[cmiles74]

I don't read Ed Zitron, aside from when he appears here on Hacker News, and I also find his tone to be over-the-top. I think we might agree on that much.

These articles are lengthy but, to my understanding, Ed's idea is...

* AI companies have committed to purchasing X amount of compute

* Data centers are being constructed to meet this demand, they'll need to charge amount Y

* AI companies do not have sufficient revenue to pay amount Y

IMHO this isn't surprising, personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls. This doesn't seem like a big enough market for the huge dollar amounts I'm seeing thrown around.

I'm curious why you think Ed is so far off the mark on this. To me, it seems like we are headed for a big correction on the whole AI thing.

[mike_hearn]

Not the OP but Zitron makes clear errors:

• He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.

• All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.

• He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.

[cmiles74]

> All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.

Agreed, but I'd argue that Ed doesn't have much else to work with. I'd like to see journalists take this tack and start asking these executives to either back up their statements or back down from them. They should be held accountable for their statements.

Even if we dial down these numbers by a magnitude they are still insanely large and the AI companies do not seem to be making enough money to balance things out.

> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.

I agree that older hardware from Nvidia doesn't become worthless when Nvidia releases new, more powerful hardware. I have to point out that it certainly loses a great deal of value and that's not nothing.

> He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.

Is that really the case? I mean, I read about the build out of these data centers being delayed all of the time. I read this last week and it seems roughly in line with Ed's ravings:

> A JPMorgan analysis last month found that more than 60% of data-center capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed.[0]

[0]: https://www.msn.com/en-us/news/technology/america-s-data-cen...

[JacobAsmuth]

H100s installed 4 years ago are more expensive to rent now than they were on day 1. It is not at all clear that older hardware is losing its value in a world where the next gen model is smarter and faster due to improved training+inference algorithms (e.g. custom kernels) but runs on the same hardware.

[LogicFailsMe]

It's either new GPUs make the old ones worthless or old GPUs make the new ones too expensive because they're still useful, it depends which ranter you're reading at the time.

Just like Michael Burry kept comparing NVDA to CSCO and now he doesn't do so anymore now that NVDA's P/E is ~31 and CSCO's is ~41. Funny that.

[ai_critic]

It helps if you look at Zitron's work history and experience. He's a hype man and a games journalist. His opinions on this are whatever sells, not exactly whatever is correct.

This is alarmingly obvious whenever he talks out of his depth about things like how companies actually use AI and reason about business decisions.

[michael-ax]

accuracy and precision are not the same thing. he's delivering one, you're asking for the other. no?

[ai_critic]

To put it more bluntly: he provides neither in his pursuit of rage views.

[sumeno]

They don't immediately become worthless, but they don't last all that long either

https://www.tomshardware.com/pc-components/gpus/datacenter-g...

[CuriouslyC]

This doesn't match my experience, in academia I saw ~40-45% utilization NVIDIA GPU clusters that went 6 years with <20% failure rate. Might be a TPU thing?

[CraigRood]

I'm FAR form an expert on this, but I believe that the operating costs such as power + cooling form a big part of the lifecycle. I have no doubt that at some point within the 6 years that are being booked, that replacing entire working racks won't be more cost efficient.

[CuriouslyC]

That is current practice, yes. The economics of replacing racks then selling the old ones to people who will salvage and resell working components works out better than trying to repair/retrofit in place.

[dofm]

> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless.

I am the OP and I totally agree with you on this one point. In fact the progress being made by open weights models strongly suggests that some of this hardware has much more of a life.

The overarching point he makes about incomplete data centres is that the current offering is running successfully on that very incomplete capacity, right?

What he is saying is that he cannot believe the demand exists to fill any of the unbuilt stuff, but much of it is still commitments that are going to have to be paid for, unless they can be backed out. He points to Nadella essentially confirming there will be overcapacity.

He also makes an interesting point that people tend to think "I can't get a GPU right now" means "there is intense, live demand for GPUs in data centres" when in fact the reason you can't get one is buy-and-hold. Including much of that new replacement hardware: it is being bought even the old stuff would (let us stipulate will) do the job.

I think he (or someone who interviewed him) recently said it reminded them less of the dot com boom and more of the Chinese real estate bubble.

[mike_hearn]

Future demand is unknowable. He might be directionally correct but wrong in magnitude, or right about everything, or wrong about everything. Unfortunately people who call bubbles never make their claims falsifiable or do anything else to build confidence, like take short positions. Zitron attacks the very notion that he might put skin in the game like that as obviously crazy.

I don't know to what extent we can say the current offering is running successfully. Anthropic have had visible capacity constraints for 18 months now with lots of throttling and quota capping going on. Those are good signs that demand does exceed supply at the current price point.

Additionally, Mythos has not launched publicly and one reason seems to be that it's too slow/expensive to make widely available, i.e. is capacity constrained.

But supply/demand is always in equilibrium, in some sense. So you could argue that it's currently balanced, or would be if priced correctly. That tells you little about future demand though.

[dofm]

All of this is fair, but it's also important to weigh your criticisms of Zitron's claims against the absolutely unsupported claims being made by Altman on a regular basis. They never show their working in even the way he does.

FWIW on capacity constraints, my gut instinct is that like every other startup these AI companies are are really only now beginning to do the serious efficiency work, because they had money and resources to throw at scaling without it; never optimise too early is pretty much a startup mantra.

[mike_hearn]

Sure, I'm not claiming that anyone who isn't Zitron must therefore be completely reliable.

I think all the labs have done a lot of efficiency work for a long time, tbh. You can see the evidence in their papers, open source releases and product design choices like model routers. They know they need to reduce their cost base a lot to become profitable.

[hn_throwaway_99]

> personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls.

That seems like a giant paucity of imagination. I can easily name a lot of areas where AI is already having a large impact and it's not hard to imagine the impact growing:

1. Customer service. Yes, we all like to laugh at the silly chatbot mistakes, linked list reversals and Instagram oopsies, but a lot of companies are putting a lot of effort (and spend) into AI for customer service.

2. The legal profession is already spending a lot on AI, and it will only grow. Again, we all like to read about hallucinated case citations, but those are solvable problems (honestly I felt they were more human problems than tech problems to begin with) and there are so many areas in research and document summarization that AI is really good at.

3. Radiology. There are lots of arguments over whether AI will "replace radiologists", but that's besides the point. The largest radiology groups in the country already use AI software to check for specific missed diagnoses, and the expected spend on AI will grow, a lot.

4. Enterprise knowledge management. Services like Glean are popular and growing.

I can easily go on.

[monodeldiablo]

You annihilated your own argument with the inclusion of radiology. The only successfully deployed "AI" in use by radiologists (that I'm aware of) are bespoke image analysis models, not LLMs. And that space is rapidly fragmenting as there's a frustrating and seemingly irresolvable tension between sensitivity, generalizability, and accuracy.

[sabretooth1405]

Everyone I know hates AI customer service. A couple of prominent food delivery apps here in India switched to AI chatbot customer services and it’s been horrible since then. It’s been almost impossible to get refunds since then, even when there’s straight up fraud involved without screaming ok twitter.

Now ofc it can be said that they haven’t implemented it properly but at some point it needs to be considered that why isn’t no one figuring it out?

[c0n5pir4cy]

I would argue that all 4 of these that you have mentioned can be handled with relatively small models very well.

The real question is what situations are the flagship, larger models useful in and will that produce enough demand.

[sumeno]

Radiology isn't using chat bots

[JamesBarney]

I don't know if Ed is far off the mark. But this article does nothing to help illuminate it.

He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.

He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.

It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.

Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.

[czhu12]

Code generation isn't big enough of a market?