What is vibe coding —
and why does AI-generated code matter in a deal?
Vibe coding represents a tectonic shift where developers build applications using plain-language prompts rather than line-by-line syntax. But beneath this staggering speed lies a potential minefield of black-box technical debt, intellectual property ambiguity, and security risks.
Start here: The plain-language definition
Vibe coding is a software development workflow where a programmer describes what they want to build in plain natural language, and AI agents write, debug, and deploy the actual source code. Coined by AI pioneer Andrej Karpathy, the term captures a shift from syntax-level keyboard typing to high-level intent-matching. Developers no longer manually type every semicolon; they guide an AI engine through an iterative, conversational loop of testing and refinement.
Think of vibe coding like hiring a hyper-fast contractor who speaks fluent code but has zero common sense. The developer acts as the supervisor, pointing out mistakes and directing the build while “vibing” at the macro level. When executed by elite engineers, vibe coding delivers massive speed-to-market and slashes R&D costs. But when abused by inexperienced teams, it produces highly fragile software systems where no living human actually understands how the underlying code functions.
“Vibe coding allows a two-person team to build what used to require an entire engineering department. But if your acquisition target is ‘vibing’ without strict architectural governance, you aren’t buying a scalable platform—you are buying a house of cards written by an AI that nobody on the current team can debug.”
Three ways vibe-coded systems behave in a portfolio asset
During due diligence, do not treat all AI-assisted code the same way. Categorize the target’s development practices into these three levels to map your operational exposure:
Four vibe coding risks to audit in Tech DD
While rapid deployment looks impressive on a pitch deck, PE investors must look under the hood. Audit these four risks to ensure a target's velocity doesn't become a post-close liability.
The Maintenance Black Box
When developers rely on AI to write complex logic, they often stop reading the generated code. If the original developer leaves your target company, the remaining team will struggle to refactor or debug a codebase they didn't write and do not fully comprehend.
Intellectual Property Leakage
Code generated by commercial LLMs sits in a legal gray area regarding copyright eligibility, and public AI models can ingest proprietary code if developers don't configure enterprise-grade privacy boundaries. You must audit the targets' AI policies to avoid compliance contamination.
Silent Security & Library Bloat
AI tools frequently suggest deprecated libraries, hallucinate non-existent software packages, or overlook basic secure coding practices (like input validation). This leaves the target application vulnerable to data breaches and ballooning technical debt.
Dependency Locking and Fragility
Vibe-coded systems are often built by stacking dozens of minor modules. Without human-architected "abstraction layers," a single update to an underlying API or third-party dependency can break the entire software chain, causing catastrophic downtime.
How vibe-coded assets alter your financial model
Vibe coding dramatically lowers the initial CapEx required to build a minimum viable product. On paper, this makes a target look exceptionally capital-efficient, with lean R&D teams delivering features at a breakneck pace. However, the true cost of vibe coding is back-loaded. If the underlying codebase is a chaotic tangle of AI-generated script, your post-acquisition operating expenses (OpEx) will spike as you are forced to hire expensive, senior systems architects to untangle and rewrite the software for enterprise scale.
When modeling the target’s growth, do not assume that their historical R&D efficiency will scale linearly. If the target has achieved its current scale through unmitigated vibe coding, your integration and product-scaling timelines may double. You must budget for immediate, post-close engineering investments to establish unit testing, code refactoring, and proper documentation standards.
The single signal that proves vibe coding maturity
In our due diligence engagements at idbokx, the ultimate indicator of a healthy, AI-accelerated engineering organization is Automated Test Coverage and CI/CD Guardrails.
We do not care if a target’s developers use AI to write 90% of their code—provided they have implemented a rigorous, automated testing pipeline. An elite team treats AI as a raw generator but utilizes deterministic, automated tests to validate the output. If the target’s repository has over 80% test coverage and strict static analysis tools that automatically reject insecure or non-standard AI-generated pull requests, you are buying a highly leveraged, modern software engine. If they lack automated testing, you are buying an unstable prototype.
Inheriting a codebase built on AI-accelerated code?
Our AI & Vibe Coding Tech Audit Framework maps the origin of your target’s codebase, isolates unmaintainable black-box logic, and assesses IP/security compliance—ensuring your acquisition scales predictably post-close.
