AI’s equity implications
While X_Stereotype doesn’t currently track the carbon emissions of its operations, it’s in the works. Because climate change is something that disproportionately impacts communities of color, measuring and reducing impact is a “priority problem” for the platform, Adams explained.
“As fast as AI moves, this next wave of conversation is going to come up,” Adams said. “How is this being powered? How is this being distributed? Is it ethically distributed AI?”
Avoiding the Jevons paradox
When Karan Walia first began developing the AI technology that now powers his company Cluep, he was training the models on computers that pale in comparison to the efficiency available in today’s models.
“It took us two years just to train our initial AI model to recognize human feelings within your textual conversations on social media,” Walia said. “[Now], you can get up and running with a model in two days.”
Those hardware efficiencies have major implications for the energy demands of AI tools. But as often happens, efficiency gains can be outpaced by wider adoption, resulting in an overall increase in demand on resources. This phenomenon, referred to as the Jevons paradox in economics, often stands between potential climate solutions and real emission reductions.
“Ultimately, the energy itself needs to be produced in sustainable ways that are less harmful,” Walia said.
But creating guardrails to prevent inefficient and harmful uses of AI can also promote better practices across the industry. “There can and there should be regulation of how much compute your AI models are utilizing in production,” he added.
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