Vibe coding is transforming software development, allowing teams to turn ideas into working applications faster and with fewer technical resources. But while AI can dramatically accelerate coding, it can’t eliminate the risks of cutting corners. The ease of creating functional software may even encourage teams to overlook critical development steps.
Speed has always been a competitive advantage in software development, but moving too fast can carry a steep price. Below, members of Forbes Technology Council highlight essential development steps teams may be tempted to skip when vibe coding and explain why those shortcuts can prove costly later.
Identifying The Customer’s Real Problem
Make sure you understand the real customer problem. When AI makes coding fast, teams may skip the hard work of chiseling the product to truly fit the need—leading to software that ships quickly but misses the mark. – Ashutosh Madeshiya, Axiarete AI
Conducting Code Security Reviews
Security reviews are the step teams most often skip. Vibe coding helps teams build quickly, but reviews still take time. When deadlines are tight, reviews are usually the first thing to go. According to Veracode’s 2026 GenAI Code Security Report, AI-generated code passed security tests only 56% of the time—nearly half of the code failed. If you catch a flaw during review, it is easy to fix. If the same flaw shows up in production, it will be an incident. – Alma Jones, Comerica Bank
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Vetting Open-Source Packages
AI is notorious for pulling in tons of new open-source packages as a shortcut to build new features. If companies don’t actively review those packages, check their support health, and verify the functionality they provide, tech debt will pile up, and companies could introduce new security issues and bugs unnecessarily. – Aaron Mitchell, HeroDevs
Confirming A Product Is Worth Building
The step teams will be tempted to skip is deciding whether a product is truly worth building and for whom. When execution becomes instant, the costliest mistake is no longer building too slowly; it’s scaling the wrong idea faster. – Jehan Hamedi, Vizit
Reviewing Software Architecture
I’d be careful about skipping an architecture review before letting an AI agent work through the codebase. Someone still needs to decide how the new functionality fits into the existing system. If those decisions happen during implementation, they can get buried in the generated code and become harder to unwind later. I’d settle the architecture first and let AI handle the implementation. – Elton Chan, Second Talent
Challenging Assumptions About AI Code Quality
The problem is about more than skipping a specific development step; it is also about the assumptions teams make about the quality and security of the code being developed. Development teams have historically skipped steps in the service of speed, but it is now happening faster. Everyone thinks they are a developer, but many people (I have run into a few) have little to no clue what the essential steps are and inappropriately assume AI tools inherently develop good code. – Aman Raheja, HPE
Enforcing Coding Standards
We as the IT team have to maintain the code and fix any defects. Therefore, it is important to evaluate the code against our own coding standards, leverage automated testing to prevent bugs, and have an LLM document the code architecture (data model, patterns) for long-term maintenance. – Buyan Thyagarajan, Eigen X
Thoroughly Testing AI-Generated Code
Vibe coding can tempt teams to skip rigorous testing of AI-generated code. The challenge is velocity: Traditional code testing often can’t keep pace with how quickly AI creates code, and developers want to move code swiftly into production. But ignoring proper testing creates blind spots that can compound into reliability, security and quality issues later. Teams need testing approaches that can actually scale alongside AI-driven development. – Shubha Govil, Sauce Labs
Ensuring Reviewers Can Spot Security Flaws
Teams skip the judgment step, not the review step. Most still review AI-generated code but assume every reviewer can tell the difference between secure output and insecure output, and that skill is unevenly held. AI writes code that looks right far more often than it writes code that is right, so a quick read just confirms the happy path and misses the auth check. The expensive version of this isn’t one bad merge; it’s a team that has learned to trust output they can’t actually evaluate. – Andrea Scaduto, SecureFlag
Running Adversarial Security Tests
Teams skip real security testing in the pipeline. Vibe coding does not change the fact that developers create vulnerabilities and vulnerability researchers exploit them. That has always been the cycle; the only change is speed. Without adversarial testing before release, structural flaws get embedded in production architecture, and removing them there is far more expensive than preventing them—especially if damages are realized via a breach. – Adriel Desautels, Netragard
Defining Acceptance Criteria And Tests
Teams often skip defining acceptance criteria and writing tests before generating code, because when a prototype appears in minutes, stating what “correct” means feels like bureaucracy. Once the codebase outgrows anyone’s head, every AI-generated fix can silently break something else, and nobody can tell an improvement from a regression. Early speed is repaid with debugging and rework. – Anthony Spadaro, Vantomic Inc.
Vetting Dependency Versions Before Merging
Don’t skip a dependency review before merging. A Purdue study of more than 117,000 dependency changes found AI agents pick vulnerable package versions more often than humans, but the expensive part comes later: Nearly 37% of the agents’ bad picks needed a major-version upgrade to fix, versus just under 13% for humans. Skip the gate and you ship exposure that takes weeks to unwind. Let AI write code. Don’t let it pick dependencies alone. – Steve Carter, Nucleus Security
Planning For Extensibility And Maintenance
Consider future extensibility, security and maintenance. Vibe coding looks a lot like continuously adding parts. Generally, there has been limited refinement of the quality of the code. – Nolan Garrett, TorchLight
Designing Features And User Experiences
Because execution is cheaper, it is tempting to skip careful design of features and user experience and just iterate on what AI comes up with. This can be costly in the form of lost creativity, as it is hard to go back to other approaches on a different development path once you are down the path of a certain execution. – Rohit Patel, ndoc.ai
Clarifying Goals For Speed, Quality And Scale
As vibe coding accelerates output, teams skip clarifying what they are optimizing for: quality, speed or scale. Without this clarity, code fails to deliver long-term business value or scale beyond immediate demos. To avoid costly rewrites, teams must separate one-way from two-way decisions and embed design patterns into prompts. When AI grasps full context, it generates code that aligns with real business needs. – Vishal Saxena, Octus
Restricting AI Agents’ Access
Always scope what the AI can touch. Coding assistants often run with the developer’s own credentials, so an app built in an afternoon can inherit production database access and connections to unvetted MCP servers. Give each agent its own narrowly scoped identity and an audit trail from day one. Retrofitting permissions after launch is slow and expensive, and one bad command can wipe live data. – Linus Hakansson, Gravitee
Gathering Feedback From Users And Colleagues
It’s easy to start missing out on the human conversations around the code. When software is easy to generate, teams may skip sitting with users, talking through trade-offs with colleagues, and getting honest feedback. The code can look finished long before anyone has agreed what problem it should solve. Discovering that gap after launch is costly. – Lakshmanan Alagappan, Genpact UK
Reviewing And Understanding Generated Code
The development step teams may be tempted to skip is reviewing and understanding the code after it works. Vibe coding can translate ideas and plans into working products very quickly, but if you continue onward without a succinct understanding of what was built, the first change or bug you encounter can become that much harder to deal with. Speed is useful, but someone still needs to be accountable for what AI has built after it has done its work. – Adithyan RK, Hyring.com
Protecting Sensitive Data In AI Prompts
I’ve observed that teams using vibe coding can sometimes overlook a critical step: deciding what data is appropriate to share with AI tools. In the rush to move faster, sensitive or confidential information may inadvertently end up in prompts. That shortcut can create significant privacy, security and compliance risks later. – Prashanthi Kolluru, KloudPortal Technology Solutions Pvt Ltd.
Documenting Why The Code Works
Write down why the code works; don’t just confirm that it does. Vibe coding lets teams generate working features fast without anyone fully understanding the logic; too often, the attitude is “it ran, it looks right, ship it.” That’s fine until something breaks months later and nobody, including the AI, can explain what the code was supposed to do. Skipping documentation trades a fast launch for an undebuggable mystery. – Hastimal Jangid, Coozmoo Digital Solutions, Inc.
