Due Diligence · 6 min read

Technical Due Diligence vs AI Due Diligence: Key Differences

Why traditional technical due diligence is not enough when evaluating AI-native companies, and what the additional AI layer covers.

By Sasan Ghorbani · Independent AI Advisor · April 22, 2026

Technical due diligence has a clear scope: evaluate the codebase, assess architecture, quantify technical debt, and determine engineering risk. It was built for software companies where the primary question is whether the technology does what it claims to do and whether it can scale.

For AI-native companies, that scope is necessary but not sufficient. The additional layer that AI introduces — the model dependencies, pricing architecture, data economics, and PMF signals unique to AI products — requires a different lens.

What technical due diligence covers

A standard technical due diligence engagement examines:

• Code quality and architecture design
• Scalability and performance under load
• Security posture and vulnerability exposure
• Technical debt and estimated remediation cost
• Development team structure and key-person risk
• IP ownership and third-party license risk
• Deployment infrastructure and DevOps maturity

These are legitimate and important questions. A company with poor code quality, unmanaged technical debt, or significant security exposure is a worse investment regardless of how compelling the AI narrative is.

Where technical due diligence falls short for AI companies

The problem is not what technical due diligence covers. It is what it does not ask.

Technical due diligence was not designed to evaluate model dependencies. It does not naturally surface the question of what happens to a business when the third-party model it is built on drops its API price by 80% — or when a competitor ships the same capability using a cheaper model. It does not assess whether the pricing architecture can survive commoditisation of the underlying AI layer. It does not evaluate whether retention metrics reflect genuine product-market fit or feature novelty.

These are the questions that determine whether an AI company is a durable business or a well-executed arbitrage on temporary model scarcity.

What AI due diligence adds

AI due diligence runs in parallel with technical due diligence and covers the territory specific to AI-native businesses:

• Model architecture and dependency risk — Is the company building on top of a single foundation model with no proprietary layer? What is the switching cost if that vendor changes pricing or deprecates the model? Does the company have any data advantage that compounds over time?
• Pricing architecture review — Does the pricing model reflect the actual cost structure of the AI layer? Is gross margin sustainable at scale, or is it temporarily inflated by underpriced compute or promotional API pricing?
• Product-market fit analysis — Are retention cohorts consistent with genuine stickiness, or are they shaped by novelty? Is expansion revenue driven by real workflow integration or by feature exploration?
• Competitive moat assessment — What does the company actually own that a well-funded competitor could not replicate in six months?
• Infrastructure cost structure — What are the true AI infrastructure costs at current scale and at 5x scale?

How they work together

Technical due diligence and AI due diligence are complementary, not competing. A company that fails technical due diligence on code quality or security is a worse investment regardless of its AI merit. A company that passes technical due diligence but fails on pricing durability or PMF signals is equally problematic — it is just harder to see.

For AI-native companies at Series A and beyond, both should be commissioned. Technical due diligence answers the engineering questions. AI due diligence answers the commercial and structural questions that determine whether the AI is a genuine business or a well-timed product.

The practical implication

When a deal team says they did technical due diligence on an AI company, the follow-up question is: who evaluated the pricing architecture, the model dependencies, and the PMF signals? If the answer is no one, the due diligence is incomplete — regardless of how thorough the code review was.

The companies most likely to disappoint AI investors are not the ones with messy codebases. They are the ones with clean codebases, compelling narratives, and commercial structures that cannot survive the next 18 months of AI commoditisation.