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How AI Is Forcing McKinsey and Its Peers to Rethink Pricing

Published

2 months ago

May 24, 2026

nThe hour is up

For the better part of a century, the economics of management consulting have rested on a beautiful fiction: that the value of advice can be measured in time. An analyst’s hours, a partner’s days, a team’s weeks on site — these were the denominator around which entire firms were built, pyramids of talent whose profitability depended on billing more hours than competitors at rates clients would reluctantly accept. The fiction held because nobody had a better alternative.

Artificial intelligence has now supplied one.

The pressure is visible in the numbers, in restructured partner pay, and in the quiet desperation with which firms like McKinsey, BCG, and Bain are repositioning themselves not as advisers but as delivery partners. The consultancy industry’s pricing model — the bedrock of a $700 billion global market — is cracking. The question is not whether it will change. It already is. The question is who benefits.

A familiar disruption, an unfamiliar pace

The consulting industry has survived disruptions before. Offshoring squeezed margins in the 2000s. The post-2008 austerity wave hammered public-sector mandates. The pandemic briefly collapsed travel-dependent engagement models. Each time, the billable-hour survived, battered but intact.

This time is structurally different. What AI is compressing is not demand for advice — that remains robust — but the labour input required to produce it. The Management Consultancies Association’s January 2026 member survey found that 77% of UK consulting firms have already integrated AI into their systems, with 76% deploying it specifically for research tasks and 68% having increased automation of core workflows. Meanwhile, the global AI consulting and support services market, valued at $14 billion in 2024, is forecast to expand at a compound annual growth rate of 31.6% to reach $72.8 billion by 2030 — a trajectory that reflects how thoroughly the tools are reshaping both supply and demand.

When AI compresses the time required to produce work, hourly billing stops being a proxy for value. It becomes a liability.

The AI consulting pricing model is already shifting — and McKinsey is leading it

In November 2025, Michael Birshan, McKinsey’s managing partner for the UK, Ireland, and Israel, made an admission that would have been unthinkable five years ago. Speaking at a media briefing in London, Birshan told reporters that clients were no longer arriving with a scope and asking for a fee. Instead, they were arriving with an outcome they wanted to reach and expecting the fee to be contingent on McKinsey’s ability to deliver it. “We’re doing more performance-based arrangements with our clients,” he said. About a quarter of McKinsey’s global fees now flow from this outcomes-based pricing model.

That 25% figure is both significant and revealing — significant because it marks a genuine departure from decades of billable-hour orthodoxy, revealing because it shows that three quarters of McKinsey’s revenue remains anchored to the old model. The transition is real. It is not complete.

The driver is largely internal. McKinsey’s Lilli platform — an enterprise AI tool rolled out firm-wide in July 2023 — is now used by 72% of the firm’s roughly 45,000 employees. It handles over 500,000 prompts a month, auto-generates PowerPoint decks and reports from simple instructions, and draws on a proprietary corpus of more than 100,000 documents, case studies, and playbooks. By McKinsey’s own reckoning, Lilli is saving consultants 30% of their time on research and knowledge synthesis. When a tool saves 30% of the hours that used to justify an invoice, the invoice requires a different rationale.

BCG has pursued a parallel path. Its internal assistant “Deckster” drafts initial client presentations from structured datasets in minutes. BCG disclosed in April 2026 that roughly 25% of its $14.4 billion 2025 revenue — approximately $3.6 billion — derived from AI-related work, the first time any Big Three strategy firm has made that figure visible. Bain’s “Sage” platform performs comparable functions. PwC, which became OpenAI’s first enterprise reseller, committed $1 billion to generative AI in 2023 and subsequently deployed ChatGPT Enterprise to 100,000 employees. KPMG followed with a $2 billion alliance with Microsoft.

Collectively, the Big Four and major strategy houses poured more than $10 billion into AI infrastructure between 2023 and 2025. The investments were real. The pricing implications they’re now confronting were perhaps underestimated.

What is outcome-based pricing in consulting — and why does AI accelerate it?

Outcome-based pricing ties a consulting firm’s compensation to measurable results — revenue growth, cost reduction, market-share gains — rather than to the hours or scope of work delivered. It existed before AI, but AI transformation projects suit it naturally: they are multi-year, multidisciplinary, and generate data that makes performance tracking tractable.

As Kate Smaje, McKinsey’s global leader of technology and AI, noted in November 2025, the shift “developed over the past several years as McKinsey started doing more multi-year, multidisciplinary, transformation-based work.” AI didn’t originate the model. It made it commercially necessary.

The structural problem no press release addresses

Here is where the analysis must get uncomfortable for the firms themselves.

The productivity gains AI is generating inside McKinsey, BCG, and Bain are not, in any consistent way, being passed on to clients. One detailed analysis of MBB pricing practices published in 2025 concluded bluntly: firms’ external pricing “hasn’t moved” even as internal AI tools have displaced significant analyst labour. Clients are still paying as if junior consultants spent 80-hour weeks building the models from scratch. In many cases, Lilli or Deckster did it in an afternoon.

This creates a credibility problem that compounds over time. Sophisticated procurement teams at large corporations are beginning to ask questions about methodology, tool usage, and the provenance of deliverables. Deloitte Australia’s AU$440,000 refund to a government client over unverified AI-generated outputs — reported in 2025 — turned what had been a theoretical concern into a profit-and-loss event. Ninety percent of enterprise buyers, according to subsequent surveys, now want explicit AI governance disclosures built into contracts.

The Financial Times has reported that McKinsey is already adjusting its internal partnership economics in response, planning to shift a greater share of partner remuneration into equity as AI-driven outcome-based pricing makes consulting revenues more volatile and harder to predict quarter-to-quarter. Partners, in other words, are being asked to absorb the risk that used to sit with clients. That is a profound structural change — and one the recruitment and retention of top talent will have to accommodate.

The Amazon McKinsey Group launched in January 2026 — a joint venture combining McKinsey’s strategy capability with AWS cloud infrastructure and AI tooling — represents the most explicit attempt yet to fuse the advisory and implementation roles into a single, outcome-accountable offer. Engagements are scoped for transformations expected to deliver at least $1 billion in measurable client impact. It is a bet that scale and technology integration can justify premium fees in ways that billable hours increasingly cannot.

The counterargument: not all hours are created equal

It would be wrong to read this as consulting’s obituary. The critics of outcome-based pricing are not wrong to worry.

The model introduces its own distortions. When fees depend on measured outcomes, consultants have an incentive to define those outcomes narrowly, to work on problems whose success is easily attributable, and to avoid the ambiguous, long-horizon strategic work that generates the least data but often the most genuine value. A firm paid to raise revenue by 8% in 18 months may not tell a CEO that the business model is structurally broken. A firm paid by the hour has no such structural inhibition.

There is also the question of risk allocation. Outcome-based contracts push downside exposure onto the consulting firm, which sounds appealing to clients until they realise that firms will price that risk into their upside. McKinsey isn’t offering to share downside and cap upside. The performance-based arrangements being described are, in practice, hybrid structures — some fixed base, performance kickers on top — not pure contingency. That’s a meaningful distinction.

Sceptics within the industry point to a second problem: attribution. Did McKinsey’s intervention raise the client’s revenue, or did a favourable macroeconomic tailwind? Determining causality in complex business environments is genuinely hard, and the history of performance-based arrangements in other professional services — notably investment banking and private equity advisory — suggests that disputes over attribution tend to be costly and corrosive.

“Outcomes-based pricing didn’t start because of AI,” Smaje acknowledged in November 2025. The honest implication of that statement is that it won’t be resolved by AI either.

What firms, clients, and the talent market face next

The second-order effects of this pricing shift will ripple well beyond contract structures.

The consulting pyramid — the hierarchy of analysts, associates, managers, partners, and senior partners whose labour cost structure has remained largely stable for three decades — is under genuine pressure. McKinsey’s own research has estimated that approximately 45% of activities traditionally performed by consultants could be automated with existing technology. If Lilli handles research, synthesis, and deck generation, the case for the analyst class — the bottom of the pyramid that cross-subsidises partner economics — becomes harder to sustain.

Hiring data from 2025 suggests firms are already adjusting. The UK Management Consultancies Association survey projected 5.7% consulting revenue growth in 2026 and 7.4% in 2027, with AI services driving the greatest expansion for 66% of firms. Yet headcount growth is not tracking revenue growth — a gap that implies productivity gains are being captured by existing staff rather than expanded teams.

For clients, the shift creates genuine leverage — but only for those sophisticated enough to use it. Enterprise buyers who understand what AI can and cannot do, who can write performance metrics that are both meaningful and attributable, and who are prepared to challenge deliverable provenance will extract real value from the new model. Those who outsource that judgment to the firms themselves will find that outcome-based pricing, in practice, looks a lot like billable hours with better marketing.

The talent market will bifurcate. Consultants who can manage AI-augmented workflows, design outcome metrics, and demonstrate delivery accountability will command premiums. Those whose competitive advantage was research bandwidth and slide-deck velocity — tasks now automated at scale — face a more difficult conversation. Research published in late 2025 found that consultants using AI tools completed tasks 25% faster at 40% higher quality, but the strategic thinking, relationship management, and client judgment that justify senior fees remain, for now, distinctly human.

The tension that will define the next decade

There is a phrase circulating in elite consulting circles that captures the bind precisely: firms are being asked to be accountable for outcomes they do not fully control, using tools whose productivity gains they have not fully disclosed, in a market where clients are only beginning to understand what to demand.

The billable hour was imperfect. But it had the great virtue of simplicity: time spent, time charged. What replaces it will be messier, more contested, and more lucrative for the firms that define the terms before their clients do.

McKinsey’s quiet overhaul of partner pay is the most honest signal of what the industry privately believes: that the revenue model is becoming structurally volatile, and that the people at the top of the pyramid need to share in the uncertainty their AI tools have created. That is not a reassuring message dressed up as progress. It is a reckoning.

The hour was always a fiction. The question now is what honest accounting looks like when a machine has done the work.

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

Published

43 minutes 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.