Save The Planet
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Worse is Google that insists on shoving a terrible AI-based result in your face every time you do a search, with no way to turn it off.
I'm not telling these systems to generate images of cow-like girls, but I'm getting AI shoved in my face all the time whether I want it or not. (I don't).
wrote on last edited by [email protected]Someone posted here a while ago - if you use the URL https://www.google.com/search?q=%25s&udm=14 it doesn't include the AI search. I've updated my Google search links to use that instead of the base Google URL.
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Meanwhile I'm down town I'm my city cleaning windows in office buildings that are 75% empty but the heat or ac is blasting on completely empty floors and most of the lights are on.
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It's actually because small trucks were regulated out of the US market. Smaller vehicles have more stringent mileage standards that trucks aren't able to meet. That forces companies to make all their trucks bigger, because bigger vehicles are held to a different standard.
So the people who want or need a truck are pushed to buy a larger one.
They can meet them. But the profit margin is slimmer than if they use the giant frame.
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I guess.
It still smells like an apologist argument to be like "yeah but using it doesn't actually use a lot of power".
I'm actually not really sure I believe that argument either, through. I'm pretty sure that inference is hella expensive. When people talk about training, they don't talk about the cost to train on a single input, they talk about the cost for the entire training. So why are we talking about the cost to infer on a single input?
What's the cost of running training, per hour? What's the cost of inference, per hour, on a similarly sized inference farm, running at maximum capacity?Maybe you should stop smelling text and try reading it instead.
Running an LLM in deployment can be done locally on one's machine, on a single GPU, and in this case is like playing a video game for under a minute. OpenAI models are larger than by a factor of 10 or more, so it's maybe like playing a video game for 15 minutes (obviously varies based on the response to the query.)
It makes sense to measure deployment usage marginally based on its queries for the same reason it makes sense to measure the environmental impact of a car in terms of hours or miles driven. There's no natural way to do this for training though. You could divide training by the number of queries, to amortize it across its actual usage, which would make it seem significantly cheaper, but it comes with the unintuitive property that this amortization weight goes down as more queries are made, so it's unclear exactly how much of the cost of training should be assigned to a given query. It might make more sense to talk in terms of expected number of total queries during the lifetime deployment of a model.
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Someone posted here a while ago - if you use the URL https://www.google.com/search?q=%25s&udm=14 it doesn't include the AI search. I've updated my Google search links to use that instead of the base Google URL.
You can also use alternatives like startpage and ecosia which use google results, I believe.
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Laughs in total recall
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I'm really OOTL when it comes to AI GHG impact. How is it any worse than crypto farms, or streaming services?
How do their outputs stack up to traditional emitters like Ag and industry? I need a measuring stick
How is it any worse than crypto farms, or streaming services?
These two things are so different.
Streaming services are extremely efficient; they tend to be encode-once and decode-on-user's-device. Video was for a long time considered a tough thing to serve, so engineers put tons of effort into making it efficient.
Crypto currency is literally designed to be as wasteful as possible while still being feasible. "Proof-of-work" (how Bitcoin and many other currencies operate) literally means that crypto mining algorithms must waste as much computation as they can get away with doing pointless operations just to say they tried. It's an abomination.
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Maybe you should stop smelling text and try reading it instead.
Running an LLM in deployment can be done locally on one's machine, on a single GPU, and in this case is like playing a video game for under a minute. OpenAI models are larger than by a factor of 10 or more, so it's maybe like playing a video game for 15 minutes (obviously varies based on the response to the query.)
It makes sense to measure deployment usage marginally based on its queries for the same reason it makes sense to measure the environmental impact of a car in terms of hours or miles driven. There's no natural way to do this for training though. You could divide training by the number of queries, to amortize it across its actual usage, which would make it seem significantly cheaper, but it comes with the unintuitive property that this amortization weight goes down as more queries are made, so it's unclear exactly how much of the cost of training should be assigned to a given query. It might make more sense to talk in terms of expected number of total queries during the lifetime deployment of a model.
You're way overcomplicating how it could be done. The argument is that training takes more energy:
Typically if you have a single cost associated with a service, then you amortize that cost over the life of the service: so you take the total energy consumption of training and divide it by the total number of user-hours spent doing inference, and compare that to the cost of a single user running inference for an hour (which they can estimate by the number of user-hours in an hour divided by their global inference energy consumption for that hour).
If these are "apples to orange" comparisons, then why do people defending AI usage (and you) keep making the comparison?
But even if it was true that training is significantly more expensive that inference, or that they're inherently incomparable, that doesn't actually change the underlying observation that inference is still quite energy intensive, and the implicit value statement that the energy spent isn't worth the affect on society
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I'm not really saying that the curve itself is changing (sorry, I was really not clear), only that those other variables reduce actual energy demand later in the day because of the efficiency gains and thermal banking that happens during the peak energy production. The overproduction during max solar hours is still a problem. Even if the utility doesn't have a way of banking the extra supply, individual customers can do it themselves at a smaller scale, even if just by over-cooling their homes to reduce their demand after sundown.
Overall, the problem of the duck curve isn't as much about maxing out the grid, it's about the utility not having instantaneous power availability when the sun suddenly goes down. For people like me who work from home and have the flexibility to keep my home cool enough to need less cooling in the evening, having solar power means I can take advantage of that free energy and bank it to reduce my demand in the evening.
I get what you were saying now, but having solar would absolutely reduce my demand during peak hours.
It's a neat idea to over-cool in order to reduce consumption later on!
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I am trying to understand what Google's motivation for this even is. Surely it is not profitable to be replacing their existing, highly lucrative product with an inferior alternative that eats up way more power?
To make search more lucrative, they've enshitified it and went too far, but for a short time there were great quarterly resukts. Now they're slowly losing users. So they try AI to fix it up.
It's also a signal to the shareholders that they're implementing the latest buzzword, plus they're all worried AI will take off and they've missed that train.
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And when it did it also altered the results, making them worse, because it was trying to satisfy "fuck" as part of your search.
wrote on last edited by [email protected]If you can't search for "fuck," you can't search for "fuck google."
With apologies to Lenny Bruce.
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When I’m told there’s power issues and to conserve power I drop my AC to 60 and leave all my lights on. Only way for them to fix the grid is to break it.
Literally rolling coal to own the cons
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I don’t disagree with you but most of the energy that people complain about AI using is used to train the models, not use them. Once they are trained it is fast to get what you need out of it, but making the next version takes a long time.
This is a specious argument.
Once a model has been trained once they don't just stop training. They refine and/or start training new models. Showing demand for these models is what has encouraged construction on 100s of new datacenters.
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Also they can build nuclear power generators for the data centers but never for the residential power grid.
There's no money in selling residential energy.
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I am trying to understand what Google's motivation for this even is. Surely it is not profitable to be replacing their existing, highly lucrative product with an inferior alternative that eats up way more power?
They don't want to direct you to the thing you're searching for anymore because that means you're off their site quickly. Instead they want to provide themselves whatever it is you were searching for, so you will stay on their site and generate ad money.
They don't care if their results are bad, because that just means you'll stick around longer, looking for an answer. -
️ Immediate to Short-Term (Days to Centuries)- Hours to weeks: Power grids fail; nuclear reactors melt down without maintenance[11].
- Months to decades: Urban areas flood as drainage systems fail; buildings decay from weather and plant growth[6][11].
- 100–300 years: Steel structures collapse; concrete buildings crumble[5][7]. Most cities become overgrown forests[6].
Medium-Term (Thousands of Years)
- 1,000 years: Visible surface structures (e.g., roads, monuments) are buried or eroded. Plastics fragment but persist chemically[5][7].
- 10,000–250,000 years: Nuclear isotopes (e.g., plutonium-239) remain detectable in sediments and ice cores[7]. Mining tunnels fill with sediment but leave identifiable "industrial fossils"[7].
- 500,000 years: Microplastics and polymer layers in ocean sediments endure[5][10].
Long-Term (Millions of Years)
- 1–7 million years: Fossils of humans and domesticated animals persist. Geological strata show elevated carbon levels and mass extinction markers[4][8]. Deep mines and landfills remain as distinct layers[7][10].
- 50–100 million years: Continental drift subducts surface evidence; satellites decay or drift into space[3][10]. Only deep geological traces (e.g., mine shafts, isotope ratios) might endure[3][10].
- 250 million years: Next predicted mass extinction eradicates all mammals, including any remaining human traces[9].
Near-Permanent Traces
- Space artifacts: Lunar landers, Mars rovers, and Voyager probes persist for billions of years[3][10].
- Radio signals: Human broadcasts travel through space indefinitely at light speed[5].
Key Factors
- Detection likelihood: Aliens or future species could find traces for 100+ million years via deep geological analysis or space exploration[5][10].
- Total erasure: Requires Earth's destruction (e.g., solar expansion in 5 billion years)[10].
Citations:
[1] Human extinction https://en.wikipedia.org/wiki/Human_extinction
[2] What If Humans Suddenly Went Extinct? https://www.youtube.com/watch?v=yuOKTZISXhc
[3] How long would it take for all traces of humans to be gone? https://www.reddit.com/r/answers/comments/1azu120/how_long_would_it_take_for_all_traces_of_humans/
[4] What would happen to Earth if humans went extinct? https://www.livescience.com/earth-without-people.html
[5] How long before all human traces are wiped out? https://www.newscientist.com/lastword/2215950-how-long-before-all-human-traces-are-wiped-out/
[6] Vanishing Act: What Earth Will Look Like 100 Years After Humans Disappear - Brilliantio https://brilliantio.com/if-people-dissapeared-what-will-happen-to-earth-in-100-years/
[7] If humans became extinct, how long would it take for all ... https://www.sciencefocus.com/science/if-humans-became-extinct-how-long-would-it-take-for-all-traces-of-us-to-vanish
[8] Nature will need up to five million years to fill the gaps caused by man-made mass extinctions, study finds https://www.independent.co.uk/climate-change/news/mass-extinctions-five-million-years-nature-mammals-crisis-animal-plants-pnas-aarhus-a8585066.html
[9] Humans Will Go Extinct on Earth in 250 Million Years; Mass Extinction Will Occur Sooner if Burning Fossil Fuels Continues [Study] https://www.sciencetimes.com/articles/49951/20240430/humans-will-go-extinct-earth-250-million-years-mass-extinction.htm
[10] How long would it take for all evidence of humanity to be ... https://worldbuilding.stackexchange.com/questions/153618/how-long-would-it-take-for-all-evidence-of-humanity-to-be-erased-from-earth
[11] What Would Happen If Every Human On Earth Just Disappeared? https://www.scienceabc.com/humans/life-like-humans-suddenly-disappeared.htmlStephen Baxter over here. I'm going back to bed, it won't change anything...
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You can also use alternatives like startpage and ecosia which use google results, I believe.
Both of which are probably training their own AI as middle men or stealing your search terms to tell Walmart what type of peanut butter you're most likely to buy if they could lock it up on a plastic covered shelve.
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Worse is Google that insists on shoving a terrible AI-based result in your face every time you do a search, with no way to turn it off.
I'm not telling these systems to generate images of cow-like girls, but I'm getting AI shoved in my face all the time whether I want it or not. (I don't).
Firefox has a plugin that blocks the AI results. It works pretty well most of the time, but it occasionally has hiccups when Google updates stuff or something.
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How is it any worse than crypto farms, or streaming services?
These two things are so different.
Streaming services are extremely efficient; they tend to be encode-once and decode-on-user's-device. Video was for a long time considered a tough thing to serve, so engineers put tons of effort into making it efficient.
Crypto currency is literally designed to be as wasteful as possible while still being feasible. "Proof-of-work" (how Bitcoin and many other currencies operate) literally means that crypto mining algorithms must waste as much computation as they can get away with doing pointless operations just to say they tried. It's an abomination.
I legit don't know much about tech, and it ts showing. I didn't know streaming was so efficient.
What I. Trying to get at (I still have to read that article in the parent comment) is that how is AI any worse than crypto? As far as I can tell crypto impact, while bad, was relatively minor and it drastically decreased in popularity; it's kind of logical AI does the same, unless it's impact is way higher.
Meanwhile we have cargo ships burning bunker crude .
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Let's not forget billionaires in this consideration.
