You’ll see plenty of answers claiming that AI can only handle “simple” projects, or insisting that humans must always review AI-generated code. The subtle flaw in many of those arguments is the hidden premise: they assume an AI coding agent can never reach sufficiency, let alone perfection—an assumption that’s hard to test and easy to overstate.

So let me come at this from a different angle. A major strand of computer science has always been about building tools that open up computing to everyone—what we often call the democratization of computing. From your first CS class onward, you’ve been both pushing that boundary and benefiting from it. AI is simply the next instrument in that long tradition: not a replacement for learning, but a force multiplier for access.

AI is not replacing the need to learn computing. It is becoming a powerful tool in the broader mission of democratizing computing.

“Computing should never be a black box controlled by a technical elite. Instead, it should be a transparent and accessible set of tools, concepts, and capabilities that empower everyone,” says Professor Hal Abelson. From Turtle Geometry to Scratch, from MIT App Inventor to GitHub Copilot, the history of computing has been a story of steadily expanding access. AI, when framed properly, is not the end of computer science education but an entry point for those previously excluded from programming. Just as MIT App Inventor allowed children and non-programmers to build impactful mobile apps through a visual interface, AI can now help users express computational ideas in natural language. It is a form of accessibility, not obsolescence.

Democratizing computing does not mean outsourcing human thought to machines. Nor does it mean we stop learning to think computationally. Understanding the underlying principles of computing — abstraction, algorithms, data structures, systems thinking, and ethics — is more important than ever. These are the foundations that allow us to use AI wisely, critically, and responsibly.

Ultimately, AI is just the latest chapter in a decades-long effort to make computing a universal language. Just as calculators didn’t eliminate the need to understand math, AI tools don’t eliminate the need to understand how systems work — they only change how we interact with them.

Now, more than ever, we need to study computer science — not just to write code, but to understand, question, and shape the digital world we live in.