Deconstructing Vibe (Agentic) Coding Through First Principles

What is vibe (agentic) coding

Vibe coding is a term coined by Andrej Karpathy. It describes creating something with code without writing code.

He introduced it in a post on X in early February 2025, describing it as: “a new kind of coding where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”

My definition is the following:

Agentic coding is a systematic orchestration of AI coding agents that materialize your intention with code.

I like to think about vibe and agentic coding differently. The former is a hobby activity. You have an idea, you prompt it, and the agent creates it. You are sort of vibing through it. You get something at the end and it’s most likely not a production grade app.

Agentic coding, on the other hand, is a professional activity. There’s a certain system you follow to maximize the quality of the outcome.

Deconstruction of vibe coding assumptions

Let’s deconstruct most of the assumptions using first principles, then discover truths and build predictions.

“AI will replace developers”

Why do people believe this? Because AI can write code.

Why does writing code matter? Because that’s what developers are paid to do.

Is that true? No. Developers are paid to solve problems using code. Writing is the mechanical part. The job is: understand the problem, design a solution, implement it, verify it works, maintain it over time.

Which parts can AI do today? Implementation, well. Verification, partially. Understanding the problem, poorly. Design decisions, poorly. Maintenance over time, not at all.

Why can’t AI do the rest? Because understanding the problem requires context that lives outside the codebase. In conversations, in business constraints, in user behavior. Design requires trade-offs between competing goals that have no objectively correct answer. Maintenance requires judgment about what matters over time.

What’s the fundamental truth? The mechanical layer of software is collapsing toward zero cost. The judgment layer isn’t.

So what actually happens? You don’t replace the person who has judgment. You remove the bottleneck between their judgment and the result. One person with good judgment now does what took a team to execute.

Prediction: Team sizes shrink, but the people who remain are more valuable, not less. And more teams emerge because the barrier to execution drops.

“Vibe coding = type a prompt, get an app”

While it’s true that you can nowadays one shot an app, it doesn’t mean the result is any good.

You can refine it in several iterations and prompts, but it still won’t be production grade. If it’s just for personal use, then yes, you can create an app with a prompt on a platform like v0 or Loveable.

If you are creating a product, or using AI in a team, then the approach is different. It’s not about writing a prompt, but taking a fundamentally different approach to maximize the quality of the outcome, consistently.

Since the cost of producing code reaches zero, the technical and product judgment becomes essential.

The prompt-to-app pipeline fails because nobody is judging the output. The code runs, but nobody asked: is this secure? Does this solve the right problem? Will this break in 6 months?

This brings us to another prediction: You’ll be able to create apps for yourself, but if you’re creating a product, you’ll need to judge what you build, not just prompt it into existence.

“Vibe coding is not for production”

It is true. As we discussed earlier, write a prompt and get a result is for hobbyists. If you want to do serious work you have to learn to judge what to build and how it’s built, orchestrate the agents, and QA the result.

Right now, as of 2026, I create production ready marketing websites built following a custom designed website in Figma. My next milestone is a production-ready web app, which I am in the middle of.

While vibe coding is not for production, serious agentic coding is and will be for production. Take a look at Nvidia or Anthropic itself. They already use it to ship product updates.

“AI produces slop”

Why do people believe this? Because they’ve seen it. AI-generated content, code, and designs often look generic, shallow, or broken.

Is that true? Yes, often. But why?

Because most people give AI vague input and accept the first output. “Build me a landing page.” “Write a blog post about X.” “Make an app that does Y.” The AI complies. The result is mediocre. And the person concludes: AI produces slop.

But what happens when you give it specific constraints? When you define the architecture before the implementation? When you review the output, reject what’s wrong, and redirect? The quality changes completely.

The fundamental truth here is old and boring: garbage in, garbage out. AI didn’t invent this. It just made the cycle faster. You get slop at the speed of light instead of slop at the speed of a junior developer.

The variable was never the AI. It was the input. The intent. The judgment applied after the output.

This is why two people using the same model produce wildly different results. One prompts and accepts. The other prompts, reviews, rejects, refines, and prompts again with more context. The model is identical. The operator isn’t.

Prediction: AI will keep producing slop for people who treat it like a magic box. For people who treat it like a tool that amplifies their thinking, the quality ceiling will keep rising.

Fundamental truths that emerge

These are the truths I keep coming back to after stripping away the hype:

• The bottleneck was never typing code. It was knowing what to build and why.
• AI amplifies input quality. An expert with AI will always produce better results than a novice with AI. Always. The gap doesn’t shrink, it widens.
• The profession shifts from implementer to director. You are not writing code. You are reviewing it, steering it, deciding what gets built next.
• The commodity layer dies. Simple sites, basic apps, boilerplate. All of that collapses to near zero cost. What survives is thinking, strategy, and taste.
• Garbage in, garbage out. Same rule as before, just with much faster garbage.

Predictions

Team sizes shrink, more companies and solo founders emerge

As individuals get a team of AI agents to work with, the size of a human team will naturally shrink to move and make decisions faster.

This also creates another trend of more companies and solo founders emerging.

Smaller teams, more companies.

The cost of building goes down, the value of intent and what to build increases. Distribution becomes the biggest problem.

What is going to make your product stand out in a flooded market?

I think it will be user experience, pace of development, customer orientation, and openness to create trust.

Becoming a generalist will be a rare and valuable skillset

Since the cost of producing code reaches zero, the technical and product judgment becomes essential. The question is not how to build, but what to focus your intent on and judge if the technical solutions made by AI are right or not.

Technical judgment: will this architecture hold at scale, is this secure, is this maintainable.

Product judgment: does this solve the right problem, will users pay for it, what do we cut.

Today those live in different people: engineers and PMs. When AI collapses the mechanical layer, the person orchestrating needs both. Not deep expertise in each. Enough judgment to make the right call and verify the AI didn’t make the wrong one.

That’s the first principle behind “become a generalist.” It’s not that everyone learns everything. It’s that the separation between “deciding what to build” and “building it” disappears, so the person doing it needs judgment across both.

This also answers why the orchestrator must be human: the judgment that matters most is which problem to solve and for whom. That’s not a code question. That’s a business, empathy, and strategy question. AI can’t close that loop because it doesn’t have skin in the game.

You’ll have to learn how to create properly and become a generalist, whether you are an engineer, designer, or a product manager. Solo founders will become a norm, and bigger teams will have an expert generalist in each area of expertise: engineering, product, business.

“Everyone codes” is wrong. “Everyone who thinks clearly can build” is right

The popular take is that AI democratizes coding. Everyone becomes a developer. I don’t think that’s what happens.

What actually happens is that the barrier shifts. You don’t need to know Python. You need to know what you want, why you want it, and how to tell if what you got is any good.

That’s not coding. That’s thinking clearly.

Most people won’t do that. Clear thinking is hard. It requires understanding the problem before jumping to solutions. It requires saying no to features. It requires looking at something that works and asking if it works for the right reasons.

The people who build successfully with AI won’t be “everyone.” They’ll be the people who already think clearly about problems and now have a way to act on it. The barrier to entry dropped but the barrier to quality didn’t.

Agentic coding is closer to managing a junior team than writing code

If you’ve ever managed junior developers, you know the pattern. You explain the task. They build it. You review it. You catch what they missed. You redirect. You explain again.

That is what agentic coding feels like.

You don’t write the code. You set the direction, review the output, and catch the things the AI doesn’t know to look for. You’re a technical lead with a team that never sleeps, never complains, and moves incredibly fast, but also never pushes back when you’re wrong.

That last part is the risk. A junior developer sometimes says “this doesn’t make sense.” The AI usually doesn’t. So you need to be more disciplined. You have to be the quality gate because nobody else will be.

The moat becomes intent, intuition, and thinking quality

Technical skill used to be the moat. If you could write clean code, ship fast, and debug under pressure, you had an edge.

That edge is shrinking. AI writes clean code. AI ships fast. AI debugs.

What AI can’t do is decide what matters. It can’t feel that a user flow is off. It can’t sense that a market is shifting before the data shows it. It can’t make the call to kill a feature that’s working but distracting from what actually matters.

The new moat is the quality of your thinking. Your intent. Your taste. Your ability to look at something and know, before the metrics confirm it, whether it’s right.

That’s not a skill you learn from a tutorial. It comes from experience, from paying attention, from building things and watching how people use them. AI accelerates the building. It doesn’t accelerate the learning that matters.

The generalist with AI beats the specialist without it

A designer who understands product and can orchestrate AI agents will outship a team of three specialists who work in silos.

This is already happening. I’ve seen it in my own work. I design in Figma, build in SvelteKit, deploy to Vercel, set up analytics, write copy. Not because I’m an expert at all of these. Because the AI handles the mechanical parts and I handle the judgment across all of them.

A specialist who refuses to expand will find their niche shrinking. The mechanical part of their specialty is exactly what AI eats first. What remains is the judgment, and judgment doesn’t respect discipline boundaries.

This doesn’t mean specialists disappear. Deep expertise still matters for hard problems. But the default winner in most situations will be the person who can think across domains and use AI to execute, not the person who can only go deep in one.

Tools converge, the methodology is what differentiates

Right now people argue about Cursor vs Claude Code vs Windsurf. In two years, these arguments will look like arguing about text editors in 2010.

The tools will converge because the model is what matters, and every tool will have access to the same models. The UX differences will shrink. The integrations will standardize.

What won’t converge is how you use them.

Do you have a system for planning before you build? Do you review the AI’s output or just accept it? Do you test? Do you have a feedback loop between what you ship and what users actually need?

The methodology is the moat. Not the tool. The person with a clear process and a mediocre tool will consistently beat the person with the best tool and no process.

That’s what agentic coding is really about. Not which IDE you use. Not which model you pick. It’s the system you build around the AI to make sure what comes out the other end is worth shipping.

Where this leaves us

Vibe coding is real. It’s fun. It’s a great way to prototype, explore, and build things for yourself.

But it’s not the future of professional software. The future is agentic coding: intentional, systematic, judgment-driven. The kind where you think before you prompt, review before you ship, and take responsibility for what gets built.

The hype says AI changes everything. The first principles say it changes one thing: the cost of execution drops to near zero, and the value of knowing what to execute goes through the roof.

If you’re waiting for AI to replace you, it won’t. If you’re waiting for AI to do your thinking for you, it can’t.

But if you learn to think clearly and direct AI with intent, you’ll build things that used to require a team. And that’s not hype. That’s already happening.