But electricity consumption is a minority of energy consumption. It was always true that decarbonization was going to massively increase the use of electricity.

▲ acc_297 2 days ago | parent | next [-]

Yes but the task becomes that much harder - we are scaling up natural gas generation not to phase out coal but simply to meet demand that wouldn't exist without the fierce competition to build the biggest LLM. Any feasible plan made 5 years ago which may have worked to transition a large industry from fuel burning energy sources to electricity generation (renewable or otherwise) is made 10x harder by the introduction of this rapid rollout in datacentre capacity.

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jeffbee 2 days ago | parent [-]

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

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acc_297 2 days ago | parent [-]

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

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jeffbee 2 days ago | parent [-]

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

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acc_297 2 days ago | parent [-]

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 a day ago \| parent [-]
		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.

▲ schiffern 2 days ago | parent | prev [-]

  >But electricity consumption is a minority of energy consumption.

Everything is a minority of energy consumption.

https://commons.wikimedia.org/wiki/File:US_energy_consumptio...

If your standard is "I won't do anything unless it's the majority of energy consumption," you're really just saying don't do anything period.

  >It was always true that decarbonization was going to massively increase the use of electricity.

That was the plan (and it could have worked too), but what actually happened is the new decarbonized energy is supplementing fossil fuel instead of replacing it.