What I Told My Friend About His Daughter's CS Degree

A former student wrote to me last week. His daughter wants to major in Computer Science and he wanted to know if that was still a good idea given what AI is doing to the field. He wasn't sure. Here’s my thoughts on this.

The easy coding work is getting automated. Not eventually; now. Anyone graduating in four years who spent their degree learning to write syntax will find that ground has shifted. The tools handle that. They handle it well on clean, well-specified problems with clear success criteria.

The problem is that real engineering isn't mostly clean problems. It's ambiguous requirements, incomplete context, and decisions that require judgment across competing constraints. That's where models degrade, and that's where the humans who understand what's happening will have real leverage.

And here's some suggestions:

Learn to specify problems precisely. Most AI failures start with vague input. The engineer who can decompose a messy requirement into unambiguous pieces is the one directing the tools, not cleaning up after them.

Study systems, not syntax. Operating systems, networks, software architecture. Models know syntax. They're weaker on tradeoffs, on performance across boundaries, on why a design decision made ten years ago is causing a problem today.

Learn to evaluate, not just produce. Can you tell whether a solution is actually correct? Can you define what correct means before you start? That's the gap between someone who uses AI well and someone who ships bugs confidently.

Get comfortable with ambiguity. Seek out projects where the problem isn't handed to you. Open source, research, capstone work. The ability to figure out what the problem actually is doesn't show up on most syllabi.

The pattern holds across majors. Law schools spent years worrying that LexisNexis would replace lawyers. It replaced the first-year associate doing document review, which freed the actual lawyers to do more law. Accounting software automated the ledger work in the 1980s. Accountants who understood tax strategy and client judgment did fine.

The jobs that survived automation in every field shared a common trait: they required deciding what mattered, not just executing against a clear target. CS is no different. The syntax is the ledger work. The judgment is the practice.

So what does the next four years look like, and beyond? Models will keep improving on well-specified tasks. The baseline on routine coding, testing, and documentation work rises every year. At the same time, the judgment-heavy work gets more valuable. Architecture decisions, requirement analysis, debugging complex systems, knowing when a technically correct solution is the wrong answer: these compound in value as the easy work gets cheaper.

New categories of work always appear. The internet eliminated some jobs and created others that didn't exist before. AI is doing the same. The students who treat AI as a tool they understand rather than a black box they depend on will be better positioned than those who don't. That has been true of every major technology shift in engineering for the past fifty years.

We need engineers who can think. We always have!