... Any fool can vibecode. But any VibeCoder can also make a fool of themselves. That’s true democratization.

— Steven Broschart

Right now, two narratives are running parallel through the AI debate that don't really fit together.

The first goes:

Now anyone can build software.

The second:

AI-generated code is full of security problems and hard to maintain.

Neither is really completely wrong. And yet neither describes the situation particularly well.

Because the interesting observation is a different one: Vibe-coding doesn't make the difference between people smaller, but more visible. The simpler tools become, the more clearly it suddenly shows who:

  • understands structure,
  • brings technical depth,
  • makes good decisions,
  • and who really just reproduces standard solutions.

Exactly that can be observed everywhere right now.

A while ago, I built an internal monitoring tool myself using vibe-coding. Nothing spectacular, but functional within a few hours. A short time later, I came across a similar product online. A quick glance was enough to recognize the typical "handwriting" of Claude Code:

  • the same component logic,
  • the same helper functions,
  • the same UI patterns,
  • the same structure.

The product worked. But it had no originality whatsoever.

And that's exactly where the real misunderstanding of many democratization narratives lies: Yes, AI does democratize tools. Entry barriers fall dramatically. But that's still no guarantee for convincing results.

Why language models work so well specifically with code

Precisely in programming, the approach 'the LLM does the work' often works surprisingly well, and for the same reason that language models still have clear limitations in other areas.

In an earlier essay, I described how language models structurally tend toward the middle: toward the statistically most likely answer, toward consensus, toward standard solutions. In strategy work, that's often a problem. Good strategy often emerges precisely where you leave the middle.

With programming, the situation is almost reversed. When you build a React frontend, you rarely search for the most original architecture. Usually you want exactly that solution that has proven itself in practice and that other developers immediately understand. The same applies to API designs, database queries, or infrastructure patterns. Standard problems often benefit from standard solutions.

And that's exactly where language models are strong. The tendency toward statistical middle becomes an advantage with code: it reproduces established patterns, known libraries, and common architectural decisions.

There's something else too: code can be tested. A strategy can seem plausible for months before it becomes visible that it doesn't work. Software, on the other hand, collides relatively quickly with reality:

  • it runs,
  • or it doesn't;
  • it scales,
  • or it breaks apart.

This harsh feedback makes software development significantly more robust for AI systems than many other knowledge domains.

And the productivity gain is real. Not theoretical, but practically tangible. Tasks that used to take hours or days now often take minutes.

Why results are still so different

Yet a misunderstanding is emerging right now: many believe that through vibe-coding, everyone can now achieve roughly the same results. In practice, the opposite happens.

Two people can work with the same models and the same tools, and produce completely different results. One builds something that looks like hundreds of other AI projects: functional, but interchangeable.

The other builds something that:

  • seems well-thought-out,
  • is clearly structured,
  • shows technical depth,
  • and appears sustainably viable.

The difference isn't in the model. It's in the person.

Successful vibe-coding users usually bring four things with them:

First: technical depth. They understand the problem behind the prompt.

Second: context. They explain to the AI why their case isn't standard.

Third: skepticism. They critically examine results instead of just adopting them.

And fourth: They make architectural decisions themselves. The AI may write components, but not the actual structure of the system.

That's exactly where quality emerges.

And that's exactly why the role of the programmer is also shifting. The real difficulty lies less and less in writing code, but rather in understanding problems technically, structuring systems meaningfully, and making good decisions.

The real risk often lies invisibly underneath

The technical differences become particularly visible with detail questions like tech stack decisions. If someone who's never programmed builds a product with AI, they inevitably get recommendations:

  • which framework,
  • which database,
  • which hosting,
  • which libraries.

An experienced developer can assess:

  • whether that's scalable,
  • how actively a project is maintained,
  • what security problems might arise,
  • or whether you're just building up technical debt.

A beginner can't. At least not without first having to familiarize themselves with it. They don't adopt the recommendation out of laziness, but because they lack the criteria to evaluate it at all.

And that's exactly what makes vibe-coding simultaneously powerful and risky:

It creates the ability to act faster than the ability to judge.

The real problem is speed

There's a second dynamic on top of that: software is now being created faster than it can be reviewed.

Especially in the AI environment, you see it everywhere:

  • agent frameworks,
  • plugins,
  • toolchains,
  • open-source projects.

Everything grows at a pace that traditional review processes can hardly keep up with. The problem here isn't even primarily bad intent. But acceleration. Those who develop more slowly lose market share, reach, and attention. Those who review thoroughly are often slower, and that suddenly becomes a competitive disadvantage.

This creates structural pressure: Speed becomes more important than care.

And that's exactly why humans paradoxically become more important, not less. Because if everything is getting faster anyway, what suddenly matters more is:

  • who makes good decisions,
  • who recognizes risks,
  • who understands architecture,
  • and who just assembles generated output.

Why this extends far beyond programming

The nervousness around vibe-coding probably has one more deeper reason. Programming was long considered something only highly qualified specialists could master. If precisely this field becomes partially automatable, it seems like a signal for many other knowledge professions.

But that's exactly where precision pays off. The real observation isn't:

Machines replace people.

But rather:

Machines take over the standardizable part of human work.

And that's exactly how something becomes visible that often remained invisible before:

  • judgment,
  • taste,
  • responsibility,
  • contextual understanding,
  • technical expertise.

These things don't disappear. They become more valuable. That's also why a statement from Jensen Huang, the CEO of NVIDIA, is interesting. He's credited with roughly the following observation:

If a technology company doesn't really understand its own technology, it's essentially just a sales organization at its core.

The statement ultimately describes the same problem at the company level: Tools alone don't create technological depth.

What follows from this

Vibe-coding is neither fraud nor magic. It's a very powerful tool. But like almost all powerful tools, the quality of the result depends more on the user than on the tool itself.

Those who bring technical depth, structural understanding, and care can build things today that would have been barely possible a few years ago. Those who don't often mainly produce faster software that looks like everything else. And that's exactly why AI doesn't make the difference between people smaller. It makes it more visible.