Introduction
This is the first in a series of articles, because there's A LOT to discuss and I don't want to overwhelm you with a single, hour-long read. I've read and watched plenty on this topic and most of it is quite good. My goal with this series is to share my own perspective, processed from what I've learned and experienced, and to explore every critical angle as clearly and deeply as possible.
Disclaimer: The opinions and ideas in this article are my own, and I don't claim they're 100% true for everyone. I'd love to hear your thoughts, so feel free to comment or reach out (LinkedIn, X) if you want to discuss further.
This isn't my first time writing about this subject. I previously covered the pros and cons of vibe coding, which was a much narrower focus and just a small part of the broader AI takeover conversation. Check out that article below:
This series will be much broader though. We'll cover a range of topics related to technology, career, the future of coding, and how roles might evolve. The title, "Will Developers Survive AI Takeover," isn't just for developers. Even if you're not a developer, you'll find something valuable here, especially if you work with, hire, mentor, or aspire to become a developer one day.
Why Write About This Now?
That's a fair question. Well, I'm one of those 'old school'-ish engineers who started coding long before anyone imagined AI could write even the simplest code. With nearly 10 years of professional experience, I've watched technologies, frameworks, and best practices come and go. Software engineering isn't linear, it's more like a spiral, with trends constantly evolving. But just because something is popular today doesn't mean it won't be replaced by something entirely different tomorrow, or won't lose it's hype. Here are a few examples:
- Frameworks: AngularJS was once the go-to for web development, but then React and Vue.js rose in popularity. Even so, Angular itself evolved into Angular (2+), proving that frameworks adapt rather than disappear.
- Architectural Patterns: Monolithic apps were the standard, then microservices became the trend. Now, serverless and modular architectures are gaining ground, yet monoliths still have their place and actually, they can be the best choice in many scenarios, don't underestimate their power.
- Best Practices: Object-oriented programming used to be the gold standard, but over time, its drawbacks became more apparent. It's still more common than functional programming, but no longer the undisputed champion.
- Tools: SVN was replaced by Git, which is so effective that I doubt anything will replace it anytime soon, it's simply genius.
- UI/UX Design: Desktop-first design was the norm, then mobile-first took over. Today, responsive and adaptive design principles blend both approaches for better user experiences.
- Testing: Manual testing was once the default, then automated frameworks like Selenium became essential. Now, continuous integration and delivery (CI/CD) have made testing an integral part of the development pipeline.
- Clean Code: When the concept first emerged, it was like a bible for software engineers. But now, many realize those practices aren't always helpful, in fact, sometimes, they make code a nightmare to review, maintain, and change. I'm speaking from my own experience, that's definitely true.
AI is Popular, Sure, But Not Yet Mainstream
Here's what I mean: AI is trendy and exciting, and everyone seems to be talking about it, especially on LinkedIn and X. But despite the buzz, it's not truly mainstream yet. By 'not being mainstream' I mean that Most companies still aren't using AI widely. Sure, developers use it daily, but mostly as an assistant. That's a key distinction. There's a big difference between using AI as a helper and fully integrating it into your company's core operations.
Many organizations are still hesitant, and I get it. If you're a CEO, you don't want your codebase to become unmanageable or risk piling up technical debt because of AI-driven changes. Relying solely on AI is risky, it should be done mindfully, with careful observation and analysis at every step before handing over major responsibilities.
However I'm a 100% sure AI will reach that level pretty soon, when almost all the companies will start to incorporate AI into their workflow and that's what I believe will be the peak era of AI. When something gets too mainstream, it always works like an equalizer. You (whether you're a company or an individual person) always want to do much better than your competitors, so you need to find something that'll give you an advantage over them. Something that'll amplify your skills and abilities and give you an edge.
Equalizer
Let's talk more about how AI is equalizing so much in the software industry right now. Let me share a quick story from my own experience:
When I was a rookie, I wanted to learn literally everything (which was a big mistake - you can only do so much, especially when you're inexperienced). I mostly wrote back-end code, but I wanted to be super competent in front-end too, not just JS frameworks, but also fancy markups, designs, and dynamic visuals with HTML and CSS.
One time I discovered "codepen.io" and was absolutely blown away by what could be created with HTML, CSS, and JS. That was the moment I realized programming was more art than art itself. I thought: I'm going to learn it, no matter how hard it is, and do things like that myself. I started learning from the lowest level, even tried to read code from other authors, which was the hardest part. After grinding and working for weeks, I still struggled to get some things right for several reasons: first, markup isn't really my thing, I don't like writing markup at all, second, the other authors doing amazing stuff had years of experience compared to my weeks, and third, it still wasn't my primary focus, I was still a back-end developer and didn't want to take away too much time from that skill (thankfully).
You're probably wondering why I told you this story. Because right now, if I wanted to create the same thing I struggled with back then, I'd just have to write:
"Hey, copilot, please generate a login page with neon lights,
glowing effects, cursor-based changes, a minor futuristic flicker,
and Matrix-style green numbers on successful login."
Copilot would generate it for me in seconds. It might not get it right on the first try, but honestly, neither did I. I had to try hundreds of things before I got it close to what I had in mind. AI gets you much further, much faster. That's why it's such a powerful equalizer: I can create something with little to no knowledge almost as quickly as someone who's honed that skill for years.
AI is making developers equal in some regards. Of course I don't mean in terms of skill, that's a hard NO! Skill remains skill. I mean in terms of what you can generate as a software engineer, what can you create, the product itself. Granted, you still need to have some skill to maintain what you create, but as long as you have enough skills to understand where AI is wrong and you might be right, you're fine.
Some might argue: "but what are you gonna do when there's no AI to help you generate something"? But, let's be real, most of the time, AI will be around, it's everywhere. You can even find AI in pretty surprising places like refrigerators that track food freshness and suggest recipes. The only time I don't have access to the internet is when being on a plane, and honestly, I don't get how some of you folks manage to code up there. I can barely open my laptop without elbowing a stranger, my knees are in my chest - hurting, my back's begging for mercy - hurting, and you're telling me you're out there coding and actually being productive at 30,000 feet? What?!
AI can be an equalizer for companies too, especially if they just adopt it because everyone else is, without adjusting it to their own needs. It's not a magic wand that will solve all your problems overnight. You need to invest real time and effort into building a strategy for how AI fits into your business. Like any tool, it can make your company faster and more efficient, or it can waste resources and money if used carelessly.
If you're a startup planning to use AI to build your products, go for it, but remember, as you grow into a mid-sized or large organization, you'll need to maintain what you build. You don't want to end up like those companies that launch something impressive but can't actually manage or sustain it. It's like constructing a skyscraper without a team to maintain the elevators, plumbing, or safety systems. Eventually, the whole thing becomes a liability instead of an asset. And that's exactly where AI can play a huge role as an amplifier.
Amplifier
In the short term, AI is a great equalizer, but in the long run, it can become a powerful amplifier. Starting new projects is exciting, but keeping that excitement alive is much harder, especially as projects evolve and drift from your original vision. The real pain comes when you return to a project after weeks or months, only to find changes you can't understand, made by someone who's no longer around, or worse, by an AI.
Trust me, I've seen AI break code in ways that are both hilarious and horrifying. Even simple things like HTML and CSS can get messed up pretty good: I've opened pages, written based on my AI prompt, where half the markup was corrupted and the rest barely rendered. And the changes weren't even that complex. Now imagine that happening in a massive financial backend and AI quietly breaks a feature that's worked for years, and unless you test for it specifically, you might never notice.
If you've worked on large, complex systems, you know bugs are inevitable, no matter how good your tests are. When that happens, you need deep technical skills and domain expertise to catch and fix those issues before they hit production and cost you real money. That's where AI becomes an amplifier: in the long term, your skills matter more than ever. When AI goes mainstream, your expertise will set you apart, so keep refining and polishing it. Don't get discouraged, your effort absolutely matters, maybe even more than before.
When it comes to adopting AI as a company, the real question isn't "if", it's "how." It"s no longer about if you're using AI, but rather how intelligently and responsibly you're doing it.
Here are a few questions worth asking yourself and your team before diving in:
Am I adopting AI because it truly adds value, or just because it's trendy?
Have I clearly identified the areas where AI can make a meaningful, positive impact on the business?
Have we compared the real costs versus the actual benefits? Are we genuinely seeing a return on investment?
Did we just copy existing solutions and best practices, or did we thoroughly analyze and adapt them to fit our unique business model and needs?
Do we have solid security measures in place? The ones that define what data can be shared publicly and what must remain local or private?
If you haven't discussed these points with your team yet, take the time to do so, even a quick conversation can make a big difference. AI can be a powerful amplifier for your success, but only if it's implemented thoughtfully and with caution.
Summary
In a world where technology doesn't just march forward but spirals ahead, AI is a great equalizer, leveling the field for rookies and veterans alike. But as companies rush to embrace the latest AI trends, the real challenge isn't creating something impressive, it's making sure you can sustain it.
The future belongs to those who adapt, strategize, and use AI to amplify their strengths rather than blindly chasing the hype. Don't get stuck in a skyscraper you can't maintain, build with purpose, and let AI help you climb higher.
Just remember: AI is a tool. Use it wisely, and it can be your greatest asset. Use it carelessly, and it can become your downfall. The choice is yours.
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