AI-Assisted Development Is Not Vibe Coding
"Vibe coding" means throwing prompts at a model and hoping it works. That is not what AI-assisted development is. The difference matters, and it is the difference between amateur hour and professional craft.
AI-Assisted Development Is Not Vibe Coding
There is a term circulating in the industry right now: "vibe coding." It refers to a style of development where you type a prompt at an AI model, look at what it generates, feel roughly okay about it, and ship. The vibe is right. The code went somewhere. Good enough.
I want to push back on something. Not on using AI to write code. I do that every single day. I have built a chip tune synthesizer, an animated film studio, a volatility trading dashboard, a book, and a browser-based game museum, all with AI assistance at the center of every session. But what I am doing is not vibe coding, and the distinction matters more than most people are willing to admit right now.
What Vibe Coding Actually Is
Vibe coding is not lazy or stupid. It is a reasonable response to a genuinely confusing moment. AI models can now produce functional code from natural language descriptions. If you do not already know how to program, the temptation is to treat this as an unlock. You describe what you want. You accept what the model gives you. You move it into production. You tell yourself the model is reliable enough that critical evaluation is not really necessary.
That is the vibe. The vibe is: this looks about right, and checking closely would slow me down.
The problem is not the AI. The problem is the lack of a theory about what is happening. The vibe coder has no model of what the code is doing, which means they have no model of what it might do wrong, which means they cannot catch the errors the model makes. And the model does make errors. It makes them confidently. It makes them in ways that are invisible if you are not actively looking for them.
Security vulnerabilities are one category. Subtle logic errors are another. Architectural decisions that feel sensible locally but compose into a mess at scale are a third. The vibe coder meets all of these in production, where they are expensive, and has no theory about why they are happening.
What AI-Assisted Development Actually Requires
When I use Claude in agent mode to build something, the session does not start with a prompt. It starts with me knowing what I am trying to build and why. I have a mental model of the system: its components, their boundaries, the data flowing between them, the failure modes I care about.
The AI is not directing that. I am directing it. The AI is amplifying my ability to execute.
That distinction sounds subtle but it is the whole thing. When the model proposes an approach I would not have chosen, I notice. Not because I know better than the model on every technical question. Because I know what the system is supposed to do, and I can evaluate whether the proposal gets me there. When the model makes an error, which it does regularly, I can see it because I have a theory of what correct looks like.
This requires skill. It requires the ability to read code, understand it, and evaluate it against a standard you carry in your head. It requires knowing what questions to ask the model to surface its assumptions. It requires the discipline to slow down when something feels off rather than accepting it because the vibe seems right.
Agent mode specifically asks more of you, not less. In a normal editor workflow, the model suggests one line at a time and you approve or reject it. In agent mode, the model executes sequences of operations autonomously. It reads files, writes files, runs commands, makes architectural decisions in real time. The power is real. But so is the surface area for error. You need to be paying close attention, not coasting on vibes.
A Concrete Example
I recently directed the production of a three-and-a-half-minute animated rap film called The Intruder. It involves seven passes of animation rendering, voice synthesis through ElevenLabs, autotune processing with Rubberband, a custom chip tune music score, and a final FFmpeg mix pipeline that takes all the stems and produces a finished MP4.
None of that code appeared from nowhere. The AI generated large portions of it. But every generation was preceded by a design decision I made: which vocal persona, what BPM, how to handle the timing retime when ElevenLabs clips run long, what FFmpeg filter chain achieves the sidechain pump effect I wanted. And every generation was followed by evaluation: does this actually do what I intended, are there any edge cases I need to handle, does this compose correctly with the other parts of the system.
That process took about twelve passes across multiple sessions. A vibe coder would have stopped at pass three when it "basically worked." I kept going because I had a clear standard in my head and the output had not met it yet.
The difference between those two approaches is not whether AI was used. Both used AI heavily. The difference is whether the human was directing a craft process or just accepting outputs and hoping.
Why It Matters
There is a version of the future where "vibe coding" normalizes across the industry and the result is a lot of software that nobody fully understands, deployed by people who cannot debug it, maintained by no one. The models will get better, but the fundamental problem is not model quality. It is the absence of a human who can evaluate and take responsibility.
Security is the sharpest edge of this problem. Models will generate code with SQL injection vulnerabilities. They will omit authentication checks. They will store secrets in ways that are trivially discoverable. They will do this without any indication that something is wrong, because they are predicting plausible code, not reasoning about whether it is safe. The vibe coder will not catch any of this.
Professional accountability is another edge. If you deploy something and it goes wrong, someone will ask you to explain it. "The AI wrote it and I thought it looked fine" is not a professional answer. It may not even be a legal one, depending on the domain.
The industry is going to figure this out the hard way. There will be a wave of AI-generated incidents: breaches, outages, embarrassing failures. And then there will be a correction, and the correction will be: you need engineers who understand what the AI is generating, not operators who are just approving it.
The Professional Standard
The right framing is not "can AI write code?" The right framing is "what does it take to work with AI at a professional standard?"
That standard looks like this: you come to the session with intent. You direct the work. You evaluate every output. You maintain architectural coherence across sessions, because the model has no persistent memory of what you are trying to build. You catch errors before they compound. You take responsibility for the result.
None of that is easy. But it is not harder than traditional software engineering. It is approximately the same bar, with a very powerful new tool available inside it.
The vibe coder is not using that tool well. They are using it as a replacement for skill they do not have. The AI-assisted engineer is using it as an amplifier for skill they do have.
That amplification is genuinely transformative. I can build things now that would have taken a team months, and I can build them alone in weeks. But that leverage only exists because I know what I am doing and I can direct the process intelligently.
Drop the underlying skill, and you drop the leverage with it. You are left with vibes and hope. In production, that is not a strategy.
I wrote a book called AgentSpek that covers the methodology for working with AI in a disciplined way, including how to think about prompt craft, agent mode oversight, and architectural intent. The projects section shows what this approach produces in practice. If you are new here, start here.