Ask anyone familiar with the dire state of the climate and they’ll tell you humankind is at an inflection point. We either reduce carbon emissions and stabilize the global climate, or we fail and face catastrophic damage and potential societal collapse. So, in true human fashion at this critical point, we develop one of the most energy-intensive information technologies ever created – generative AI. The advent of AI increased emissions at major tech companies as they race to build energy-hungry data centers to support generative AI. A recent article from The Hill reports that:
“Google revealed that its greenhouse gas emissions rose 13 percent in 2023 and 48 percent since 2019, clashing with its goal of becoming net zero by the end of the decade… Microsoft has seen its emissions jump 29 percent since 2020, according to its annual sustainability report released in May. The tech giant, which aimed to be carbon negative by 2030, similarly cited AI as the cause of its growing emissions.”
The article cites AI development as the underlying cause. Yet at the same time, some sustainability professionals exhibit “toxic positivity” surrounding the advent of AI, with ESG Today reporting that:
“Overall, the survey found that 55% of sustainability professionals believe that AI’s impact on global sustainability progress will be net positive.”
So there’s seemingly a disconnect between AI’s impacts and how it is being perceived by the ESG community. I believe that much of this can be boiled down to a definitional problem with AI. AI covers a lot of different applications and technologies. In the sustainability space, the technology is identifying energy inefficiencies, tracing supply chains, gathering disparate data from disconnected sources, and modeling the climate. This is vastly different than the generative technologies used by large language models like ChatGPT and image generation AI like Stable Diffusion. Those generative applications are largely the ones driving energy demand and the construction of new data centers. So those 55% of sustainability professionals might be right if AI is used strategically and in limited applications to support sustainability. However, we’re not seeing strategic use of AI – we’re seeing a generative AI arms race that demands more data centers, more electricity, and more emissions.
As sustainability professionals, we may need to push back on the AI arms race at our companies, or at the very least arguing for smarter implementation of AI so emissions goals aren’t thrown out the window. Expectations for positive responses should be tempered, however: executives may not be ready to listen until after the AI bubble has already popped when we can sell the emissions reductions story as a silver lining.
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