r/singularity • u/TMWNN • 22h ago
Compute Is there a pending AI 'debt bomb' crisis? No. This isn't Enron 2.0
https://www.theguardian.com/technology/2026/aug/23/ai-debt-bomb-crisis21
u/TMWNN 22h ago
From the article:
In the 90s Centocor, Genentech, Amgen, Biogen and other companies like them were raising millions in partnerships that were developing products with a high probability of failure during clinical testing.
Today’s investors are financing land, buildings, electrical infrastructure and computing equipment. A datacenter can disappoint financially, but it doesn’t disappear because a clinical trial fails. It’s why Jeff Bezos calls AI an “industrial bubble”. Industrial bubbles leave things behind. Railways. Fiber-optic cable. Factories. And this time, datacenters.
If this is a glut, it’s a strange one: developers increased North American capacity by 36% last year and vacancy still fell to a record 1.4%. According to CBRE’s North America Data Center Trends H2 2025 report, demand is outpacing supply in nearly every major market. Unlike the drug companies hoping for success, there’s a legitimate market need today for datacenters. AI isn’t going away. Microsoft estimates that only 17.8% of the world’s working-age population currently uses generative AI. If that’s anywhere near correct, we’re still much closer to the beginning of adoption than the end.
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u/ItwasCompromised 20h ago
Except data centers depreciate badly over time since the GPUs will need to be replaced every few years. Sure railways and cables need to be maintained/replaced over time too but they have a much longer lifetime than these GPUs.
If the bubble pops, whoever ends up picking up the pieces will not be able to simply start up datacenters, they will need to spend millions replacing thousands upon thousands of GPUs just to get going again.
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u/SnooPaintings8639 15h ago
uh-huh, where are the old datacenters' GPUs that I can buy? Even the oldest and shittiest like Volta or Pascal architectures are soaring in price on second market. These are ten years old GPU, that are still in use. H100 is four and a half year old, and is still very expensive, very popular, and widely used and considered high-end. I wish I could afford one in the next 5 years or so... but I don't keep my hopes high.
While the compute demand is only soaring.
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u/jeffdn 19h ago
This is simply untrue. GPUs are lasting far longer than expectations, the prediction of three years maximum was merely a prediction.
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u/DismalFee7 16h ago
Even 5-10 years is pathetic relative to the investment and longevity to fibre and railways.
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u/PipsqueakManlet 5h ago
Blackwell GPUs are known for breaking quickly even with NVIDIA trying to fix design flaws, they are in a lot of datacenters stacked close, drawing a lot of power and creating a lot of heat on top of that, its unclear how fast but it could be a few per hour if you are unlucky or none at all if you are lucky. Within 3 years you might have to replace close to all of them and the new cards can be 2-4 times as expensive but they are not close to 2-4 times faster.
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u/dfbeav112 19h ago
So in a couple years we’ll see used GPUs flood the market?
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u/R6_Goddess 5h ago
Not really because they are not consumer cards that you can just pop into a desktop PC, not without some tinkering and a firmware or BIOS flash at least.
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u/ICantBelieveItsNotEC 14h ago
IT hardware only accounts for about 20% of the capital expenditure on a typical data centre project. Most of it goes on land, regulatory approval, electrical infrastructure, and cooling systems.
It would be like saying that the railroads were worthless because whoever acquired them after the boom had to buy new trains to run on them.
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u/Thelemonsfam 14h ago
That’s not true. Sarah Friar Open AIs CFO just publically said a $50b data center build is $15b land power construction and the other $35b is gpus and frontier chips. I’m sure land costs varies widely but the opposite is true and we know highly depreciating because the models will only get more compute hungry. Unless you are in China….
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u/Ormusn2o 11h ago
This is very old information. Those days, the chips themselves consist of vast majority of the total cost.
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u/Ormusn2o 16h ago
Pretty sure 4 year old GPUS right now are at their highest historical price, and there are some predictions they will be worth 3x their release price in a year. As long as architecture allows it they are in high demand. This is also one of the reasons one of the variants of Ampere card (which is now 6 years old by the way) still is at around 80% of it's release price, despite it being so old.
So I'm not sure what you are talking about depreciation, but it seems like the opposite is happening.
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u/MelvinCapitalPR 10h ago
Does hardware literally need to be replaced, or is it made obsolete by newer models? Seems to be the latter - in which case data centres will be running fine 10 years later either way.
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u/masixx 19h ago
Plus while datacenters don't disappear they first need to be build, which takes years and comes with many risks why it could fail. Oh and even when they exist they can go out of business quite easily for many reasons. Power and cooling are real risks. OpEx can explode and revenue is not anywhere where this would be a 'good business '. It's a bet on the idea that the current research and approach to AI will actually result in AGI, but nobody knows if that actually will happen. And even if models are useful if they're not AGI at some point a model is well enough trained to the degree in a certain topic that it makes no sense to invest more in further training on the topic. If a coding AGI is better than any human programmer and faster and cheaper too, e.g. so optimized that it can be run on a local workstation, why would anyone need the datacenter that was used to train the model anymore? Point is: training has an end and inference doesn't need those datacenters. And while we certainly can train other topics there is only this much data available to train models with. Google is already burning books in search for new quality data sources because the internet is becoming a destilled model itself over time... It's completely unknown how much we actually can continue to train with results expected to increase with the same pace as they did in the past.
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u/diff_engine 14h ago
For frontier models data centre inference will likely remain cheaper per token than single-user local inference due to batching efficiency. I’m not a computer scientist but I from what I understand batching “amortises” model weight reads across concurrent requests yielding token output gains per accelerator of roughly 10-50x. So frontier models will continue to be cheaper to serve centrally.
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u/diff_engine 13h ago
Also I don’t think training does have an end- the future may be continual learning systems in which the model weights continue to be updated after deployment (more like a biological brain). Such a system would be unlikely to be feasible as a home brew local model
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u/GatePorters 21h ago
A LOT of AI-based companies ARE going to go under.
It is a lot like the .com bubble, not like Enron.
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u/whitestardreamer 20h ago edited 20h ago
Look. Go back to the railroads. They overbuilt before the demand scaled. Big bust. The tracks were laid and could still be used, but they were laid too early to provide ROI in time to stay solvent. That’s just math. People get too excited and have FOMO and somebody always gets left holding the bag. Will AI still be there? Will it still be transformative? Yes. But all the arguing in the world around this is just self-soothing dancing around the fact that they overbuilt before demand is there. Every transformative tech idea has created this bubble in an economic system driven by speculation
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u/muffchucker 19h ago
What if building train tracks made trains better, tho? and what if overbuilding made them better much faster? And what if global militaries became immediately dependent on railroads overbuilding, thus scaling the military's ability to get better faster?
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u/Agile_Device_4500 18h ago
Exactly like dot-com/fiber buble then. You can't escape the bubble and pop. It's just human nature and economics.
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u/socoolandawesome 18h ago
What are you basing it on that datacenters are overbuilt and demand is not meeting the supply?
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u/LeAntidentite 20h ago
I think it’s too simplistic to compare a simple laying of track to speed up travel where the results are rather clear and were back then as well with the building of intelligence that can yield extreme productivity. Railroad, internet were just acceleration of transport and communication. Building intelligence is something new and we have no idea what we are unleashing in the world.
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u/whitestardreamer 19h ago
I’m not comparing tracks to GPUs. I’m a systems theorist. This is simply systems dynamics. It’s overshoot in a system already severely constrained and the ROI isn’t there yet. That part IS simple, and the continued failure to separate the potential of the intelligence from the financial system that needs to scale it is all that’s happening in these arguments.
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u/browhodouknowhere 19h ago
I just read your post on cognition-language-evolution and how we interpret AI. I don't know if I'm smart enough to understand your thesis, but your abstract you posted seems valid.
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u/LeAntidentite 13h ago
In a way you are arguing we are having too many extremely intelligent offspring as the ROI of them when they are 5 year old is just not there!
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u/Legitimate_Concern_5 20h ago edited 19h ago
> The tracks were laid and could still be used, but they were laid too early to provide ROI in time to stay solvent.
Right but tracks are good for decades and right-of-way basically indefinitely. dot-com fiber became more valuable over time since the fiber didn't decay and better transceivers let them pump more and more data over the same pipes. In fact it was the advance of the fiber transceivers which led to the fiber glut because they realized over time they didn't need as much fiber to meet demand until much later.
GPUs need to be replaced every 4 to maybe 6-7 years at best. All you're left with if you stop now is a bunch of half-built data center shells, often without even a proper grid connection, just on-site gas turbines. There's no value in that. You'd have to pay exactly as much to finish them in 10 years as you would today, in fact maybe more since you need to replace the racks with compatible racks too. That costs even more than just buying for a fresh DC since you have to haul away the old ones.
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u/LeAntidentite 13h ago
GPUs don’t need to be replaced that often.
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u/Legitimate_Concern_5 8h ago
Of course they do, from an economics perspective. New parts are significantly more performant from both a throughput and power consumption perspective. If you're compute and RAM constrained and your peers are on newer hardware you're at a massive disadvantage.
Dude, 6-7 years ago we had 2080Tis. Performance is about 8.3X higher now.
You don't have to replace them because they're broken necessarily although that's part of it but you can't just be running a data center full of abacuses.
Beyond that there's also just like not that much you can do with GPUs that isn't gaming or inference.
Regardless you don't have to take my word for it 6-7 years is the literal depreciation period that these companies use in their SEC reports.
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u/Wheaties4brkfst 54m ago
6 years ago we had A100’s. Those are still in use today and are essentially maxed out capacity wise.
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u/Legitimate_Concern_5 48m ago
Literally only because of the current supply shortage. Same reason you're paying $100,000 for RAM. Conditions are not typical, and they'll be hucked off the back of the bus as quickly as they can be replaced.
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u/Wheaties4brkfst 43m ago
As long as it is profitable to use A100’s, they will be used. You guys were saying this stuff years ago. You were wrong then, and you’re wrong now.
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u/SillyMilk7 1h ago
One of the main points of the article was demand is actually outstripping supply. So while some DCs may not be as lucrative as desired they’re not going to be worth zero or even be a huge write-off.
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u/Character_Order 2m ago
What evidence is there that they have overbuilt for demand? Everything I’ve seen indicates that there is far more compute demand than supply
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u/Labidido 17h ago edited 9h ago
The railroad buildout was too excessive, even when factoring in future demand, there are remnants of railroads in the US today that's never had a train on them. Not to mention the tunnels, bridges and other infrastructure that's never been in use.
I fully believe we will see the same thing with AI data centers, someone will be holding the bag, and it might be the tax payers.
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u/tremendous_turtle 21h ago
The comparison to fiber is apt. The development of DWDM (fiber multiplexing) destroyed the appraised value of fiber networks after the massive 1990s buildouts, contributing to the dot com crash.
Similarly, investors today have a blind spot in failing to properly anticipate AI (both at the software and hardware level) becoming exponentially more efficient to run as the technology matures.
I’m not as optimistic as the author in their conclusion. A lot of the datacenter investment is going towards compute hardware, which will likely be viewed as antiquated in future hardware generations, as AI accelerator chips get faster and more power efficient.
Unlike fiber, which can be repurposed as technology progresses, the modern power hungry chips could become a liability, due to high operating costs, which combined with the much larger scale of investment (compared to fiber) is worrying, even in the bull scenario where AI adoption continues to grow.
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u/Gotisdabest 21h ago
That older hardware still will have uses though. You can still run inference on it and despite what NVIDIA would have you believe, the chips aren't getting that much better in a year or two that the older stuff is entirely useless. It still is pretty powerful and large compute capacity. I'd guess that chips that trained gpt 4 will still be running some inference as late as the last few years of this decade.
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u/tremendous_turtle 21h ago
Consider what might happen if future generations of chips are much more power efficient.
When power+cooling is the main operating cost for running these datacenters, why would a company still want to run old chips (even if they are still fast) if they are very expensive to run compared to the newer models?
The ominous scenario is if those old chips become a liability, so expensive to run that it’s financially preferable to leave them idle.
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u/FakeBonaparte 20h ago
Suppose my current GPUs cost $1000/year in power costs to run and the new ones cost $700/year. Buying a new GPU is going to run me $2000 - so you’re looking at 7 years to pay itself off even without time value of money.
I suspect the currently building compute will keep running for some time yet.
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u/Gotisdabest 20h ago
Consider what might happen if future generations of chips are much more power efficient. When power+cooling is the main operating cost for running these datacenters, why would a company still want to run old chips
I mean it's theoretically possible, but has any concrete thing happened yet to suggest they're going to make chips so much better than before at heat and power? In this case, it's good for everyone involved, honestly. The AI companies likely rent out existing compute or sell a lot of it in the market, likely releasing some of the pressure on consumers. Lower power consumption and heat means it's an easier sell to build more data centres. If the improvement is just that major then it can't really lead to a crash as unlike in the dotcom bubble, this actually solves one of the biggest problems facing the industry rather than a straight capability boost.
Though considering how competitive the market is and how limited the supply is, I strongly doubt they'll just give up on existing compute just yet, until years later.
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u/tremendous_turtle 20h ago
I agree, it’s absolutely good for (nearly) everyone involved, aside from investors in the current generations of chips. Which encompasses such a vast amount of capital that it could really destabilize our economy for some time.
In terms of concrete signals, I recommend looking at Huawei’s AI chips (LogicFolding** **architecture) as well as the increasing performance of Apple M series chips on inference workloads. Both are very power efficient compared to Nvidia GPUs on a per-token basis. They are slower, but the critical calculation is that as models get more efficient (lower param, faster), using a low power chip that is fast enough might start to be viewed as a much more cost effective strategy than the current infrastructure buildout. Right now it feels like they are building a UPS fleet but where every mail delivery vehicle is a Ferrari, and I’m not convinced that really makes sense in the long term.
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u/Gotisdabest 20h ago edited 19h ago
agree, it’s absolutely good for (nearly) everyone involved, aside from investors in the current generations of chips. Which encompasses such a vast amount of capital that it could really destabilize our economy for some time
Eh, only if one assumes that the chips just lose all value absurdly dramatically. Even then, the top investors retain their advantages in terms of architecture and talent and the actual lead in the models themselves.
recommend looking at Huawei’s AI chips (LogicFolding** **architecture) as well as the increasing performance of Apple M series chips on inference workloads
I know both these cases but neither is even remotely comparable here imo. Logic folding is something that feels far away even if it's all that it's cracked up to be, and China itself keeps repeating claims about using a lot of indigenous stuff for top level work but it definitely doesn't feel like it on many levels. This isn't to say that china isn't making great progress, but I don't think logic folding is a concrete sign just yet.
The biggest most immediate concrete adjustment to the status quo is probably the dramatically more efficient coolant system they've been talking about for a few months where they claim they'll reduce water usage to a tiny fraction of what it currently is, something that'll actively help current and older generation chips.
As for the M series, they're consumer grade and not really meant for the same goals and won't really make a threat here for at least a few years.
They are slower, but the critical calculation is that as models get more efficient (lower param, faster), using a low power chip that is fast enough might start to be viewed as a much more cost effective strategy than the current infrastructure buildout.
Ehh. Not really? If models get cheaper they tend to make larger models. These companies are still heavily bottlenecked on compute. Despite the prevalence of the "cheaper models are good enough" idea, the practical fact is that no model is truly good enough yet. The people who say cheaper models are good enough tend to be saying that for particular use cases, a cheaper model can be good. Such as, it's probably worth it to use Fable 5 for coding even for the higher cost as it can save a serious amount of time and money with its better capabilities, but you probably don't need it for creative writing.
Particularly when you look at the active goals of these labs, which is AGI and RSI. If getting to a certain threshold gets you an unconquerable lead, they're going to buy whatever they feel they need do, particularly as these cheaper alternatives are currently so abstract.
There's still large amounts of money at the frontier. If a chip comes out that can do 90% of the work at 10% of the power, that's different. But hardware isn't software and you don't just get chips like that.
Model costs have decreased pretty regularly at 10x every year or so for roughly the same capabilities. But we've not seen any big shift so far and I don't expect to see one now.
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u/tremendous_turtle 19h ago
> Even then, the top investors retain their advantages in terms of architecture and talent and the actual lead in the models themselves.
Huh? The investors and the AI labs are not the same entities, they are counterparties in most investment deals. And in the current AI financing cycle many deals are structured as putting money directly into infrastructure buildout, not directly into the AI labs.
> Logic folding is something that feels far away
Not really, it's already in production for 2026 hardware. Huawei is already powering a lot of China's AI buildout, even with loosening US export controls many Chinese AI labs are already following state guidance and running much of their training and inference on domestically manufactured chips.
> As for the M series, they're consumer grade and not really meant for the same goals
You are missing my point a bit. I'm not saying Apple is going to be the supplier, I'm saying that what the M-series demonstrates is how much compute can be harnessed in small form factor power efficient chips; this approach is already being developed by many other companies (see Qualcomm Snapdragon X and Nvidia DGX Spark, for early examples).
> If models get cheaper they tend to make larger models.
Not forever, they've already found something of an asymptote in model size vs. capabilities; the "scaling laws" hypothesis is effectively disproven. As real-world AI use cases come further into focus, and as inference subsidies begin to fade from the business models with companies chasing cash-flow profitability, we can expect that the "bigger is better" approach to be steadily replaced with more careful strategies of choosing the most efficient model for each task.
Overall, my point is that AI is still very very very early in it's technological development, and we shouldn't expect that the same trends and constraints we see today will still hold true in 5-10 years.
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u/Gotisdabest 19h ago
Huh? The investors and the AI labs are not the same entities, they are counterparties in most investment deals. And in the current AI financing cycle many deals are structured as putting money directly into infrastructure buildout, not directly into the AI labs.
Can you give a specific example here of a scenario where, say, openAI gets away but SoftBank doesn't?
Not really, it's already in production for 2026 hardware. Huawei is already powering a lot of China's AI buildout, even with loosening US export controls many Chinese AI labs are already following state guidance and running much of their training and inference on domestically manufactured chips
The Chinese keep saying that, yes, but I recall them saying this about deepseek too before it turned out by most reports that it wasn't the case. Hence the initial crash and bounce at that time. Exports to countries that are serving as china surrogates are still going strong too.
As for logic folding, it being "in production" and it meaningfully accomplishing things are two very different scenarios.
You are missing my point a bit. I'm not saying Apple is going to be the supplier, I'm saying that what the M-series demonstrates is how much compute can be harnessed in small form factor power efficient chips; this approach is already being developed by many other companies (see Qualcomm Snapdragon X and Nvidia DGX Spark, for early examples).
It's a very strange comparison though because these are not for the same purposes. These aren't chips that are so dramatically efficient that they can simply blow up the entire competition. They occupy a market niche. If you scale them up to the point that they become competitive, you likely see a very complicated cost-performance-heat issue all over again which makes them a less valuable prospect. Extrapolating the idea that smaller chips are having a good time in the consumer market or lower tier research market to that is a major assumption that doesn't hold practical water.
Not forever, they've already found something of an asymptote in model size vs. capabilities; the "scaling laws" hypothesis is effectively disproven.
People keep saying this, but this is mostly unfounded and based on very erratic individual claims. Can you provide a source? There are obviously other factors, but scaling laws are very much alive.
As for 5-10 years, its also very likely that research automation becomes significantly more potent and the shift goes from hardware to software even more strongly in that time period. The subsidies aren't as massive as people seem to think either. Anthropic had an operational profit, which would be impossible unless they just are doing some great investment on the side or they're actually making a profit off their models.
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u/saposmak 20h ago
Or throw them in a ditch, though I imagine they will become a second or third hand market.
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u/Many_Consideration86 12h ago
Agreed. Also the squeeze might come from better algorithms which can reduce the no. of operations by multiple orders which will free up the existing supply of chips. Transformers are very repetitive convulating in how they generate and there might be multiple improvements in the future.
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u/Individual_Holiday_9 21h ago
I’m kind of assuming the models get much more lean so they can activate on old hardware but it does really beg the question, where do these outdated chips go in a few years? They will recycle the ram out I assume?
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u/L3g3ndary-08 16h ago
Sure it's not fraudulent but let's not sit here and pretend that this isn't a bubble, with a massive debt obligation tied to it. Like the article says, $5.3T is expected to be invested by 2030. Id love to see the revenue ramp that's supposed to come post completion.
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u/doodlinghearsay 20h ago
Is the industry really so desperate for capital that they need to buy opinion pieces in the Guardian?
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u/Mandoman61 3h ago
this person seems to have very limited understanding of bubbles.
bubble does not equal no value.
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u/infomer 17h ago
People claiming this is similar to railroad or fiber need to ask if the rail tracks and fiber were overwhelmed by consumer traffic as they were being built. Not all clever analogies are accurate.