SimpleOne explained why vibe coding speeds up releases but hurts code and code review

SimpleOne released an analysis of vibe coding without the usual war between camps. The company acknowledges that code generation through AI truly accelerates product releases, but the price is paid through code quality, code review complexity, and growing dependence on external models.

Why developers argue

The main complaint from engineers isn't that AI writes non-functional code, but that it often writes it excessively and noisily. The article gives a simple example: a task with user discounts turns into a function with multiple levels of nesting, repeated checks, and comments that paraphrase the code itself. Such a PR can be merged because tests are green, but reading, extending, and reviewing it is noticeably more difficult. In the end, technical debt accumulates not from one major failure, but from hundreds of small decisions in the spirit of "well, it seems to work."

The second problem is the shift in the developer's role from solution author to chat operator. When a person less frequently thinks through architecture and algorithm on their own, they faster lose the skill to explain why the system is built that way. This is particularly painful during onboarding and interviews, where it's important not just to show results, but to defend the thinking process.

The third complaint is already about business risks: the strongest models belong to external providers, so along with acceleration comes dependence on someone else's infrastructure, data storage policies, and legal restrictions.

Why business doesn't wait

But business has a different metric of success. If a product can be assembled in a week instead of a quarter, the team reaches the market sooner, gets feedback faster, and understands sooner whether there is any demand at all. In the material, this is connected with the overall pace of the technological race: there are more startups, competition is denser, and the window for entering the market is shorter. So the question for management sounds not like "is this code ideal," but like "will we manage to occupy the niche before competitors and not spend extra months in development."

• Assemble the first prototype faster and show it to users
• Verify a hypothesis more cheaply before hiring a full development team
• Rewrite a failed implementation faster after initial feedback
• Reach clients sooner, start sales, and collect real data

The authors go further and formulate a thought uncomfortable for the engineering community: the market increasingly forgives products for rough edges if they solve an important problem. Examples are given of services and tools that periodically crash or slow down, but retain their audience due to usefulness. For internal systems, B2C applications, and quick prototypes, this changes the balance: sometimes it's more profitable for companies to accept imperfect code now than to spend time polishing the architecture and miss the release deadline.

However, for banks, healthcare, and government infrastructure, this approach remains too risky.

Where the boundary lies

SimpleOne's key takeaway is that the debate about vibe coding is useless to conduct in the categories of "good" and "evil." It's not a replacement for all development, but a tool for a specific class of tasks. Where the cost of an error is moderate, the product lives in short iterations, and code will likely need to be rewritten anyway, code generation through AI can be a rational choice. Where a failure impacts money, security, or regulation, human engineering remains mandatory.

"The goal of vibe coding is to quickly deliver a cheap working product."

The practical recipe from the article is also quite grounded: don't immediately drag such an approach into the core of the business, but test it on internal tools, employee interfaces, prototypes, and auxiliary services. If the time to the first result shrinks dramatically and the number of bugs remains manageable, the application zone can be expanded. If code review chokes, production grows incidents, and the team stops understanding its own code, then the boundary has already been reached.

In this sense, vibe coding requires not faith, but metrics and discipline.

What this means

Vibe coding is unlikely to kill the profession of developer, but it certainly changes the criteria for choosing between speed and quality. For part of the market, "good enough" AI code will become the norm, especially in prototypes and internal products. For critical systems, requirements won't change: reliability, explainability, and control remain more important. The main question now is not whether the team likes the approach itself, but where its cost is justified and where it isn't.

Teams that learn to honestly separate fast experiments from zones with a high cost of error will benefit the most.