Cracking the code of vibe coding

Not all vibes are good.

Are you wondering WTF all this vibe coding stuff is that’s been sweeping your social feeds? You probably get the gist that it’s about having a vision of an app or software you want to build and vibing your way into it with the help of an AI tool.

If you’re an old-school coder like me, you might be laughing a bit at the idea that you can just blink software into reality — especially when you think about all the blood, sweat, and tears it traditionally takes to build excellent products. It’s like a joke that it can suddenly be so easy.

Well, stop laughing. Vibe coding is real, and in this article, I’ll explain it and break down how it’s changing how we imagine, build, and grow products and companies going forward. I’ll also make a case for embracing vibe coding without losing your soul, including four frameworks to optimize your success with the good vibes from creating genuinely helpful, well-crafted software and apps.

Same cycle, new vibe

Vibe coding is also just one more example of what I call the Great Democratization Cycle. We’ve seen it in photography as it evolved from darkrooms to digital cameras, which eliminated film processing, to smartphones and Instagram filters, making everyone a high-end “photographer.” The same goes for publishing (from printing presses to WordPress), video production (from studio equipment to TikTok), and music creation (from recording studios to GarageBand on a laptop and now AI tools like Suno on your smartphone).

Each wave democratized image creation while simultaneously changing what it meant to be a professional in the field.

Software development is in no way exempt. We’ve come a long way from traditional coding (1970s-2010s), which was expert-driven and hard to get into. From the low-code/no-code movement (2010s) to AI-assisted development (early 2020s; i.e., Github Copilot), the path to easy software development has accelerated to where we find ourselves today: vibe coding. Thanks to platforms like Windsurf, Cursor, LoveableDev, and Replit, anyone can take their app or software idea to execution within minutes.

However, it’s not that simple — not for the non-technical entrepreneur who feels empowered or the veteran developer who feels dissed.

The seduction of simplicity

Let me take you back to my first coding project. As a pimply-faced teen in my parent’s basement, I spent countless sleepless nights wrestling with syntax errors, debugging mysterious crashes, and finally experiencing that incomparable rush when my creation actually worked. The journey was killer, but it shaped my understanding of how software fundamentally operates.

Fast forward to 2025, and we’re witnessing a revolution called “vibe coding” — a term popularized by AI researcher Andrej Karpathy that’s taken the tech world by storm. The premise? Simply describe what you want in natural language, and AI generates the code. No more syntax struggles. No more Stack Overflow deep dives at 2 AM. Just vibes.

It’s intoxicatingly easy.

I recently tested this myself. I prompted CursorAI, an AI-enabled IDE (integrated development environment), and built an app called DaddyTime that helps discover new cool things I can do with my son (he’s three).

Within 30 minutes, I had a fully functional progressive web app that:

Image by Author.

Connected to local events in my area
Integrated with a weather service (to suggest indoor or outdoor activities)
Correlated ideas with local weather
Integrated with my Google calendar for booking events

The entire process took less than 30 minutes — no coding required, just a conversation with an AI.

This isn’t an exaggeration — it’s our new reality.

And that’s precisely what worries me.

The craft crisis

This AI-driven accessibility is undeniably powerful. Designers can prototype without developer dependencies. Domain experts can build tools to solve specific problems without learning Python. Entrepreneurs can validate concepts without hiring engineering teams.

But as we embrace this new paradigm, we face a profound question: What happens when we separate makers from their materials?

Consider this parallel: Would we celebrate a world where painters never touch paint, sculptors never feel clay, or chefs never taste their ingredients? Would their art, their craft, retain its soul?

When we remove the intimate connection between creator and medium — in this case, between developer and code — we risk losing something essential: the craft. And it’s not just about producing working software. It’s about:

Understanding systems at a fundamental level, which allows you to solve problems when things inevitably break
Creating elegant, maintainable solutions that stand the test of time
Building mental models that inform higher-level architectural decisions
Developing an intuition for performance, security, and edge cases

A Microsoft engineer was brutally honest about AI-generated code, stating that LLMs are “not good at maintaining or extending projects” over time and often “get lost in the requirements and generate a lot of nonsense content.”

This isn’t surprising. AI excels at mimicking patterns but lacks the deeper understanding that comes from years of hands-on experience. It can produce code that works initially but falls apart under pressure.

As one tech CTO warned, overreliance on AI can lead to “hidden complexities” — quick fixes that “become unmanageable during scaling or debugging.” The 75% that AI solves quickly often leaves the critical 25% — making code production-ready — a looming challenge.

And don’t even get me started on the security risks this poses. You could be a few clicks away from leaking all your data by signing up for some cool thing that popped up in your IG feed. That’s not a vibe, is it?

Beyond technical debt: creative debt

There’s something even more concerning than technical debt lurking in our AI-coded future: creative debt.

True innovation often emerges from constraints and deep domain knowledge. When you wrestle with a programming language’s limitations, you’re forced to think creatively within boundaries. This tension produces novel solutions and unexpected breakthroughs.

When we remove this friction entirely, we risk homogenizing our solutions. If everyone asks AI for “a responsive e-commerce site with product filtering,” we’ll get variations on the same theme — technically correct but creatively bankrupt implementations that feel eerily similar.

The danger isn’t just bad code; it’s boring products and AI slop.

The knowledge gap widens

Vibe coding creates two distinct tracks for engineers:

Those who understand the foundations and can wield and direct AI effectively
Those who depend entirely on AI outputs without comprehending what’s happening under the hood

This bifurcation has serious implications. When problems arise — and they will — the second group will be helplessly dependent on AI to fix issues it may have created in the first place.

What happens when the AI can’t solve the problem? Who do we turn to then?

Image meme via Reddit.

As The Guardian aptly observed, “Now you don’t even need code to be a programmer. But you do still need expertise.” This expertise gap will only widen as more people build software without understanding its inner workings.

The swiss army knife imperative

Recent headlines confirm a troubling trend: Amazon plans to terminate over 14,000 managerial positions to save $3.5 billion annually. Meta, Microsoft, and countless others are making similar moves. The message is clear — operational efficiency is king, and specialization is becoming a luxury.

This streamlining creates a new mandate: everyone must become a Swiss Army knife of skills.

Vibe coding accelerates this transformation. When anyone can generate functional code through conversation, the specialization that once protected technical roles evaporates. The implications ripple through organizations:

Product managers can’t hide behind documents and wireframes — they’ll need to generate working prototypes
Designers can’t simply hand off mockups — they’ll need to implement their designs
Marketers can’t request custom tools — they’ll build their own analytics dashboards
Executives can’t claim technical ignorance — they’ll need to understand the systems they oversee

This isn’t just speculation. Amjad Masad, CEO of Replit, revealed that “75% of Replit customers never write a single line of code” already. The future is arriving faster than we think.

In this new landscape, value shifts dramatically from technical implementation to problem identification. As one entrepreneur noted, “If you have an idea, you’re only a few prompts away from a product.” The bottleneck is no longer development speed — it’s knowing which problems are worth solving.

Mental models for the vibe coding era

To navigate this shift, we need new mental models. Here are four frameworks I’m using to make sense of the vibe coding revolution:

1. The creation-maintenance divide

Vibe coding excels at creation but struggles with maintenance. This creates a fundamental split:

Creation: Easy, accessible, democratic
Maintenance: Complex, requiring deep expertise, increasingly valuable

Smart organizations will develop dual skillsets — rapid vibe coding for prototyping and proof-of-concepts, alongside rigorous engineering practices for production systems.

2. The three tiers of software creators

As coding barriers fall, a new hierarchy emerges:

Prompt Engineers: Those who use AI to implement existing patterns
Solution Architects: Those who combine AI capabilities in novel ways
System Innovators: Those who create entirely new paradigms AI hasn’t seen

Your value as a software creator will increasingly depend on moving up this ladder.

3. Intellectual leverage vs. Execution leverage

Traditional software provided execution leverage, automating repetitive tasks. Vibe coding gives us intellectual leverage, automating thinking itself. This means the highest-return activities shift from “building things right” to “building the right things.”

4. The specialization paradox

As tools become more powerful, success depends less on specialization and more on synthesis. The most valuable person isn’t the deep expert in a single domain but the connector who understands multiple domains well enough to identify novel intersections.

Finding the balance: augmentation, not replacement

I’m not suggesting we abandon AI-assisted coding — that would be like rejecting power tools in favor of hand saws. But we need to approach this revolution thoughtfully, preserving craft while embracing innovation.

Here’s what I propose:

For individual creators:

Learn the fundamentals first. Build a strong foundation in programming concepts before relying heavily on AI. I refuse to hire engineers who can’t code without an LLM
Use AI as a collaborator, not a replacement. Let it handle boilerplate while you focus on architecture and novel features.
Understand what the AI produces. Take time to read and comprehend generated code before implementing it.
Challenge AI outputs. Instead of accepting the first solution, ask, “Is there a better way?”
Develop T-shaped expertise. Deep knowledge in one area, with broad understanding across many.

For teams and organizations:

Establish robust review processes for AI-generated code. Don’t skip quality assurance just because AI wrote it.
Create balanced teams with both AI enthusiasts and traditional craftspeople who can provide valuable checks and balances.
Invest in education that emphasizes system thinking and architecture, not just prompt engineering and vibe coding.
Document diligently. With less human-written code, thorough documentation becomes even more crucial.
Restructure around problem spaces, not technical specializations.

For the community:

Value and celebrate craftsmanship. Let’s not lose sight of the artistry in well-crafted code.
Develop ethical frameworks for responsible AI coding that preserves innovation while mitigating risks.
Share learning resources that combine AI tools with foundational programming knowledge.
Create new certification paths that validate understanding, not just implementation ability.

Distribution: the new cheat code and business moat

While we debate craft versus convenience, vibe coding has another dimension that deserves attention: the democratization of distribution.

Pieter Levels, the indie hacker behind Nomad List and a dozen other profitable solo ventures, recently dramatically demonstrated this shift. Using vibe coding techniques through CursorAI, he built and launched RemoteOK Jobs 2.0 in just six hours, then shared the entire process on social media.

“I had the idea at breakfast,” he posted. “By dinner, it was live with 5,000 users.”

This isn’t just fast development — it’s a fundamental collapse of the creation-to-market timeline. When Levels built his first successful product in 2014, it took him weeks of coding. Now, with AI assistance, he’s compressing that cycle to less than a day while reaching audiences in the millions.

The implications are profound:

The idea-execution gap vanishes. When you can go from concept to working product in hours instead of months, more ideas get tested in the wild.
Audience trumps technical complexity. Levels’ success isn’t primarily about his coding skills — it’s about his deep understanding of his audience and distribution channels. He knows exactly who he’s building for and how to reach them.
Marketing > Building. As Levels bluntly said: “I spent 20% of my time building and 80% telling people about it. That ratio used to be reversed.”

This hints at a future where technical implementation is so streamlined that distribution becomes the primary differentiator. The winners won’t necessarily be those who build the best product in a technical sense but those who make the right product for a specific audience and get it in front of that audience fastest.

For entrepreneurs, this means investments in audience building, community development, and marketing channels may yield higher returns than investments in technical infrastructure.

For larger organizations, it means the teams that understand customers and distribution will increasingly drive product development, not the other way around.

In this brave new world, the greatest advantage goes to the creators who combine:

Deep audience understanding
Rapid implementation through vibe coding
Established distribution channels
A willingness to launch fast and iterate publicly

That’s a very different skill set than what created tech success a decade ago, and it favors domain experts, community builders, and audience cultivators over traditional technical specialists.

Disrupting the disruptors: no-code’s no-future?

Perhaps the most fascinating second-order effect of vibe coding is how it threatens to make the no-code/low-code movement obsolete almost overnight.

For the past decade, platforms like Webflow, Bubble, and Airtable have carved out a valuable middle ground: visual interfaces that let non-developers build functional software without coding. These platforms found product-market fit by eliminating the need to write code while still requiring users to understand logical structures and workflows.

Vibe coding leapfrogs this paradigm entirely. Why learn a proprietary visual interface when you can simply describe what you want in plain language?

This disruption of the disruptors creates cascading effects:

No-code platforms must evolve or die, likely by integrating AI to become “AI-enhanced no-code.”
Visual programming becomes a transitional technology rather than the end-state many predicted.
The value shifts from tools to prompts and patterns, creating new opportunities for “prompt marketplaces” and pattern libraries.
Traditional developers and no-code creators increasingly compete

in the same space.

The survivors will be those who recognize that the true value lies not in the implementation method but in a deep understanding of human problems and creative solutions.

A call for thoughtful evolution

People have always created the most compelling software with vision, empathy, and a deep understanding of human needs. AI can help us execute that vision more efficiently but can’t replace the human spark that drives genuinely transformative products.

Consider that what separates good products from great ones is rarely technical perfection — it’s the human touch, the careful consideration of edge cases based on real-world experience, and empathetic design that anticipates user needs.

As Karpathy himself noted, vibe coding isn’t about abandoning thought — it’s about thinking at a higher level: “I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works.” The key word is “mostly.” The gap between “mostly works” and “delights users” is where human creativity and craft still reign supreme.

So, the question is: Will we use AI to amplify human creativity, freeing us from drudgery so we can focus on innovation? Or will we surrender our craft entirely, becoming mere prompt engineers orchestrating increasingly generic software?

The choice is ours.

For my part, I’m embracing AI as a powerful collaborator while fiercely protecting the craft that drew me to this field. While good vibes might get you a working prototype, it’s the marriage of human creativity with technological tools that creates truly extraordinary products.

Not all vibes are good — but with intention and care, we can ensure that the products we build in this new era combine the best of both worlds: AI efficiency and human ingenuity.

The code of the future shouldn’t just run. It should sing.

Originally published on medium.com.