451–460 of 668 posts
We are way past peak LLM and it shows. They are basically advertise spacing heating as if it's some sort of advancement, while the tech seems to have stagnated, and they re just making the horses faster. The market should have punished this
The dot com bubble saw crazy deals and valuations, followed by a crash.
some companies emerged from it and went on to be a giant company like Amazon. Let's hope this AI boom have some similar outcomes.
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He single-handedly cost people more than anyone with his bearish takes lol
Its not a good argument against him. I read his articles and he is absolutely correct about the state of things. Predicting the crash is a fools errand. I don't use that as a argument to discredit what he actually writes regarding the raw economics of the AI industry. I say this as someone who has been holding NVDA stock since 2016 and can cash out for a large sum of money. To me its all theoretical money until I act…
Framing it in gigawatts is very interesting given the controversy about skyrocketing electric prices for residential and small business users as a result of datacenters over the past three years, primarily driven by AI growth. If, as another commenter notes, this 10GW is how much Chicago and NYC use combined , then we need to have a serious discussion about where this power is going to come from given the dismal stat…
Watt is the hottest new currency in big tech. Want to launch something big? You don't have to ask for dollars or headcount or servers or whatever else used to be the bottleneck in the past. There's plenty of all this to go around (and if not it can be easily bought). Success or failure now depends on whether you can beg and plead your way to getting a large enough kilowatt/megawatt allocation over every other team th…
https://subscriber.politicopro.com/article/eenews/2025/09/22...
onlyrealcuzzo wrote: > Google is pretty useful. It uses 15 TWh per year. 15TWh per year is about 1.7GW. Assuming the above figures, that means OpenAI and Nvidia new plan will consume about 5.8 Googles worth of power, by itself. At that scale, there's a huge opportunity for ultra-low-power AI compute chips (compared with current GPUs), and right now there are several very promising technology pathways to it.
This one datacenter should be able to perform a 51% attack on any of the big cryptocurrencies with that much compute. An interesting hedge in case the AI bubble pops.
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> As in, there are lots and lots of investment funds/pension funds/other such like financial entities which are very heavily tied to the "performance" of equity, and I'm talking about trillions (at this point) of dollars, and if that equity were to get a, let's say, 20 or 30% hair-cut in a matter of two-three months (at most), then we'll for sure be back in October 2008 mode. Just curious, can you detail how it would…
Anytime there's a massive draw down equities an asset-liability mismatch shows up (margin calls) because someone was borrowing money to spend in the short term against the value of assets that have now disappeared. It might not be the catastrophic cascading failure of the GFC, but someone somewhere in the pile will get exposed.
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Its not a good argument against him. I read his articles and he is absolutely correct about the state of things. Predicting the crash is a fools errand. I don't use that as a argument to discredit what he actually writes regarding the raw economics of the AI industry. I say this as someone who has been holding NVDA stock since 2016 and can cash out for a large sum of money. To me its all theoretical money until I act…
He’s been absolutely wrong on most things but spreading FUD is how he makes money, like Gary Marcus
But it goes both ways? Because AI promoters are also spreading FUD. That's how they make money. Because their livelihoods are tied to this technology and all the valuations. So is spreading FUD for you just a condition on whether or not you agree with the person?
onlyrealcuzzo wrote: > Google is pretty useful. It uses 15 TWh per year. 15TWh per year is about 1.7GW. Assuming the above figures, that means OpenAI and Nvidia new plan will consume about 5.8 Googles worth of power, by itself. At that scale, there's a huge opportunity for ultra-low-power AI compute chips (compared with current GPUs), and right now there are several very promising technology pathways to it.
This one datacenter should be able to perform a 51% attack on any of the big cryptocurrencies with that much compute. An interesting hedge in case the AI bubble pops.
Earlier quoted context omitted.
I work in the datacenter space. The power consumption of a data center is the "canonical" way to describe their size. Almost every component in a datacenter is upgradeable—in fact, the compute itself only has a lifespan of ~5 years—but the power requirements are basically locked-in. A 200MW data center will always be a 200MW data center, even though the flops it computes will increase. The fact that we use this unit…
All life is basically refining energy - standing up to entropy and temporarily winning the fight.
Framing it in gigawatts is very interesting given the controversy about skyrocketing electric prices for residential and small business users as a result of datacenters over the past three years, primarily driven by AI growth. If, as another commenter notes, this 10GW is how much Chicago and NYC use combined , then we need to have a serious discussion about where this power is going to come from given the dismal stat…
Earlier quoted context omitted.
Almost every model trained by the majors has paid for itself with inference fees. I’m not saying there isn’t a bubble, but I am saying if the researchers and strategists absolutely closest to the “metal” of realtime frontier models are correct that AGI is in reach, then this isn’t a bubble, it’s a highly rational race. One that large players seem to be winning right now.
> Almost every model trained by the majors has paid for itself with inference fees. Even if we assume this is true, the downstream customers paying for that inference also need it to pay for itself on average in order for the upstream model training to be sustainable, otherwise the demand for inference will dry up when the music stops. There won't always be a parade of over-funded AI startups burning $10 worth of tok…