Key Takeaways

  • Bain & Company is generating partial AI replicas of software products to help private equity firms evaluate technical defensibility.
  • The practice fits into a wider shift toward AI-assisted code generation and scenario modeling within software due diligence.
  • Rapid enterprise adoption of generative tools is making replication-style assessments more practical for investors.

Private equity teams have been hunting for new ways to understand whether a software target actually has the product strength its pitch decks promise. Bain & Company is testing a novel path by using generative AI to recreate slices of a target's platform based on natural-language descriptions. The firm calls this "vibecoding." Even though the term has a slightly playful tone, the underlying use case is serious and tied to capital allocation decisions.

Instead of relying entirely on demos, documentation, and engineering interviews, the consulting firm produces a working partial replica of the target product. It is not meant to be a polished rebuild. The goal is to see whether key features can be reconstructed quickly, and whether the moat is as wide as claimed. If a core workflow can be replicated in days, it hints that the differentiation may rely more on execution than irreplaceable intellectual property. Investors see that as a helpful signal, especially in competitive bidding cycles.

Generative development tools have improved enough to make this kind of evaluation viable at dealmaking speed. According to Gartner, 70% of new enterprise applications will use generative AI-based tools by 2027, up from less than 5% in 2023. That shift makes it easier to imagine consultants spinning up partial systems from scratch, because the ecosystem itself is pushing toward faster application creation.

IDC estimates that organizations will allocate $308 billion to AI-centric systems in 2026, with nearly half of that tied to software-oriented investments. These figures highlight how AI-supported development and analytics are becoming standard parts of enterprise workflows.

McKinsey has emphasized the economic upside of generative AI in software engineering, with projected annual economic value between $2.6 trillion and $4.4 trillion. Software engineering continues to be one of the functions most affected by faster coding, automated testing, and rapid prototyping. These trends help explain why private equity teams are experimenting with AI-driven replication during diligence. If engineering velocity rises broadly, competitive advantage might hinge more on domain expertise, user experience, and market access, making technical moats trickier to justify.

Development standards are shifting, and responsible AI guidelines are becoming more central to enterprise decision-making. Frameworks like the NIST AI Risk Management Framework and the ISO/IEC 42001 AI management standard shape how organizations validate models and control operational risks. The firm operates inside that environment, ensuring its vibecoding approach aligns with existing expectations around governance. Private equity firms, especially those managing institutional capital, tend to prefer methods that map to recognized frameworks rather than purely experimental practices.

Deals move fast, and diligence teams often juggle limited information with fixed timelines. Investors want to know whether a target's architecture is resilient or if it is simply holding together under optimistic assumptions. Vibecoding provides a quick comparative benchmark. Assessors can test how the replica behaves, see what breaks, and understand which modules depend heavily on unique craftsmanship. It is not perfect, but it offers another angle of visibility.

Engineering teams inside targets sometimes worry that such replicas diminish their value. Yet the exercise does not aim to replicate an entire product; it focuses on capabilities that matter for competitive positioning. A partial rebuild that highlights sticky differentiation can actually strengthen a target's valuation story. When vibecoding exercises show slow replication or high complexity, buyers gain more confidence that the product is defensible.

Private equity sponsors also appreciate how these replicas open conversations and probe real engineering bottlenecks. Deal teams might ask what happens if the rebuilt workflow behaves in unexpected ways, or if an algorithm is simple to mimic but difficult to scale. These discussions often reveal organizational maturity or long-term roadmap clarity, areas that traditional diligence methods do not always uncover.

Developer productivity improvements are becoming more measurable. Bain & Company’s 2025 Technology Report noted that organizations adopting advanced software development practices, including AI-enabled engineering, report developer productivity gains of 20% to 30%. With that kind of shift underway, investors want more grounded insight into how a target actually builds software. Vibecoding supports that aim through hands-on assessment rather than slide-driven narratives.

Looking ahead, more consulting firms and corporate development teams will likely experiment with similar model-driven analysis. The tooling will keep improving, especially as platform providers like OpenAI and Anthropic release more capable code-generation models. Not every investor will adopt vibecoding, though. Some prefer traditional engineering interviews or architecture deep dives, while others see replicas as a useful complement rather than a core component of diligence.

The boundary between product evaluation and software creation is narrowing, and diligence processes are beginning to reflect it. This approach demonstrates how AI is reshaping not only how products are built, but also how they are valued. In a competitive deal market, any method that helps investors understand true defensibility has a way of catching on.