When you generate a scenario, your selected drivers are passed to a large language model along with your chosen time horizon, tone, and domain. The model returns five sequential stages — each with a description, a one-word theme, a divergence value, and 3–4 investable category archetypes — visualized on a futures cone calibrated to the four classical bands (Probable, Possible, Plausible, Preferable). Tone selection shifts the categories: optimistic surfaces emerging plays, baseline the consensus thesis, challenging the defensive moats and incumbents-who-survive.

The 100 macro drivers in this workspace were synthesized from publicly available frameworks published by the World Economic Forum and McKinsey & Company, then de-duplicated and re-categorized across the STEEP framework:

Additional database sources can be added to bring more focused context if desired. Each driver carries a source attribution surfaced on its card:

Drivers that appear in both WEF and McKinsey carry both labels (de-duplicated overlap).

Foresight OS doesn't weight or rank its 100 drivers — each carries equal standing. When a user selects active drivers and clicks Run Simulation, those drivers are passed to Claude (Anthropic's LLM), which synthesizes the scenario narrative and future-cone classification based on how it reasons the selected drivers would interact.

This is a more powerful approach than a numeric scoring scheme. A fixed formula can only capture interactions its designer anticipated in advance, and it treats every combination of drivers the same way regardless of context. An LLM instead reasons fresh about each combination — picking up second-order effects, historical analogues, and domain nuance a static weight could never encode. The same drivers can yield meaningfully different, well-reasoned scenarios depending on time horizon, tone, and lens, because the model is reasoning about interactions rather than running arithmetic.

The tradeoff is less determinism and auditability than a scoring model would offer — identical inputs can produce somewhat different framing across runs, and the reasoning lives in the model rather than an inspectable rule set. Scenarios should be treated as exploratory, AI-informed outputs meant to provoke strategic thinking, not validated quantitative forecasts.

Each of the 100 drivers carries a title, a one-sentence descriptor, a STEEP category, and a source attribution. The descriptor is a concise signal statement — not an extended analysis. When you generate a scenario, the platform passes the selected driver titles to the language model as equal-weight inputs. When you generate a Ripple Effects flow, it passes both titles and descriptors, giving the model slightly richer signal to reason about interactions.

There is no numeric weighting applied to drivers. All selected drivers are treated as equal inputs, and the model synthesizes their interactions, tensions, and compounding effects autonomously. Effective weighting is entirely user-driven:

• Selection — only the drivers you select are passed to the model. Unselected drivers are invisible to generation.
• STEEP balance — if your selection contains five Technology drivers and one Society driver, the model's outputs will naturally lean toward the Technology cluster. Varying the STEEP mix produces substantively different scenarios even from the same topic area.
• Exclusion toggle — the Active Drivers bar in the Scenario view lets you temporarily remove individual drivers from a generation without deselecting them, useful for testing how sensitive a scenario is to any single driver.
• Driver count — a small, focused selection (3–5 drivers) produces tight, coherent scenarios; a larger selection (10+) produces richer, more complex outputs with broader scope.

Future releases will introduce optional driver signal-strength ratings to allow explicit up- or down-weighting of individual inputs before generation.

• Global Risks Report (annual)
• Future of Jobs Report
• Top 10 Emerging Technologies
• Strategic Intelligence Platform — driver and transformation taxonomies

• Technology Trends Outlook (annual)
• McKinsey Global Institute reports — productivity, AI, energy transition, demographics
• McKinsey Quarterly — business model, talent, capital markets coverage

Of the 100 drivers, attribution breaks down as: 22 from both WEF and McKinsey (de-duplicated overlap), 29 from WEF only, 22 from McKinsey only, and 27 synthesized sub-drivers. This roadmap will broaden over time to incorporate additional public sources — likely Gartner, IEA, IMF, IPCC, and government foresight units — to reduce single-source dependency and increase methodological diversity.