The Harsh Reality Behind the $26 Billion Crypto Liquidation: Liquidity Is Killing the Market
Original Article Title: There's Not Enough Money In The World
Original Article Author: @plur_daddy, Financial Analyst
Original Article Translation: Dangdang, Odaily Planet Daily
Editor's Note: Gold and silver plummeted, US stocks tumbled across the board, and the cryptocurrency market was hit hard with a 24-hour trading volume of over $26 billion. Bitcoin once experienced a flash crash to the $60,000 level, plunging nearly 20% in a single day. From its peak of $126,000 in October last year, the BTC price has been halved. What's even more alarming is that the market has seen almost no meaningful resistance.
Everyone is frantically searching for reasons: US tech stocks dragging down crypto, Trump's nomination of Warsh triggering hawkish expectations, a strong dollar, poor job data... These explanations all sound reasonable. However, in the view of the author of this article, they are mostly superficial and not the core issue. The real underlying reason is that there is simply not enough money in the world. The AI massive capital expenditure cycle itself is shifting from "injecting liquidity" to "extracting liquidity," leading to a substantial shortage of global financial capital.
The following content is from the original author and will systematically break down how this mechanism operates.
We are undergoing a paradigm shift in the market, driven by a financial capital shortage caused by the AI capital expenditure cycle. This has a profound impact on asset prices because capital has been excessively abundant for a long time. The Web 2.0 and SaaS paradigms that drove the market prosperity of the 2010s were essentially extremely light capital business models, allowing a large amount of surplus capital to flow into various speculative assets.
Yesterday, while discussing the market structure, I suddenly had an "epiphany moment." I believe this is the most differentiated article I have written in a long time. Below, I will break down layer by layer how everything functions.
There is actually a highly similar mechanism between AI capital expenditure and government fiscal stimulus, which helps us understand its underlying logic.
In fiscal stimulus, the government issues bonds, and the private sector then absorbs duration risk; the government then receives cash and injects it back into the economy. This cash circulates in the real economy, creating a multiplier effect. Its net impact on financial asset prices is positive precisely because of the existence of this multiplier effect.
When it comes to AI capital expenditure, mega-cap tech companies either issue bonds or sell government bonds (or other assets), with the private sector absorbing the duration risk; these companies then receive the cash and put it to use. This cash similarly circulates in the real economy, generating a multiplier effect. Ultimately, the net impact on financial asset prices remains positive.
As long as this funding comes from the “dry powder” in the economic system (idle capital not yet deployed), this process can run smoothly. It works well, almost able to “lift all boats.” In recent years, this has been the dominant paradigm—AI capital expenditure acting as an incremental stimulus, injecting a stimulant into the economy and markets.
The issue is: once the dry powder is depleted, any dollar inflows into AI capital expenditure must be diverted from elsewhere. This triggers a convexity scramble for capital. When capital becomes scarce, markets are forced to reevaluate: where is capital best deployed? Meanwhile, the cost of capital (i.e., interest rates set by the market) rises.
Let me reiterate: when money becomes tight, there is an “elimination game” among assets. The most speculative assets suffer disproportionately—just as they disproportionately benefited when capital was abundant but lacked productive use. In this sense, AI capital expenditure actually plays a role in “reverse QE,” leading to a negative asset allocation rebalancing effect.
Fiscal stimulus typically does not face this issue because the Fed often ends up being the ultimate absorber of duration risk, thereby avoiding “crowding out” effects for other capital purposes.
The term “money” used here can be interchangeable with “liquidity.” However, the word “liquidity” can be confusing as it has different meanings in different contexts.
Let me give you an example: money or liquidity is like water. You need the water level in the bathtub to be high enough for financial assets (those floating rubber ducks) to rise together. To do this, there are several ways:
· You can increase the total amount of water (rate cuts / QE)
· You can unclog the inflow pipes (similar to current operations like RRP (reverse repurchase) / RMP (reserve management purchases))
· Or you can reduce the rate at which water drains from the bathtub.
When discussing liquidity in the economy, the focus is almost always on the money supply. However, in reality, demand for money is equally important. The issue we currently face is: excessive demand, leading to a significant crowding-out effect.
There have been reports in the media that the world's "deepest pockets" — Saudi Arabia and SoftBank — have essentially been squeezed dry. Over the past decade, the world has been on a frenzy of asset gorging, and now it is "full." Let's take a closer look at what this means.
Suppose Sam Altman (Founder of OpenAI) reaches out to them to honor their previous commitments. Unlike in the past when they still had dry powder, now they must sell something first to free up money to give to him. So, hypothetically, what would they sell?
They would review their investment portfolio and select the least confident assets: sell some underperforming bitcoins; sell some SaaS software assets facing disruption risk; redeem funds from underperforming hedge funds. And these hedge funds, to meet redemptions, must sell assets. Asset prices fall, confidence is eroded, margin availability tightens, triggering more passive selling elsewhere. These effects propagate layer by layer and amplify in the financial markets.
What's worse, Trump chose Warsh. This is particularly problematic because he believes the current problem is too much money, when in fact, the opposite is true. As a result, since his selection, the pace of these market changes has significantly accelerated.
I have been trying to understand why memory chip manufacturers like DRAM / HBM / NAND (such as SNDK, MU) outperform other stocks by far. Of course, underlying product prices have indeed surged. But more importantly, these companies are now and in the near future in a state of excess profitability—although it is clear that their profitability is cyclical and will eventually fall back. As the cost of capital rises, the discount rate increases. The result is that assets with longer duration and speculative on future expectations will be hit, while assets with near-term cash flow will benefit relatively.
In such an environment, crypto assets naturally face a "doomsday scenario" as they are the frontline probes of liquidity condition changes. This is also why the market feels like it is "falling endlessly."
Highly speculative retail momentum stocks can hardly hold any gains, and even sectors where fundamentals are improving are struggling.
Due to demand for money exceeding supply, sovereign bonds and credit rates are both rising.
This is not a time to be comfortably extremely long. This is a phase that requires defense, extremely selective positions, and careful risk management. I'm not telling you to go all into cash, and this article is not a trading signal. You should take it as a kind of background framework to help you understand what's going on.
I personally sold my gold and silver at the peak, and currently, most of my position is in cash. I am not eager to buy anything. I believe that if you are patient enough, this year will present extremely rare opportunities.
Finally, thanks to the genius friends who thoroughly discussed these issues with me in the group chat, including @AlexCorrino, @chumbawamba22, @Wild_Randomness.
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