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…
I mean gigawatts is a concise metric to get a grasp of the amount of gpu compute they install, but the honesty seems a bit strange to me imo.
I didn’t see the step where Larry has to sell any stock, and hence puts downward price pressure on Oracle share prices. What is the source of the cash in steps 3, 4, and 7?
It is us, index fund owners :clown: Disclaimer: I also have a small amount of money in vanguard IRA
According to the image of the steps, Oracle’s share price is going up, presumably more than it would have without engaging in these steps. How can that cost index fund owners? They would be benefiting from the share price increase.
This is throwing more cards on the house of cards. Nvidia is “investing” in OpenAI so OpenAI can buy GPUs from NVidia. Textbook “round tripping.” I generally like what’s been happening with AI but man this is gonna crash hard when reality sets in. We’re reaching the scary stage of a bubble where folks are forced to throw more and more cash on the fire to keep it going with no clear path to ever get that cash back. If…
Does this mean they pay for it through consumer GPU sales?
Last quarter, NVIDIA reported data-center revenue of $30.8 billion and gaming revenue of only $3.3 billion.
But real GPUs are being built, installed and used. It's not paper money, it's just buying goods and services partly with stock. Which is a very solid and time honored tradition which happens to align incentives very well.
No one's implying it's fake money or resources, only that will no clear path to profit eventually the money will stop flowing and valuations will implode.
It’s a global AI race, there is more at stakes than profit.
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…
DC infra is always allocated in terms of watts. From this number, everything else is extrapolated (e.g. rough IT load, cooling needed, etc).
100 square miles is small in the American southwest. And a solar farm would disrupt the ecosystem much less than many other land uses. Adding shade and cover will benefit many species.
A few nuclear power plants have a much smaller environmental impact.
Burning 10GW of fossil fuels for 20 years while waiting for the nuclear plants to finish building will do far more damage to the environment than 100 square miles of shade in the desert.
Water is a critical resource in dwindling supplies in many water-stressed regions. These data centers have been known to suck up water supplies during active droughts. Is there anyone left at the EPA that gets a say in how we manage water for projects like this?
Water is much less of an issue than the media makes it out to be. It's a problem in some specific areas, yes, but power is a much better concern.
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
It's 100% plausible and believable that there's going to be a spectacular bubble popping, but saying we are way past peak LLM would be like saying we were way past peak internet in 1999-2001 -- in reality, we weren't even remotely close to peak internet (and possibly still aren't). In fact, we were so far from the peak in 2001 that entire technological revolutions occurred many years later (e.g., smartphones) that just accelerated the pace even further in ways that would've been hard to imagine at the time. It's also important to note that AI is more than text-based LLMs -- self-driving cars and other forms of physical "embodied" AI are progressing at exponential pace, while entirely new compute form factors are only just now starting to emerge yet are almost certainly guaranteed to become pervasive as soon as possible (e.g., real AR glasses). Meanwhile, even plain-old text-based LLMs have not actually stagnated.
"GPUs per user" would be an interesting metric. (Quick, inaccurate googling) says there will be "well over 1 million GPUs" by end of the year. With ~800 million users, that's 1 NVIDIA GPU per 800 people. If you estimate people are actively using ChatGPT 5% of the day (1.2 hours a day), you could say there's 1 GPU per 40 people in active use. Assuming consistent and even usage patterns. That back of the envelope math…
I'm kinda scared of "1.2 hours a day of ai use"...
Sorry, those figures are skewed by Timelord Georg, who has been using AI for 100 million hours a day, is an outlier, and should have been removed.