Source: Arthur Hayes
Compilation | Odaily Planet Daily (@OdailyChina); Translator | Azuma (@azuma_eth)
Looking around, humans have transformed Earth's natural environment into a different world. Some changes are awe-inspiring, while others are shocking, but without exception, they all began as an idea in the mind of one or more evolved primates—humans.
Because the brain needs to process a vast amount of information every day, we constantly construct various narratives (Narratives) to make the world coherent and meaningful. It is precisely for this reason that narratives themselves ultimately shape reality.
For investors, to predict future price fluctuations in the market, one must understand which "collective delusions" the market participants collectively believe. The same company, with unchanged future cash flows, may receive drastically different valuation multiples simply because the market believes a different story.
And the simplest way to achieve a "valuation reset" (re-rate) for an originally dull, boring enterprise is to replace it with a new narrative that aligns with the current market hotspots, making investors willing to chase it regardless of cost.
Is There an AI Bubble?
This leads to the core question of whether AI is currently in a bubble.
However, before discussing the AI bubble, it's more worthwhile to answer a more fundamental question: "What exactly are we investing in?" — to put it in terms of a relationship, it's "What exactly is our relationship?"
At least from my somewhat "Luddite" perspective, the key is how the market defines AI capital expenditure (AI CAPEX) — does it belong to Technology or to Real Estate?
The mainstream narrative in the market today is that this multi-trillion-dollar AI infrastructure construction belongs to "technology," and therefore deserves high-growth valuations.
But my view is precisely the opposite. AI CAPEX is essentially just another boring and mundane real estate investment. The only difference is that this time, the data centers are filled not with office buildings, but with computing power. This computing power will ultimately give birth to silicon-based lifeforms and drive the development of human civilization, its significance possibly even surpassing the railway revolution of the past.
The reason we must distinguish between "real estate" and "computing power" is that newly mature hedge fund managers, banks, private credit funds, and even governments today mistakenly believe they are lending to tech giants like Apple (Apple), not providing real estate financing to Lehman Brothers (Lehman Brothers).
I believe the eventual bursting of the AI bubble will be due to financial intermediaries overbuilding data centers, along with all the supporting infrastructure needed for data center construction, including energy, electricity, and everything required for training and inference of AI chips.
Therefore, the AI bubble is more like a 2008-style credit bubble, rather than an earnings bubble like the 2000 internet bubble.
During the 2000 internet bubble, most listed internet companies had almost no revenue, let alone profits, such as Pets.com, so that bubble was essentially a problem of "earnings failing to materialize."
The 2008 financial crisis was different. What truly triggered the crisis was the slowdown in US home price appreciation, which raised concerns among banks and financial institutions about the repayment capacity of mortgage assets, making it a credit crisis.
The AI bubble will follow a similar logic. The real turning point is not when leading AI companies stop making profits, but when data center construction growth begins to slow, or when Hyperscalers lower their future data center construction guidance.
Even if leading AI companies can still earn massive profits, their forward valuation multiples (Forward Multiple) will contract due to declining growth expectations. The first to fall will be those AI companies with the most fragile credit conditions and highest leverage.
Subsequently, these risks will quickly transmit to the balance sheets of highly leveraged financial institutions holding significant AI debt assets. Ultimately, the government will intervene again in the name of "national security," ensuring that these over-leveraged AI companies and their supporting financial institutions do not collapse.
And this misallocated capital will ultimately flow into the crypto market... sending Bitcoin to the moon once again.
The Credit Risk of AI CAPEX
Whenever someone suggests "AI is in a bubble," AI bulls almost invariably counter with "Jevons Paradox" as a rebuttal. Jevons argued that when the price of a commodity falls, its usage will grow significantly, causing the overall market size to continue expanding, even exponentially.
If you believe AI capital expenditure itself represents the demand for computing power, then according to Jevons Paradox, there is indeed nothing to worry about. As computing power costs continue to decline, demand for AI Token-consuming applications and AI Agents will grow exponentially, so lending to AI infrastructure is naturally a money-good business.
But I believe this is actually a misreading of Jevons Paradox. To understand why Jevons Paradox doesn't mean all credit flowing to AI CAPEX will be repaid, let's look at what a Hyperscaler is actually doing when building a data center.
Essentially, it is first undertaking a real estate development project. It constructs a building to house server racks, then procures the latest generation of semiconductor chips for AI model training and inference. And as industrial technology—especially semiconductor manufacturing—continues to advance, the floating-point operations (FLOPs) provided per kilowatt-hour of electricity will continue to increase exponentially.
Years from now, whether it's Nvidia, AMD, Intel, Huawei, or SMIC, they will release new generations of AI chips far more efficient than today's. At that time, the same data center, consuming less power, will be able to produce over 1000 times more Intelligence.
This means two things can be simultaneously true: on one hand, the construction of physical infrastructure like AI data centers can become saturated; on the other hand, the consumption of AI Tokens can still grow exponentially.
So, the real question worth considering is, do you want to hold a real estate business—what Hyperscalers are doing today; or do you want to hold the AI application layer?
A common rebuttal from AI bulls is that Hyperscalers are both landlords and tenants. They rely on the massive cash flows generated by their Web2.0 "attention-selling" businesses to provide credit support for issuing debt to build data centers; simultaneously, they leverage their AI capabilities to sell the "wisdom apple" from the Garden of Eden to the world.
If you truly believe this story, then I hope you own their stocks, not their bonds. There's a reason bonds are called Fixed Income—no matter how successful the company ultimately becomes, the best outcome for creditors is just getting their principal back plus a bit of interest.
If Google successfully bets on AI and creates revolutionary products that change the course of human civilization, sending its stock price soaring, shareholders certainly deserve to cheer; but bondholders will still only get their principal back.
Conversely, if Google ends up as just a "data center landlord" renting out masses of depreciated Nvidia GPUs but cannot earn enough revenue to service debt and interest, creditors will suffer heavy losses. And it's highly questionable how much a data center packed with outdated chips is really worth.
The CFOs of Hyperscalers and Wall Street financiers are not stupid. They know they are essentially in the real estate business. Therefore, they must find some "greater fools" who believe they are investing in high-tech, not real estate.
These greater fools include insurance companies under alternative asset management giants like Apollo, and the taxpayers of various countries who will ultimately foot the bill for the government's implicit guarantee of AI credit.
If you carefully review the deliberately obscure financial statements, you'll find—a significant portion of the debt issued to finance AI CAPEX is kept off balance sheet, with little clear connection to the core profitable businesses supporting the stock valuations.
How we define AI CAPEX determines how we understand the entire AI investment cycle. It is this narrative that explains why severe capital misallocation will occur, and why the scale of this bubble could surpass that of the railway bubble.
More importantly, because the AI bubble is a credit bubble, not an earnings bubble, when the crisis erupts, the government will inevitably step in to save the ultimate buyers who mistakenly bought traditional real estate debt as new tech equity assets.
Don't think the AI bull market is over just because of recent adjustments in the AI sector, especially in highly leveraged markets like South Korea. On the contrary. The truly insane "blow-off top" phase may have just begun.
Just last week, the Fed had the opportunity to raise interest rates to combat inflation that remains above trend levels by any statistical measure, but it chose not to, opting instead to remain on hold, with even former Chairman Powell voting to keep rates unchanged.
So, what does AI credit allocation matter to forgotten crypto players struggling in a sideways bear market? It matters because it determines in what way, why, and how much money future governments will print to fill the financial holes created by uncontrolled AI CAPEX investment.
The rest of this article will elaborate on this theory and explain why governments will ultimately have no choice but to print money for a rescue.
As AI CAPEX growth slows while credit continues to expand, Bitcoin will bottom out and begin a long-term uptrend. When policymakers finally realize that the AI GDP growth they pinned their hopes on is essentially just another ordinary real estate bubble, they will be forced to launch monetary easing that could even exceed the scale of the 2008 Global Financial Crisis (GFC).
And ultimately, this will propel Bitcoin past $1 million, or even higher.
The Second Derivative Determines Everything
I always have to remind myself: "Investing doesn't truly trade on growth itself, but on the acceleration of growth."
In other words, what we focus on is actually the Second Derivative—whether growth is accelerating or decelerating.
This aligns with intuition. When an asset is still in an accelerating growth phase, people constantly weave stories about its infinite future possibilities. Thus, you hear bold statements in the market like, "I'd rather hold a Hyperscaler to bankruptcy than miss the chance to build AGI."
However, all growth eventually enters a deceleration phase. The problem is, the price behavior of most assets often follows this pattern:
- Accelerating growth phase: Price continuously makes new highs;
- Decelerating growth phase: Price moves sideways;
- Only when growth itself (the first derivative) turns negative does the price truly start to fall.
No one can accurately predict how long the interval will be between growth starting to decelerate and actually turning negative, but many investors, myself included, instinctively believe—even when growth has started to decelerate, asset prices can still rise indefinitely.
If the AI bubble is essentially a credit bubble, the importance of the second derivative becomes even more pronounced. Because the entire society's willingness to continuously finance AI CAPEX is based on an assumption—AI investment will forever maintain accelerating growth.
Once that acceleration disappears, increasing debt becomes increasingly dangerous, but the reality is, no one knows when to stop until they actually get punched in the face.
Either wait for the financial crisis to erupt; or wait for "Kenny G" (Odaily note: here referring to Ken Griffin who recently bought AI god portfolios at low prices) to sweep up all your assets at the market bottom.
Therefore, even when investment growth has begun to slow, credit scale often continues to expand. Only when AI CAPEX budgets actually start declining will the market experience that classic "Wile E. Coyote" moment—where the character has already run off the cliff but doesn't realize there's no ground beneath until he looks down, then instantly plummets.
At that point, the market will begin to identify who, by holding vast amounts of junk AI CAPEX debt, has become over-leveraged.
Let's apply this logic to the US subprime crisis. One of my favorite courses in college was studying US housing policy and the mortgage market. The instructor had served as Deputy Secretary of Housing in the Clinton administration. Coincidentally, I took this course in the spring of 2008—right when Bear Stearns collapsed, making it incredibly timely.
The core message of the course was that the government, to achieve the socially equitable goal of "homeownership for all," continuously encouraged more people to buy homes, leading to persistent credit expansion. However, by 2006, many first-time homebuyers were essentially unable to afford the monthly payments after their loan rates reset. Their only chance to continue making payments was if home prices kept rising at an increasingly faster pace.
Of course, I'm still waiting for the government to deliver on my promised "forty acres and a mule." In that case, might as well just print money to build houses.
The following four-panel chart shows:
- The S&P 500 index;
- US construction loans and construction activity;
- The Case-Shiller National Home Price Index.
By the end of 2005, US home price appreciation had already begun to slow, which also coincided with the peak in actual construction investment spending (the orange line in the first panel). However, real estate credit (the purple line) continued to flow into the market until the stock market peaked and began a slight correction.
2006 to 2007 could be called the "no man's land" before the crisis erupted. Home prices were still rising, but the rate of increase was continuously slowing; subsequently, the stock market peaked in mid-2007 (the pink dashed line in the chart); the real "Wile E. Coyote moment" occurred in August 2007—the collapse of three credit hedge funds under BNP Paribas; the crisis then spread, ultimately bringing down Bear Stearns and Lehman Brothers in September 2008... and before that, the S&P 500 had already fallen about 50% from its high.
What truly triggered the financial meltdown was investors finally discovering who held those toxic "Frankenstein" financial derivatives. Ultimately, the government had to simultaneously take over both the debt and equity of these institutions to avert a new Great Depression.
This point is very important, as we will return to this logic later when discussing how the government might rescue the AI industry.
The second chart is also worth noting. It shows that the starting point of capital misallocation was precisely when home price appreciation began to slow. If new credit was still being used to build more housing, the problem wouldn't be severe, but if the entire system started relying on new debt to repay old debt, then risk began accumulating. The rising ratio of construction loans to construction investment spending perfectly illustrates this process.
Now, apply the same analytical framework to AI. The key variable here is the CAPEX spending plans of various companies.
Currently, the market believes that real estate (here meaning AI) is technology; the more technology invested, the higher future profits. Therefore, the market rewards Hyperscalers that announce increased capital expenditure budgets by driving up their stock prices.
I expect the announced growth rate of AI CAPEX will begin to slow in mid to late 2027, and by 2028, the market will clearly enter a "decelerating growth phase."
Simultaneously, a seemingly contradictory phenomenon will occur: although CAPEX growth begins to decline, the scale of credit flowing to AI will continue to expand. The reason is that lenders believe they are investing in technology, not real estate. Coupled with governments worldwide emphasizing the need to dominate the global AI race, continuing to finance everything related to AI CAPEX seems the most rational choice.
Thus, 2027 will become the "no man's land" similar to 2006-2007. The recent sharp correction in AI stocks is just a normal pullback within a bull market. The true peak of the AI bubble will occur next year.
After that, the market will instead start rewarding Hyperscalers that "exit the arms race first" and actively cut CAPEX budgets. Unlike the early bubble phase from 2022 to mid-2026, Hyperscalers will find it increasingly difficult to support AI investments solely with their own free cash flow. They will have to rely more on issuing bonds and equity to raise funds.
Pressure on balance sheets will also force management to seriously consider: "Is it really worthwhile to keep borrowing to build more data centers, just to house more chips that will constantly depreciate?"
At least for US Hyperscalers, the emergence of Chinese frontier AI models that are significantly cheaper yet comparable in performance will completely douse their "silicon deity" fantasies. After all, if two products are of the same quality, or one is only slightly inferior, most people will choose the cheaper one.
As the intelligence generated per kilowatt-hour by AI chips continues to grow exponentially, and with Chinese competition driving down the cost per Token, a rational Hyperscaler CFO would not continue to worsen their balance sheet merely to build more data centers.
Even according to Jevons Paradox, if demand for AI Tokens eventually explodes, that growth may not come fast enough to offset the negative impact of the massive debt issued years earlier. Ultimately, the market will first punish the participants with the weakest credit. At that point, people will truly realize how much capital has been wasted in this AI investment wave.
I cannot predict which Hyperscaler will be the first to overplay its hand, triggering bond investors to collectively exclaim: "Oh shit!"
However, before discussing why banks, aware of the enormous risks, still feel compelled to keep lending to AI, let's look at the chart below. It shows the scale of CAPEX investment already committed by major Hyperscalers versus the cash on their balance sheets.
The entire AI bull market narrative is supported by leverage on the scale of trillions of dollars. Among these companies, one will eventually fall from grace like the once market-adored AI genius Leopold Aschenbrenner. The difference is that those coming to their rescue won't be the greedy, neurotic East Coast hedge fund managers of Wall Street, but the printing presses in the hands of Warsh and Treasury Secretary Bessent.
The Bank Credit Dilemma
Many believe that the impending slowdown in AI CAPEX growth means the AI bubble is nearing its end. If that were true, why would banks continue lending?
The reasons are simple: First, because it's profitable; Second, because the government wants them to; Third, because they know that even if the loans eventually go bad, the government will step in to rescue them.
Louis-Vincent Gave of Gavekal Research published a very interesting article last week. He argued that Warsh's interest rate policy actually follows a very simple logic—actively steepening the Yield Curve.
Doing this has two benefits. First, it makes bank lending more profitable; Second, it helps gradually dilute the US's massive debt burden through inflation.
Ultimately, banks will keep creating new loans, i.e., creating new money. This newly added capital will finance US re-industrialization and continue supporting AI construction. This aligns closely with the "Hamiltonian Economics" recently mentioned repeatedly by Treasury Secretary Bessent.
Judging by all objective economic indicators, the Fed should have raised rates at its recent meeting, but it didn't. Instead, long-term Treasury yields subsequently surged rapidly.
- Odaily note: The yield on 30-year US Treasuries rose rapidly after the Fed stood pat.
Many view this as a policy error by the Fed, but from a bank's perspective, it's a godsend.
The reason is simple. Banks can fund themselves at a cost close to the Federal Funds Rate, and the Fed is deliberately keeping this rate below nominal economic growth, even below the actual inflation rate.
Then, banks lend this money out as long-term loans to AI data center developers, rare earth mining companies, defense contractors, etc. The steeper the yield curve, the higher the Net Interest Margin banks can earn.
And as shown in the chart below of commercial and industrial loan volumes, the more incentive banks have to keep creating new money through lending.
- Odaily note: The white line is the 10-year Treasury yield minus the effective federal funds rate (reflecting yield curve steepness); the yellow line is US commercial bank commercial & industrial loan balances.
From a political perspective, this is a sustainable Fed policy. Even though the Trump administration's Justice Department once investigated and even prosecuted some Fed governors (like Lisa Cook and Powell), these voting members still supported keeping short-term rates at negative real levels.
In other words, Warsh effectively has a "coalition of volunteers," including people from the Trump camp and those within the system who were deeply affected by "Trump Derangement Syndrome" (TDS).
From a monetary policy perspective, the Fed's recent actions also allow Treasury Secretary Bessent to issue short-term Treasury Bills (T-Bills) at yields below the nominal economic growth rate. If the market cannot digest the massive weekly issuance of T-Bills, the Reserve Management Program (RMP) will fill the demand gap by printing money.
To suppress the "unruly," persistently rising long-term Treasury yields, Bessent can also implement Treasury Buybacks—first issuing short-term T-Bills monetized by the Fed, then using those funds to buy back 10-year or 30-year Treasuries, thereby pushing down long-term rates.
Notably, Warsh, known for advocating shrinking the Fed's balance sheet, shows no intention of restricting, let alone stopping, the RMP program's balance sheet expansion. It's all just a kabuki-style UFC performance on the White House lawn.
If you are a credit officer at a "Too Big To Fail" (TBTF) bank hoping for future promotions and raises, you would almost certainly approve loan applications for "critical industries" like AI, defense, etc.
The reason is simple. It both boosts bank profits and aligns with the policy direction of the Fed and Treasury. Even if the loans eventually blow up—and mathematically, the probability is quite high—the government will swiftly roll out "bazooka-sized" rescue packages.
There's almost no downside risk here. This is how "Window Guidance" actually operates in the US.
I believe the 2026 version of the "Treasury-Fed Accord" has already quietly happened, just not formally announced. Otherwise, how would you define the current situation?
- The Fed maintains negative real interest rates;
- The Fed prints money to buy Treasury-issued T-Bills;
- The Treasury encourages banks to lend to critical industries;
- If the loans go bad, the ruling government will provide implicit guarantees to cover the losses.
If this isn't fiscal and monetary policy coordination, I don't know what is. So, I'm extremely bullish on the market right now; the truly massive money printing is far from over.
The US Sovereign Wealth Fund
Now, let's stretch our imagination a bit. What if the US government not only rescues banks after a crisis occurs but proactively buys AI company stocks when signs of crisis appear? After all, bold thought experiments are always interesting.
In fact, the Trump-era rescue of AI has already begun. Under the banners of "national security" and "US-China competition," the US government has started borrowing money and directly buying equity in companies in so-called "critical industries" like rare earths and semiconductors.
This is essentially a dollar liquidity-increasing operation, which can also be understood as Equity QE. Because the dollars originally sitting in government accounts are directly injected into financial markets.
The following lists some cases where the US government, using borrowed funds from the CARES Act, CHIPS Act, and Defense Department budgets, directly holds equity in relevant companies.
Unfortunately, for us crypto investors whose wealth entirely depends on the scale of money printing, under the existing legal framework, there is very little room left for the government to conduct similar equity investments.
However, the Trump administration and Treasury Secretary Bessent have clearly shown an attitude that, as long as the law allows, they would not hesitate to use borrowed money to bottom-fish AI stocks.
Thus, a new question arises: Is there a way to print money in advance to buy AI stocks, without waiting for a crisis to erupt, and without needing Congressional approval?
The answer is yes! And this is precisely the most interesting part.
Under the Federal Reserve Act, under so-called "Emergency and Exigent Circumstances," the Fed can directly print money and provide unlimited liquidity loans to Special Purpose Vehicles (SPVs) established by the US Treasury.
During the 2008 financial crisis and the 2020 pandemic, the Treasury used the Exchange Stabilization Fund (ESF) to support first-loss equity, after which the Fed provided loans to the SPV to purchase various financial assets to stabilize the market.
Currently, the ESF account still holds about $28 billion. Bessent could use this as the initial capital for a new SPV, again under the guise of safeguarding national AI security.
Following past practice, the Fed is typically willing to provide up to 10x leverage for an SPV. This means Bessent could theoretically leverage about $280 billion to invest in unprofitable AI companies. Of course, compared to today's AI companies with market caps in the trillions, $280 billion is hardly "heavy firepower."
Could the scale be further expanded? For example, could the Treasury set up an SPV with no first-loss capital buffer at all, letting the Fed lend directly and limitlessly? Technically possible, but doing so would mean the Fed must withstand enormous political pressure—because it would be seen as secretly conducting unlimited-scale Equity QE.
So, does the Fed really care about political pressure?
The answer is both yes and no. The new Chairman Warsh has repeatedly emphasized that AI will soon become a miracle for boosting US productivity. In other words, ideologically, he himself believes in the grand narrative painted by AI entrepreneurs.
If Trump told him that to save Sam Altman and OpenAI, the government must directly step in to buy stocks—because there aren't enough retail investors willing to put real money into a frontier AI company that isn't yet profitable, while simultaneously, Dario Amodei's Anthropic is already profitable and has stronger models.
Then, Warsh would most likely comply without hesitation. Of course, procedurally, approving SPV loans still requires affirmative votes from three other Fed governors. But considering that in the recent FOMC meeting, people including Cook, Powell, and others have already aligned with Warsh (supporting keeping rates unchanged), if Warsh really pushes the Fed down this path, I see little substantive resistance.
After all, compared to theoretical concerns about whether money should be printed, personal investment returns in stock portfolios are always more convincing.
If the Treasury uses printed money to support star AI companies issuing new shares before they go public, it is actually cashing out the unrealized profits on the books of early investors and employees. This is the purest form of "Liquidity Creation."
Because before the government steps in to provide a floor, that capital doesn't exist. It's precisely the government's willingness to provide a bid for valuations that lack fundamental justification in the primary market that allows these paper wealth to be "realized."
From an accounting perspective, the government gains two benefits.
First, as long as an AI company has government backing, investors will flock to it. After all, following someone with a printing press to speculate in stocks, at least initially, almost always makes money. Thus, the SPV's books would quickly accumulate massive unrealized gains. Trump could package these paper gains as government "profits," even claiming they could theoretically offset the fiscal deficit. If AI is truly the most important technological revolution in human history, then just the paper gains in the stock market could, in accounting terms, even "eliminate" the entire US fiscal deficit.
Second, the millionaires, billionaires, and trillionaires created as a result must pay federal and state capital gains taxes when they sell their shares. These new taxes could also reduce the fiscal deficit, allowing the government to borrow less and further claim that the US debt-to-GDP ratio is declining. At least initially, the bond market would believe this story, so Treasury yields would fall, and the market would reward the government for this "accounting magic."
However, I must emphasize, Trump has not invented the Philosopher's Stone. He is merely kicking the can down the road, hoping the next administration—preferably still Republican—will have to deal with it.
Why is this model destined for disaster? Let's conduct a simple thought experiment. Suppose you want to become a billionaire overnight without doing any work. So, you spend a few thousand dollars to incorporate a company, issuing a total of 1 billion and 1 shares.
Then, you sell 1 share to your mother for $1. Since the latest transaction price is $1, on paper, your other 1 billion shares are worth $1 billion. Next, you take this "wealth" to a bank, hoping to borrow $100 million to buy mansions, Lamborghinis, and various luxuries. The bank will tell you directly: "Not a chance."
You'd be confused. Because to you, the loan's Loan-to-Value (LTV) ratio is only 10%, the risk seems low, but the bank's answer is simple:
"If we need to sell these shares to repay the loan in the future, there's simply no market liquidity."
The same logic applies to the AI SPV. If the SPV becomes the single largest shareholder of an AI company, and other investors buy the stock only because the government is in it, then once the government prepares to exit, there will be no real buyers in the market. Moreover, when politicians—like Ro Khanna, Nancy Pelosi, etc.—start selling their shares, all investors will rush to sell before the government does, turning the paper gains originally meant to "offset national debt" into nothing. They would not only become actual losses but also increase government debt further.
Worse, the Treasury must still repay the money it originally borrowed from the Fed. So for this SPV, it's essentially an investment that can only be bought, not sold. The Fed can only continuously roll over the SPV's loans, ensuring a margin call is never triggered.
Ultimately, to sustain the entire system, the expansion of the Fed's balance sheet will become permanent.
However, this isn't Trump's concern. Because politically, he wins both ways—on one hand, unprofitable US AI companies get funding to continue competing with China; on the other, the paper "wealth" created by AI drives tax revenue growth and stimulates current economic activity.
Simultaneously, unrealized gains plus new taxes create the illusion that "US debt-to-GDP is declining." Thus, the market continues willing to lend to the US government at lower rates.
The US government could do this now, preemptively preventing the AI bubble from bursting. Of course, it could also wait until AI CAPEX growth slows and the market starts a full-scale sell-off of AI stocks before intervening.
Since the US government has already started directly buying corporate equity, why not buy more? Combining "bank window-guided lending" with "government direct purchases of AI stocks" could theoretically ensure an AI credit crisis never happens—at least not before the 2028 US presidential election.
Some might ask, with so much money printed from 2022 till now, why hasn't Bitcoin broken $126,000? Patience, the next part has the answer.
When Will Bitcoin Bottom?
The bottom of the last cycle occurred after the market discovered that white boy Sam Bankman-Fried had stolen FTX customer funds, with CZ helping to facilitate that discovery.
Simultaneously, ChatGPT launched commercially, and the AI wave began.
Starting in October 2023, the US liquidity environment changed. As funds from the Overnight Reverse Repo Facility (RRP) persistently flowed out, dollar liquidity began increasing. Subsequently, bank credit and government borrowing also grew. Bitcoin rose as a result, peaking in October 2025; but Bitcoin didn't continue rising—it only rose about 2x from its previous all-time high because AI credit and AI stocks absorbed the newly created fiat liquidity.
As AI capital expenditure (CAPEX) expansion accelerated, devouring all available fiat liquidity, Bitcoin—obvious in hindsight—fell 50%.
In mid-2026, the liquidity environment reversed. The growth rate of announced AI CAPEX for the next 18 months will begin to slow, but the bank and government credit channels are just starting to create dollars and funnel them to the AI industry.
If banks fail to fulfill their "patriotic duty" to continue providing credit, the government will strongly push them to lend to AI. If that still fails, the government will reduce banks' lending risks to AI by providing equity support to specific AI companies and offtake agreements similar to those for Intel and IBM.
Bitcoin will bottom during the early phase of this credit misallocation. The financialization of AI—the amount of dollars and yuan chasing quality AI projects exceeding the actual number of quality projects—will ultimately cause capital misallocation.
Writing this article in late July 2026, I don't know at what price Bitcoin will ultimately bottom; perhaps the bottom is already in.
The market needs time to digest concerns over Strategy (formerly MicroStrategy) selling Bitcoin. Also, the market needs a new narrative: if Strategy can no longer issue stock or find investors to buy its preferred shares and use those funds to keep buying Bitcoin, then why can Bitcoin still rise?
Perhaps Bitcoin will oscillate between $60,000 and $70,000 for a while, possibly dipping to $50,000. However, during all this, AI capital waste will continue accelerating, laying the foundation for Bitcoin's bottoming and subsequent slow rise.
If my view is correct—that the scale of truly valuable AI CAPEX projects is smaller than the scale of credit flowing to "AI"—then the Bitcoin price will eventually reflect this excess liquidity. This will help Bitcoin bottom, even if Digital Asset Treasury (DAT) companies like Strategy can no longer buy Bitcoin in a Bitcoin-per-share accretive manner through stock and corporate bond markets.
I will keep observing several indicators to verify this logic:
- Whether AI CAPEX growth is slowing;
- Whether AI loan volumes are increasing;
- Whether hyperscalers are increasing off-balance-sheet commitments.
If we enter the capital-waste phase of this AI credit boom, the next question is: What will regulators and governments do? Will they print money in advance? Or, lacking political space, wait for the eventual crisis before intervening with rescue packages?
Fortunately, as long as we hold Bitcoin non-leveraged, we don't care when the rescue arrives. Because we know that, due to distorted government incentives, they will ultimately choose to print money to save the system. The AI CAPEX credit frenzy already scales proportionally to GDP similar to the railway construction era. This means the scale of capital misallocation already exceeds the US subprime crisis.
Therefore, the future rescue scale will exceed the trillions of dollars printed by the Fed and major global central banks between 2009 and 2013. Bitcoin was born in response to the "irresponsible bailout of bankers" during the subprime crisis. If you think about it, that's quite remarkable. This time, Bitcoin already exists, and it might fulfill many people's dreams—rising to $1 million or even higher.
Considering the current bleak state of crypto capital markets, imagining such a future isn't easy. But in my view, this precisely creates interesting asymmetric opportunities. Maelstrom has held a significant amount of Bitcoin for a long time.
Aside from Bitcoin, what new narrative over the next six months could drive up a large-cap token? Ethereum is currently the most hated, most forgotten large-cap "shitcoin" in the market. It hasn't even broken its 2021 all-time high of $5,000, while most top-10 shitcoins by market cap have.
In my view, the next narrative is that enterprise RWA (Real World Asset) chains, like Robinhood's, will use customizable Ethereum Layer2s like Arbitrum. Ethereum will become the securities settlement layer for these chains. Therefore, even if the actual Gas revenue flowing to Ethereum constitutes a small proportion of the entire system, ETH remains the shitcoin driving "the tokenization of everything."
I am a critic of RWAs. Maelstrom constantly receives numerous trash project funding pitches where teams claim they will ride the asset tokenization wave, but on the other hand, traditional finance (TradFi) loves discussing: "In the future, all assets will be tokenized and run on some private or public chain."
I strongly believe that if this future materializes, these TradFi RWA projects must run on public blockchains. And Robinhood using Arbitrum to launch its own chain will lower the career risk for TradFi practitioners—they can replicate the same model, ultimately building Ethereum-based solutions.
This narrative is very strong. And ETH, as a shitcoin, is the second-largest crypto asset by market cap and has existed since 2015, giving it a Lindy effect second only to Bitcoin (the longer something exists, the higher its probability of continued existence). Plus, Tom Lee of Bitmine has endorsed ETH for institutional investor allocation, enabling fund managers to bet on the trend of capital market tokenization.
My rough target price for ETH by the end of 2026 is $5,000, roughly a 2.6x increase from the current price. I like this trade because I can deploy a fairly large nominal position while being able to accept the very low risk that ETH might drop 75% one day due to some technical flaw.
Furthermore, ETH is highly liquid. Therefore, even if it constitutes a large proportion of Maelstrom's portfolio, I can still exit within minutes. Finally, I will also sell out-of-the-money puts to earn extra yield, while accepting the risk of buying ETH at a discount if the price falls below the strike.
The AI bubble once sucked liquidity away from the crypto markets, but that has ended. As the market narrative gradually shifts from "invest in AI at any cost" to "what's my return on investment," and finally to "when do I get my principal back"... governments that have bet entire economic policies on AI will start worrying—maybe this bubble really could burst.
To avoid admitting their mistake and prevent that outcome, they will undertake massive capital misallocation, and capital misallocation on that scale will ultimately create a cryptocurrency bull run we haven't seen since 2021.
