I'm just starting college and already feel like everyone else is far ahead of me. I've been interested in computers since high school—I've installed Arch Linux, experimented with Hyprland, and followed coding and AI news—but I never had enough time to properly learn programming. Now I'm working through Python and C, and I'm enjoying both, but the projects I can currently build feel like something an AI could generate in seconds.
I'm worried it might take me a year or more to build a solid foundation, and that by the time I'm capable of creating something meaningful, AI will be even more advanced. I also spend a lot of time alone and could easily devote 12 hours a day to studying, although I'm not sure an isolated grind is actually the right approach.
I know that consistently learning over four years could probably lead to a decent job, but I don't want to settle for merely being good enough. I want to become genuinely excellent at this field. The problem is that I don't constantly have revolutionary ideas, and I'm afraid I'll spend years learning only to discover that I started too late or never figured out what direction to pursue.
For someone beginning a computer science degree with plenty of motivation but no clear long-term path, what should the process of becoming exceptional actually look like?
5 Answers
Everyone who looks ahead may simply have started earlier; that doesn’t mean they’re fundamentally more capable. You have four years, which is a huge amount of time if you use it consistently. Focus on deliberate practice and keep building projects instead of waiting for a billion-dollar idea. Start with problems that matter to you, even if they seem small. AI can generate code, but someone still needs to understand the problem, make good design decisions, verify the result, and turn it into something useful.
The exact number of hours isn’t important, but becoming world-class takes a long period of focused, deliberate practice. Simply spending 12 hours at a screen isn’t the same as improving for 12 hours. Set aside some time for concentrated study, then apply what you learn through projects, debugging, reading documentation, and reviewing your own work. A sustainable routine will take you much further than trying to force an extreme schedule.
Most people can’t maintain genuinely difficult practice all day, every day. A few focused hours done consistently can be far more valuable than a huge amount of unfocused screen time.
AI makes programming faster, but it doesn’t eliminate the need to understand programming. The better you understand fundamentals, the better you can prompt, direct, test, debug, and improve AI-generated code. Keep learning the basics while using AI as a tool rather than treating it as a reason to stop. If the technology discourages some people from entering the field, that may even reduce competition, but your main advantage will still come from being able to think clearly and build reliable systems.
There is no single moment when you discover the one thing you’re supposed to do. Explore different areas through coursework and small projects, pay attention to what holds your interest, and gradually move toward harder problems. You don’t need an original world-changing idea to become excellent; deep understanding, persistence, and the ability to solve real problems are already rare and valuable.
Even experienced developers still feel behind. The field changes constantly, and after decades people are still learning new tools, languages, and areas of engineering. That feeling doesn’t necessarily mean you’re failing; it often comes with working in a field where there is always more to learn. The useful skills—logic, debugging, system design, communication, and good engineering habits—transfer across technologies.
You don’t need thousands of hours before you’re allowed to build something worthwhile. Keep making things, learn from what breaks, and gradually choose harder problems.