I graduated with a Computer Science degree about two months ago. For most of university, I genuinely enjoyed programming: working through difficult problems, debugging, understanding what went wrong, and finally getting something to work. Over the last couple of years, however, AI tools have changed how programming feels to me. Instead of investigating problems myself, I can often ask an AI for an answer immediately. Even when the result is imperfect, I sometimes feel more like I'm managing prompts and generated code than actually solving problems. I understand that these tools can improve productivity, but they have taken away much of the curiosity and satisfaction I used to feel.
I'm currently being paid to work with one of my professors on medical and AI research projects, but the available data is extremely limited. I'm increasingly doubtful that the project can produce a meaningful result, and some days there simply isn't much useful work for me to do. Being paid while feeling unproductive has made me question myself even more.
Before choosing Computer Science, I also considered medicine. Looking back, part of its appeal was the status, financial security, and clear career path rather than a strong fascination with medicine itself. I chose CS because I was genuinely more interested in it, but now that my enthusiasm has faded, I'm wondering whether I made the wrong choice.
I'm trying to work out whether this is burnout, disappointment with my current research role, frustration with AI-driven programming, career comparison, or a genuine change in interests. How can I tell whether I've actually lost interest in the field? For experienced developers, how do you keep programming intellectually rewarding while using AI? More generally, how do you find your direction again when something you once loved no longer feels satisfying?
3 Answers
Don’t choose medicine simply because programming currently feels bad. You already identified that status, money, and security were major reasons it attracted you, which is useful information. Talk to doctors, observe the day-to-day work, and compare that reality with several different technology roles before committing to years of training. You may also find a hybrid direction, such as healthcare technology, medical data, automation, or process improvement, but only pursue it if the actual work appeals to you rather than the image of the career.
A lot of professional programming is less like solving puzzles and more like maintaining large systems, fixing ordinary bugs, writing documentation, and delivering under constraints. That can be disappointing even without AI. If you stay in tech, consider areas where the underlying problem matters more than producing routine code, such as embedded systems, infrastructure, security, graphics, performance, or research engineering. In those areas, you still need to understand tradeoffs, reliability, and architecture, and AI-generated code is not something you can safely accept without deep review.
Exactly. In a healthy team, AI can speed up implementation, but people still judge you on whether you understand the design and can defend the solution. A workflow built around blindly producing and approving generated code would make almost anyone miserable.
AI doesn’t have to replace the part of programming you enjoy. Use it as a rubber duck or debugging assistant instead of asking it to generate the whole solution. Give it documentation and limited, read-only context, ask it to help interpret an error, and then write or refactor the code yourself. You can also set aside projects where you solve the core problems without AI, perhaps using manuals, documentation, or source code as your references. That preserves the learning and discovery while still letting you use AI for tedious work.
That distinction helped me too. Having AI explain a confusing error is very different from handing it the entire problem and accepting whatever code it returns.
Careers can also be useful without being a lifelong passion. Look for work that is tolerable, financially sustainable, and aligned with your values, then keep the more playful or challenging projects for your own time. Passion often returns when it is no longer being measured by deadlines and productivity targets.