Career Payoff Explorer

This is a toy model with illustrative numbers, not a calibrated empirical study. The point is to make the risk shape of different career paths legible at a glance — fat right tails for tournaments, fat left tails for risk roles, narrow capped distributions for ladder careers — not to claim that the dollar amounts are right.

Each career has a hand-picked quantile distribution representing lifetime earnings (salary + bonus + equity monetization, pre-tax, $M) across an idealized 30-year career in a neutral economy under status-quo AI. Anchor points span p0.5 to p99.9. Values between anchors are interpolated log-linearly when both endpoints are positive, linearly otherwise.

Time scaling: (years/30)^0.85. Sublinear because peak earning years cluster mid-career and very long horizons hit retirement saturation.

Economy multiplier varies by career — investing and tournament are highly economy-sensitive (β ~0.7–0.8); risk mgmt is barely affected (β ~0.15). Bad economies shrink upside AND deepen the left tail.

AI scenario applies a percentile-dependent multiplier. AGI compresses functional consulting and BigLaw, expands the right tail of tournament careers (AI-native unicorns) and risk management (more breaches, more demand, but bigger left-tail catastrophes).

Rank column orders selected careers by median earnings under current settings.

⚠ Anchor values are eyeballed approximations. The shape of each curve is the load-bearing claim; the dollar values are not.