Wise Coding
Vibe coding makes engineers wiser.
As someone who has built systems from user interface to backend database across decades of technology shifts, who was testing early JavaScript on Netscape Navigator when most developers dismissed it as a toy, I can confirm that AI has genuinely changed how I work.
Every few years, a new technology promises to democratize coding. Make it accessible to everyone. No code. Eliminate the need for programmers. The tools change. The promise doesn’t. And the reality never quite matches the headline.
But this time is different. AI is a genuine breakthrough.
I felt this once before, watching JavaScript transform from a browser curiosity into the language that would reshape the entire web. That same recognition hit me when I first used AI for development. This is real. This changes things.
What concerns me isn’t the technology. It’s the narrative wrapped around it.
Media frames AI as a race. Humans versus machines, programmers fighting for survival, jobs on the line. This framing misses the point entirely. The breakthrough isn’t that AI replaces what engineers do. The breakthrough is that AI amplifies it.
The future I see isn’t about survival. It’s about value augmentation. Human use genuinely clever technology to create more than we could before. That’s the opportunity. That’s what makes this moment matter.
Some people vibe code for experiment, for prototyping. I vibe code for serious applications.
The difference isn’t the tool. It’s what you bring to it.
This is what I actually do when I “vibe code”:
I design my workflow. I design my database schema: table by table, column by column, key by key. I think through input-process-output logic before I write a single prompt. I make architectural decisions about microservices versus monolith. Then I use AI to generate the syntax faster.
The thinking didn’t change. The sequence didn’t change. The knowledge didn’t become optional either.
What changed is I type less and decide more.
That’s what makes engineers wiser. It’s not what the headlines are selling.
The Alarm Industry
A recent news headline claimed that vibe coding “allows amateur programmers to create custom software applications.” The framing suggests anyone can now build applications by describing what they want.
This sells newspapers only, not truth.
Media benefits from creating an alarming clickbait “AI replaces programmers” generate clicks, “Anyone can build apps now” drives engagement. Narratives like these demand a race where someone must lose.
But this isn’t what AI companies actually say. Anthropic, OpenAI, Google, they all position these tools as assistance, not replacement. Human-in-the-loop. Augmentation. Collaboration.
The alarm is a media construction. And it’s distorting how organizations think about AI adoption.
The Wrong Question
The alarming narrative asks: Will programmers survive?
Wrong question. It fixates on scarcity—who wins, who loses, who gets replaced.
The right question: How do we use clever technology to create more value?
AI is clever. No argument. But cleverness without direction produces noise, not outcomes. The value isn’t in the tool’s capability. It’s in knowing what to build and why.
Prompt-to-Interface Is Not Prompt-to-App
Here’s what vibe coding delivers well: interfaces.
Describe a product catalog, a checkout flow, a dashboard—AI generates it quickly. The screens look real. The buttons look clickable. The demo impresses.
This is prompt-to-interface. It’s valuable for prototyping, for communicating ideas, for testing concepts before committing resources. Designers, product managers, business analysts, professionals who think visually can now render their thinking faster than ever.
But an application isn’t an interface.
Consider e-commerce. Customers see a beautiful product catalog and a smooth checkout button. What makes the business work is everything invisible: inventory sync across warehouses, shipping carrier integrations, payment reconciliation, return authorization workflows, fraud detection, tax calculations by jurisdiction, fulfillment status updates.
The checkout button is the tip of the iceberg. The fulfillment workflow is the business.
Even a chatbot isn’t simple.
Building a ChatGPT wrapper takes an afternoon. Building a production chatbot is a different matter entirely: chat thread management, content accuracy validation, AI hallucination detection, rule-based guardrails, context window optimization, fallback handling when the model fails. The wrapper is the demo. The invisible architecture is the product.
The pattern repeats everywhere. What looks simple on screen hides layers of technical decisions that determine whether software works reliably or embarrasses you in production.
Prompt-to-interface gives you the tip. It doesn’t give you the mass below the waterline where all the logic, integrations, data IO, error handling, manage repositories, and business rules that make software actually function.
The Thousand-Prompt Reality
Can AI generate that deeper complexity?
In my experience, achieving working input-process-output logic requires not a few prompts but hundreds. Sometimes thousands. Edge cases surface. Integrations break. Architecture needs rethinking. Debugging consumes hours.
At that point, you’re not vibe coding. You’re doing full-scale development—just with a different interface. The effort isn’t eliminated. It’s redistributed.
And here’s the critical difference: with technical foundation, those thousand prompts move you forward systematically. Without it, you’re circling, trying variations, hoping something works, unable to diagnose why it doesn’t.
Prototyping and production require different knowledge, different processes, different expectations. Both are legitimate. Conflating them is where organizations get hurt.
The Myth of One-Size-Fits-All
AI-assisted coding reduces syntax obstacles. That’s real value.
But syntax is one layer. Application development spans many: backend architecture, database design, security, performance tuning, integration. Each is a distinct stream of knowledge. Years of learning. Hard-won pattern recognition. Judgment built from failure.
The promise that one AI tool handles all of this? Overselling.
Perhaps the future looks different. AI agents specialized by domain: one for database design, one for security review, one for performance optimization. Human developers visualize logic on a holistic dashboard, drag and drop to connect the dots. It’s plausible. The technology trends point there.
But even then, the operator needs to understand what those agents do. Which agent to invoke when. How to evaluate their output. When to override their recommendations.
The dashboard doesn’t eliminate technical knowledge. It assumes it.
Tools get more powerful. The need for technical judgment doesn’t disappear, it only moves higher.
Wise Coding
The real opportunity isn’t about survival. It’s about leverage.
Engineers who understand system design, data relationships, and workflow architecture create more value with AI than without it. Not because they type faster, because they focus on decisions that matter while AI handles the tedious parts. This is value augmentation. AI amplifies what you know. It doesn’t substitute for what you lack.
The marketing professional using AI to prototype an interface isn’t becoming a programmer. They’re gaining a communication tool for an effective way to visualize ideas and share concepts with technical teams. That’s valuable. It’s simply not “building custom software applications.”
The distinction matters. When organizations confuse prototyping with production, they ship facades that collapse under real use. When they expect prompt-to-app magic, they set projects up for failure.
The question isn’t whether AI is clever. It is.
The question is whether you’re wise enough to use clever technology for value creation, not alarm, not hype, not the fantasy that complexity disappears because the interface looks easy.
Vibe coding makes engineers wiser because it frees them to focus on what they’ve always been paid to do: think through problems, design solutions, make architectural decisions that determine whether software works or fails.
For everyone else, AI offers different value: prototyping, exploration, communication, acceleration within your actual domain of expertise.
The value is real. But it’s not the same value.
That’s wise coding. Knowing the difference.
