When researchers at Oxford published their 2013 paper estimating that 47% of US jobs were at high risk of computerisation, they produced a point estimate: one number for each occupation representing the probability of automation. Point estimates are easy to communicate but they are not how uncertainty actually works. The AI capability improvement rate, the enterprise adoption speed, the regulatory environment, and your own response to these changes are all variable. A Monte Carlo approach to career risk modelling acknowledges this by running thousands of scenarios across different assumptions and producing a distribution of outcomes rather than a single number.
You do not need to build a formal simulation to benefit from this thinking. The practical value is in identifying which career moves remain beneficial across the widest range of plausible futures, rather than betting everything on one AI trajectory. The Fossil Score assessment gives you a useful starting point: your current position across five dimensions, each of which you can move.
What the research says
92M
jobs displaced by 2030
WEF Future of Jobs 2025
170M
new roles created by 2030
WEF Future of Jobs 2025
41%
of employers plan AI-driven headcount reductions
WEF 2025
55K
job cuts explicitly attributed to AI in 2024
Challenger, Gray and Christmas
A Monte Carlo career simulation applies probabilistic scenario modelling to career planning under AI uncertainty. Instead of asking 'will AI replace my job' and getting a single estimate, you model the range of possible outcomes across different assumptions about AI capability improvement rates, employer adoption speeds, regulatory changes, and your own response actions.
Point estimates assume a single trajectory for AI capability and adoption. A job with 60% automation probability in the median scenario might range from 20% to 90% across plausible scenarios. Knowing the distribution is more useful than knowing the single median estimate when planning career moves.
The most impactful variables are: the rate of AI capability improvement relevant to your task type, the speed of adoption in your specific industry, whether regulatory environments constrain deployment in your sector, and your own response. Your personal actions are one of the variables, which is what makes career risk models useful rather than purely fatalistic.
You do not need to run a formal simulation. What the concept teaches is useful: think about your career risk across a range of scenarios rather than assuming one will occur. Planning for a range of outcomes rather than one expected outcome produces more robust decisions.
The moves that hold up across the widest range of AI trajectories are: adding AI literacy to your current role, developing deeper domain expertise, and building a professional reputation independent of your current employer. These are valuable whether AI adoption is fast or slow.
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