Wake Up! Stop Trying to Catch the Falling Knife: The Harsh Truth Behind the $26 Billion Crypto Liquidation

Odaily星球日报Опубліковано о 2026-02-06Востаннє оновлено о 2026-02-06

Анотація

In a sharp market downturn, Bitcoin plunged nearly 20% to $60,000, with over $2.6 billion in crypto liquidations within 24 hours. While many attribute the crash to factors like tech stock declines or hawkish policy expectations, the author argues the core issue is deeper: a structural shortage of global financial capital. The shift is driven by the AI capital expenditure cycle, which initially acted as incremental stimulus but is now draining liquidity. Unlike the capital-light Web 2.0 and SaaS models that fueled the last bull market, AI requires massive upfront investment. When "dry powder" (idle capital) is exhausted, funds for AI spending must be pulled from elsewhere—sparking a "convex competition" for capital. This leads to a repricing of assets based on utility and raises the cost of capital. Speculative, long-duration assets like crypto are hit hardest, while cash-generating assets benefit. The author compares liquidity to water in a bathtub: when demand exceeds supply, assets sink. Even deep-pocketed investors like Saudi Arabia and SoftBank may need to sell holdings to fund commitments, triggering cascading sell-offs. The conclusion: this isn’t a time for aggressive long positions. Defensive strategies, selective holdings, and patience are advised until clearer opportunities emerge.

Author | @plur_daddy

Compiled | Odaily Planet Daily (@OdailyChina)

Translator | DingDang (@XiaMiPP)

Editor's Note: Gold and silver both plummeted, U.S. stocks fell across the board, and the cryptocurrency market was even more brutal, with over $26 billion liquidated in 24 hours. Bitcoin once flash-crashed to the $60,000 mark, plummeting nearly 20% in a single day; from its high of $126,000 in October last year, the price of BTC has been halved. What's even more frightening is that the market showed almost no significant resistance.

Everyone is frantically searching for reasons: U.S. tech stocks dragged down crypto market; Trump's nomination of Warsh sparked hawkish expectations; the strong dollar, poor employment data... These explanations sound reasonable. But in the view of the author of this article, they are more superficial than the core of the problem. The real underlying reason is: Money in the world is becoming insufficient. The massive AI capital expenditure cycle itself is shifting from "injecting liquidity" to "draining liquidity," leading to a substantial shortage of global financial capital. The following is the author's original text, which will deconstruct step by step how this mechanism operates.

We are experiencing a paradigm shift in the market, due to a shortage of financial capital caused by the AI capital expenditure cycle. This has profound implications for asset prices, as capital has been excessively abundant for a very long time. The Web 2.0 and SaaS paradigm that drove the market boom of the 2010s was essentially a extremely capital-light business model, which allowed a large amount of excess capital to flow into various speculative assets.

Yesterday, while discussing the market landscape, I had an "aha moment." I believe this is the most differentiated article I've written in a long time. Below, I will deconstruct, layer by layer, how all of this operates.

There is actually a highly similar mechanism between AI capital expenditure and government fiscal stimulus, which helps us understand the underlying logic.

In fiscal stimulus, the government issues treasury bonds, so the private sector absorbs the duration risk; then the government gets the cash and spends it. This cash circulates in the real economy and creates a multiplier effect. The net impact on financial asset prices is positive, precisely because of this multiplier effect.

In AI capital expenditure, mega-cap tech companies either issue bonds or sell treasury bonds (or other assets), again the private sector absorbs the duration risk; then these companies get the cash and put it to use. This cash also circulates in the real economy and creates a multiplier effect. Ultimately, the net impact on financial asset prices is still positive.

As long as these funds come from the "dry powder" (idle, unused capital) within the economic system, this process runs smoothly. It worked very well, almost "lifting all boats." In the past few years, this has been the dominant paradigm—AI capital expenditure acted like an incremental stimulus policy, injecting adrenaline into the economy and markets.

The problem is: Once the dry powder is exhausted, every dollar flowing into AI capital expenditure must be pulled from somewhere else. This triggers a convex battle for capital. When capital becomes scarce, the market is forced to reassess: where is the "most useful" place to deploy capital? Simultaneously, the cost of capital (i.e., the market-determined interest rate) rises.

Let me emphasize again: When money becomes scarce, a "knockout tournament" occurs among assets. The most speculative assets suffer disproportionate losses—just as they disproportionately benefited when capital was extremely abundant but lacked productive uses. In this sense, AI capital expenditure actually plays a role of "reverse QE," bringing negative portfolio rebalancing effects.

Fiscal stimulus typically doesn't face this problem because the Fed often ultimately becomes the absorber of duration risk, thus avoiding "crowding out" other uses of capital.

The "money" mentioned here can be used interchangeably with "liquidity." But the word "liquidity" is confusing because it has different meanings in different contexts.

Let me use an analogy: Money or liquidity is like water. You need the water level in the bathtub to be high enough for the financial assets (those floating rubber ducks) to all rise together. There are several ways to do this:

  • You can increase the total amount of water (rate cuts / QE)
  • You can unclog the inlet pipes (operations like the current RRP (Reverse Repo Program)/RMP (Reserve Management Purchases) "plumbing work")
  • Or you can reduce the speed at which water drains from the bathtub.

Discussions about liquidity in the economy almost always focus on the money supply. But in fact, the demand for money is equally important. The problem we are facing now is: Demand is too high, leading to significant crowding-out effects.

Media reports suggest that the world's "deepest pockets"—Saudi Arabia and SoftBank—are basically tapped out. The whole world has been gorging on assets for the past decade and is now "stuffed." Let's look specifically at what this means.

Suppose Sam Altman (founder of OpenAI) reaches out to them, asking them to fulfill their previous commitments. Unlike in previous periods when they still had dry powder, now they must first sell something to free up money for him. So, hypothetically, what would they sell?

They would look at their investment portfolios and pick the assets they have the least confidence in: sell some underperforming Bitcoin; sell some SaaS software assets facing disruption risk; redeem funds from hedge funds with long-term poor performance. And these hedge funds, to meet redemptions, must sell assets. Asset prices fall, confidence weakens, the availability of margin tightens, triggering passive selling in more places. These effects cascade and amplify through the financial markets.

Worse still, Trump chose Warsh. This is particularly problematic because he believes the current problem is too much money, when in fact, we are facing the opposite problem. This is why the pace of these market changes has noticeably accelerated since he was selected.

I have been trying to understand: Why have memory chip manufacturers like DRAM / HBM / NAND (e.g., SNDK, MU) performed far better than other stocks. Sure, the underlying product prices are indeed soaring. But more importantly, these companies are now and will be in the near future in a state of supernormal profits—even though it's clear their profits are cyclical and will eventually fall back. When the cost of capital rises, the discount rate increases accordingly. The result is: Speculative assets with longer duration, reliant on future expectations, get hit, while assets with near-term cash flows benefit relatively.

In such an environment, crypto assets naturally get "decimated," as they are the frontline probes for changes in conditions. This is also why the market feels like it's "falling endlessly."

Highly speculative retail momentum stocks can hardly hold any gains, and even sectors with improving fundamentals are struggling hard.

As demand for money exceeds supply, sovereign bond and credit interest rates are both rising.

This is not a time to be complacently extremely long. This is a stage for defense, being extremely picky with holdings, and seriously managing risk. I am not telling you to sell everything; this article is not a trading directive. You should treat it as a contextual framework to help you understand what is happening.

I personally sold gold and silver near the highs, and most of my positions are now in cash. I'm in no hurry to buy anything. I believe that if you are patient enough, extremely rare opportunities will emerge this year.

Finally, thanks to the brilliant friends in the group chat who helped me thoroughly discuss these issues, including @AlexCorrino, @chumbawamba22, @Wild_Randomness.

Пов'язані питання

QAccording to the article, what is the core reason behind the recent massive liquidation in the cryptocurrency market?

AThe core reason is a global financial capital shortage caused by the AI capital expenditure cycle shifting from injecting liquidity to draining liquidity, leading to a scarcity of capital and forcing a reevaluation of where capital is most useful.

QHow does the AI capital expenditure cycle differ from government fiscal stimulus in terms of impact on financial assets?

AGovernment fiscal stimulus often has the Federal Reserve as the ultimate absorber of duration risk, avoiding a 'crowding-out effect' on other capital uses, whereas AI capital expenditure can act as 'reverse QE' with negative portfolio rebalancing effects once dry powder is exhausted.

QWhat role do 'dry powder' (idle capital) and its exhaustion play in the current market paradigm shift?

AAs long as dry powder is available, AI capital expenditure functions like incremental stimulus, boosting the economy and markets. Once it is exhausted, every dollar flowing into AI capex must be pulled from elsewhere, triggering a convex battle for capital and a reassessment of the most useful investments.

QWhy are highly speculative assets like cryptocurrencies particularly vulnerable in the current environment?

AThey are the front-line probes for changes in liquidity conditions. When capital becomes scarce and costs rise, long-duration, speculative assets reliant on future expectations are disproportionately hit, while assets with near-term cash flows benefit relatively.

QWhat investment strategy does the author recommend based on the analysis provided?

A

Пов'язані матеріали

Jito Revives with New Exchange JTX Buyback: Self-Salvation or Lifeline?

Jito, a Solana-based MEV and liquid staking infrastructure protocol, has announced new governance proposal JIP-38 and the launch of a new self-custody trading platform, JTX. The proposal establishes a rigid value-capture mechanism, mandating that 100% of the DAO's share of revenue from JTX—80% of its platform fees—will be used for programmatic, on-chain verifiable open market buybacks and permanent burns of the JTO token. This commitment is set to last at least from JTX's launch until Q4 2027. The move comes as Jito faces significant challenges in its core liquid staking market, with protocol-staked SOL declining from 18 million to under 10 million. Intense competition from protocols like Sanctum and Jupiter, coupled with continuous monthly token unlocks (1.15% of max supply), has pressured JTO's price, which fell over 96% from its all-time high to a low of $0.21 earlier this year, before recovering to around $0.63. JIP-38 formalizes Jito Network as a "token-centric" network, where all major revenue streams flow to the DAO for governance by JTO holders. While the JTX buyback is a firm commitment using new revenue, decisions on other income streams and the post-2027 strategy will be determined by future governance votes. The proposal is seen as a strategic pivot to create a new revenue source and directly align token value with ecosystem growth, though its success depends heavily on JTX's ability to compete effectively in the crowded Solana trading landscape.

Foresight News23 хв тому

Jito Revives with New Exchange JTX Buyback: Self-Salvation or Lifeline?

Foresight News23 хв тому

The More Proficient AI Becomes at Answering, Why Do Humans Need Deep Thinking More? Fudan Releases the 2026 Blue Book on Intelligent Development in Humanities and Social Sciences

As AI capabilities rapidly expand, particularly in generating sophisticated text, analyzing data, and automating complex tasks, the need for human deep thinking becomes more critical, not less. The "2026 Blue Paper on Intelligent Development for Humanities and Social Sciences" from Fudan University argues that the relationship between AI and these fields is shifting from "one-way empowerment" to "bidirectional fusion." While AI transforms research methodologies, the humanities must guide its purpose, application, and governance. The core challenge is no longer processing vast information, but defining worthwhile problems, establishing genuine causal mechanisms, and constructing verifiable evidence chains. AI excels at producing coherent, fluent outputs but risks oversimplifying complex social realities into standardized formats it can easily process. For instance, in areas like climate-society systems, the difficulty lies not in handling more variables, but in understanding the fundamental mismatches between natural and social systems. Similarly, in automated research, AI can efficiently search for statistically significant results or generate papers quickly, potentially masking flawed assumptions or "packaging" statistical noise as discovery. The speed of paper production does not equate to the speed of genuine knowledge advancement. This underscores the non-transferable human responsibility for judgment. Deep thinking must be embedded into research workflows, governance systems, and organizational structures. Key principles include: * **Maintaining the Evidence Chain:** While AI can handle tasks like data processing, researchers must retain oversight over problem definition, conceptual translation into metrics, causal interpretation, and defining the scope of conclusions. Frameworks like STRIDES aim to document decisions and enable audit trails. * **Ensuring Meaningful Human Oversight:** In public governance, AI systems should operate in an "assistive" rather than an "agentic" mode. Human operators must retain genuine intervention, correction, and explanation rights to prevent "responsibility theater," where humans merely rubber-stamp algorithmic decisions. * **Translating Principles into Practice:** AI governance needs enforceable mechanisms across a system's lifecycle—pre-deployment risk assessment, runtime monitoring and human-in-the-loop controls, and post-hoc review and accountability—tailored to the level of risk involved. * **Defining Direction, Not Just Answers:** Humanities and social sciences provide the essential framework for navigating value conflicts (e.g., efficiency vs. fairness) and analyzing the social consequences of technology, questions AI alone cannot resolve. Building lasting capacity requires more than isolated projects. It demands integrated infrastructure—shared data standards, tools, interdisciplinary training, and collaborative mechanisms—as measured by initiatives like the "Chinese Universities AI4SSH Index." The ultimate imperative is clear: as AI becomes better at answering questions, humans must become more deliberate and responsible in deciding which questions are worth asking, critically evaluating the answers, and steering the technology's impact on society.

marsbit43 хв тому

The More Proficient AI Becomes at Answering, Why Do Humans Need Deep Thinking More? Fudan Releases the 2026 Blue Book on Intelligent Development in Humanities and Social Sciences

marsbit43 хв тому

Торгівля

Спот
活动图片