The AI credit bubble may become one of the most important forces shaping Bitcoin’s next major market cycle. While investors continue to treat artificial intelligence primarily as a technology and earnings story, an increasingly large share of the infrastructure supporting the sector is being financed through bonds, private credit, long-term leases and off-balance-sheet structures.

That distinction changes everything.

A traditional technology bubble collapses when expected revenues fail to appear and equity valuations fall. A credit bubble becomes significantly more dangerous because falling asset values can damage lenders, refinancing markets, institutional portfolios and the broader financial system.

Arthur Hayes, co-founder of BitMEX and chief investment officer of Maelstrom, argues that the current artificial intelligence boom belongs in the second category. In his view, the eventual collapse of the AI credit bubble could resemble the global financial crisis more closely than the dot-com crash.

The underlying assets may be graphics processors, high-performance servers and advanced computing clusters, but much of the infrastructure surrounding them is economically similar to real estate. Data centers require land, power connections, cooling systems, transmission infrastructure, construction financing and long-term leases.

If expected demand for AI computing fails to grow quickly enough, the problem will not be limited to lower technology valuations. The owners of those facilities will still have debt to service, energy contracts to honor and lease payments to collect.

Hayes believes that governments will eventually intervene to protect strategically important AI infrastructure and the financial institutions that funded it. That intervention could involve central bank liquidity, fiscal support, guarantees, asset purchases and other forms of monetary expansion.

Bitcoin would not necessarily rise during the first phase of the crisis. It could initially fall alongside stocks, credit and other risk assets. But if the response generates another major expansion of dollar and yuan liquidity, Bitcoin could become one of the primary assets capturing the monetary consequences.

This is the mechanism behind Hayes’s argument that Bitcoin could eventually move toward $1 million.

The prediction is dramatic, but the more useful question is not whether the exact target will be reached. The more important question is whether the AI credit bubble is creating the conditions for another systemic bailout and, consequently, a new global liquidity cycle.

Bitcoin Is Not Currently Trading Like the Winner of the AI Boom

Bitcoin was trading near $64,000 on August 5, 2026, after spending several months trapped inside a broad corrective range. At the same time, parts of the U.S. stock market continued to trade near record levels, supported by enthusiasm surrounding artificial intelligence, data centers, semiconductors and infrastructure spending.

This divergence matters.

During previous phases of the crypto cycle, investors often treated Bitcoin as a direct expression of expanding technological optimism and global risk appetite. In 2026, that relationship has weakened.

AI-related equities have absorbed a disproportionate share of available capital. Companies connected to computing infrastructure, semiconductor supply chains, power generation and cloud services have continued to attract institutional demand, while Bitcoin has struggled against restrictive real yields, a stronger dollar and weaker crypto-specific liquidity.

The latest Block2Learn analysis of the U.S. stock market near record highs examined how market performance has become increasingly dependent on artificial intelligence investment and a concentrated group of large companies.

Bitcoin, by contrast, has remained sensitive to monetary conditions. Its limited upside participation suggests that it is not currently receiving the same flow of capital as the AI trade.

That does not necessarily invalidate the long-term Bitcoin thesis. It may instead reveal the sequence through which the AI credit bubble could influence crypto.

During the expansion phase, capital flows toward the assets directly associated with AI. During the eventual contraction, Bitcoin may initially suffer because liquidity disappears. Only after governments and central banks respond could Bitcoin begin to benefit from the monetary reaction.

The distinction between the current phase and the future response is essential. Hayes is not arguing that every dollar invested in artificial intelligence immediately moves Bitcoin higher. He is arguing that the eventual failure of the credit structure could force policymakers to create the liquidity that drives Bitcoin’s next secular expansion.

Why Hayes Compares the AI Credit Bubble to 2008 Instead of 2000

The central argument in Hayes’s “Situationship” essay is that investors are using the wrong historical comparison.

The common analogy is the dot-com bubble. During the late 1990s, investors assigned enormous valuations to internet companies with limited revenues, weak business models and, in many cases, no credible path to profitability.

When expectations collapsed, technology stocks fell dramatically. The Nasdaq Composite lost most of its value, speculative companies disappeared and equity investors suffered significant losses.

However, the financial system itself survived. Banks were not forced to absorb losses on a scale comparable to the 2008 crisis. The technology correction was destructive for shareholders, but it did not create the same systemic credit event.

The housing bubble was different.

Real estate purchases, construction and financial speculation were supported by enormous quantities of debt. Mortgages were originated, packaged into securities, transformed into derivatives and distributed throughout the banking and shadow-banking systems.

When house-price appreciation slowed, the entire structure became vulnerable. Borrowers had relied on rising property values, lenders had underestimated default risks and investors had purchased securities whose apparent safety depended on assumptions that were no longer valid.

The crisis did not begin simply because house prices fell. It began because the debt built on those prices could no longer be serviced, refinanced or trusted.

Hayes believes the AI credit bubble follows a similar structure. The current boom is not supported exclusively by equity investors purchasing expensive technology stocks. It is increasingly supported by lenders financing data centers, power systems, chips, land and long-term computing capacity.

The Bank for International Settlements reported in March 2026 that hyperscalers have increasingly used debt and off-balance-sheet arrangements to fund infrastructure expansion. Special-purpose entities and joint ventures can own the data-center assets, raise private debt and lease the capacity back to large technology companies.

Economically, these structures can function like borrowing even when the associated debt does not appear directly on the hyperscaler’s balance sheet. The BIS described them as a form of “shadow borrowing” that strengthens the connections between technology companies, private credit funds, banks and insurers.

That is the key difference between an equity bubble and the AI credit bubble.

A decline in technology stocks damages investors. A failure in heavily financed infrastructure can damage investors, lenders, pension funds, insurers, banks, private-credit vehicles and the broader credit market.

The Data Center Boom Is Becoming a Debt Machine

The scale of the financial commitments already attached to artificial intelligence infrastructure is becoming difficult to ignore.

Microsoft, Meta, Oracle, Amazon and Alphabet have collectively committed approximately $1.09 trillion to future lease payments that had not yet commenced as of August 2026. Most of those commitments are connected to data centers required to support cloud computing and artificial intelligence.

These future payments were almost four times the approximately $285 billion in lease liabilities already recognized on the companies’ balance sheets.

The commitments are disclosed in corporate filings, but accounting treatment generally delays recognition as a liability until the facility becomes available for use. This means a large portion of the economic obligations connected to the AI credit bubble has not yet entered the most commonly followed leverage metrics.

Microsoft had disclosed approximately $329.1 billion in uncommenced leases. Meta reported nearly $279 billion before signing another $68 billion of data-center leases in July. Oracle disclosed approximately $260 billion, while Alphabet and Amazon reported $85.2 billion and $137.2 billion respectively.

These figures do not mean that $1 trillion should simply be added to reported corporate debt. The payments are spread over many years, have different contractual structures and must be discounted to calculate their present value.

Nevertheless, they demonstrate the extraordinary scale of the fixed commitments being created.

If demand for computing power continues to expand rapidly, the facilities could generate sufficient revenue to justify their construction. The leases would support the next phase of cloud growth, AI inference and enterprise adoption.

If demand disappoints, companies may be required to pay for long-lived capacity that cannot easily be reduced or redeployed.

This is the central vulnerability of the AI credit bubble. Computing technology evolves rapidly, but the financial commitments funding the infrastructure are often fixed for periods of 15 years or longer.

The useful economic life of the chips may be far shorter than the life of the lease.

Data Centers Are Technology on the Inside and Real Estate on the Outside

The market frequently values data-center spending as if every dollar represented a direct investment in technological innovation. In reality, the infrastructure combines two very different economic systems.

Inside the facility are graphics processors, networking equipment, memory, storage and high-performance servers. These assets can produce valuable computing services, but they also depreciate quickly.

New generations of chips can provide significantly greater performance and energy efficiency. A data center filled with expensive hardware can therefore lose technological competitiveness long before the building or financing obligation expires.

Outside the computing racks, the project resembles a large industrial property development.

It requires land acquisition, planning permission, substations, cooling systems, grid connections, water infrastructure, backup generation, security and long-term power agreements. These components involve large upfront costs and long construction periods.

The International Energy Agency estimates that global data-center electricity consumption could increase from approximately 415 terawatt-hours in 2024 to around 945 terawatt-hours by 2030. That would represent annual growth of approximately 15%, more than four times the growth rate expected from other sources of electricity consumption.

Accelerated servers used primarily for artificial intelligence are projected to account for almost half of the increase.

The AI infrastructure cycle is therefore not only a semiconductor story. It is also an electricity, natural gas, nuclear power, renewable energy, grid expansion, construction and financing story.

That complexity creates multiple points of failure.

A project may have access to chips but lack sufficient power. It may secure power but experience delays in grid connections. It may become operational after demand has moved to a different region or more efficient computing architecture.

The AI credit bubble does not require AI technology to fail. It only requires the financial return on certain infrastructure projects to be lower than lenders and investors expected.

The Most Important Variable Is Not Growth but Acceleration

Hayes places particular emphasis on the second derivative of spending.

Markets rarely wait for a sector to begin contracting before repricing it. Prices often peak when the rate of growth stops accelerating, even if absolute revenues and capital expenditures are still increasing.

Suppose a hyperscaler increases annual AI capital expenditure from $50 billion to $80 billion, then to $130 billion. The market sees an accelerating investment cycle and rewards the companies positioned to supply it.

If the following annual budget increases from $130 billion to $150 billion, spending is still rising. However, the pace of the increase has slowed dramatically.

For equity investors, that deceleration can challenge the assumption of unlimited future demand. For lenders, the consequences may be even more significant.

Credit may continue expanding after investment growth begins to slow. Projects already announced must still be financed. Facilities under construction require additional capital. Existing debt must be refinanced.

This produces a dangerous period in which the growth of the underlying economic activity decelerates while the amount of credit connected to it continues to rise.

The same pattern appeared before the global financial crisis. U.S. home-price growth began to slow before mortgage lending and construction finance fully contracted. Credit continued flowing because the system had not yet accepted that its central assumption was breaking.

Hayes expects announced AI capital-expenditure growth to begin decelerating between the second half of 2027 and 2028. He argues that lenders may continue providing capital during this phase because governments have framed artificial intelligence as a strategic national priority.

The exact timing is uncertain. However, the mechanism is credible.

The AI credit bubble would become unstable not necessarily when spending falls, but when investors recognize that spending can no longer accelerate at the rate required to validate existing valuations and financing commitments.

Why Credit Could Continue Expanding After the Warning Signs Appear

In a normal commercial market, weaker expected returns should reduce investment. Artificial intelligence is not currently operating inside a normal commercial framework.

Both the United States and China consider AI leadership strategically important. The technology is connected to national security, military systems, economic productivity, communications, healthcare, surveillance and control over future digital infrastructure.

This strategic dimension can distort capital allocation.

Lenders may believe that governments will not allow critical AI projects to fail. Investors may assume that the largest technology companies will continue honoring their contracts regardless of temporary losses. Private-credit funds may view hyperscaler guarantees as substitutes for traditional project economics.

The result is moral hazard.

When participants believe that an industry has become too important to fail, they demand less compensation for risk and finance projects that might not survive under purely commercial conditions.

The BIS reported that direct-lending funds had quadrupled their exposure to AI and information-technology sectors over five years, bringing the allocation to approximately 15% of their portfolios. The institution also warned that these loans tend to be larger than lending in other sectors while pricing and maturity structures remain broadly similar.

This raises questions about whether credit markets are receiving sufficient compensation for concentration, construction, technology and refinancing risk.

The AI credit bubble can therefore continue expanding even after the first evidence of overcapacity appears.

Projects may be completed because abandoning them would crystallize losses. Loans may be extended because refinancing appears less damaging than default. Governments may encourage continued investment because slowing down could be interpreted as losing the strategic race.

This is how a manageable misallocation can become a systemic problem.

The Weakest Link May Not Be a Hyperscaler

Microsoft, Alphabet, Amazon and Meta generate substantial cash flows. Their financial strength is one reason many investors dismiss the possibility of an AI-driven credit crisis.

However, the primary risk may not sit directly on the balance sheets of the strongest technology companies.

The infrastructure ecosystem includes property developers, power providers, utilities, chip-financing companies, special-purpose vehicles, private-credit funds, construction firms, colocation providers and regional data-center operators.

Many of these entities have weaker balance sheets and greater refinancing needs than the hyperscalers ultimately using the capacity.

A dedicated vehicle may finance a facility with a combination of sponsor equity, bank loans and private debt. The hyperscaler commits to lease the facility or purchase computing capacity but owns only a minority interest in the project.

If construction costs rise, the vehicle may require more capital. If the completion date is delayed, the guarantee from the final customer may not yet be fully effective. If interest rates remain high, refinancing can become more expensive.

The debt may then be sold to bond investors, insurers or pension funds seeking yield.

This structure distributes exposure throughout the financial system. It can also make the location of the ultimate risk difficult to identify.

During the global financial crisis, the uncertainty surrounding ownership of mortgage-linked securities contributed to the freezing of interbank and wholesale funding markets.

The AI credit bubble could create a similar information problem. Investors may understand that certain data-center loans are deteriorating without knowing which private funds, insurers or banks hold the largest exposures.

The failure of one highly leveraged developer would not automatically create a systemic crisis. But it could force markets to reprice the entire sector.

Why the Recent Korean Sell-Off Matters

The violent reversal in South Korean equities provides an early example of how concentrated AI positioning and leverage can amplify market stress.

The Block2Learn analysis of the KOSPI crash and Korea’s leveraged AI unwind examined how foreign-capital withdrawals, semiconductor concentration and leveraged positions contributed to one of the sharpest market reversals of the cycle.

Hayes does not interpret the Korean sell-off as proof that the AI bull market has already ended. He views it as a correction inside a larger expansion that could eventually enter a final speculative phase.

That interpretation is plausible.

Credit bubbles rarely collapse immediately after the first warning. Early stress can produce temporary deleveraging, policy support and another period of price appreciation.

The danger is that investors interpret every recovery as confirmation that the underlying structure is safe. Higher asset prices then support more borrowing, more construction and larger commitments.

The AI credit bubble may therefore become more dangerous after surviving its first major correction.

A final acceleration in AI equities, data-center construction and credit issuance would increase the volume of capital exposed when growth eventually slows.

How an AI Debt Crisis Could Reach the Broader Economy

A data-center credit event would not remain isolated inside the technology sector.

The first transmission channel would be corporate credit. Spreads on AI-linked bonds would widen as investors demanded greater compensation for default and refinancing risk.

New issuance would become more expensive. Projects dependent on continuous access to capital could be delayed or cancelled.

The second channel would be private credit. Funds holding illiquid loans could experience lower valuations, redemption pressure or difficulty raising new capital.

Because private assets are not continuously traded, reported losses may appear slowly. However, the absence of a visible market price does not eliminate the economic damage.

The third channel would be banks.

Banks provide construction loans, revolving credit facilities, bridge financing and funding lines to special-purpose vehicles and private-credit funds. They may also be exposed through derivatives, guarantees and warehouse facilities.

The fourth channel would be employment and economic growth.

Data-center construction has become an important component of investment in advanced economies. A sudden decline would affect construction companies, utilities, equipment manufacturers, semiconductor producers and regional economies built around expected facilities.

The fifth channel would be equity markets.

AI-related companies now represent a substantial share of major U.S. indices. A sharp repricing would reduce household wealth, pension values and corporate confidence.

The AI credit bubble could therefore produce simultaneous stress across equities, credit, employment and investment.

That is the point at which political pressure for government intervention would become difficult to resist.

Why Governments Would Probably Intervene

Hayes’s Bitcoin thesis depends on one critical assumption: authorities would respond to the collapse with large-scale monetary and fiscal support.

This cannot be guaranteed, but several factors make intervention plausible.

First, artificial intelligence has been defined as a matter of national security. Allowing large portions of domestic computing infrastructure to fail could be interpreted as surrendering strategic capacity to geopolitical competitors.

Second, the financial exposures may extend into systemically important institutions. If banks, insurers, pension funds and private-credit vehicles hold substantial AI-related obligations, uncontrolled defaults could threaten financial stability.

Third, governments have already established the precedent that strategically important sectors can receive emergency support. During previous crises, authorities rescued banks, supported money-market funds, guaranteed corporate credit and funded critical industries.

Fourth, the political cost of mass layoffs and abandoned infrastructure would be substantial.

A government might not directly rescue every failed data-center developer. It could instead provide loan guarantees, subsidized refinancing, tax credits, power contracts or public purchases of computing capacity.

Central banks could lower interest rates, reopen emergency facilities or purchase assets to stabilize credit markets.

The AI credit bubble would then transform into a sovereign and central-bank balance-sheet problem.

Private losses would be partially socialized, financial conditions would be loosened and the supply of liquidity would increase.

That transition is the bridge between artificial intelligence debt and Bitcoin.

The Liquidity Transmission Mechanism to Bitcoin

Bitcoin does not need governments to purchase it directly. It needs the policy response to alter the monetary environment in which investors allocate capital.

The first transmission mechanism would be lower real interest rates.

Bitcoin does not generate contractual cash flows. When inflation-adjusted yields are high, investors can obtain attractive returns from government securities without accepting the volatility of crypto assets.

When real yields fall, the opportunity cost of holding Bitcoin declines.

The second mechanism would be expansion of central-bank balance sheets or emergency liquidity facilities. New reserves increase the amount of money circulating through the financial system and reduce immediate funding stress.

The third mechanism would be fiscal spending. Government guarantees, subsidies and strategic investment programs could increase deficits and Treasury issuance.

If markets become unable or unwilling to absorb that debt at politically acceptable yields, central banks may eventually be pressured to provide support.

The fourth mechanism would be currency debasement expectations.

Investors do not need inflation to immediately accelerate. They only need to believe that authorities will repeatedly protect debtors by reducing the real value of outstanding obligations.

The fifth mechanism would be renewed demand for scarce monetary assets. Gold and Bitcoin can benefit when investors seek alternatives to sovereign liabilities.

This is why Hayes expects Bitcoin to fall first and rise later.

During the initial phase of the AI credit bubble collapse, investors would sell liquid assets, leverage would be reduced and the dollar could strengthen. Bitcoin could decline sharply.

After the policy response becomes visible, the market would begin discounting lower real yields, larger deficits and greater monetary expansion. That is the phase in which Bitcoin’s structural scarcity could become valuable.

The 10-Year Real Yield Is Bitcoin’s Immediate Macro Test

The long-term bailout thesis does not eliminate the importance of current monetary conditions.

The 10-year U.S. Treasury inflation-protected yield stood at 2.43% on August 3, 2026. It had reached 2.47% on July 31, placing it close to levels not sustained since before Bitcoin became a traded asset.

Bitfinex has identified 2.5% as an important threshold for its current Bitcoin macro thesis. The exchange argues that a move above that level lasting at least two consecutive weeks would invalidate the assumption that real rates continue to provide a supportive long-term environment.

The threshold is not a mechanical law. Bitcoin could still rise with real yields above 2.5% if demand, adoption or liquidity conditions changed sufficiently.

However, the level is important because Bitcoin has limited historical experience operating in such a restrictive real-rate environment.

Bitfinex noted that the 10-year real yield increased from 2.24% at the beginning of July to 2.41% on July 30. The exchange also highlighted that Bitcoin’s observable price history largely exists below the 2.5% threshold.

This creates an immediate conflict between the current macro environment and the future AI credit bubble thesis.

Hayes’s argument is structurally bullish over a long horizon because he expects a future crisis to force monetary expansion. The current real-yield environment remains restrictive and can continue suppressing Bitcoin before that crisis occurs.

Investors who ignore this distinction risk becoming correct about the long-term outcome while suffering major losses during the transition.

Why Bitcoin Could Initially Collapse With AI Assets

Bitcoin is frequently described as digital gold, but its behavior during liquidity shocks often resembles that of a high-volatility risk asset.

When funding conditions tighten, investors sell what they can sell. Bitcoin trades continuously, has deep global liquidity and can be used as collateral across crypto markets.

These characteristics make it vulnerable during forced deleveraging.

If the AI credit bubble begins to collapse, hedge funds and institutional investors may reduce exposure across multiple asset classes. Crypto derivatives could experience liquidations, ETF flows could turn negative and stablecoin liquidity could contract.

Mining companies exposed to high power costs or AI infrastructure projects might also sell Bitcoin reserves to strengthen their balance sheets.

The first phase could therefore produce a significant Bitcoin drawdown rather than an immediate rally.

The Block2Learn Bitcoin daily technical analysis examined the strategic importance of the $60,000 area. A systemic risk-off event could force the market to test or break that support before any liquidity-driven recovery begins.

This is consistent with Hayes’s sequence: Bitcoin falls during the recognition of the credit problem and begins a secular rise only when policymakers respond.

The $1 million thesis should therefore not be interpreted as a straight-line projection.

It is a crisis-and-response scenario.

What Would Be Required for Bitcoin to Reach $1 Million?

A Bitcoin price of $1 million would imply a network value approaching $20 trillion based on the circulating supply expected during the second half of the decade.

That would place Bitcoin in the same broad valuation category as the world’s largest monetary and sovereign reserve assets.

Reaching that level would require much more than another conventional crypto bull market.

Global investors would need to view Bitcoin as a strategic monetary asset rather than primarily a speculative technology exposure. Institutional allocation would need to deepen, custody infrastructure would need to remain resilient and regulatory access would need to remain available across major financial jurisdictions.

The monetary response to the AI credit bubble would also need to be exceptionally large.

Hayes argues that the intervention could exceed the response to the 2008 financial crisis because both the United States and China would consider AI infrastructure strategically essential.

A combined expansion of dollar and yuan liquidity would create a broader global impulse than a U.S.-only intervention.

Even under that scenario, $1 million should be understood as an upper macroeconomic destination, not a precise forecast with a guaranteed timetable.

Bitcoin could capture only a portion of the new liquidity. Gold, equities, real estate and sovereign bonds would compete for capital.

Governments might also impose regulations, taxes or capital controls that reduce Bitcoin’s ability to absorb monetary demand.

The target is mathematically possible. Its probability depends on the scale of the crisis, the policy response and Bitcoin’s position within the global financial system at the time.

Three Scenarios for the AI Credit Bubble and Bitcoin

Base Scenario: The AI Boom Continues and Bitcoin Remains Macro-Constrained

In the Block2Learn base case, the AI credit bubble continues expanding through 2027. Capital expenditure remains high, data-center demand grows and credit markets continue financing new infrastructure.

Some projects experience delays or lower returns, but the largest companies maintain sufficient cash flow to prevent systemic stress.

Artificial intelligence equities remain volatile but avoid a complete collapse. Governments support the sector through tax incentives, energy policy and strategic procurement rather than emergency bailouts.

Bitcoin remains primarily influenced by real yields, ETF flows, dollar liquidity and crypto market structure.

Under this scenario, Bitcoin can recover from the current range and participate in a broader risk cycle, but the path toward $1 million does not begin immediately.

Bullish Bitcoin Scenario: Credit Breaks and Policymakers Launch the Big Liquidity Response

In the most bullish long-term scenario for Bitcoin, AI capital-expenditure growth decelerates sharply during 2027 or 2028.

Overleveraged data-center projects begin defaulting. Private-credit funds mark down loans, corporate spreads widen and banks face growing exposure through lending facilities and guarantees.

Equity markets fall as investors recognize that infrastructure spending has exceeded monetizable AI demand.

Governments initially describe the problem as contained. However, the crisis spreads through employment, construction and financial institutions.

The Federal Reserve and other central banks reduce rates, establish liquidity facilities and support credit markets. Governments provide guarantees and strategic funding for AI infrastructure.

Real yields fall, fiscal deficits expand and global liquidity accelerates.

Bitcoin initially declines but later begins a major revaluation as investors seek protection from monetary expansion.

This is the scenario in which Hayes’s $1 million target becomes conceptually plausible.

Bearish Bitcoin Scenario: AI Delivers Productivity and Real Yields Stay High

The most challenging scenario for Bitcoin is not necessarily an AI collapse. It could be AI success without a credit crisis.

Suppose artificial intelligence produces sufficient revenues and productivity improvements to validate infrastructure investment. Data-center capacity is absorbed, corporate profits remain strong and credit losses stay manageable.

Economic growth remains resilient, inflation stays above target and governments have no reason to launch large-scale monetary support.

The 10-year real yield moves above 2.5% and remains there.

In this environment, the AI credit bubble never becomes a systemic crisis. It transforms into a successful capital-investment cycle.

Bitcoin would still possess scarcity and adoption value, but the macro liquidity catalyst behind the $1 million thesis would be absent.

Capital could continue preferring productive technology assets and high-yielding government securities.

This is the scenario Bitcoin investors must not ignore.

The Indicators Investors Should Monitor

The first indicator is hyperscaler capital-expenditure growth. The absolute amount of spending matters less than whether annual growth is accelerating or decelerating.

The second indicator is the volume of uncommenced leases and long-term purchase obligations. These commitments reveal the future fixed-cost structure that has not yet fully entered reported liabilities.

The third indicator is corporate-credit pricing. Rising credit default swap spreads, weaker bond prices and more expensive new issuance would indicate that lenders are beginning to question the economics of the AI credit bubble.

The fourth indicator is private-credit performance. Loan amendments, maturity extensions, payment-in-kind interest and reduced fund distributions can reveal stress before formal defaults occur.

The fifth indicator is data-center utilization. Capacity growth must be supported by actual demand for AI training and inference.

The sixth indicator is energy availability. Delayed grid connections, rising electricity prices and competition for natural gas or nuclear capacity could reduce project profitability.

The seventh indicator is the 10-year real yield. A sustained move above 2.5% would maintain pressure on Bitcoin and increase financing costs across infrastructure markets.

The eighth indicator is government language. References to AI infrastructure as critical, strategic or systemically important would increase the probability of future public support.

The ninth indicator is global liquidity. Federal Reserve assets, Treasury cash management, Chinese credit creation, stablecoin supply and dollar funding conditions will help determine whether the monetary environment is becoming supportive.

The final indicator is Bitcoin’s relative performance. If Bitcoin begins outperforming AI equities before formal policy easing, it may indicate that markets are starting to price the future response rather than the current crisis.

The Block2Learn View: Hayes May Be Right About the Mechanism but Early on the Timing

The Block2Learn position is that Hayes has identified a credible structural risk.

Artificial intelligence is no longer financed exclusively through the retained earnings of highly profitable technology companies. Debt, private credit, long-term leases and special-purpose structures are becoming increasingly important.

The BIS evidence and corporate lease disclosures support the argument that the AI credit bubble has a genuine financial dimension.

However, identifying a future credit problem is not the same as identifying the moment when the system breaks.

The boom may continue longer than bears expect. Governments can support investment before defaults occur. AI revenues may grow sufficiently to absorb a meaningful portion of the infrastructure.

Efficiency gains could also increase demand by reducing the cost of inference, partially offsetting the risk of overcapacity.

The most probable path is therefore not an immediate collapse followed by Bitcoin at $1 million.

A more realistic sequence includes continued AI investment, periodic corrections, rising credit concentration, a phase of spending deceleration and only later a possible refinancing crisis.

Bitcoin may remain under pressure while real yields are high. It could suffer another major drawdown when the credit cycle turns.

The decisive bullish phase would begin only when authorities demonstrate that they are prepared to absorb private losses and expand liquidity.

Hayes may therefore be directionally correct while remaining early in timing.

For investors, this distinction is fundamental.

The AI credit bubble is not a reason to purchase Bitcoin with unlimited leverage today. It is a macro framework for understanding why the next systemic rescue could produce a historic repricing of scarce monetary assets.

Learning Path: Understanding the Connection Between Credit, Liquidity and Bitcoin

The relationship between artificial intelligence and Bitcoin cannot be understood by following technology headlines alone.

Investors must understand how infrastructure is financed, how credit expands, why refinancing risk develops and how central banks respond when private debt threatens financial stability.

The Block2Learn Learning Path provides a structured progression through monetary systems, macroeconomics, market liquidity, asset analysis, trading and wealth strategy.

The first step is understanding that money and credit are not the same. Central banks can create base money, while commercial banks and non-bank lenders expand credit through loans, securities and financing structures.

The second step is learning how liquidity moves between markets. Capital does not enter every asset simultaneously. It can first concentrate in AI equities, later retreat into cash and government bonds, and eventually rotate toward Bitcoin when monetary policy changes.

The third step is distinguishing technological success from investment success. Artificial intelligence can transform the economy while many individual data-center projects still generate poor returns.

The fourth step is understanding real yields. Bitcoin’s scarcity becomes more attractive when the inflation-adjusted return available from sovereign debt declines.

The final step is scenario construction. Investors should not rely on a single forecast. They should evaluate the probability of continued expansion, controlled deleveraging and systemic collapse.

The AI credit bubble may become the bridge between today’s technology boom and tomorrow’s monetary response.

Whether Bitcoin reaches $1 million will depend on how large that bubble becomes, where the debt ultimately resides and how aggressively governments react when the cycle turns.

The immediate market may remain dominated by high real yields and narrow liquidity. The long-term structure is different.

If artificial intelligence becomes too strategically important to fail and too leveraged to unwind safely, the final beneficiary may not be the companies that built the largest data centers.

It may be the asset designed to exist outside the balance sheets used to rescue them.