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As I expected, the author made no attempt to calculate the carbon footprint of the human artists and other workers that these AIs can replace, or the carbon savings from productivity gains or other similar efficiencies. If a call center gets closed because the workers were replaced by AI that’s a ton of commuting and such that’s no longer needed.
Those people aren’t deleted from existence, they still produce just as much carbon they just don’t have jobs.
Productivity gains are meaningless when it just means that bosses will demand more work in less time
They may do something that doesn’t involve as much commuting. They’ll be doing something that wasn’t being done before. Productivity gains means more work gets done, overall.
The point is that the author’s analysis is simple-minded to the point of uselessness. If I were to object to building a hospital in a neighborhood because whenever a hospital is added to a neighborhood it results in a sharp increase in the number of people dying there, you’d rightly call me out for looking at just one specific number while ignoring the overall benefits. It’s not like these AIs just pop into existence and spew CO2 like some sort of Captain Planet villain’s pollution factory, the AIs are doing something and that has value.
This is why I’ve been researching and working on FOSS AI systems. My own AI cannot fire me. ;)
Can we make a single breakthrough in technology that doesn’t totally fuck our planet? That’d be just swell!
Um… doesn’t ChatGPT run on Azure? A world leader in sustainability that has been carbon neutral for over a decade?
This is the best summary I could come up with:
Answer: reach for the Gartner Hype Cycle, an ingenious diagram that maps the progress of an emerging technology through five phases: the “technology trigger”, which is followed by a rapid rise to the “peak of inflated expectations”; this is succeeded by a rapid decline into the “trough of disillusionment”, after which begins a gentle climb up the “slope of enlightenment” – before eventually (often years or decades later) reaching the “plateau of productivity”.
This hype has given rise to much anguished fretting about its impact on employment, misinformation, politics etc, and also to a deal of anxious extrapolations about an existential risk to humanity.
Which, in turn, means CO2 emissions on a large scale – about which the industry is extraordinarily coy, while simultaneously boasting about using offsets and other wheezes to mime carbon neutrality.
The implication is stark: the realisation of the industry’s dream of “AI everywhere” (as Google’s boss once put it) would bring about a world dependent on a technology that is not only flaky but also has a formidable – and growing – environmental footprint.
A study in 2019, for example, estimated the carbon footprint of training a single early large language model (LLM) such as GPT-2 at about 300,000kg of CO2 emissions – the equivalent of 125 round-trip flights between New York and Beijing.
Ways of seeingIn an intriguing blogpost, Om Malik describes why Apple’s fancy, soon-to-be-released headset Vision Pro will change photography.
The original article contains 897 words, the summary contains 238 words. Saved 73%. I’m a bot and I’m open source!
The world it’s going to the fuck, isn’t news… just enjoy your life while it last. 🤣🤣…
