AI is not only a device 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 traders, this modifications the best way technical due diligence ought to be approached.
Conventional due diligence nonetheless seems at structure, testing, documentation, technical debt, and safety. However with an AI-generated codebase, that’s not sufficient.
It is usually essential to find out whether or not the product will be independently understood, maintained, deployed, and prolonged by an engineering group. The evaluation ought to determine hidden dependencies, undocumented assumptions, architectural weaknesses, and platform-specific parts that might enhance the fee or complexity of future improvement 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 group, beneath management, legally sound, and prepared for additional progress. These are sometimes the areas the place dangers stay hidden from a normal technical evaluate.
Why Vibe-Coded Codebases Want a Totally different Sort 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 intensive use of AI. The issue is that they’ll present the state of the system with out revealing how properly the group truly understands and controls it.
In a vibe-coded startup, some architectural choices could have emerged instantly from conversations with an AI device. The code works, however nobody documented why a specific strategy was chosen, which alternate options have been thought-about, or what limitations have been launched alongside the best way. In some instances, the group can keep the product solely so long as the one who remembers the context behind these choices stays concerned. For an investor, that’s not only a code high quality situation – it’s a enterprise danger tied to undocumented data held by one or two individuals.
One other essential query is whether or not the product that was truly constructed corresponds to the product the founder, buyer, or investor meant to create. Vibe-coded functions can look surprisingly full in a demo, with polished interfaces, a number of screens, and an extended record of seemingly completed options. However visible completeness doesn’t essentially imply practical readiness.
As soon as reviewers stroll by way of actual end-to-end eventualities, they might uncover that customers have no idea what to do subsequent, the UI doesn’t replicate the precise enterprise course of, web page names and interface components don’t match the terminology utilized by the enterprise, or particular person options exist with out being related right into a coherent workflow. Important end-to-end use instances could also be lacking solely. In different phrases, the applying could show options with out supporting the precise person journey.
Regulation provides one other layer. Using vibe coding doesn’t robotically make a product a high-risk AI system. Nonetheless, Article 6 of the EU AI Act classifies sure AI techniques as high-risk relying on their meant objective and space of use, together with techniques that fall beneath Annex I or Annex III. In such instances, extra necessities could apply to danger administration, documentation, testing, and conformity evaluation.
The price of missed points will also be important. In response to figures cited by EDUCBA, technical debt can enhance post-acquisition prices by 30-50%, whereas greater than 60% of surveyed M&A professionals reported that know-how points missed throughout diligence had a cloth influence on the end result of a deal.
That’s the reason reviewing a vibe-coded product ought to reply not solely “Does the code work as we speak?” but additionally the extra essential query: “Does the corporate perceive what it owns, and might it develop it safely tomorrow?”
What Traders Truly Have to Test
When reviewing a vibe-coded startup, traders have to look past whether or not the product works and whether or not the group can ship options shortly. The important thing query is how resilient the enterprise is when you take away the present tempo of AI-assisted improvement 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 group used third-party AI companies, coding assistants, app turbines, or prebuilt parts, traders ought to evaluate their licensing phrases, industrial – use restrictions, and the origin of vital elements of the codebase. A working product doesn’t robotically imply that possession rights are clear for each part it depends on.
Structure and Scalability
The following query is what occurs if site visitors, buyer numbers, or knowledge volumes enhance tenfold. AI instruments will be efficient at constructing an MVP shortly, however they typically optimize for the instant activity somewhat than long-term structure. Traders want to grasp 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. Essentially the most harmful issues are sometimes present in code that appears right and passes primary checks however fails to account for real-world assault eventualities.
Documentation and Group Data
A robust evaluate ought to assess not solely whether or not documentation exists, however whether or not the group truly understands the system. What occurs if the one who constructed many of the product by way of conversations with an AI device leaves tomorrow? Can different engineers perceive vital flows, deploy the system, resolve incidents, and safely modify core enterprise logic? If important data exists solely in a single particular person’s reminiscence or in previous immediate histories, that turns into an operational danger.
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 objective and market, laws reminiscent of GDPR, industry-specific guidelines, or the EU AI Act could apply.
That is why technical debt due diligence traders 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 might materially change the economics of the funding.
What Traders Actually Have to Test: A Sensible Guidelines
AI-Generated Code Due Diligence: A Sensible Guidelines
If the earlier stage of due diligence identifies the place the principle dangers are, this stage ought to present methods to confirm them in observe. Robust AI-generated code due diligence ought to rely not on the group’s assurances, however on verifiable proof: repositories, change historical past, dependencies, checks, documentation, and deployment processes.
1. Set up What You Truly Personal
The primary query will not be 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 applying has a whole and accessible codebase or stays depending on a closed AI/no-code platform. Test whether or not the supply code will be exported, whether or not it may be constructed and deployed independently, and whether or not the applying will depend on proprietary platform companies.
Additionally determine the place the database, person accounts, information, authentication, integrations, and different vital parts are hosted and who controls them.
A product that works as we speak however can’t be independently developed, deployed, or migrated could carry important platform and vendor lock-in danger.
2. Construct a Dependency Stock
Evaluate not solely third-party libraries and packages, but additionally the exterior companies the applying will depend on. Establish the databases, authentication suppliers, file storage, electronic mail companies, cost suppliers, e-signature platforms, APIs, and different SaaS parts utilized by the product.
For every vital dependency, decide why it’s getting used, which elements of the applying 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 evaluate package deal variations, licenses, deserted libraries, recognized vulnerabilities, and dependencies which will have been launched with out correct analysis. AI-generated functions can accumulate each pointless libraries and exterior companies just because they have been handy throughout improvement.
The purpose is to grasp not simply what the applying will depend on, 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 information 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 Exams in Follow
Having a checks folder proves little or no by itself. The check suite ought to be executed, vital flows ought to be reviewed for protection, and the checks ought to be checked to make sure they’ll truly detect failures somewhat 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 applying could have little or no software program engineering background and will not understand how the underlying structure was carried out.
As a substitute, assess the structure instantly from the out there supply code, configuration, infrastructure, database, integrations, and deployed utility. Map the principle parts, knowledge flows, enterprise logic, exterior companies, and dependencies to find out how the system truly works.
The purpose is to determine architectural weaknesses, tightly coupled parts, 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 applying is definitely constructed, configured, deployed, and operated. Decide whether or not the out there supply code and infrastructure configuration are ample to deploy the applying exterior the unique AI Builder or improvement setting.
Test the construct and deployment course of, setting configuration, secrets and techniques, databases, file storage, exterior companies, area configuration, and different infrastructure parts required to run the product.
The important thing query is whether or not the applying will be reliably deployed and operated within the setting required by the enterprise – and what extra engineering work can be wanted to attain 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 fastened earlier than the deal, what ought to be addressed earlier than scaling, and what can stay as acceptable technical debt. That is what helps traders estimate not solely the standard of the present product, however the true value 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 situation for the group – as soon as the deal closes, they turn into the client’s accountability. That’s the reason an impartial codebase evaluate is particularly essential earlier than signing, whereas there may be nonetheless time to grasp 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 applying works and serves present prospects efficiently. The evaluate ought to independently assess whether or not the structure is maintainable, whether or not there are vital 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 group can proceed creating the system with out relying continuously on its unique creators.
In observe, an M&A code audit AI-generated codebase ought to separate points that may be fastened by way of routine refactoring from dangers that might materially have an effect on post-acquisition integration prices. One set of findings could imply just a few weeks of deliberate engineering work; one other could level to the necessity to substitute a core part or partially redesign the structure.
An impartial code audit provides consumers and traders an exterior evaluation from specialists who usually are not beneath stress to current the product in the absolute best gentle earlier than the deal closes.
The purpose of the audit is to not uncover as many flaws as attainable. It’s to offer each side an goal technical image: which dangers are actually important, how a lot effort remediation could require, and which points ought to be taken into 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 robotically must be fastened by rewriting it from scratch. For a startup, AI instruments are sometimes a rational strategy to validate an thought quicker, 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 improvement, modernization, and code auditing, so we consider AI-generated code within the context of your complete system somewhat than in isolation. Our specialists can assess structure, backend and frontend, infrastructure, safety, dependencies, code high quality, and improvement processes as a complete. That is particularly essential in due diligence, the place a technical situation issues most when it creates monetary, operational, or scalability dangers for the enterprise.
That have additionally helps us distinguish actually vital limitations from strange technical debt that may be addressed regularly. As a substitute of defaulting to “rewrite every thing,” the SCAND group identifies what will be preserved, what ought to be improved by way of refactoring, and which parts could genuinely must be changed.
For founders who need to deliver an AI-built product to a extra mature engineering state earlier than a funding spherical or the following stage of progress, SCAND additionally provides Put up-Vibe-Coding Growth – from technical evaluation and codebase stabilization to additional improvement and scaling.
Conclusion
AI-generated code will not be a crimson flag by itself. The true query is whether or not the corporate understands what it has constructed, can keep it with out counting on just a few people, and is aware of which technical dangers might 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 traders and consumers, it supplies a clearer image of the product behind the demo.
Incessantly Requested Questions (FAQs)
Is AI-generated code robotically a crimson flag for traders?
No. Traders are normally extra involved with maintainability, safety, possession, scalability, and group data than with how the code was produced. AI-generated code turns into a danger when the corporate can not clarify, help, or safely evolve the system.
How is due diligence totally different for a vibe-coded startup vs. a historically constructed one?
The core checks stay related, however vibe-coded merchandise require additional consideration to code provenance, undocumented AI-driven choices, dependency dangers, safety gaps, and data focus. Reviewers additionally have to confirm whether or not the group can keep the system with out counting on one particular person or device.
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 objective, performance, and danger class. Some merchandise could fall beneath high-risk necessities, whereas many others is not going to.
Can a startup put together for technical due diligence earlier than traders ask?
Sure. Founders can evaluate structure, safety, dependencies, documentation, check protection, IP possession, and deployment processes prematurely. An impartial audit also can 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, group dependency, compliance, future prices, and transaction readiness.