Oracle has become a test of how much debt markets will tolerate in the AI buildout. Rising credit costs and growing use of swaps show investors backing the AI story while seeking protection in case promised workloads and cash flows fail to arrive on time.
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A sophisticated ransomware attack by the INC Ransom group has crippled the OnSolve CodeRED emergency notification platform, impacting hundreds of US municipalities. The incident has forced the permanent decommissioning of the legacy system and exposed the personal data of millions of residents.
Oracle has become a test of how much debt markets will tolerate in the AI buildout. Rising credit costs and growing use of swaps show investors backing the AI story while seeking protection in case promised workloads and cash flows fail to arrive on time.
Morgan Stanley is warning on Oracle has become a litmus test for how far markets are willing to underwrite the AI boom on faith alone. What started as a technical note on widening credit spreads is now shaping into a bigger argument about whether the AI economy can deliver hard returns fast enough to justify the debt being piled up in its name.
On Wall Street, Oracle is increasingly being treated less as a staid database vendor and more as a proxy for AI credit risk. Its cost of default protection has jumped to multi‑year highs, and some traders openly talk about the company as a “barometer” for how much leverage investors will tolerate in the race to build data‑centre capacity. Analysts point to a balance sheet swollen by bond issues, project finance and long‑dated leases, all anchored to an AI demand curve that still depends heavily on a handful of blockbuster contracts.
Morgan Stanley credit analysts Lindsay Tyler and David Hamburger believe that a financing gap, balance sheet expansion, and the risk of technological obsolescence are just some of the threats facing Oracle. According to ICE Data Services, the cost of insuring Oracle’s debt against default over the next five years rose to an annual 125 basis points on Tuesday.
Morgan Stanley warned in a report on Wednesday that the price of Oracle's 5-year credit default swaps (CDS) could break the record high set in 2008. Concerns over the company's heavy borrowing to achieve its AI ambitions continue to drive significant hedging activity among banks and investors.
That sentiment is echoed in the growing use of credit‑default swaps as a defensive hedge. Investors are not walking away from the AI story, but they are paying up for insurance in case promised workloads and cash flows fail to arrive on schedule. For now, the message is caution rather than panic—yet the parallels with pre‑2008 complacency are hard to miss.
Oracle’s situation also exposes the fragility of what many in Silicon Valley like to describe as an “AI circular economy”. In the optimistic version of that model, capital flows into chips and data centres, those assets power new AI services, and the profits loop back into the next generation of infrastructure. In practice, the loop can easily become a closed circuit of mutual dependence. Oracle needs sustained, high‑margin AI workloads to service its obligations; its biggest AI partners, in turn, are betting on Oracle and a small club of other hyperscalers to deliver near‑limitless capacity at predictable prices.
One San Francisco‑based venture capitalist who has backed several AI infrastructure start‑ups calls Oracle’s bind “the first real stress test of the AI flywheel”. In their view, the market is not rejecting the AI thesis but “testing whether the plumbing—the financing structures, contracts and incentives—can actually support the scale of the promises being made”. If that plumbing leaks, they warn, the damage will not be confined to one balance sheet.
Among equity analysts, there is a growing split between those who see a necessary shake‑out and those who fear a broader AI bubble. A tech strategist at a large US brokerage argues that “some compression in AI valuations and credit spreads is healthy”, describing Oracle’s experience as “the market forcing discipline on capex that has run ahead of visible cash returns”. They frame it as a transition from a phase where AI spending was rewarded almost automatically to one where every extra dollar of debt has to be justified in terms of payback period and risk.
Perhaps nobody embodies artificial intelligence mania quite like Jensen Huang, the chief executive of chip behemoth Nvidia, which has seen its value spike 300% in the last two years.
A frothy time for Huang, to be sure, which makes it all the more understandable why his first statement to investors on a recent earnings call was an attempt to deflate bubble fears.
"There's been a lot of talk about an AI bubble," he told shareholders. "From our vantage point, we see something very different."
Pro‑AI advocates in Silicon Valley push back on the idea that the debt build‑up is inherently reckless. A senior executive at a leading model developer says the industry is “in the grid‑building phase”, likening today’s spending to the early days of electricity and the internet.
“We are laying the rails for the next 50 years of computing,” they argue. “If we’re still asking where the ROI is in 2030, then worry. But between now and 2027, the right question is whether we’re building fast enough, safely enough and in the right places.”
Even they concede, however, that the window for delivering proof is narrowing. AI’s strongest boosters now talk openly about 2026–2027 as the period when “virtual co‑workers” and AI agents should start showing up in productivity data, not just product demos. That timeline is being watched closely by macro economists and central banks already modelling how AI could reshape growth, inflation and labour markets over the next decade.
Overlaying the debt story is a more subtle shift in how AI is reshaping trust in financial infrastructure. Bankers and regulators have been warned that AI‑generated voices and deepfakes have effectively broken legacy voice‑based security. Systems that once felt like the cutting edge—voiceprints, challenge phrases, frictionless call‑centre authentication—are now being quietly redesigned or retired as institutions accept that synthetic audio can mimic customers too convincingly to rely on sound alone.
Fraud specialists expect losses linked to deepfake scams and synthetic‑identity abuse to climb sharply over the next few years. For lenders and insurers, that translates into higher expected loss rates, more complex investigations and heavier compliance overheads. All of that feeds back into the cost of capital for the very firms leaning hardest into AI. The technology that is supposed to generate efficiency and margin is simultaneously forcing expensive upgrades to security and risk management, blunting some of the promised gains.
This is why the years 2026 and 2027 are emerging as a de facto deadline for demonstrable AI return on investment. On corporate ledgers, boards will want to see AI clearly improving margins, cutting error rates and compressing delivery times in ways auditors can track. Sector‑by‑sector, early adopters in finance, healthcare, logistics and professional services will be expected to show that AI is lifting output per worker, not just shuffling tasks around. At the macro level, treasuries and central banks will look for signs that AI is nudging productivity trends, not just inflating capex lines and credit risk.
In other words, this is the moment when AI stops enjoying “moon‑shot” status and starts being judged like any other capital project, subject to hurdle rates, risk‑adjusted returns and intense political scrutiny.
Their warning is blunt: if the numbers disappoint, it will be much harder to justify the next wave of AI‑driven borrowing, and some of today’s high‑profile projects may end up being remembered as sunk costs rather than foundational infrastructure.
For AI diplomats and policymakers, Oracle’s predicament and the wider AI debt build‑up mark the beginning of an accountability era. The political narrative is already shifting from uncritical enthusiasm to pointed questions about who bears the risk if the AI boom misfires—creditors, taxpayers, workers or a mix of all three. That shift will shape everything from industrial policy and competition law to prudential rules for banks underwriting AI megaprojects.
The most optimistic voices in the field still believe an AI circular economy is achievable: one in which value, safety and trust circulate together, reinforcing each other rather than cancelling out. But getting there will require more than rhetorical support for innovation. It will demand transparent financing structures, interoperable security standards that can withstand synthetic media, and cross‑border cooperation on AI‑enabled fraud and systemic risk. Oracle’s elevated spreads, and the nervous jokes on trading floors about “2008 déjà vu”, are early reminders that the burden of proof now sits squarely with those betting the heaviest on the AI future.
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