Frugal AI: The Secret to Cutting Costs and Carbon in Your Tech Stack

As we integrate more AI models into our tech stacks, the demand on our data center infrastructure is also growing exponentially.

According to the International Energy Agency, data centers, AI, and cryptocurrency account for around 2% of global electricity demand. While AI energy usage will continue to rise as its adoption increases, it is important to recognize that currently, AI’s energy consumption is a mere fraction of the technology sector’s power consumption and is slim compared to other industries. 

For instance, according to the U.S Energy Information Administration, in the United States, the industrial sector accounts for 33% of all electricity used in the world. Dig deeper, and 77% of all industrial electricity goes to manufacturing, 12% to mining, 7% to construction, and 5% to agriculture. And while AI’s current energy demand is far lower than many other facets of our lives, fast forward to 2026, and AI’s energy footprint is expected to grow tenfold. With this context, we must be conscious of how using AI at our jobs and in our lives impacts energy usage. 

And that’s where the concept of frugal AI comes in – a practical approach to making AI systems more efficient while maintaining their power and utility.

What is Frugal AI?

Frugal AI is all about moderation and designing AI systems that are resource-efficient from the ground up. At Schneider Electric, we’ve embraced this approach, particularly through our collaboration with AFNOR to create global standards for frugal AI. The goal? Ensure AI systems do more with less by using fewer resources like electricity, computing power, and bandwidth while maintaining top-tier performance.

Here’s what you should focus on:

1. Assess Whether AI is Necessary:
   Not every business problem needs to be solved with an AI solution. Yes, AI is powerful, but sometimes conventional methods are more efficient. Before deploying AI, ask: Is AI the best solution here? Is there another more efficient solution? AI should only be used when it’s the most effective tool for the job, especially in areas like process automation or reducing redundant tasks. 
2. Use AI More Efficiently:
   Once you’ve committed to an AI approach, the next step is optimization. For SaaS companies, this could mean fine-tuning model parameters or, in some cases (depending on loads, edge and cloud servers efficiency, carbon intensity of local electricity), using edge computing to offload resource-heavy processes from central servers. If your goal is to improve operational efficiency, ensure you’re also accounting for the energy it uses. Can you reduce the frequency of certain tasks or help you switch to greener energy sources to run computations? These minor tweaks can lead to massive savings.
3. Refine the AI Over Time:
   AI isn’t a “set it and forget it” system. Continuous optimization is key. Can you reduce the size of the datasets you're training on? Can you opt for smaller neural networks with lower precision? Every byte counts, and these trade-offs can lead to a more environmentally friendly AI stack.

Why Does This Matter?

While AI has the potential to save time, improve system reliability, or even reduce costs, we can't throw an AI solution at every single business problem without a second thought. Ask yourself when it’s the right time to leverage AI and when it’s the right time to stick to more traditional options. 

And with customers increasingly looking at their partners’ sustainability efforts, it’s on you to lead with solutions that scale and are energy-conscious. 

To help you meet this demand, let’s break down what frugal AI means in practice and how you can implement it without sacrificing growth. 

Practical Ways to Deploy Frugal AI

The good news is that you don’t have to sacrifice performance to be frugal. Here are three strategies to consider to help you optimize AI infrastructure sustainably:

• Run Fewer Computations: Do you really need to forecast data every five minutes? Instead, try once an hour. Fewer computations mean less energy consumption.
• Leverage Green Energy: Running AI systems on renewable energy sources is a no-brainer. By migrating workloads to greener data centers, you’re directly reducing your carbon footprint.
• Optimize Hardware: Are you still relying on outdated, power-hungry hardware? Investing in energy-efficient servers can deliver performance boosts and energy savings.

Tech Leaders Must Drive the Charge

The AFNOR Spec on Frugal AI is a landmark step in setting global standards for environmentally responsible AI systems. Technology leaders have the opportunity and responsibility to implement these standards. By doing so, you’re not just being mindful of resources but also positioning your company as a leader in sustainable innovation. 

Efficiency is the new competitive edge, and the move toward smarter and more thoughtful AI solutions will set the tech leaders of tomorrow apart. 

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