AI

The Mythos Meeting: Anthropic’s Dangerous AI and the White House’s Calculated Gamble | 2026

Published

3 months ago

April 18, 2026

The Amodei–Wiles meeting signals a seismic U.S. AI policy pivot. Why Washington is now courting the Anthropic Mythos model it once tried to destroy.

Imagine the scene: a Friday afternoon in the West Wing, the air carrying the particular weight of decisions that cannot be undecided. Dario Amodei, the quietly intense CEO of Anthropic, sits across from Susie Wiles, the White House Chief of Staff whose political instincts are said to be the closest thing to a gyroscope this administration possesses. Between them, unspoken but omnipresent, is a question that has convulsed Washington’s national-security establishment for weeks: what do you do with an AI so dangerous that even its creators are frightened of it—and so potent that refusing to use it might be the most reckless choice of all?

That meeting, confirmed by Axios, CNN, and the Associated Press, is not merely a diplomatic thaw between a tech company and its government tormentor. It is the moment Washington finally admitted what it has known all along: that frontier AI has outrun every framework, every regulation, and every posture of ideological hostility that American politics could muster. The implications—for U.S. national security, for the global AI arms race, and for the governance of technology at civilizational scale—are seismic.

What Mythos Is, and Why It Terrifies the People Paid to Worry

To understand the Dario Amodei–Susie Wiles meeting and its national security implications, you must first understand what Anthropic’s Claude Mythos Preview actually does. Launched on April 7, 2026, Mythos is not a chatbot upgrade. It is, in the judgment of the cybersecurity community, a watershed event—a model of such extraordinary capability in identifying software vulnerabilities that it reportedly discovered thousands of zero-day flaws across major operating systems and browsers before breakfast.

Anthropic’s co-founder and policy chief Jack Clark, speaking at the Semafor World Economy Conference this week, described Mythos as having capabilities that could pose “severe” fallout for public safety, national security, and the economy. Washington Times He was not speaking hyperbolically. He was warning. Clark added that Mythos is not a “special model”—”there will be other systems just like this in a few months from other companies, and in a year to a year-and-a-half later, there will be open-weight models from China that have these capabilities.” PBS

This is the paradox that has split Washington clean in two. Mythos can map the defensive perimeter of any digital system with an acuity no human team could match. It can find the crack in the levy before the flood. But it can also—in theory, in the wrong hands, with the wrong prompts—hand an adversary the blueprint for that same attack. Its Mythos tool can identify cybersecurity threats but also present a roadmap for hackers to attack companies or the government. CNN One U.S. official, in a phrase that deserves to be carved somewhere permanent, told Axios: “They’re using this Mythos cyber weapon to find friendly ears in the government. They’re succeeding.” Axios

Recognizing this dual-use reality, Anthropic did not release Mythos publicly. Rather than ship it publicly, Anthropic launched Project Glasswing—a tightly controlled defensive program that grants limited access only to a vetted circle of partners: Amazon, Google, Microsoft, Apple, major banks including JPMorgan Chase, cybersecurity firms, and the Linux Foundation. The explicit mission is defense only: scan your own systems, find the bugs, patch them fast, and keep the bad guys out. Zero Hedge Anthropic also pledged up to $100 million in usage credits and $4 million in donations to open-source security groups.

It is, by any reckoning, an extraordinary act of self-regulation from a private company. It is also the act that made the U.S. government desperate to get inside the tent.

The Meeting: What We Know, and What It Really Means

The meeting, first reported by Axios, comes after tensions have run hot between the Trump administration and the safety-conscious Anthropic, which has sought to put guardrails on the development of AI to minimize potential risks. It marks a breakthrough in Amodei’s effort to resolve the company’s bitter AI fight with the Pentagon. Axios

The White House said the meeting was “introductory,” calling it “productive and constructive.” “We discussed opportunities for collaboration, as well as shared approaches and protocols to address the challenges associated with scaling this technology,” the White House said in a statement. “The conversation also explored the balance between advancing innovation and ensuring safety.” CNN

The diplomatic language obscures the pressure beneath. Treasury Secretary Scott Bessent joined the meeting, a notable escalation of seniority. “This is a big problem. Everyone’s complaining. There’s all this drama. So this got elevated to Susie to hear Dario out, determine what is bullsh-t and start to plot a way forward,” a Trump adviser told Axios. Axios

Those familiar with the negotiations describe what the White House is actually seeking: next steps are expected to be about how government departments engage with Anthropic’s new Mythos Preview model. Axios This is not abstract policy discussion. Some government agencies want access, and the White House and Anthropic are discussing the terms under which that might be possible. Two sources told Axios there are ongoing discussions, and agencies may get access to Mythos in the coming weeks. Axios

What Amodei wants in return is equally clear. He has drawn two lines in the sand that have proved non-negotiable: no use of Claude for mass domestic surveillance, and no deployment in fully autonomous weapons systems. Amodei noted that Anthropic has proactively deployed its models to the Department of War and the intelligence community, and was the first frontier AI company to deploy models in the U.S. government’s classified networks and at the National Laboratories. Attack of the Fanboy The Pentagon’s position—that it needs AI available for “all lawful purposes” without carve-outs—strikes many observers outside the building as, at minimum, an extraordinary demand to make of a private-sector partner.

From Pentagon Blacklist to White House Courtship: The Policy U-Turn

The speed of this reversal deserves its own chapter in any future history of American governance.

In late February, President Trump directed federal agencies to stop using Anthropic’s technology. In early March, the Defense Department formally designated Anthropic a supply-chain risk, effectively blocking its models from use on Pentagon contracts. CNN The designation—previously reserved for companies with ties to foreign adversaries—was applied to a San Francisco AI safety company because it refused to remove ethical guardrails. A federal judge in California, granting Anthropic a preliminary injunction, wrote that “nothing in the governing statute supports the Orwellian notion that an American company may be branded a potential adversary and saboteur of the U.S. for expressing disagreement with the government.”

Yet even as that legal fight raged, Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell summoned executives from JPMorgan Chase, Goldman Sachs, Citigroup, Bank of America, and Morgan Stanley and urged them to use Anthropic’s new Mythos model to detect cybersecurity vulnerabilities in their systems. The Next Web The left hand of government was blacklisting what the right hand was urgently deploying.

Key officials in the Trump administration see Anthropic and its leaders as woke doomsters, and some relished slapping on the “supply chain risk” designation. But some of those same officials, and many others, also see Anthropic’s tools as best-in-class when it comes to AI for national security purposes. One Defense official told Axios at the height of the Pentagon-Anthropic feud that the only reason the talks were ongoing was: “these guys are that good.” Axios

This is the grotesque comedy—and the cold logic—of American AI policy in 2026. Ideological hostility colliding with operational necessity. The government cannot afford the luxury of its own grievance.

Geopolitical Stakes: China, Europe, and the New AI Arms Race

The Dario Amodei Susie Wiles meeting on AI national security cannot be understood outside its broader geopolitical frame. Jack Clark’s comment at Semafor was not idle—it was a countdown. A source close to negotiations told Axios: “It would be grossly irresponsible for the U.S. government to deprive itself of the technological leaps that the new model presents. It would be a gift to China.” Axios

China’s AI labs—DeepSeek, Zhipu, Baidu’s ERNIE—are advancing at a pace that was unimaginable eighteen months ago. The release of DeepSeek’s R1 model in early 2025 rattled markets and shattered the comfortable assumption that America’s compute advantage translated automatically into a capability lead. Beijing’s military-civil fusion doctrine means that any advance in Chinese commercial AI carries direct implications for the People’s Liberation Army. Anthropic has passed up several hundred million dollars to cut off use of Claude by firms linked to the Chinese Communist Party and shut down CCP-sponsored cyberattacks that attempted to abuse the system. Attack of the Fanboy

Europe, for its part, is watching from a peculiar position: deeply invested in AI safety regulation through the EU AI Act, yet without a frontier model lab of its own capable of matching Anthropic, OpenAI, or Google DeepMind. The UK’s NCSC and regulators are scrambling to assess Mythos’s risk profile. The asymmetry is uncomfortable: American and Chinese labs are racing to build and deploy the most powerful AI systems the world has seen, while Europe writes governance frameworks for systems that are already obsolete by the time the ink dries.

In this context, the U.S. government’s approach to Anthropic’s Mythos Preview and cybersecurity defense is not merely domestic policy. It is a strategic posture in a new kind of arms race—one where the weapons are invisible, the battlefield is software infrastructure, and the most dangerous adversary may be inaction itself.

The Opinion: Washington Must Choose

Let me say plainly what the diplomatic language of this week’s meetings cannot: the United States government does not have a coherent AI strategy. It has a collection of competing institutional impulses—the Pentagon’s maximalism, the intelligence community’s pragmatism, the Treasury’s alarm about financial infrastructure, and the White House’s moment-to-moment political management—loosely tethered by the fiction of a unified executive branch.

The Anthropic Mythos White House access negotiations expose this incoherence in full. A company is simultaneously being sued by one arm of the government and being courted by three others. The same model is being called a national-security threat and a national-security imperative, often by people in the same building. This is not policy. It is cognitive dissonance with a budget.

What Washington must do—and what this meeting, however “introductory,” at least gestures toward—is make a choice. Either frontier AI labs like Anthropic are strategic national assets to be cultivated under a framework of responsible access and negotiated guardrails, or they are private entities whose autonomy makes them inherently adversarial to state power. You cannot hold both positions at once, regardless of how many executive orders you issue.

The Anthropic model—safety-conscious development, controlled deployment through Project Glasswing, categorical refusal of certain military applications—is not naïveté. It is a serious attempt to thread a needle that governments have proven incapable of threading themselves. The Pentagon’s insistence on unrestricted access is not hardheadedness. It is institutional anxiety dressed as operational necessity. Between these poles, there is a deal to be made. But making it requires the kind of institutional self-honesty that bureaucracies resist until the cost of denial becomes catastrophic.

The cost is visible. Civilian agencies like the Departments of Energy and Treasury are responsible for safeguarding critical sectors like the electric grid and financial system. Axios Those systems are being probed, daily, by adversaries who will not wait for Washington to resolve its internal politics. Every week the impasse continues is a week the electric grid goes unscanned, the financial system goes unpatched, and the advantage shifts.

What Comes Next: For Regulators, Enterprises, and Citizens

The practical near-term architecture of whatever deal emerges from the Mythos negotiations is beginning to take shape. An internal Office of Management and Budget memo lays out strict protocols for safe access, data handling, and usage limits so that major departments can deploy Mythos against their own sprawling digital estates. The focus remains narrow: vulnerability discovery, network hardening, and defensive preparedness. Zero Hedge

For enterprises, the implications of Anthropic’s Mythos model for cybersecurity defense extend well beyond Washington. If Project Glasswing’s 40-plus organizations can use Mythos to discover and patch vulnerabilities faster than adversaries can exploit them, the model for critical infrastructure protection changes fundamentally. Security becomes proactive rather than reactive. The question is whether the access framework can scale—and whether Anthropic can maintain meaningful guardrails as it does.

A real compromise would likely mean granting Anthropic broader federal access for cybersecurity and software testing while preserving the safety commitments the company says define the product. For Washington, the tradeoff is stark: use a powerful model to harden government systems, or pressure the company to weaken the very restraints that make its technology acceptable in the first place. Prism News

For citizens, this matters in ways that extend far beyond any individual’s awareness of AI policy. The security of the national power grid, the integrity of the financial system, the resilience of government networks—these are not abstract concerns. They are the infrastructure on which daily life depends. The Mythos Preview is not, in the end, a tech industry story. It is a story about who gets to decide how the most powerful tools in human history are deployed, and under what terms.

The Kicker: The Future Is Already in the Room

Here is what the optimists and the catastrophists both miss: the most important fact about this moment is not that Anthropic’s Mythos model exists, nor that the White House is courting it, nor even that China is close behind. The most important fact is that every frontier model released from here forward will carry something like Mythos’s capabilities. The Pandora’s box is already open. The question is not whether to touch what’s inside. The question is whether to pick it up with gloves on—or with bare hands.

The Amodei-Wiles meeting, whatever its immediate outcome, represents the first serious acknowledgment by the American executive branch that the era of AI as an abstract policy problem is over. The technology is here, it is geopolitically consequential, and it will not wait for regulatory consensus. Washington can lead this transition with deliberate guardrails and structured public-private partnership, or it can continue managing it through institutional contradiction and inter-agency feuding until an adversary—human or algorithmic—exploits the gap.

The Friday meeting in the West Wing was quiet. But the decisions made in its aftermath will be anything but.

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AI Capex Bubble 2026: The Hidden $662B Debt Nobody Reports

Published

11 hours ago

July 11, 2026

Abdul Rahman

Every earnings season now brings a fresh wave of headlines about hyperscaler AI capital expenditure hitting a new record. The “big four” — Amazon, Microsoft, Alphabet, and Meta — are on track to spend roughly $725 billion combined in 2026, a 77% jump from the $410 billion deployed in 2025 (UnboxFuture). That number gets reported constantly. What almost nobody is reporting with the same prominence is a separate figure that may matter more: roughly $662 billion in data center lease commitments that hyperscalers have already signed but not yet begun — obligations that currently sit entirely off balance sheet.

Why the Off-Balance-Sheet Number Changes the Whole Picture

Under GAAP accounting rules governing when a lease “commences,” these signed-but-not-started commitments don’t appear in the capital expenditure figures analysts and investors typically scrutinize when assessing hyperscaler financial health. According to reporting citing Moody’s early-2026 analysis, this shadow liability is larger than the combined on-balance-sheet debt of the same companies (Anomaly Investments).

That detail matters enormously for one specific argument AI infrastructure bulls have relied on: the claim that this buildout is being conservatively self-funded from operating cash flow rather than risky leverage. Once the full picture of committed-but-unrecognized obligations is accounted for, that defense becomes much harder to sustain.

The Debt Is Already Showing Up, Not Just Theoretical

This isn’t a purely hypothetical concern about future liabilities. Big tech companies have already issued more than $100 billion of bonds in 2026 specifically to help fund AI capital expenditure, and investors have responded by demanding record levels of protection against potential defaults through credit default swaps — essentially insurance policies against bond default (IEEE ComSoc).

Individual company examples illustrate the shift toward leverage: Oracle issued an $18 billion bond specifically tied to its data center expansion; CoreWeave secured a $2.6 billion loan alongside a $1.75 billion bond package; and OpenAI and Oracle reportedly entered into a $100 billion vendor financing arrangement (Anomaly Investments). At Amazon specifically, capital expenditure over the trailing twelve months has reached $151 billion — a figure that now exceeds the company’s entire operating cash flow, pushing free cash flow into negative territory.

The Depreciation Assumption Almost No Coverage Questions

Here’s an angle genuinely underexplored across most financial media: the depreciation schedules hyperscalers use for AI hardware assume a five-to-six-year useful life. But given how rapidly GPU generations are turning over and how intensively AI workloads are pushing hardware utilization, critics argue the real economic life of this equipment is closer to two to three years. That gap between assumed and actual depreciation is estimated to understate true asset depletion by roughly $176 billion between 2026 and 2028 alone — a figure that grows as accelerating token consumption pushes hardware utilization beyond the assumptions built into current depreciation schedules (Anomaly Investments).

Layered on top of that is the energy cost curve: running the current roughly 30-gigawatt installed base of AI infrastructure costs approximately $27 billion annually today, but that figure is projected to climb to between $45 and $90 billion per year as capacity scales toward 2029 — and crucially, these are first charges against revenue, not optional or deferrable costs.

The Revenue Gap: Who’s Actually Paying for All This?

The most commonly cited justification for the capex surge is that the pure-play AI vendors — OpenAI, Anthropic, and others — represent a massive and rapidly growing revenue opportunity. The reality is more nuanced. OpenAI’s roughly $20 billion annualized revenue run rate, while genuinely impressive for a company with barely any consumer products three years ago, represents only about 3% of projected 2026 hyperscaler capex. Anthropic’s roughly $9 billion run rate, despite showing 9x year-over-year growth, occupies a similarly small share. The entire cohort of pure-play AI vendors combined — including Cohere, Mistral, Perplexity, and others — likely accounts for less than $35 billion in projected combined 2026 revenue against a hyperscaler capex figure exceeding $700 billion (Futurum Group).

That gap is the crux of the bubble debate: hyperscalers are betting the infrastructure will ultimately serve enterprise adoption and their own AI services broadly, not just third-party AI vendor revenue — but that bet requires enterprise AI monetization to arrive at a scale that, as of mid-2026, remains largely unproven outside of code generation and basic customer service automation.

The Skeptic’s Case, From Inside Goldman Sachs Itself

The most prominent voice of institutional skepticism doesn’t come from an outside critic — it comes from within Goldman Sachs itself. Jim Covello, the bank’s Head of Global Equity Research, has consistently argued the economics of the generative AI transition are fundamentally flawed, stating in mid-2026 that the industry has moved “further away” from justifying the scale of capital expenditure compared to two years prior (UnboxFuture). Covello has specifically flagged circular capital flows between cloud providers and AI startups — where hyperscalers invest in AI companies that then spend that same capital purchasing compute from those same hyperscalers — as a red flag reminiscent of vendor financing patterns seen in the dot-com era.

The valuation comparison to that era is explicit and increasingly common among strategists: US technology and AI equities carry EV/EBITDA multiples near 25x, close to historical extremes and above the telecom valuations that preceded the 2000 dot-com peak. More specifically, capex is currently expanding roughly 46 percentage points faster than revenue growth — a gap that exceeds the 32-point divergence observed during the 2001 telecom excess cycle (Allianz Research). Separately, Bank of America strategists have pointed out that AI stock concentration has reached levels matching prior bubble peaks, with the “AI Big 10” (Nvidia, Microsoft, Alphabet, Amazon, Meta, Apple, Tesla, Broadcom, Micron, and AMD) now making up 41% of the S&P 500 — comparable to the concentration of tech and telecom stocks during the actual dot-com bubble (Yahoo Finance).

The Bull Case Isn’t Naive Either

It would be inaccurate to frame this purely as informed skeptics versus blind enthusiasm. Goldman Sachs’ own broader research (distinct from Covello’s individual view) models roughly $7.6 trillion in cumulative AI capital expenditure between 2026 and 2031, built on the expectation that token consumption will increase 24-fold by 2030, driven largely by enterprise AI agents becoming embedded in production workflows rather than remaining experimental (Sesame Disk / Goldman commentary). Microsoft has disclosed an $80 billion backlog of Azure orders it currently cannot fulfill due to power constraints — genuine evidence that demand, at least for existing capacity, is outpacing even the current aggressive build-out pace (Futurum Group).

Leverage levels also remain more conservative than headlines suggest in absolute terms: the top five US capex providers reported a combined $385 billion in debt at the end of 2025, with leverage ratios still roughly 20% below the “high spender” cohort from the 2000 dot-com peak, according to Allianz Research analysis — meaning rising debt levels are a trend worth monitoring closely, not yet an acute crisis.

What Happens If the Bubble Skeptics Are Right

Historical infrastructure cycles offer a specific and somewhat counterintuitive lesson: the investors who fund the initial frenzied build-out phase rarely capture the long-term rewards. If the AI capex cycle follows the pattern of the 1998-2001 fiber optic buildout, hyperscalers may eventually be forced to write down the value of data centers and GPUs purchased at today’s prices and utilization assumptions. But that collapse in computing costs, paradoxically, could pave the way for a new generation of leaner, genuinely profitable software companies to build on top of the resulting cheap, overbuilt infrastructure — much as fiber-optic overbuild eventually enabled the 2000s streaming and cloud computing boom, even after the original telecom investors were wiped out.

What This Means for Investors and Businesses

For equity investors, the practical signal to watch isn’t the headline capex number — it’s the widening gap between capex growth and revenue growth, and whether that gap begins narrowing through 2027 as enterprise adoption either accelerates or disappoints. For businesses evaluating AI vendor relationships, the circular-financing pattern flagged by Covello is worth diligence: understanding whether an AI vendor’s revenue depends partly on capital originally supplied by the same hyperscaler providing its compute is a legitimate red flag for assessing that vendor’s underlying financial independence. For fixed-income investors, the rising credit default swap pricing on hyperscaler-linked debt is itself a market signal worth tracking as an early indicator of shifting sentiment, independent of equity price action.

The Bottom Line

The AI infrastructure buildout genuinely is the largest corporate capital expenditure cycle in recorded history, and it’s happening for real, defensible reasons tied to a genuine technology shift. But the debate over whether it constitutes a bubble isn’t really about whether AI technology is useful — it’s about whether the timing of returns can keep pace with public equity markets’ patience, and whether the $662 billion in off-balance-sheet lease commitments, aggressive depreciation assumptions, and circular vendor financing arrangements represent manageable financial engineering or the early architecture of a genuinely serious correction. Both cases have real evidence behind them. What’s clear is that the headline capex figure everyone quotes is no longer the most important number in this story.

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AI Bubble Warning 2026: Why BIS, IMF and Bank of England Fear a Market Crash

Published

6 days ago

July 5, 2026

Abdul Rahman

Global financial regulators have moved from quiet skepticism to open warning, marking one of the most significant shifts in central-bank rhetoric since the aftermath of the 2008 crisis. The Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the Bank of England have each flagged the risk that a correction in artificial-intelligence valuations could cascade through the global financial system, according to the BIS Annual Economic Report 2026 and reporting compiled by Wikipedia’s tracking of the unfolding episode.

From Confidence to Contagion Fear

The warnings did not emerge in a vacuum. In late June 2026, South Korea’s KOSPI index was forced into a trading halt after Samsung and SK Hynix shares each lost roughly 12% in a single morning, a shock that rippled into the Nasdaq, which fell 2.2% the same day. By the following week, Oracle had recorded its worst trading week since the dot-com crash, sliding 19%, after Apple raised product prices in response to soaring chip costs. The sell-off, detailed in Wikipedia’s account of the June 2026 rout, spread across global chip manufacturers before the BIS issued its formal caution on June 29.

Pablo Hernández de Cos, general manager of the BIS, framed the moment as one of “progress” colliding with “peril,” pointing to inflationary pressure, elevated public debt, and what the institution calls AI exuberance as compounding financial vulnerabilities.

Why This Cycle Looks Different — and Why It Doesn’t

Comparisons to the 1999–2000 dot-com bubble are now routine among Wall Street strategists. Deutsche Bank’s global economics team has described 2026 as resembling “1999 meets 1990,” according to Fortune’s coverage of the growing exuberance debate. JPMorgan’s chief executive Jamie Dimon has repeatedly used the phrase “irrational exuberance,” borrowed from former Fed chair Alan Greenspan, to describe dealmaking activity that he says is running “gung-ho.”

Yet analysts at Fidelity note a structural difference from 2000: hyperscalers are largely funding AI capital expenditure from earnings rather than debt, keeping the capex-to-free-cash-flow ratio below 1, compared with nearly 4 at the dot-com peak, based on Fidelity’s bubble-indicator research. That distinction matters for systemic risk, since debt-fueled busts tend to transmit further into the banking system than equity-only corrections.

The Systemic Transmission Risk

Oliver Wyman’s analysis of a potential AI-led market collapse estimates that an equity crash on the scale of the early 2000s could erase approximately $33 trillion in value — more than annual US GDP — a scenario that would compound if financing tied to data-center and digital-infrastructure debt turns out to be more opaque than banks currently report, according to Oliver Wyman’s assessment of financial-sector exposure. US equity market capitalization currently sits at close to twice GDP, a higher multiple than at the dot-com peak.

Prediction markets have already begun pricing the risk. Polymarket data cited by Tekedia shows the probability traders assign to an AI investment-frenzy collapse by the end of 2026 climbing to 26%, up sharply in recent months as valuations in chip and hyperscaler stocks stretched further.

What Regulators Are Asking Institutions to Do

The BIS is not calling for a halt to AI development. Instead, it is urging financial institutions to build greater transparency into AI-related financing, particularly the private-credit channels that now fund a large share of data-center buildouts, and to stress-test balance sheets against valuation drops of 30%, 40%, or even 50% in AI-exposed equities. The Bank of England has separately warned that investors have not been adequately cautioned about downside scenarios tied to companies such as OpenAI, whose valuation more than tripled between October 2024 and the following year.

For markets in the UK, US, Singapore, and East Asia’s chip-manufacturing hubs, the message from regulators is consistent: the innovation is real, but the financing structure underneath it has not been fully stress-tested against a reversal in sentiment.

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AI Bubble Risk 2026: BIS Warns Private Credit Could Trigger Financial Crisis

Published

1 week ago

July 3, 2026

Abdul Rahman

The Bank for International Settlements has told the world’s central banks something few wanted to hear in the middle of an AI-fueled bull run: the financing behind the boom now resembles the early architecture of a credit crisis. In its flagship Annual Economic Report, the Basel-based institution known as the central bank of central banks said that if AI returns disappoint and investors reassess risk, falling asset values combined with sudden funding withdrawals could transmit stress across the broader financial system, as first detailed by The Economy.

From Hyperscaler Capex to Systemic Fragility

The scale driving this concern is difficult to overstate. Microsoft, Amazon, Alphabet, Meta, and Oracle are collectively on pace to spend more than $1 trillion on AI infrastructure across 2025 and 2026 combined, a sum the BIS says already outpaces the group’s combined earnings and free cash flow. That gap is why hyperscalers have turned to debt markets at a pace unseen since the buildout of broadband infrastructure, with investment-grade bond issuance by major AI players exceeding $100 billion in six months, according to Oliver Wyman’s analysis of Dealogic and SIFMA data.

Fortune’s review of the BIS report frames the comparison in historical terms the institution itself invoked: the canal mania of the 1830s, Britain’s railway bubble of the 1840s, and the dot-com crash of 2000, each beginning with a genuine technological breakthrough that attracted more capital than commercial returns could ultimately justify, per Fortune. The BIS stops short of calling the AI boom a bubble outright, but its language leaves little room for comfort.

Private Credit’s Opacity Problem

The more acute concern sits outside public markets entirely. Private credit lending to AI companies surged from roughly $3 billion in 2010 to $40 billion last year, the BIS found. Because these loans flow through a web of investment funds, insurers, pension funds, and asset managers with little public disclosure, regulators cannot easily determine where losses would land if AI returns fall short. Unlike banks, these lenders have no deposit base and no central bank liquidity backstop, leaving forced asset sales as one of the few levers available if investors demand their money back.

That vulnerability is no longer theoretical. Blue Owl paused quarterly redemptions on a retail-facing direct lending fund earlier this year, an early sign of the liquidity strain described by Forbes. BlackRock’s TCP Capital Corp wrote down a private loan to an Amazon-seller aggregator to zero from full value, while bankruptcies at First Brands Group and Tricolor Holdings last September, each carrying billions in debt, have sharpened scrutiny of underwriting standards built during the ultra-low-rate years of 2020 and 2021.

Direct lending funds, an ecosystem now exceeding $1 trillion, have quadrupled their exposure to the AI and IT sectors over five years, and that exposure now represents about 15% of their portfolios, the BIS report notes. The Financial Stability Board, which monitors risk across 24 central banks, has separately warned that “significant data challenges” make the sector’s true exposure nearly impossible to map, with bank exposure estimates ranging anywhere from $220 billion to $500 billion depending on methodology, a spread detailed by IndMoney’s market analysis.

Why the Timing Is Especially Dangerous

The AI credit question is colliding with a second global shock that has nothing to do with technology. The closure of the Strait of Hormuz following the outbreak of the Iran conflict in February cut more than 10 million barrels of crude oil a day from global supply, a disruption larger than either the 1973 oil embargo or the 1979 Iranian revolution, according to the BIS report cited by Fortune. That energy shock has kept inflation risk elevated even as central banks weigh whether to ease policy, creating a scenario the BIS describes bluntly: the same monetary tightening needed to contain energy-driven inflation could be exactly what pops the AI-financed debt bubble.

Credit markets are already pricing in some of this tension. Spreads on bonds issued by AI-related companies rated BBB or higher have widened noticeably since the first quarter, briefly approaching a 20-basis-point increase in March, even as equity markets continue to price substantial further upside, a divergence flagged in the Economy’s coverage. Debt coming due from weaker private credit borrowers is projected to jump from $56.6 billion in 2026 to $215 billion by 2028, according to S&P Global data cited by IndMoney, concentrating refinancing risk at precisely the moment AI infrastructure utilization rates are becoming the market’s most important, and least verifiable, number.

What Happens if the Bet Doesn’t Pay Off

Not every analyst agrees the danger is systemic. The CFA Institute’s Enterprising Investor blog has pushed back on comparisons to the 2008 crisis, arguing that private credit’s structural mismatch is fundamentally different from the overnight funding of illiquid mortgage assets that caused the Global Financial Crisis, and noting that a well-diversified multi-strategy portfolio would likely be only marginally affected even by a serious AI correction, per CFA Institute.

But the BIS itself is not predicting collapse so much as demanding preparation. Its central recommendation is for what it calls “robustness” rather than the more fragile “resilience” the global financial system has shown so far, a distinction the institution says matters because a shock, whether a renewed inflation surge or a sharp AI-led repricing, could trigger a broader credit crunch. If half of the projected $6 trillion in AI capital spending through 2030 ends up debt-financed, the resulting credit buildup would exceed all broadband infrastructure investment since the birth of the commercial internet, Oliver Wyman’s modeling shows, and an equity crash on the scale of the early-2000s dot-com bust would, at today’s valuations, wipe out roughly $33 trillion in value, more than the entirety of US GDP.