MIT and Carnegie Mellon already rebuilt their programs around AI. Most schools haven't moved. Here's what changed, why it matters, and a free tool to check where your program actually stands.
I want to start with the uncomfortable question that kicked this whole thing off for me. We spend four years — and a serious amount of money — training engineers for a specific first job. The junior role. The one where you cut your teeth on the grunt work and slowly climb. But what if that exact rung is the first thing AI takes off the ladder?
That's not a doom-pitch. It's a question I think every engineering student, professor, and department head should be sitting with right now. So I went looking for the data, and then I built a tool to make it practical. This post is the companion to the video — the receipts, the links, and the part you can actually do something with.
The entry-level rung is being automated faster than the senior rung it's supposed to lead to.
The numbers that made me stop scrolling
I'm wary of hype, so I only kept the figures that come from real payroll and labor data, not vendor decks. A few of them genuinely surprised me:
13%drop in employment for 22–25 year-olds in the most AI-exposed jobs since 2022 (Stanford Digital Economy Lab)25%cut in big-tech entry-level hiring in a single year — while experienced hiring rose92Mjobs the WEF expects to be displaced by 2030 (with 170M created)
The thing that ties them together isn't "AI is coming for everyone." It's narrower and sharper than that: AI is best at the book-learning, rote tasks that a fresh graduate brings to the table — and experience is becoming the thing that protects you. The newest engineers have the least experience, so they're the most exposed. That's the whole argument in one sentence.
What the good schools already did
Here's the part that gives me hope, because it shows this is solvable. The leading programs didn't add one AI elective and call it a day — they wove it into the discipline:
- MIT built machine learning into a flagship design course back in 2021.
- Carnegie Mellon launched seven AI-engineering master's programs.
- University of Florida rolled AI literacy across its entire curriculum.
- ABET's proposed AI accreditation criteria start applying in the 2027–28 cycle — so this becomes the floor, not the ceiling.
The common signal: AI is embedded into the engineering, not bolted on beside it. That's the bar. The question is how far the rest of us are from it.
So I built a way to measure it
Talking about readiness in the abstract goes nowhere. So I turned it into a simple diagnostic: eight capabilities — from "AI in the core" to deployment skills to faculty currency — each scored 0 to 3 (Absent → Exposure → Applied → Embedded). You rate where you stand and how urgent each one is, and it sorts your gaps into a ranked, act-now list. Not a vibe — an actual plan.
In the video I run it against two anonymized real cases: a frontier program that scores 92% ("frontier-ready"), and a traditional program that lands at 33% ("at risk"). Same eight questions, wildly different shapes. It makes the gap impossible to hand-wave away.
Free Download
The AI Readiness Chart Calculator
The exact tool from the video — Excel with live charts (category bars + a priority × readiness map + a benchmark radar) plus a PDF. Score your program in about ten minutes and see where you land between the two cases.
The reframe I'll leave you with
I don't think the takeaway is "engineering is dead" — that overclaims, and honestly it's not true. The survivable version is this: don't train students for the job. Train them to be the ones AI works for. Command the tools instead of competing with them. That shift is entirely within a department's control — if they choose to make it before their students graduate.
If you're a student, run the calculator against your own program and ask the hard questions in office hours. If you teach or run a department, run it with your team — and pay attention to where people disagree, because that's where the real conversation is.
📌 I'll be sharing the link around — on the channel, in the community tab, and across socials. If this was useful, the most helpful thing you can do is pass it to one person who needs it: a classmate, a professor, a department head. Here's the shareable link.
Written as a companion to the video. More breakdowns at hilmybox.com.