Financial Fragility at the Top of the AI Stack

Executive Summary

Enterprise security programs have spent the better part of two years building vendor-concentration playbooks for cloud and AI dependency: multi-cloud architecture, model-provider diversification, and dependency mapping aligned to CSA’s AI Controls Matrix (AICM). Those programs generally treat concentration as an operational and geopolitical exposure — what happens if a hyperscaler region goes dark, or a government export-control action removes a model overnight. A parallel exposure appears to have received far less attention inside security organizations, likely because it looks, at first glance, like a finance problem rather than a security one: the frontier AI labs and the infrastructure built to serve them run on financial arrangements that independent analysts, credit rating agencies, and now central banks describe as structurally fragile.

The evidence has accumulated quickly. Leaked 2025 financial documents, whose authenticity the Financial Times reported it had verified, showed OpenAI generating approximately $13 billion in revenue against an operating loss of roughly $21 billion; subsequent reporting on the same disclosures put the company’s full net loss — after a roughly $30 billion non-cash charge tied to investor-rights accounting — closer to $38.5 billion, with internal projections pointing to further large losses through the end of the decade [1][2][12]. Anthropic is on a faster projected path toward profitability but is simultaneously restructuring its pricing model in a way that shifts cost unpredictability onto enterprise customers [3][13]. Beneath both companies sits a web of circular financing — Nvidia investing in OpenAI while OpenAI commits the proceeds to purchasing Nvidia chips and Oracle cloud capacity, Microsoft financing OpenAI while capturing the resulting Azure spend — that Bloomberg and other outlets have mapped in detail and that credit rating agencies now cite explicitly when downgrading vendors in the chain [4][5][6]. In July 2026, S&P Global Ratings downgraded Oracle’s long-term issuer credit rating to BBB- — one notch above speculative grade — citing Oracle’s concentrated financial exposure to OpenAI as a primary driver [6]. The same month, Moody’s warned that the combined capital spending of six major AI infrastructure providers would approach $785 billion in 2026, financed increasingly through debt and off-balance-sheet structures that strain investment-grade credit profiles built for a slower-moving industry [5].

Security researcher Bruce Schneier and co-author Nathan E. Sanders have gone further, arguing in an August 2026 essay that if OpenAI and Anthropic fail in the financial markets, the United States should nationalize them rather than allow frontier AI capability to disappear or fall into unaccountable private hands [7]. Whether or not that specific policy prescription gains traction, the essay crystallizes a scenario that enterprise risk programs appear not to have modeled yet: a frontier AI provider undergoing financial distress, restructuring, forced sale, or government intervention — not a technical outage, but a solvency event with the same practical effect on dependent enterprises.

This whitepaper argues that financial fragility at the top of the AI stack is a legitimate, board-relevant input to enterprise AI vendor risk management, distinct from but complementary to the operational and geopolitical concentration risks CSA has already documented. It examines the unit economics driving the fragility, the circular financing architecture amplifying it, the pricing shifts already transferring risk to customers, and the emerging central-bank view that this is now a financial-stability concern rather than a sector-specific one. It closes with recommendations for treating AI vendor financial health as a named, monitored line item in third-party risk programs, aligned to CSA’s AI Controls Matrix (AICM) v1.1 and the concentration-risk research CSA has already published.

Introduction and Background

For most of the enterprise AI adoption curve, security and procurement teams have evaluated frontier model providers the way they evaluate any strategic software vendor: on capability, security posture, compliance certifications, and contractual terms. That evaluation model assumes the vendor is a going concern whose primary risks are technical (an outage, a vulnerability, a data breach) or contractual (a price increase, a feature deprecation). It does not typically ask whether the vendor’s underlying business model can sustain itself, because for most enterprise software categories, that question was settled years ago by the time the vendor reached enterprise scale.

Frontier AI labs do not fit that pattern. OpenAI, Anthropic, and their principal infrastructure partners have reached extraordinary revenue scale and market valuation — OpenAI was valued at $852 billion after closing a $122 billion funding round in March 2026, and Anthropic reached $965 billion following a $65 billion Series H round in May 2026 — while simultaneously posting among the largest operating losses reported by any privately held technology company in recent years [8][9][24]. This is not, by itself, unusual for a capital-intensive technology sector in a land-grab phase; e-commerce, cloud infrastructure, and telecommunications all went through periods of loss-funded growth. What distinguishes the current period is the scale of capital involved, the speed at which enterprises have made these companies load-bearing infrastructure for revenue-generating processes, and the degree to which the financing supporting that infrastructure has become circular and opaque rather than diversified and transparent.

CSA’s AI Safety Initiative has already published extensive research establishing that concentration in AI compute, capital, and model provision constitutes a durable structural risk to AI-dependent enterprises, examining the operational failure modes (outages, jurisdictional exposure, strategic lock-in) that result [10][11]. That body of work treats the concentration itself — the fact that a handful of firms sit beneath most enterprise AI deployments — as the central risk. This paper takes a narrower and more specific view: even holding concentration constant, the financial condition of the firms at the top of that concentrated stack is deteriorating in ways that, this paper argues, constitute a distinct category of enterprise exposure that most current vendor-risk frameworks do not separately track. A vendor can be technically excellent, operationally resilient, and still be financially unable to sustain its current pricing, product roadmap, or independent existence.

The Economics Nobody Priced In

Unit Economics at the Frontier

The core economic problem facing frontier AI labs is straightforward to state and difficult to escape: training and operating frontier models is extraordinarily expensive, the resulting models depreciate in commercial value within months as competitors release comparable or superior capability, and the market for inference — the actual product being sold — is being commoditized faster than most labs can recover their training costs. OpenAI’s leaked financial documents — whose authenticity the Financial Times reported it had verified — showed a 2025 operating loss of approximately $21 billion against booked revenue of roughly $13 billion, with total spending of $34 billion including $19 billion in research and development [1]. Later reporting on the same disclosures put OpenAI’s full 2025 net loss closer to $38.5 billion once a roughly $30 billion non-cash charge tied to investor-rights accounting is factored in [2] — a distinction worth holding onto, since the two figures describe different things rather than corroborating a single number. Internal projections reviewed by industry analysts put 2026 losses at a further $14 billion or more, with cash burn accelerating rather than decelerating as infrastructure commitments come due [12]. OpenAI has committed to more than $1 trillion in infrastructure spending over the coming years and, by its own projections, does not expect positive free cash flow until 2029 [12].

Anthropic’s trajectory looks healthier on several dimensions — its annualized revenue run rate reached roughly $47 billion by late May 2026, up from $9 billion at the end of 2025, and the company has stated it expects to reach operating profitability two to three years ahead of OpenAI’s own projected timeline [13][26]. But Anthropic’s economics are subject to the same structural pressure facing every frontier lab: the price of the underlying product is falling fast. Inference pricing across the industry has declined at rates industry analysts describe as roughly an order of magnitude or more within a single year, driven by open-weight Chinese models like DeepSeek that now trail the performance of leading Western frontier models by only a few months while pricing tokens as much as 70 times cheaper on lower tiers [14]. In direct response to this pressure, OpenAI cut prices on two of its GPT-5.6 model tiers by 20 percent and 80 percent respectively in July 2026, in a move reporting linked to both rising customer cost-sensitivity and competitive pressure from Anthropic ahead of both companies’ anticipated public listings [15][16]. Reporting at the time characterized this as a preemptive price war neither company wanted but neither could avoid starting, given the risk that the other would move first [15].

The table below summarizes the divergence in reported financial posture between the two dominant US frontier labs as of mid-2026, drawing on the most recent independently reported figures available at the time of writing. Because these figures have moved quickly throughout 2026, readers using this table to inform a live vendor risk assessment should verify each figure against current reporting before relying on it.

Metric	OpenAI	Anthropic
2025 reported loss	~$21 billion operating loss; ~$38.5 billion net loss including a ~$30 billion non-cash charge [1][2]	Not separately disclosed; burn compressing toward single digits of revenue by 2027 [13]
Annualized revenue run rate (mid-2026)	~$25 billion [12]	~$47 billion (May 2026), up from $9 billion at year-end 2025 [13][26]
Most recent valuation	~$852 billion ($122 billion round, March 2026) [9][24]	~$965 billion (May 2026, $65B Series H) [9]
Projected path to positive free cash flow	Not before 2029 [12]	Operating profitability projected 2–3 years ahead of OpenAI [13]
Multi-year infrastructure commitment	Reported in excess of $1 trillion [12]	Not separately disclosed at comparable scale

This divergence matters for enterprise risk purposes not because one company is “safer” than the other in absolute terms, but because it illustrates that the financial condition of frontier AI providers is neither stable nor uniform, and it is changing quickly enough that a vendor risk assessment performed in early 2026 could already be materially out of date by the time this paper is published.

Commoditization Undercuts the Investment Thesis

The deeper structural issue is that the products generating this revenue are converging toward commodity status faster than the capital invested to produce them can be recovered. Schneier and Sanders make this point directly: frontier models depreciate within months of release as newer models supersede them, the leading models from different vendors perform comparably on most benchmarks, and open-source and non-US labs trail the frontier by a matter of months while giving capability away at a fraction of the price [7]. When the underlying product commoditizes, the payback window for the capital that built it narrows, and the investment thesis underlying hundreds of billions of dollars in committed infrastructure spending becomes harder to sustain. This is precisely the dynamic Moody’s identified in July 2026 when it characterized the aggregate infrastructure buildout among six major AI infrastructure providers — projected at roughly $785 billion for 2026 alone, financed heavily through debt — as an “unprecedented” strain on credit quality across the sector [5].

The Circular Financing Architecture

How the Money Loops

A second and related fragility comes from how the AI buildout is financed. Rather than raising independent capital that is deployed across a diversified base of customers and suppliers, the largest deals in the AI infrastructure ecosystem increasingly loop the same dollars among a small number of counterparties. Microsoft has invested more than $13 billion in OpenAI over several funding rounds while OpenAI has committed to spending approximately $250 billion on Microsoft’s Azure cloud platform [4][17]. Nvidia invested $30 billion into OpenAI’s $122 billion funding round, closed in March 2026 at an $852 billion valuation, while continuing to be one of OpenAI’s largest chip suppliers, and separately took a stake in CoreWeave alongside a multibillion-dollar commitment to purchase CoreWeave’s cloud services [4][17][24]. Oracle is constructing roughly $300 billion in data center capacity for OpenAI under the Stargate initiative, financed substantially through debt that S&P Global Ratings has now flagged as a credit risk specifically because more than half of Oracle’s remaining performance obligations trace back to a single customer [6][17].

Bloomberg’s detailed mapping of these arrangements, along with independent analysis from economics commentators, describes the effect plainly: money invested by chipmakers and cloud providers into AI labs flows back to those same chipmakers and cloud providers as revenue, in a loop that can make demand appear more organic and revenue appear more robust than the underlying economics support [4][18]. None of this is necessarily fraudulent or even unusual for a capital-intensive buildout with legitimate supply constraints — participants argue that locking in compute supply through paired financing and purchase commitments is a rational response to genuine chip scarcity [18]. But from a risk-management perspective, the practical effect is that a demand shortfall or profitability disappointment at any single node in this network — OpenAI failing to monetize as projected, for instance — does not stay contained to that node. It propagates to Oracle’s balance sheet, to Nvidia’s reported revenue, to CoreWeave’s asset valuation, and back around to the financing capacity of the original AI lab, in a structure with far less independent diversification than it may appear to have from the outside [4][17].

Credit Markets Are Already Pricing This In

Credit rating agencies have moved from describing this as a forward-looking risk to acting on it. Beyond the Oracle downgrade, Moody’s placed a negative outlook on the company and estimated that the six major hyperscale and AI infrastructure providers it tracks now carry roughly $460 billion in direct debt combined with $1.2 trillion in lease commitments, a debt load Moody’s explicitly connected to “unprecedented” AI capital spending pressuring credit quality even at companies with otherwise investment-grade balance sheets [5]. CoreWeave, which finances its Nvidia GPU fleet through complex debt structures, carries a sub-investment-grade rating that leaves it especially exposed to any slowdown in AI infrastructure demand [5].

The exposure is no longer confined to the technology sector’s own balance sheets. Private credit funds — principally Blackstone, Blue Owl Capital, Apollo, PIMCO, and BlackRock — now originate the majority of AI data center debt, much of it through off-balance-sheet special purpose vehicles, with outstanding loans to AI-related companies growing from near zero to more than $200 billion in a few years and Morgan Stanley projecting an additional $800 billion in private credit financing for data centers over the following two years [19]. The Federal Reserve Bank of Chicago published research in February 2026 finding that while direct bank exposure to AI-adjacent industries remains modest at roughly 0.8 percent of average bank total assets, that exposure carries meaningful tail risk because stress in one AI-adjacent industry — for example, an AI software company unable to sustain its infrastructure spending — can propagate across multiple interconnected AI-adjacent industries simultaneously [20]. The Bank of England’s July 2026 Financial Stability Report went further, warning that AI-related investment financing has become “unprecedented historically” in its use of public debt, private credit, leveraged finance, and structured finance, and modeling a scenario in which a sharp correction in AI equity valuations could reduce UK GDP by as much as 2.2 percentage points through financial contagion channels [21]. Two separate central banking institutions describing AI financing structures as a financial-stability concern, within weeks of each other, is a meaningfully different signal than the same concern raised by industry analysts alone.

When the Market Says No: The Nationalization Debate

It is against this backdrop that Schneier and Sanders published their August 2026 essay arguing that if market forces reject OpenAI and Anthropic — if investors conclude the companies cannot generate the returns their valuations imply and funding dries up — the United States government should nationalize them rather than allow frontier AI capability to be liquidated, sold piecemeal, or left in the hands of whichever private actor can afford the distressed assets [7]. Their argument rests on two observations already discussed in this paper: that both companies were founded on explicit public-interest missions before market incentives pulled them toward conventional corporate behavior, and that the underlying unit economics — expensive training, fast depreciation, and commoditizing output — do not obviously support the valuations markets have assigned them [7].

Enterprise risk leaders do not need to take a position on the nationalization argument itself to recognize what it signals: a publicly argued scenario from a credentialed security researcher now exists in which a frontier AI provider does not merely raise prices or experience an outage, but undergoes a solvency event serious enough to draw government intervention. That scenario differs materially from the operational and geopolitical failure modes CSA’s prior concentration-risk research has examined — a cloud region outage, an export-control suspension of model access — because it implicates the provider’s continued independent existence, ownership structure, and possibly its governing terms of service, rather than a temporary service disruption [10][11]. A nationalized or government-brokered successor entity might operate under materially different data handling commitments, security obligations, pricing, or contractual terms than the private company an enterprise originally signed with, and the transition process itself could introduce operational discontinuity regardless of the eventual outcome’s merits.

This is not a near-term prediction that either company will fail. Anthropic’s improving path to profitability and both companies’ continued ability to raise record funding rounds argue against an imminent crisis [9][13]. It is, rather, an argument that “frontier AI provider undergoes financial distress serious enough to trigger a change-of-control or government-intervention event” has moved from a hypothetical too remote to plan for, to a scenario publicly argued by a credentialed security researcher and covered across mainstream and trade press, and therefore one that enterprise risk registers should name explicitly rather than leave implicit inside a generic “vendor risk” category.

Consumption Risk Shifts to the Customer

While the possibility of a lab-level solvency event remains a tail scenario, a more immediate and already-observable consequence of frontier lab financial pressure is the transfer of cost and forecasting risk onto enterprise customers. Anthropic’s mid-2026 pricing restructuring illustrates the pattern. The company moved away from a fixed per-seat subscription model, priced between $40 and $200 per user monthly, toward a hybrid structure that separates a lower base platform fee — $10 to $20 per user by role — from metered charges tied to token consumption and Agent SDK usage [3]. Forrester’s analysis of the change is direct about its effect: costs are now driven by consumption, meaning token usage, model selection, coding-agent adoption, and commitment levels have a far greater impact on enterprise spend than the number of licensed seats, and a small number of intensive users or automated agents can generate a disproportionate share of an organization’s total AI bill [3].

This shift is not unique to Anthropic. Forrester notes it reflects a broader industry convergence toward access-plus-consumption pricing across OpenAI, Google, and other providers, driven by the same unit-economic pressure discussed above: as inference costs fall and competitors undercut fixed-price offerings, vendors need pricing structures that let them capture value from the heaviest users rather than subsidizing them under a flat per-seat rate [3]. From the enterprise’s perspective, the effect is a transfer of forecasting risk and budget volatility from the vendor to the customer, at precisely the moment automated agents and agentic workflows are increasing the unpredictability of token consumption. Forrester’s recommended mitigations — validating usage forecasts before committing to a contract tier, negotiating quarterly consumption corridors, and establishing predefined overage pricing and expansion discounts in advance — are financial and procurement practices, but they now sit squarely inside the scope of enterprise AI vendor risk management because the underlying driver is the vendor’s own need to manage its unit economics [3].

Security and risk teams should read this pricing shift as a leading indicator rather than an isolated commercial event. A vendor restructuring its pricing to shift consumption risk onto customers is a vendor managing its own margin pressure in real time. That pressure is the same pressure driving the broader commoditization and price-war dynamics described earlier in this paper, and it is reasonable to expect further pricing, contract-term, or service-level changes from frontier labs as they continue to search for a sustainable unit-economics model.

Enterprise Exposure: Why This Is a Security and Risk Problem

Framing frontier lab financial fragility as a security and risk management concern, rather than purely a finance or investor-relations concern, rests on three practical observations. First, enterprises have already embedded frontier model dependencies deeply enough into production security operations, customer-facing products, and revenue-generating workflows that a lab-level disruption — whether from a solvency event, a forced restructuring, or simply an abrupt pricing or product change made to preserve margin — has the same operational blast radius as the outages and access restrictions CSA’s prior concentration-risk research has already documented [10][11]. A 2025 Futurum Group survey of AI enterprise decision-makers found that OpenAI’s models led platform adoption at 61 percent, a concentration Futurum noted was already prompting larger enterprises to pursue multi-model diversification strategies — meaning any disruption to the leading provider remains a material risk for a majority of enterprise adopters even as that diversification accelerates [25]. The mechanism causing that disruption — technical failure, geopolitical action, or financial distress — is secondary to the fact that the enterprise did not architect for the loss of that dependency.

Second, product and platform discontinuation driven by a vendor’s need to redirect capital toward higher-margin lines of business is already happening, not merely theoretical. OpenAI’s April 2026 decision to discontinue the standalone Sora web and app experiences, with the underlying API following in September 2026, forced enterprises that had built production workflows atop that product to urgently reassess their roadmaps on a vendor-driven timeline they did not control [22]. A frontier lab under sustained margin pressure has a strong incentive to prune less profitable product lines regardless of how deeply individual enterprise customers depend on them, and financial fragility at the top of the stack makes this kind of abrupt discontinuation more likely, not less.

Third, and most directly relevant to the security function specifically, financial pressure inside frontier labs has coincided with visible reductions in the safety and governance infrastructure enterprises implicitly rely on when they treat a frontier model as a trusted component of their own security posture. Reporting in August 2026 documented that OpenAI had disbanded the internal team responsible for assessing whether its models could pose catastrophic risks, following earlier reductions to related safety and alignment functions; OpenAI publicly disputed this characterization, stating that responsibility for the team’s risk areas had been redistributed into other groups rather than eliminated [23][27]. Enterprises that have not independently validated a frontier model’s behavior — relying instead on the vendor’s own safety testing and disclosure practices — are exposed not only to the vendor’s financial condition but to the possibility that cost pressure has reduced the rigor of the safety functions standing behind the product, whichever characterization of the reorganization proves most accurate.

Taken together, these three observations support treating frontier AI lab financial condition as a distinct, named entry in enterprise third-party risk registers — not folded into a generic “AI vendor risk” category, and not deferred to procurement or finance teams as purely a commercial matter. CSA’s own research on foundation model concentration makes a closely related point from the insurance industry’s perspective: because a single upstream model failure, safeguard regression, or access restriction affects every enterprise that depends on that model simultaneously, the resulting losses are correlated rather than independent, and neither traditional insurance underwriting nor traditional vendor risk scoring is well equipped to price that correlation [11]. Financial fragility is one of the mechanisms — alongside technical failure and geopolitical action — by which that correlated loss event could be triggered, and it is one enterprises can monitor for in advance.

Recommendations

Immediate Actions

Enterprise risk and security teams should add frontier AI provider financial condition as an explicit, separately tracked line item in existing third-party and AI vendor risk registers, distinct from the operational and security-posture criteria already tracked for these vendors. This should include, at minimum, monitoring publicly available signals — credit rating actions, disclosed or leaked financial results, major funding rounds, and pricing model changes — on a recurring cadence rather than only at contract renewal. Organizations negotiating or renewing frontier model contracts should review pricing terms specifically for consumption-based structures that shift forecasting risk onto the enterprise, and should require usage forecasting validation, consumption corridors, and predefined overage terms before committing to a tier, following the mitigation approach Forrester has outlined for the current pricing environment [3].

Short-Term Mitigations

Organizations should conduct a dependency inventory identifying which revenue-generating or security-critical workflows rely on a single frontier model provider without a validated fallback, and should test multi-provider or multi-model failover before it is operationally required, consistent with recommendations CSA has already made in its compute-concentration research [10]. Legal and procurement teams should negotiate contractual continuity provisions — advance notice of material product discontinuation, data portability commitments, and audit rights — that would preserve the enterprise’s ability to migrate on its own timeline rather than the vendor’s, informed by the precedent set by abrupt product discontinuations such as OpenAI’s Sora wind-down [22]. Security teams should not rely solely on a frontier vendor’s self-disclosed safety and evaluation practices, particularly in light of documented reductions to vendor-side safety governance functions, and should incorporate independent, ongoing evaluation of model behavior into their own assurance processes rather than treating vendor certification as sufficient [23][27].

Strategic Considerations

At the governance level, boards and executive risk committees should treat “primary AI vendor undergoes a change-of-control, restructuring, or government-intervention event” as a distinct scenario in business continuity and tabletop exercise planning, separate from the technical-outage and export-control scenarios already in common use. This does not require predicting whether or when such an event might occur; it requires having a documented response posture — migration options, contractual triggers, and decision authority — in place before it does. Enterprises should also weight financial stability and demonstrated path to sustainable unit economics as an explicit criterion in frontier model vendor selection and multi-sourcing strategy, alongside the technical capability and security posture criteria already in standard use, recognizing that a vendor’s current capability advantage may not persist if its underlying business model cannot be sustained.

CSA Resource Alignment

This paper’s findings connect directly to a growing body of CSA AI Safety Initiative research on AI concentration risk, and enterprises should treat that body of work as a coherent whole rather than a series of unrelated notes. CSA’s AI Compute Concentration: Security Risk in a New Political Economy is the most direct predecessor to this paper, adopting the same Schneier and Sanders framing used here and analyzing the circular financing arrangements among OpenAI, Nvidia, Microsoft, and Oracle as a durable structural risk to AI-dependent enterprises; this paper extends that analysis specifically into the unit-economics, pricing, and nationalization-scenario dimensions that note identifies but does not develop in depth.

CSA’s Foundation Model Concentration: The Uninsurable AI Risk provides the risk-transfer lens this paper builds on, establishing that foundation-model dependency produces correlated rather than independently diversifiable losses across dependent enterprises — the same structural property that makes a frontier lab’s financial distress a systemic rather than isolated event, and the reason this paper recommends treating vendor financial condition as its own tracked risk category rather than folding it into generic AI risk. CSA’s AI Capital Concentration and the Regulatory Capture Risk documents the extreme concentration of AI venture capital in a handful of firms and its downstream governance implications, complementing this paper’s focus on operating economics with a capital-markets perspective on the same underlying concentration.

Enterprises implementing the recommendations in this paper should do so through CSA’s AI Controls Matrix (AICM) v1.1, particularly its supply chain management, transparency, and accountability domain, which provides the control structure for documenting AI vendor dependencies, contractual continuity requirements, and third-party risk assessment criteria referenced throughout this paper’s recommendations. Because AICM is a superset of CSA’s earlier Cloud Controls Matrix, organizations that have already mapped cloud vendor concentration risk to CCM controls can extend that same control structure to the AI-vendor financial-fragility risk described here without adopting an entirely new framework.

Conclusion

The AI industry’s concentration risk has, until now, been analyzed primarily as an operational and geopolitical problem: what happens when a small number of providers control the compute, models, and capital that enterprises depend on, and one of those providers becomes unavailable through outage or policy action. This paper has argued for a complementary view: the providers at the top of that concentrated stack are themselves financially fragile, in ways that credit rating agencies, central banks, and security researchers are now describing in increasingly concrete terms. That fragility is already changing enterprise cost structures through consumption-based pricing, and it introduces a tail scenario — a solvency event serious enough to prompt government intervention — that enterprise business continuity planning has not yet named. Security and risk leaders do not need to resolve the debate over whether OpenAI and Anthropic’s economics will ultimately prove sustainable. They do need to ensure their organizations are not caught unprepared if the market, credit agencies, or policymakers answer that question before the enterprises depending on these providers have planned for the answer.

References

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