Will Developers Survive After All? Expensive Vibe Coding Is Changing the Rules of the Game

Not long ago, the narrative was simple and very clickable: AI agents would code faster, cheaper, and practically without human involvement, so development teams would start shrinking. For some, it was a vision of beautiful automation; for others — a professional apocalypse. The problem is that reality, as usual, does not read LinkedIn headlines.

Today, we increasingly hear the opposite: companies that got carried away with “vibe coding” are starting to look at the spreadsheet. And spreadsheets can be brutal. As model usage scales up, so do the bills for tokens, extra iterations, fixes, debugging, and maintaining AI-generated code. Suddenly it turns out that the person who understands system architecture and can make a sensible decision on the first or second try is not such an expensive luxury after all.

That does not mean AI in programming has failed. Quite the opposite. AI is becoming very strong support for developers, but it increasingly looks less like a full replacement for an entire team. And that is probably the most important shift in thinking worth noticing today.

What actually happened to all this vibe coding?

“Vibe coding” is a trendy term for work in which the user does not so much program in the traditional sense as describe intent, refine the result, and guide the model toward a solution. In theory, it sounds great: instead of writing hundreds of lines of code, you tell AI what you want, and it delivers the app.

At a small scale, it really works. A simple landing page, a script automating a boring task, a quick prototype, an MVP to show a client — here AI can save hours, sometimes days. Especially for people who are not programmers, it gives them a completely new sense of agency.

The stairs start later.

Because when a project grows, less Instagram-friendly things appear:

• dependencies between modules,
• data security,
• performance,
• compatibility with existing infrastructure,
• tests,
• refactoring,
• maintenance after a few weeks, when nobody remembers why a given part works exactly that way.

And then it turns out that AI can generate a lot of code, but a lot of code is not the same as a good system.

Rising token costs: a small detail, a big problem

In the excitement around automation, it is easy to overlook one thing: models do not work for free, and with heavy usage the cost can rise faster than a team expects.

It is not just about a single prompt. In practice, the process often looks like this:

1. You ask AI to write a function.
2. You refine the prompt because the result does not fit.
3. You ask for a refactor.
4. You add context from additional files.
5. You ask it to fix a bug.
6. The model breaks something else.
7. You repeat the round.

Each loop means more tokens. When one person does this for an hour, the cost does not seem dramatic. But in a company where a dozen or several dozen people use AI tools every day, things start to get interesting. Or rather, expensive.

There is another effect too: the more complex the project, the more context you need to provide the model. And the more context, the higher the token usage. Suddenly a simple experiment costing a few zlotys turns into a regular operating expense that has to be justified in business terms.

And that raises a question many companies were not asking loudly enough a year ago: is it really cheaper to hand everything over to an AI agent, or is it more sensible to give a human good tools and let them work faster?

Why some companies are going back to hiring people

This is not a romantic return to “the good old days.” It is plain economics.

A company does not pay for the mere fact that code was generated. A company pays for solving a problem. If AI solves it faster, great. But if it requires many iterations, constant supervision, and later corrections by more experienced people, the calculation looks different.

Experienced developers are regaining their position not because AI turned out to be weak, but because they:

• know when to use AI and when not to,
• spot errors and hallucinations faster,
• design architecture better,
• understand the consequences of changes in a system,
• can reduce generation costs through better prompts and a smarter workflow.

In other words: a good developer with AI becomes more productive, not necessarily replaceable.

It is a bit like the calculator. When it appeared, it did not eliminate the need to understand mathematics. But it dramatically increased the speed of people who already knew what they were doing.

AI is not taking the profession away from developers. It is changing the job description

The most sensible companies are no longer treating AI like a magical employee with no salary. More and more often, they see it as:

• an assistant for generating boilerplate,
• help with research and quick solution checks,
• a tool for creating tests,
• support for documentation,
• a partner for rapid prototyping.

That is an important difference. Because if AI is a tool, then the advantage goes not to the person who “hands everything over to it,” but to the one who knows how to guide it well.

For managers, this means a shift in how teams are viewed. Instead of asking “how many people can we replace?”, it is better to ask: how do we make current people deliver more with AI without destroying quality and budget?

For students and people choosing a field of study, this is also good news. Programming is not disappearing. What is increasing in value are higher-level skills:

• problem analysis,
• system design,
• understanding business logic,
• communication with AI tools,
• evaluating output quality.

Bluntly put: typing syntax by hand will be worth less and less. Engineering thinking — more and more.

What does this mean for non-technical people?

This is where things get especially interesting. Because if AI can generate a working piece of an application, then people outside IT can enter the world of building tools, automation, and simple digital products without years of programming study.

But again: this does not mean everyone will become a backend senior after a weekend with a chatbot. It is about something more practical — the barrier to entry for building digital solutions has dropped dramatically.

A small business owner can create a simple lead management system faster. A student can build a portfolio project. A manager can prepare an internal tool for a team. Someone following trends can simply better understand what is real today and what is just marketing wrapped in an overly confident tone.

Still, it is worth learning this properly, not by the “click and see what happens” method. That is exactly when costs, frustration, and chaos grow fastest.

Where AI in coding delivers the biggest return

Not every use of AI makes the same business sense. The biggest return usually appears where you can shorten work time without giving up full control over the project.

Good examples:

• creating first versions of apps and prototypes,
• generating simple integrations and scripts,
• automating repetitive tasks,
• preparing technical documentation,
• cleaning up existing code,
• quickly testing ideas before investing in larger development.

Smaller benefits, and sometimes greater risk, appear when we try to run critical systems through AI without supervision:

• critical business systems,
• complex architectures,
• areas with high security requirements,
• projects where the cost of an error is high.

That does not mean “do not use AI.” It means: use it where it strengthens the human, not where it is supposed to pretend to be one.

Managers: look not only at speed, but also at the cost of decisions

From a management perspective, it is easiest to get excited about one number: “we did it 3 times faster.” But speed without context can be deceptive.

If the generated code later requires many fixes, is not readable, is hard to extend, or nobody on the team wants to touch it after a month, then the savings were temporary. Technical debt has a remarkable talent for coming back exactly when there is no budget for it.

So the more sensible questions are:

• how much does AI really cost us over a month,
• where does the tool shorten work, and where does it only shift it in time,
• can the team assess the quality of generated code,
• does AI lower costs, or only create the illusion of acceleration.

The companies winning today are usually not the ones that “implemented AI everywhere,” but the ones that implemented it where it made operational sense.

Students and people choosing a field: is IT still worth it?

Short answer: yes, but with a different mindset than a few years ago.

If someone imagines that learning programming will consist solely of memorizing syntax, they may indeed be disappointed. AI is taking over that layer better and better. But if someone wants to understand systems, create products, solve problems, and work at the intersection of technology and business, the field is still enormous.

In practice, it is worth developing three areas at the same time:

• technical fundamentals and programming logic,
• the ability to work with AI tools,
• domain skills, meaning understanding a specific industry or process.

That combination is what gives an advantage today. Not “either human or AI,” but the human who can use AI better than others.

If you want to enter this world practically

For many people, the biggest blocker is not lack of motivation, but chaos at the start. Terminal, API, configuration, commands, integrations — it all sounds like something easy to postpone until “someday.” And then you end up watching yet another video about how AI is changing the world instead of building anything that actually works yourself.

That is why learning based on a concrete process makes sense. If you are a manager, student, business owner, or simply want to see what modern app building looks like without traditional coding, a good direction is the course Claude Code - how to program without writing code.

It is practical material for non-technical people: from installing Claude Code in the terminal, through connecting your account and API, to building and launching your first app. Without pretending everything will happen by itself. But with a demonstration of how to actually use a tool that can shorten the path from idea to working solution.

Why does this make sense for this group?

• a manager will better understand what can really be delegated to AI,
• a student will build a portfolio project faster,
• a business owner will see where processes can be automated,
• a person without a technical background will stop seeing AI as a black box.

In practice, it is also a good antidote to hype. After such a course, it is easier to distinguish real capabilities from marketing stories in which a single prompt supposedly replaces an entire IT department.

Will developers survive? Yes, but not everyone in the same role

The real answer is neither “AI will take everyone’s jobs” nor “nothing will change.” A lot will change — just not necessarily in the way the loudest headlines predicted.

Developers will survive because we still need:

• understanding the problem,
• designing solutions,
• evaluating quality,
• responsibility for deployment,
• maintaining and evolving the system.

What will become less valuable is doing tasks that are completely mechanical, repetitive, and easy to describe in prompts. That is where AI will keep getting stronger.

So the winners will not be those who get offended by new tools, nor those who hand over the controls without reflection. The winners will be those who learn to work with models: wisely, efficiently, and with full awareness of the limitations.

The most interesting irony in this whole story

We were supposed to reach a moment where companies lay off developers because AI codes more cheaply. Meanwhile, it is increasingly turning out that AI without a human can be surprisingly expensive, and a good specialist with sensible AI support delivers a better result for more reasonable money.

This is not a failure of artificial intelligence. It is a sign that the market is maturing.

After the fascination phase comes the accounting phase. And when accounting begins, the value of people who can think, choose tools, and make good decisions rises again. So, classic.

So if someone is wondering today whether it is worth developing technical skills or learning to work with AI, the answer is: definitely yes. Just not in order to blindly believe the model will do everything. Rather, to know when to use it, how to use it, and when it is better to take the wheel yourself.

And that is no longer a temporary trend. It is the new standard of work.