Vibe Coding
How do we feel about it? Is it the art of Software Engineering a relic of the past? Or the key to AI-driven innovation?
Let's be real. What we're calling vibe coding today—people throwing vague prompts at AI and expecting magic—isn't just another step in programming's evolution. It's a fundamental rupture in how we create software.
I've been watching Engineers react to this shift with skeptical fascination. The fascination comes from seeing people with zero technical knowledge suddenly building dope shit by describing what they want in plain English. The skepticism? That it mostly actually works… at least until it doesn’t.
What's Actually Happening Here
The term "vibe coding" captures something real: the collapse of traditional knowledge barriers in software creation. But underneath the memes and the moral panic, something more interesting is happening.
Programming has always been about layers of abstraction. We started with machine code, moved to assembly, jumped to higher-level languages, then frameworks, and now we're approaching a layer where natural language itself becomes the interface to computation.
This one doesn't just hide implementation details. It hides the very concept of implementation.
Not Just Another Tool in the Box
Whenever new technology emerges, there's this predictable chorus of "it's just another tool" or "real engineers will still be valuable." That's comforting but misses the point.
AI isn't just another abstraction layer like high-level languages were to assembly. Previous abstractions required you to understand programming concepts. They just made them more convenient. You still needed to think algorithmically.
This is different. A great model doesn't just abstract away implementation. It abstracts away the need to think like a programmer at all.
That's not an incremental change. That's a phase transition.
Language Creates New Possibilities
What's also fascinating is how the term "vibe coding" itself is shaping reality. By naming this practice, by giving legitimacy to what would previously have been dismissed as "not real programming," we're creating space for entirely new approaches to software creation.
The term validates non-traditional paths into tech. It challenges the gatekeeping that's been baked into software culture. It suggests that technical intuition might be as valuable as technical knowledge.
And, I’ll admit, that can be frustrating as hell to someone who spent years mastering the craft.
Where This Actually Leads
Most discussions about AI and coding fall into simplistic traps: either "AI will replace programmers" or "AI is just a tool that makes programmers more productive."
The reality is messier and more interesting.
What we're witnessing isn't the death of programming. It's a bifurcation. Two distinct disciplines are emerging from what was once a single field:
System Engineering
Designing, architecting, and maintaining the complex systems that underpin our digital world. This requires deep technical knowledge and won't be replaced by vibes anytime soon.Product Engineering
Creating specific solutions to business problems using AI as the primary implementation layer. This is where vibe coding thrives, and it's becoming its own discipline with its own expertise.
These aren't the same field anymore. They require different skills, different mindsets, and different types of people.
The New Knowledge Hierarchy
Here's where things get really interesting. Vibe coding doesn't eliminate the need for expertise. It creates demand for new types of expertise:
Prompt Engineering
Not just writing good prompts, but understanding the conceptual boundaries of what current AI can and cannot accomplish.Result Verification
Developing reliable methods to verify that AI-generated code actually does what it's supposed to do, without necessarily understanding all implementation details.System Design
Creating architectures that work reliably even when individual components are generated by AI with varying levels of reliability.
None of these are simple skills. They're just different from traditional programming skills, and they'll be valuable in different contexts.
What Actually Matters Now
The most valuable people in this new landscape won't be those who cling to traditional programming or those who blindly embrace vibe coding. They'll be those who can bridge these worlds. People who understand enough about implementation to know what's possible, but who can translate between human intent and computational reality.
If you're panicking about AI replacing Software Engineers, you're headed down the wrong path. The real question is: how does your role evolve when implementation is no longer the bottleneck?
Some possibilities:
Becoming the person who can frame problems in ways that make them easily solvable
Developing expertise in verifying and validating solutions
Building the frameworks and platforms that make AI-assisted development reliable
Creating the tools that let others build with confidence
Learning From Historical Parallels
The closest parallel to what's happening now isn't any previous programming paradigm shift. It's what happened to other fields when they were transformed by computation.
Think about graphic design before and after desktop publishing. Or architecture before and after CAD. Or animation before and after computer graphics.
In each case, the field didn't disappear. It transformed. Some traditional skills became less valuable. New skills emerged. The nature of expertise shifted.
The people who thrived weren't those who resisted change or those who abandoned all traditional knowledge. They were those who understood which fundamentals remained valuable and which needed to evolve.
The Only Path Forward
The future belongs to those who can navigate the middle ground. People who can bring enough technical knowledge to avoid AI's pitfalls, but enough flexibility to leverage its capabilities.
AI isn't just another tool. Nor is it the end of programming either. It's the beginning of something different. Something that will eventually just be called "coding" again, but will mean something new.
The question isn't whether you embrace AI or resist it. The question is: what aspects of software creation still require human expertise, and how do you position yourself at that intersection?
That's where the opportunities are. That's where the future is being built.
And it's going to be a wild ride!