• by Laurie Sullivan , Yesterday

Power-thirsty data centers are dominating the news cycle with Google, Meta, Microsoft, and others spending billions to support artificial intelligence.

Advertisers concerned about using too much energy or harming the environment through searches or media buys may want to consider another way to connect with potential and existing customers.

For advertisers that are storing data, searches or making media buys, they may want to consider another way to connect with potential and existing customers.

The median energy use of one text prompt on Google Gemini is equivalent to watching TV for less than nine seconds and consumes five drops of water, the company said in a new research paper. It emits 0.03 grams of carbon dioxide. The company is proud of its findings, because the figures are substantially lower than many public estimates, it wrote in a post.

Google this week released a methodology to determine how much electricity and water a full-stack AI infrastructure requires. It includes everything from overhead to energy used by idle chips -- even ways to cool the equipment to ensure they do not overheat.

Software efficiencies and clean energy have lowered the median energy consumption of a Gemini Apps text prompt by 33x during the last year and CO2 by 44x, the company wrote in the post.

During the past 12 months, the energy and total carbon footprint of the median Gemini apps text prompt dropped by 33x and 44x, respectively, while delivering higher-quality responses, the company wrote.

"We are not just measuring the power from chips -- we also look at used TPUs and GPUs in Google's system," said Savannah Goodman, head of Advanced Energy Labs at Google, in a video. "The energy consumption, carbon emissions and water consumptions were a lot lower than what he have been seeing in some public estimates."

The research paper, released Thursday also suggests that despite all this progress in AI, the industry lacks first-party data from the largest AI model providers, and that needs to change.

Lack of a consensus has led to published estimates for similar AI tasks, and a lack of agreement regarding methodologies may have contributed to a lack of first-party data when it is needed most.

"Google has a unique perspective on the operational realities of maintaining a large-scale, globally-distributed AI production fleet, and serving software products at scale—such as web search," according to the paper.

The paper also went on to describe ways to characterize and optimize "environmental impact of AI model serving requires a comprehensive view of energy consumption—including the power drawn by the host machine’s CPU and DRAM, the significant energy consumed by idle systems provisioned for reliability and low latency, and the full data center overhead as captured by the Power Usage Effectiveness (PUE) metric."