Private equity tech due diligence is broken. For decades, the process looked exactly the same. A private equity firm wanted to buy a software company, so they hired a bunch of expensive engineers to stare at a code repository for two weeks. They looked for messy syntax, security holes, and structural rot. It was slow, reactive, and completely detached from how software actually functions in the real world.
Bain & Company is quietly throwing that playbook out the window. Instead of just reading a target company's source code, Bain's M&A teams are using generative AI to spin up functional replicas of the target's entire software product. They call it vibe coding. You might also find this related article useful: The Massive Dutch Pension Shakeup International Managers Cannot Afford To Ignore.
The goal isn't to steal the intellectual property. The goal is to stress-test the product's core architecture by building a working lookalike in hours using natural language prompts. If an AI agent can recreate a startup's core product over a weekend based purely on public descriptions and high-level technical parameters, it tells the private equity firm everything they need to know about that company’s competitive moat—or lack thereof.
The shift from syntax to simulation
Traditional tech diligence focuses on code quality. Buyers want to know if the engineering team built a clean, scalable foundation or a pile of unmaintainable garbage. But that approach misses the forest for the trees. It doesn't tell you how fast a competitor can copy the software, and it doesn't tell you how easily the product can adapt to new AI capabilities. As highlighted in latest reports by Harvard Business Review, the results are significant.
By using vibe coding—where developers prompt AI agents to handle the creative implementation and write the raw code—Bain is changing the questions investors ask.
Instead of asking "Is this code well-written?", they are asking:
- Can a basic AI model clone this software’s primary feature set in less than five hours?
- How dependent is this product on proprietary data versus easily replicable logic?
- What happens to the company's valuation if a non-technical founder can "will" a competing app into existence next month?
This isn't a theoretical exercise. Bain’s 2026 M&A data shows that one in five strategic buyers have walked away from a deal specifically because of AI-related risks to the target's business model. The threat of rapid, AI-driven commoditization has made traditional code reviews insufficient.
Why working software is no longer an investable moat
The rise of agentic software development means that "it works" is no longer a valid validation of a tech company's value. Anyone can build a functional prototype now. If your software's primary value proposition is a slick interface sitting on top of standard APIs, you don't have a software company. You have a feature that an LLM can replicate on demand.
When Bain builds these AI replicas, they are testing the limits of what today's top models can do. Current benchmarks show that models like GPT-5.1 still struggle with complex end-to-end application development, accurately hitting requested feature targets only about 24.6% of the time on the first try.
But that 24.6% changes the math for a buyer. It means a competitor doesn't need a massive team of engineers to build a minimum viable product anymore. They just need a sharp product manager who knows how to guide an AI agent.
The Subprime Mortgage of Code: Vibe coding acts like a variable-interest loan. It gives startups immediate gratification through rapid feature delivery, but it can accumulate massive technical debt and structural insecurity behind the scenes.
If a target company built their own product using loose vibe coding methods, Bain's simulation approach will expose it instantly. AI-generated backends frequently ship with overly broad permission settings, hardcoded API keys, and chaotic duplicate functions because the AI lacks a unified memory of the project's architecture.
The legal and structural landmines buyers are looking for
Cloning a target's software using AI also highlights massive legal liabilities that traditional diligence often misses until it's too late. When developers rely heavily on pure vibe coding, copyright protection evaporates.
In most jurisdictions, software must be the result of human creative expression to qualify for copyright protection. If an AI agent independently chooses the design logic, researches third-party packages, and writes the code based only on a high-level prompt, the resulting software may not be protectable at all. A buyer looking at a target company might find they are paying millions for a codebase that anyone can copy legally.
Furthermore, AI agents are notorious for pulling in open-source code fragments without verifying license terms. This introduces a massive compliance risk. If the AI accidently drops a copyleft-licensed snippet into a commercial product, it can compromise the entire intellectual property stack.
How to prepare your technical stack for an AI audit
If you are a founder or an engineering leader looking at an exit in the next few years, you need to expect buyers to run these exact simulation tests on your product. You can't just clean up your repository before the data room opens. You have to change how your team builds.
- Move away from pure prompting. Stop letting engineers push raw AI output directly to production without deep human verification. Force a strict human-in-the-loop review process where experienced developers act as structural architects.
- Enforce strict tool validation. If your team uses AI coding agents, ensure those agents validate parameters against explicit schemas (like Pydantic) rather than letting the model guess data structures.
- Audit your dependencies. AI agents tend to optimize for immediate task completion, not supply chain security. They will pull down external npm or Python packages blindly. Implement automated tools to log exactly where and why every package enters your codebase.
- Document your proprietary data moats. Since code logic is becoming easier to replicate via AI, your value lies in your data, your integrations, and your workflow design. Clearly isolate these elements so buyers can see exactly what an AI agent cannot clone over a weekend.
The era of evaluating software companies by counting engineering heads and reading static lines of code is over. Buyers are using the technology to build their own versions of your product before they ever make an offer. The only way to survive that audit is to ensure your software has an architectural depth that vibes alone can't replicate.
