AI: Oracle's Credit Default Swaps and the AI Hedge
Oracle’s exploding CDS price shows traders are using Oracle as a safe haven against AI.
Hi Fintech Futurists —
Today’s agenda is below.
AI: Big Tech’s rush to fund massive AI infrastructure with record levels of debt, especially Oracle’s heavily leveraged, high capex strategy, signalled by surging CDS spreads and negative free cash flow, has turned the AI boom into a growing credit risk.
LONG TAKE: Understanding the Robotics economy and its financial applications.
PODCAST: Building the $500MM+ Binance-based Digital Asset Treasury, with BNB Network CEO David Namdar
CURATED UPDATES: Machine Models, AI Applications in Finance & Investment Outlook
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Lex Sokolin
AI: Oracle Is Now an AI Hedge
Markets have been correcting down after an aggressive run-up this year, and investors are focusing on how to isolate the risks from overvaluation and circular revenue in the AI industry. Given that both fixed income and equities are correlated with AI revenue — not to mention overall risk-on appetite — we are interested in any data or indicators that show a change in regime.
Enter $500B Oracle.
Over the past two months, Oracle has gone from an AI champion to Wall Street’s go-to instrument for betting against the AI boom. You may want to avoid trying to short NVIDIA and having your face blown off. But Oracle has debt, a large chunk of it is exposed to OpenAI as a counterparty, and therefore that debt has a chance to default, and there are derivatives markets that can provide insurance against such default.
This is why Credit Default Swaps (CDS) on Oracle’s debt have surged in price and volume, turning the company into the preferred hedge against AI. You can see the premium bumping up magnificently below.
We’ve previously discussed that AI is a capital cycle as much as a technology revolution. Circular investment in data centers, funded by debt collateralized by the future data center revenue, has run into the trillions. In fact, data center and AI infrastructure spend is projected at roughly $6.7–7T by 2030, with ~$5.2T of that is AI-specific data centres and related infrastructure.
This doesn’t mean the underlying fundamentals aren’t there. Just that expectations — and leverage — are high.
This is mirroring the dramatic rise in tech investment by the hyperscalers. They are busy hyper-scaling their infrastructure. And some of this is worth it — for example, Google’s recent progress in TPU chip architecture seems to be a path out of the NVIDIA paradox.
Data centers are now catching up with offices when it comes to construction spending in the US. That’s a pretty tremendous stat, on which we should meditate.
So credit markets are now hedging against this. The question they are all asking is what happens if this spending doesn’t pay off fast enough, or if funding costs spike? While demand for inference is currently strong and rising, there is a ton of embedded volatility when you try to model out the future.
That’s where Oracle comes in.
Since Oracle announced the $300B OpenAI deal in September, its market cap has fallen by $300B+, even as broader software indices were flat to slightly up over the same period.
Oracle is targeting $166B in cloud revenue by 2030, leaning heavily on OpenAI from 2027 onward. To support this, Oracle has planned massive capex, rising towards $80B per year by 2029, and its net debt is expected to roughly quadruple from 2021 levels, with free cash flow projected to be negative for several years.
Oracle, OpenAI, and SoftBank are also fronting a $500B AI infrastructure initiative, with about $18B in project finance loans being arranged by a club of roughly 20 banks for a New Mexico data centre campus that Oracle will ultimately lease. UK press reports say the company has been seeking to raise up to $38 billion of additional debt to fund AI infrastructure build-outs.
Credit markets have noticed.
Oracle’s CDS becomes the “AI Bust Hedge”. The cost of five-year credit default swaps (CDS) on Oracle rose 44% in a month to 87 basis points, following roughly $75 billion of AI-focused investment-grade bond issuance by Big Tech in September/October. The annual cost of insuring $100 of Oracle debt is now about $1.04, roughly double the level in September 2025, making Oracle one of the most repriced credits in large-cap tech.
Why Oracle?
Oracle has a weaker balance sheet vs. peers, with S&P rating Oracle BBB+, below Microsoft or Alphabet, and Oracle’s net debt is around $90 billion, with leverage ratios above 3x EBITDA.
The combination of huge AI capex, rising leverage, and weaker credit ratings makes Oracle a high-beta AI credit proxy. When we look at these financial models ranging out to 2030, it is reasonable to be skeptical. Can we really continue projecting 100% revenue growth year over year at an enormous scale.
Maybe yes. But maybe no.
The Oracle trade doesn’t exist in a vacuum. It sits inside a broader re-rating of investment-grade credit in the face of AI capex. Global stocks are down ~2.7% in November amid AI over-investment fears, but investment-grade (IG) bond spreads are still near 27-year lows, with US IG trading only 10 bps above its tightest level.
Boaz Weinstein’s Saba Capital has been selling CDS protection (i.e., short CDS spreads) on big tech names, including Oracle, Microsoft, Meta, Amazon and Alphabet to banks that want to hedge their AI loan and bond exposures. Banks and asset managers are using Oracle CDS as a macro hedge, while sophisticated credit funds like Saba are taking the other side, collecting premiums on what they see as overpriced AI fear.
For a deeper dive on the debt in the sector and historical lessons, see the related coverage below. Of course, we want tech to win over skepticism, but we are concerned about the enormous concentration of wealth, power, and platform that is happening with hyperscalers.
Start-ups and open-sourced decentralized alternatives are more important than ever.
👑Related Coverage👑
⭐Recommended Reading⭐
We also recommend the following article to learn more about TGUs vs. GPUs and how Google is positioning for its competition with NVIDIA and OpenAI.
Robotics: From Industrial Automation to Humanoid Intelligence (link here)
The traditional robotics industry has developed a vertically integrated value chain, comprising four main layers:
Core components (controllers, servos, reducers, sensors, batteries, etc.) have the highest technical barriers, defining both performance ceilings and cost floors.
Control systems act as the robot’s “brain and cerebellum,” responsible for decision-making and motion planning.
Complete machine manufacturing reflects the ability to integrate complex supply chains.
System integration and application development determine the depth of commercialization and are becoming the key sources of value creation.
🎙️ Podcast: Building the $500MM+ Binance-based Digital Asset Treasury, with BNB Network CEO David Namdar (link here)
In this episode, Lex speaks with David Namdar - CEO of the BNB Network Company, kicking off with his journey from early Bitcoin adoption in 2012 to co-founding Galaxy Digital and now leading the BNB Network Company. Namdar explains the evolution of public markets’ engagement with crypto, highlighting how regulatory hurdles and speculative cycles shaped market participation. He outlines the rise of Digital Asset Treasury (DAT) companies, crediting Michael Saylor’s MicroStrategy for pioneering the model by converting $400 million in cash to Bitcoin - now holding over $75 billion in BTC.
We examine how Binance, with 290 million users and 40% of global crypto volume, supports BNB as a deflationary asset, burning up to $2 billion per quarter. Finally, Namdar shares why BNB, not Bitcoin, is the focus of his new DAT initiative, offering U.S. investors exposure to an underrepresented but powerful asset.
Curated Updates
Here are the rest of the updates hitting our radar.
Machine Models
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning - Matthew G. Hanna & Liron Pantanowitz & Brian Jackson & Octavia Palmer & Shyam Visweswaran & Joshua Pantanowitz & Mustafa Deebajah & Hooman H. Rashidi
A Critical Field Guide for Working with Machine Learning Datasets - Sarah Ciston & Mike Ananny & Kate Crawford
AI Applications in Finance
⭐ AI-Driven Payment Systems: From Innovation To Market Success - Merve Ozkurt Bas
The Rise Of Generative Ai Agents In Finance: Operational Disruption And Strategic Evolution - Inesh Hettiarachchi
Financial Modeling in Corporate Strategy: A Review of AI Applications For Investment Optimization - Olufunmilayo Ogunwole & Ekene Cynthia Onukwulu & Micah Oghale Joel & Ejuma Martha Adaga & Augustine Ifeanyi Ibeh
Investment Outlook
⭐ Private Equity Outlook 2025: Is a Recovery Starting to Take Shape? - Bain & Company
⭐ Global Venture Capital Outlook: The Latest Trends - Bain & Company
⭐ Global Private Markets Report 2025: Braced for shifting weather - McKinsey & Company
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Contributors: Lex, Laurence, Matt, Farhad, Daniel, Michiel, Luke
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"Google’s recent progress in TPU chip architecture seems to be a path out of the NVIDIA paradox."
This will depend on their ability to maintain high quality developer relations across generations. How will AI companies like OpenAI tradeoff developer man hours versus hardware savings?