In software M&A, the question used to be whether a target had good code. Increasingly, the question is whether that code matters enough to survive a copy test.
That is the practical role vibecoding is starting to play in acquisitions. Consulting firms, including Bain & Company, have been using AI-generated replicas to recreate target software quickly enough to gauge how hard it would be for a rival—or the buyer itself—to reproduce the product. The point is not to perfectly clone every system. It is to pressure-test the defensible moat: if a similar product can be rebuilt cheaply and quickly, then the original codebase may not support the valuation being discussed.
That matters because deal outcomes can change on the basis of that exercise. According to reporting cited by The Decoder, one private equity investor walked away from a bid after a Bain-generated recreation of an analytics platform suggested the target’s technical edge was easier to replicate than it looked on paper. That is a notable shift in diligence logic. Instead of treating product architecture as an intangible story told by founders and engineers, buyers can now use AI replicas as an empirical check on reproduction cost.
How vibecoding scales in diligence
The pattern described in recent coverage is less a one-off stunt than a process that has been industrialized. In 2023, Bain reportedly had a dedicated engineering team working on this kind of recreation exercise. By now, the work has scaled to hundreds of rough prototypes built by regular consultants. That detail matters. When a technique moves from a specialized group to a repeatable workflow, it stops being a novelty and starts becoming a standard input into commercial decision-making.
The technical appeal is straightforward. A vibecoded replica does not need to be production-grade to be useful. It needs to approximate the target’s workflows, UI behavior, integration surfaces, and the rough shape of its product logic well enough to answer a narrow question: how hard would it be to reproduce this?
That makes the exercise useful at scale because it standardizes comparison across deals. Rather than relying only on a code review, a vendor demo, or a founder’s explanation of architecture, buyers can ask a more concrete question: what is the reproduction cost for this product, and what part of the value sits in code versus distribution, data, workflow embedding, or customer relationships?
That is also why the technique can change bidding behavior. If a replica is easy to assemble, the buyer may conclude that the software is not the scarcity asset it appeared to be. If the product resists replication because of complicated integrations, domain-specific logic, or hard-to-port operational knowledge, then the buyer may see a stronger moat and a more durable pricing case.
What buyers are really testing
Vibecoding does not make architecture irrelevant. It makes architecture legible.
A company still needs code, but code alone may no longer define the defensible moat. Buyers are likely to place more weight on properties that are harder to reproduce with AI assistance: proprietary data pipelines, deeply embedded customer workflows, operational know-how, and systems that depend on hard-to-extract context. Modularity cuts both ways here. Clean interfaces and decoupled services can improve engineering quality, but they can also make pieces of the product easier to mimic if the surrounding advantage is thin.
For product and platform teams, this changes how technical differentiation is presented. A product with portable data models, auditable pipelines, and strong integration boundaries may look attractive if those features reflect disciplined engineering around scale and compliance. But if the same architecture reveals that the core experience can be reassembled from generic components, the buyer may discount the claimed moat.
In other words, the diligence lens is moving from “How sophisticated is the code?” to “How reproducible is the business?” That is a subtler and more consequential test. It forces teams to show where value sits outside the repository: in switching costs, workflow lock-in, proprietary training data, operational constraints, or regulated processes that are difficult to emulate quickly.
Governance becomes part of the deal
The expansion of AI-generated replicas into M&A diligence also creates a governance problem.
First, there are IP and licensing boundaries. If a replica is built from observed product behavior, documentation, and public-facing interfaces, buyers will still need to know what data sources were used and whether any licensed material was incorporated into the exercise. Second, there is data-handling risk: diligence artifacts can expose customer-facing flows, internal logic, or sensitive integration details. Third, there is auditability. If a replica helps determine a bid or justifies a lower price, the buyer needs to be able to explain why the model’s output was considered credible and what assumptions shaped the reconstruction.
That makes oversight more than a legal afterthought. Firms using vibecoding in deal evaluation will need documentation around prompts, inputs, sources, reviewer judgments, and the limits of the replica. Otherwise, the test can become misleading—either overstating weakness where hidden assets exist, or overstating strength when the replica fails to capture a product’s real operational depth.
The risk is not just misuse; it is overinterpretation. A rough AI-generated replica is a diagnostic, not a verdict. In diligence, that distinction matters because the cost of a false negative or false positive can be an abandoned bid, a missed acquisition, or an inflated price.
Why this changes market positioning
For software vendors, the strategic implication is uncomfortable but clear: code moat assessment is becoming more empirical and more scalable. If a target can be quickly approximated by consultants using AI tools, sellers may need to work harder to explain what is actually unique.
That should push product leaders to align architecture and go-to-market more tightly. Systems that are defensible should be defensible for reasons that survive a replication test: durable data advantages, operational complexity, compliance burdens, or network effects tied to actual customer usage rather than surface-level features. Teams that can show reproducibility controls, strong observability, and evidence of difficult-to-copy operational workflows may be better positioned when diligence turns adversarial.
For buyers, vibecoding offers a new kind of leverage. It lowers the marginal cost of asking a hard question: if we had to rebuild this, how much would it take? That does not eliminate the need for human judgment. It does, however, shift the burden of proof.
As vibecoding moves from a niche engineering experiment to a more common due-diligence tool, technical teams should expect more scrutiny on reproducibility, architecture, and governance. The companies most likely to fare well will be the ones whose value can be demonstrated beyond the codebase itself. That is the direction this market is heading, and it is already affecting deal outcomes.
