Vibe coding represents innovation and a critical engineering liability
Vibe coding is developing fast. It’s a practice where people, sometimes developers, often not, use AI tools to generate software with minimal manual input. You prompt the AI, get results, and execute. It’s fast. Projects that used to take months now launch in weeks. Some pull this off with no hand-written code at all. Entire products are going from idea to monetization without traditional software engineering teams behind them.
This new wave isn’t just hype. One product, Workcade, a gamified productivity app, attracted hundreds of users in its first week. Another example: a non-technical founder built a 100,000-line AI-generated app and turned a profit within weeks. These aren’t small wins. They point to a capability shift driven by AI that could affect execution speed, team structure, and budget strategy.
But let’s be clear. Just because AI can write the code doesn’t mean it’s good code or secure code. Enrichlead is the perfect case study. The app was built with Cursor, entirely AI-generated, no hand-written code involved. Its founder, Leo Jr., not a developer, launched quickly, saw early traction, and got attacked within 48 hours. Subscription walls were bypassed. Infrastructure costs spiked. The app’s large language model (LLM) made up sales leads out of nothing. That’s not a glitch. That’s product risk multiplied at scale.
This matters for anyone serious about integrating AI into product development. Once you use an AI to build, you must still manage the outcome, security, performance, compliance, scalability. If you skip that part, you’re building faster, but with no guardrails. For a C-level audience, ask yourself this: can your reputation handle a public meltdown because your AI-assistant “hallucinated” user data or exposed your product to attack?
David Beale, a well-respected voice in DevOps, said it straight, this isn’t new behavior. Developers have always pulled ideas from forums, pasted code from GitHub, and built by reference. What changes now is the speed, scale, and automation. And that means we’re no longer in control by default. We need stronger execution discipline, not less.
Steven Donaghy, Engineering Manager at Microsoft, had the right take: “AI is like alcohol. It amplifies what you already are. If you’re a great coder, it makes you better. If you’re terrible, the output is even worse.”
Bottom line, AI can accelerate your strongest teams. But if you cut out the engineers and hope for the best, you’re not innovating. You’re outsourcing control, and inviting technical chaos.
Vibe coding should be selectively applied
There’s a place for vibe coding. It works particularly well at the beginning of a project. AI tools help teams start faster, generating working prototypes, UI scaffolds, basic logic, all within hours, not weeks. This early velocity has value. Teams avoid over-planning, produce useful outputs quickly, and test concepts before investing heavily.
But this speed doesn’t scale on its own. As teams move from prototypes to actual products, the requirements shift. You need consistency, reliability, auditability, and compliance. These aren’t optional. Finance, healthcare, logistics, most sectors work under some form of regulation. That means code can’t be “good enough” based on feel. It needs to meet security guidelines, coding standards, and performance benchmarks.
Steven Donaghy from Microsoft made an important point here. He said AI helps most at the start and end of a project. In the beginning, it’s a tool for breaking through inertia. At the end, it accelerates repeatable tasks. But in the middle, where systems are integrated, and scale, logic, infrastructure, and user safety matter, you need precision. That’s not something AI provides by default.
Adam D’Angelo, Director at Slalom, sharpened the argument: large language models can introduce vulnerabilities, generate code with problematic licenses, or enable compliance failures without warning. In a regulated environment, uncontrolled AI outputs can create legal exposure at scale. You don’t need that risk.
For CTOs or CEOs considering full adoption, pause and assess where the tools actually fit your workflows. Use them to unlock creativity and speed, but insert structure before you move into productization. Blending AI into real production pipelines requires clear separation between experimentation and execution, with humans in control at each critical checkpoint.
Vibe coding isn’t a yes or no choice. Apply it with precision. It shouldn’t be driving your main product without human oversight. Avoid installing casual experimentation into systems where faults cost money, trust, or time. Choice of tools is strategy. Keep your leadership team aligned on that principle.
The responsible integration of vibe coding
AI-generated code isn’t automatically well-structured, secure, or compliant. It’s synthetic output based on probabilistic patterns. That doesn’t mean it’s useless, it means you need systems and standards in place to make it trustworthy. If you’re adopting vibe coding in your organization, you need rules around it. Not just policies, frameworks that enforce code quality, security checks, and version control around anything AI touches.
Adam D’Angelo from Slalom flagged the major risks: LLMs can produce code with vulnerabilities, like injection paths, XSS issues, or flawed authentication logic. They can also pull in open-source components with conflicting licenses, opening up legal problems if you’re not tracking them. These issues won’t always show up immediately. They emerge later, at scale, under load, or during audits.
Letting teams operate purely on “vibes” encourages shortcuts. Repetitive reliance on AI-generated solutions without understanding the underlying logic creates weak technical cultures. D’Angelo called it “learned helplessness.” That’s not a personnel issue, that’s an organizational fragility. It’s something leadership must measure and fix.
So, what’s the move? You implement strategic constraints. That means gating AI-generated outputs behind code review. It means hiring and upskilling for hybrid fluency, people who can work with AI tools but still know how and when to override them. It means making engineering discipline a non-negotiable, especially in environments where reliability and security define product viability.
David Beale, from the DevOps space, offered a smart perspective. He doesn’t dismiss vibe coding. He recommends mastering it, intentionally. Don’t panic. Don’t overreact. Instead, train your teams, build oversight layers, and use vibe coding as a multiplier, not a crutch.
For executives, this becomes a leadership issue. Do your developers understand what the AI just wrote? Do your architects have final say? Are your compliance teams looped into code review procedures? If the answer is unclear, AI isn’t your current advantage, it’s your next risk.
Progress in AI-assisted coding isn’t just technical. It’s operational, legal, and strategic. Treat it that way. Use it where it adds value, apply pressure where quality matters, and above all, stay in control.
Key highlights
- Vibe coding accelerates shipping but invites systemic risk: AI-generated code enables rapid, low-cost product builds, even by non-technical users, but often lacks proper security, quality control, and basic fail-safes. Leaders should treat it as an acceleration tool, not a viable engineering replacement.
- Use vibe coding strategically only where speed matters more than stability: The approach is most effective in early prototyping and repetitive implementation tasks but should not be applied to core systems or regulated environments. Executives must define clear usage boundaries to prevent legal, financial, or operational exposure.
- Guardrails and training are essential for safe AI-powered development: Without code reviews, compliance checks, and continuous education, teams risk over-dependence on AI and degraded engineering judgment. Leaders should mandate oversight frameworks and invest in hybrid developer skills to sustain product quality.
