The AI buildout is well and truly underway with OpenAI CEO Sam Altman leading the charge, having made various deals with companies including Oracle and Nvidia to guarantee the infrastructure needed to train future AI models is installed. But as this ensues, the spotlight has been thrown on how much energy these models will need.
“People talk about how much energy it takes to train an AI model relative to how much it costs a human to do one inference query… But it also takes a lot of energy to train a human — it takes, like, 20 years of life and all the food you eat during that time before you get started.”
Eating machines
Altman was speaking during an AI summit in India earlier this year when making the remarks to The Indian Express.
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The OpenAI chief was being questioned about the substantial amount of energy that AI has already been consuming – and will be projected to continue consuming – for both training and inference. Bringing these models online, after all, and keeping them running require a huge amount of resources, not only in terms of the energy to power the data centers, but the water for cooling, and the components and resources in building the hardware.
Altman’s defence hinged on the idea that people, too, require plenty of energy in order to reach utility — while AI can be trained much quicker. This view, in essence, frames machine intelligence as a like-for-like competitor with human intelligence.
Energy efficiency
Altman argued that humans are deeply inefficient, and that you should compare the total energy spent to create a human expert versus a machine expert. There’s also an argument that energy efficiency of AI could improve over time.
But detractors were scathing in their criticism of this entire point of view – saying this framing doesn’t take into account the fact that the human brain operates on roughly 20 watts of power. This isn’t to mention the ethically gray and dehumanizing nature of the remarks.
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