The arrival of any new technology always creates two camps: those who embrace it and those who reject it. While voices in the middle exist, they’re less vocal and often overlooked. Being reasonable isn’t nearly as attention-grabbing.

When AI became commercially available in a user-friendly way (think ChatGPT), people quickly split into two camps: one predicted mass job loss within months (though we’re years past that now), while the other viewed AI as a technological savior set to resolve every problem and replace humans entirely.

Turns out... Both camps are right! In Europe and North America, the white-collar job market in 2025 shows elevated unemployment (EU: 6.2%; US hiring at weakest since 2009) and widespread layoffs (e.g., Amazon’s 14,000 cuts), driven by a mix of factors. AI does have a role in that. It did accelerate displacement, especially in entry-level roles (40% of employers plan reductions; potential 10-20% unemployment spike), via automation in tech, finance, and admin. It’s amplifying vulnerability, but not the primary driver yet. Dominant causes include recession signals, slowing growth, high borrowing costs, inflation, and US tariffs hitting Europe, displacing cca. 1.6M jobs globally. AI exacerbates but trails these structural issues.

So that means that people are losing jobs, and/or not being able to get them, and that higher-ups have managed to find a solution to their problem by cutting down costs of labor and replacing it with AI. But then again, being a voice in the middle is boring. Why am I even trying to be factual here and not rage-bait for clicks? Ugh, silly me!

When AI-assisted coding became attainable to developers via IDEs such as Cursor, productivity jumped immensely. Even in the earliest stages, we all knew that software engineering was going to change forever. Before IDEs like Cursor, you had to copy-paste code from your text editor to ChatGPT/Cursor chatbot, and hope you didn’t exceed the context window. Nowadays, you can instruct a model to review your codebase, make a plan, align on requirements with you in a collaborative fashion, and execute it.

At that point, we, once again, ended up with two camps. On one end, you had so-called “vibe coders”, people who blindly trust AI. On the other hand, hardcore AI rejectionists. The puritans of software engineering, if you wish. In this case, I can’t say that both camps are justified in their own way, but the truth is that the job of software engineering is somewhere in between. There are tasks that you can absolutely delegate to AI. And there are tasks that you simply can’t. Not because AI is not good enough, or will not get good enough in the future, but because you cannot replace the human element in certain tasks and jobs with a machine.

Again, what’s up with all that middle-ground exasperation? Make up your mind!

Humans tend towards comfort over harsh conditions for life. That’s why we innovate and adapt the environment to our liking. So it’s not a surprise that vibe coding, the practice of using AI as a replacement for doing the hard work of thinking, reasoning, and working, is the default preference. The only problem with AI-written code is the fact that AI models are prone to hallucinations, bad/obsolete training data, and often miss the aforementioned human decision-making and forward-thinking component. The result? Bedraggled code.

LinkedIn saw the emergence of “Vibe coding cleanup specialists”. I’m pretty sure there is a demand for such workers. Because if vibe coding is the future of software engineering, then at some point, there will be an emergence of a movement that wants to adhere to best practices in AI-written code, including writing tests and following battle-tested design patterns. And that’s the influence of the AI-rejectionists/manually-written-code puritans camp. Humans have been working with technology for far longer than AI has been around. You can read all about tech, and you can ingest all the knowledge in the world, but you’ll never be able to beat experience. You’ll never be able to beat intuition born through hard work and getting burned by mistakes.

The idea will be peddled as self-healing AI-written code.

Code is supposed to be read; writing it is easy. Code is supposed to execute and perform necessary functions, and in order for you to know that’s the case, you need to test it. At some point, you’ll have to make changeovers, so wouldn’t it help if code were designed in a way where modifications can be gradual, incremental, and isolated? So this is where I’m making my stand. AI-written code will have to follow certain principles of “clean” design (which I know is a triggering word for some), and it will have to be well-tested and validated before being given to humans for review.

All of this will demand skills and a deep understanding of the technology one works with. Yes, AI can write a lot of code very fast, and most likely, this code is better than what you would write manually. The only metric of how “good” a code is, is whether or not it passes tests (which you still have to orchestrate and understand how to write), and whether or not it’s written in a way that’s easy to understand and follows battle-tested design patterns. This is where humans come in. It’s your experience, it’s your talent, it’s your skills, all put in a line. How well can you communicate, how clear can your instructions be, how well can you predict future edge-cases... No amount of AI assistance can help you with that. This takes years of practice, which nobody (and nothing) can replace.

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