Energy Hog ...

Illustration: Molimit46/Getty Images

AI, love it or hate it, it's here and isn't going away unless our antiquated power grid surrenders given just how truly inefficient digital is when it comes to processing analog output of neural nets as AI must use analog in order to react to the real world in real time as analog uses differential equations to map the differences in textural, visual and audio data prior to delivering said content to digital systems to process and show a user the best way to check out the 

Grand Canyon.

Remember, digital counts, analog measures.

RIGHT NOW, GENERATIVE artificial intelligence is impossible to ignore online. An AI-generated summary may randomly appear at the top of the results whenever you do a Google search. Or you might be prompted to try Meta’s AI tool while browsing Facebook. And that ever-present sparkle emoji continues to haunt my dreams.

This rush to add AI to as many online interactions as possible can be traced back to OpenAI’s boundary-pushing release of ChatGPT late in 2022. Silicon Valley soon became obsessed with generative AI, and nearly two years later, AI tools powered by large language models permeate the online user experience.

One unfortunate side effect of this proliferation is that the computing processes required to run generative AI systems are much more resource intensive. This has led to the arrival of the internet’s hyper-consumption era, a period defined by the spread of a new kind of computing that demands excessive amounts of electricity and water to build as well as operate.

There is no free lunch 

“In the back end, these algorithms that need to be running for any generative AI model are fundamentally very, very different from the traditional kind of Google Search or email,” says Sajjad Moazeni, a computer engineering researcher at the University of Washington. “For basic services, those were very light in terms of the amount of data that needed to go back and forth between the processors.” In comparison, Moazeni estimates generative AI applications are around 100 to 1,000 times more computationally intensive.

Going beyond environmental concerns, these data centers have the potential to overwhelm local power grids with their energy needs. “In Washington, there's a Microsoft data center building in Quincy,” says Moazeni. “I know there's a lot of concern that the power they are burning is basically sucking up all of the energy in that area.” Around the world, the server farms that train and operate AI models may compete with local residents and businesses for power—possibly leading to blackouts during peak times.