Why software roles, senior expectations, and Computer Science are evolving instead of disappearing
By Jae S. Jung
As we come to a close for 2025, one thing is clear. Over the past few years, AI has taken center stage in technology discussions. What began as curiosity quickly turned into urgency and, in some cases, fear.
One of the strongest narratives to emerge has focused on the future of programmers. With AI now capable of generating code, scaffolding applications, and accelerating development at a pace few anticipated, many are questioning whether programming roles will continue to exist and whether Computer Science remains a viable path forward.
The intensity of these reactions suggests this topic reaches beyond tools or trends. It challenges how people think about their careers, their education, and the long-term shape of the technology industry.
But the conclusion that programming is becoming obsolete misses what is actually happening.
AI is not eliminating programmers. It is changing what valuable technical work looks like.
AI excels at accelerating implementation tasks.
It can generate boilerplate code, scaffold applications, translate logic between languages, and assist with repetitive development work. These capabilities are impressive, but they primarily replace tasks, not roles.
Writing code by itself has never been the highest-value part of software development. The real value has always come from understanding the problem, shaping the solution, and ensuring what gets built works correctly within real-world constraints.
AI speeds up execution. It does not replace judgment.
Even when AI generates code, someone still needs to review it, validate it, and understand how it behaves under real conditions.
Security, performance, data integrity, and edge cases cannot be delegated blindly. You need enough understanding to recognize when assumptions are incorrect, when something appears correct but fails under specific conditions, or when a small decision introduces long-term risk.
This is especially important for people entering the field. Writing code remains one of the best ways to learn how systems work. AI can assist with learning, but it cannot replace the mental models that come from building something yourself and seeing how changes ripple through a system.
If you cannot understand the code, you cannot confidently trust it.
As AI accelerates development, the bottleneck moves away from writing code and toward designing systems.
The harder problems now involve how components fit together. Databases, APIs, authentication and authorization, caching strategies, messaging systems, logging, monitoring, and data flow between services all become more critical as systems become easier to build and faster to change.
This naturally pushes programmers toward architectural thinking, even if their titles do not change. Understanding how the pieces interact becomes more important than writing any single piece of code.
This shift has existed for years. AI simply makes it unavoidable.
Many of these expectations already existed at higher levels of experience. Senior engineers and architects have long been responsible for system design, tradeoffs, and long-term decisions.
What changes now is the decision surface.
As AI reduces time spent on implementation, senior roles expand further into defining system boundaries, governing how AI is used, setting standards, and owning the long-term risk associated with architectural decisions.
Less time is spent reviewing individual lines of code. More time is spent shaping how systems behave, scale, and fail over time.
Senior roles will not remain static. They will continue to evolve alongside the tools.
The software industry has grown rapidly over the past decade. With that growth comes some level of oversaturation.
In many industries, disruption eventually forces a recalibration of roles, expectations, and value. AI may simply be the catalyst that accelerates this process in software.
This does not necessarily mean fewer opportunities. It may mean clearer differentiation between those who understand systems deeply and those whose value is tied primarily to execution speed.
Disruption does not always remove work. Sometimes it redefines where value truly sits.
I do not believe that AI's role in the near or immediate future is limited to simply enhancing programmers or making development incrementally more efficient. That framing understates what may be unfolding.
What feels more accurate is that we are still in the earliest stages of understanding what this technology represents. Much like the early days of the internet, organizations today are reacting in real time. They are experimenting, adjusting, and moving quickly, often without a clear long-term map.
There are parallels to the dot-com era, but also important differences. The internet bubble eventually burst, yet the underlying technology reshaped entire industries and gave rise to companies that defined the next generation, such as Google and the iPhone. AI feels closer to that kind of foundational shift.
From my perspective as a solutions architect, AI has already helped me explore new ideas, challenge existing assumptions, and advance how I think about system design. It does not replace judgment or experience, but it expands what is possible within the same amount of time.
This does not mean every use case will succeed or every investment will pay off. But it does suggest that reducing AI to a simple productivity tool misses the broader transformation that may be underway.
We may not fully understand what we have yet, and that uncertainty is exactly why conversations about roles, skills, and the future of technical work feel so charged.
Rather than signaling the end of programming, this moment feels like the beginning of a broader transformation. One that shifts the role toward deeper thinking, stronger systems, and more intentional design.
Computer Science is not becoming irrelevant. It is becoming more demanding. Foundational concepts such as algorithms, data structures, systems design, and problem solving matter even more in an AI-assisted world. The difference is how those fundamentals are applied.
Programming is not disappearing.
It is evolving into a discipline with greater scope, responsibility, and long-term impact.
