The first time you turn a written idea into a working application using an AI coding tool, it can feel slightly unreal.
You describe what you want. The tool creates a screen. You ask it to change the workflow, connect some data, or add another interaction. A few iterations later, you have something you can actually use.
It may be rough. The code may not be ready for production. But the idea is no longer trapped in a document or presentation. You can interact with it, test it, show it to someone, and begin learning.
That is what interests me about vibe coding.
The term is often surrounded by much bigger claims. Some people describe it as the beginning of a world where anyone can build software and engineers are no longer necessary. Others dismiss it as a novelty that produces impressive demonstrations but little of lasting value.
I think both interpretations miss the more useful change already happening.
Vibe coding is making it easier for people to turn ideas into evidence.
What vibe coding actually changes
Vibe coding is an AI-assisted way of building software by describing what you want in natural language and iterating on the generated result.
The person guiding the tool may still need to understand product behavior, user experience, data, technical constraints, or code. But they no longer need to write every line manually before they can see whether an idea works.
For product teams, this shortens the distance between discussing a possibility and experiencing it.
A product manager can explore a workflow without waiting for the idea to enter an engineering backlog. A designer can test how an interaction behaves rather than limiting the exploration to static screens. A founder can make an early concept tangible enough to put in front of potential customers.
The output is not necessarily a product. In many cases, it should never become one.
Its value is that it gives the team something concrete to learn from.
A working prototype changes the conversation
Product teams regularly spend hours debating ideas that remain abstract.
Imagine a team trying to simplify customer onboarding. One person wants to collect more information upfront. Another wants the product to recommend a configuration. Someone else believes most of the setup should happen automatically.
All three ideas might sound reasonable in a meeting.
A document can explain each direction. A design can show what the screens might look like. But a working prototype reveals things that are difficult to see until someone tries to use the experience.
The recommended configuration may feel premature because the product does not yet know enough about the customer. The shorter workflow may remove information people genuinely need. The automated experience may appear effortless until the system makes the wrong assumption and gives the user no obvious way to correct it.
Once a prototype exists, the conversation becomes less theoretical. People are no longer reacting to three different versions of the idea in their heads. They are reacting to the same experience.
That usually leads to better questions and better decisions.
The prototype is a decision tool
I do not see vibe coding as a reason for product managers to become software engineers. I see it as an expansion of the product toolkit.
Product people have always used whatever helps make a problem easier to understand: customer interviews, journey maps, spreadsheets, wireframes, data analysis, clickable prototypes, and written requirements.
Working software can now become another form of product thinking.
When I build a prototype, the goal is not to prove that I can create an application. The goal is to reduce uncertainty.
Can the workflow be made simpler? Does the interaction feel natural? What information does the user need before they can make a decision? Where will the product require human oversight? Which assumptions need to be validated before the organization commits engineering capacity?
A useful prototype does not need to answer every question. It needs to answer the most important question facing the team at that moment.
This is also why disposable prototypes are valuable. If a prototype helps eliminate a weak direction, clarify a requirement, or expose a technical constraint, it has done its job. It does not need to survive.
Faster experimentation should change investment decisions
The cost of building an early product concept has traditionally influenced which ideas get explored.
When every experiment requires design capacity, engineering time, prioritization, and a place on the roadmap, teams naturally test fewer ideas. They often spend more time debating which direction sounds right because building multiple directions is too expensive.
AI-assisted development changes those economics.
A team may be able to explore several approaches before asking engineering to build any of them properly. That does not remove the need for prioritization. It moves some of the learning earlier, when changing direction is still inexpensive.
This is especially valuable in enterprise products, where a seemingly simple workflow can involve permissions, integrations, configuration, security, and several different user roles. A working prototype can reveal that complexity much earlier than a polished presentation.
The result should not simply be faster delivery. The more meaningful benefit is better-informed investment.
A prototype is not a production product
The danger begins when something that looks finished is assumed to be finished.
Modern AI coding tools can generate polished interfaces, database connections, authentication flows, APIs, and deployment configurations surprisingly quickly. To someone using the application, the result may appear complete.
But visible functionality represents only part of a production system.
Production software must also account for authorization, data protection, accessibility, testing, observability, performance, failure recovery, dependency management, maintainability, and long-term ownership.
These responsibilities do not disappear because the initial application was easy to create.
This distinction is particularly important for enterprise products. A prototype might demonstrate that a workflow is useful, but it does not prove that the workflow can safely access customer data, support complex permissions, meet regulatory requirements, or operate reliably at scale.
Research from Google Cloud’s DORA program describes AI as an amplifier of an organization’s existing strengths and weaknesses. AI can improve throughput, but weak engineering foundations can also lead to instability. That is a useful way to think about AI-assisted development: faster creation increases the importance of the systems surrounding it.
DORA’s State of AI-assisted Software Development
The question is not whether AI can generate the code. The question is whether the organization can responsibly operate what has been created.
Small software may become much more common
Some of the most useful applications of vibe coding may never become customer-facing products.
Every organization has small operational problems that are too narrow to earn a place on an engineering roadmap. Teams clean the same reports every week, move information between systems, maintain complicated spreadsheets, or repeat manual steps because building an internal application would cost more than the problem appeared to justify.
AI-assisted development changes that calculation.
If someone can create a lightweight tool that saves several hours of repetitive work each week, the tool may be valuable even if it supports only one team. Not every application needs to scale to thousands of users or become a permanent platform.
This creates room for personal utilities, temporary applications, research tools, team-specific workflows, and small internal products that previously would not have been economical to build.
That is a meaningful opportunity, but it also introduces a governance problem.
Enterprises need boundaries, not blanket restrictions
If more employees can create software, organizations will eventually need visibility into what is being created.
A small application can become important surprisingly quickly. It may connect to business data, use an improperly stored API key, or become part of a workflow that nobody officially owns. The original creator may move to another team while the business continues to depend on the tool.
Trying to ban this kind of creation is unlikely to work. Employees have always adopted spreadsheets, scripts, and unapproved SaaS tools when official systems did not solve their problems.
A better response is to establish sensible boundaries.
Organizations need to decide what employees can build independently, what data those applications may access, which authentication and authorization patterns are required, and when engineering or security review becomes necessary.
They also need a clear path for promoting a useful experiment into an owned and supported product.
The objective should be to enable responsible experimentation while preventing invisible operational risk.
Engineering becomes more important, not less
Vibe coding does not make software engineering irrelevant. It makes the distinction between generating code and engineering a dependable system easier to see.
Engineers make decisions about architecture, resilience, security, maintainability, performance, and how a system will evolve as requirements change. They understand what happens when dependencies fail, usage grows, data becomes sensitive, or assumptions turn out to be wrong.
That expertise matters even more when organizations can create more software, more quickly.
What may change is when engineering becomes involved and where its time is best spent.
Product managers and designers may build richer early prototypes themselves. Weak ideas can be discarded before they consume significant engineering capacity. Engineers can then focus their attention on the opportunities that have earned further investment and on the technical foundations required to make them reliable.
That is not the removal of engineering. It is a better use of engineering.
Product judgment still determines what is worth building
Vibe coding makes building easier. It does not tell us what deserves to exist.
Product leaders still need to understand the customer problem, determine whether the opportunity matters, evaluate the business case, recognize the risks, and decide what should happen next.
They also need to know when a rough prototype is sufficient and when an idea requires deeper design, engineering, security, and operational investment.
This is why I find vibe coding more useful than the hype around it.
The most important change is not that more people can claim to have built an application. It is that product teams can explore possibilities that previously would have remained inside a document, presentation, or meeting.
They can test earlier, discard weak ideas sooner, and make important concepts tangible before committing significant resources.
The companies that benefit most will not necessarily be the ones that generate the most code. They will be the ones that use this new speed to learn faster while maintaining the judgment and discipline required to build products people can trust.
That is a far more interesting future than simply asking whether everyone is now a software engineer.