Top of Mind for Clients: AI, Private Credit, and the Risky Intersections

Recent client conversations have converged around two questions:

Whether artificial intelligence (AI) represents a speculative bubble
 How concerned we should be about private credit 

This note outlines our perspective and where we see the most risk.

Jump to Conclusion.

AI - Compelling Technology; Winners Remain Uncertain

What We See: Extraordinary Depth and Accelerating Adoption 

AI's unprecedented potential for technological advancement is evident in anecdotal cases:

• A man with no medical background used AI to create a personalized cancer vaccine for his dog.
• A 23-year-old with no advanced training solved a 60-year-old math problem in 80 minutes using AI. 
• An autonomous driving system detected that a driver was experiencing a medical episode and transported him to the emergency room.

Enterprise adoption is also gaining traction. As of March, over 30% of companies in technology, education, and finance report using AI in business functions, with all sectors expecting increased adoption over the next six months.

This trend is similarly reflected in our own experience. Since ChatGPT’s release in 2022, the Burney Analytical team has seen meaningful productivity gains by using AI tools for data validation and organization, coding, and company and manager due diligence.

Why is the Stock Market Rattled? 

In most technological cycles, early leaders are not guaranteed long-term dominance. At this juncture, market confusion reflects a combination of: 

• Broad recognition of AI’s significance
• Limited clarity on monetization, business model durability, and capital efficiency

Two structural features of the stock market add to the problem:

1. The stock market is not fully rational—especially in the short term.

Sentiment and uncertainty lead to distortions and dislocations, with some AI-related stocks selling off on fear rather than fundamentals.

• MSFT trades at ~21x forward earnings with ~40% net profit margin
• COST trades at ~50x forward earnings with ~3% net profit margin

2. The stock market is inherently forward-looking.

Since Q4 2025, much of the stock market’s anxiety has centered on how to position for AI. Investors are actively searching for “the next winner,” leading to frequent rotations and short-term volatility. Mega Techs are now being evaluated not just on CapEx (capital expenditure), but on visible ROI (return on investment). Following last Wednesday’s earnings release,

• We saw both Alphabet (GOOGL) and Meta (META) increased CapEx guidance
• But investors found clearer ROI signs in Alphabet’s results vs. Meta’s
• Consequently, Alphabet rose ~10% the following day, while Meta declined ~9%

How Should Investors Think About AI?

In 1913, Henry Ford introduced the modern moving assembly line. Its net effects:

• Ford Model T assembly time decreased from ~12 hours to ~90 minutes.
• U.S. auto production increased from ~200,000 vehicles in 1910 to ~2,000,000 in 1923.
• The price of the Model T declined from ~$850 in 1908 to ~$300 in the 1920s.
• Ford’s employment grew from ~14,000 workers in 1910 to ~60,000 by 1920.

AI is a real and POSITIVE structural shift, but the question financial markets are grappling with is who ultimately captures its economic value. Jensen Huang described AI as a “five-layer cake,” from bottom to top: energy, chips, infrastructure, models, and applications. Winners are—and will continue to—emerge within and across these layers, creating opportunities for investors who are diligent and adaptable. 

Source: Nvidia

Private Credit - Systemic Risk Comparable to Equities/Economy 

What We See: Stable Defaults and Continued Activities

Cliffwater Direct Lending Index (CDLI) data indicate a healthy private credit market as of the end of 2025, with defaults, non-accruals (pre-default), leverage, and PIK (payment-in-kind, a form of loan modification) all showing no signs of unusual stress or adverse trends. 

• Defaults fell from 1.35% in Q3 2025 to 1.27% in Q4 2025 (latest data available)
• PIK Income, portfolio watchlists, and trailing 1-year credit losses were approximately unchanged over the same period

While headlines draw attention to fund redemptions, actual investor behavior is more measured. In Blue Owl’s case, tender offers at steep discounts attracted less than 1% of shares, suggesting limited desire to exit at distressed prices. Flows into private credit continue, though they have become more selective. In Q1, Goldman Sachs’ private credit BDC saw 7.1% inflows (according to Fitch Ratings). JPMorgan is also planning to launch a new private credit interval fund, underscoring continued opportunity and sustained investor demand.

Why the Deteriorating Sentiment: 'Cockroaches' and Liquidity Constraints

The media has been beating the drum on payment-in-kind (PIK) risk for several years. In reality, about half of current PIK exposure is present at origination, indicating a significant portion is structural rather than stress-driven. Defaults only became more visible in late 2025, with several high-profile cases, all involving fraud or alleged fraud. 

• August 2025, the owner of multiple telecom-services companies declared bankruptcy after BlackRock’s private-credit investing arm and other lenders allege “breathtaking” fraud.
• September 2025, subprime auto lender Tricolor filed to liquidate. Fraud was alleged.
• September 2025, First Brands Group filed for bankruptcy. Its former CEO Patrick James was later charged with defrauding lenders.
• October 2025, two regional banks alleged in filings that they were victims of fraud on loans to a distressed-debt investor, igniting broader investor fears about banks’ loan portfolios. 

By November 2025, investors began seeking exits, and Blue Owl was unfortunately caught in the shift in sentiment. Private credit loans typically have a 5–7 year maturity, and unlike public bonds, managers generally intend to hold them to maturity. Interval funds provide quarterly liquidity gated at 5%, funded through revolvers, CLO warehouse lines, and portfolio cash flows from loan repayments and interest. Private BDCs are not legally required to provide periodic liquidity, but many adopt a similar schedule as interval funds. In either case, we believe that investors should not treat these vehicles as if they were as liquid as ETFs, and managers should maintain the 5% gate in order to protect remaining investors. 

How Should Investors Think About Private Credit Risk? 

It is important for investors to recognize that, although private credit typically involves unrated or lower-rated companies, corporate direct lending—what most wealth clients hold—is often structured as senior secured debt, backed by the company’s balance sheet and sits at the top of the capital structure. For losses to occur in private direct lending, financial stress must first erode the equity and junior debt layers.

The majority of private direct lending loans are "sponsored", meaning a private equity manager is actively involved with the borrower. The private credit sector is also tangled with banks and insurers. U.S. banks hold nearly $300 billion in direct exposure to private credit (per Moody’s), and U.S. life insurers hold more than 31% of their assets in private bonds (most are a form of private credit; data per BlackRock).

Structurally, in our view, it is unlikely for private credit to be systematically underwater while banks, private equity, and the broader economy remain healthy. A recent piece from Morningstar echoes this conclusion from a different angle, noting that, as a lender, private credit may be more stable than banks due to much lower leverage, less asset-liability mismatch, and more diversified holdings. 

The Risky Intersections - Private Debt in Software and Data Centers

While AI is a positive force for the broader economy and private credit portfolios are currently healthy, we see elevated risks in two specific AI-related segments. 

Software Debt - Concentrated Exposure; Uncertain Outlook 

Historically, software has been an attractive business. The median annualized growth in EBITDA earnings among private credit software borrowers has been 36% over roughly the past two years (per KBRA), leading to software's steadily rising portfolio weights since 2013 and outsized exposure in private credit today. 

• Software revenue is largely contracted and recurring
• Near-zero cost of goods sold (COGS)
• Gross margins of 70–80%
• Loan-to-value (LTV) ratios of 30–40%

Now, AI is disrupting the enterprise software and undermining seat-based pricing models. Business risk is becoming increasingly bifurcated, and trajectories may vary significantly across positions in the value chain. Companies also have limited time to adapt, with a wall of maturities approaching in 2028.  For private credit, all of a sudden, software’s asset-light structure with limited collateralizable assets is no longer advantageous and instead implies lower recovery rates if loans deteriorate. 

Data Center Debt -  Capital Intensive; Demand Not Guaranteed

Private credit is increasingly funding AI data centers. Morgan Stanley estimates that, although nearly half of the $2.9 trillion in funding needed for data centers from 2025–2028 will come from hyperscalers’ capex, private credit could provide roughly $0.8 trillion, making it a significant second source of funding.

Data centers present the opposite profile of software: rather than asset-light, they are asset-heavy, requiring substantial upfront capital while still facing the risk of lower recoveries due to potential technological obsolescence. Lenders to unprofitable or overleveraged projects could incur losses if some AI-driven buildouts prove uneconomic due to:

• Delays and shortages (power supply, equipment, water cooling, labor)
• Rapid technology shifts from technological advances—efficiency gains, declining demand, oversupply, and outdated infrastructure

Conclusion