It’s no surprise that AI has a carbon footprint, which refers to the amount of greenhouse gases (carbon dioxide and methane, primarily) that producing and consuming AI releases into the atmosphere. In fact, training AI models requires so much computing power, some researchers have argued that the environmental costs outweigh the benefits. However, I believe they’ve not only underestimated the benefits of AI, but also overlooked the many ways that model training is becoming more efficient.
Greenhouse gases are what economists refer to as an “externality” — a cost borne inadvertently by society at large, such as through the adverse impact of global warming, but inflicted on us all by private participants who have little incentive to refrain from the offending activity. Typically, public utilities emit these gases when they burn fossil fuels in order to generate electricity that powers the data centers, server farms, and other computing platforms upon which AI runs.
Consider the downstream carbon offsets realized by AI apps
During the past few years, AI has been unfairly stigmatized as a major contributor to global warming, owing to what some observers regard as its inordinate consumption of energy in the process of model training.