AI-era career ratings

Rate the career paths you’re weighing — like stocks.

Picking a career is the biggest bet most of us ever make, and most of the advice is vibes. CareerStar treats it like an analyst treats a stock: every U.S. occupation gets one 0–100 score — real growth and pay data, discounted by how exposed the work is to AI, blended with how well it fits you. No horoscope. Math you can check.

Rate my paths →or browse all 730 careers

730 careers rated· built on real U.S. government data· every score is explained, not guessed

Tested against a real decade, not vibes. I scored 2014’s careers with 2014 data, then checked what actually happened by 2024: rank correlation ρ = 0.39, and 48% of the careers that really declined were flagged (33% by chance). Where it missed, I say so. See the back-test →

01 · The process

How it works

Name your paths

Careers, whole fields, or just your interests — start anywhere, even from “I don’t know yet.”

The math rates each one

Return (growth + pay) discounted by AI risk, blended with your fit — the same shape as a risk-adjusted return in investing. An AI writes the plain-English why, never the number.

Read the verdict — and the receipts

A ranked comparison with stars on a curve, bulls & bears for every career, and the exact math behind each score, one click away.

02 · Model risk, made visible

Five judges, not one oracle

Any single formula is one opinion about how much AI risk should count. So every comparison is scored by five rival models — from “ignore AI entirely” to “safety is everything.” When they agree, that’s conviction. When they don’t, that’s a real finding about your choice — and you can pick which judge scores your ranking, with each one’s strengths and blind spots stated up front.

Standard

the balanced judge — AI risk discounts the reward

+ The balanced read — and the only model here that's been back-tested against a real decade (2014→2024).

− Its blend weights are judgment calls, not fitted constants — which is exactly why the four rivals exist.

Growth maximalist

the optimist — growth and pay only, ignores AI on purpose

+ The purest read of the official projections — and on the pre-LLM decade, raw projections were the single best predictor.

− Blind to AI by design. A highly exposed career with good growth numbers scores well right up until it doesn't.

Defensive

the safety-first judge — a career's AI shelter matters most

+ Built for the bad scenario — the only judge that prices a career's moat, so it catches shelter the others ignore.

− Punishes exposed-but-booming fields hard: it will underrate software developers in every timeline where AI disappoints.

Sharpe-style

the efficiency judge — reward per unit of risk

+ The efficiency lens: flags high-reward careers whose risk is quietly outsized, which additive models let slide.

− Ratios flatter the timid — a low-reward, low-risk career can score 'efficient' while going nowhere.

Naive 1/N

the plain average — the baseline the others must beat

+ No opinions to be wrong about — the honest baseline every clever model has to beat to earn its complexity.

− Weights every signal equally, which nobody actually believes — including it.

Rate your paths, then switch judges live →

03 · The receipts

Why you can check it

Deterministic math. The same inputs always produce the same score, from formulas published in full on the methodology page — including why each line beat its alternatives.
Back-tested, misses named. The model was pointed at 2014 and graded against what 2024 actually did — including the careers it got wrong, listed by name.
Stress-tested. Every comparison is re-scored under 729 weight variations and five rival models; results that don’t survive are flagged as close calls, not sold as verdicts.
Open data. Every rating is in one downloadable CSV, and the code is on GitHub. If a number looks wrong, audit it.

Ninety seconds from “no idea” to a ranked answer.

Free, no sign-up, nothing stored. The worst case is you disagree with the math — and you can see all of it.

Rate my paths →