Tag: Job Market

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AI Computers Stanford Work

The Seam in the Pipeline

Stanford’s undergraduate computer science degrees fell by about 70 students this year — almost 14 percent in a single cycle. That’s a sharp drop at a school that, for a long stretch, felt like the factory floor for Silicon Valley.

A single year is not a trend. Mehran Sahami, Stanford’s computer science chair, called it natural variation — cycles the department has seen before. He’s right to be cautious. But the number lands inside a pattern that’s harder to wave off.

Nationally, computer science enrollment at four-year colleges is down roughly 8 percent, after nearly two decades of uninterrupted growth. Entry-level software jobs have thinned. Laid-off mid-career engineers are competing with new graduates. AI tools now write a lot of the code that used to be the first rung on the ladder.

I got curious how MIT looked next to Stanford. The comparison is messy in a useful way.

MIT doesn’t have one “computer science” degree — computing is split across several Course 6 majors. Classic Computer Science and Engineering (6-3) awarded 265 bachelor’s degrees this year. The newer Artificial Intelligence and Decision Making major (6-4) awarded 114. Electrical Engineering and Computer Science (6-2) added 72 more, plus smaller joint programs.

Look at majors instead of diplomas and the shift sharpens. 6-3 slipped from 706 students to 672 — about 5 percent this year, and down 18 percent from its 2022 peak of 823. 6-4, which started in 2022 with 37 students, is now at 372. Intro programming enrollment has been sliding since its 2022–23 high. Course 6 overall fell last year for the first time in a decade — but only modestly.

Stanford’s story is a clean 14 percent drop in one degree. MIT’s is a reallocation: students moving from traditional software construction toward a track built around judgment and decision-making instead of a coding sequence. The footprint is still large. The composition has already changed.

That distinction matters.

After the dot-com bust, CS enrollment fell too. Sahami noted that the students who enrolled then graduated straight into the Facebook and Google growth years. A downturn in the pipeline isn’t automatically a verdict on the field. It can be a lag. It can also be a signal that the old promise — learn to code, collect a golden ticket — no longer maps onto the first job.

I don’t know which this is yet. A Census Bureau paper cited in the Stanford piece found that recent graduates in highly AI-exposed fields have seen employment and starting-job quality deteriorate on a scale comparable to a large recession. That’s not a cycle to dismiss. It’s also too early to call it destiny. Models change. Firms eventually spend the productivity they just captured. What’s left tends to look less like junior ticket-closing and more like judgment, systems design, knowing what the tools should be pointed at.

I’ve watched enough technologies overshoot and settle to be wary of panic and nostalgia alike. The question isn’t whether computer science is “over.” It isn’t. It’s what kind of computing education still compounds once the first-year coding job stops being the obvious on-ramp.

Stanford is still talking in cycles. MIT has already built a major that assumes the on-ramp has moved. Both may be right for a while.

The seam, as usual, is in the middle — between the degree that used to be a ticket and the skill that still is. That’s the part I’ll keep watching.

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AI Work

Betting on Ourselves in the Age of AI

Every time tech takes a leap, we assume we’re finally obsolete. The current panic, which Greg Ip recently picked apart in the Wall Street Journal, is AI. We hear endless predictions of “economic pandemics”—server farms wiping out white-collar jobs overnight, leaving everyone broke and adrift.

It’s a terrifying story. It also completely ignores history.

Ip highlights the main flaw in the doomsday pitch: it misreads how markets work. We treat labor like a fixed pie. If a machine eats a slice, we assume that slice is gone forever.

“Technological advancements always cost some people their jobs—those whose skills can be easily substituted by tech. But their loss is more than offset through three other channels. The new technology enhances the skills of some survivors… it helps create new businesses and new jobs; and it makes some stuff cheaper…”

That cycle holds up. Take the 1980s spreadsheet panic, a perfect parallel. When Lotus 1-2-3 and Excel hit the market, bookkeepers freaked out. Then the number of accountants and financial analysts exploded. Software didn’t kill the need to understand money. It just did the math, letting people focus on strategy.

We’re seeing the exact same thing with software development. Coding isn’t dead. As AI makes writing basic code cheaper, demand for software just goes up. That requires more humans to architect systems and supervise the AI. The pie just gets bigger.

But my skepticism about the AI apocalypse goes beyond economics. It’s about why we pay people in the first place.

We don’t just buy services; we buy accountability. Ip notes that radiologists kept their jobs because patients want a real person explaining their scans. Google Translate has been around since 2006, yet the number of human translators has jumped 73%. When the stakes are high—a legal contract, a medical diagnosis—we want a human in the room. We want a real person on the hook.

The danger isn’t that AI will replace us. The danger is that we panic and forget our own adaptability. The transition will hurt, and specific jobs will disappear. We’ll need safety nets. But betting against human ingenuity has always been a losing wager.

Large language models are tools, not replacements. They handle the cognitive heavy lifting, much like tractors handled the physical heavy lifting. Tractors didn’t end farming; they just killed the plow.

Work will change. We’ll have to figure out which of our skills are actually “human.” But as long as we want the presence and accountability of other people, there will be jobs. We just have to evolve. And we do. It’s the human spirit. Or is this time “really different”?