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n-kling.bsky.social
go dot
@n-kling.bsky.social
390 followers 1K following 250 posts
2 wrongs don't make a right. Love art, cat, dogs, rats, library and information science, information policy, stolen art and art forgery stories and info. Very late diagnosed adhd etc... so you can imagine all of the things I care very much about.
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Reposted by go dot
peacebird.bsky.social
Peter Max was commissioned to paint border signs too
March 7, 2024 at 4:16 PM
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edzitron.com
I am deeply worried for the future.

NVIDIA's revenue is a chain of pain - selling GPUs bought with debt that are rented to unprofitable LLM companies subsidized by VC who sell access to unprofitable AI companies subsidized by VC.

The end will be gruesome.
www.wheresyoured.at/nvidia-isnt-...
NVIDIA Is Dependent On Endless Debt and Credit - As Are Most Of The Customers Of AI Compute, Creating A Deadly Cycle That Ends In Disaster NVIDIA is currently making hundreds of billions in revenue selling GPUs to companies that either plug them in and start losing money or, I assume, put them in a warehouse for safe keeping. This brings me to my core anxiety: why, exactly, are companies pre-ordering GPUs? What benefit is there in doing so? Blackwell does not appear to be “more efficient” in a way that actually makes anybody a profit, and we’re potentially years from seeing these GPUs in operation in data centers at the scale they’re being shipped — so why would anybody be buying more? I doubt these are new customers — they’re likely hyperscalers, neoclouds like CoreWeave and resellers like Dell and SuperMicro — because the only companies that can actually afford to buy them are those with massive amounts of cash or debt, to the point that even Google, Amazon, Meta and Oracle are taking on massive amounts of new debt, all without a plan to make a profit. NVIDIA’s largest customers are increasingly unable to afford its GPUs, which appear to be increasing in price with every subsequent generation. NVIDIA’s GPUs are so expensive that the only way you can buy them is by already having billions of dollars or being able to raise billions of dollars, which means, in a very real sense, that NVIDIA is dependent not on its customers, but on its customers’ credit ratings and financial backers. To make matters worse, the key reason that one would buy a GPU is to either run services using it or rent it to somebody else, and the two largest parties spending money on these services are OpenAI and Anthropic, both of whom lose billions of dollars, and are thus dependent on venture capital and debt (remember, OpenAI has a $4 billion line of credit, and Anthropic a $2.5 billion one too). Yet it gets worse from there. Who, after all, are the biggest customers renting AI compute? That’s right, AI startups, all of which are deeply unprofitable. Cursor — Anthropic’s largest customer and now its biggest competitor in the AI coding sphere — raised $2.3 billion in November after raising $900 million in June. Perplexity, one of the most “popular” AI companies, raised $200 million in September after raising $100 million in July after seeming to fail to raise $500 million in May (I’ve not seen any proof this round closed) after raising $500 million in December 2024. Cognition raised $400 million in September after raising $300 million in March. Cohere raised $100 million in September a month after it raised $500 million. Venture capital is feeding money to either OpenAI or Anthropic to use their models, or in some cases hyperscalers or neoclouds like CoreWeave or Lambda to rent NVIDIA GPUs. OpenAI and Anthropic then raise venture capital or debt to pay hyperscalers or neoclouds to rent NVIDIA GPUs. Hyperscalers and neoclouds then use either debt or existent cashflow (in the case of hyperscalers, though not for long!) to buy more NVIDIA GPUs. Only one company actually makes a profit here: NVIDIA. Aside: I should add there are also NVIDIA resellers like Dell or Supermicro, the latter of which buy NVIDIA GPUs, put them in servers, and sell them to neoclouds like Lambda or CoreWeave. At some point, a link in this debt-backed chain breaks, because very little cashflow exists to prop it up. At some point, venture capitalists will be forced to stop funnelling money into unprofitable, unsustainable AI companies, which will make those companies unable to funnel money into the pockets of those buying GPUs, which will make it harder for those companies buying GPUs to justify (or raise debt for) buying more GPUs. And if I’m honest, none of NVIDIA’s success really makes any sense. Who is buying so many GPUs? Where are they going? Why are inventories increasing? Is it really just pre-buying parts for future orders? Why are accounts receivable climbing, and how much product is NVIDIA shipping before it gets paid? While these are both explainable as “this is a big company and that’s how big companies do business” (which is true!), why do receivables not seem to be coming down? And how long, realistically, can the largest company on the stock market continue to grow revenues selling assets that only seem to lose its customers money? I worry about NVIDIA, not because I believe there’s a massive scandal, but because so much rides on its success, and its success rides on the back of dwindling amounts of venture capital and debt, because nobody is actually making money to pay for these GPUs. In fact, I’m not even saying it goes tits up. Hell, it might even have another good quarter or two. It really comes down to how long people are willing to be stupid and how long Jensen Huang is able to call hyperscalers at three in the morning and say “buy one billion dollars of GPUs, pig.” No, really! I think much of the US stock market’s growth is held up by how long everybody is willing to be gaslit by Jensen Huang into believing that they need more GPUs. At this point it’s barely about AI anymore, as AI revenue — real, actual cash made from selling services run on GPUs — doesn’t even cover its own costs, let alone create the cash flow necessary to buy $70,000 GPUs thousands at a time. It’s not like any actual innovation or progress is driving this bullshit! In any case, the markets crave a healthy NVIDIA, as so many hundreds of billions of dollars of NVIDIA stock sit in the hands of retail investors and people’s 401ks, and its endless growth has helped paper over the pallid growth of the US stock market and, by extension, the decay of the tech industry’s ability to innovate. Once this pops — and it will pop, because there is simply not enough money to do this forever — there must be a referendum on…
December 8, 2025 at 5:42 PM
Reposted by go dot
edzitron.com
It no longer makes sense to buy NVIDIA's AI GPUs. The space and power doesn't exist to turn them on, providing AI compute and LLM services is deeply unprofitable with no end in sight, and demand for these services simply doesn't exist at scale. It's time to stop.
www.wheresyoured.at/nvidia-isnt-...
Why Is Anybody Still Buying GPUs? This Is All Insane! AI bulls will tell you about the “insatiable demand for AI” and that these massive amounts of orders are proof of something or rather, and you know what, I’ll give them that — people sure are buying a lot of NVIDIA GPUs! I just don’t know why. Nobody has made a profit from AI, and those making revenue aren’t really making much. For example, my reporting on OpenAI from a few weeks ago suggests that the company only made $4.329 billion in revenue through the end of September, extrapolated from the 20% revenue share that Microsoft receives from the company. As some people have argued with the figures, claiming they are either A) delayed or B) not inclusive of the revenue that OpenAI is paid from Microsoft as part of Bing’s AI integration and sales of OpenAI’s models via Microsoft Azure, I wanted to be clear of two things: This is accrual accounting, meaning that these numbers are revenue booked in the quarter I reported them. Any comments about quarter-long delays in payments are incorrect. Microsoft’s revenue share payments to OpenAI are pathetic — totalling, based on documents reviewed by this publication, $69.1 million in CY (calendar year) Q3 2025. In the same period, it spent $8.67 billion on inference (the process in which an LLM creates an output). This is the biggest company in the generative AI space, with 800 million weekly active users and the mandate of heaven in the eyes of the media. Anthropic, its largest competitor, alleges it will make $833 million in revenue in December 2025, and based on my estimates will end up having $5 billion in revenue by end of year. Based on my reporting from October, Anthropic spent $2.66 billion on Amazon Web Services through the end of September, meaning that it (based on my own analysis of reported revenues) spent 104% of its $2.55 billion in revenue up until that point just on AWS, and likely spent just as much on Google Cloud. While everybody wants to tell the story of Anthropic’s “efficiency” and “only burning $2.8 billion this year,” one has to ask why a company that is allegedly “reducing costs” had to raise $13 billion in September 2025 after raising $3.5 billion in March 2025, and after raising $4 billion in November 2024? Am I really meant to read stories about Anthropic hitting break even in 2028 with a straight face? Especially as other stories say Anthropic will be cash flow positive “as soon as 2027.” These are the two largest companies in the generative AI space, and by extension the two largest consumers of GPU compute. Both companies burn billions of dollars, and require an infinite amount of venture capital to keep alive at a time when the Saudi Public Investment Fund is struggling and the US venture capital system is set to run out of cash in the next year and a half. The two largest sources of actual revenue for selling AI compute are subsidized by venture capital and debt. What happens if these sources dry up? And, in all seriousness, who else is buying AI compute? What are they doing with it? Hyperscalers (other than Microsoft, which chose to stop reporting its AI revenue back in January, when it claimed a $13 billion, or about $1 billion a month, in revenue) don’t disclose anything about their AI revenue, which in turn means we have no real idea about how much real, actual money is coming in to justify these GPUs. CoreWeave made $1.36 billion in revenue (and lost $110 million doing so) in its last quarter — and if that’s indicative of the kind of actual, real demand for AI compute, I think it’s time to start panicking about whether all of this was for nothing. CoreWeave has a backlog of over $50 billion in compute, but $22 billion of that is OpenAI (a company that burns billions of dollars a year and lives on venture subsidies), $14 billion of that is Meta (which has yet to work out how to make any kind of real money from generative AI, and no, its “generative AI … Also, I also forgot to mention that CoreWeave’s backlog problem stems from data center construction delays. That and CoreWeave has $14 billion in debt mostly from buying GPUs, which it was able to raise by using GPUs as collateral and that it had contracts from customers willing to pay it, such as NVIDIA, which is also selling it the GPUs. So, just to be abundantly clear: CoreWeave has bought all those GPUs to rent to OpenAI, Microsoft (for OpenAI), Meta, Google (OpenAI), and NVIDIA, which is the company that benefits from CoreWeave’s continued ability to buy GPUs. Otherwise, where’s the fucking business, exactly? Who are the customers? Who are the people renting these GPUs, and for what purpose are they being rented? How much money is renting those GPUs? You can sit and waffle on about the supposedly glorious “AI revolution” all you want, but where’s the money, exactly? And why, exactly, are we buying more GPUs? What are they doing? To whom are they being rented? For what purpose? And why isn’t it creating the kind of revenue that is actually worth sharing? Is it because the revenue sucks? Is it because it’s unprofitable to provide it? And why, at this point in history, do we not know? Hundreds of billions of dollars that have made NVIDIA the biggest company on the stock market and we still do not know why people are buying these fucking things.
December 8, 2025 at 5:42 PM
Reposted by go dot
edzitron.com
Big tech really has no way to make back the money it spent on GPUs. Data centers take years and billions of dollars to build, creating a giant hole of costs to fill before you even turn them on - and on a yearly cycle, every GPU loses value the second you buy it.
www.wheresyoured.at/nvidia-isnt-...
There’s No Way For Big Tech To Make Back Its Capital Expenditures On AI So, why does this create an artificial time limit? Let’s start with a horrible fact: it takes about 2.5 years of construction time and $50 billion per gigawatt of data center capacity. One way or another, these GPUs are depreciating in value, either through death (or reduced efficacy through wear and tear) or becoming obsolete, which is very likely as NVIDIA has committed to releasing a new GPU every year. At some point, Wall Street is going to need to see some sort of return on this investment, and right now that return is “negative dollars.” I break it down in my premium piece, but I estimate that big tech needs to make $2 for every $1 of capex. This revenue must also be brand spanking new, as this capex is only for AI. Meta, Amazon, Google and Microsoft are already years and hundreds of billions of dollars in, and are yet to see a dollar of profit, creating a $1.21 trillion hole just to justify the expenses (so around $605 billion in capex all told, at the time I calculated it). You might argue that there’s a scenario where, say, an A100 GPU is “useful” past the 3 or 6 year shelf life. Even if that were the case, the average rental price of an A100 GPU is 99 cents an hour. This is a four or five-year-old GPU, and customers are paying for it like they would a five-year-old piece of hardware. The same fate awaits H100 GPUs too. Every year, NVIDIA releases a new GPU, lowering the value of all the other GPUs in the process, making it harder to fill in the holes created by all the other GPUs. This whole time, nobody appears to have found a way to make a profit, meaning that the hole created by these GPUs remains unfilled, all while big tech firms buy more GPUs, creating more holes to fill. In really, really simple terms: Big tech keeps buying more GPUs despite the old GPUs failing to pay for themselves. To fix this problem, big tech is buying more GPUs. Newer generation GPUs — like NVIDIA’s Blackwell and Vera Rubin — require entirely new data center architecture, meaning that one has to either build a brand new data center or retrofit an old one. Big tech is spending billions of dollars to make sure it’s able to turn on these new GPUs, at which point you may think that they’ll make a profit. Even when they’re turned on, these things don’t make money. The Information reports that Oracle’s Blackwell GPUs have a negative 100% gross margin. How exactly are these bloody things meant to make more money than they cost in the next six years, let alone three? They don’t make a profit now and have no path to doing so in the future! I feel like I’m going INSANE! So, now that you know this, there’s a fairly obvious question to ask: why are they still buying GPUs? Also…where the fuck are they going?
December 8, 2025 at 5:42 PM
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scotgirl72.bsky.social
Someone needs to point blank ask him when he was diagnosed with dementia and when he started “treatment”.
December 8, 2025 at 8:52 PM
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ceej.online
As biblical salvation is exclusive to human beings, there is unfortunately no way to offer our robot buddy absolution. But still, the ability to sin and be cast into fire for all eternity represents an important step forward for artificial intelligence
December 8, 2025 at 10:41 PM