Doesn't training the model consume the most energy in most cases?

All serious, viable plans for decarbonization include a massive increase in electricity consumption, due to electrification of transportation, industrial processes, etc, along with an increase in renewable energy production. This isn't new, but AI datacenters are a very large net new single user of electricity.

If the amount of money already poured into AI had gone into the rollout of clean energy infrastructure, we wouldn't even be having this conversation, but here we are.

It makes perfect sense from a policy perspective, given that there are a small number of players in this space with more resources than most governments, to piggyback on this wave of infrastructure buildout.

It also makes plenty of financial sense. Initial capex for adding clean energy generation is high, but given both the high electricity usage of AI datacenters, and the long-term impact on the grid that someone will eventually have to pay for, companies deploying AI infrastructure would be smart to use the huge amount of capital at their disposal to generate their own electricity.

It's also, from a deployment standpoint, pretty straightforward — we're talking about massive, rectangular, warehouse-like buildings with flat roofs. We should have already mandated that all such buildings be covered in solar panels with on-site storage, at a minimum.

I agree that in general, if the goal is to limit CO2 emissions and use renewable sources of energy, we ought not to focus on AI first, because it is dwarfed by many other things that we take for granted today. My canonical example I give folks is that the latte they order every day from Starbucks involves substantially more energy and water use than most uses of ChatGPT on a daily basis.

But as we move to digitize more and more of this world, and now create automated cognitive labor, we should start with the right foundations. I'd rather we not try to disentangle critical AI infrastructure from coal power plants, and I'd rather we try to limit the compute available to workloads in ways that encourage people to use the tech actually befitting of their use case rather than throw it all into the most expensive model every time.

So it seems like the better goal is to just aim for more clean energy.

There’s a lot of focus on the carbon cost of various digital goods. I get it. Destroying the environment is a big problem. But like, maybe we also should not make a bunch of plastic crap and ship it around the world a bunch of times.

  Do we as a society want to wade into the morass of telling people what kinds of activities they can use energy for?

  If we care about saving a watt-hour, there are lots of places to look. 

For anyone who's curious on specifics re:AI emissions, the recent MIT article is the gold standard in terms of specificity, neutrality, and nuance: https://www.technologyreview.com/2025/05/20/1116327/ai-energ... .

I also did some napkin math here in 2024.12: https://bsky.app/profile/robb.doering.ai/post/3lckwra33vk2t TL;DR: Eating one less burger affords you ~300 chatbot inferences, and avoiding a flight from ATL to SFO affords you ~16,000.

Air conditioning for example would be a good place to save energy, as the world wide energy consumption is a multiple of AI's consumption. But climate change will push the need (not luxury) for air conditioning up, which is the Catch-22 in this case.

The International Energy Agency (IEA) estimates that 10% of the globally generated energy is used for sir conditioning. But it would nevertheless be a good idea to require AI companies to care for renewable energy before they reach similar consumption levels.

Regarding the "morass" … we tell people how fast they can drive, or companies to limit air pollution (at least in some countries) so no problem here.

This really applies to any application which consumes high percentages of the resources available. (Compare, data centers are responsible for almost 80% of the electricity consumption in the Dublin area according to the paper.) The rational of purpose and resource demand and expected effects is secondary to this. The primary question is about (significant) quantities.

Here's some stats showing the growth of the millionaire class, now up to 7% of the population: https://www.statista.com/chart/30671/number-of-millionaires-...

At the same time, here's some stats showing extreme poverty falling off a cliff: https://www.statista.com/statistics/1341003/poverty-rate-wor...

Such a thing was completely unthinkable before disruptive tech and the associated mega-rich became the new normal 200 years ago.

Having said that, you're correct to point out that negative externalities haven't really entered our minds until about 50-75 years ago, but it seems tech progress has even made clean, green living at scale possible at least in principle.

Speak for yourself. Environmentalism has been a thing for longer than I have been alive. Clearly we care.

And before you reply with even more toxic cynicism stand behind an idling 1960, 1980, 2000 and 2020 sedan and tell me you can’t tell the difference.

Those in AI data centers never stop running and completely utilize their capacity. The difference in power usage is astronomical.

A typical NVIDIA server GPU consumes 700W, and a server might have eight of them, so 5.6kW.

A PlayStation 5 consumes 200W total.

https://www.technologyreview.com/2025/05/20/1116327/ai-energ...

They are a big thing. Old people still donate to them.

They are a big reason Africans don't grow GMOs that can help children avoid blindness.

https://en.wikipedia.org/wiki/Greenpeace