Workforce explorer · illustrative scenario
Where might people work?
Explore how demand, automation, personal preference, interests, and education shape paid work.
These are adjustable examples, not employment statistics or forecasts. People retain choice over their work.
Choose a scenario or open a function to change its assumptions.
The distribution in this scenario
Rounded people-equivalents at the chosen weekly hours, not a prediction of individual headcount. People outside the matched roles are not automatically unemployed.
—Roles covering technical gaps
—Roles requested for chosen human contribution
—Unfilled roles that interested people could cover after training
Change the assumptions
Work one function at a time.
Open a function to change demand, automation, consumer choice, interest, and readiness. Demand and interest weights are each normalised across the functions, so a weight is not itself a percentage.
Science has two examples: producing knowledge, and helping people understand and discuss it. Most scientific production is automated in this scenario; personal explanation is a possible human contribution to assess. See how science fits.
Event scheduling and logisticsCalculating…
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Hosting a shared experienceCalculating…
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Coaching and learning practiceCalculating…
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Conversation and personal supportCalculating…
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Craft making and shared practiceCalculating…
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Human sport and live performanceCalculating…
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Scientific research and discoveryCalculating…
This example starts by assuming a fully automated research function and no added human contribution. It is a scenario to change, not an estimate of current scientific capability. Research can still be funded when it has no human roles.
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Understanding and explaining scienceCalculating…
Most scientific production is automated in this scenario. People can be paid to help others understand science through explanation and discussion, where personal interaction improves the outcome or experience. AI can explain too: the human benefit must be assessed. Deep scientific education can take years.
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
Political discussion and community organisingCalculating…
This row models paid support for political discussion and community organising. The controls do not allocate voting rights or elected offices. Human political authority remains a principle of the framework even if you set paid support roles to zero. Training readiness concerns the support role, not anyone’s right to participate.
Relative share of the service workload.
Share of baseline work that machines can cover.
Share of automatable work customers or public programmes choose to have people deliver.
Relative share of people whose first preference is this function.
Share of that interested group currently prepared for the work.
How the numbers are calculated
- Demand: divide the weekly service workload by the demand weights. Assume the chosen workload has a customer or a public budget able to pay for it.
- Human hours: keep work outside technical automation, then add the automatable work deliberately chosen for human delivery. The two parts do not overlap.
- Roles: divide those human hours by the paid hours per person per week.
- People: divide the people by their interest weights, then apply training readiness within each interested group.
- Match: each function is limited by the smaller of requested roles and its interested, trained people.
This simplified model gives each person one first-preference function. It assumes equal weekly hours and uses baseline human time for the work chosen for human delivery. Real services may require different amounts of human time. People can have several interests and qualifications, change preferences, combine roles, or learn new skills; those transitions need a richer model.
Quality gains are assessed separately on the jobs page. They do not automatically convert into purchases: price, ability to pay, public priorities, and alternatives affect demand. The model assumes funding for the requested service volume; it does not calculate tax revenue, wages, or the cost of training.
Increasing training readiness is a scenario for successful education, not an instant result. Some skills take many years to develop. The model assumes that any technical gaps shown can be covered by trained people; it cannot establish feasibility or service quality.