Technical Due Diligence

25% of Y Combinator's W25 batch have codebases that are 95% AI-generated. TechCrunch: 25% of YC W25 have 95%+ AI-generated codebases What counted as a competitive edge a year ago is now a risk factor in technical due diligence (TDD): AI-generated code contains security vulnerabilities in 45% of cases, is rarely tested, and builds up technical debt that can mean as much as a 20% discount at valuation. For CTOs and investors evaluating AI-built platforms, a new set of review criteria applies.

45%

of AI-generated code tasks contain security vulnerabilities (Veracode 2025)

40%

of agentic-AI projects will be scrapped by the end of 2027, per Gartner

40%

more exposed secrets in repositories using GitHub Copilot (6.4% vs. 4.6%)

Up to 20%

valuation discount from technical findings in due diligence

Why the pace of AI is changing technical due diligence

The speed at which AI tools turn out working code has created a new problem: platforms look convincing in the demo but are technically fragile. YC CEO Garry Tan captured the trend in a single line: "The age of vibe coding is here." Garry Tan: The age of vibe coding is here (2025) A TDD audit surfaces exactly the shortcuts a demo never reveals: missing tests, exposed secrets, inconsistent architecture.

Gartner forecasts that more than 40% of all agentic-AI projects will be scrapped by the end of 2027 – driven by escalating costs, unclear business value, or inadequate risk controls. Gartner: 40%+ Agentic AI Projects Canceled by 2027 That is why investors now scrutinize AI-built platforms more closely: test coverage, secrets hygiene, and architectural consistency, before the term sheet hits the table.

Red flags: what stands out in AI-generated codebases

No test coverage

AI tools generate functional code, but almost never the tests to go with it. A platform without unit tests, integration tests, and E2E tests is flying blind – no investor accepts that as production-ready. In due diligence, test coverage below 60% is an immediate red flag.

Hardcoded secrets and API keys

In 2025, more than 28.6 million new hardcoded secrets were discovered in public GitHub commits – a 34% increase year over year. OECD.AI: AI Coding Assistants Drive Surge in Secret Leaks Repositories with GitHub Copilot active show a roughly 40% higher rate of exposed secrets (6.4% vs. 4.6% of all public repositories): API keys, database passwords, and tokens sitting directly in the source code. GitGuardian: GitHub Copilot and the risk of leaked secrets

Missing error handling

AI-generated code focuses on the happy path. Edge cases, error handling, retry logic, and graceful degradation are systematically absent – which leads to cascading failures in production.

Inconsistent architecture

Dependency bloat

AI models add dependencies without checking what is already in place. The result is dozens of redundant packages and outdated versions piling up – a bloated supply chain that widens the attack surface.

No CI/CD pipeline

Without continuous integration, there are no automated checks – no lint enforcement, no security scans, no automated deployments. Every release is a manual risk.

Missing access control

AI agents routinely forget to wire authentication middleware into downstream components. The result: individual routes or API endpoints are left unprotected, even when a login page exists. The Hacker News: AI Code Expands Attack Surface (2026)

The valuation impact: as much as a 20% discount

Technical debt feeds straight into company valuation. Technical findings in due diligence can knock as much as 20% off the valuation, and roughly 60% of deals fall through over problems that only surface in the tech review. Sphere: Technical due diligence can cut valuation by up to 20%

Kriterium	Production-Ready Codebase	Vibe-Coded Platform
Test coverage	> 70% with CI enforcement	0–5%, no automated tests
Security scans	SAST/DAST integrated into CI/CD	No scans, no audits
Secrets management	Vault/ENV, no secrets in code	Hardcoded API keys in .env files
Architecture	Consistent patterns, documented	Incoherent, generated file by file
Dependency management	Automated updates, SBOM	Outdated packages, no tracking
Error handling	Retry logic, circuit breaker, logging	None – happy path only
Valuation impact	Full valuation	Up to 20% discount

A worked example: three additional senior engineers over twelve months, at a fully loaded headcount cost of €110,000–180,000 per person, amount to roughly €330,000–540,000 in extra spend that investors deduct from the valuation.

What a TDD report must contain

A professional technical due diligence report for AI-built platforms covers:

Architecture assessment: System architecture, data model, API design, consistency of patterns
Code quality analysis: Automated scans (SonarQube, Snyk), technical debt quantified
Security audit: SAST/DAST results, secrets scan, dependency vulnerabilities, OWASP Top 10
Infrastructure review: Deployment process, monitoring, backup strategy, scalability
Team assessment: Technical capability, documentation, development processes
Compliance check: GDPR, NIS2, data residency, supply chain security
Risk assessment: A prioritized list with estimated remediation effort and timeline

Cleveroad: Technical Due Diligence Key Elements 2025

How to make your codebase audit-ready

Quick wins (weeks 1–2):

Run a secrets scan and strip out every hardcoded credential
Dependency audit: run npm audit / yarn audit and apply critical updates
Stand up a basic CI/CD pipeline with linting and security checks

Short term (months 1–2):

Build out a test suite: cover critical business logic at minimum
Produce architecture documentation
Implement monitoring and logging (Grafana + Prometheus)

Medium term (months 2–4):

Security hardening: RBAC, MFA, rate limiting
Infrastructure as code (Terraform) for reproducible deployments
Establish a performance baseline and run load tests

In a free intro conversation, we assess your AI-built platform for TDD readiness – from security findings to architectural weaknesses. We deliver the full build at a fixed price of €36,000: a working app on EU-sovereign infrastructure.

Frequently asked questions

What is a technical due diligence?

A systematic review of the entire tech stack – architecture, code quality, security, infrastructure, and team capability. Investors increasingly bring in external TDD reviewers to assess technical health ahead of a funding round.

How does technical debt affect valuation?

Technical findings in due diligence can knock as much as 20% off the valuation, and roughly 60% of deals fall through over problems that only surface in the tech review. A 12-month backlog that takes three additional senior engineers to clear quickly adds up to several hundred thousand euros.

What are the most common red flags in AI-generated codebases?

No test coverage, hardcoded secrets and API keys, missing error handling, inconsistent architecture, dependency bloat, no CI/CD pipeline, and missing access control.

When should I make my codebase audit-ready?

Months before the funding round. Around 30% of failed M&A deals collapse over technology integration problems, often a consequence of unvetted tech stacks. Quick wins (secrets scan, dependency audit) take 1–2 weeks.

Is Gartner's forecast on AI project failure taken seriously?

Yes. Gartner expects more than 40% of agentic-AI projects to be scrapped by the end of 2027. For due diligence, the implication is clear: investors are scrutinizing more closely.

Can AnvilStack help with TDD preparation?

Yes. The platform assessment is free and evaluates your codebase for TDD readiness – from security findings to architectural weaknesses. We deliver the full build to a verifiable outcome at a fixed price of €36,000.