Work, adaptability, AI use, finances, ownership and options.
Clear enough to use.
Transparent enough to question.
FutureStake combines seven parts of your economic position into a directional baseline. This page shows how answers become scores, what evidence informs the model and where the current alpha has limits.
Observable signals converted to a common 0–100 scale.
Where you are supported, exposed and able to act.
What this is not: a prediction, a measure of personal worth or a peer percentile.
From answers to a useful first move
The assessment starts with the future you want, measures seven parts of your current position and then tests how that position responds across five hypothetical futures.
Explore
Capture goals, concerns and life context.
Assess
Score seven observable dimensions.
Connect
Find strengths, gaps and interactions.
Stress-test
Recalculate across five futures.
Act
Prioritize one move and a roadmap.
Your job describes possible disruption. The rest describes your capacity to respond.
Adaptability and AI use show how you can participate. Financial flexibility shows what you can absorb. Ownership shows whether upside can reach you beyond wages. Optionality shows whether you have credible alternate paths.
Explore the five-scenario stress test+
FutureStake reweights the same profile against slower AI progress, steady adoption, rapid acceleration, recession plus AI adoption, and an AI-driven opportunity boom.
Scenario scores measure positioning if those conditions occur. They do not estimate the probability of those conditions.
Seven components. One directional baseline.
Multiple-choice answers become numeric inputs, usually from 0–4. Each component combines several inputs, rounds the result and limits the component to a 0–100 scale.
Higher means stronger support within the factors measured. It does not mean a person has an equivalent probability of success.
See the seven components and weights+
| Component | Weight | What it measures |
|---|---|---|
| Career Advantage | 20% | Role defensibility, responsibility, relationships and portability |
| AI Adaptability | 15% | Use, workflow depth, learning speed and willingness to redesign work |
| AI Opportunity | 15% | Potential leverage, customer access and ability to capture value |
| Financial Flexibility | 15% | Income concentration, accessible runway, capital and constraints |
| Ownership and Capital | 15% | Productive assets and readiness to own AI-created value |
| Human Advantage | 10% | Judgment, relationships, leadership and accountability |
| Optionality | 10% | Mobility, reinvention capacity and access to alternate paths |
See current instrument choices and known issues+
Consumer instrument revision
The consumer-constructs-v1.2 instrument retains component weights and arithmetic but changes the evidence collected. Its scores are not directly comparable to earlier instruments.
- Profit participation: share of personal pre-tax income from business profits, contractual profit-sharing or royalties—not wages, hourly fees or investment returns.
- Judgment: frequency of decisions with responsibility to others. Frequency does not establish quality.
- Mobility: perceived ability to start new earned income within six months covering at least half of essential living costs.
- Business: experience running an income-generating business.
“Cannot assess” uses 2 as an explicit midpoint placeholder. It is not measured evidence or a population average. Capture contributes 3 points per level to AI Opportunity and Ownership & Capital; Judgment contributes 7 to Human Advantage; Mobility contributes 2.5 to Career Advantage and 7 to Optionality; Business contributes 2 to AI Opportunity and 5 to Ownership & Capital before component caps.
These are operational alpha choices, not externally validated thresholds. The existing overall-score upper-bound issue remains a separate release item.
Published evidence informs the factors—not your outcome.
FutureStake uses research to choose relevant constructs and frame plausible change. Those sources do not validate the proprietary scoring formula or predict what will happen to one person.
Publisher names identify sources and do not imply endorsement.
See how evidence connects to each assessment section+
| Section | Evidence signal |
|---|---|
| Looking Ahead | WEF projects substantial job creation and displacement by 2030, supporting a model that tests both risk and upside. |
| Your Context | IMF estimates AI may affect almost 40% of jobs globally and about 60% in advanced economies. |
| AI and You | WEF estimates 39% of existing skills may change and 59 of every 100 workers may need training by 2030. |
| Career and Work | ILO estimates one in four workers has some occupational GenAI exposure, while transformation is more likely than full replacement. |
| Financial Position | Federal Reserve data show 55% of U.S. adults had emergency savings sufficient for three months of expenses in 2024. |
| Ownership and Opportunity | IMF research notes that AI productivity gains may increase capital returns as well as labor income. |
| Choices and Execution | WEF ranks analytical thinking, resilience, flexibility, leadership and social influence among leading core skills. |
Useful for reflection and planning. Not externally validated.
- No population percentiles or proprietary peer benchmarks.
- No claim that a score predicts job loss, income or personal success.
- No legal, financial, tax, bankruptcy, investment or employment advice.
- Scenario scores are stress tests, not forecasts or probabilities.
- One-point changes are not established as meaningful movement.
See privacy, future benchmarking and longitudinal principles+
Privacy
Draft answers remain in the user’s browser. Email delivery transmits only the report summary required for that message. Anonymous usage events measure the journey without survey answers. Alpha feedback is saved only after explicit consent.
Future benchmarking
Peer percentiles are disabled. The planned framework requires at least 100 valid responses in a broad cohort and at least 50 in any displayed comparison cell, with small intersections suppressed and direct identifiers excluded.
Change over time
The baseline is an absolute framework score. Future movement must transparently distinguish “you changed,” “the world changed” and “we learned more.” Model revisions require provenance and an explicit policy for historical scores.
Future product architecture
Alpha testers receive the full analysis and roadmap. Automatic monitoring, subscriptions, alerts, history tracking, dynamic scores and peer comparisons are not active.