Passing Technical Due Diligence With an AI-Generated Codebase

Somebody is about to read your repository. Not skim it — read it, with a checklist, on behalf of someone writing a check. If most of that code came out of an AI assistant, the reasonable reaction is mild dread.

Calm down slightly. Diligence teams in 2026 are not shocked to find AI-generated code; roughly 46% of new code shipped this year is machine-written, and the reviewers know it. What they are looking for is not authorship. It is whether the thing can be operated, extended, and handed to engineers who did not build it.

That reframing matters, because it tells you what to fix in the weeks before the data room opens and what to leave alone.

What the reviewer is actually deciding

A technical diligence engagement answers four questions for the investor or acquirer, in roughly this order of weight:

1. Is there a liability hiding here? Leaked secrets, unlicensed code, exposed customer data, a compliance claim the product cannot back up.
2. Can this be maintained by someone else? If the founder or the one contractor vanishes, does the product survive?
3. Will it scale to the plan being funded? Not "is it web scale" — will it survive 10x the current traffic and the next twelve months of features?
4. What does it cost to fix? Every finding gets converted into engineer-months, and engineer-months get subtracted from the valuation or added to the plan.

Notice what is missing. Nobody is grading your code style. A reviewer who has done this fifty times knows that ugly code that ships is worth more than elegant code that does not.

The findings that actually threaten a deal

The first four are deal-shaping. The rest are price-shaping: they show up in the report as remediation cost rather than a red flag.

Secrets deserve their own note. GitGuardian counted 28.65 million new hardcoded secrets in public GitHub commits during 2025, up 34% year over year, and AI-assisted commits leaked at roughly 3.2% versus a 1.5% baseline. A diligence team will run a history scan, and finding a live production key in commit 40 tells them something about engineering discipline that no amount of architecture explanation walks back. Handle it before they look, using the approach in scanning an AI codebase for secrets.

What they look at, in the order they look

The git history first. It is the cheapest signal in the room. Commit cadence, message quality, who wrote what, whether work happens in branches with review or lands directly on main at 2am. A history of 300 commits all authored by one account saying "update" tells the reviewer there is no second engineer and no review process — which becomes a key-person risk finding.

Then the repository shape. Can they find the entry point, the data model, and the deployment configuration in five minutes? An AI-built repo often has a clean surface and a chaotic interior: three folders that all look like the API layer, two config systems, dead routes from abandoned attempts. Cleaning this up is cosmetic in effort and substantial in impression.

Then the data model. This is where the durable value sits, and where AI-generated projects are weakest. Reviewers look for tables without foreign keys, denormalized copies that drift, missing indexes on obvious query paths, and the absence of any tenant boundary. A shaky schema means the roadmap they are funding is more expensive than the deck implies.

Then security and access control. Not a pen test — a targeted look at authentication, authorization between accounts, secret handling, and what the client bundle exposes. Escape.tech found more than 2,000 vulnerabilities across 5,600 vibe-coded apps, and diligence engineers have read the same research you have.

Then operations. How does code get to production, what happens when it breaks, is anyone alerted, and can you roll back? "We push to main and hope" is a finding.

Finally, the cost story. Infrastructure and API spend per active customer, and whether it improves or degrades with scale. AI-heavy products often have per-user costs that no one has modeled, which turns a good gross margin slide into a discussion.

A four-week preparation plan

You cannot rebuild the product before diligence. You can remove the findings that cost the most and cost the least to fix.

Week 1 — stop the bleeding. Scan git history for secrets, rotate every credential you find, and move configuration to a real secret manager. Lock down anything publicly reachable that should not be. Confirm tenant isolation with a manual test: log in as account A and try, by hand, to read account B's data.

Week 2 — prove ownership. Collect signed IP assignment for every contractor and freelancer who touched the code, including anyone found in the commit history that you had forgotten about. Generate a dependency license list and resolve anything copyleft. Confirm you actually own the accounts your product runs on — domain, cloud, app stores, model providers — rather than an ex-developer's personal login.

Week 3 — make it operable. Write the README the reviewer needs: architecture in one page, how to run it locally, how it deploys, what the environment variables are, where the data lives. Set up a deploy pipeline with a rollback, even a simple one. Add tests to the three paths where a bug costs money — auth, billing, and whatever your core write operation is.

Week 4 — get an outside read. Have someone who did not build the product review it against the same checklist a diligence firm would use. It is far better to see the findings in a friendly report than in a term-sheet negotiation. This is exactly what an independent AI code audit is for, and it doubles as a preview of the investor code review itself.

How to talk about AI-generated code in the room

Do not hide it and do not apologize for it. The strong version of the answer sounds like this: we used AI tooling to reach product-market fit quickly, we know exactly which parts were generated and which were reviewed, here is the audit we commissioned, here is the remediation list with owners and dates, and here is what we deliberately deferred and why.

That answer converts an unknown into a managed known — and managed knowns do not kill deals. What kills deals is a founder who cannot explain how a core flow works because a model wrote it and nobody read it. If you are in that position, the honest fix is to close the gap before the meeting, which usually means a focused pass to fix the AI-generated code rather than a rewrite.

Be equally straight about known technical debt. A prioritized, costed debt list reads as engineering maturity. A claim that there is none reads as a founder who has not looked.

Frequently asked questions

Will AI-generated code fail technical due diligence? Not by itself. Reviewers care whether the system is secure, operable, and maintainable by new engineers — not who typed it. What fails diligence is unmanaged risk: live secrets, no tenant isolation, unclear IP ownership, and a founder who cannot explain how the product works. All of those are fixable before anyone looks.

How long does technical due diligence take? For an early-stage round, typically one to three weeks of reviewer time, often including a code walkthrough, an architecture session, and interviews with whoever built it. Larger acquisitions run longer and go deeper on compliance and unit economics. Your preparation window is whatever gap exists between term sheet and data room, which is usually shorter than you would like.

Should I rewrite the code before diligence? Almost never. A rewrite in progress is a worse signal than working code with a documented remediation plan, because it means the thing being valued does not exist yet. Fix the security and ownership issues, document the architecture, and present the rest as a costed plan.

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If a round or an acquisition is approaching and nobody outside your team has read the code, the diligence report will be the first honest review it gets. SprintX runs pre-diligence audits on AI-built products and remediates what we find, so the buyer's engineer sees a managed system instead of a surprise. Send us your repo before they ask for it.