Banks across the United States are now sorting software companies into winners and losers based on their exposure to artificial intelligence, creating a two-tier lending system that is driving up borrowing costs and killing deals for firms seen as vulnerable to AI disruption. The Federal Reserve’s January 2026 lending survey confirms that commercial lenders are less likely to approve loans for companies threatened by AI while favoring those expected to benefit. That split is rippling through private credit markets, where pulled deals, frozen redemptions, and postponed refinancings signal a deepening credit squeeze for an entire sector.
The Fed’s New AI Litmus Test for Borrowers
The clearest evidence of this shift comes from the regulator itself. The Board of Governors of the Federal Reserve System published its latest loan officer survey, which for the first time showed banks explicitly differentiating commercial and industrial loan approval decisions based on how borrowers relate to AI. Firms that stand to gain from adopting or supplying AI are finding doors open; firms whose business models face displacement are finding them shut. This is not a soft preference or an informal bias. It is a documented pattern in the Fed’s own survey data, drawn from senior lending officers at major U.S. banks who are being asked to underwrite technology risk that could transform revenue trajectories long before existing contracts roll off.
The practical result is a widening gap in credit access. A software company building AI tools or integrating them deeply into its product can expect smoother negotiations with lenders, more flexibility on covenants, and a better chance of securing revolving credit facilities that support growth. A legacy enterprise software provider whose recurring revenue could evaporate as AI agents replace traditional workflows faces tighter terms, higher spreads, or outright rejection. That divergence is accelerating because lenders are not simply reacting to current earnings. They are pricing in a future where AI rewrites competitive dynamics across the software industry, and they are doing so with the blunt instrument of loan approvals rather than waiting for quarterly results to confirm their thesis. For borrowers, the message is clear: demonstrate an AI upside story or risk being treated as a melting ice cube.
Pulled Deals and Postponed Refinancings
The lending chill is already producing real casualties in the deal market. Software companies have seen their loan prices drop amid growing fear that advances in artificial intelligence will disrupt them. Investors concerned that AI will soon displace enterprise software have triggered pulled deals and hung loans against software firms, amplifying price swings in a market that had been relatively stable for years. Syndicated loan desks, wary of being stuck with exposures they cannot distribute, are demanding steeper discounts or walking away altogether. The result is a feedback loop: falling loan prices make new issuance harder, which raises borrowing costs, which further depresses valuations and feeds investor skepticism about the sector’s long-term resilience.
European digital service provider Team.blue offers a concrete example of how this pressure plays out. The company postponed an extension of its 1.353 billion euro ($1.60 billion) term loan after lenders balked at the risk profile and pricing. That postponement is telling because it suggests lenders and borrowers could not agree on terms that reflected the new risk calculus around AI disruption. When a company with more than a billion euros in outstanding debt cannot simply roll its financing forward, the market is sending a clear signal about how seriously it takes the threat to traditional software and hosting revenue streams. For similar mid-market borrowers, the episode is a warning that refinancing windows once taken for granted may now close abruptly if investors decide a business looks more like an AI victim than a beneficiary.
Private Credit Feels the Strain
The stress is not confined to broadly syndicated loan markets. S&P Global Ratings has highlighted that record issuance by technology borrowers, rising leverage, and looming maturity walls are straining private credit liquidity in 2026. Private credit volumes for lower-rated borrowers have been outpacing broadly syndicated loan volumes, meaning that many of the software companies squeezed out of traditional bank lending have been turning to direct lenders willing to charge higher rates. That dynamic worked when software revenue was considered durable and predictable, supported by long-term subscriptions and high switching costs. AI disruption risk is now testing that assumption hard, forcing private funds to revisit underwriting models that treated software cash flows as quasi-bond-like.
The tension between projected hyperscaler capital expenditure and the troubles facing software borrowers creates an unusual split within the tech credit universe. Companies building data centers, chips, and other AI infrastructure are attracting enormous capital and in some cases securing tighter spreads, while companies selling the software that AI could replace are struggling to refinance existing debt. Looming maturity walls add urgency: firms that borrowed heavily during the low-rate era now face refinancing windows in which lenders are far less accommodating and due diligence questions focus on AI exposure. The gap between AI beneficiaries and AI casualties is widening not just in equity markets but in the less visible world of corporate debt, where the consequences of a downgrade or a failed refinancing can be existential, triggering covenant breaches, forced asset sales, or distressed exchanges.
Blue Owl and the Valuation Reckoning
Perhaps the starkest illustration of how AI anxiety is destabilizing private credit came when Blue Owl Capital permanently halted redemptions at a private credit fund aimed at retail investors. The tension in that episode was tied directly to loan portfolios with software exposure, and the decision to freeze redemptions rather than sell assets at a loss revealed how difficult it has become to assign reliable values to software loans in a market gripped by AI uncertainty. When a fund cannot let investors withdraw without risking a fire sale, the underlying marks on its loan book come into question, and with them the confidence of both existing and prospective investors. That raises the prospect of a broader valuation reckoning across private credit vehicles that ramped up exposure to software during the boom years.
The Blue Owl decision also underscores the structural vulnerability of semi-liquid funds that promise periodic liquidity while holding inherently illiquid assets. Retail-focused products built on private loans to software and services companies thrived when credit spreads were tight and default expectations were low. Now, as AI risk forces lenders to reassess business models, those same funds are discovering that exit doors for underlying assets can narrow quickly. For managers, the choice is between crystallizing losses by selling into a weak market or shutting the gate on redemptions and hoping conditions improve. For regulators, the episode raises fresh questions about whether retail investors fully understand the liquidity and valuation risks embedded in these strategies when technology shocks hit.
How Investors and Borrowers Are Repricing AI Risk
The emerging two-tier credit market is forcing both lenders and borrowers to rethink how AI risk should be priced and disclosed. Some banks and private funds are pushing software companies to provide detailed scenario analyses showing how AI could erode or enhance revenue, along with concrete plans for integrating new tools into their products. According to reporting on large-cap U.S. and European credit markets, investors are increasingly using credit spreads to differentiate between perceived AI winners and losers, with even modest shifts in sentiment translating into millions of dollars in additional annual interest for leveraged borrowers. In parallel, rating agencies are incorporating AI exposure into sector outlooks, which can influence everything from covenant packages to allowable leverage ratios.
For software executives, the new environment means capital structure strategy cannot be separated from technology strategy. Firms that can credibly present themselves as AI-enabled (whether through in-house development, strategic partnerships, or acquisitions) are more likely to secure refinancing on acceptable terms. Those that cannot may find themselves effectively shut out of traditional markets and pushed toward more expensive rescue financing. Market data platforms such as financial terminals already show a widening dispersion in trading levels for software credits that once moved in lockstep, reflecting investor attempts to sort durable franchises from those at risk of obsolescence. As the AI cycle unfolds, the line between technological adaptability and creditworthiness is blurring, reshaping not only which software companies survive but also who gets to decide their fate.
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*This article was researched with the help of AI, with human editors creating the final content.
Grant Mercer covers market dynamics, business trends, and the economic forces driving growth across industries. His analysis connects macro movements with real-world implications for investors, entrepreneurs, and professionals. Through his work at The Daily Overview, Grant helps readers understand how markets function and where opportunities may emerge.
