AI AND GREEN REVOLUTION
The excitement around AI can be seen in every shade of the tech spectrum, but a less talked about focus is the intersection of AI and sustainability.
AI is already reengineering energy consumption in electronics and will steer us toward an era where manufacturers can deliver the environmental change they have promised throughout the years.
Shaping Consumer Choices and the Wider Grid
AI will have a significant role in analyzing and influencing consumer behavior toward more sustainable choices. It can already predict complex patterns in energy usage and it will inform the design of energy-efficient products and encourage consumers to adopt greener habits.
Imagine your devices understanding your schedule and making the decision to do its most intensive tasks, like washing your clothes or charging personal devices, while power is being delivered to the grid via solar.
Electronics are changing
AI’s reach in enhancing energy efficiency is transforming the very guts of consumer electronics. Fridges, washing machines, and even lighting systems are now being embedded with AI that modulates their functions in response to real-time data. This not only trims down energy bills but also curtails the rate at which we dispose of electronics.
Environmental Cost of AI
It’s essential to address the elephant in the room: the energy required to run and train AI systems. This presents a paradox in the narrative of AI as a green solution. Yet, we’re already seeing the development of more energy-efficient AI equipment and powering data centers with renewable energy, addressing this concern head-on.
In a short amount of time, we’re already seeing extreme efficiency gains for AI Hardware efficiency. The two most popular AI Superchips, reaching 95% of the market, are NVIDIA’s DGX A100 and H100.
The H100, the latest NVIDIA offering has a projected power consumption of ~10.2 kW, which is about 1.6x higher than the DGX A100. However, it sports 3.5x higher tensor cores than its predecessor.
Manufacturing Revolution Through AI
A compelling example is the optimization of cutting paths. By intelligently analyzing design files and material properties, AI algorithms can determine the most efficient cutting sequences, reducing material waste and power usage.
In aerospace manufacturing, where precision and material conservation are paramount, AI-driven CNC machines have already significantly reduced titanium waste, a costly material, by optimizing cutting paths.