There are reports that emissions footprints by Google has increased by 13 per cent in 2023 as compared to 2022, and the entire rise can be attributed to electricity consumption in its data centers and supply chains. The electricity consumption in 2023 for Google increased by 17 per cent. The same trend is going to sustain because of the deployment of AI tools.
AI could be of help in climate change and could be transformative across various sectors. The same AI is responsible for heavy emissions.
A simple query put to ChatGPT could use between 10-33 times more energy than consumed by a regular Google search. Image-based searches could consume even more energy.
LLMs sift through more data while processing and formulating apt responses. It requires more electric signals. More work generates and releases more heat. It requires cooling by ACs and other forms of cooling in data centers.
With the spread of AI, the electricity consumption is likely to go up. Data centers at present account for 1-13 per cent of global electricity demand. This could double by 2026. In Ireland, the share has reached 18 per cent as it has a large number of data centers because of incentivization.
The US has the largest number of data centers. There is consumption between 1.3 per cent and 4.5 per cent.
AI takes a huge environmental toll. Apart from electricity, there is water consumption. GPT-4 at Iowa (US) is reported to have consumed 6 per cent of the districts water supply in July 2022.
In the coming years, in India, there will be deployment of AI and data centers. Its environmental effect will be huge. The expansion should be planned so that there is minimum adverse impact. The processes should be efficient and should minimize emissions footprint.
There is a positive outlook too. AI could reduce emissions globally. A BCG study puts this reduction to 5-10 per cent by 2030. It will generate a value $1.3 trillion to $2-6 trillion through additional revenues and cost savings. AI can facilitate monitoring and predictions of emissions in existing processes. It can optimize these by eliminating wastages and inefficiencies.
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