Foresight Framework  ·  Grey Swan / Archimedes  ·  v12.0

Can institutions keep up with rapid technological change?

Grey Swan turns public evidence into disciplined scenario probabilities. It tracks whether advances in AI and productive capability are being matched by the energy, health, education, trust, coordination and compute systems needed to absorb them, while climate adds pressure across the whole system.

The model has now been run three times: an October 2025 baseline, a Spring 2026 update and the new Fall 2026 run.

Fall 2026
Latest run
v12.0-R1
Current model
October 2025
Baseline
Spring 2027
Next run
Model overview Spring 2026 Fall 2026 2050 Settlement

What is a Grey Swan?

A Grey Swan is a high-impact future whose warning signs are already visible. The framework asks what happens if those signs are acted on early, and what happens if action remains fragmented or delayed.

The two policy postures are Do The Right Thing (DTR), where leaders make material coordinated reforms, and Let It Rip (LIR), where deployment and disruption outrun the response.

What is different in v12?

v12 keeps the original two postures and four outcomes but improves what is measured. Institutional trust is restored explicitly, economic growth and productivity are visible, compute is measured as productive infrastructure rather than broadband speed, and a new posture layer estimates how likely DTR and LIR actually are.

The Fall 2026 baseline judges the current global posture at roughly 10% DTR and 90% LIR.

Three-run trajectory
How have the conditional scenario probabilities changed since the baseline?
Choose an outcome. Each panel shows the probability at 2030, 2040 and 2050 across the October 2025 baseline, Spring 2026 and Fall 2026 runs. These are the comparable DTR/LIR conditional probabilities; the new Fall posture-weighted outlook is shown separately on the Fall page.
Do The Right Thing
Let It Rip
2030
2040
2050
Fall 2026 conditional probabilities are held at Spring levels by design during the v12 transition baseline.All data public · no private telemetry
What the model measures

Six levers, climate underneath them, and one final gate

Grey Swan does not judge the future from AI capability alone. Progress has to survive contact with the systems that make capability usable.

01

Energy and grids

Reliable, affordable power, grid access, connection constraints, system adequacy and resilience as demand rises.

02

Health security

Surveillance continuity, preparedness capacity and the workforce needed to sustain care through shocks.

03

Education and skills

Whether educators and learning systems are building meaningful AI capability rather than simply acquiring tools.

04

Trust and information

Underlying institutional legitimacy plus operational AI accountability, transparency, audit and contestability.

05

Coordination and supply security

Resilient networks and practical cross-border compacts that work in implementation, not only in communiqués.

06

Compute and AI access

Who controls advanced compute and whether productive access exists for smaller firms, universities and public bodies.

Cross-cutting condition

Climate change

Climate is not a seventh silo. Heat, water stress, food insecurity, migration, health burdens and infrastructure damage can weaken every other lever.

Final test

Wealth-Diffusion Gate

Even if productivity and AI capability rise, Full Empowerment is blocked unless gains reach households and workers through stronger financial resilience, labour participation and improving shared prosperity.


The four possible futures

The outcomes are system states, not predictions of one dramatic event.

Full empowerment

Capability with resilience and fairness

Productive capability spreads widely, institutions remain resilient and gains reach ordinary households, workers, firms and public services. The Wealth-Diffusion Gate must be open.

Managed disruption

Shocks absorbed, gaps remain

Disruption continues but systems cope more often than not. Progress is genuine, although incomplete and vulnerable to reversal.

Uneven transition

Progress is patchy and brittle

Some sectors and places advance while others stall. Capability, skills and wealth cluster, producing real gains alongside widening gaps.

Lost control

Problems outpace institutional capacity

Not a Hollywood collapse. Pressures compound faster than institutions can absorb them, making shocks harder to manage and recovery slower and more expensive.


Run history

Three readings, one evolving framework

The baseline established the scenarios. Spring 2026 worsened the conditional odds. Fall 2026 holds those odds but adds a new answer to a harder question: which posture are we actually following?

October 2025 baseline

The original Grey Swan

Established DTR versus LIR, four outcomes, six Archimedes levers, climate as a cross-cutting condition and the Wealth-Diffusion Gate.

Foundational paper →
Spring 2026 · v11.9

Deterioration from baseline

Trade fragmentation and degraded health surveillance pushed conditional probabilities toward Lost Control. Geopolitical and health flags were active; the Wealth-Diffusion Gate remained closed.

Read the Spring update →
Fall 2026 · v12.0-R1

Conditional hold, harder real-world outlook

The inherited conditional odds do not move. But the new posture assessment puts DTR at only 10%, making the posture-weighted real-world outlook substantially darker.

Read the Fall update →

Further work

From warning system to positive settlement

Grey Swan asks where the current trajectory is taking us. A 2050 AI Settlement asks what would have to change for a better outcome to become plausible.

Discussion paper · June 2026

A 2050 AI Settlement

The paper turns the Grey Swan framework from diagnosis toward a positive systems proposition. It asks what would have to move together if AI and climate transition are to become shared human capability rather than a more efficient form of concentration. The six Archimedes pillars remain central, climate is the physical stress condition, and the Wealth-Diffusion Gate becomes the decisive test of whether productivity and capability gains reach households, workers, SMEs, regions and public systems.


Evidence and method

Public, reproducible and deliberately conservative.

Grey Swan uses public-access evidence from established international statistical agencies, regulators, system operators and widely used independent datasets. Core v12 source families include the IMF, World Bank, ILOSTAT, OECD/OECD.AI, WHO, IEA and major grid operators, UNCTAD, Freedom House, Transparency International, public Gallup/OECD trust aggregates, the Caldara-Iacoviello Geopolitical Risk dataset and public Epoch AI compute estimates. Official and operator data are preferred; modeled estimates are flagged and given less weight. No private telemetry or proprietary data are used.