About
A calmer, data-grounded way to think about what’s next.
PivotFromTech is for one person: the software, data, IT, design, or product worker who watches AI swallow a little more of their job each quarter and wonders where to go. We rank careers by how hard they are to automate, and we’re straight about what a move really costs you in pay, in retraining, and in the parts of the old job you’d miss. We can point you somewhere solid. We can’t promise it works out.
The score
Five weighted signals, one number from 0 to 100.
Every career gets an AI-Resistance Score from 0 to 100, where higher means more durable. It’s the inverse of how exposed a job is to automation: we weigh five signals by how much each one protects a role, then combine them into a single number.
| Signal | Weight | What it captures |
|---|---|---|
| Physical / non-routine work | 30% | Hands-on, in-person work that changes from one job to the next, which is the hardest kind for software to automate. |
| AI task-overlap (inverse) | 25% | How little of the daily work overlaps with what today's AI can already do. |
| Licensing / regulatory moat | 20% | Licenses, boards, and safety rules that slow adoption and keep a human legally on the hook. |
| In-person / interpersonal demand | 15% | Direct human care, trust, and presence that stays genuinely hard to replace. |
| Labor-market outlook (BLS) | 10% | BLS-projected growth and hiring. Strong numbers mean the market isn't betting on the job being automated away. |
We rate each signal Low, Medium, or High resistance, worth 25, 60, or 90 points, then take the weighted average and round to a whole number. Higher means more resistant: a job that’s hard to automate scores high. The math is simple on purpose, so anyone can check it.
Sources
Grounded in public research.
- Frey & Osborne (2013 / 2017). Their “Future of Employment” study, which gives us the automation-probability framework behind the physical, non-routine signal.
- U.S. Bureau of Labor Statistics. The Occupational Outlook Handbook, our source for median pay, projected growth, and entry requirements on every career.
- Major AI-impact reports. Consultancy and think-tank studies that estimate how exposed each occupation's tasks are to generative AI.
- Academic LLM-exposure research. “GPTs are GPTs” and similar papers that measure how much of a role overlaps with what current AI can do.
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