college majors that DO and DON’T make money

Business / revenue-focused summary of majors

The video frames college major choices primarily through economic outcomes (starting salary, ROI, lifetime earnings) plus practical constraints (difficulty, hours, additional training).

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High-earning / “does make money” majors (and implied business logic)

Engineering

• Why it pays: High demand for technical problem-solving roles.
• Key metrics stated:
  • Median starting salary: ~$74,000
  • Projected starting salary range: ~$66,000
• Subfield example:
  • Higher: nuclear engineering (higher earnings mentioned)
  • Lower (but still high): biomed engineering
• Tradeoff / operations constraint: “engineering is brutal,” implying heavy workload and lower work-life balance during school.

Computer Science (CS)

• Why it pays: Still high demand despite fears about market disruption/automation.
• Key metrics stated:
  • Average starting salary: ~$77,000
• Risk noted (high level):
  • CS job market is “cooked”
  • AI may replace software engineers eventually, but “not anytime soon” (per speaker)
• Actionable career “playbook” (tips to get employable):
  • Prepare for lots of math (more than just coding; includes proofs)
  • Work on side projects (build tangible artifacts: app/portfolio/game)
  • Embrace basic professionalism (“shower” joke used to break stereotype)

Healthcare

• Why it pays: Clinical roles have strong compensation; AI/tech integration increases long-run demand.
• Key metrics stated:
  • Nursing starting salary: ~$65,000
  • Physician assistant starting salary: ~$104,000
  • “Hundreds of thousands by mid-career isn’t uncommon” (no exact CAGR/figures given)
• Tradeoff: Brutal hours + long training pipeline (MCAT → medical school → residency → etc.).

IT / Information Technology

• Why it pays: Practical demand as companies shift to cloud and modern infrastructure.
• Key metrics stated:
  • “Salaries into the six figures” for cloud specialists (no precise number)
• Operations / entry barrier claim:
  • Lower barrier than many high-paying fields
  • May not require a bachelor’s degree (associates/certs suggested)
• Concrete example given:
  • Speaker “hired one a few months ago” (cloud specialist demand claim)
• Role taxonomy (market map):
  • System administrators
  • Network engineers
  • IT project managers
  • Cyber security analysts
  • Cloud specialists

Finance (shown as high ROI, but high intensity)

• Why it pays: Strong compensation + fast progression in certain sub-industries.
• Key metrics stated:
  • Average ROI on a bachelor’s in finance: 1,842%
  • Average starting salary: ~$85K
  • Expected lifetime earnings: ~$9.7 million
• Tradeoff / workload: “brutal hours”
• Concrete workplace policy example:
  • September 2024: JP Morgan implemented an 80-hour weekly cap on junior investment bankers
• Career progression example (investment banking):
  • Analyst: $85–$100K
  • Associate (2–3 years): $150–$225K
  • Vice President (after 3–4 more years): $300K–$550K
  • Further progression to Director (total comp “into the seven figures”)
• Skills note: Quant analyst roles require very strong math (IMO-level mentioned), but many finance roles rely more on applied stats/modeling than heavy STEM.

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Lower-paying / “doesn’t make as much money” majors (and how to mitigate)

Fine Arts

• Key metrics stated:
  • Average starting salary: ~$49,000
  • Benchmark: average bachelor’s degree ~$55,000
• Mitigation strategy (career alignment):
  • Degree doesn’t determine pay—role does
  • Example pivot: fine arts → marketing/communications (better outcomes than “staying purely arts”)

Education (explicitly framed as poor ROI)

• Key metrics stated:
  • Negative lifetime ROI: -555.43%
  • “149K to be exact” (presented as the scale of loss; wording implies net cost/outcome)
  • Primary school teaching starting salary: ~$41,000
• Higher-paying alternative roles (no numbers given):
  • Technical writer
  • HR specialist
  • Corporate trainer
• Constraint: genuine love required; pay is described as insufficient.

Humanities (lower starting salaries, but transferable skills)

• Key metrics stated (examples):
  • History starting salary: ~$49,000 (~11% below average college grads)
• Business-relevant transferable skills emphasized:
  • English → writing/communication → marketing, communications, social media
  • History → research/analysis → business analytics
  • Philosophy → critical thinking → tech/law/finance/consulting
• Actionable recommendation (initiative required):
  • Don’t passively assume the degree limits you—choose career mapping intentionally
  • Example framing: balance money vs passion (marketing vs corporate vs finance depending on fit)

Social sciences / Psychology (lower ROI unless you add grad school)

• Key metrics stated:
  • Bachelor’s starting salary: ~$50–60K
  • Clinical psychologist (with doctorate) median annual salary: ~$96K
• Mitigation paths:
  • Use psychology skills in industry (selling ideas, managing teams, communications)
  • Alternative “monetization route” mentioned: content creation (humor/example)

Theology

• Key metrics stated:
  • Average starting salary: ~$42,000
• Mitigation strategy:
  • If financial stability matters: roles in educational institutions, clinics, nonprofits

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Execution-focused frameworks / playbooks embedded in the talk

The video doesn’t formalize OKRs/SWOT, but it repeatedly applies an informal decision framework:

• Earnings feasibility framework: compare starting salary, then consider lifetime earnings/ROI

• Constraint tradeoff model: high pay is paired with workload/difficulty (engineering brutality; healthcare training; finance brutal hours)

• Role-mapping strategy: degree → transferable skills → target role/industry (e.g., fine arts → marketing; humanities → communication/analytics/critical thinking; psychology → comms/team management)

• Employability playbook (for CS):

  • Math readiness
  • Build side-project portfolio
  • Demonstrate professionalism/basic readiness

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Notable sponsorship / product mention (non-business)

• Opera (browser) mentioned as a sponsor, positioned as improving research workflow via:
  • tab grouping, split-screen reading
  • sidebar streaming player
  • AI assistant (“Arya”) for Q&A and visual explanation
• Not directly tied to major selection economics.

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Presenters / sources

• Presenter: Not explicitly named in the subtitles (solo speaker).
• Sponsor: Opera (mentioned as “the sponsor of today’s video”).
• Named external organization: JP Morgan (cited for an 80-hour weekly cap example, September 2024).
• Other cited “source” types (no direct links provided):
  • A study reporting education degree negative lifetime ROI of -555.43% and “149K” figure.