“Vibe coding is the way of the future!!”
“Vibe coding is bad.”
Neither captures it. Like anything that actually matters, the real answer is: it depends.
AI dramatically reduces the time and human effort needed in the implementation phase of software development. That is real and I am not arguing against it. But it does not mean AI can write all of your code reliably. It does not mean AI will design secure code. It does not mean AI will produce sound, optimized architecture. Left to its own devices, AI will produce something that works. Most of the time. But “works” and “right” are not the same thing, and when you let AI make the architectural decisions, you tend to get one extreme or the other: over-built, which costs more than it needs to, or under-built, which leaves you exposed to mild traffic spikes or the person jiggling door handles who can walk into your data almost by accident.
So What Is Vibe Coding, Really?
In its purest sense, vibe coding is when you are not looking at the code. Not really looking at how it is being distributed. Not concerned with anything except the vibes. It is a reasonable way to prototype. It will allow almost anybody to produce something that looks visually impressive on the surface, but because there is no real architectural depth, and architectural depth is not just backend, it is UI and UX too, it starts to feel amateurish quickly when you try to use a fully vibe-coded product to do real work.
Vibe coding is speaking an idea into existence. There is genuinely a lot of value in that. Just not for delivering production-ready applications, not when there are any stakes whatsoever.
I was talking to a client who was excited to show me an application she had vibe coded. A CRM. She had done a good job. Genuinely. Solid design, coherent, had the features she wanted. She had never developed anything in her life. The problem was that when it came time to solve the email problem, she did not have a solid answer, and neither did the LLM she was using. Not because there were no patterns to pull from, but because email was a thing they would get to later. Anyone who has ever tried to implement email understands how hairy it gets. Rolling your own email solution is not for the faint of heart. She had no reason to know that. Thankfully, she showed me what she was building before the sunk cost fallacy took hold.
What she produced was a fantastic representation of her needs. Extremely valuable for communicating them. It was not, and could never have been, viable without real technical architectural insight, taste, and judgment applied to it.
Outsource Implementation, Not Thinking
If you have read anything I have written or talked to me for more than twenty minutes in the last two years, you have heard some version of this. Use LLMs as a thought partner, as a way to organize ideas, as a way to do basic pressure testing. You still have to be the one providing the ideas. You still need to be the one making the critical decisions. It is still your taste that is the deciding factor between slop and something genuinely useful.
If you have written any code in the last ten years, you have been on Stack Overflow. Probably a lot. Maybe not recently. I personally have not visited it in at least two years, but that is the point. Generative AI provides the same service Stack Overflow once did, with an important upgrade: it adapts the solution to your specific use case. That adaptation can look like sound architectural judgment. It can appear as though taste is no longer required. That is a mistake people make at every level, from someone who has never written a line of code to a principal engineer. Every single one of them, at some point, figures out just how wrong that assumption was. Many have not been burned severely enough yet.
AI is phenomenal at implementation, especially when you have a clear picture of what done looks like, when done has testable parameters, and when those parameters can be automated. Setting those conditions is your job. In that way, it is like managing an employee. Your job is to set them up for success. But the analogy breaks down the moment you expect the employee to develop taste and judgment on their own. You can impart pieces of your taste into a quality AI harness. It is not the same thing.
AI-Augmented SDLC Has Entered the Chat
And that is why what I ship I would never call vibe coded.
When I use AI to rapidly ship product, I use it as a thought partner and as an accelerator for implementation. I do not outsource key decisions. I do not let it run wild and push things to production without review. But the AI-augmented SDLC I work from also incorporates vibe-coded experiments and vibe-coded prototypes. I just do not ship prototypes or experiments. I ship products that survive contact with the real world.
The methodology runs on four principles. They sound simple. They are harder than they look in practice.
Decompose the problem. Not the surface problem. The real one. Break it into its actual constituent parts before touching any tools. This is where most people skip ahead, and it is where most of the debt accumulates.
Describe the desired outcome. With enough precision that anyone working toward it knows when they have arrived. A destination that feels inevitable. This is a required discipline, not a nice-to-have.
Apply taste and judgment. The model will give you something. The question is whether it is the right something. This is the layer that cannot be automated. It is also the layer that compounds. Better judgment early means substantially less rework later.
Hold the line. Until the work is actually right, not just done. This is the step that most sets AI-augmented SDLC apart from vibe coding. It is where the quality gap is especially pronounced.
Before any generation starts, there is a structured interrogation of the problem space. Scope and intent, dependencies, data shape, error topology, test contract. The spec lives with the code. Future-me, or future-AI, always has the reasoning, not just the output.
So when I describe a build, it can sound like vibe coding from the outside. A few hours from idea to working product. But what actually happened is I spent an hour on the spec, two hours running the build, and an hour or so cleaning things up. The speed is real. The speed is possible because the thinking happened first.
I may ask the model to come up with five different approaches to a problem. Maybe I supply two or three, it generates two or three of its own, and then I run them. I analyze them. I see which approaches will actually get the best results. I decide which trade-offs I am willing to make as the architect. AI can compress technical research spikes from months to days. But I do not tell it to decide for me.
The biggest difference between vibe coding and shipping with AI is this: the vibe coder does not feel a sense of responsibility for what is being shipped. A developer who uses AI understands that every single line going to production has their name on it, and they are responsible for the consequences of that code. Because generative AI is nondeterministic by nature, a vibe coder is pulling the lever on a slot machine hoping for the jackpot. Sometimes they hit. Often they do not. I prefer to count cards at a blackjack table. I am watching the game. I know when to hit, when to stay, when to step away. There is still random chance involved. I am dealing with the same nondeterministic system. But I am containing it. Winning more than I am losing because I am paying attention to the mechanics of what is happening.
Right Tool, Right Job
Here is what I did not say explicitly at the start, but it has been the thread running through all of this: I do vibe code. On low-stakes items, absolutely. A throwaway utility script, a one-off internal tool that three people will ever see, a prototype I need to think through visually. Vibe coding is fine for all of that. The four-part methodology does not need to be applied to everything. That would be over-engineering the process, not the product.
The differentiator is the quality gate. Vibe coding is not a different methodology from what I do. It is a different risk tolerance. The process can look nearly identical from the outside. What is different is what you accept at the end.
Which means the real question is never “did you vibe code?” The question is: what were the stakes, and did your quality bar match them?
At the end of the day, vibe coding is a powerful tool when applied to the right problem. The problem is, humans tend to pick up a hammer and think that everything is a nail. And here is where it gets a little recursive. Knowing when to vibe code and when not to is itself an act of taste and judgment. You have to apply the exact thing that vibe coding outsources in order to know when it is safe to outsource it. The people who do not understand that are the ones who might ship something genuinely cool, but then are bankrupt immediately because their stuff could not hold up to the harsh prodding from the real world.
The tools are remarkable. They are going to keep getting better. None of that changes the fact that you still have to be the one deciding what good looks like.