If your student is choosing a major right now, there's a good chance you've heard some version of this advice: skip the "impractical" degree, go technical, and "AI-proof" your career. Computer science, data science, engineering, anything with a clear, codeable skill attached.
It's well-meaning advice. It's also increasingly out of step with where the job market is actually heading.
Why "AI-proof" doesn't mean what it used to
AI is remarkably good at the parts of work that can be reduced to a repeatable process: writing first-draft code, summarizing documents, analyzing data sets, retrieving facts. As those tasks get cheap and automated, so does the market value of doing them, just doing them.
That shift is already showing up in the data. Research from Stanford's Digital Economy Lab, using payroll data covering millions of workers, found that employment for software developers aged 22 to 25 fell roughly 20% from its late-2022 peak through mid-2025, while employment for developers 30 and older in those same AI-exposed roles actually grew. The entry-level execution work that junior employees have traditionally been hired to do while they learn is exactly the work AI has gotten good at.
What doesn't get automated is everything AI still can't do on its own: exercising judgment, reading a room, telling a story people believe, deciding what "good" actually looks like, and leading other humans toward a shared goal.
Those aren't soft, secondary skills. They're increasingly the skills that determine who gets hired, promoted, and trusted to run something. And they're exactly what a broad liberal arts education is built around: writing, reading widely, thinking critically across disciplines, and communicating clearly.
Two paths this opens up
Traditional employment. Employers who've hired liberal arts graduates for years consistently point to the same traits: strong writing, creative problem-solving, and the ability to work well on a team. That's not just anecdotal. In NACE's most recent employer surveys, problem-solving, communication, and teamwork have ranked as the top attributes employers seek, year after year, ahead of any specific technical skill. As AI absorbs more of the routine execution at a company, judgment and people skills increasingly are the job. That puts broadly educated graduates ahead, not behind.
Entrepreneurship. This is the bigger shift. Starting a business used to require serious technical overhead. AI has knocked much of that barrier down. Someone with no coding background can now build a working app or launch a small business in a weekend. That means the technical build is no longer the hard part or the differentiator. The hard part is everything AI can't do for you: identifying a real problem worth solving, communicating why it matters, and earning a customer's trust. That's judgment, creativity, and communication. Again, the core of a liberal arts education.
What this means for your student's major
None of this is an argument against technical skills. Plenty of liberal arts grads benefit from picking up data literacy or basic coding alongside their core coursework, and the two aren't mutually exclusive. It's an argument against narrowing a student's education purely out of fear that a broader degree won't "pay off."
The earnings data backs this up more than the stereotypes suggest. Georgetown's Center on Education and the Workforce found that 14 of 19 humanities and arts majors produce median earnings above $65,000 by mid-career, in a fairly narrow band overall. STEM majors do earn more on average, but that average hides enormous variance, and some STEM majors post lower earnings than several humanities majors once graduates are established. A major isn't destiny in either direction.
As AI takes over more routine execution, the ability to think, write, and lead becomes the differentiator, not a liability.
A few things worth encouraging in any student, regardless of major
- Strong writing. Clear writing reflects clear thinking, and it's the fastest way to turn AI-generated drafts into something actually useful.
- Comfort with AI tools, plus the judgment to edit them. Letting a tool draft something is easy. Knowing what's actually good is the skill that matters.
- Wide reading, outside their major. History, literature, and philosophy build the pattern recognition and context that AI doesn't have.
- Ongoing curiosity. The students who keep learning after graduation tend to keep winning in the job market, regardless of what their diploma says.
The bottom line for families weighing "practical" versus "impractical" majors: a broad, well-rounded education isn't the risky choice anymore. It's the strategic one.
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