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Vibe coding due diligence has quietly become a standard part of software deals, and it cuts in two directions at once. Buyers now use AI tools themselves to test how defensible a target's product really is, and the AI-generated code inside a target invites a newer set of questions about how it was made and checked. If you are a founder building with AI tools, or an investor looking at a company that was, the questions on both sides are new enough that many teams walk into a process unprepared. This post maps what changed, what buyers actually test, the technical risk areas that official guidance now names, and what a founder should have ready before a process starts. Source
The clearest signal that diligence has shifted comes from Bain and Company. The Financial Times reported that Bain now builds rough AI-generated replicas of acquisition targets' products so prospective buyers can test the moat before they pay for it. What began in 2023 as a side project for a handful of Bain engineers is now standard practice across its private equity advisory teams, which have produced hundreds of these prototypes for clients evaluating deals. Rebecca Burack, head of Bain's global private equity practice, told the FT the firm uses the technique "to show what a software company can and can't do, to understand where it fits in the value chain and to understand whether it is the actual code that is the defensible part of the business or something else." One private equity investor told the FT that a Bain-built recreation of an analytics platform up for sale contributed to their decision to walk away from the auction. The context is a sharp repricing: PE-led technology, telecom and media deal value fell 69 percent in the first quarter of 2026 versus the final quarter of 2025, per KPMG figures cited in the same report. Source
The replication exercise is not about proving a product can be cloned line for line. It answers a cheaper question: how much of this company's value survives contact with a competent team and a modern AI coding tool? If a junior consultant can rebuild the core workflow over a long day, the moat was never the code. That pushes diligence toward the parts AI cannot replicate: proprietary data, integrations that took years of partner negotiations, regulatory approvals, switching costs baked into customer workflows, and brand. The FT report notes that public markets have already cut more than a third from the valuations of large enterprise software names this year on exactly this logic, and that mid-market SaaS businesses loaded with acquisition debt face the sharpest version of the question. For a founder, the practical implication is uncomfortable but clarifying: if your pitch deck's defensibility slide says "our technology," a buyer may test that claim with an actual rebuild attempt during diligence. Source
Official guidance gives any diligence team a ready-made taxonomy for reviewing AI-generated code, and we recommend using it as the review map. The Canadian Centre for Cyber Security's guidance on managing security risks in vibe coding (ITSM.60.043) names several categories that a technical reviewer could probe directly. Code provenance: AI models draw on training data that can include vulnerable, outdated or intentionally malicious code, and similar patterns can appear in generated output, so nobody should assume generated code is secure or robust. Supply chain integrity: the guidance recommends a trust-tier analysis of AI vendors, review of each provider's security policies and data-handling terms, and a software bill of materials to track open-source dependencies. Embedded malware and library risk: AI-generated code may pull in packages containing malicious code, unknown vulnerabilities or insecure configurations. It also names slopsquatting, a supply chain attack in which threat actors register fictitious package names that AI coding tools commonly hallucinate, then wait for developers to install them. Source
The same guidance flags two risks that matter specifically in an acquisition context, because liability transfers with the company. First, information leaks: giving an AI tool access to sensitive information can leak it, either through the resulting code or back through the AI provider, and the guidance recommends selecting providers whose terms contractually prohibit training on customer input. A diligence team can reasonably ask which AI tools wrote the code, what data was pasted into them, and what each provider's data terms say. Second, architecture: when a person writes code they design the security structure deliberately, but an AI generates what it predicts fits, without understanding every security pitfall it encodes. The guidance's bottom line is that producing secure, reliable, production-ready code still requires technical expertise, and that review, testing and approval requirements should rise with the risk of the code. A thorough buyer could ask who reviewed the generated code, and how, and a prepared founder has that answer ready. Source
The founder-side answer to this diligence style is preparation, not persuasion. Have a current SBOM and dependency scan results, and be able to show that no dependency traces to a hallucinated package name. Document which AI tools contributed to the codebase, under which accounts and data terms, and what review each generated change received. Clean up the classics before anyone looks: secrets in history, dead dependencies, unowned licenses, missing tests around the money paths. Keep the records that prove provenance, because "a senior engineer reviewed everything" is a claim, while a pull request trail is evidence. Our guides on how to audit vibe coded apps before launch, vibe coding IP ownership and vibe coding technical debt walk through the self-audit version of this work; the same artifacts are what a diligence team could request. If you want an outside team to run that audit and remediation with you, our vibe coding for founders service is built for exactly this stage. Source
Everything above is what the sources say; this section is our read. The Bain story matters less for what Bain does than for what it normalizes: replication testing is cheap enough to become a default step, and once it is a default step it stops being a negotiating tactic and becomes a pricing input. Founders should expect the question "what stops someone from rebuilding this" in every software deal from now on, asked with working prototypes rather than hypotheticals. The counterintuitive upside is that vibe coded companies are not automatically weaker in diligence. A small team that used AI tools well, kept provenance records, reviewed output and can show a clean SBOM may present better documentation than a larger team with years of unexamined legacy code. Diligence rewards evidence of process, and AI-assisted teams can generate that evidence as they go, if they bother to keep it. Source
Vibe coding due diligence is the review of a software company whose code was partly or largely generated by AI tools, combined with buyers' own use of AI to test how easily the product could be replicated. It covers code provenance, dependencies, data handling by the AI tools used, security review practices, and the durability of the product's competitive advantage. Source
The Financial Times reported that Bain and Company builds AI-generated replicas of acquisition targets so buyers can test whether the code itself is the defensible part of the business. The technique, which started as a 2023 engineering side project, is now used across Bain's private equity advisory teams, and at least one investor walked away from an auction after seeing a replica. Source
Slopsquatting is a supply chain attack in which threat actors register package names that AI coding tools commonly hallucinate, then wait for developers to install them. In diligence it matters because a single malicious dependency in a target's codebase becomes the acquirer's problem on day one, so dependency scanning and a software bill of materials are reasonable diligence requests, and the guidance recommends SBOMs as baseline practice. Source
Not by itself. The valuation question buyers ask is whether the defensible part of the business is the code or something else, such as data, integrations, regulatory position or workflow lock-in. A company with AI-generated code, clean provenance records and strong non-code moats can still diligence well; a company whose only moat was that its code was hard to write faces a harder conversation. Source
A current software bill of materials, dependency and vulnerability scan results, a record of which AI tools contributed code and under what data terms, the pull request and review history showing human review of generated changes, license and IP assignment records, and secrets and access hygiene evidence. Government guidance on vibe coding risk recommends SBOMs and provider data-term review as baseline practice. Source
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