By: Audrey Denise B. Cachuela
Here’s a number worth sitting with: nearly half of all AI-generated software has security vulnerabilities baked right into it. 45% of AI-generated code samples introduced known security vulnerabilities across Java, Python, C#, and JavaScript, regardless of model size or vendor (Veracode, 2025). The AI app boom has made software development faster than it has ever been, but speed of build and fitness for production are two entirely different things, and the tools themselves will not tell you which one you have. Redwerk, a software engineering firm with twenty years of delivery work, has spent the past two years being called in after AI-assisted builds hit walls they were never designed to handle.
45% of heavy AI coding tool users are shipping to production every single day (Source: TechRadar, 2026). That pace is genuinely useful when you are prototyping or pressure-testing an idea. Where things go sideways is when that same prototype architecture gets carried forward, without question, into a system expected to serve thousands of real users.
Most Organizations Are Still Figuring Out What They Actually Built
88% of organizations now use AI in at least one business function, up from 78% the year before. Two-thirds of them have not begun scaling it across the enterprise. They are still running pilots, working out what the tools can reliably do, and figuring out where the edges are before committing further. (Source: McKinsey, 2025)
That is not a criticism. Caution makes sense when the tools are moving this fast. The problem is that for many teams, the pilot never really ended. They kept shipping, kept adding features, and the codebase that was supposed to be temporary became the thing running in production. Software quality in AI development does not degrade all at once. It erodes gradually, and by the time the numbers start showing it, the system is already load-bearing.
Three Problems That Show Up in Every Audit
Engineering teams working with AI-generated software tend to hit the same categories of trouble regardless of industry or company size. The specifics vary, but the underlying failure modes are consistent enough that they show up in almost every SDLC audit.
Scaling AI-built applications is usually where the cracks appear first. Load assumptions baked into a demo-ready codebase are not the same as the ones a live product needs. Database queries that return fast at low volume start timing out under concurrency. API integrations that worked fine in testing fall apart under real throughput. Reworking that from the data layer up, while customers are actively using the product, is expensive and politically difficult in ways that catching it earlier simply is not.
The security risks of AI-generated code are harder to spot and tend to carry more serious consequences when they surface. Java clocked a failure rate above 70% in testing. Cross-Site Scripting failed 86% of the time across relevant tasks. The part most teams have not absorbed yet: switching to a newer or bigger model did not move those numbers. The vulnerability is structural, not a version problem. (Source: TechRadar, 2025)
Technical debt is the least visible of the three and often the most damaging over time. Researchers tracked 304,362 verified AI-authored commits across 6,275 real GitHub repositories and found that more than 15% of commits from every AI coding assistant they tested introduced at least one quality, security, or maintainability issue. Nearly a quarter of those issues, 24.2%, were still sitting unresolved in production repositories when the study wrapped. (Source: arXiv, 2026)
The Timing Is What Makes It So Expensive
Understanding why these problems surface late matters because it changes how you think about prevention. Failures after an AI-assisted build usually show up weeks or months in production, once the distance between what the system was built to do and what it is being asked to do has widened past the point of easy recovery. By the time the symptoms appear, tracing them back to the root cause takes real forensic work.
Konstantin Klyagin, Founder of Redwerk, puts it plainly: the real work starts when companies need to turn those early AI-assisted versions into systems that can hold up under actual growth. That requires engineering judgment and quality assurance discipline that AI coding tools do not supply on their own, and it requires them earlier in the SDLC than most teams think.
This is where the cleanup work shows up across the board. Redwerk repairs the codebases of early-stage teams and works with growing startups like Pridefit that have adopted AI to accelerate feature delivery. Those teams keep the freedom to ideate and prototype quickly, while Redwerk audits and tunes the resulting code so that it fits the existing architecture and does not introduce regressions later.
This Is Not Just a Startup Problem
It would be easy to frame this as a scrappy team problem, the result of founders moving fast without enough engineering oversight. But the same pattern runs through enterprise AI deployments at scale. 40% of enterprises are projected to demote or decommission their autonomous AI agents by 2027, and the reason is not that the technology failed. It is that organizations deployed without the controls, monitoring, and accountability structures needed to manage what happens when something breaks in production. (Source: Gartner via TechRadar, 2026)
Resources and headcount do not automatically solve a governance problem. Large organizations are running into the same deferred reckoning as smaller ones, just at a higher cost and with more stakeholders watching when it lands. The companies coming out of the current AI app boom with products that hold up are the ones that built readiness into the launch process rather than treating it as a follow-up task.
What the AI App Boom Gets Right, and Where It Still Falls Short
AI coding tools are genuinely useful and worth using. They compress timelines and lower the cost of early validation, giving teams real leverage in software development that simply did not exist a few years ago. The honest version of that picture also includes what they do not cover: architectural judgment, security governance, and a software testing process rigorous enough to catch what the code generator missed. Those gaps do not close with a model upgrade. They require structured quality assurance applied at the right points in the SDLC by people who know what they are looking for.
As the AI app boom continues to push more half-ready software into production, vibe code cleanup is only becoming more necessary. Redwerk works with engineering teams at exactly that point: auditing what was built, identifying where the architecture will not hold, and rebuilding what needs to survive in production. Their sister company, QAwerk, handles the software testing and quality assurance layer across manual, automated, performance, and security disciplines. If an AI-assisted build is running in production and has not been through a formal audit, that is the right place to start.
