Why 'knowing the domain' beats 'knowing the syntax' in the age of AI

For decades, knowing how to code was the ultimate filter — the border between those who could build the future and those who could only imagine it. Programming was an exclusive language, and mastering it was the passport to innovation.

But something fundamental is changing. With the rise of large language models, the ability to “speak code” is no longer the bottleneck. Anyone who can articulate a problem clearly — in natural language — can now build, automate, and experiment with software. The new literacy is context and intent, not syntax.

The Rise of Vibe Coding

Figure 1. Vibe coding: the engineering of managing coding agents

A recent paper, A Survey of Vibe Coding with Large Language Models (arXiv:2510.12399), formalizes this shift. The authors describe “vibe coding” as a new software engineering paradigm where developers — or domain experts — express what they want in natural language, and AI agents translate that intention into functioning code.

As the paper puts it:

"Vibe Coding democratizes development by lowering technical barriers. Traditional development required extensive programming knowledge before implementing ideas. Natural language becomes the primary creation interface [4, 5, 1]. Domain experts—medical practitioners, educators, designers—articulate needs without computer science education [35]. This diversifies innovation sources, materializing underrepresented perspectives [276, 90]. Economic impact manifests through creator economy expansion: domain experts monetize specialized tools without technical co-founders. This parallels previous democratization waves, representing software literacy’s evolution from specialized skill to broadly accessible capability [277]."

That’s a polite academic way of saying: you don’t need to be a coder anymore to create technology.

From Coders to Context Designers

In the era of vibe coding, the most valuable skill is no longer the ability to implement an algorithm — it’s the ability to frame a problem precisely enough for an AI to solve it. The engineer’s role shifts from writing code to curating context.

Large language models are increasingly capable of handling the heavy lifting: writing boilerplate, integrating APIs, generating tests, and refactoring entire modules in seconds. The human task becomes more abstract — defining goals, understanding users, validating outcomes, and, most importantly, knowing why something should be built in the first place.

Technically, vibe coding relies on prompt-to-code translation, feedback loops, and memory contexts that allow AI agents to act as continuous collaborators rather than static tools. But the real transformation is cultural: programming is no longer a private language — it’s a shared conversation.

The Democratization of Software Creation

This is the next wave of software literacy. Just as spreadsheets let non-programmers do analytics and early web builders allowed anyone to publish online, vibe coding opens the door for domain experts to innovate directly — without waiting for a technical co-founder.

Educators can build adaptive learning tools. Doctors can prototype decision-support systems. Investigative journalists can automate parts of their analysis. All without touching a compiler.

This isn’t just productivity — it’s diversity. When the power to automate and create software extends beyond professional programmers, we get new voices, new biases (yes), but also new perspectives that never made it through the technical gate before.

The End of Algorithmic Gatekeeping

Many hiring processes still test humans through exercises designed for another era — whiteboard coding, algorithm puzzles, sorting challenges. Ironically, the systems used to test “problem-solving skills” are now solved instantly by the very AI tools candidates are supposed to use at work.

It’s time to rethink what we value. Being a good engineer in 2025 isn’t about knowing every edge case of a data structure. It’s about integrating knowledge, building safely with AI, and understanding the broader environment — legal, ethical, and human — in which your system operates.

Humans Still Matter — Just Not the Same Way

In my own work building desenmascara.me, I see this transition daily. The technical core — the AI model, the database, the interface, the analysis pipeline — is important. But the real breakthroughs happen when domain expertise meets automation: understanding how scammers operate, how users think, and how to translate that into patterns an AI can detect.

Figure 2. Example of domain expertise for edge cases on scam websites

That’s not programming — that’s reasoning, modeling, and empathy. It’s the essence of what vibe coding elevates: humans defining intent, AI handling execution.

The Bottom Line

Vibe coding doesn’t make programmers obsolete — it redefines them.
It moves software creation from a narrow technical discipline to a broader human capability. The best developers of the future won’t be those who type the fastest, but those who think the clearest.

The next generation of engineers, designers, and researchers will all share one thing: They don’t just code. They vibe.

The article itself is AIL 3