficient, or AI models more optimized, the lower “cost” of computation often leads to more usage - more data processed, more models trained, more apps built. In other words: efficiency alone doesn’t guarantee sustainability if overall consumption keeps growing.
That said, let's review another important aspect of the consumption and AI sustainability:
5. The Human Factor: How Users Can Make AI Use More Sustainable
Even small user habits have a measurable effect on digital energy demand.
Here’s how to make your AI use more sustainable:
Use AI Mindfully
- Avoid excessive or repetitive queries.
- Use simple search engines for straightforward answers instead of running large AI models.
- Skip long prompts - even small interactions consume compute cycles.
- Avoid running AI queries for simple tasks you do out of habit rather than efficiency - a lot of us got used to ask AI model for things we could answer faster with a quick search or our own common sense. That small habit adds unnecessary computational load every time the model spins up to process your request. If the task is simple o checking a fact, listing obvious information, or doing something you already know how to do - skip the AI query. Save the heavy machinery for when it actually adds value.
Even a single ‘hi’ or ‘thanks’ entered into a chat with an AI isn’t zero-impact. While a typical query to ChatGPT may only use ~0.3 Wh of energy (comparable to running a LED bulb for a few minutes), when multiplied by millions or billions of queries the footprint becomes meaningful.
Make Conscious User Choices
- Opt out of unnecessary AI features when possible (like AI search overviews).
- Research sustainable platforms such as Ecosia, which offsets its digital footprint.
- Choose “light” or “basic” modes in AI tools when full precision isn’t required.
- Use GPT-4o or “Instant” mode instead of GPT-5 for basic text generation or quick ideas.
- When using image or video AI tools, select low-resolution previews before generating high-quality outputs.
- For chat-based tools, avoid long or repetitive prompts that make the model process excess tokens.
While responsibility shouldn’t rest solely on users, small conscious choices at scale still matter.
Key Facts About AI Consumption
- Sam Altman (CEO of OpenAI) states that the average ChatGPT query with GPT‑4o model uses about 0.34 Wh of energy and around 0.000085 gallons of water (≈ one-fifteenth of a teaspoon).
- A tool highlighted by TechCrunch estimated that for another model (Llama 3.3 70B) writing a typical email used about 0.1841 Wh.
- Larger / more complex models can consume far more: one estimate for GPT‑5 suggests up to 40 Wh per query in some cases, with an average consumption of 18Wh.
Having this stats we can easily calculate the following:
With 2.5 billion requests a day through the model, we could see energy usage as high as 45 GWh and this is massive. For context - a single modern nuclear power plant generates around 1 to 1.6 GWh gigawatts of electricity per reactor. If OpenAI’s GPT-5 were to operate at roughly 18 Wh per query across global usage, the total demand could equal the output of two to three nuclear reactors, enough energy to power a small country.
If we decide to re-calculate the same with the more optimistic stats - it's also fine, but the overall picture should be clear.
In Summary
Sustainable AI isn’t about slowing innovation, but about making it smarter and thinking about the user behavior. Every layer of the system, from code to cloud to consumer, plays a part in reducing the environmental cost of intelligence.
By optimizing algorithms, greening hardware, building efficient data centers, and using AI smarter - we are actually making an impact.
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