Finance is a prized frontier for investors eager to leverage artificial intelligence to generate alpha. Earlier this week, news about OpenAI hiring 100 investment bankers from JPMorgan, Morgan Stanley, and Goldman Sachs to eliminate the manual tasking doled out to junior bankers underscored the relevance of its application in industry.
Yet one recent high-profile misstep proves a sobering point: AI is hardly infallible. Anthelion Capital Partners’ overweight investment in now-bankrupt car-parts supplier First Brands is a stark reminder that even the most sophisticated AI platforms can be blindsided, particularly when dealing with esoteric investments or credits lacking transparency.
The AI-Powered Blow
Money manager Anthelion Capital Partners had proudly touted its AI-driven platform’s ability to pick up on market signals missed by human competitors. The firm’s technology, which reportedly leverages large language models to scour data on companies’ customers and suppliers, was supposed to offer a competitive edge.
Instead, Anthelion built an unusually concentrated position in the loans of First Brands, an investment that became a significant blow for the AI-driven investor. The firm, a collateralized loan obligation (CLO) issuer, had about $5 million of First Brands debt as part of a roughly $400 million investment vehicle.
Damage Control And Crystallized Losses
Since the filing, the company has fully sold out of its position in First Brands’ debt. The sales, which started at levels at or above 90 cents on the dollar and averaged between roughly 70 cents and 90 cents, helped Anthelion avoid the deep losses now facing some other creditors. However, these sales still would have crystallized losses early in the life of the CLO, which was issued in June.
Representing 1.29% of the CLO’s loan portfolio, this was the highest concentration among CLOs printed in 2025, according to analysis by Valitana. The firm’s appetite for the supplier’s debt prior to its collapse into bankruptcy last month underscores the potential risk of relying on algorithms in complex credit markets.
The Broader Warning On Credit Selection
Anthelion’s situation provides a crucial lesson for Wall Street: AI is a powerful utility, but it is not a panacea, especially when faced with the inherent opacities of leveraged credit. In the CLO market—a roughly $1 trillion arena backed by pools of leveraged loans—the lack of full transparency into funding operations and balance sheet composition can often elude even the most advanced algorithmic scanners.
Although Anthelion’s exposure was dwarfed by other creditors, such as Jefferies’ specialist invoice-finance fund, Point Bonita Capital, which had about $715 million invested, the firm’s experience serves as a clear warning about over-reliance on AI to select investments in credits with opaque financial operations.
While the overall US CLO exposure to First Brands being only 0.21%, the willingness of an AI-driven firm to take such an outlier-concentrated bet illustrates that technology is still vulnerable to the pervasive spread of fragile debt across the financial system.
It’s Always Been Done This Way
Traditional means of perfecting security interests and liens is often a fragmented, manual, and uncoordinated endeavor. To search for a debtor’s existing liabilities, a lender must typically conduct a Uniform Commercial Code (UCC) search in each relevant state’s central filing office (usually the Secretary of State), as this is where public notice of claims against personal property—like inventory, equipment, or accounts receivable—is recorded.
This state-level system is not a centralized national registry, meaning that for a multi-state debtor, the lender must perform separate searches across multiple state jurisdictions, each with its own specific search logic, indexing delays, and reporting format.
This reliance on multiple, disparate, and non-interoperable systems—combined with the risk of name-matching errors and indexing delays—creates a significant potential for “collateral blind spots,” making a comprehensive view of a debtor’s full financial encumbrances extremely difficult to attain.
The Future Is In The Foundation
While current AI models struggle with the lack of transparency, like in the First Brands case, the focus is shifting from simply detecting market signals to securing the underlying collateral itself. Emerging applications of Big Data, combined with distributed ledger technology (like blockchain), offer the potential to create real-time, tamper-proof records of the assets backing loans.
By using AI to analyze this transparent, on-chain data, investors could move beyond surface-level financial statements to gain genuine visibility into a borrower’s operational health, significantly reducing the that plagued this type of esoteric credit. This technological evolution aims to make the foundations of leveraged finance fundamentally less opaque and thus, more resilient.
Shared, Yet Competing Interests
The widespread adoption of DLT for collateral management faces a major hurdle: interoperability and standardization. Implementing a shared ledger system requires consensus on technology standards across a vast and fragmented ecosystem of competing financial institutions, lenders, custodians, and regulators.
Current institutional efforts, while groundbreaking, often involve proprietary systems: JPMorgan’s Kinexys (Onyx), for instance, utilizes tokenized assets for collateral settlement on its private network, and bank consortia like Fnality International are building parallel networks for wholesale settlement using digital currency. Similarly, Swift is integrating a blockchain-based ledger to its messaging network to handle tokenized value.
While each step modernizes one part of the market, the sheer number of different, isolated institutional ledgers risks creating a fragmented landscape of digital silos, reinforcing the very inefficiencies the technology aims to overcome. Establishing universal protocols remains a slow, complex, and costly endeavor, echoing common challenges in the adoption of any new financial technology.
Anthelion’s overweight position in First Brands debt reaffirms the need for human judgment and robust fundamental analysis to complement, not be replaced by, the growing power of AI and the blockchain in the high-stakes world of credit investing. Directing the power of these tools toward operational and financial transparency will further enhance debtors’ quest for perfection of lien.
Anthelion Capital Partners’ overweight investment in First Brands is a stark, expensive reminder: even the most sophisticated AI can be blindsided. This is particularly true when dealing with esoteric investments or credits where transparency may not be fully embraced by borrowers.