Vibe Coding Due Diligence: What Investors Will Find
Vibe coding can produce a working product at remarkable speed. It can also produce three authentication systems, a database nobody can explain and a production environment held together by an API key copied into a settings file at 1.17am.
Investors are unlikely to object because AI helped write the code. They will object if nobody can show what the product owns, how it works, where customer data goes, what fails under load and how the company would recover without asking the original prompt to remember what it meant.
That distinction matters as AI-assisted development becomes ordinary practice. Stack Overflow's developer trust analysis reported that 84 per cent of respondents used or planned to use AI tools in 2025, while only 29 per cent trusted them. Investors face the same sensible tension: speed is attractive, but unverified speed is not yet an asset.
Due diligence is evidence, not a code-style referendum
A technical diligence review is not a contest to identify which lines were written by a person. It asks whether the software can support the claims being made about the business.
In July 2026, NIST published a cybersecurity due-diligence quick-start guide built around provenance, resilience, foundational cyber practices, ownership and supply-chain visibility. It is written for ICT supplier assessment, but the questions travel rather well.
Who built the product? Which components and services does it depend on? Can the team maintain them? Are secure development practices visible? Does the system recover when a supplier, credential or deployment fails? Those questions separate a promising demonstration from something an investor can reasonably value.
1. Who understands the architecture?
A founder does not need to recite every function. Somebody does need to explain the applications, data stores, external services, identity boundaries, environments and critical user journeys.
Vibe-coded products often grow through local requests. Add payments. Add teams. Fix login. Make it mobile. Each instruction can succeed while the architecture becomes less coherent. Reviewers will trace journeys end to end: registration, authorisation, payment, file storage, notifications and account deletion. A credible team can answer with a current diagram, named owners and the relevant evidence.
2. Are authentication and data access correct?
Authentication that appears to work is not the same as access control that is correct. Reviewers will look for server-side authorisation, tenant isolation, role boundaries, session handling, password-reset behaviour, secrets management and controls around privileged operations.
They will also follow the data. What sensitive information is collected? Why is it needed? Which processors receive it? How is it encrypted? How long is it retained? Can the company export or delete it accurately?
These checks deserve independent tests, particularly when the same AI tool wrote the feature and its reassuring test suite. The OWASP Secure Coding with AI guidance recommends manually written security-critical tests for authentication, authorisation, input validation and cryptography, plus adversarial testing and independent analysis. The author of the answer should not be the only examiner.
3. Are recurring AI-built failure patterns present?
Recent research into the security of vibe-coded applications identified recurring problems including placeholder logic, unfiltered input and exposed secrets. The researchers connected them to context loss, locally optimised changes and incomplete security knowledge.
A diligence review tests the actual product. Are mock checks still present? Can untrusted input reach a database query, file path or command? Are secrets committed to history? Do rate limits exist? Are dependencies maintained? Does error handling fail closed?
Newer models and better prompts may reduce defects. They do not remove the need to establish what is in this codebase. A buyer acquires the repository, systems, contracts and incidents in front of it, not the average improvement curve of a model.
4. Is there a controlled route to production?
Useful evidence includes protected repositories, reviewed changes, automated tests for critical journeys, dependency and secret scanning, separate environments, repeatable deployments, monitoring and a tested rollback path.
GitHub's June 2026 release of security validation for third-party coding agents is instructive. Agent-created changes can be checked with CodeQL, dependency analysis and secret scanning before a pull request is finalised. The point is not that one platform has solved software security. It is that AI-generated code is routed through ordinary engineering controls rather than granted diplomatic immunity because it arrived quickly.
5. Does the product survive real customers?
A successful demo proves the happy path for one user with cooperative data. Due diligence asks what happens when 10,000 users arrive, a payment webhook is delivered twice, an API slows down, a job fails halfway through or a customer uploads something unexpectedly enormous.
Reviewers may examine load tests, database indexes, retries, idempotency, caching, cost controls and the ability to scale without manually editing production. They will look at alerts and incident history. They may ask the team to restore a backup, not merely point at the screen where backups are said to exist.
6. Does the company own and understand its dependencies?
Investors need to know that the company owns or is licensed to use the source code, assets, datasets, packages, generated content and contractor work it is selling.
Prepare an inventory of material dependencies and licences. Record which AI coding services were used, what repository or data access they received, and which vendors would be difficult to replace.
Snyk research based on nearly 10,000 developer environments argues that coding agents, tools, instructions, integrations and permissions have become part of the software development supply chain. If an agent can install packages, run commands, read secrets and push changes, its configuration is not merely a preference. It is a production risk with a cheerful autocomplete box.
7. What will the maintenance burden cost?
Technical debt is not automatically fatal. The concern is debt that is unknown, concentrated in critical systems or impossible for the current team to manage.
Reviewers will look for duplicated logic, oversized modules, weak coverage, abandoned dependencies, fragile migrations, undocumented integrations and areas only one person understands. Findings may affect valuation, deal structure, hiring and the first hundred days after investment. An honest remediation backlog with owners and estimates is usually more valuable than concealed uncertainty.
Build the evidence pack before the data room opens
Founders should assemble a one-page architecture and data-flow diagram; repository, environment and service inventory; security findings; critical-journey test results; deployment and rollback evidence; backup and restore results; dependency and licence register; incident history; and a prioritised remediation plan.
Then test the narrative against the product. Create a fresh environment. Restore a backup. Rotate a secret. Remove a user's access. Trace a payment. Reconcile a failed job. Let somebody who did not build the system explain it.
What investors want to find
Investors want evidence that the product does what customers are paying for, that serious risks are known, and that the team can improve the system without a ceremonial rewrite after every feature.
Vibe coding can be a legitimate route to product discovery and early delivery. It becomes an investment problem when speed has replaced architecture, generated tests have replaced scrutiny and nobody can distinguish a working feature from a temporary truce.
If your AI-built product is approaching investment but its architecture, security evidence and remediation priorities still live in separate conversations, show Venturist what you have built. A focused technical due-diligence audit can turn an impressive prototype into an evidence-backed plan before an investor starts asking the same questions.