Artificial Intelligence (AI) is no longer a futuristic concept, it’s here, and it’s transforming our daily lives. From chatbots to self-driving cars, AI is revolutionising industries, streamlining processes, and even tackling climate change. But there’s a hidden cost to this innovation, and it’s one we rarely stop to think about: energy consumption.
AI, particularly large-scale deep learning models, requires immense computational power. Training a single deep learning model can consume as much energy as five cars do in their entire lifetimes. That’s a staggering statistic. In fact, training some of the most advanced AI models can produce as much carbon emissions as an entire country. But even at an individual level, AI requests add up. A single AI request for a large model, such as GPT-4, can consume as much as 0.5 kWh (equivalent to charging a smartphone 50 times or driving an electric car 1-2 miles).
So, while AI is a powerful tool for progress, it’s also contributing to an increasing environmental footprint. But does that mean we should stop using AI altogether? Of course not! Instead, we need to become more mindful of our AI usage and adopt smarter, more sustainable practices.
How Can We Reduce AI’s Energy Consumption?
The good news is that there are practical steps we can take to limit AI’s environmental impact—many of which require minimal effort. Here are three simple changes you can make today:
1. Use AI More Mindfully
AI-powered tools are incredibly convenient, but do we really need them for everything? Every AI request, from generating an email response to asking a chatbot a simple question, consumes energy. Here’s how you can reduce unnecessary usage:
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Think before you prompt: Do you really need AI to generate a short email or a quick response, or could you write it yourself?
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Limit AI-driven apps: Many applications now use AI in the background without us even realising it. Disable unnecessary AI-powered features if they aren’t essential.
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Batch your queries: Instead of sending multiple AI-generated requests throughout the day, try grouping them together. This can cut down on redundant processing.
2. Choose Energy-Efficient AI Services
Not all AI systems are created equal—some companies are actively working towards making their AI platforms more energy-efficient. When using AI-powered services, consider the following:
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Opt for companies prioritising sustainability: Some tech giants, like Google and Microsoft, are investing heavily in making their AI models greener. Look for companies that use renewable energy sources to power their data centres.
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Use smaller, optimised AI models: If you don’t need the full power of large-scale AI models, opt for smaller, more efficient alternatives that consume less energy.
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Switch to cloud-based AI services: Cloud providers with energy-efficient data centres are generally more sustainable than running AI on personal hardware.
3. Reduce Idle AI Usage and Device Energy Waste
You might not realise it, but even when you’re not actively using AI, background processes can still consume power. Simple adjustments can make a big difference:
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Turn off AI-driven features when not needed: Many smart assistants, auto-recommendation systems, and AI-enhanced apps run constantly in the background. Disable these when they’re not necessary.
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Power down unused devices: Laptops, smartphones, and smart home devices contribute to energy waste. Make a habit of turning them off or putting them in low-power mode.
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Optimise AI training and usage: If you work in tech or data science, advocate for more sustainable AI training practices, such as transfer learning and model pruning, which reduce unnecessary energy consumption.
The Future: A More Sustainable AI Revolution
It’s easy to focus on AI’s environmental impact and feel overwhelmed, but the future isn’t all doom and gloom. Many companies and researchers are already working on solutions to make AI more sustainable.
Tech giants like Google and Microsoft have pledged to power their data centres with 100% renewable energy. Google’s AI research division is even using AI itself to optimise energy use in its data centres, cutting energy consumption by up to 40%. Similarly, Microsoft aims to become carbon-negative by 2030, investing in carbon capture technologies and AI-powered solutions for climate monitoring.
On the hardware side, advancements in AI-specific processors like GPUs and TPUs are making AI computations far more energy-efficient. Emerging technologies like quantum computing also hold the promise of drastically reducing the power required to run AI models in the future.
Working with Forward Role
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