AI

OpenAI Robotics Chief Caitlin Kalinowski Quits Over Pentagon Deal: A Matter of Principle

Published

4 months ago

March 8, 2026

On the morning of Saturday, March 8, 2026, Caitlin Kalinowski — one of the most accomplished hardware engineers in Silicon Valley and, until that day, OpenAI’s head of robotics — posted a resignation letter that read less like a grievance and more like a brief filed before history. “This wasn’t an easy call,” she wrote on X and LinkedIn. “AI has an important role in national security. But surveillance of Americans without judicial oversight and lethal autonomy without human authorization are lines that deserved more deliberation than they got.” A second post was more surgical: “My issue is that the announcement was rushed without the guardrails defined. It’s a governance concern first and foremost.” A third, offered perhaps for those who suspected personal animosity toward colleagues or leadership, offered a quiet clarification: “This was about principle, not people.”

In the compressed, often performative world of tech resignations, these three statements were remarkable for what they were not: they were not vague, not self-promotional, and not hedged. The OpenAI Pentagon deal — announced roughly a week earlier amid the wreckage of Anthropic’s collapse from government favor — had acquired its most credible internal critic. The question, for investors, policymakers, and the millions who have handed their most intimate intellectual tasks to ChatGPT, is what happens next.

The Backdrop: Why Anthropic Said No and OpenAI Said Yes

To understand why Caitlin Kalinowski quit, you first need to understand why Anthropic effectively lost its seat at the table.

In late February 2026, the Trump administration moved to designate Anthropic as a “supply-chain risk” after the company refused to remove safety constraints from AI systems being evaluated for Pentagon deployment. The designation — extraordinary in its scope — effectively barred Anthropic from key federal procurement channels and sent a chill through the broader AI safety community. The Economist reported that Anthropic’s chief executive had offered a public apology for language critical of the Pentagon’s approach, while simultaneously filing suit to contest the supply-chain designation — a posture that satisfied no one cleanly but illustrated the profound bind facing any AI company that takes its own safety commitments seriously in a Washington now hungry for deployable capability.

OpenAI moved with speed. Within days of the Anthropic fallout becoming public, the company announced an agreement to deploy AI systems — including models built on the GPT-4 architecture — on classified Department of Defense networks. The deal, as presented, included a set of claimed “red lines”: no use for domestic surveillance of American citizens without judicial oversight, and no deployment in autonomous lethal decision-making without explicit human authorization. These commitments were described as contractually enforceable and backed by technical safeguards. Reuters confirmed the structure of the agreement on March 7, noting that OpenAI had made internal commitments about the scope of permitted use cases.

The problem, as Kalinowski’s exit would make clear, was not the destination — it was the journey, and whether sufficient architecture had been built along the way.

Kalinowski’s Stand: From Meta AR to OpenAI Robotics — A Line in the Sand

Caitlin Kalinowski was not a peripheral figure at OpenAI. She had been recruited in November 2024 from Meta, where she had served as the lead hardware engineer for Project Orion — Meta’s most ambitious augmented reality effort and, by most technical assessments, the most sophisticated AR device yet produced by a major tech company. Her hiring was seen as a signal that OpenAI was serious about the physical layer of AI: robots, sensors, embodied intelligence, hardware that could operate in the real world rather than the controlled environment of a data center.

For someone in that role, the Pentagon partnership was not abstract. Robotics and hardware sit precisely at the intersection where AI meets the physical domain — which is to say, precisely where the most consequential questions about lethal autonomy and surveillance hardware arise. Unlike a software engineer working on a language model far removed from physical deployment, Kalinowski’s domain was the place where the rubber, quite literally, meets the road.

TechCrunch’s detailed reconstruction of events suggests that internal deliberations about the Pentagon deal’s scope were truncated — that the timeline was driven by the political opportunity created by Anthropic’s exclusion rather than by a mature internal governance process. Whether that account is entirely accurate is difficult to verify from the outside. What is verifiable is that Sam Altman himself subsequently acknowledged the rollout had been “opportunistic and sloppy,” and that the company moved to amend its terms following the announcement — a remarkable concession that validated, at minimum, the procedural objection at the heart of Kalinowski’s departure.

That amended framework, as the Financial Times reported, attempted to more precisely delineate the scope of permissible military use and to establish clearer governance mechanisms. Critics — including some who did not share Kalinowski’s decision to resign — noted that the amendments came after, not before, the public announcement: a sequencing that undermined the credibility of the original process.

The Economic and Geopolitical Stakes

The Sam Altman Pentagon deal controversy arrives at a moment of extraordinary financial and strategic sensitivity for OpenAI. The company’s most recent private valuation exceeded $150 billion, a figure premised not simply on its current revenue but on a projected future in which OpenAI becomes foundational infrastructure for both the private economy and, increasingly, the national security apparatus. Defense-tech investment in the US has surged since 2022; the convergence of frontier AI capability with DoD contracting is now a central axis of Silicon Valley’s growth narrative.

The economics of the Pentagon deal, properly understood, are attractive. Government contracts offer revenue stability that consumer subscriptions do not; classified deployments command premium pricing; and a sustained DoD relationship confers a strategic moat against competitors — including international ones — that money alone cannot buy. Seen through that lens, the decision to pursue the partnership is commercially rational.

But the consumer dimension is where the math becomes more complicated. Fortune’s analysis noted that ChatGPT uninstalls in the US surged by 295% in the week following the Pentagon announcement — a figure that, if sustained even partially, represents a meaningful threat to the subscription revenue base that currently underpins OpenAI’s operating economics. Simultaneously, Claude — Anthropic’s flagship product — rose to the top two positions in the US App Store, a direct beneficiary of the perception, however imperfectly calibrated, that it represents a more principled alternative.

This dynamic illuminates a tension that will define AI’s next chapter: the revenue logic of government partnerships and the trust logic of consumer adoption do not always point in the same direction. OpenAI is now navigating both simultaneously, with the credibility cost of the governance misstep weighing on both.

Geopolitically, the stakes extend well beyond OpenAI’s balance sheet. The United States’ ability to project technological leadership — and to persuade democratic allies that American AI is the right foundation for their own defense and economic infrastructure — depends in part on the perception that US AI development operates within a comprehensible, principled framework. A high-profile resignation by a senior AI executive citing surveillance and lethal autonomy concerns is precisely the kind of signal that adversaries amplify and allies register with discomfort. Beijing’s AI governance narrative — that American AI is militarized, ungoverned, and therefore unsafe for partner nations — receives unintended reinforcement when the governance critiques come from inside the house.

The implications for the US-China AI competition are layered. China’s state-aligned AI development model faces its own credibility constraints with potential partners in the Global South and among non-aligned democracies. But every governance stumble on the American side narrows the differentiation. The OpenAI military AI deal ethics debate is, in this sense, not merely a domestic regulatory question — it is a soft-power variable in a competition that will run for decades.

The Governance Failure at the Center of It All

It is worth being precise about what Kalinowski did and did not say. She did not argue that AI has no role in national security — she said explicitly the opposite. She did not claim that the deal’s stated red lines were illegitimate. What she argued, with notable precision, was that the process was broken: that the guardrails had not been defined before the announcement was made, and that deliberation had been sacrificed to speed.

This is a governance critique, not an ideological one — and it is, arguably, the harder critique to dismiss. An ideological objection to military AI can be engaged with on policy grounds. A process objection, particularly when corroborated by the CEO’s own admission that the rollout was “sloppy,” points to institutional dysfunction of a different and more consequential kind.

The question it raises is structural: does OpenAI — or any frontier AI company operating at this scale and velocity — have governance mechanisms capable of handling the decisions now being placed before it? The company’s board was restructured in late 2023 following the brief and chaotic dismissal of Sam Altman; it has since been reconstituted with a stronger commercial orientation and reduced representation of the safety-first voices that originally dominated it. Whether that reconstituted board is equipped to deliberate with appropriate rigor on questions of OpenAI Kalinowski resignation surveillance, lethal autonomy, and classified military deployment is a question that regulators in Brussels, London, and Washington are now, quietly, asking.

The European Union’s AI Act, which entered its enforcement phase in 2025, contains explicit provisions on high-risk AI uses — provisions that may bear on the contractual structures OpenAI is now building with the DoD. UK regulators, operating under a principles-based framework rather than the EU’s rules-based approach, have been watching the American developments with a mixture of concern and, one suspects, a measure of competitive calculation. If US AI governance appears compromised, the argument for European regulatory leadership becomes stronger — and European AI champions benefit accordingly.

What Happens Next

Several trajectories are now in play simultaneously, and the interactions between them will shape not just OpenAI’s future but the broader architecture of AI governance.

Inside OpenAI, the Kalinowski resignation will accelerate an internal reckoning that was already underway. The company will face pressure — from remaining senior technical staff, from its investors, and from the amended Pentagon framework itself — to build genuine governance infrastructure rather than contractual scaffolding. Whether that means reinstating a more powerful safety function, establishing an independent oversight board with real authority over defense-related deployments, or something more novel remains to be seen. What is clear is that the talent-retention argument for getting this right is now materially stronger: engineers of Kalinowski’s caliber do not leave quietly, and her departure will be a reference point in every recruiting conversation the company has with senior hardware and robotics talent for the foreseeable future.

For the Pentagon, the episode underscores that procurement speed and governance adequacy are not the same thing. The DoD has a long and often uncomfortable history of deploying technologies — from predictive policing algorithms to drone targeting systems — before the ethical and legal frameworks have caught up. The [OpenAI Amended Pentagon Deal] represents an opportunity to establish a more rigorous template, but only if the amended terms carry genuine enforcement teeth rather than serving as public relations scaffolding.

For Anthropic, the short-term consumer gains are real but precarious. Rising to the top of the App Store on the strength of a competitor’s stumble is a brittle form of growth; sustaining that position will require Anthropic to demonstrate not just principled postures but capable products. The [Anthropic Supply-Chain Risk Ruling] also remains unresolved: the company’s legal challenge to its federal designation is pending, and its outcome will determine whether Anthropic can eventually re-enter the defense market on its own terms — or whether it becomes, by exclusion if not by choice, the AI company that the US government declined to include.

For global AI regulation, the episode has provided a concrete and high-profile case study that will inform legislative debates from Brussels to Tokyo. The argument that voluntary self-governance by frontier AI companies is adequate has been meaningfully weakened — not by an external critic but by the resignation of one of those companies’ own senior executives, citing the inadequacy of internal deliberation.

Caitlin Kalinowski’s three posts on the morning of March 8 were short. Their implications are not. In resigning over what she called a governance concern rather than a personal grievance, she has done something that critics and regulators have struggled to do from the outside: she has placed the question of how these decisions get made — not merely what decisions get made — at the center of the debate. In an industry where process is usually treated as a means to an end, that reframing may prove to be the most consequential thing she has done at OpenAI, and she did it on her way out the door.

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

Published

10 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.