Nearly half of all new code shipped this year was written by AI. A quarter of a recent Y Combinator batch launched with codebases that were 95% AI-generated. The first wave of those businesses is now reaching acquisition conversations, and buyers have adjusted faster than most founders realize.
Bain’s 2026 M&A report found that one in five strategic dealmakers walked away from a deal because of AI-related risk in the target’s business. FE International estimates that technical and compliance risks tied to AI can shave 15 to 30% off a valuation multiple. Technical diligence on AI-assisted codebases has become its own workstream, with its own specialists and its own line in the price.
None of this disqualifies you. But the founders who understand what buyers now check will keep their multiple. The ones who don’t will watch it get negotiated down line by line.
Why buyers suddenly care how your code was written
AI made it possible to ship a working product without anyone on the team fully understanding how it works. That’s a new category of risk, and the early data explains the caution. OX Security found that 62% of AI-generated code ships with at least one vulnerability. GitGuardian’s 2026 report found AI-assisted commits leak hardcoded credentials at roughly twice the rate of ordinary commits. Even developers themselves have grown warier: trust in AI-generated output fell from 77% in 2023 to around 60% today, even as adoption became near universal.
Buyers aren’t reacting to a trend. They’re reacting to what their technical teams keep finding under the hood.
What the diligence workstream actually inspects
Beyond the usual code review, expect a buyer’s technical team to dig into five areas.
Provenance. Your commit history tells a story. Ten thousand lines landing in a single commit at 2am reads very differently from steady, reviewed pull requests. Buyers increasingly ask you to estimate what share of the codebase was AI-generated and, more importantly, what share was reviewed by a human who understood it.
Security posture. Automated scans for hardcoded secrets, exposed endpoints, and broken authentication logic come first. AI-generated code fails these scans at above-average rates, so anything touching payments, logins, or personal data gets extra scrutiny.
Dependency and license hygiene. AI tools pull in packages liberally, and occasionally reproduce code under restrictive open-source licenses. License contamination is a classic deal-delayer because it clouds the IP the buyer is actually purchasing.
IP ownership. Purchase agreements include representations that you own your code. AI-generated code complicates that in ways most founders haven’t thought about: the tools’ terms of service, whether output can carry copyright at all, and whether contractors used AI tools under accounts you don’t control. Expect AI usage disclosures to appear in your reps and warranties.
Operability. Can your team debug production without asking the AI? Buyers will ask about past incidents, monitoring coverage, and mean time to resolution. Fast shipping is worthless to an acquirer if nobody can trace a failure to its cause after you leave.
The questions behind the questions
All five checks reduce to one thing: can the buyer quantify the risk? A codebase that’s 80% AI-generated but reviewed, tested, and documented is quantifiable. A codebase that’s 30% AI-generated with no record of what was checked is not. Unquantifiable risk gets priced as a discount, or as a walked deal.
That’s why “we don’t really know” is the most expensive answer in diligence. Not because it reveals a technical problem, but because it reveals that the founder can’t tell the buyer where the problems are.
How to close the gap before you sell
The gap between what your product does and what your team can explain is fixable, and fixing it is the highest-leverage exit preparation available to a technical founder right now. Four moves matter most.
Get a security review on every path that touches money or personal data, and fix what it finds. Document your architecture at the level a new senior engineer would need to get productive in a week. Run a license scan across your dependencies and generated code. And keep an honest internal record of what was AI-generated, what was reviewed, and what was rewritten, because that record is exactly what a buyer’s technical team will try to reconstruct without you.
None of this is glamorous. All of it is cheaper than a 20% haircut on your exit.
How we look at it
At saas.group, we’re seeing AI-assisted codebases in a growing share of the businesses we evaluate, and our position is the same one we take on financials: we’re not looking for perfection, we’re looking for clarity. A founder who says “these modules were vibe-coded early on, here’s what we rewrote and here’s what still needs work” builds more trust than one with a polished demo and vague answers.
We’ve said before that clean businesses close faster. Code is now part of clean. If your codebase grew faster than your understanding of it, start closing that gap now, ideally 12 months before you want to be in a process.
And if you want a straight answer on how your business would hold up in diligence, AI-generated code and all, talk to our M&A team: Pavel Prokofiev, ACA (pavel@saas.group).
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