AI bubble red flags are here, and retails are going to end up paying for the infrastructure build out

Hello Fellow Apes,

This post is a follow up to the post I made about 8 months ago.

https://www.reddit.com/r/ValueInvesting/comments/1oyu1hi/ai\_bubble\_market\_crash\_healthcare\_and\_value/?utm\_source=share&utm\_medium=mweb3x&utm\_name=mweb3xcss&utm\_term=1

For those who are curious, my investments in Molina and Clover performed very well. Centene, however, never dropped to the price level I was targeting, so I chose not to buy any shares.

That said, the main purpose of this post is not to revisit those individual investment decisions. Instead, I want to return to the broader topic of what I see as an impending AI bubble, especially in light of the recent moves involving Micro, Micron, and SoftBank. In my view, these developments may represent one of the first meaningful signals that the AI bubble is beginning to approach its breaking point.

I also want to be clear about the scope of this discussion. I will not be focusing on oil shocks, debt levels, interest rates, geopolitical risks, or other macroeconomic events. Those factors are important, but they are outside the focus of this post.

This post is specifically about the AI bubble and how it may compare to the dot-com bubble. My goal is to examine the similarities, the warning signs, and whether the current enthusiasm surrounding AI is beginning to resemble the speculative excesses we saw during the late 1990s.

To better understand this, we need to examine the cash conversion cycle and the recent events.

What is a cash conversion cycle? The cash conversion cycle is how long a company’s cash gets tied up between paying suppliers and collecting cash from customers. In simple terms

How many days does it take to turn cash → inventory/products → sales → cash again?

for the accounting people out there

Cash Conversion Cycle = Inventory Days + Receivables Days - Payables Days

Inventory days = how long inventory sits before being sold.

Receivables days = how long customers take to pay after the sale.

Payables days = how long the company can wait before paying suppliers.

Now we juxtapose that with recent events.

1. Super Micro stock plunges as $7 billion equity raise overshadows booming backlog https://www.marketwatch.com/story/super-micro-stock-plunges-as-7-billion-equity-raise-overshadows-booming-backlog-c5df2fc8

1. SoftBank Attempt to Get $6 Billion OpenAI Margin Loan Stalls https://finance.yahoo.com/markets/stocks/articles/softbank-attempt-6-billion-openai-042525869.html

1. Is Micron Quietly Preparing for the Collapse in AI Demand? https://finance.yahoo.com/markets/stocks/articles/micron-quietly-preparing-collapse-ai-130225317.html

The AI trade appears to be shifting from “scarce chips print money” to “who can fund the supply chain and survive the cash conversion cycle?”

That distinction matters. Going back to the cash conversion cycle, some of these companies look like they are trying to jump from the order phase straight back into the cash phase, before the actual cash from sales has fully materialized. In other words, demand may be real, but the cash is not necessarily there yet. This raises an obvious question: if these companies are making so much money, why do they suddenly need more liquidity while their stocks are booming?

When a company says, “Demand is enormous, orders are booming, AI servers are selling like crazy,” but then also says, “We need billions in new liquidity right now,” the market should be asking: Are you actually generating cash, or are you just moving a massive amount of expensive hardware through the system? That is the part I think many people are overlooking.

The issue is not necessarily that these companies lack revenue. The issue is that they can be revenue-rich but cash-poor. A business can show strong revenue growth and even positive accounting profits while still bleeding cash. That is especially true in hardware. For AI infrastructure suppliers, cash leaves the business early. GPUs, memory, networking equipment, power and cooling systems, racks, and other components all have to be purchased before the customer fully pays. If gross margins are only in the single digits or low double digits, then a $1 billion server order may not actually generate that much profit after component costs, logistics, warranties, credit risk, financing costs, and overhead.

This is why hypergrowth in hardware can be dangerous. The faster these companies grow, the more inventory they need. The more inventory they need, the more cash they consume. Growth itself can create the cash crunch. The scary part is that raising equity while the stock is hot is rational. It may simply be opportunistic. But it also tells you that management believes now is the window. If the business were effortlessly printing cash, it probably would not need emergency-looking liquidity while the stock market is booming.

And importantly, backlog is not cash. Orders are not cash. Announced demand is not cash. A backlog can look impressive, but suppliers still need to be paid in real money, not future revenue. This is why the current AI infrastructure cycle rhymes with the dot-com bubble. The analogy is not perfect, but it is there: huge capex promises, aggressive financing, narrative-driven valuations, circular demand (the good old circle-jerk memes we have been seeing), and companies leaning on capital markets because the AI buildout is outpacing organic cash generation. That last part should be a major red flag: companies are leaning on capital markets because the AI buildout is moving faster than the cash generation underneath it.

Let's not forget that back orders can be canceled. "How Oracle’s Canceled Nvidia Server Order At Supermicro (SMCI) Has Changed Its AI Infrastructure Investment Story" https://finance.yahoo.com/sectors/technology/articles/oracle-canceled-nvidia-server-order-180557762.html Oracle reportedly canceled 300–400 Nvidia-based server racks from Super Micro, worth an estimated $1.05B–$1.40B. We're just going to ignore things like this?Are we

Some people will argue that this is different from the dot-com bubble because many dot-com companies had little revenue, weak products, or no real business model. That is true. This AI cycle has real revenue, real demand, and very profitable buyers like Microsoft, Amazon, Google, Meta, and Oracle.

But that does not eliminate the risk. It changes the nature of the risk.

The problem is not that AI demand is fake. The problem is that the balance sheet is being forced to front-run that demand. Customers are ordering enormous amounts of AI infrastructure, so suppliers like Super Micro Computer need to stock inventory, secure components, and extend working capital before the cash comes back in. If those orders quickly convert into high-margin cash, then dilution and financing look survivable. But if margins stay thin, customers delay orders, component prices move against them, inventories build up, or financing windows tighten, then these liquidity raises start looking less like opportunism and more like stress signals.

The bubble probably starts to crack when the market shifts from asking, “How much AI demand is there?” to asking, “How much cash are you actually making from AI sales?” That is the question that matters. Not bookings. Not backlog. Not hype. Actual cash generation from actual AI sales.

My speculation is that these questions will start becoming louder sometime between September and December. Right now, the recent news feels like confirmation that things are not going as smoothly as the headline demand story suggests. These companies appear to be preparing for a tougher phase of the cycle.

The three cases look different, but they point to the same broader issue:

1. Super Micro Computer: “Orders are huge, but we need cash now to buy the components required to fulfill them.”

1. Micron: “AI memory demand is huge right now, but memory is historically boom-and-bust. What happens if supply catches up or AI demand slows?”

1. SoftBank: “We are already extremely deep into OpenAI and the broader AI trade, so we have to keep financing the bet.”

Taken together, this does not mean AI is fake. It means the AI infrastructure trade may be entering the phase where the market stops rewarding the story alone and starts asking whether the cash flows can support the scale of the buildout.