You keep saying this. It doesn't mean anything to "directionally agree with Zitron" in the context of this Dan Luu piece. All you're saying is "you're skeptical of AI and the AI business model". Bully for you! Lots of people are. Nobody is dunking on you for having that skepticism. They're dunking on Ed Zitron for making a long series of patently risible specific predictions.

> On a spectrum between tulip wilting and fiber build out, we know deprecation cycle of DC hardware leans towards tulips i.e. <10 years (very generous) vs 20+ years for fiber layout, a lot of which is actually infra/earth works etc.

Counterargument: As advancements in transistor densities slow down, the rationale for increasing depreciation cycles makes more sense. As the performance gap between new & 5-year-old hardware continues to shrink, then the need to replace older hardware similarly shrinks, justifying longer depreciation cycles.

> we know deprecation cycle of DC hardware leans towards tulips i.e. <10 years (very generous) vs 20+ years for fiber layout

[1] is a reasonable discussion of DC cost models, which calculates depreciation as part of the annual cost.

> here is the rest of the order magnitude more $$$ that justifies existing spent relative to time frame coming from?

That money comes from long term debt (ie bonds by public companies[3]) and new investment into neo-cloud companies (ie, IPOs like 4).

The justification comes revenue. Eg, the NScale IPO above[4] has $51B in long term contracted revenue with an annual run rate of $500M.

[1] https://epoch.ai/data-insights/ai-datacenter-cost-breakdown

[2] https://www.cushmanwakefield.com/en/united-states/insights/d...

[3] eg https://www.yondrgroup.com/newsroom/press-release/yondr-secu... (but you'll find lots of similar bonds issued)

[4] https://dealroom.co/news/143730-nscale-eyes-september-us-ipo...

Luu is pretty specific about the predictions Zitron is making, has taken the time to pull them out and date them, and they're both risible and not rescuable with vibes.

His point isn't that Google or Meta are doing well or have bright futures. Luu is generally critical of tech giant engineering and product culture. He's critical of Google in particular in this very article.

But the point of the article is that it's not enough to have directionally satisfying vibes. If you made concrete forward-looking predictions and they're catastrophically wrong, that matters. If you make backwards-looking predictions that were literally wrong the moment you published them, that matters even more.

"Did you read the article" is a frowned-upon response on HN. The better way to write that kind of response, per the guidelines, is "the article mentions that". So: the article mentions that.

Ah I understand what you're saying. Yes I agree that if you pull back to, "We're in a bubble," then you can say that Zitron is correct, at least fundamentally, despite being wildly inaccurate in almost every other prediction he makes.

But Zitron isn't just blogging about how we're in a bubble. The assertions he makes are not minutiae, he basically continuously says that all the big SW firms are walking corpses. He's not having a rational conversation about the long term prospects for companies who invest in AI. There is a population of people who (rightfully) hate Google et al and want them to fail, and he just stokes their anger and frustration.

He doesn't add anything substantial, and (as the article indicates), even when he brings economic figures into the conversation, he's frequently wrong or misrepresents them.

Hasn't AI been horrible for FAANG fundamentally?

- They've all been compelled to build the same horribly expensive AI infra, to serve similar models that have no ability to lock-in customers

- Google Search has to compete with LLMs

- Meta hasn't demonstrated a credible argument on how they're planning to use AI. AI 'friends' would kill their business model. Their saving grace ironically is that people absolutely hate interacting with AIs. Same goes for other AI assistants.

- Hyperscalers have to compete for the same hardware as AI companies, driving their costs up

- AI turned out to be excellent at both porting software to more optimized stacks and deleting the 'prestige' of building these ultra-inefficient microservice containerized stuff. I haven't read a single article about somebody bragging about this stuff. When it comes to tech (which is not AI), usually its about Zig, Rust and going native.

- So if customers really start feeling the heat of rising costs, they have a realistic path of optimizing their compute usage by using AI to rewrite the worst-offending components. I think one of the few things in which AI has demonstrated measurable economic value is rewriting software in Rust to be more efficient

Of the three Google is in the best position. Meta and MS are in trouble. Zuckerberg will survive because he has control of his company, but Nadella is not going to survive Copilot if it don’t work.

more consistent with? more consistent than?

> These companies have been caught tweaking their numbers... they build data centers through holding companies, who have to absorb the costs and massive capex based financial liabilites, so that the brand-name big-tech companies get to keep their expenses off their books.

This is about as far from "tweaking their numbers" as you can get. It's a standard way infrastructure-heavy industries structure their investments and people would be asking questions if they didn't do this!

> hyperscalers opted to lengthen the depreciation timelines of their GPUs.

Yes and so they should! GPU depreciation timelines used to be 3 years!!

Google is famously still running 10 year old TPUs at 100% utilization, and 10 year old H100s are worth more now on the second hand market than they were when they were bought.

H100 spot prices have only dropped from $5 in May 24 to $3.20 now despite the release of the B200: https://semianalysis.com/gpu-pricing-index/