TLDR Reddit Mentions | ChartExchange

TLDR: Once again, we're about to embark on a journey that I believe will be significantly larger than all past financial crashes combined.
This AI financial bomb (when, not if, it goes off) will obliterate the global money supply as we know it. In the past, at the very least, some assets remained that could be utilized later on. This time, nothing of any value will remain. Meaning that everything will be vapourized once this comes to its final, inevitable conclusion.
Timeline:
\- Initial shocks are already starting (South Korea)
\- Plenty more within the next 2 years.
\- Optimistically, we're looking at 5 years (likely far less).
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We have a problem, yet again.
A massive financial hit is about to be taken by everyone on the planet. Whether they signed up, or not.
We have to get some stuff out of the way first.
\- What is AI?
\- Second, money. It won't make sense at first but it will later on.
\- Third. Past crashes.
\- Fourth, data centers.
\- And last but certainly not least. How much damage will be done if this doesn't pan out?
# Artificial intelligence
Artificial intelligence is software that learns patterns from huge amounts of data and then generates new outputs that look intelligent. It doesn't think like a person. It predicts what comes next:
\- the next word, the next note, the next line of code.
Here's what it already does well:
Music:
\- Describe a style or mood and it writes complete songs in seconds.
Art:
\- Type a description and it creates detailed images or edits existing ones almost instantly.
Research:
\- Ask a full question in plain English and it summarizes information from across the web far faster than a traditional search, though the answers still need checking.
Code:
\- Tell it what you want a program to do and it writes working code, debugs problems, or explains existing scripts.
These tools are already used daily by millions of people. They are limited, can be wrong, and burn enormous amounts of computing power. But the results are good enough that companies and investors are pouring money into them at historic speed.
# Money
Money only has the buying power we give it.
If I think something is worth $100 and someone else thinks that same thing is worth $200, you can guess who the vendor is selling it to.
I don't have the thing, they do and the vendor will raise their price for everyone else. Because they can.
In most cases, people trade time and ability, for money.
If 1 hour of labour is worth $20 and they work 50 hours a week, they get $1,000.
That's about $50,000 a year.
Assume $35,000 after taxes.
From that, they have to make payments on a vehicle, pay for insurance, buy gas, and overall maintenance on that vehicle. Tires, brakes and oil changes to keep getting to their job.
Also pay a mortgage or rent, food, clothing, hydro, cell phone, internet and so on.
After 20 years, their $1 million, taxed down to $700,000, should leave plenty left over but we all know how it went.
Assuming a million people did the same, That's a trillion dollars essentially vapourized while the government spent $300 billion on social services.
I could get into inflation and sales tax here but it's not necessary for the point.
# Past Financial Crashes
Financial crashes do a lot of damage to everyone. Take the Wall Street crash of 1929, the most devastating financial collapse in American history. It began in October 1929 after the "Roaring Twenties". A decade where massive industrial expansion fueled intense public speculation. Regular people, frustrated by low interest rates on their bank accounts, poured their savings into the stock market. But beneath the surface, the real economy was fracturing; overproduction left factories and farms with unsellable inventory, leading to job cuts and falling wages.
Despite these clear warning signs, blind investor optimism kept pushing stock prices far above their actual, underlying value. The illusion finally shattered in September 1929 when experienced shareholders realized the growth was unsustainable and began selling off their holdings. This sparked a wave of mass panic, triggering a frenzied, unstoppable market sell-off. Even though the nation’s top bankers stepped in to buy up shares at inflated prices to fake a recovery, the bleeding couldn't be stopped. By the time the market finally bottomed out in 1932, the stock market had lost an incredible 90% of its pre-crash value. This rapid erosion of confidence completely broke the banking system, dragging the entire world into the Great Depression.
[https://en.wikipedia.org/wiki/Wall\_Street\_crash\_of\_1929](https://en.wikipedia.org/wiki/Wall_Street_crash_of_1929)
The dot-com bubble was a stock-market bubble that built up through the late 1990s and peaked on March 10, 2000. Fueled by the rapid spread of the World Wide Web, easy venture capital, falling interest rates, lower capital-gains taxes, and speculative fervor, investors poured money into almost any company with a “.com” name or internet-related business model. Often ignoring traditional metrics such as profitability or revenue. The Nasdaq Composite rose roughly 600% between 1995 and its peak, with many stocks soaring on the “get big fast” mantra and lavish marketing spending. Established tech, media, and telecom firms also benefited.
The bubble burst in 2000 through 2002. Rising interest rates, a Barron’s warning about cash-burning startups, the Microsoft antitrust ruling, and a cascade of high-profile failures triggered a sharp sell-off. From its peak, the Nasdaq fell about 78 % by October 2002, wiping out trillions in market value. Many pure-play internet firms went bankrupt or were acquired at fire-sale prices, while survivors such as Amazon and Cisco saw massive drops in valuation. The crash left a lasting legacy of caution among investors, tighter scrutiny of accounting practices, and a more measured approach to funding technology startups.
[https://en.wikipedia.org/wiki/Dot-com\_bubble](https://en.wikipedia.org/wiki/Dot-com_bubble)
We also can't forget the 2008 financial crisis.
A major worldwide financial crisis centered in the United States took place in 2008. The causes included excessive speculation on property values by both homeowners and financial institutions, leading to the 2000s United States housing bubble. This was exacerbated by predatory lending for subprime mortgages and by deficiencies in regulation. Cash out refinancings had fueled an increase in consumption that could no longer be sustained when home prices declined.
The first phase of the crisis was the subprime mortgage crisis, which began in early 2007, as mortgage-backed securities (MBS) tied to U.S. real estate, and a vast web of derivatives linked to those MBS collapsed in value. A liquidity crisis spread to global institutions by mid-2007 and climaxed with the bankruptcy of Lehman Brothers in September 2008, which triggered a stock market crash and bank runs in several countries. The crisis exacerbated the Great Recession, a global recession that began in late-2007, as well as the United States bear market of 2007–2009. It was also a contributor to the 2008–2011 Icelandic financial crisis and the euro area crisis.
[https://en.wikipedia.org/wiki/2008\_financial\_crisis](https://en.wikipedia.org/wiki/2008_financial_crisis)
As much as those seem to mirror what's going on with AI, I'd more likely relate it to the rail line over build of the 19th century.
The 19th-century railroad boom serves as the ultimate historical warning for modern technology overbuilds, culminating in a speculative bubble that shattered the American economy. Spurred by massive government land incentives and frenzied Wall Street speculation, rival companies raced to lay thousands of miles of redundant, parallel tracks to capture market share. Investors poured unlimited capital into any venture with "railroad" in its name, convinced this revolutionary network would permanently rewrite global commerce. Promoters aggressively prioritized rapid footprint expansion over sustainable business models, constructing an interconnected empire on a fragile mountain of corporate debt.
When economic reality finally caught up with the hype, the infrastructure bubble burst with catastrophic force during the Panic of 1893. Because the sparsely populated regions couldn't generate enough freight traffic to service the massive loans, overbuilt lines became instantly unprofitable. In a matter of months, a synchronized chain reaction of failures brought down the era's ultimate corporate titans, including the legendary Northern Pacific and Union Pacific railroads. This was not a localized crash. It was a systemic liquidation that froze the nation’s industrial heartbeat.
At the absolute peak of the wreckage, more than 150 different railroad companies went completely bankrupt, instantly paralyzing over 30,000 miles of track.
This massive corporate avalanche represented $2.5 billion in 1893 dollars, which adjusts to roughly $92 billion today, based on standard consumer inflation. However, because the 19th-century economy was so much smaller, that $2.5 billion loss wiped out a staggering 25% to 30% of the entire nation’s invested railroad capital overnight. Wiping out speculative investors and forcing a massive wave of industry consolidation, it left a tiny handful of powerful banking syndicates to inherit the nation's core infrastructure at a deep discount.
[https://theconversation.com/for-tech-giants-a-cautionary-tale-from-19th-century-railroads-on-the-limits-of-competition-91616](https://theconversation.com/for-tech-giants-a-cautionary-tale-from-19th-century-railroads-on-the-limits-of-competition-91616)
# Data Centers
What are they? How are they useful? How long have they been around? What the hell is going on right now?
Past purpose:
Data centers started as the modern descendants of mainframe computer rooms from the 1950s and 1960s. For most of their history they served as centralized, highly reliable facilities that stored data and ran the computing workload businesses and governments needed: accounting systems, databases, email servers, corporate applications, and later web hosting.
By the 1990s and 2000s, they became the quiet backbone of the internet and digital economy. Every credit-card swipe, online purchase, email, streaming video, search query, and cloud-based software service eventually passed through one. They replaced slower paper-based processes (think weeks for bank clearances) with near-instant digital ones. Until the early 2020s most people never thought about them because they simply worked in the background, owned or rented by banks, retailers, tech companies, and cloud providers (Amazon Web Services, Microsoft Azure, Google Cloud, etc.).
Current purpose:
They still do all of the above. Traditional cloud computing, enterprise IT, financial transactions, content delivery, streaming, and general internet services continue to run in data centers.
What changed is the addition of a new, extremely power-hungry workload: artificial intelligence.
Data centers now also train large AI models (the expensive, multi-month process of building systems like the ones behind ChatGPT and similar tools) and run inference (the day-to-day work of answering user queries, generating text/images/code, powering AI features in apps, etc.).
Newer facilities are increasingly purpose-built for this: denser racks packed with specialized chips (GPUs and other accelerators), advanced liquid cooling, and much higher power densities than traditional servers required. Many of the largest new campuses are designed primarily or exclusively around AI clusters.
Percentage of current use directed toward AI:
Exact figures vary by source and whether they measure power consumption, IT capacity, or new builds, but recent analyses converge on a clear range:
AI-optimized servers are estimated to account for roughly 30% of total data center power consumption in 2026 (Gartner).
Broader estimates of AI workloads (training + inference) put the current share in the 15–40% range depending on methodology, with the higher end reflecting power draw in modern facilities.
New capacity is far more skewed: a large fraction (often half or more) of net new data-center capacity coming online is intended for AI/ML GPU clusters.
# Potential Financial Bomb
Traditional non-AI workloads still make up the majority of the installed base, but AI is driving nearly all the recent growth in power demand, construction, and capital spending. The share is rising quickly as more inference capacity comes online and older servers are replaced or supplemented by AI-optimized ones.
Civilian water treatment and wastewater facilities across the United States are being expanded to support the data-center boom. These upgrades (new treatment plants, capacity expansions, pipelines, storage, and reuse systems) are frequently financed through public mechanisms:
municipal bonds, state revolving loans, federal grants, tax-increment financing, and other taxpayer backed tools.
The scale is significant. Independent research estimates that meeting projected data-center water demand could require $10 billion to $58 billion in new water infrastructure capacity nationwide in the coming years. Individual projects already run into the tens or hundreds of millions of dollars per community. In some cases, developers contribute or reimburse costs. In others, the public system carries a large share of the upfront investment and ongoing obligation.
The risk here is structural. Public water systems are built for decades of use. If a major data-center campus is delayed, scaled back, or abandoned, the expanded treatment capacity, staffing, debt service, and maintenance don't vanish with it. Those fixed costs remain on local taxpayers. The private operator can walk away; the public infrastructure stays.
This pattern is already visible in multiple states. It's the quiet public side of the private infrastructure race: communities are being asked to underwrite permanent capacity for a technology build-out whose long-term economics are still uncertain.
Unlike the physical fiber-optic cables of the dot-com era which can sit in the ground for decades, modern AI microchips have a brutal, short operating life of only five years before they become completely deprecated or obsolete. If the consumer and corporate revenue streams fail to scale up fast enough to pay off these loans, the true value of these highly specialized assets will instantly vanish.
But the hidden corporate debt is just the fuse. The real payload of this financial bomb explodes when you look at the true math of infrastructure (financing specifically). The difference between the initial principal and the long-term debt service.
These multi-billion-dollar data centers aren’t bought with upfront cash. They're financed through high-interest, long-term debt instruments that balloon the final cost.
First, look at the corporate and opaque shadow debt. The core tech sector and private equity firms have taken on roughly $8.5 trillion in principal to fund these projects.
But because this is funded through high-yield corporate bonds and private credit over a standard 15-year amortization period at current 6.5% interest rates, satisfying that debt actually requires a staggering $13.5 trillion once you account for interest.
Second, the cost bleeds directly into public infrastructure through municipal and utility grid debt. To keep these data centers running, public grids require massive reinforcement, high-voltage substations, and water-cooling pipelines.
This requires 20-to-30-year municipal and utility bonds. The compounding interest on these bonds transforms a $3 trillion public investment into a $6 trillion to $8 trillion taxpayer obligation, paid for by everyday citizens through spiked utility bills and local taxes.
Third, as governments dig in to protect domestic silicon supply chains and declare AI a matter of national security, they're stepping in with sovereign bailouts and subsidies.
State-backed loans, equity injections, and central bank liquidity programs are projected to pile an additional $5 to $7 trillion onto national debt burdens globally.
Finally, when the bubble inevitably contracts, we face secondary economic wealth destruction. The resulting defaults on municipal bonds, bank write-downs of stranded data center assets, and permanent losses in retail retirement portfolios will add an estimated $5 trillion in unrecoverable, systemic economic damage.
When you tally up every single layer of this crisis, the corporate shadow debt, the interest compounding over decades, the taxpayer grid overhauls, the sovereign national security bailouts, and the secondary wealth destruction, the final number stops looking like a corporate tech bubble and starts looking like an existential threat. When it's all finalized and the dust clears, the total systemic liability of this infrastructure arms race approaches a staggering, incomprehensible
# $50 trillion.
That's a multi-trillion-dollar weight strapped directly to the back of the global economy.
And that brings us right back to where we started. A financial hit of this magnitude can't be contained within Silicon Valley or Wall Street. It bleeds out into your spiked utility bills, your frozen retirement portfolio, your local taxes, and the devalued buying power of your hourly wage. A massive financial hit is about to be taken by everyone on the planet.
# Whether they signed up for AI, or not.

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