Why "Vibe Coding" Is Not My Future | Prime Reacts

“Vibe coding” is gaining attention as a way to build software by letting AI generate code with minimal human inspection—but the core pushback is that the approach is too expensive, too error-prone, and too risky for anything beyond throwaway prototypes. The most immediate practical objection comes from cost: using OpenAI’s “jippy 45” (as referenced) can rack up over $200 for 2,000 quick requests, making “vibe coding” feel financially out of reach for casual experimentation.

At the center of the debate is what “vibe coding” actually means. Andre Kathi’s framing—“fully give in the vibes,” “embrace exponentials,” and “forget the code even exists”—is treated as a philosophy of not validating diffs, not analyzing generated output, and simply accepting changes until the app runs. In that model, tools like Cursor paired with LLMs (including mentions of Sonnet and “open AI 01 03”) are used to translate prompts (and potentially voice via “super whisper”) into working UI and logic. The appeal is speed and convenience: ask for concrete UI tweaks (“decrease the padding on the sidebar by half”), accept the patch, and move on.

But the counterargument is that “not caring about the code” breaks down the moment software needs correctness, maintainability, and security. The transcript repeatedly returns to a lived experience: generated code can compile and look fine while still failing at runtime, producing cascading bugs, or creating states where the app becomes unusable (including an example of “maximum update depth exceeded”). Even when the AI can fix issues, the workflow can turn into endless back-and-forth—asking, describing, and re-asking—until something works. That’s framed as slower than writing code directly, because human developers can be precise about intent, while natural-language prompts are inherently ambiguous.

There’s also a structural critique: LLMs may deliver the “fun” first 90% of a solution, but the last 10%—the part that makes a system robust—can be the hardest and most failure-prone. The transcript argues that programming doesn’t behave like image generation, where repeated sampling can converge on a visually acceptable result. In software, one wrong detail can break functionality or introduce vulnerabilities.

Security and accountability become the decisive line. Shipping AI-generated code without understanding it raises the risk of subtle issues—cross-site scripting vulnerabilities, insecure patterns, or even accidental exposure of secrets like API keys. The transcript emphasizes that LLMs don’t “know” your project’s constraints; they predict likely next tokens based on training, so bad practices can slip in even without hallucinations.

Still, the transcript doesn’t dismiss AI coding entirely. It distinguishes “vibe coding” from using AI as an assistant: autocomplete, boilerplate generation, and targeted help (like generating tedious type definitions) can be genuinely useful when developers remain in control. The conclusion is cautious: vibe coding may work for some low-stakes prototypes, but it’s unlikely to be a default approach for real projects—especially in teams where plans, milestones, and correctness matter.