A Worst-Case Scenario: AI Destroys 80% of College

Quick note: This is a stress test, not a forecast. We’re asking a hard “what if” so you can make better decisions in the real world.

Why run this stress test now?

• AI already does junior-level tasks: first drafts, basic analysis, slide builds, simple code.
• Employers are slowly shifting toward skills tests and work samples over degree labels (with exceptions for licensed fields).
• Colleges feel pressure from rising tuition discounts and continued program cuts.
  Sources are linked in the full article on the site.

What would have to be true (simple version)

1. AI “doers,” not just chat. Tools that complete tasks across apps as well as a junior hire.
2. Cost beats campus. AI tutoring and project feedback become cheap and high quality.
3. Hiring shifts to proof. More weight on portfolios and skills tests, less on major names.
4. School fragility continues. More tuition discounts, teach-outs, and mergers.
5. Policy stays neutral. No big, lasting bailout for lecture-heavy majors.

The 3–5 year domino chain (worst-case)

Year 0–1: AI tools become normal at work. Entry tasks shift to AI. More job ads try skills-first screens.
Year 1–3: Degree requirements loosen in more roles; portfolios rise. Tuition-dependent schools cut majors or merge.
Year 3–5: A few AI-native programs run like studios and clinics. Survivors cluster in licensed, lab-heavy, and frontier research programs.

Who likely survives (the “Protected 20%”)

• Medicine, nursing, allied health (clinical hours, patient safety, licensure).
• Engineering with Accreditation Board for Engineering and Technology (ABET) limits (prototyping, instruments, physical risk).
• Hard sciences with complex gear (fabrication labs, biosafety labs, specialized instruments).
• Top-tier research (true frontier R&D).

Who is most exposed (the “Commoditized 80%”)

• Lecture-centric, test-centric majors where outputs are essays, decks, basic dashboards, or baseline code.
• Programs AI tutors can practice at scale without labs, clinicals, or licenses.

Unintended consequences (worth naming)

• College-town economics: Fewer renters and diners hurt local shops.
• Sports fallout: Cuts to programs ripple into leagues.
• 529 plan shifts: Families delay or redirect education savings toward short credentials.
• Accreditation pressure: Faster approval for competency-based and skills-first paths.
• “AI-proof” testing arms race: More proctoring and AI-vs-AI checks.
• Insurance & liability: Stronger rules for human-in-the-loop review in health, engineering, and finance.
• Corporate mini-universities: Employers build in-house academies and fund micro-credentials.
• International student flows: If non-STEM demand drops, some schools lose key revenue.
• Civic life: Fewer campus “third places” can weaken community for young adults.

Playbook (fast)

Learners (ages ~18–28)

• Make AI your teammate. One top model for writing/analysis; one helper for code; one research tool.
• Portfolio of Proof: Every 2–3 weeks, ship a finished work sample (decision memo, market teardown, Key Performance Indicators (KPI) dashboard, prototype, case brief).
• Show your process: Note how you used AI and how you checked it.
• Choose projects over lectures when you can.
• Manage debt: Favor majors with labs, licenses, or strong placement. Think Return on Investment (ROI).

Degree-holders

• Rewrite your résumé around deliverables and the KPI you moved.
• Document “man + machine”: how you use AI, where you verify, how you handle edge cases.
• Run a 90-day skills sprint built from real job postings; publish two artifacts that close your gaps.

Employers

• Move from pedigree to paid trial tasks (4–8 hours). Allow AI; score judgment and Quality Assurance (QA).
• Hire for interface skills: design a safe human-in-the-loop workflow and own outcomes.
• Stand up a 6–12 week internal academy with clear KPI; tie completion to pay.

Colleges & universities

• Flip lectures into studios/clinics. Grade supervised production.
• AI-first syllabi: allowed tools, audit trails, red-team checks, process grading.
• Double down on physical moats: labs, clinics, instruments.
• Portfolio graduation: public work, every term.
• Employer councils with teeth: refresh job tasks every quarter; update the Learning Management System (LMS).

Your 90-Day plan (one page you can print)

Week 1–2: Pick a domain (ops, analytics, design, growth, compliance, clinical support). Set up your AI stack. Select two job-relevant tools (for example, cloud + BI).
Week 3–10: Ship four artifacts tied to real tasks:

1. Decision memo with assumptions
2. Market teardown with a one-page dashboard
3. Simple prototype (no-code or code)
4. Risk or compliance brief in your domain
   Add a process note to each.
   Week 11–12: Send 10 Try-Me Task offers (2–4 hour scoped tests).
   Week 13: Convert 2–3 of those into paid gigs, internships, or apprenticeships.

Join the debate (comment below)

Tell me where I’m wrong or if you were 18 today, would you enroll—or build a portfolio first?

Ideas to spark replies:

1. What would have to be true for your job to be 50% automated in three years?
2. Name one lab/clinical experience AI can’t replace this decade.
3. If you hire: which work test beats a degree for your roles?
4. Which major wins or loses first—and why?
5. What’s one unintended consequence nobody is pricing in?

Rules: Bring data or examples. Attack ideas, not people. Give one tip someone can use this week.

Where Myford University fits

We are building our 8–12-Hour Accelerators (currently under development) to turn learning into deliverables you can ship on Monday.

• MBA Accelerator: decision memos, market tear-downs, KPI dashboards, cap-table models.
• College Accelerator: analytical writing, structured problem-solving, research briefs.
• PhD-Level Thinking Toolkit: epistemology, research design, systems thinking, bias checks.

Myford University — learn fast, apply it faster.

Want to read the full article? Read it here.