Private equity has always had a particular kind of curiosity: not just “Is this company profitable?” but “What exactly makes it hard to copy?” In software, that question is notoriously difficult. A product can look simple on the surface—an interface, a workflow, a few integrations—while the real advantage hides in messy details: edge cases, data models, operational reliability, security posture, onboarding friction, and the unglamorous engineering decisions that only show up after months of use.
Now, according to reporting from the Financial Times, some private equity groups are experimenting with a new way to answer that question. Instead of relying solely on traditional diligence—code reviews, customer interviews, architecture diagrams, and spreadsheets—they are using “vibecoding” AI replicas to recreate software products quickly and then stress-test what they’ve built. The premise is straightforward: if an AI system can rapidly generate a working approximation of a target product, investors can measure how defensible the original really is. If replication is easy, the competitive moat may be thinner than it appears. If replication is hard—because of complexity, tacit knowledge, or deep domain logic—then the moat may be real, even if the company’s marketing doesn’t fully explain it.
The phrase “vibecoding” is doing a lot of work here. It suggests a style of coding where the system doesn’t merely translate specifications into code, but instead infers structure and behavior from examples, descriptions, and observed patterns. In practice, that means the AI isn’t just writing boilerplate; it’s attempting to reproduce the “feel” of a product—its workflows, its outputs, and its internal logic—by learning from signals about how the software behaves. The result is an AI-generated replica: a version of the product that may not be identical line-by-line, but aims to match functionality closely enough to be tested.
What makes this approach notable is not that AI can generate code. That’s been widely demonstrated for years. What’s different is the investment mindset behind it. Private equity is treating AI replication as a diligence instrument—almost like a stress test for competitive advantage. Rather than asking whether the target company is good at building software, the question becomes: how quickly could a capable buyer (or competitor) reproduce the product’s value proposition?
That shift changes what diligence looks like. Traditional diligence often focuses on what exists today: revenue quality, churn, customer concentration, contract terms, and the current state of the engineering team. AI replication adds a second dimension: the replicability of the product itself. It’s a way to estimate the cost and time required to rebuild the offering from scratch—or at least from a close approximation—using modern tooling.
In other words, the diligence process starts to resemble reverse engineering, but with a twist. Instead of manually mapping features and reconstructing systems, the investor uses AI to accelerate the reconstruction. Then they evaluate the replica’s performance against the original: does it behave the same way under realistic conditions? Does it handle the same failure modes? Can it integrate with the same ecosystem? Does it scale? Does it remain stable when the inputs get messy, which is where most software advantages actually live?
This is where the “competitive advantage” part becomes more than a buzzword. Many software companies claim differentiation through “platform” language, but the real differentiators are often operational. They might include:
1) Data handling and correctness
A product’s value can depend on subtle transformations, validation rules, and consistency guarantees. Two systems can appear similar in demos while diverging sharply when confronted with real-world data.
2) Reliability under load and over time
A replica might pass functional tests but fail when subjected to sustained usage, concurrency, or long-running workflows.
3) Security and compliance posture
Even if an AI replica can mimic features, it may struggle to replicate the security model, permissioning logic, audit trails, and compliance-related constraints that are deeply embedded in mature systems.
4) Integration depth
Software moats frequently come from how well a product plays with other tools—APIs, webhooks, identity providers, billing systems, and internal enterprise workflows. Replicating the “happy path” is one thing; replicating the integration reality is another.
5) Product ergonomics and workflow nuance
Some advantages are experiential. The “vibe” of a product—how quickly users can accomplish tasks, how the UI guides them, how the system responds to mistakes—can be hard to quantify but easy to feel. AI replicas can approximate interfaces, but matching the full workflow experience is harder than it sounds.
The FT report frames this as an experiment rather than a universally proven method. That matters, because the temptation with any new diligence technique is to treat it as a shortcut to certainty. But software replication is not a single problem with a single answer. It’s a spectrum of difficulty depending on the product category, the maturity of the target system, and the availability of information.
For example, consider two types of software targets:
A) Products with clear external behavior and limited internal complexity
If a product’s value is mostly visible through its outputs—say, a tool that transforms inputs into predictable results—AI replication may succeed quickly. The replica can be validated by comparing outputs across test cases.
B) Products with heavy internal coupling and tacit knowledge
If the product relies on complex domain logic, proprietary data structures, or operational know-how, replication becomes harder. Even if the AI can generate code, it may not reproduce the underlying assumptions that make the system robust.
This is why AI replication can be useful even when it doesn’t perfectly match the original. The goal isn’t necessarily to build an identical clone. The goal is to learn something about the effort required to reach parity. If the replica gets 70% there quickly but stalls at 90% because of hidden complexity, that gap is itself informative. It tells investors where the moat likely resides.
There’s also a strategic reason private equity would care about speed. In many deals, time is money—not just for the buyer, but for the seller too. If an investor can compress diligence timelines, they can move faster, negotiate with more confidence, and potentially outcompete other bidders. AI replication offers a way to turn weeks of manual analysis into days of iterative testing, at least for certain product types.
But speed introduces its own risks. An AI replica might produce something that looks plausible yet fails in ways that only show up under specific conditions. That’s why the testing framework matters. The diligence process described in the reporting implies that the replica is used to benchmark performance and identify where the competitor may be strong. That suggests a structured evaluation rather than a one-off demo.
In a well-designed diligence workflow, the replica would be tested across multiple dimensions:
Functional fidelity:
Does it reproduce the same user-facing behavior? Are outputs consistent? Do workflows complete successfully?
Robustness:
How does it handle invalid inputs, missing data, unusual sequences of actions, and partial failures?
Performance:
What are the latency characteristics? How does it behave under load? Does it degrade gracefully?
Maintainability:
Can the generated system be understood and modified? Or does it become a fragile black box?
Security and permissions:
Does it enforce access controls correctly? Does it log events appropriately? Does it resist common misconfigurations?
Operational readiness:
Can it be deployed reliably? Does it require extensive manual tuning? How does it behave in production-like environments?
The “vibecoding” angle also raises an interesting question about what investors are actually measuring. When an AI replica is generated, it may rely on patterns learned from training data and general software conventions. That means the replica’s success could reflect not only the target product’s complexity, but also the AI system’s prior knowledge and the generalizability of the product’s design.
So the diligence outcome might be partly about the target product and partly about the AI toolchain. That doesn’t invalidate the approach, but it does mean investors need to interpret results carefully. A product might appear easy to replicate because it resembles common architectures. Another product might appear hard because it uses unusual patterns or domain-specific logic—even if the underlying engineering is not particularly sophisticated.
This is where the “unique take” on the story becomes important: AI replication could shift diligence from “What is the company’s engineering capability?” to “How much of the product is standard versus bespoke?” That’s a different lens. It reframes competitive advantage as a composition problem: how much is generic software engineering, and how much is specialized knowledge embedded in the system?
If that’s the case, then AI replicas become a kind of measurement device for “bespoke-ness.” Investors can quantify the portion of the product that is likely to be reproducible by a competent team using modern tools. They can also identify where the target company’s engineers have created non-obvious value—value that may not be captured by code volume or headcount.
There’s another layer: the feedback loop between diligence and acquisition strategy. If an investor can replicate a product quickly, they might decide to pursue a different approach post-acquisition. Instead of preserving the existing engineering team and rebuilding slowly, they might choose to refactor aggressively, migrate to a new stack, or consolidate overlapping components across portfolio companies. Conversely, if replication is difficult, they might prioritize retaining key engineering talent and protecting the existing architecture.
In that sense, AI replication isn’t just about deciding whether to buy. It can influence how the buyer plans to operate after the deal closes. It can shape integration plans, roadmap priorities, and even the valuation narrative.
However, there are practical limitations that will likely show up quickly, especially as AI replication becomes more common. Some of the biggest challenges include:
Information gaps:
AI systems need inputs—documentation, UI flows, API behavior, sample data, and sometimes direct access to the product. If the target product is opaque, replication may stall.
Non-determinism and hidden state:
Many systems depend on internal state, background jobs, caching layers, and asynchronous workflows. Replicating those accurately can be difficult without deep observ