Are we repeating the telecoms crash with AI datacenters?

The article claims that AI services are currently over-utilised. Well isn't that because customers are being undercharged for services? A car when in neutral will rev up easily if the accelerator pedal is pushed even very slightly, because there's no load on the engine. But in gear the same engine will rev up much less when the accelerator is pushed the same amount. Will there be the same overutilisation occurring if users have to financially support the infrastructure, either through subscriptions or intrusive advertising?

I doubt it.

And what if the technology to locally run these systems without reliance on the cloud becomes commonplace, as it now is with open source models? The expensive part is in the training of these models more than the inference.

OpenAI has 800,000,000 weekly users but only 20,000,000 are paying while 780,000,000 are free riding. Should they by accident under provision then they could simply remove the freebee and raise the prices for the paying clients. But that is not what they want.

IMHO the investors are betting on a winner-takes-it-all market and that some magic AGI will be coming out of OpenAI or Anthropic.

The questions are:

How much money can they make by integrating advertising and/or selling user profiles?

What is the model competition going to be?

What is the future AI hardware going to be - TPUs, ASICs?

Will more people have powerful laptops/desktops to run a mid-sized models locally and be happy with it?

The internet didn't stop after the dotcom crash and the AI wont stop either should there be a market correction.

The thing that makes AI investment hard to reason about for individuals is that our expectations are mostly driven by a single person’s usage, just like many of the numbers reported in the article.

But the AI providers are betting, correctly in my opinion, that many companies will find uses for LLM’s which are in the trillions of tokens per day.

Think less of “a bunch of people want to get recipe ideas.”

Think more of “a pharma lab wants to explore all possible interactions for a particular drug” or “an airline wants its front-line customer service fully managed by LLM.”

It’s unusual that individuals and industry get access to basically similar tools at the same time, but we should think of tools like ChatGPT and similar as “foot in the door” products which create appetite and room to explore exponentially larger token use in industry.

After reading the article and the comments, here are a few points people are missing from their analysis:

- OverUtilized/UnderCharged: doesn't matter because...

- Lead Time vs. TCO vs. IRS Asset Deprecation: The moment you get it fully built, it's already obsolete. Thus from a CapEx point of view, if you can lease your compute (including GPU) and optimize the rest of the inputs for similar then your CapEx overall is much lower and tied to the real estate - not the technology. The rest is cost of doing business and deductible in and of itself.

- The "X" factor: Someone mentioned TPU/ASIC but then there is the DeepSeek factor - what if we figure out a better way of doing the work that can shortcut the workflow?

- AGI partnerships: Right now, you see a lot of Mega X giving billions to Mega Y because all of them are trying to get their version of Linux or Apache or whatever at parity with the rest. Once AGI is settled and confirmed, then most all of these partnerships will be severed because it then becomes which company is going to get their AI model into that high prestige Montessori school and into the right ivy league schools - like any other rich parent would for their "bot" offspring.

So what will it look like when it crashes? A bunch of bland empty "warehouses" with mobile PDU's once filling all their parking lot space gone. Whatever "paradise" that was there may come back... once you bulldoze all that concrete and steel. The money will do something else like a Don McLean song.

Welp Gemini got me. Using G3 to improve what I write, generate specific images, and use NotebookLM to dive into some research materials. Tried to do a bit each day with my free credits, but hit the limit too often. G2.5 was not nearly as useful. So I upgraded my baselevel Google workspace plan. Recently spoke to someone who is also using G3 a lot with good results. YMMV re: G3, but Google hooked me, and now I pay more. However I think it is worth it for what I do. G3 is my helpful, nerdy work mate. I never plan to use agenic AI. Not using ChatGPT much if any at all anymore. Sorry Sam.

I am still a little skeptical about utilisation rates. If demand is so extreme, wouldn't we see rental prices for H100/A100 prices go up or maintain? Wouldn't the cost for such a gpu still be high (you can get em 3k used).

Yes or no conclusions aside (and despite its title, the article deserves better than that), the key point is I think this one: “But unlike telecoms, that overcapacity would likely get absorbed.”

Stylistically, this smells like it was copy and pasted from straight out Deep Research. Substantively, I could use additional emphasis on the mismatch between expectations and reality with regards to telco debt-repayment schedule.

Some of the utilization comparisons are interesting, but the article says 2 trillion was spent on laying fiber but that seems suspicious.

I'm curious about the telecom collapse. How was it not in the pipeline that much better use of the fibres was around the corner? Surely they would have known that people were looking into it with some promise?

Telco capex was $100 billion at the peak of the IT bubble, give or take. There's going to be $400 billion investments in AI in 2025. Just saying.

The 2001 telecoms crash drove benefits for companies that came later in the availability of inexpensive dark fiber after the bubble popped. WorldCom, ICG, Williams sold off to Verizon, Level 3, Teleglobe, and others. That in turn helped future Internet companies gain access to plentiful and inexpensive bandwidth. Cable telephony companies such as Cablevision Systems, Comcast, Cox Communications, and Time Warner, used the existing coaxial connections into the home to launch voice services.

Don’t think looking at power consumption of b200s is a good measure of anything. Could well be an indication of higher density rather than hitting limits and cranking voltage to compensate

Is there a way in which this is good for a segment of consumers? When the current gen of GPUs are too old, will the market be flooded with cheap GPUs that benefit researchers and hobbyists who therwis would not afford them?

Simultaneous claims that 'agentic' models are dramatically less efficient, but also forecasts efficiency improvements? We're in full-on tea-leaves-reading mode.

Yes

No, because at least dark fiber is useful. AI GPUs will be shipped off to developing nations to be dissolved for rare earth metals once the third act of this clown show is over.

Amazing article, I found it fascinating.

> You can already use Claude Code for non engineering tasks in professional services and get very impressive results without any industry specific modifications

After clicking on the link, and finding that Claude Code failed to accurately answer the single example tax question given, very impressive results! After all, why pay a professional to get something right when you can use Claude Code to get it wrong?

This seems to be either LLM AI slop or a person working very hard to imitate LLM writing style:

The key dynamic: X were Y while A was merely B. While C needed to be built, there was enormous overbuilding that D ...

Why Forecasting Is Nearly Impossible

Here's where I think the comparison to telecoms becomes both interesting and concerning.

[lists exactly three difficulties with forecasting, the first two of which consist of exactly three bullet points]

...

What About a Short-Term Correction?

Could there still be a short-term crash? Absolutely.

Scenarios that could trigger a correction:

1. Agent adoption hits a wall ...

[continues to list exactly three "scenarios"]

The Key Difference From S:

Even if there's a correction, the underlying dynamics are different. E did F, then watched G. The result: H.

If we do I and only get J, that's not K - that's just L.

A correction might mean M, N, and O as P. But that's fundamentally different from Q while R. ...

The key insight people miss ...

If it's not AI slop, it's a human who doesn't know what they're talking about: "enormous strides were made on the optical transceivers, allowing the same fibre to carry 100,000x more traffic over the following decade. Just one example is WDM multiplexing..." when in fact wavelength division multiplexing multiplexing is the entirety of those enormous strides.

Although it constantly uses the "rule of three" and the "negative parallelisms" I've quoted above, it completely avoids most of the overused AI words (other than "key", which occurs six times in only 2257 words, all six times as adjectival puffery), and it substitutes single hyphens for em dashes even when em dashes were obviously meant (in 20 separate places—more often than even I use em dashes), so I think it's been run through a simple filter to conceal its origin.

No, because the datacenters will get used. The demand side exists, whether it’s LLM AIs or something completely different that isn’t AI related. That’s very different from a crash where there is absolutely nothing valuable/useable/demanded underneath the bubble.

Nice article; far from bullet-proof, but it brings up some interesting points. HN comments are vicious on the topic of AI non-bubbles.

> This is the opposite of what happened in telecoms. We're not seeing exponential efficiency gains that make existing infrastructure obsolete. Instead, we're seeing semiconductor physics hitting fundamental limits.

What about the possibility of improvements in training and inference algorithms? Or do we know we won't get any better than grad descent/hessians/etc ?

No. AI data center, or any data center is designed with incorrect data structure resulting in over utilisation of computing resource.

Hardware growth is slow and predictable, but one breakthrough algorithm completely undercuts any finance hypothesis premised on compute not flowing out of the cloud and back to the edges and into the phones.

This is a kind of risk that finance people are completely blind to. Open AI won't tell them because it keeps capital cheap. Startups that must take a chance on hardware capability remaining centralized won't even bother analyzing the possibility. With so many actors incentivized to not know or not bother asking the question, there's the biggest systematic risk.

The real whiplash will come from extrapolation. If an algorithm advance shows up promising to halve hardware requirements, finance heads will reason that we haven't hit the floor yet. A lot of capital will eventually re-deploy, but in the meantime, a great deal of it will slow down, stop, or reverse gears and get un-deployed.

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Holly cow, we've found an exception to Betteridge's Law of Headlines! Talk about burying the lede!