Private equity buyers use AI to clone target software, threatening enterprise tech valuations

A quiet revolution in private equity due diligence is threatening the valuation of enterprise software companies, as acquisition advisors turn to artificial intelligence to test whether a target company's product is truly defensible. In a practice known as vibecoding, dealmakers are using generative AI to rapidly build functional replicas of an acquisition target's software from the outside. The goal is simple yet devastatingly effective: if a group of consultants can recreate a company’s core product using plain-language prompts in a matter of days, potential buyers are left wondering what exactly they are paying a premium for. This emerging practice is fast becoming a standard hurdle in software-as-a-service acquisitions, fundamentally redefining how the industry assesses technological moats.

At the center of this shift is the global consulting firm Bain & Company, which has integrated vibecoding into its "outside-in" due diligence processes. What began as a specialized task for a dedicated team of software engineers has now scaled across the firm, with ordinary consultants routinely generating rough prototypes of target software products[1][2]. By utilizing advanced generative AI coding tools to interpret high-level intent rather than relying on manual programming, these consultants have built hundreds of software mockups[3]. The rapid replication of features provides potential buyers with a tangible, interactive demonstration of how easily a proprietary system can be duplicated, stripping away the mystery of a target's codebase[1][4].

According to leadership at the firm, the ability to rapidly clone an application's features alters the entire perspective of a transaction. Leaders within Bain's global private equity practice describe the shift as the difference between viewing an acquisition target in two dimensions versus three dimensions[1]. The primary objective is to evaluate what a software company can and cannot do, to pinpoint its exact position in the broader value chain, and to determine whether its underlying code is the defensible element of the business or if the value lies elsewhere[1]. This hands-on stress-testing allows investors to bypass polished marketing decks and directly confront the physical realities of the technology they are buying[1][5].

The implications of this testing are already altering the outcomes of high-stakes negotiations[1]. In one notable instance, a private equity investor reportedly abandoned a bid for an analytics platform after a vibecoded replica created by Bain demonstrated that the target’s core technology could be easily reproduced[1]. This skepticism is mirrored in the public markets, where investors have significantly repriced legacy enterprise software giants, wiping away massive portions of the valuations of companies like Salesforce and ServiceNow[2]. In private markets, the fear of AI disruption has contributed to a dramatic cooling of deal flow[3]. According to industry data, the total value of private equity-led technology, media, and telecom transactions collapsed by nearly seventy percent in the first quarter of this year compared to the final quarter of last year, as buyers step up their scrutiny of AI-related risks[3].

This surge in software replication has been accelerated by a booming market for AI-driven development tools[6]. Products like Anthropic's Claude Code, Replit, and Cursor are turning high-level design specifications and plain-language descriptions into functional applications, dramatically compressing development cycles that once took months or years[7][6]. The broader tech sector has reacted with intense financial activity, with major acquisitions of AI-native coding platforms validating the strategic importance of this technology[6]. These platforms allow even non-technical analysts to bypass traditional development bottlenecks, such as writing wireframes, checking syntax, and manual debugging, allowing them to focus entirely on the overall logical flow of the system[8][9].

While the ease of cloning software features has sent a chill through the technology sector, it has also sparked an intense debate over what actually constitutes a competitive advantage in the modern era[10]. Many industry analysts argue that a software company's true value rarely resides solely in its code or user interface[10]. Instead, the real defensibility often lies in complex ecosystems, proprietary data integrations, regulatory compliance, and deep customer relationships that cannot be replicated by prompting an AI[8][10]. For example, a healthcare software platform might be easily cloned from a functional standpoint, but replicating its deep integration into hospital electronic health records or its decade-long trust with clinical institutions is an entirely different challenge[10].

Nonetheless, the widespread use of vibecoding as a due diligence tool forces software startups and established vendors alike to reevaluate their business models[2]. Companies that offer thin-wrapper solutions—software that primarily consists of basic workflows, standard dashboards, and simple API connections—are highly vulnerable to having their valuations gutted[2]. To survive this new era of scrutiny, founders must prove that their platforms offer deep structural value, specialized domain expertise, or unique distribution channels that AI tools cannot easily replicate[8]. Software can no longer be sold simply on the premise that it was hard or expensive to build[2].

Ultimately, the rise of vibecoding in merger and acquisition strategies marks a permanent pivot in how technology is valued[5]. As the marginal cost of writing software approaches zero, the historical premium placed on proprietary codebases is evaporating[1][3]. Acquirers are no longer taking a developer’s word on the complexity of their platform; they are asking AI to rebuild it before they even sit down at the negotiating table[5]. For the software industry, this means that the ultimate test of a business is no longer whether its product works, but whether its value extends far beyond the code itself.