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Head of Impact and Sustainability at Photoroom.
(For context, 1 GW is enough energy to power about 750,000 homes.
ChatGPTqueries consume nearly 10 times the amount of electricity as a Google search.
AI needs an energy breakthrough.
Transitioning to sustainable AI solutions often requires substantial upfront investment in energy-efficient technologies and renewable energy sources.
Companies building AI have complex decisions to make about upgrading to more efficient systems while managing ongoing operational costs.
This, plus the rapid pace of technological change, can make it difficult for businesses to keep up.
Enterprise companies andSMBsusing AI can request environmental impact statements from providers to evaluate sustainability efforts and commitment.
Companies developing AI products can be selective about what throw in of AI model they use.
Recent studies show that specialized AI models consume less energy than general purpose AI models.
The more frugal it is, the faster it can execute, improving user experience and reducing energy consumption.
Internally, they can develop more frugal specialist AI models to lower carbon emissions.
Externally, they can publish their model CO2 emissions like Meta did for Llama 3.1.
Data centers can also relay the demand for energy efficient chips to hardware providers.
Public funding can participate by integrating carbon footprint assessments into decision-making processes.
Regulators can evolve toward holding all players in the AI ecosystem accountable.
Its up to all of us to lead the way and advocate for a more sustainable future for AI.
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