[jeffbee]

I'm not sure if that's true of not. As the article indicates, training for AI is naturally demand responsive. Training can move on the clock, and it can move around the world to minimize carbon footprint. See the PDF I just love to share: "Revamped Happy CO2e Paper" https://arxiv.org/pdf/2204.05149

[acc_297]

Agree on training. But that google paper was written when the only image model available for broad public consumption was dall-e 2 and video models were more than a year away. It gets a mention in a more recent 2024 paper [1] which goes into detail about how inference rather than training creates the difficult to manage energy load which grids struggle to meet. If consumer interests and demands drive the trend in what companies offer in terms of inference capability then it's fair to worry that the impact on sustainability goals will be an afterthought.

[1] https://dl.acm.org/doi/pdf/10.1145/3630106.3658542

[jeffbee]

The second author of that paper is the person who got turfed out of Google for refusing to use actual energy consumption and insisting on using their flagrantly wrong estimate of inference energy costs. E.g. the rebuttal by Dean https://x.com/JeffDean/status/1843493504347189746

[acc_297]

The number was correct to a reasonable degree under the assumptions stated by the author in the paper that tweet references since they obtained estimates from consumer grade hardware and the carbon intensity associated with average kilowatt produced in the United States not a hyperscale datacentre run using "ML best practices" although this distinction is left out of various lay media citations. The number also did not pertain to inference it was associated with training a particular model from pre-2019.

[jeffbee]

I'm sure you see the problem with assuming a very inefficient and poorly-utilized system but at the same time assuming Google scale, and multiplying those factors together.