Modern engineering teams are moving faster than ever, often with the help of AI-powered coding tools. A McKinsey study found developers can complete tasks up to twice as fast with generative AI (How generative AI boosts developer productivity, McKinsey & Co., 2024). This hyper-accelerated workflow sometimes called "vibe coding" when characterized by code generating copilots, minimal process, and a "just ship it" ethos is changing how software is written and deployed. Developers (and even non-developers) can now simply describe what they want in natural language and get functional code in return. As Andrej Karpathy put it, “The hottest new programming language is English” (https://x.com/karpathy/status/1617979122625712128?lang=en).
This AI-fueled productivity comes with a catch. Pushing out code at break-neck speed, without the usual scrutiny, introduces hidden problems that may only surface later as security vulnerabilities, unstable architecture, or fragile code quality. At Workstreet, we're actively addressing these challenges internally, with the potential to transform our solutions into client services. From this ongoing work, we explore below how AI-assisted vibe coding impacts development and deployment, what issues typically slip under the radar, why traditional safeguards struggle to keep pace, and the risks of ignoring these blind spots.
AI coding tools have undeniably changed the development workflow. Tasks that used to take hours of manual coding can now be done in minutes with the aid of "pair-programming" copilots. Entire boiler-plate modules or complex functions are generated on-the-fly, allowing smaller teams to produce much more functionality in less time. Code is often written and immediately integrated into projects with minimal human touch beyond initial prompting.
In vibe-coding style, a developer might outline a feature in a sentence or two, let the AI write most of the code, do a quick sanity check, and then deploy possibly skipping lengthy design reviews or exhaustive testing. The result is an explosion of new code and rapid-fire deployments. Security analysts have observed a surge in code output (more pull requests, more lines of code) when generative AI is introduced (https://apiiro.com/blog/faster-code-greater-risks-the-security-trade-off-of-ai-driven-development/). This acceleration even enables "citizen developers"—staff with little formal coding experience—to create applications by simply describing requirements to an AI (https://www.youtube.com/watch?v=pdJQ8iVTwj8).
However, writing code is only part of software engineering. Software quality, security, and maintainability still require careful thought and those are exactly the things getting less attention in a move-fast environment. AI assistants lack context about your organization's security policies or architecture vision. They'll happily generate code that works for the prompt given, but they won't consider unwritten requirements (like "don't expose customer data" or "ensure this module fits our overall design pattern"). And when developers lean heavily on these tools, there's a temptation to trust the AI's output at face value. In short, AI has made it trivially easy to crank out code but not to build secure, robust systems.
When you're pushing code out the door at high speed, security is often the first casualty. AI coding assistants can introduce hidden vulnerabilities that a rushed team might miss. These models generate code based on patterns in training data—they don't inherently know secure from insecure practices. A suggestion that looks correct might actually be riddled with flaws (https://ee.stanford.edu/dan-boneh-and-team-find-relying-ai-more-likely-make-your-code-buggier).
For example, an AI could generate a database query that works but lacks input validation, opening the door to SQL injection. Another might scaffold an authentication flow that superficially functions but skips critical checks. Real-world codebases have seen huge jumps in missing input validation and authorization as AI-generated code proliferates.
Another concern is sensitive data handling. In the race to get features working, developers may inadvertently expose API keys or personal information in code. One analysis noted a 3× increase in repositories containing hard-coded secrets after AI tools became popular. The AI isn't aware it just embedded a credential or returned a social-security number, and if the developer doesn't catch it, that sensitive data goes live.
Blind trust in AI output only magnifies the danger. Developers often assume "the AI probably knows what it's doing" and merge code without a thorough review, letting serious vulnerabilities sail into production. Attackers love unvetted code: they know fast-moving teams overlook security, and AI can churn out similar bugs at scale.
Traditional safeguards like periodic security scans are too slow for daily or hourly deploys. If a vulnerability gets flagged days later, it may already have been exploitable in prod. And because AI-generated functions lack clear rationale or docs, auditors struggle to verify them against standards, creating a governance gap.
Speed-over-substance coding skews architecture and piles up technical debt. AI suggestions can resemble the work of a short-term contractor who "doesn't thoughtfully integrate their work into the broader project" (https://sloanreview.mit.edu/article/how-to-manage-tech-debt-in-the-ai-era/). You might solve the same problem three different ways, depending on how you prompted the AI. Over time, this patchwork design makes the system harder to understand and maintain.
Code churn, the volume of code written then rapidly rewritten, has reportedly doubled in some AI-heavy teams. But anyone using an AI-enabled IDE knows the reality is likely much worse, approaching 4x higher rates by spring 2025. While AI delivers impressive first drafts, the multiple iterations required to integrate and refine this code often nullifies the initial speed advantage. It's the development equivalent of sprinting ahead, only to discover you've been running in the wrong direction.
AI-assisted development can become a technical-debt accelerant. It's like getting a brand-new credit card that lets you accumulate debt in ways you never could before (https://sloanreview.mit.edu/article/how-to-manage-tech-debt-in-the-ai-era/). Inefficient algorithms, duplicate modules, missing tests, etc. all lurking until they explode in future maintenance cycles. Systems built in a rush often ignore scalable design principles, ending up with monoliths that buckle under real-world load.
Established processes like manual code review, weekly scans, compliance checklists weren't designed for AI-driven velocity. Humans can't scrutinize every diff when an assistant is generating thousands of lines a week. Reviewers rubber-stamp to keep up, and issues slip through.
Automated tools struggle too. Classic static analyzers may not understand AI-generated patterns, creating either noise or blind spots. If your security tools run only after release, they're irrelevant to an hourly deploy cycle. And the people writing much of this new code might lack security training; there simply aren't enough seasoned reviewers to cover the rapidly expanding surface.
Metrics can also drive the wrong behavior. If leadership rewards ticket velocity or lines of code, AI-enabled teams will deliver (potentially at the expense of quality). Combining output-based incentives with generative AI creates "incentives ripe for regrettable code" (Bill Harding, https://www.geekwire.com/2024/new-study-on-coding-behavior-raises-questions-about-impact-of-ai-on-software-development/)
The landscape of development is evolving at break-neck speed. High-velocity, AI-assisted vibe coding unlocks serious productivity gains but ignoring its blind spots in security, architecture, and quality can be catastrophic. By modernizing safeguards, keeping humans in charge, and building a culture that values robust engineering practices, teams can move fast without breaking things. The goal isn't to slow down; it's to pair our new turbocharged capabilities with disciplined engineering, ensuring today's rapid progress doesn't become tomorrow's irreversible setback.
