Every day, we are bombarded with headlines about how Artificial Intelligence (AI) is “disrupting” every industry in its path. Software development is at the epicenter of this hype. With the rise of sophisticated AI-powered tools, the same question surfaces repeatedly: Will AI replace human coders, or merely augment them?
I find it particularly hilarious to see YouTube videos claiming a “layman” built, deployed, and monetized a full-scale app in minutes using AI. In reality, these “apps” are usually fragile, buggy, and lack the security or scalability needed for the real world. Building a robust application requires a deep understanding of software architecture and best practices—things an AI can mimic, but not truly understand.
The Problem with Predictions
Before we dive in, let me clarify: I do not take “future of tech” predictions seriously (not that i do for any other speculative field except from science and logic).
I will accept predictions only for fully reproducible scientific experiments or mathematical theorems but not for social or technological trends.
Most predictions about the future of AI are built on current trends and shaky assumptions that rarely survive the long run. Furthermore, the majority of these forecasts come from individuals with a vested interest in selling you a specific product or platform.
Even when the noise isn’t coming from a salesperson, it often comes from people who are not experts in the field of programming. I’ve read countless speculative “end-of-programming” articles written by people who aren’t developers at all, or best, at some point in their education or early career, they wrote a “Hello World” program in python and suddenly felt qualified to judge the future of software architecture.
What I am expressing here is based on my experience as a professional software developer for decades. I can be wrong; I have been wrong in some of my assessments before. However, I still believe that my “opinion” is worth no more or less than anyone else’s
Some notable failed predictions from experts in their respective fields include:
- Self-Driving Cars: Tesla has promised “Full Self-Driving” is just around the corner for years; we are still nowhere near that goal.
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Medical AI: In 2016, Geoffrey Hinton—the “father of modern AI”—predicted that radiologists would be replaced within five years. We are now a decade past that prediction, and radiologists are as essential as ever.
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Scientific Hubris: In 1895, the renowned physicist Lord Kelvin famously stated that “heavier-than-air flying machines are impossible.” The Wright brothers proved him wrong just eight years later.
If world-class experts cannot accurately predict the future of their own fields, speculating on the “death of the programmer” is a waste of time.
AI as a Tool, Not a Teammate
Despite my skepticism of the hype, I acknowledge that AI has made significant strides. AI-powered tools like code generators, bug detectors, and testing frameworks are already augmenting our work. They excel at automating repetitive tasks, improving code quality, and speeding up the initial development phase.
As a programmer, I use AI tools daily. I find platforms like GitHub Copilot to be valuable additions to my workflow, offering context-aware snippets that save time and reduce syntax errors. AI is also surprisingly adept at helping with database schema design and initial data analysis.
However, I see them as tools, not replacements , a view that is not shared by many AI enthusiasts who in their majority have a direct or indirect interest in promoting AI technologies.
The “Spaghetti” Reality
In my experience, projects generated exclusively by AI without human intervention invariably result in “spaghetti code”that is next to impossible to maintain, and extend. While AI is great at generating “boilerplate” (the repetitive parts of a program), it cannot replicate the critical thinking required to make high-level architectural decisions.
Final Thoughts
Experience has taught me that predicting the future is a futile exercise. The best we can do is adapt. AI is undoubtedly a powerful tool that can enhance our capabilities, but it is no substitute for human creativity.
Software development isn’t just about outputting lines of code; it’s about solving human problems. Until AI can understand the “why” behind a project as well as the “how,” the programmer’s job is secure.