Permira's analysis suggests AI can replicate software features, eroding the value of high switching costs and recurring revenue. The new moat is whether a company owns critical data or is deeply embedded in workflows.

AI's primary value in pre-buy research isn't just accelerating diligence on promising ideas. It's about rapidly surfacing deal-breakers—like misaligned management incentives or existential risks—allowing analysts to discard flawed theses much earlier in the process and focus their deep research time more effectively.

For an incumbent, mission-critical company, AI presents a significant opportunity. By leveraging their proprietary data to build AI tools, they can enhance their product, improve margins, and further solidify their market leadership, making them more attractive credit risks.

Advanced AI tools can model an organization's internal investment beliefs and processes. This allows investment committees to use the AI to "red team" proposals by prompting it to generate a memo with a negative stance or to re-evaluate a deal based on a new assumption, like a net-zero mandate.

A significant portion of private credit portfolios consists of loans to software companies, which were underwritten based on predictable, recurring revenue. AI is now fundamentally disrupting these business models, threatening to devalue the very collateral that underpins billions of dollars in these 'safe' loans.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

When evaluating software loans, Blackstone moves beyond financials to product underwriting. Its investment committee uses a specific scorecard to assess a company's risk of AI disruption, how embedded its product is in workflows, and how its technology stacks up, demonstrating a structured approach to modern threats.

While many firms are just now reacting to AI's impact, major credit investors like KKR have been actively underwriting AI-driven business model risk for nearly six years. This proactive, long-term approach to assessing technological disruption is a core part of their due diligence process, not a recent development.

Drawing a parallel to the early internet, where initial market-anointed winners like Ask Jeeves failed, the current AI boom presents a similar risk. A more prudent strategy is to invest in companies across various sectors that are effectively adopting AI to enhance productivity, as this is where widespread, long-term value will be created.