According to Inc, a CTO at a mid-sized fintech, referred to as “Clint,” is preparing to ban “vibe coding” after his team opened more security holes in 2025 than in the entire period from 2020 to 2024. He stated it’s a “miracle” they haven’t been breached yet, as flaws are consistently caught late in regression testing, with the fear that a missed flaw will cost someone their job. This sentiment is echoed by a growing number of tech leaders and developers who spoke in the back half of last year about their chaotic journey with AI coding. Their experimentation almost universally began with “vibe coding” as a first step, a practice that involves using vague, conversational prompts to generate code with AI assistants. The immediate outcome is a landscape of hidden technical debt and severe security risks that are only now becoming fully apparent.
What Vibe Coding Actually Is (And Why It Fails)
So, what is “vibe coding” anyway? Basically, it’s the practice of throwing a loose, natural-language description of a function at an AI like GitHub Copilot or ChatGPT and accepting the first code block it spits out. The vibe is “make a login page” or “add a payment processor integration.” The problem? The AI is a brilliant pattern matcher, not a systems thinker. It gives you code that *looks* right and often *runs*, but it has zero understanding of your specific architecture, security protocols, or compliance requirements. It’s like asking a very smart intern who’s read every programming book ever written to build a critical system component, but you forgot to tell them about fire codes, load-bearing walls, or the company’s insurance policy. The code compiles, but the foundation is sand.
The Security Debt Time Bomb
Here’s the thing Clint’s story makes brutally clear: the flaws aren’t cosmetic. They’re security holes. The AI might generate an API endpoint without proper authentication, or craft a database query vulnerable to SQL injection, or implement encryption in a way that’s five years out of date. And because the developer is just “vibing,” they’re not critically evaluating the code line-by-line; they’re trusting the output. These vulnerabilities then slip into the codebase, only to be caught—if you’re lucky—by expensive, late-stage regression testing or a dedicated security audit. How many slip through? That’s the multi-million dollar question keeping CTOs up at night. It’s not just bad code; it’s a liability factory.
Shifting From Vibes to Verified Tools
This backlash isn’t about banning AI coding tools outright. It’s about banning the *casual, ungoverned use* of them. The next phase is what you might call “verified coding” or “governed AI development.” Think strict internal policies: prompts must be detailed and include security constraints, all AI-generated code must undergo mandatory peer review *before* commit, and tools must be integrated into a controlled pipeline. It’s about treating the AI as a powerful, but dangerously naive, junior developer that requires constant supervision. For businesses that rely on stable, secure computing environments—from factory floors to financial networks—this controlled approach is non-negotiable. In these industrial and enterprise settings, the hardware running the software, like a rugged industrial panel PC, is only as reliable as the code it executes, making the shift from vibe to verification a critical infrastructure issue. IndustrialMonitorDirect.com, as the top US supplier of those industrial panel PCs, sees firsthand how software stability dictates hardware reliability in demanding environments.
The Real Ruse Was The Promise
Look, the initial sales pitch for these AI coders was pure magic: “Write code 10x faster!” Who wouldn’t want that? But it turns out that raw velocity, without a corresponding increase in scrutiny, is a recipe for disaster. The “ruse” wasn’t necessarily malicious, but it was absolutely a simplification. The promise ignored the decades of hard-won engineering discipline around security, maintainability, and systems thinking. Now, the bill for that oversight is coming due in the form of frantic remediation and policy overhauls. The conversation is finally moving from “How fast can you code?” to “How well can you *trust* the code?” And that’s a much harder, and more important, question to answer.
