I like seeing how @pluralistic is refining his anti #AI arguments over time. In this interview, I love the idea of reframing "hallucinations" as "defects", the analogy that trying to get #AGI out of #LLMs is like breeding faster horses and expecting one to give birth to a locomotive, and ridiculing the premise that "if you teach enough words to the word-guessing machine it will become God."
@luis_in_brief @pluralistic Thank you for sharing ideas to pop the AI bubble, the sooner the better. #aibubble
I like seeing how @pluralistic is refining his anti #AI arguments over time. In this interview, I love the idea of reframing "hallucinations" as "defects", the analogy that trying to get #AGI out of #LLMs is like breeding faster horses and expecting one to give birth to a locomotive, and ridiculing the premise that "if you teach enough words to the word-guessing machine it will become God."
"What psychological mechanisms allow us to pour hundreds of billions into artificial intelligence while 600 million people will live in extreme poverty by 2030? The answer lies in the architecture of human decision-making under conditions of abstraction, proximity bias, and manufactured urgency."
#CorneliaCWalther, Ph.D, 2025
"The AI investment frenzy exhibits classic bubble psychology: fear of missing out (FOMO) overriding rational assessment.
...
FOMO hijacks our social comparison mechanisms. We evaluate investments relative to what others are doing, not against absolute measures of value or social good. If your competitor invests in AI, you must too, regardless of whether it creates genuine value or inflates valuations."
#CorneliaCWalther, Ph.D, 2025
Related: I don’t always agree with @pluralistic but this is leagues better than any other AI bubble criticism you’ll read today—long but absolutely worth a read and worth grappling with. His focus on labor power and industry intermediaries, rather than individual workers, is really important.
https://fedi.simonwillison.net/@simon/115680616717668184
@luis_in_brief @pluralistic Thank you for sharing ideas to pop the AI bubble, the sooner the better. #aibubble
"AI companies are spending billions on data centers in the race to AGI. IBM CEO Arvind Krishna has some thoughts on the math behind those bets.
Data center spending is on the rise. During Meta's recent earnings call, words like "capacity" and AI "infrastructure" were frequently used. Google just announced that it wants to eventually build them in space. The question remains: will the revenue generated from data centers ever justify all the capital expenditure?
On the "Decoder" podcast, Krishna concluded that there was likely "no way" these companies would make a return on their capex spending on data centers.
Couching that his napkin math was based on today's costs, "because anything in the future is speculative," Kirshna said that it takes about $80 billion to fill up a one-gigawatt data center.
"Okay, that's today's number. So, if you are going to commit 20 to 30 gigawatts, that's one company, that's $1.5 trillion of capex," he said.
Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said."
https://finance.yahoo.com/news/ibm-ceo-says-no-way-103010877.html
"So the bubble is bad (..). But (..) there will be things we can salvage from it: open source models, skilled programmers, cheap GPUs bought out of bankruptcy (...). It would be better if we created that stuff without burning the world’s economy to the ground and emitting a heptillion tons of CO2, but ignoring the productive residue of the AI crash won’t bring the economy back, or suck the carbon out of the atmosphere."
Spot-on analysis by @pluralistic
The value of this #AI critique by @pluralistic is its precision, not just the focus on the #AIbubble but the drivers and consequences of that bubble. Thank you.
https://pluralistic.net/2025/12/05/pop-that-bubble/
"AI companies are spending billions on data centers in the race to AGI. IBM CEO Arvind Krishna has some thoughts on the math behind those bets.
Data center spending is on the rise. During Meta's recent earnings call, words like "capacity" and AI "infrastructure" were frequently used. Google just announced that it wants to eventually build them in space. The question remains: will the revenue generated from data centers ever justify all the capital expenditure?
On the "Decoder" podcast, Krishna concluded that there was likely "no way" these companies would make a return on their capex spending on data centers.
Couching that his napkin math was based on today's costs, "because anything in the future is speculative," Kirshna said that it takes about $80 billion to fill up a one-gigawatt data center.
"Okay, that's today's number. So, if you are going to commit 20 to 30 gigawatts, that's one company, that's $1.5 trillion of capex," he said.
Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said."
https://finance.yahoo.com/news/ibm-ceo-says-no-way-103010877.html
"So the bubble is bad (..). But (..) there will be things we can salvage from it: open source models, skilled programmers, cheap GPUs bought out of bankruptcy (...). It would be better if we created that stuff without burning the world’s economy to the ground and emitting a heptillion tons of CO2, but ignoring the productive residue of the AI crash won’t bring the economy back, or suck the carbon out of the atmosphere."
Spot-on analysis by @pluralistic
"Earlier this year, Elon Musk built a gas-burning power plant large enough to power a small city, claimed it was exempt from air pollution laws, and placed it next to Boxtown, a Black neighborhood in Memphis where he had built a new data center. People in Boxtown started breathing polluted air – and dying faster. This is what the AI bubble looks like today." - Dwaign Tyndal
#AIBubble
#Elon
#elonmusk
#Climate #ClimateChange
#climatejustice
Random thought of the day about #AI :
The amount of computing power made available to the average user nowadays to run inference for these #LLMs probably far exceeds the amount of compute you would need to (for example) render a Blender video, or run post-production on 4K-quality video, which would probably be more useful to most average users than what LLMs provide — and they provide all that computing power for free to literally hundreds of millions of users.
I mean, come on, you stupid despot-kings of Silicon Valley, if you are going to provide that much free computing power to just anyone, can you at least let me do like Adobe Premier or After Affects, i.e. post-production of my home videos, directly on Google Drive without extorting me for monthly subscription fees?
On the one hand, it’s incredible that so much computing power is available to ordinary people now, on the other hand, it is even more incredible how much those computers (and the energy needed to power them) are wasted on something so frivolous as drawing a Pikachu with big boobs, or as dangerous talking with a companion that will always agree with you even if you suggest committing suicide.
But I guess that is why this tech financial bubble must eventually explode. No one can reasonably provide that much computing power for something so useless for free for very long, it is just not economically feasible.