AI has transformed software creation into something that feels less like coding and more like conversation. No more hammering out code line by line. Instead, one can describe an idea, and the model produces functioning snippets within seconds.
This ‘vibe coding’ approach, while fast becoming a trend, is also unearthing a fresh set of challenges.
As more developers and non-technical founders rush in to experiment, the cracks are starting to show in unexpected ways.
Fragile Foundations in Vibe Coding
Chaitanya Choudhary, CEO of Workers IO, believes the problems with vibe-coding are structural. “Vibe-coded code often lacks a clear specification, rationale and consistent patterns,” he told AIM, noting that without a single source of truth, teams repeatedly re-prompt models, rediscover intent and risk regressions.
Meanwhile, in a conversation with AIM, Namanyay Goel, founder of Giga AI, pointed to another fault line. For him, the gap lies not in the promise but in the experience. Vibe coding platforms have improved safety and polish. “There’s still a significant gap between what these platforms promise and what new users actually experience,” he said.
His caution comes from watching hundreds of beginners struggle to cross the line between early success and real deployment.
The act of generating code by prompting comes with hidden costs. Models don’t “see ahead” yet; they only react to the text in front of them. This makes long-term architectural choices inconsistent.
As Choudhary puts it, “If you don’t already know core software concepts, or you prompt step-by-step in chat without a plan, the model can’t ‘see ahead’.” The outcome is a patchwork of decisions that later collapse under the pressure of scaling or maintenance.
Testing only compounds this issue. While code is produced quickly, verification lags behind. Passing happy-path tests might give false confidence, but systems need edge-case checks, fuzzing and property-based tests to be reliable. He explained that his platform, Workers IO, is investing in this layer, but admits that most casual vibe-coders lack both the time and the setup to implement such rigour.
Even when code runs, integration with production environments is rarely smooth. API quotas, authentication, CI/CD pipelines and performance bottlenecks often break snippets that look perfect in chat. The drift worsens over time as dependencies evolve. As Choudhary frames it, the vibe-coded script may “look right” syntactically, but collapses once it meets real-world production usage.
And then there is the challenge of maintenance. With no stable specification or documentation, evolving a project becomes rediscovery each time. Teams end up chasing intent through prompts rather than relying on consistent design principles. What begins as acceleration often slows into rework.
The Beginner’s Wall
If Choudhary points to structural flaws, Goel’s research reveals a different layer: the struggles of non-technical users.
Yes, platforms can generate working prototypes, but beginners often misunderstand what they are building. “Most newcomers approach AI coding tools like they’re ordering from a menu,” he explained, but development is closer to cooking; one must know the ingredients, methods and what could spoil the dish.
He describes this as the ‘80/20 wall’. AI excels at the first 80%, which includes interfaces, forms and simple integrations.
The final 20%, which demands debugging, error handling and scalability, is where frustration peaks. For non-technical founders, this is where frustration peaks. On Reddit, Goel recalls, one user spent days asking an AI to add logging, only to find out that the logs never showed up in the console. The issue wasn’t faulty code, but the user’s inability to verify or debug.
Frontend simplicity also contrasts with backend complexity. While interfaces are smooth, authentication, databases and deployment expose the limits.
These platforms, he mentioned, market themselves as ‘low-code’ solutions. However, unless one has a strong developer background, they’re not shipping anything. Without debugging instincts, users endlessly loop through fixes without addressing the root causes.
Despite these challenges, Goel sees genuine progress. These platforms have improved error handling, safety and code quality.
What hasn’t caught up is education. “Documentation assumes too much technical knowledge. Even ‘beginner-friendly’ guides often reference concepts like environment variables, REST APIs and database schemas without proper explanation,” he added.
His advice is practical: learn first, then build. Starting with small projects and gaining debugging skills provides the base to scale ideas with AI support.
A Future That Still Needs Builders
Both founders converge on one reality: vibe coding is not magic. They reiterated that while it accelerates development, it doesn’t erase the complexity of software. Beginners must invest in learning, and professionals must enforce testing, integration, and maintenance discipline. The most successful approach may be to treat AI as a tutor as much as a co-pilot, guiding learning rather than replacing it.
The optimism remains strong. The barriers to entry are lower, and experimentation has never been faster. Yet, the essence of building, systematic thinking, problem-solving and long-term structure, remains unchanged. Vibe coding, in other words, may make the start easier, but it has not yet made reaching the finish line easier.
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