581–590 of 985 posts
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Took me a while to find again, as there are a lot of such papers in this area. https://www.arxiv.org/pdf/2511.18517
A single author, in a physics department. Seems unlikely to be groundbreaking or authoritative.
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I think it's a bit different because a rental car generates direct revenue that covers its cost. These GPU data centers are being used to train models (which themselves quickly become obsolete) and provide inference at a loss. Nothing in the current chain is profitable except selling the GPUs.
> and provide inference at a loss You say this like it's some sort of established fact. My understanding is the exact opposite and that inference is plenty profitable - the reason the companies are perpetually in the red is that they're always heavily investing in the next, larger generation. I'm not Anthropic's CFO so i can't really prove who's right one way or the other, but I will note that your version relies on…
Despite the flashy title that's the first "sober" analysis from a CEO I read about the technology. While not even really news, it's also worth mentioning that the energy requirements are impossible to fulfill Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc. None of this is on par with a human. The code snippets are straight out of Stackoverflow...
If you believe this, you must also believe that global warming is unstoppable. OpenAI's energy costs are large compared to the current electricity market, but not so large compared to the current energy market. Environmentalists usually suggest that electrification - converting non-electrical energy to electrical energy - and then making that electrical energy clean - is the solution to global warming. OpenAI's energy needs are something like 10% of the current worldwide electricity market but less than 1% of the current worldwide energy market.
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Networking gear was famously overbought. Enterprise hardware is tricky as there isn’t much of a resale market for this gear once all is said and done. The only valid use case for all of this compute which could reasonably replace ai is btc mining. I’m uncertain if the increased mining capacity would harm the market or not.
BTC mining on GPUs haven't been profitable for a long time, it's mostly ASICs, GPUs can be used for some other altcoins which makes the potential market for used previous generation GPUs even smaller.
That only scales if the coin goes up in value due to the extra "interest". Which isn't impossible but there's a limit, and it's more often to happen to smaller coins.
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How different is this from rental car companies changing over their fleets? I don't know, this is a genuine question. The cars cost 3-4x as much and last about 2x as far as I know, and the secondary market is still alive.
> How different is this from rental car companies changing over their fleets? New generations of GPUs leapfrog in efficiency (performance per watt) and vehicles don't? Cars don't get exponentially better every 2–3 years, meaning the second-hand market is alive and well. Some of us are quite happy driving older cars (two parked outside our home right now, both well over 100,000km driven). If you have a datacentre with…
Think 100 cards but only 1 buyer as a ratio. Profit for ebay sellers will be on "handling", or inflated shipping costs.
eg shipping and handling.
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On a personal level it will work out for him just fine. All he has to do is siphon off a fraction of that money to himself and/or an entity that he controls. He's like Elon Musk in that respect: always doubling the bet on the next round, it is a real life Martingale these guys are playing with society on the hook for the downside. https://en.wikipedia.org/wiki/Martingale_(betting_system)
How would he siphon off? He has no stake in OpenAI. And any other stake must be public. Or are you suggesting something more shady? Elon is the complete opposite of martingale. He has helped produce value beyond just bets. Spacex, Tesla and so on.
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Took me a while to find again, as there are a lot of such papers in this area. https://www.arxiv.org/pdf/2511.18517
Is this AI paper written by a reputable subject matter expert? It seems to be written by a physicist and also be the only academic work by this author in English
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Took me a while to find again, as there are a lot of such papers in this area. https://www.arxiv.org/pdf/2511.18517
Is this AI paper written by a reputable subject matter expert? It seems to be written by a physicist and also be the only academic work by this author in English
I have read it. It is nothing new on the subject, but it was just the recent paper I saw on HN and the person was asking for the link.
The crux is an LLM is and can never be intelligent in the sense of an AGI. It is easier to think of it as a way to store and retrieve knowledge.
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It’s the ship of Theseus in corporate form. Even if all the people are gone but the culture hasn’t changed, is the criticism inaccurate?
I mean, okay, but you're taking the current leadership's words and claiming they are incorrect because IBM management was not great at identifying trends decades ago. Historical trend is not an indicator of the future and it's not engaging in good faith on the conversation if overspending on AI can be backed by revenue in the future. You're attacking the messenger instead of the message.
Despite the flashy title that's the first "sober" analysis from a CEO I read about the technology. While not even really news, it's also worth mentioning that the energy requirements are impossible to fulfill Also now using ChatGPT intensely since months for all kinds of tasks and having tried Claude etc. None of this is on par with a human. The code snippets are straight out of Stackoverflow...
Your assessment of Claude simply isn’t true. Or Stackoverflow is really good. I’m producing multiple projects per week that are weeks of work each.
I've found Claude's usefulness is highly variable, though somewhat predictable. It can write `jq` filters flawlessly every time, whereas I would normally spend 30 minutes scanning docs because nobody memorizes `jq` syntax. And it can comb through server logs in every pod of my k8s clusters extremely fast. But it often struggles making quality code changes in a large codebase, or writing good documentation that isn't just an English translation of the code it's documenting.