Discipline among disruption – private credit investing in the age of AI

February 19, 2026

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AI is forcing investors to rethink business models and the embedded risks. For private credit, distinguishing durable cash flows from disruption risk has become critical, argue Rafael Torres and Gianpaolo Pellegrini.

The rapid development of artificial intelligence is reshaping how investors assess risk in any company where the business model could be heavily and quickly disrupted by machine learning technology/artificial intelligence. Concerns about disruption have already spread beyond software to legal technology, consulting, real estate services, insurance brokerage and comparison platforms, with market reactions at times reflecting uncertainty rather than fundamental change.¹

While AI developments continue to evolve, they are already improving productivity and potentially reducing the cost of building software, prompting investors to reassess companies that rely on standardised, off-the-shelf products or labour-intensive development.

However, disruption risk is not likely to be uniform. Software that is deeply embedded in critical workflows – such as enterprise resource planning, billing, financial systems, or public-administration platforms – tends to benefit from high switching costs and operational dependencies, making rapid displacement unlikely. In many cases, we believe AI is more likely to enhance these systems than replace them.

At the same time, the economics of AI remain uncertain. Building and running AI models is capital intensive, and many providers are still in early monetisation phases. While technological progress is rapid, its long-term commercial impact is still unfolding.

These developments are particularly relevant for private credit because software companies have been a major focus of private-equity investment in recent years.² Their growth profiles, recurring revenues and high margins have often supported elevated valuation multiples. To meet return targets, sponsors frequently structured transactions with relatively high leverage, commonly in the range of 5-7x EBITDA and, in some cases, using revenue-based metrics such as annual recurring revenue to justify additional borrowing.³

In this context, AI represents a potential credit risk not because software demand is disappearing, but because valuation assumptions and growth expectations may prove optimistic for certain business models. If AI increases competition, compresses pricing power, or accelerates technological obsolescence in specific segments, equity cushions based on high multiples could decline quickly.

For highly leveraged companies, this creates refinancing and downside-protection risks for lenders. As a result, some large private-credit portfolios with meaningful exposure to leveraged software companies are now facing greater scrutiny from investors.

Our own private-debt portfolios have largely avoided this risk due to our more conservative underwriting approach. Credit selection has consistently prioritised leverage based on cash-flow metrics rather than revenue multiples, combined with meaningful equity commitment from sponsors and a focus on business-model durability. In practice, this meant we did not participate in highly leveraged software transactions where pricing depended heavily on aggressive growth assumptions. While this limited deal participation during periods of strong software-sector activity, it also reduced our exposure to segments now viewed as more vulnerable to AI-driven disruption.

As a result, software exposure across our Pan-European private-debt strategies remains limited. The small number of software-related investments that we hold are concentrated in companies whose products are embedded in core business processes and used daily by customers. These include systems linked to operational workflows, billing infrastructure, or financial processes where reliability, data integration and switching costs create natural protection against rapid displacement. In these cases, we believe AI is more likely to enhance functionality and efficiency than to undermine the business model.

We have also generally avoided software solutions whose primary function is to replace human labour in relatively standardised tasks or that offer limited differentiation. In areas such as translation, basic legal-document processing, customer-service automation, or commoditised marketing tools, AI is more likely to create sustained competitive pressure and erode pricing power over time. Distinguishing between these types of business models is central to our credit analysis.

A similar approach applies in our parallel-lending portfolios, where exposure to software producers and AI-sensitive business models remains minimal and well below portfolio risk thresholds. Where technology-enabled companies are present, they tend to operate in niche financial-services segments or payroll and accounting services and are actively incorporating AI tools to improve efficiency rather than facing displacement risk. Portfolio leverage levels are typically below 3.5x EBITDA and diversification is high, limiting the impact of any single issuer.

We have also deliberately avoided financing AI data-centre infrastructure structured through special-purpose vehicles. In our view, the risk profile of such investments has risen alongside rapid technological change. Advances in computing architecture and alternative infrastructure concepts could introduce obsolescence risk more quickly than traditional infrastructure cycles would suggest. While AI infrastructure will remain essential, the pace of innovation increases uncertainty around long-term asset values.

More broadly, AI is becoming an increasingly important dimension of credit analysis. As lenders, the focus is less on predicting technological winners and more on identifying where business models could face revenue erosion, margin pressure or valuation compression. At the same time, many of our portfolio companies are adopting AI tools to improve productivity and efficiency, which can strengthen credit profiles rather than weaken them.

The current environment illustrates how technological change can create both risk and opportunity. While AI may challenge certain software and services models, it also reinforces the importance of disciplined underwriting, leverage control and careful assessment of competitive positioning. For credit investors, understanding how technology interacts with cash-flow durability remains more important than reacting to short-term market narratives.

 

References

1.Reuters, as of 13th February 2026. “From software to real estate, US sectors under the grip of AI scare trade.”
2.Bain and Company, as of 23rd September 2025. Deals rise in 2025, but easy wins may be over.
3.S&P Global, as of 12th February 2026. AI disruption worries spill over to private credit markets.

 

This material is not intended to be relied upon as a forecast, research, or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed by Muzinich & Co. are as of February 2026, and may change without notice.

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