Vibe coding empowers non-coders to build applications using AI
The rise of AI coding tools like Bolt has made it surprisingly easy for non-engineers to generate working applications. All it takes is a simple prompt. You describe an idea, and the software gives you something usable in minutes. That’s powerful. It removes barriers that would’ve made development impossible for many people just a few years ago.
But the output still isn’t good enough. Inexperienced users often don’t understand the systems they’re creating. The frontend might look functional, but under the surface, the code is flawed, missing tests, filled with disorganized logic, or entirely unsecured. These gaps show up fast when you hand the code over to someone who actually understands it. That’s what happened in this case: the app worked at a surface level but failed in reliability and structure as soon as it was reviewed.
There’s nothing wrong with using AI as a starting point. But treating these tools like a replacement for development expertise is short-sighted. If you’re deploying real applications inside a company, or even just exposing them to external users, you can’t afford to ignore structure and security. The tools don’t yet understand those constraints as deeply as a trained engineer does. They’ll do just enough to get something you can click, but not enough to make it trustworthy.
The bigger conversation is about expectations. C-suite leaders need to understand that automation at this level still demands oversight. You gain speed and accessibility, yes. But fully replacing engineering skill is not happening here, not yet.
A 2024 developer survey cited here is worth paying attention to: 66% of developers report they experience a “productivity tax” from using these AI coding tools. That means they spend extra time cleaning up generated code to make it stable, scalable, and secure. That’s not increased efficiency. That’s just technical debt created faster.
Vibe coding tools hold significant promise as educational aids for new programmers
Now let’s talk about a real success story. A theoretical physicist with a PhD from Stanford, who transitioned into software development, used AI tools like ChatGPT, GitHub Copilot, and Gemini to self-educate. No CS degree. No formal training. But he was highly motivated. He used these tools not to skip learning, but to learn faster.
This is an important distinction. These tools are not replacements for understanding, they’re accelerators of it. For people who are already curious and disciplined, AI can reduce the time it takes to build necessary technical fluency. Think of them as high-speed search engines, with context. They don’t just give you an answer. If you ask for it, they explain why it’s the answer. That’s what this individual did. He solved bugs, understood the reasons behind them, and retained that knowledge.
Executives who are looking at workforce upskilling need to understand this: vibe coding isn’t just for hobbyists. It’s a scalable path toward technical literacy for those outside traditional engineering roles. This matters because tech teams are under constant pressure and talent gaps won’t shrink on their own. Developing new engineers internally, through tools like these, gets you closer to closing that gap. And it doesn’t require pulling talent away from existing product or infrastructure work.
But this only works if the mindset is correct. These tools reward curiosity and action. They don’t create value for passive users. The real opportunity isn’t in treating AI like a magic box. It’s in treating AI like a learning engine, one that compresses years of trial-and-error into weeks or months of guided interaction. That’s how you build new technical operators without waiting for four-year degrees or formal retraining cycles.
This isn’t the future. It’s happening right now.
Interfaces can mask underlying technical gaps that are critical in real-world development
Tools like Bolt are designed to reduce friction. You open the interface, write a basic prompt, and the system generates a working application. It’s intuitive. You don’t need to know where to find the terminal or how to install dependencies. The entire experience is aimed at non-developers. That, in itself, expands who can build digital tools.
But simplicity hides complexity. Non-technical users may believe they’re building sustainable software, when in fact, they’re only producing temporary functionality. The underlying systems, version control, APIs, live environments, all still require precision. Without that, deployment becomes fragile. The application might run, but it won’t scale or integrate cleanly.
In the example discussed, the lack of understanding around commands like npm run dev, or assumptions about how subreddits and app instances behaved, led to user errors that would never happen with even minimal developer experience. There were also fundamental misunderstandings about how app versions evolve, revealing how critical the knowledge gap still is.
For business leaders, this means short-term gains might come at long-term cost. Applications built in these no-code or low-code environments that aren’t governed by technical review end up producing layers of confusion and instability. Development governance still matters, even when the tools appear foolproof on the surface.
This isn’t a flaw of Bolt in particular. All AI coding solutions are built on abstractions. But abstraction without oversight leads to false confidence. And in enterprise environments, that translates directly into risk. A clean interface and a working preview do not guarantee production readiness. Internal product owners and innovation leads must ensure that actual engineering talent is brought in early, not just as a cleanup crew at the end.
Security remains a critical concern with AI-generated applications built by non-coders
Security doesn’t happen automatically. In the app built through Bolt, there were no guardrails to prevent data exposure. No authentication. No authorization. Input fields were live, review submissions were unsecured, and backend systems were unprotected by default.
In this case, it didn’t matter. It was a test app for reviewing terrible public bathrooms on Reddit. But leadership can’t afford to treat that outcome casually. When AI-generated applications start to collect real data, email addresses, locations, passwords, flaws like these become liabilities. The GDPR doesn’t care if your tool came from AI. It only cares whether you protected user information.
AI tools focus on generation, not governance. They will create forms, pages, and submission workflows, but they do not automatically enforce best practices around data storage or encryption. That has to be commanded explicitly, or added during cleanup by someone with security expertise. Most non-developers don’t know where to begin with these steps.
Executives need to recognize that this tooling introduces new risks if left unchecked. The speed at which someone can ship insecure applications is accelerating. That means organizations need new standards for auditing internally-built tools, even the small ones. Especially the small ones. Anything customer-facing or user-data-driven must go through security review, even if it was created through vibe coding over a weekend.
Waiting until something breaks is not a strategy. Bolt, in this case, responded helpfully to debugging prompts, but the underlying risk remained. People without security knowledge can inadvertently launch applications that expose an organization to compliance violations and reputational harm.
Ensure that every AI-generated product, no matter how trivial it appears, is reviewed as though it were being launched to production. Because eventually, it might be.
Vibe coding can create a false sense of capability
One of the key weaknesses of AI-assisted code generation is not its speed, but the illusion of completeness. You can produce a working interface in minutes, and it may even behave as intended on the surface. But the moment a skilled developer inspects the codebase, the flaws are immediately visible. In the example covered, once Bolt delivered a functional bathroom review app, it quickly became clear the code lacked organization, modularity, and core development practices.
Code was inlined. There were no unit tests. Components were oversized and difficult to maintain. The GitHub project was poorly structured, a simple problem with serious downstream effects when collaborating or scaling. This isn’t a question of whether the app ran. It’s about whether it could be understood, evaluated, and extended by others. In this case, it couldn’t, not without significant rework.
For executives, the risk isn’t in building quickly, it’s assuming the result is fully operational or ready to evolve. That mistake can lead to delays in releases, added workload for engineering teams, and trust issues across departments. It also undercuts the idea that AI tools can effectively replace early-career developers. Even with help from tools like Bolt, the app still required intervention and eventual cleanup by people with foundational experience.
Speed does not eliminate the value of craftsmanship. In fact, the faster something is built, the more important it becomes to review whether it meets technical, structural, and compliance standards. It’s easy to deploy an MVP. It’s much harder to deploy it responsibly. That’s where these generated platforms fall short.
Collaboration with experienced developers remains essential to polishing AI-generated code
AI coding assistants are not substitutes for engineering experience. They are amplifiers. But the direction of that amplification depends on user input. If the user lacks basic understanding of software structure, the AI can only follow instructions, it cannot assess thoroughly whether the output meets requirements around maintainability, reusability, or scalability.
In this case, the author leaned heavily on developer friends to interpret error messages, improve file structure, and validate front-end logic. One engineer pointed out poor code organization and suggested flattening the directory. Another flagged that all styling was inlined within the components, making it harder to parse and adjust. A third called out the absence of unit testing as a major roadblock to understanding component-level quality.
These individuals weren’t just helping; they were essential to making the app usable. If they hadn’t reviewed the work, the project would have remained brittle and insecure. This reflects a larger truth: AI can generate the foundation, but engineers still hold the responsibility for architectural integrity. Without that oversight, these tools produce work that might feel complete, but is ultimately unreliable.
For business leaders, the implication is clear. AI tools can remove some of the friction from software development, but they do not eliminate the need for engineering review. Engaging qualified developers early, and bringing them back at key stages, ensures that what’s built is more than a demo. It’s stable, workable, and safe to integrate.
Letting AI handle all of development without professional validation introduces long-term risk. Working with capable engineers, even as consultants or reviewers, helps extract the true value of these tools while maintaining quality control across your technology portfolio.
The developer community expresses serious concerns about code quality and maintainability in AI-generated outputs
The developer response to AI-generated code is consistent: the output may function, but few professionals trust it without significant revision. In the case described, technical peers inspecting the codebase noted several persistent flaws, inline styling that cluttered component files, a lack of modularity, an oversized component (LocationDetails.tsx) that should have been split into smaller pieces, and the complete absence of unit tests.
Each of these issues points toward the same problem: code produced by AI tools like Bolt often lacks key qualities needed for long-term maintainability. It becomes harder to onboard collaborators, harder to test changes, and riskier to deploy at scale. Experienced developers immediately notice this. The generated projects don’t follow consistent software engineering patterns unless they’re explicitly directed to do so, something a non-engineer usually doesn’t know to ask.
For leadership, the strategic takeaway is straightforward. If your teams, or your customers, are using AI to accelerate development, you should assume the code they’re working with will need manual debugging, cleanup, and structural redesign. Otherwise, your teams will suffer from mounting inefficiencies.
The survey referenced in the article quantifies this clearly: 66% of developers report experiencing a “productivity tax” when using AI-assisted coding platforms. Time saved during generation is often lost later during code reviews, integration, and refactoring. This added cost affects scheduling, staffing needs, and delivery timelines.
Executives planning to integrate AI into their development toolchains should support code standardization workflows from the outset. Enforce code reviews, define testing protocols, and include senior engineers in product cycles where AI-generated assets are involved. Without these systems in place, teams will inherit code that looks operational but underperforms in enterprise environments.
The talent and experience of your existing engineering team are still the best quality controls available. AI can rapidly produce, but engineering judgment makes output reliable.
Final thoughts
AI-powered coding tools are moving fast. They lower the barrier to entry, speed up experimentation, and help non-technical teams ship ideas. That’s good progress. But speed without structure creates risk. Apps that look functional can hide messy code, missing tests, and real security issues, especially when built by those without development experience.
For decision-makers, the signal is clear: vibe coding is not a replacement for engineering discipline. It’s a supplement. You can use these tools to get started faster, prototype without delay, and support internal innovation. But you still need engineers to clean up, secure, and scale what’s built. Skipping that step doesn’t save time, it pushes cost downstream.
This is also an opportunity. AI-assisted coding can support learning, accelerate onboarding, and help non-technical employees build fluency. But it only works when there’s a foundation of curiosity and clear technical guidance. Some of your most capable future developers won’t have CS degrees. Give them the tools, but protect the process.
If you’re building anything that matters, anything customer-facing or data-driven, AI tools won’t keep you safe on their own. You still need technical oversight, consistent standards, and code quality that scales. Use the tools for leverage, not for shortcuts. That’s the way to get real value from this wave of automation.
