Consultancy Bain & Company is increasingly using artificial intelligence to recreate software products when assessing acquisition targets, as private equity investors adapt to rapid advances in generative AI that are reshaping how technology businesses are valued, according to a report by the Financial Times.
The approach, described internally as “vibecoding,” involves using AI tools to generate code from prompts in order to quickly build functional replicas of target companies’ software. These prototypes are then used to evaluate how easily a product could be replicated and to assess the durability of its competitive positioning.
Bain said the technique has expanded from a specialist engineering capability introduced in 2023 to a broader tool now used across its private equity advisory teams, reflecting the growing integration of generative AI into deal diligence workflows.
According to the firm, the AI-generated models allow investors to move beyond static product reviews and instead interact with working versions of software platforms, helping identify whether a company’s advantage lies in its underlying code, data, workflow design or broader ecosystem positioning.
The shift comes at a time when concerns about AI-driven disruption are increasingly influencing software valuations, with public markets already re-rating several major enterprise software groups amid expectations of faster replication cycles and compressed moats.
In private markets, the impact has been reflected in a slowdown in technology-focused buyouts, with deal activity in software, telecoms and media declining sharply as investors reassess risk and return assumptions in light of AI disruption.
Private equity professionals and advisers say the ability to rapidly simulate products is changing how competitive advantage is tested, with some investors reportedly using AI-built replicas as part of final-stage investment decisions.
Bain executives note that the objective is not only to assess current defensibility, but also to model how target businesses might evolve over a multi-year horizon as AI reshapes product development and cost structures.
The firm argues that the technique enhances traditional diligence processes by providing a more tangible assessment of product strength in an environment where software creation costs are falling and innovation cycles are accelerating.
As a result, investors are increasingly forced to weigh not just current performance metrics, but also the potential for rapid technological displacement when evaluating acquisition targets in the software sector.
