A Vibe Coder Career Is a Dead End

Let me set the record straight right away: LLMs are useful. The question is not whether LLMs can write code — they can. The question is why vibe coding might be your worst career investment.

I started noticing the shift when developer conversations changed completely. Now they only discuss how to get Claude to write code for them. Or the end goal: how to get AI to do everything without human intervention.

Until recently, I mostly ignored the hype. I read the headlines, occasionally asked Claude or ChatGPT to help me debug, but nothing more. It was time to learn vibe coding!

"What Are You Building with Vibe Coding?"

A Telegram bot. A completely new project. Some dashboards with real-time updates. Nothing particularly complex, but not entirely trivial either. Just a standard REST API with a React frontend.

I set up the full AI coding pipeline: Claude MCP, Playwright and Postgres, several agents working on different branches, and detailed documentation files. I started vibe coding.

Claude updated schemas, wrote endpoints, clicked buttons in Chrome, checked Postgres data, and opened pull requests. Everything worked. My first reaction was:

"Holy crap! This is insane!"

The gold rush is inevitable. I won't have to spend time writing code anymore. I just need more agents, more automation. The factory must grow! I have an army of junior developers at my disposal, available 24/7.

I was easily adding 2-3 features every day. The barrier between thought and implementation simply vanished. It was so thrilling.

As the project grew more complex, things started to change. Claude repeated the same mistakes, got stuck in loops. Context switching became a massive problem. I went from 4-5 parallel branches to two, sometimes just one. I could no longer simply ask for features to be implemented. I had to stop thinking everything through carefully.

In the end, I was still limited by my own mental capacity. Context switching between multiple AI-generated branches only works for small tasks. For complex systems, I still had to think through the solution myself. Claude was just typing the code for me.

I spent more time testing and writing instruction files for Claude than I'd ever spent on a project of this size. I've worked with juniors fresh out of bootcamp before, but none of them needed this much hand-holding.

Regardless, I shipped the project to my three test users, and everything started falling apart. Messages wouldn't synchronize, users got assigned the wrong accounts. I had to beg Claude to fix bug after bug. How did I end up in this situation? This is a complete mess and chaos.

The last time something like this happened to me was when I worked with an outsourced team. Nobody on it cared about code quality; they were all focused on shipping fast. I had to review too many pull requests opened by people who didn't know what they were doing and didn't particularly care. I had only a surface-level understanding of what was going on, like some kind of conductor who... Hmm. Sounds familiar.

And this is the future of software development? Am I sure I'm not confused? Why invest in this kind of work at all?

"Those Who Are First Will Get an Advantage"

Vibe coding skills aren't that hard to master. It took me a few weeks to go from zero to full competence. Even if this becomes the industry standard, anyone can get up to speed quickly enough. LLMs aren't a new abstraction layer — they're just a different interface paradigm. By swapping syntax for natural language, we're trading determinism for uncertainty.

Meanwhile, everything I learned about vibe coding was already obsolete. I read Hacker News this morning. Companies are releasing products that automate exactly the workflows I'd mastered. In this field, first movers can't gain an advantage because the ground is plowed over completely every time.

There is no competitive advantage to preserve. There are no deep technical skills to master.

The barrier to entry in vibe coding is collapsing so fast that "first movers" end up being mere beta testers. You're spending money on R&D for tools that will turn your skills into a commodity.

"It's All About Knowing How to Write Prompts"

What's my prompting strategy? I switch to plan mode and describe what I need. Then I repeatedly say "if anything is ambiguous or unclear, ask clarifying questions" until I'm satisfied with the result. That's it. It works.

Compare that to learning something like Rust, which I've been struggling with for months. It's not just about the syntax — it's entirely new concepts. You can't absorb them all at once.

Prompting is not some complex skill that requires extensive training.

People spend thousands of hours learning to write quality code. They learn to design data schemas that can adapt to new requirements, to structure systems where bugs are easy to find and fix. All of this is a far cry from prompting skills.

"I Don't Care, It Makes Me 10x Faster"

Faster at what? Prototyping? Boilerplate? Those have a very short shelf life. The vast majority of software developers work on production systems, not greenfield projects.

What LLMs are good at is writing code very quickly. Imagine two writers. One types at 50 words per minute, the other at 200. Will the faster writer finish four times sooner? No. Because they spend most of their time on plot, characters, and crafting a coherent story.

Have you ever worked on a project that just wouldn't move forward? Everything was simply slow. The app was slow. Adding features was slow. Fixing bugs took forever. Did you really think the reason was that the developers couldn't type code fast enough? Or was it actually bad architecture, bad culture, broken communication, unclear requirements, poor technology choices?

The claim that AI significantly accelerates development requires, at a minimum, careful examination. The testing burden alone wipes out many of its advantages. You need far more tests to ensure the AI doesn't break anything. Much more than usual. And the effort of building software shifts from writing code to safeguarding and context switching.

It's a different way of building software, with its own pros and cons.

"It Makes My Job Easier"

Vibe coding forces you to trade clarity for speed.

You ship features fast, but you no longer have a mental map of your software. Striking a balance here is extremely difficult. During my experiment, I noticed that I increasingly resisted making manual code changes. It was easier to tell the LLM "this doesn't work" and paste a stack trace. I started asking the LLM to handle even trivial changes, like "now make the button blue."

Why? Because I'd lost track of where things were and how they behaved. I couldn't even remember which file the button was in. Sure, I reviewed the pull requests. You know how hard it is to properly review code? To build a mental model of what's happening in it? Now imagine you have more than a dozen pull requests in your queue. Are you really going to review them all, or just hit "Approve" and hope for the best?

Then came the moment when I hit a wall. Despite repeated pleas, Claude couldn't fix a bug. I was forced to take matters into my own hands. And God, it was hard. Thinking is hard work, and I'd been dodging it for quite a while. It was like trying to run a marathon after months on the couch. Getting back up to speed took so long that I lost all my productivity gains.

While I was writing this article, another bug report came in. I have no idea where the bug is coming from or where to start.

And this is supposed to be "LLMs made my job easier."

"So You Don't Use LLMs?"

After reading all of the above, you might think I'm a Luddite and an AI hater. I'm not.

AI helped me write this article. English isn't my native language, and my writing skills are imperfect. I had to clean up grammar, improve text flow, and express my thoughts more clearly. But I'm not a professional writer and don't claim to be one. This is just a blog post, not an essay or a book.

I also use AI for coding. Yes, it's remarkable.

But the process is nothing like vibe coding. I happily use Claude Code on a very short leash with a specific goal, and I understand that it costs me more than just the price of tokens. I don't leave it to do all the work like a robot vacuum, hoping it won't suck up a shoelace from the floor by the time I get back.

I'm not even against vibe coding itself. Sometimes you just have to save time to get the job done. Perfection can wait if the feature was needed yesterday. Technical debt is a tool that can be used wisely. Too many products have died a slow death while developers polished code that nobody would ever use. But constant vibe coding is going too far.

The concept of autonomous AI development is just a fantasy. You can't replace experience with a tool. The most valuable developers have a clear mental map of where everything is and what it does.

Using LLMs is not equivalent to writing code. It won't give you the same benefits and certainly won't produce higher-quality results. It's technical debt.

"Soon Everyone Will Be a Developer"

I've seen amazing businesses built on Excel spreadsheets and no-code tools. Of course you can build an app with Claude. But that doesn't make you a software developer. And I'm saying this for your own benefit, because...

Unlike the people who build tools for creating products, the vibe coder creates an enormous mess. I've heard plenty of horror stories from people who inherited AI-generated codebases. Nobody thought about anything. Why worry when AI can handle any task anyway?

The real difference lies in what professional developers actually create: architecture, design and debugging of complex systems, security, maintainability. They earn hundreds of thousands of dollars not because they can quickly throw together an MVP.

To create something special, you need domain knowledge accumulated through time and effort — even if aided by AI.

"AI Won't Take Your Job, but Someone Using AI Will"

This is yet another unfounded claim designed to make people rush to learn AI. It implies that if you don't learn to use AI today, you'll be irrelevant tomorrow.

I don't think that's true, but if you genuinely believe AI will soon be able to do the complex work of a developer, then why are you investing in it? What will happen to your salary when the skill bar drops significantly? If AI writes code better than you, why would anyone hire you specifically?

Either AI needs years to learn to write production-ready code — meaning there's no rush — or it will soon make coding so trivial that the work will pay minimum wage. There's no intermediate step where the title of "AI whisperer" will be well-compensated.

If our future is "AI-enhanced" development, then even with gradual adoption, you'll stop being a coder and become a babysitter. Your workday will consist of reviewing AI-generated pull requests. You'll understand almost nothing, and you'll be working on a codebase whose mental model you're incapable of building.

That's not development. That's middle management masquerading as QA, reviewing tickets it can't solve, from workers who can't think.

"It Will Only Get Better From Here"

For LLMs to keep improving, we need one of three things: more data, more compute, or a breakthrough discovery.

Data is becoming harder to find. Legal norms, ethics, and public opinion evolve much more slowly than technology. AI companies will likely exhaust all high-quality text data sources somewhere between 2026 and 2032, and synthetic data (using LLMs to generate new data) causes model collapse and bias amplification.

Compute isn't limitless either. Data centers are concentrated in specific regions. We don't have the power grid infrastructure to supply capacity at the scale they need. Other power-hungry technologies, like electric vehicles, compete for grid capacity. And looking at the bigger picture — for example, meeting our climate goals — redirecting even more power to GPUs is unlikely to be a top policy priority.

Breakthroughs are rare. Modern LLMs are based on Google's papers: "Attention Is All You Need" (2017) and BERT (2018). Those were written almost a decade ago. Since then, growth has come from scale, not new architectures. Each new release gets less impressive because transformers are hitting fundamental limitations that incremental improvements can't fix.

You can hope for breakthroughs, but they're inherently unpredictable. We're more likely to see small models become more capable than to see significant improvements in frontier models.

"You're Just Being Skeptical"

The AI industry is built on subsidized resources and is burning through venture capital without offering a clear path to profitability. Data centers get discounted land, tax breaks, and infrastructure upgrades paid for by society.

AI companies socialize losses and privatize gains, yet remain far from profitable.

They claim they can make everyone 10x, even 100x more productive, yet they can't figure out how to turn a profit. Why can't they themselves harness that productivity?

When Google appeared, it had superior algorithms. Yahoo and AltaVista, despite their vastly greater resources, couldn't compete. When Apple released the iPhone, it was such a remarkable product that BlackBerry and Nokia simply died off.

Today, every billionaire has their own pet AI. None of them is significantly better than the others. Each release slightly edges ahead of competitors in arbitrary benchmarks, without pulling far ahead of similar open-source models. This can't go on forever.

"What If You're Wrong?"

I read in the news: AI is winning, and soon we'll all lose our jobs. So I need to decide: double down on software development, switch to vibe coding, or try something entirely different?

I've concluded that today, vibe coding is nowhere near as useful as a competent software developer. But I'll revisit it in six months. Today, experienced software developers still have plenty of room to maneuver, so I'm betting on getting better at that — with or without AI.

If AI soon becomes so good that it can build programs on its own, software development as we know it will die. If AI replaces me, I'll be sad. But I'm not interested in becoming a project manager who spends all day managing AI agents. If that happens, I'll be competing with everyone who can write a prompt. I'm not going to stake my career on becoming slightly better at prompting than millions of others.

Since I see no clear signs that this will happen in the foreseeable future, I'm betting that we're actually much further from it than AI companies want us to believe, and that they keep making bold claims to raise even more funding.

If I'm right, I won't have wasted my time learning temporary skills instead of building real experience.