Morgan Stanley’s Oracle Warning: A Deep Dive into Big Tech Debt and the AI Industry Trajectory

Executive Summary

Morgan Stanley’s stark warning about Oracle’s debt approaching 2008 crisis levels reflects a broader credit market recalibration as Big Tech companies embark on an unprecedented AI infrastructure spending spree.

While Oracle faces the most acute pressure among hyperscalers—with its five-year credit default swaps surging to 125 basis points and both S&P and Moody’s assigning negative outlooks—other Magnificent Seven companies maintain substantially stronger credit profiles, with ratings ranging from AA- to AAA.

The AI industry is projected to grow at a 25-36% CAGR through 2030, though Artificial General Intelligence (AGI) remains 5-10 years away according to leading researchers, with critical breakthroughs still required in autonomous reasoning and self-directed learning.

Oracle’s Debt Position: A Comparative Analysis

Oracle’s Unique Vulnerability

Oracle stands alone among the major hyperscalers in terms of credit distress.

The company’s total debt has ballooned to over $104 billion as of fiscal year 2025, up from $90.5 billion in FY2023—a 15% increase in just two years.

More concerning, Morgan Stanley forecasts Oracle’s financial obligations, including bonds and data center leases, could nearly triple to $290 billion by fiscal year 2028.

The critical distinction is Oracle’s negative free cash flow, which fell to its lowest level in over two decades in 2025—the first time since 1999.

S&P Global Ratings and Moody’s have both revised Oracle’s outlook to negative, warning that adjusted leverage could exceed 4x debt-to-EBITDA in fiscal years 2027-2028.

Credit Default Swap Dynamics

Oracle’s CDS market has experienced explosive growth, with trading volume reaching approximately $5 billion over seven weeks ending November 14, 2025, compared to just $200 million during the same period last year.

The cost to insure Oracle’s debt against default for five years surged to 125 basis points, the highest in three years.

Morgan Stanley analysts forecast these CDS spreads could breach 150 basis points near-term and potentially reach 200 basis points—matching the 2008 financial crisis peak of 198 basis points.

Magnificent Seven Debt Comparison

The Bifurcation of Creditworthiness in the AI Era

The contemporary technology sector is witnessing a pronounced decoupling of credit profiles.

While the industry collectively engages in an unprecedented capital expenditure cycle—projected to exceed $600 billion by 2027—the capacity to service this debt varies profoundly.

A distinct hierarchy has emerged: the sovereign-tier stability of the largest hyperscalers stands in sharp contrast to Oracle’s deteriorating leverage ratios and escalating liquidity risks.

Credit Health Across Hyperscalers

Unlike Oracle, the other major technology companies maintain robust credit profiles despite their own substantial AI-related borrowing:

Key Differentiators

Microsoft

Maintains the most conservative approach, holding the coveted AAA rating—the only tech company sharing this distinction with the U.S. government.

The company has not issued bonds in 2025, choosing instead to fund AI investments through its robust cash generation while doubling data center capacity by 2027.

Apple

Carries $107 billion in debt, but with a net debt of only ~$41.5 billion after accounting for $65 billion in cash reserves.

The company’s debt-to-EBITDA ratio stands at just 0.31, and its EBIT covers interest expenses an impressive 673 times.

Nvidia

Presents a unique case—despite being the primary beneficiary of AI infrastructure spending, S&P upgraded its outlook to “positive” in late 2024, citing exceptional revenue growth and strong cash flow.

The company reduced long-term debt from $11.5 billion in January to $7.9 billion by Q3 2025.

However, analysts have flagged concerns about Nvidia’s $110 billion in vendor financing exposure to AI startups such as CoreWeave, OpenAI, and xAI, which could become problematic if AI revenue growth slows.

Meta

Executed a remarkable $30 billion bond sale in October 2025—the most extensive corporate bond offering of the year—but notably structured a separate $27 billion AI facility off-balance-sheet through a joint venture with Blue Owl Capital to preserve its AA rating.

Alphabet (AA)

With a conservative debt load of ~$48 billion against a cash pile exceeding $100 billion, Alphabet maintains a “fortress balance sheet.”

Its capital expenditures are comfortably funded by operating cash flows, insulating the company from the vagaries of the credit markets.

The Record Debt Surge

The top five AI spenders—Amazon, Google, Meta, Microsoft, and Oracle—have collectively raised an unprecedented $121 billion in debt year-to-date in 2025, more than quadrupling the $28 billion average over the previous five years.

Goldman Sachs reports that across 1,300 major technology firms, total interest-bearing debt now stands at $1.35 trillion—four times higher than a decade ago.

AI Industry Trajectory and Growth Outlook

Market Size and Growth Projections

The artificial intelligence industry is experiencing hyperbolic growth, though projections vary by research methodology.

Capital expenditure projections are equally staggering.

JPMorgan estimates that Alphabet, Amazon, Meta, Microsoft, and Oracle alone will require approximately $570 billion in capex by 2026, up from $125 billion in 2021.

AI capital expenditure is projected to reach $600 billion by 2027, up from just under $400 billion in 2025 and over $200 billion in 2024.

Generative AI as the Growth Engine

Generative AI represents the fastest-growing segment, projected to expand at a 29-34.5% CAGR from $37.1 billion in 2024 to approximately $220 billion by 2030.

Enterprise adoption is accelerating, with 65% of businesses now utilizing AI technology to optimize workflows and automate tasks.

ABI Research estimates generative AI use cases will create $434 billion in enterprise value creation annually by 2030.

Productivity and Economic Impact

The Wharton Budget Model projects AI will boost total factor productivity (TFP) growth by 0.09 percentage points in 2027, peaking at around 0.2 percentage points in the early 2030s.

Cumulative effects suggest AI will increase productivity and GDP by 1.5% by 2035, nearly 3% by 2055, and 3.7% by 2075.

However, these gains remain below transformative expectations, with permanent annual productivity effects settling below 0.04 percentage points as sectoral shifts occur.

Artificial General Intelligence: Definition, Objectives, and Timeline

Defining AGI

Artificial General Intelligence (AGI) refers to a hypothetical AI system that can match or exceed human cognitive abilities across any intellectual task, rather than being confined to narrow, specialized functions.

Key characteristics distinguishing AGI from current narrow AI include

Autonomous Goal Formation

The ability to independently decide what problems to explore, why, and how—not merely responding to instructions.

Self-Directed Learning

Continuous improvement without constant human input or task-specific retraining.

Transfer Learning

Flexible application of knowledge across diverse domains, tasks, and contexts that were never explicitly programmed.

Contextual Reasoning

Understanding nuance, ambiguity, and real-world complexity comparable to human cognition/

Core Objectives of AGI Research

The AGI research agenda centers on several fundamental objectives:

Timeline Predictions

Expert consensus on AGI timing has shifted notably in recent years, with entrepreneurs and industry leaders offering more aggressive forecasts than academic researchers:

Google DeepMind co-founder Shane Legg’s prediction from 2011—a log-normal distribution with a mean of 2028 and a mode of 2025—remains remarkably consistent with current industry sentiment.

However, Hassabis cautions that achieving true AGI requires one or two additional breakthroughs beyond current scaling approaches, particularly in reasoning, memory, and “world model” capabilities.

Current Limitations

Despite significant advances, today’s AI systems—including OpenAI’s GPT-5 -5 released in August 2025—still lack the following.

(1) Autonomous goal formation and self-motivated reasoning.

(2) Lifelong learning without catastrophic forgetting.

(3) Transfer of skills across fundamentally different domains.

(4) Commonsense reasoning about physical and social worlds.

GPT-5 represents a significant step forward, achieving “PhD-level” performance in reasoning, coding, and writing, while reducing hallucinations.

However, academic critics note it still operates within narrow transfer boundaries and lacks the self-directed learning and autonomic cognition that characterize accurate general intelligence

Key Risks and Implications

Systemic Risks in AI Financing

Goldman Sachs warns that the shift toward debt-financed AI capex “would increase the macro risks associated with the AI build-out”.

Key concerns include

(1) Circular financing arrangements

Interlinked revenue relationships among AI players introduce systemic risks—Oracle’s dependence on OpenAI contracts, for example, could generate nearly 50% of revenue from a single customer by 2028.

(2) Off-balance-sheet opacity

Meta’s use of special-purpose vehicles and limited public disclosures obscures proper leverage:

(3) Interest rate sensitivity

With half of the outstanding U.S. public debt set to turn over by 2027, elevated rates could strain debt service across the sector.

Oracle-Specific Downgrade Risk

Both Moody’s (Baa2) and S&P (BBB) maintain Oracle one notch above “junk” status.

A downgrade trigger exists if.

(1) Leverage remains above 4x debt-to-EBITDA

(2) Free cash flow weakness persists through 2026.

(3) AI infrastructure investments fail to generate expected revenue momentum.

Barclays warns of a significant funding gap starting fiscal year 2027, with the risk that cash reserves could be depleted as early as November 2026 if capital spending continues at projected levels.

Conclusion

Morgan Stanley’s warning about Oracle represents a company-specific credit concern rather than a systemic crisis across Big Tech.

Oracle’s BBB rating with a negative outlook, negative free cash flow, and projected leverage exceeding 4x distinguishes it sharply from peers like Microsoft (AAA), Apple (AA+), and Nvidia (AA- with a positive outlook).

The broader AI industry trajectory remains robust, with market growth projections of 25-37% CAGR through 2030.

However, AGI—accurate human-level artificial intelligence—remains 5-10 years away according to leading researchers, requiring fundamental breakthroughs in

(1) autonomous reasoning

(2) self-directed learning

(3) world modeling beyond mere scaling of current architectures.

For investors, the key distinction lies between companies financing AI expansion through strong operating cash flows (Microsoft, Amazon, Alphabet) versus those dependent on external debt markets (Oracle, smaller AI infrastructure providers).

As Alphabet CEO Sundar Pichai has cautioned, should the AI boom falter, “no company would be immune to the repercussions,”—but Oracle’s narrow credit margin leaves it uniquely exposed to any slowdown in AI revenue realization.