AI is now not only a instrument that helps builders write particular person items of code. In lots of startups, it now contributes to a major a part of the product-from backend logic and interfaces to integrations and infrastructure choices. For buyers, this modifications the best way technical due diligence ought to be approached.
Conventional due diligence nonetheless appears to be like at structure, testing, documentation, technical debt, and safety. However with an AI-generated codebase, that’s not sufficient.
It is usually necessary to find out whether or not the product may be independently understood, maintained, deployed, and prolonged by an engineering workforce. The evaluation ought to determine hidden dependencies, undocumented assumptions, architectural weaknesses, and platform-specific elements that might improve the fee or complexity of future growth and scaling.
That’s the reason technical due diligence for AI startups ought to assess greater than code high quality alone. It ought to present whether or not the codebase is comprehensible to the workforce, beneath management, legally sound, and prepared for additional progress. These are sometimes the areas the place dangers stay hidden from a typical technical overview.
Why Vibe-Coded Codebases Want a Completely different Form of Due Diligence
Conventional technical due diligence normally begins with acquainted areas: structure, code high quality, check protection, documentation, infrastructure, and technical debt. All of those nonetheless matter for merchandise constructed with in depth use of AI. The issue is that they’ll present the state of the system with out revealing how effectively the workforce really understands and controls it.
In a vibe-coded startup, some architectural choices could have emerged immediately from conversations with an AI instrument. The code works, however nobody documented why a selected strategy was chosen, which alternate options had been thought-about, or what limitations had been launched alongside the best way. In some instances, the workforce can keep the product solely so long as the one who remembers the context behind these choices stays concerned. For an investor, that’s now not only a code high quality difficulty – it’s a enterprise threat tied to undocumented information held by one or two folks.
One other necessary query is whether or not the product that was really constructed corresponds to the product the founder, buyer, or investor meant to create. Vibe-coded purposes can look surprisingly full in a demo, with polished interfaces, a number of screens, and a protracted checklist of seemingly completed options. However visible completeness doesn’t essentially imply useful readiness.
As soon as reviewers stroll via actual end-to-end situations, they might uncover that customers have no idea what to do subsequent, the UI doesn’t mirror the precise enterprise course of, web page names and interface parts don’t match the terminology utilized by the enterprise, or particular person options exist with out being linked right into a coherent workflow. Vital end-to-end use instances could also be lacking solely. In different phrases, the appliance could exhibit options with out supporting the precise person journey.
Regulation provides one other layer. The usage of vibe coding doesn’t mechanically make a product a high-risk AI system. Nevertheless, Article 6 of the EU AI Act classifies sure AI methods as high-risk relying on their meant goal and space of use, together with methods that fall beneath Annex I or Annex III. In such instances, further necessities could apply to threat administration, documentation, testing, and conformity evaluation.
The price of missed points may also be vital. In line with figures cited by EDUCBA, technical debt can improve post-acquisition prices by 30-50%, whereas greater than 60% of surveyed M&A professionals reported that expertise points missed throughout diligence had a cloth impression on the result of a deal.
That’s the reason reviewing a vibe-coded product ought to reply not solely “Does the code work immediately?” but in addition the extra necessary query: “Does the corporate perceive what it owns, and may it develop it safely tomorrow?”
What Buyers Really Have to Verify
When reviewing a vibe-coded startup, buyers have to look past whether or not the product works and whether or not the workforce can ship options shortly. The important thing query is how resilient the enterprise is when you take away the present tempo of AI-assisted growth from the equation and deal with the codebase as an asset that should stay viable for years.
Code Possession and IP
The very first thing to confirm is who legally owns the code and beneath what phrases it was created. If the workforce used third-party AI providers, coding assistants, app mills, or prebuilt elements, buyers ought to overview their licensing phrases, business – use restrictions, and the origin of crucial elements of the codebase. A working product doesn’t mechanically imply that possession rights are clear for each part it depends on.
Structure and Scalability
The subsequent query is what occurs if site visitors, buyer numbers, or knowledge volumes improve tenfold. AI instruments may be efficient at constructing an MVP shortly, however they usually optimize for the fast job slightly than long-term structure. Buyers want to know whether or not the prevailing system can scale incrementally or whether or not the following funding spherical will successfully finance an costly rebuild.
Safety Vulnerabilities
AI-generated code ought to be reviewed particularly for widespread safety points: API keys and secrets and techniques uncovered in client-side code, inadequate enter validation, weak authorization controls, unsecured endpoints, unverified webhooks, and outdated dependencies. Probably the most harmful issues are sometimes present in code that appears appropriate and passes primary assessments however fails to account for real-world assault situations.
Documentation and Group Data
A robust overview ought to assess not solely whether or not documentation exists, however whether or not the workforce really understands the system. What occurs if the one who constructed many of the product via conversations with an AI instrument leaves tomorrow? Can different engineers perceive crucial flows, deploy the system, resolve incidents, and safely modify core enterprise logic? If important information exists solely in a single individual’s reminiscence or in previous immediate histories, that turns into an operational threat.
Compliance
For fintech, healthtech, insurtech, and different regulated industries, compliance with knowledge safety, safety, auditability, and AI-related necessities ought to be assessed individually. Relying on the product’s goal and market, laws reminiscent of GDPR, industry-specific guidelines, or the EU AI Act could apply.
That is why technical debt due diligence buyers shouldn’t be handled merely as a seek for “dangerous code.” It’s an evaluation of future obligations: how a lot remediation could also be required after the deal, how predictable these prices are, and whether or not hidden technical points may materially change the economics of the funding.
What Buyers Actually Have to Verify: A Sensible Guidelines
AI-Generated Code Due Diligence: A Sensible Guidelines
If the earlier stage of due diligence identifies the place the primary dangers are, this stage ought to present confirm them in observe. Sturdy AI-generated code due diligence ought to rely not on the workforce’s assurances, however on verifiable proof: repositories, change historical past, dependencies, assessments, documentation, and deployment processes.
1. Set up What You Really Personal
The primary query is just not how the code was created, however whether or not you even have entry to the software program you’re shopping for.
Decide whether or not the appliance has a whole and accessible codebase or stays depending on a closed AI/no-code platform. Verify whether or not the supply code may be exported, whether or not it may be constructed and deployed independently, and whether or not the appliance is dependent upon proprietary platform providers.
Additionally determine the place the database, person accounts, recordsdata, authentication, integrations, and different crucial elements are hosted and who controls them.
A product that works immediately however can’t be independently developed, deployed, or migrated could carry vital platform and vendor lock-in threat.
2. Construct a Dependency Stock
Evaluate not solely third-party libraries and packages, but in addition the exterior providers the appliance is dependent upon. Determine the databases, authentication suppliers, file storage, e mail providers, cost suppliers, e-signature platforms, APIs, and different SaaS elements utilized by the product.
For every crucial dependency, decide why it’s getting used, which elements of the appliance depend upon it, whether or not it’s acceptable for the product’s necessities, and what would occur if the service had to get replaced.
Additionally overview package deal variations, licenses, deserted libraries, recognized vulnerabilities, and dependencies that will have been launched with out correct analysis. AI-generated purposes can accumulate each pointless libraries and exterior providers just because they had been handy throughout growth.
The purpose is to know not simply what the appliance is dependent upon, however whether or not these dependencies are intentional, acceptable, replaceable, and sustainable because the product grows.
3. Run Secrets and techniques and Safety Scans
Repositories, frontend bundles, and configuration recordsdata ought to be checked for uncovered API keys, tokens, credentials, and insecure settings. Authentication, authorization, webhooks, enter validation, and public endpoints also needs to be examined individually.
4. Validate the Checks in Apply
Having a assessments folder proves little or no by itself. The check suite ought to be executed, crucial flows ought to be reviewed for protection, and the assessments ought to be checked to make sure they’ll really detect failures slightly than merely create the looks of protection.
5. Analyze the Precise System Structure
Don’t depend on the founder or product proprietor to elucidate how the system works. In lots of vibe-coded startups, the one who created the appliance could have little or no software program engineering background and should not know the way the underlying structure was carried out.
As an alternative, assess the structure immediately from the accessible supply code, configuration, infrastructure, database, integrations, and deployed software. Map the primary elements, knowledge flows, enterprise logic, exterior providers, and dependencies to find out how the system really works.
The purpose is to determine architectural weaknesses, tightly coupled elements, pointless complexity, single factors of failure, and dependencies that might make the product tough to keep up, lengthen, or scale.
6. Confirm Deployment and Operational Readiness
Assess how the appliance is definitely constructed, configured, deployed, and operated. Decide whether or not the accessible supply code and infrastructure configuration are enough to deploy the appliance outdoors the unique AI Builder or growth setting.
Verify the construct and deployment course of, setting configuration, secrets and techniques, databases, file storage, exterior providers, area configuration, and different infrastructure elements required to run the product.
The important thing query is whether or not the appliance may be reliably deployed and operated within the setting required by the enterprise – and what further engineering work could be wanted to realize that.
7. Outline a Remediation Plan
The ultimate output of AI-generated code due diligence shouldn’t be an inventory of points. It ought to be a transparent motion plan: what have to be mounted earlier than the deal, what ought to be addressed earlier than scaling, and what can stay as acceptable technical debt. That is what helps buyers estimate not solely the standard of the present product, however the true price of creating it additional.
AI-Generated Code Due Diligence: A Sensible Guidelines
The M&A Angle: Code Audits Earlier than Acquisition
When buying a startup, technical dangers cease being an inside difficulty for the workforce – as soon as the deal closes, they turn into the client’s duty. That’s the reason an impartial codebase overview is particularly necessary earlier than signing, whereas there may be nonetheless time to know the true scope of future obligations and issue them into the corporate’s valuation.
With an AI-generated product, the duty turns into extra advanced. It isn’t sufficient for a purchaser to verify that the appliance works and serves present prospects efficiently. The overview ought to independently assess whether or not the structure is maintainable, whether or not there are crucial safety points, whether or not the logic of key modules is comprehensible, whether or not dependencies are correctly managed, and whether or not a brand new or expanded engineering workforce can proceed creating the system with out relying always on its authentic creators.
In observe, an M&A code audit AI-generated codebase ought to separate points that may be mounted via routine refactoring from dangers that might materially have an effect on post-acquisition integration prices. One set of findings could imply a couple of weeks of deliberate engineering work; one other could level to the necessity to change a core part or partially redesign the structure.
An impartial code audit offers patrons and buyers an exterior evaluation from specialists who aren’t beneath stress to current the product in the very best mild earlier than the deal closes.
The purpose of the audit is to not uncover as many flaws as doable. It’s to offer either side an goal technical image: which dangers are actually vital, how a lot effort remediation could require, and which points ought to be taken under consideration earlier than the acquisition is accomplished.
How SCAND Approaches Due Diligence for Vibe-Coded Startups
At SCAND, we don’t deal with a vibe-coded product as a technical mistake that mechanically must be mounted by rewriting it from scratch. For a startup, AI instruments are sometimes a rational strategy to validate an thought sooner, launch an MVP, and get to market. What issues extra to us is whether or not the system is prepared for the corporate’s subsequent stage of progress.
SCAND has greater than 25 years of expertise in software program growth, modernization, and code auditing, so we consider AI-generated code within the context of your entire system slightly than in isolation. Our specialists can assess structure, backend and frontend, infrastructure, safety, dependencies, code high quality, and growth processes as a complete. That is particularly necessary in due diligence, the place a technical difficulty issues most when it creates monetary, operational, or scalability dangers for the enterprise.
That have additionally helps us distinguish actually crucial limitations from peculiar technical debt that may be addressed regularly. As an alternative of defaulting to “rewrite all the things,” the SCAND workforce identifies what may be preserved, what ought to be improved via refactoring, and which elements could genuinely should be changed.
For founders who wish to convey an AI-built product to a extra mature engineering state earlier than a funding spherical or the following stage of progress, SCAND additionally provides Submit-Vibe-Coding Improvement – from technical evaluation and codebase stabilization to additional growth and scaling.
Conclusion
AI-generated code is just not a purple flag by itself. The actual query is whether or not the corporate understands what it has constructed, can keep it with out counting on a couple of people, and is aware of which technical dangers may have an effect on progress, funding, or acquisition. For founders, due diligence is a chance to determine these points earlier than they turn into a part of the negotiation. For buyers and patrons, it supplies a clearer image of the product behind the demo.
Steadily Requested Questions (FAQs)
Is AI-generated code mechanically a purple flag for buyers?
No. Buyers are normally extra involved with maintainability, safety, possession, scalability, and workforce information than with how the code was produced. AI-generated code turns into a threat when the corporate can’t clarify, help, or safely evolve the system.
How is due diligence completely different for a vibe-coded startup vs. a historically constructed one?
The core checks stay comparable, however vibe-coded merchandise require additional consideration to code provenance, undocumented AI-driven choices, dependency dangers, safety gaps, and information focus. Reviewers additionally have to confirm whether or not the workforce can keep the system with out counting on one individual or instrument.
Does the EU AI Act apply to vibe-coded merchandise?
Not just because a product was constructed with AI instruments. The EU AI Act applies primarily based on the AI system’s goal, performance, and threat class. Some merchandise could fall beneath high-risk necessities, whereas many others won’t.
Can a startup put together for technical due diligence earlier than buyers ask?
Sure. Founders can overview structure, safety, dependencies, documentation, check protection, IP possession, and deployment processes upfront. An impartial audit may assist determine points early and prioritize remediation earlier than they turn into a part of an funding or acquisition dialogue.
What’s the distinction between a code audit and technical due diligence?
A code audit focuses totally on the technical situation of the codebase, together with high quality, safety, structure, and maintainability. Technical due diligence is broader and connects these findings to enterprise dangers reminiscent of scalability, workforce dependency, compliance, future prices, and transaction readiness.
