In his February 17, 2026 article, Todd Wardzinski takes a hard look at what many developers are calling “vibe coding”—a fast-growing approach to building software by describing what you want in plain language and letting AI generate the code. The term, popularized by Andrej Karpathy, captures a real shift in how software is being created. Instead of writing every line manually, developers “set the vibe” and allow tools like Cursor, GitHub Copilot, Claude, and ChatGPT to handle implementation details.
Wardzinski acknowledges the appeal: the speed is undeniable. What once required weeks or months of evening coding sessions can now be accomplished in days. Prototypes, landing pages, simple apps, and MVPs are being built by people who might never have considered themselves developers. Because humans think in goals and intentions—not syntax and brackets—natural language feels like a more intuitive way to build. For small, self-contained projects, this method works remarkably well.
However, the article argues that the same strengths that make vibe coding exciting also make it risky. Problems tend to surface once projects grow beyond the early stage. After several months of incremental feature additions and AI-assisted fixes, the codebase often becomes fragile. A minor change can unexpectedly break multiple features. Attempts to repair one issue can introduce new ones. Developers describe this as a “whack-a-mole” cycle, where stability becomes harder to maintain with each update.
The root cause isn’t that AI is incompetent; it’s that vibe coding typically lacks durable specifications. Prompts are ephemeral. Once code is generated, the instructions that shaped it fade away. The code becomes the only record of what the system does—but it rarely explains why it was designed that way. As complexity grows, neither the developer nor the AI fully understands the evolving structure. Context windows are limited, documentation is thin, and architectural intent is lost. This is why many AI-built projects stall around the three-month mark: they outgrow their informal foundations.
Wardzinski proposes a more disciplined alternative: spec-driven development. Instead of treating prompts as disposable tasks, developers create structured, version-controlled specifications that serve as the authoritative blueprint. When something breaks, they refine the specification rather than patching scattered code segments. This approach requires more upfront effort—defining constraints, edge cases, acceptance criteria, and user flows—but it pays off in maintainability and clarity. Specifications become living documentation, enabling collaboration and reducing unintended side effects.
He also pushes back against the idea that AI eliminates the need for technical skill. While AI lowers the barrier to entry, it does not remove the need to understand architecture, dependencies, trade-offs, or system design. A poorly written specification from someone who lacks technical grounding becomes little more than a wish list. AI can accelerate output, but it cannot compensate for weak conceptual foundations. Generating code is not the same as engineering sustainable software.
The most practical path forward, he argues, blends both approaches. Vibe coding is powerful for exploration, rapid prototyping, and tightly scoped components—especially when outputs can be validated with unit or functional tests. But once a project must scale, persist, or enter production, structure becomes essential. Developers need guardrails: explicit constraints, documented decisions, and acceptance tests that confirm whether the system meets its intended design.
The industry is beginning to recognize this need for balance. Tools such as Kiro, SpecKit, CodePlain, and Tessl are emerging to formalize the relationship between natural language instructions and structured specifications. Even research initiatives like SpecLang explored structured natural language as a bridge between intent and implementation. The shared insight across these efforts is clear: freeform prompting does not scale without structure.
Another subtle but important issue is what some researchers describe as “functionality flickering”—when unspecified details change unpredictably between generations. If design choices aren’t explicitly defined, the AI fills in the gaps inconsistently. A feature may behave one way today and differently tomorrow, simply because the constraints were never made precise.
Ultimately, Wardzinski’s central message is not to reject vibe-coding but to mature beyond it. The future of AI-assisted development will belong to those who combine creative exploration with disciplined specificity. The real skill is not how enthusiastically one “vibes,” but how clearly one defines intent, constraints, and expectations. AI can execute instructions at remarkable speed—but only when those instructions are precise.
The uncomfortable truth about vibe coding
By Tech Wizard published on Mar 1, 2026
In his February 17, 2026 article, Todd Wardzinski takes a hard look at what many developers are calling “vibe coding”—a fast-growing approach to building software by describing what you want in plain language and letting AI generate the code. The term, popularized by Andrej Karpathy, captures a real shift in how software is being created. Instead of writing every line manually, developers “set the vibe” and allow tools like Cursor, GitHub Copilot, Claude, and ChatGPT to handle implementation details.
Wardzinski acknowledges the appeal: the speed is undeniable. What once required weeks or months of evening coding sessions can now be accomplished in days. Prototypes, landing pages, simple apps, and MVPs are being built by people who might never have considered themselves developers. Because humans think in goals and intentions—not syntax and brackets—natural language feels like a more intuitive way to build. For small, self-contained projects, this method works remarkably well.
However, the article argues that the same strengths that make vibe coding exciting also make it risky. Problems tend to surface once projects grow beyond the early stage. After several months of incremental feature additions and AI-assisted fixes, the codebase often becomes fragile. A minor change can unexpectedly break multiple features. Attempts to repair one issue can introduce new ones. Developers describe this as a “whack-a-mole” cycle, where stability becomes harder to maintain with each update.
The root cause isn’t that AI is incompetent; it’s that vibe coding typically lacks durable specifications. Prompts are ephemeral. Once code is generated, the instructions that shaped it fade away. The code becomes the only record of what the system does—but it rarely explains why it was designed that way. As complexity grows, neither the developer nor the AI fully understands the evolving structure. Context windows are limited, documentation is thin, and architectural intent is lost. This is why many AI-built projects stall around the three-month mark: they outgrow their informal foundations.
Wardzinski proposes a more disciplined alternative: spec-driven development. Instead of treating prompts as disposable tasks, developers create structured, version-controlled specifications that serve as the authoritative blueprint. When something breaks, they refine the specification rather than patching scattered code segments. This approach requires more upfront effort—defining constraints, edge cases, acceptance criteria, and user flows—but it pays off in maintainability and clarity. Specifications become living documentation, enabling collaboration and reducing unintended side effects.
He also pushes back against the idea that AI eliminates the need for technical skill. While AI lowers the barrier to entry, it does not remove the need to understand architecture, dependencies, trade-offs, or system design. A poorly written specification from someone who lacks technical grounding becomes little more than a wish list. AI can accelerate output, but it cannot compensate for weak conceptual foundations. Generating code is not the same as engineering sustainable software.
The most practical path forward, he argues, blends both approaches. Vibe coding is powerful for exploration, rapid prototyping, and tightly scoped components—especially when outputs can be validated with unit or functional tests. But once a project must scale, persist, or enter production, structure becomes essential. Developers need guardrails: explicit constraints, documented decisions, and acceptance tests that confirm whether the system meets its intended design.
The industry is beginning to recognize this need for balance. Tools such as Kiro, SpecKit, CodePlain, and Tessl are emerging to formalize the relationship between natural language instructions and structured specifications. Even research initiatives like SpecLang explored structured natural language as a bridge between intent and implementation. The shared insight across these efforts is clear: freeform prompting does not scale without structure.
Another subtle but important issue is what some researchers describe as “functionality flickering”—when unspecified details change unpredictably between generations. If design choices aren’t explicitly defined, the AI fills in the gaps inconsistently. A feature may behave one way today and differently tomorrow, simply because the constraints were never made precise.
Ultimately, Wardzinski’s central message is not to reject vibe-coding but to mature beyond it. The future of AI-assisted development will belong to those who combine creative exploration with disciplined specificity. The real skill is not how enthusiastically one “vibes,” but how clearly one defines intent, constraints, and expectations. AI can execute instructions at remarkable speed—but only when those instructions are precise.
