Introduction
If you're a Software Engineer, the last few years have probably felt chaotic. The industry is changing aggressively, and it's easy to feel tired, overwhelmed, or anxious sometimes. I have too. But at the same time has been very exiting to see how things change.
This article won't talk about which model is best or which tool makes you a "better engineer." There are already hundreds of articles about that. Instead, I want to share honest thoughts about AI from a human perspective, the good and the bad, and how I think things will evolve.
New models drop every week, social media floods us with lots of information and hype, and staying up-to-date feels impossible. The good news: once you accept that you can't keep up with everything, it gets easier. Focus on what actually matters and ignore the noise.
Keep in mind that the industry moves fast and some things here may already be outdated by the time you read this.
AI is breaking the silos around programming
A few years ago, programming was a huge barrier for most people. Sure, tools like Wix and WordPress made websites easier for lots of people, but building more complex applications still required coding skills.
Then ChatGPT launched in November 2022, almost four years ago, and everything changed.
How many products never saw the light because they were too expensive to build? How many small businesses couldn't afford the internal tools they needed? Now, thanks to LLMs, more people can create those applications even without deep programming knowledge. Not every problem can be solved this way of course, but a surprising number can. And LLMs will only get better.
I personally have small scripts that I use daily that were created entirely with AI, scripts whose code I have never seen. They will only be used by me, so I don't really care about the code. This applies to non-programmers too: problems that used to require buying an app or paying a subscription can now be solved in a few hours with AI. Custom tools are cheaper than ever.
Non-technical teams can also automate the tasks they've been putting off. You can create a prototype, and get feedback from others. It won't be production-ready sometimes, but it might be good enough to validate an idea.
Solid programming fundamentals can help you get more out of AI when building applications, but you don't need them to start. Experiment, build, iterate. Your next product or company might begin with a vibe-coded idea.
AI as a teacher
Studying with AI in 2026 is absolutely great, more interactive and fun than it was in the past. I wish I had these tools during college. At least for me, there is no way back.
Currently, my study workflow consist of studying documentation, reading articles and forums to understand concepts in depth, then asking AI for examples to complement my learning. Sometimes I reverse the order, starting with AI generating some code and then cross-checking with other sources.
When practicing a language topic, I still prefer writing some code myself, but focused on the topic. You might wonder: why write code manually in 2026 if AI writes all the code? Because how can you develop judgment about AI-generated code if you don't understand it? And I don't mean the syntax itself, actually that's the least important, but the security, scalability, maintainability, and performance of generated code. Or at least that's what I think works best for me.
If you're just starting in this field, I believe it's still very important to learn to code by hand, the same way mathematicians learn math by hand even though there are calculators and computers that can do calculations faster than a human. It's just part of the learning process. Once you know what conditionals and loops are, you don't need to remember the exact syntax, you just know when to use them.
As engineers, one of the worst things that you can do is to blindly trust AI and not be able to judge if it's right or not. Mastering a language and its ecosystem takes years. Knowing the right libraries and tools for a given use case is backed by years of failures and lessons learned, and that makes the difference when designing systems.
The fundamentals of Software Engineering haven't gone anywhere: concurrency, protocols, networks, algorithms, design patterns, performance, scalability, security, and testing. You still need to know how to correctly apply these concepts and tools.
Fundamentals are more important than ever
Imagine a library where all the shelves are behind locked doors, and all the keys sit at the entrance, but you don't know which key opens which door. Even with the right key, you still need to navigate the labeled shelves to find the right book.
Knowing what to prompt is the key that opens the door to the right section of the model. If you don't know the vocabulary or the topic, that knowledge is simply hidden from you. For complex problems, vague questions won't be enough. You need domain knowledge to get the right answer.
Repetitive tasks and specific needs can often be solved with a simple script or integration. But how would people even know that's possible without the vocabulary? That knowledge is there inside the model, accessible to everyone if they know what to ask. The same applies to mathematics, physics, chemistry, marketing, finance, or any other field. Only if you know the field can you access that information.
Less manual writing, more responsibility
Generating more code doesn't mean generating good quality code. AI is not responsible if something goes wrong, you are. Depending on the software you work on, errors can cost money or impact lives. Ask anyone in healthcare.
Critique the generated code like you would review a PR from another human. You don't necessarily need to understand every line, just like we don't read the assembly code a compiler generates. But you need enough understanding to judge what's good and bad. When working on a feature, put extra effort in understanding the solution. Your future self will thank you.
More important than the code itself: understand the product, customer needs, the actual problem you're solving. Everything else is just implementation detail. Strong requirements and acceptance criteria are crucial for getting good code from AI. It can easily forget the company policies or best practices you follow in your organization, so it's up to you to ensure the solution is correct.
Lastly, don't rely on LLMs for everything. Use deterministic tools for static analysis and code quality checks. Then add review steps, whether by humans or agents, through skills and scripts.
Now we can focus on what matters more: building proper test suites, discussing architectural decisions with depth, and tackling problems previously out of reach. There is no excuse. Refactors that were unmanageable and features that were too expensive are now achievable.
It Will Take Years for Universities to Adapt
Traditionally, it takes years for universities to adapt their study plans to market needs. For example, during my degree, professors discouraged the usage of Node.js. When I started working, I found it was one of the most in-demand technologies (and it still is, with TypeScript). Courses barely covered React and Angular. If I hadn't spent my limited free time as a student learning backend and frontend frameworks on my own, getting a job would have been much harder.
Now companies expect new hires to be familiar with AI tools, as you can see in recent job openings. As a student, how do you manage your limited free time to learn the most in-demand technologies on your own, and also add a ton of new AI tools and concepts on top? A new engineer might feel overwhelmed about skills, rules, agents, models, tokens, context windows, spec-driven development, RAGs, MCPs, just to mention a few.
Remember that AI is a tool, just like IDEs and compilers are. You can always learn Claude Code or OpenCode on the way, but four years of solid engineering fundamentals will pay off in the long run. Don't skip that part.
If you're just starting college, AI tools will likely be more mature and stable when you graduate, so the adaptation will be smoother for new generations of engineers. Hopefully there won't be as much AI hype by then. When a technology becomes boring, it's usually a good sign that it's mature and stable.
Disruption of software business models
Programs are now easier to replicate, making it harder to monetize solutions that can be copied quickly and with minimal investment. Companies whose business model relied on being hard to replicate from scratch are at risk. Others find their product value unclear in this new market. Not all will adapt unfortunately, and some people will lose their jobs during restructuring processes.
In January 2026, Tailwind's founder announced a 40% drop in documentation traffic and 80% drop in revenue, leading to 75% layoffs. Their model was driven by documentation traffic and one-time "lifetime access" payments, with no recurring revenue to sustain them when income dropped. AI became the middleman for information, damaging their business model.
Another example is Jetbrains. They make great IDEs, but this year I've been using IntelliJ and GoLand less and less in favor of lightweight editors like VS Code, Zed, and Neovim. For me, it's hard to justify the Ultimate license cost. More people seem to be switching to lightweight editors too.
At the same time, new AI companies are emerging and growing fast with fewer employees than traditional companies but making even more money, e.g. Cursor, and AI providers like OpenAI and Anthropic.
Open source and the AI market war
Open source is another threat for many companies, and not everyone sees it. I expect greater adoption of open-source alternatives in the coming years. Linux desktop usage is at an all-time high, and more people are trying open source software as a replacement for paid applications. I think this is good for people, given the abusive subscription culture we've seen in recent years.
Additionally, not all companies have the same muscle to invest in AI. A 100-employee company can't match a 10,000-employee company. This could hurt small and medium-sized businesses. Adopting AI means integrating it into the core of the organization, which isn't cheap. Even a $200 subscription per employee adds up to a considerable amount at scale. API usage isn't cheap either. How can they spend thousands or millions of dollars on AI without raising prices for their products and services or restructuring?
The AI market will eventually stabilize, like smartphones did. Every release felt revolutionary a few years ago; now most people don't care. Every technology matures this way.
AI companies aren't just trying to replace humans, they're trying to capture market share from each other. If a competitor launches better and cheaper models, people will switch, and sometime cause dominant players to panic. These companies need paying customers just to keep the hardware running. And let's not forget that China has very cheap and capable models these days.
Because of that, you should not stick with a single AI provider. Explore different options. You might find open-weight models that fit your needs. We don't know if they'll exist in five years.
Conclusion
Coding has never been the goal of software engineering, but the means. We are engineers, we solve problems. Code is just a way to solve them. If AI writes your code, it means now have more time to spend on better designs, architectural decisions, and proper testing. There is no excuse anymore.
It's okay to feel anxious sometimes. But learning new things is also part of the fun. Try not to pay too much attention to AI gurus on social media. Choose high quality sources of information and limit your exposure to garbage content related to AI.
Some companies will adapt faster than others. Some won't make it. If you're starting your degree, investigate your market needs and focus on that. Check LinkedIn for what companies are requiring.
Don't try to stay up to date with every trend. Choose one tool and become very good with it.
Even if you don't write or read code in the coming years, you still need to know what you're doing. Don't skip learning the fundamentals. You are responsible for the code you deliver.
Keep your brain sharp and be able to search on your own. Read articles, books and official documentation. That skill will be useful when AI struggles with complex problems.
This field has always rewarded those who stay curious. AI is just another chapter in a long history of tools that has changed how we work. It's not the first time we need to reinvent ourselves and won't be the last. Learn the fundamentals, build things that matter, and don't forget to enjoy the ride.
Top comments (1)
ngl it feels like we're just spending more time debugging ai hallucinations than actually writing the logic ourselves lately