Even though AI tools are now able to write perfectly good code, coders who understand deeper computer science concepts could still be at an advantage while using these tools.
Windsurf CEO Varun Mohan recently shared his perspective on the evolving role of software engineers in the age of AI-powered coding tools on a podcast. His insights, focusing on the shift from “solving” problems to “identifying” them, are particularly relevant given the rapid advancements we’re seeing in AI coding assistants like Cursor and Windsurf. He argues that a deep understanding of fundamental computer science principles like operating systems becomes even mor* critical as AI takes over the more rote aspects of programming.
Mohan breaks down the engineering process into three core functions: “What should I solve for? How should I solve it? And then, solving it.” He believes AI is poised to handle the vast majority, if not all, of the actual “solving it” part. He continues:
“Everyone who’s working in this space is probably increasingly convinced that solving it, which is just the pure ‘I know how I’m going to do it and just going and doing it,’ AI is going to handle the vast majority of, if not all of it. In fact, with some of the work that we’ve done in terms of deeply understanding code bases, ‘how should I solve it’ is also winding closer and closer to getting done.”
This shift, Mohan argues, returns engineering to its core purpose:
“So I think what engineering kind of goes to is actually what you wanted engineers to do in the first place, which is what are the most important business problems that we *do* need to solve, what are the most important capabilities that we need our application or product to have, and actually going in, prioritizing those, and actually going and making the right technical decisions to go out and do it.”
He acknowledges the discussion around the relevance of formal computer science education, stating:
“Now, does that mean that no one needs a CS degree? I think, I think that’s maybe a little bit overplayed a little bit. A lot of developers nowadays that build full-stack applications a handful of years ago, they probably went to college and took an operating systems course, right? And in theory, they’re not really playing around with the operating system, say the kernel scheduler, very frequently. But do those principles help them in understanding why their applications are slow? Do they help them in understanding why, why some design decisions are better than the other? Yeah, that makes them a much better engineer than, than another engineer.”
Mohan’s point underscores the idea that fundamental principles remain important, even as the tools evolve. Knowing how an operating system schedules tasks might not be necessary for day-to-day coding with an AI assistant. However, that deeper understanding can be invaluable when debugging performance issues or making architectural decisions. As AI takes over the more routine tasks, the engineer’s role becomes more strategic, focusing on higher-level problem definition and system design. This premium on deeper understanding suggests that a strong foundation in computer science could still be a significant differentiator for engineers in the AI era.