This year has been somewhat of a wake up call to the developer community in terms of A.I. It seems like each and every month now a new tool comes out that can produce code faster and more accurately than the models before it.
Needless to say, this is going to keep happening for the foreseeable future. And many newer developers find themselves at a crossroads.
Do they continue to invest time and money into a field of study that's going to evaporate in the next decade? Or do you pivot now and save yourself the headaches?
Let's separate the hype from reality. Because after two decades of supposed "developer extinction events," I've learned something important about our industry's evolution and why human developers aren't going anywhere (yet).
What's really going on
Every few months, a headline declares that programmers are becoming obsolete. The latest AI model generates a todo app in seconds, and social media floods with predictions about the end of software development careers.
You're also starting to see articles claiming that A.I. coding assistants are making junior developers worse, so they should stop using them. I would argue that the junior developer struggle is perfectly normal and that most companies are aware of that.
Many of these articles are way over-simplified and most are written by non-developers unfortunately. Because there's a lot missing to their narrative.
So before junior developers abandon their learning paths or seniors consider career changes, let's separate the hype from reality.
A.I. is still very limited
Today's AI coding tools are impressive but they are still very limited. They excel at generating boilerplate code, suggesting completions for repetitive patterns, and implementing well-defined features in familiar frameworks.
What they lack is genuine understanding of business contexts, architectural trade-offs, and system-wide implications of design decisions. They also lack the ability to make financial decisions, which is a key part of corporate development.
You might be responsible for an API that costs a company $100 a month based on usage and so you keep those constraints in the back of your mind. A.I. can't quite manage your books just yet.
When an AI model generates a React component or a Python function, it's drawing on patterns it's seen in training data—code written by human developers.
It can mimic structure and syntax remarkably well, but struggles with novel problems that it has never encountered.
It still lacks training data
Much of the software that exists in production today is relatively old, and it's kept that way on purpose. Mainly because the code is stable and presumably generating some form of income for a company. Most companies won't disrupt that unless there's some return on investment on the other side of things.
When I personally worked on a Classic ASP project years ago, my job wasn't to modernize it or to convert it to React. It was to figure out how it worked and to make sure that no matter what it kept running.
Much of the code was outdated, archaic, less secure and even deprecated (to some extent). But being a human developer, I knew those limitations and I was able to work around them.
I also worked with a very talented Classic ASP developer who had decades of experience and who knew the entire framework by heart. His personal 'training data' was much more abundant than any A.I. model at the time.
Currently, there's a high chance that most modern coding models are not well trained on older programming languages and frameworks. Simply because there's just less of them in public repos.
This makes them less than ideal for code generation as there's a good chance that hallucinations would be higher.
Limited capabilities
Take this scenario as an example. You meet with your product manager to discuss a new feature that they designed. A custom sticker feature like the ones you would see on a social media site.
As a developer (of the human kind), you know that this involves a new database schema, some form of image management (or even A.I. image generation) and an admin to manage all of the data.
You might even need to integrate it with your companies in-house asset management service which serves images to every website the company owns. This would be a very unique workflow that only a handful of people would know.
This might even involve creating a new Amazon S3 Bucket or possibly even storing images as Blob's in the database. Maybe you discuss it with your project manager to see what the budget is.
And you might even discuss it with your DBA (database administrator) to figure out what the best approach is when it comes to the database.
Most of these steps, A.I. can't do. At least not right now. It would need real-time data on the entire companies inner workings and it would require the input from multiple people in order to successfully build this feature out.
And this feature isn't even really that complex or innovative. So imagine having to build something that requires the input of even more people in real-time and that is constantly having its requirements changed on an hourly basis.
A.I. still needs code reviews
Furthermore, AI tools frequently produce code that looks correct at first glance but contains subtle bugs, security vulnerabilities, or inefficiencies that only become apparent during testing or production.
The ability to critically evaluate generated code and to know whether a solution is merely functional or truly optimal remains a distinctly human skill.
What we're witnessing right now isn't the replacement of developers but the evolution of development workflows.
The most successful engineers aren't fighting against AI tools but incorporating them as accelerators and using them to handle routine tasks while focusing their human creativity and problem-solving abilities on higher-level challenges.
This relationship between developer and AI amplifies productivity rather than diminishing the developer's role.
What's the best thing developers can do?
First and foremost, don't panic. If you read an article telling you that your job is in danger because ChatGPT wrote a 200 line to-do list application, then find a different news source.
If you're currently studying Computer Science, continue your studies and try your best to build a strong foundation on core programming principles.
The more AI tools evolve, the more important it becomes to understand core computer science concepts. Deep knowledge of data structures, algorithms, and system design isn't just academic—it's what helps you evaluate and optimize AI-generated code.
Instead of viewing AI as competition, learn to leverage it effectively. Use AI coding assistants for what they're good at—generating boilerplate code, suggesting refactoring options, or helping with documentation.
But maintain a critical eye and understand that you're the pilot, and AI is your co-pilot.
Focus on system integration and architecture
Complex systems integration, microservices architecture, and cloud infrastructure decisions require deep understanding of business needs, security implications, and performance trade-offs.
These high-level architectural skills are far beyond current AI capabilities and will remain valuable for years to come.
Stay adaptable
More importantly though, be fluid with your learning and with your career options. Things are going to change, as they always have.
In my past career, I was a Windows Desktop developer, an X++ developer, a Classic ASP developer and an ASP.NET Web Forms developer.
All deprecated and forgotten technologies. But they were still invaluable in my career growth.
Now I mainly focus on React and other JavaScript based frameworks, because that's where the market is. And there's a chance that in the future, I will be looking back at React the way that I look back at Classic ASP.
The Future is Collaborative
The future of software development isn't about AI replacing developers—it's about developers who can effectively collaborate with AI tools outperforming those who don't. By focusing on these areas, you'll position yourself to thrive in this evolving landscape.
Remember: every major technological advancement in our field has been predicted to make programmers obsolete. Instead, each one has created new opportunities for those willing to adapt and grow.
AI will be no different and it's not an ending, but a new beginning for those who embrace it wisely.