Four Bubbles Wearing One Trenchcoat: A Developer's Autopsy of the AI Boom vs. the Dot-Com Crash

by Mahan (@MahanKenway on GitHub)

There's a stretch of rural Louisiana and Kansas where, if you dig a few feet down next to certain county roads, you'll still find coils of unlit fiber-optic cable from 1999. Nobody ever turned the light on. Companies like Global Crossing and 360networks buried something like tens of millions of route-miles of glass on the belief that internet traffic was doubling every three months, financed it with debt, and were bankrupt before a lot of that fiber ever carried a single packet. Some of it sat dark in the ground for over a decade before anyone lit it up.

I keep thinking about that buried glass every time another "AI capex" headline shows up in my feed. Not because I think GPUs are about to become the new dark fiber (I'll get to why that comparison is way more complicated than it sounds), but because it's a good reminder that bubbles don't leave behind vague "irrational exuberance." They leave behind physical, dated, traceable stuff. Warehouses. Turbines. Debt schedules. Depreciation tables. If you want to know whether something is actually a bubble, you don't ask whether people online are excited about it. You go look at the concrete. Or in this case, the substation.

So that's what this is. Not another "is AI a bubble, yes or no" hot take, there are already about four thousand of those and I refuse to add a fifth. I don't even think that's the right question, honestly, because "the AI bubble" isn't one thing. When you actually pull it apart, there are at least four separate, only loosely related bubbles stacked directly on top of each other, each with its own mechanism, its own failure mode, and its own timeline. Some of them look a lot like 2000. Some of them don't look like anything from 2000 at all, because the thing that's actually fragile this time around isn't even the stock market.

Grab a coffee, this one's long. I did the reading so you don't have to, mostly.

Bubble #1: The one everyone already knows about, valuation

This is the boring one, the one that gets its own chart in every finance newsletter, so I'll go through it quick and then move on to the stuff nobody's actually talking about.

By early 2026 the Shiller CAPE ratio (a cyclically adjusted price to earnings measure that smooths out short term earnings noise) sat around 41. The dot-com peak in 1999 hit roughly 44 to 45, the highest reading in the entire history of the metric. Forty one is the second highest reading in 125 years of data, which is not exactly a comforting sentence to type out loud. Palantir was trading at a trailing P/E somewhere north of 130 on revenue of about $5 billion. That is not a typo, I checked it twice because it made me laugh out loud at my desk. Nvidia is up roughly 2,000% off its late 2022 lows, compared to Cisco's about 1,000% run into its March 2000 peak, and briefly became the most valuable company on Earth the exact same way Cisco once did. AI focused startups pulled in 61% of all global venture capital in 2025, up from about 30% just three years earlier. That's a level of capital concentration into one category with basically no historical precedent outside of dot-com itself.

None of this is subtle. If your only tool is "compare the multiples," the answer is: yes, obviously, this looks stretched, arguably more stretched than almost any period in market history except one very specific one.

But honestly? This is the least interesting bubble of the four, because everyone is already staring straight at it. Valuation bubbles get corrected the boring way, by markets doing what markets do, repricing, sometimes violently, sometimes not permanently (a bunch of dot-com survivors eventually clawed back to and past their old highs, just twenty years later, which is a rough timeline if you were counting on it for retirement). The valuation multiple is the symptom. The other three bubbles are closer to the actual disease.

Bubble #2: The one hiding inside the accounting, depreciation

This is the one that actually made me sit down and write this whole thing, because it's a purely technical, almost nerdy argument, exactly the kind of thing a room full of developers would enjoy tearing apart over lunch, and almost nobody outside finance Twitter explains it in plain english.

Here's the mechanism, and I promise it's more interesting than it sounds. When a hyperscaler buys a rack of GPUs, it doesn't expense the full cost the day it gets racked and plugged in. It depreciates it, spreading the cost over the estimated "useful life" of the hardware, and that estimate is management's call, not some fixed external rule handed down from on high. Right now the big cloud providers are depreciating Nvidia GPUs and the servers around them over five to six years. Michael Burry (yes, the Big Short guy, and yes, he's been early and flat out wrong before, worth keeping in mind before you fully bet the farm on him) has been arguing loudly that this is absurd, because Nvidia itself ships a new flagship architecture roughly every one to two years, and the real economic replacement cycle for this hardware looks a lot closer to two or three years, not five or six.

Why this actually matters

Stretching the depreciation schedule doesn't change how much cash actually left the building the day of purchase. It changes how much expense hits the income statement this year versus later. Report a longer useful life and this year's depreciation charge shrinks, and reported profit goes up, with zero change to the actual economics of the hardware sitting in the rack getting slowly outdated by the next chip generation. Burry's math puts the cumulative understatement across the whole industry at something like $176 billion between 2026 and 2028, and singles out Oracle and Meta as overstated by roughly 27% and 21% of earnings respectively by 2028, if his numbers hold up.

Is it fraud? Almost certainly not in the legal sense, accounting standards give real, legitimate wiggle room on useful life estimates, and it's genuinely hard to prove a specific number is "wrong" instead of just "optimistic." But it's a lever, and pretty much the whole industry appears to be leaning on it in the same direction at the same time, which is exactly the kind of correlated blind spot that looks completely obvious in hindsight and totally invisible in the moment. It's the accounting version of an entire industry quietly agreeing to use the same slightly-too-generous assumption, because nobody wants to be the first one to write it down and look worse than everyone else on the earnings call.

Bubble #3: The one that isn't really revenue, the circular financing web

This is the strangest one of the four, and honestly the one I think is the most underappreciated outside of people who track these deals for a living.

Trace the money through a few 2025 to 2026 headline deals and you get something like this: Nvidia takes an equity stake in OpenAI. OpenAI commits to buying enormous amounts of compute from Oracle. Oracle commits to buying tens of billions of dollars of chips from Nvidia. Microsoft and Nvidia both invest directly into Anthropic, which in turn commits tens of billions of dollars to buying Azure compute from Microsoft. AMD sells OpenAI the equivalent of a 10% equity stake in exchange for a multi-gigawatt chip supply deal. Try drawing this on a whiteboard, I dare you, you'll run out of arrows before you run out of companies.

One widely shared Bloomberg diagram that made the rounds in mid-2026 traced roughly $46 billion in direct equity stakes and $879 billion in multi-year purchase commitments circulating among a fairly small set of names: Microsoft, Oracle, Amazon, Google, Meta, OpenAI, Anthropic, xAI, CoreWeave, Nvidia, and AMD.

None of these deals are illegal, and none of them are secret, they're all announced with press releases and glossy investor decks. The uncomfortable part is what it does to how you should actually read the headline numbers. When Nvidia invests in a company that turns around and spends a meaningful chunk of that money buying Nvidia chips, the resulting "revenue" is real dollars changing hands, sure, but it's not the same signal as an independent customer showing up with their own money because they genuinely need the product. It's closer to a loop than an actual market. Bernstein analyst Stacy Rasgon flagged exactly this dynamic around the Nvidia-OpenAI deal, and UBS separately estimated the OpenAI-Nvidia relationship alone could represent something like 13% of Nvidia's projected 2026 revenue.

Layer on top of that the MIT "GenAI Divide" study, which found that roughly 95% of enterprise generative AI pilots showed no measurable profit impact for the companies actually running them, and you get a genuinely uncomfortable picture: enormous, real capital expenditure flowing in a tight loop between a handful of companies, while the actual end customer proof of durable ROI is thin. That doesn't necessarily mean the technology doesn't work, plenty of us use these tools every single day and get real value out of them (I certainly do, otherwise I wouldn't be typing this into one). It means the revenue recognition trailing the technology might be running well ahead of the value creation, which is a very specific and very familiar kind of gap to anyone who lived through the last one.

Bubble #4: The one buried in concrete and copper wire, the physical buildout

This is where the fiber optic ghost story from the intro finally earns its keep, and it's also where the comparison to 2000 gets genuinely complicated, because the differences matter just as much as the similarities do.

The scale here is not subtle. AI related spending hit roughly $375 billion in 2025 and is projected to reach around $500 billion in 2026. OpenAI alone has stacked up deals worth roughly 26 gigawatts of GPU capacity across its agreements with Nvidia, AMD, and Broadcom, which is enough electricity to make a power engineer sweat just reading the number. Jim Chanos, the short seller who called Enron before it collapsed, has been openly drawing the fiber glut parallel, pointing out that in 2000 the industry consensus was that internet traffic was doubling every quarter (it wasn't, the real number was closer to doubling annually), and that shaky consensus was enough to justify laying tens of millions of miles of cable that mostly sat dark for a decade.

Here's the honest complication though

The financing structure underneath this buildout looks meaningfully different from telecom in 2000. The fiber bubble was built on junk rated debt from companies with no real balance sheet, plus vendor financing from equipment makers like Lucent and Nortel who literally loaned their own customers the money to buy their own gear, a setup that guaranteed a domino effect the second traffic growth disappointed even slightly. Today, by most estimates, something like two thirds of 2026's AI capex is coming directly out of the operating cash flow and equity of Microsoft, Alphabet, Amazon, and Meta, companies that, whatever you think of their valuations, are genuinely some of the most cash generative businesses that have ever existed on this planet. That's a real, structural difference, not just a talking point somebody's PR team came up with.

But it's not the whole picture either, because a meaningful and fast growing slice of this buildout (the "neoclouds" like CoreWeave, the joint ventures, the special purpose financing vehicles) is running through leases and debt structures that sit off the parent company's balance sheet entirely. One recent estimate put off balance sheet debt across five major hyperscalers at something like $1.65 trillion, and credit default swap spreads on some of these names have reportedly doubled in a matter of months as bond markets start actually pricing in real risk instead of just vibes. The Bank for International Settlements flagged almost exactly this in its 2026 annual report, hyperscaler debt tied to AI buildouts growing faster than the balance sheets that are supposed to be carrying it.

And then there's a constraint that didn't even exist for fiber back in 2000: power. You can, in theory, build data centers faster than you can build power plants and transmission lines to actually feed them. Right now demand for AI compute is arguably being rationed by grid capacity rather than by customers walking away, which some analysts point out is functionally the opposite of a glut. The catch is that if you also build two or three years worth of power infrastructure on that same optimistic curve, you can turn today's shortage into tomorrow's oversupply almost overnight. That's a real risk. It's just a 2028 or 2029 risk, not a "this quarter" risk, and mixing up those two very different timelines is most of what makes this whole debate so confusing to follow in real time on Twitter.

There's one more twist worth sitting with, and it's the one that actually gives me some hope. The fiber that got buried and left dark in 2000 didn't stay dark forever. It became the physical substrate the entire streaming video, cloud computing, remote work economy runs on today, bought for pennies on the dollar by whoever survived the shakeout. The original investors got wiped out almost completely, the infrastructure itself turned out to be exactly right, just built about a decade ahead of demand and paid for by the wrong people at the wrong time. If the AI buildout rhymes with anything from 2000, it might be less "this is all going to be worthless" and more "this is going to be foundational, and most of the people currently paying the bill are probably not going to be the ones who end up owning it when the music stops."

So which bubble actually pops, and what does "popping" even look like here

If you're expecting one number and one date, I don't have it, and anyone confidently handing you one is selling something, probably a newsletter subscription. But splitting it into four does at least tell you where to actually look and what kind of event to expect from each one.

A pure valuation correction is the easiest to imagine and probably the least structurally damaging of the four. Multiples compress, a bunch of AI adjacent tickers get cut in half overnight, the profitable core survives, life goes on, roughly the same outcome the handful of dot-com companies with real revenue underneath the hype actually got.

The depreciation story resolves way more slowly and quietly, through restated guidance and gradually shortening useful life assumptions rather than one dramatic crash headline, unless something forces the issue faster, like an activist short campaign or an accounting regulator suddenly taking a public interest.

The circular revenue story is the one that resolves the moment growth merely disappoints, rather than actually failing outright, because in a closed loop, once one node slows its purchase commitments even a little, the "revenue" on the other side of that loop doesn't just grow slower, it can straight up evaporate, since it was never truly independent demand to begin with.

And the physical buildout is the one where the actual mechanism of failure, if there is one, probably shows up first in credit markets, not equities. A downgrade, a widening CDS spread, a special purpose vehicle quietly missing a payment, all well before it shows up as a scary headline stock crash. That's arguably the single biggest structural difference from 2000 worth carrying around with you: the dot-com bust was very visibly an equity story, playing out live on public tickers everyone could watch. If this one breaks, it may well announce itself first in the bond market and the off balance sheet financing vehicles nobody was really watching, which happens to be exactly the blind spot that made 2008 so much worse than most people expected going in.

What this actually means if you write code for a living

I'm not a financial advisor and none of this is a recommendation to buy or sell anything, please don't screenshot this and yolo your savings. But if you build software, especially anything with AI infrastructure underneath it, a few practical takeaways seem worth carrying around regardless of how any of this eventually resolves:

• Don't build your product's core economics around today's inference pricing being permanent. It's currently subsidized by exactly the kind of capital described above, and subsidized pricing has a funny way of quietly becoming un-subsidized pricing right around the moment you've built a whole business that depends on it staying cheap forever.
• Platform risk is real risk. If your product depends entirely on one lab's API, you're implicitly exposed to that lab's position in this exact financing web, whether you think about it that way day to day or not.
• The fiber glut precedent genuinely cuts both ways. Overbuilt infrastructure really can turn into decade defining, dirt cheap foundational capacity for whoever's still standing when the dust settles. That's an actual historical outcome, not just cope from people who bought too early. It's just usually not great news for whoever's holding the debt at the exact moment things clear.
• "Is AI useful" and "is AI overvalued" are two completely separate questions, and it's worth resisting the urge to let your answer to one contaminate your answer to the other. Plenty of internet companies back in 1999 were both genuinely useful and wildly overvalued at the same time, often the exact same company, sometimes on the exact same day.

The fiber under those Kansas county roads eventually got lit up. It just took the original investors going to zero first, and it took about a decade longer than anyone excitedly burying it in 1999 would have ever guessed. Whatever the AI buildout's version of that story turns out to be, the pattern of who actually pays for the mistake versus who inherits the infrastructure afterward is worth watching a lot more closely than whatever Nvidia's stock does on any given Tuesday.

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A note on sources

Everything above is paraphrased from public reporting and analysis rather than quoted directly. Where a specific figure or claim is doing real work in the argument, I tried to keep it traceable back to where it actually came from rather than just asking you to take my word for it:

• CNBC, Nov 2025, Michael Burry's depreciation accusation against hyperscalers
• Business Standard / Bloomberg, Sep 2025, Bernstein analyst on Nvidia-OpenAI circular financing concerns
• Yahoo Finance / 24/7 Wall St, 2026, the Bloomberg circular financing diagram and off balance sheet debt estimate
• Real Investment Advice, synthesis of the bear case including the MIT GenAI Divide study and UBS's OpenAI-Nvidia revenue estimate
• IEEE ComSoc Technology Blog and Opus Interactive, fiber optic buildout history and its comparison to AI data center capex
• Lance Roberts / ZeroHedge, 2026, power grid rationing argument and the operating cash flow vs debt financing distinction
• IntuitionLabs and IndMoney, CAPE ratio, VC concentration, and valuation multiple comparisons
• Benzinga, Jim Chanos on the fiber optic bubble parallel

Written by Mahan, self taught developer, GitHub: MahanKenway. If you've got a counter argument or think I got a number wrong somewhere in here, genuinely tell me in the comments, I'd rather get corrected than stay confidently wrong.

Tags: ai, dotcom, startups, techindustry, finance, opinion, softwareengineering, machinelearning, discuss, webdev