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FICTIONAL PLANNING REGISTER

World-event scenarios, 2027–2036

Explore 3,000 annualized scenario rows from the supplied workbook. Every row is fictional, every numerical estimate is source-authored and non-calibrated, and every consequence path is illustrative rather than predictive.

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FICTIONAL SCENARIO — NOT HISTORICAL, NOT A FORECAST The provenance of this block requires editorial review.
Confidence
Source-bounded scenario register
Sources
1
Independent corroboration
0
Date range
2027–2036
Review
Exact workbook bytes preserved; scenario assumptions, baselines, and estimates require claim-specific review
How to read this register

FICTIONAL SCENARIO — NOT HISTORICAL, NOT A FORECAST. Probability fields are source-authored analytical estimates, not calibrated forecasts and not additive. Consequence scores are comparison aids, not expected casualties, financial losses, or site rankings. Coordinates are broad centroids, not target locations.

Scenario rows
3,000
Scenario tracks
300
Years
2027–2036
Rows per year
300
Baseline source URLs
19

FICTIONAL SCENARIO — NOT HISTORICAL, NOT A FORECAST

2028-S266Track CH007Q2–Q3 2028

Large earthquake strikes a major urban fault zone — 2028 outlook

Earthquake / urban catastrophe · Istanbul or a comparable high-exposure metropolitan fault zone

Climate / health · Low 7.9% source estimate · magnitude 5/5 · priority 3/5
FICTIONAL SCENARIO — NOT HISTORICAL, NOT A FORECAST The provenance of this block requires editorial review.
Confidence
Moderate
Sources
1
Independent corroboration
0
Date range
Q3 2028
Review
Scenario premise, probability, and consequences require claim-specific review

FICTIONAL SCENARIO — NOT HISTORICAL, NOT A FORECAST

SOURCE ROW 371 · TRACK CE070

Frontier AI models and compute clusters: Public attribution triggers a retaliation spiral — 2028 outlook

In 2028, the modeled trigger or precursor is: Against a background of rapid capability gains, concentrated expertise and strategic competition over general-purpose AI, a government publicly attributes a major intrusion or espionage campaign and responds with sanctions, expulsions or disruptive countermeasures. The affected environment is model weights, training data, safety evaluations, agentic tools and high-end compute. Competing evidence and secrecy make it difficult for outside observers to judge proportionality, increasing the chance of misreading a limited response as the start of a broader campaign. The scenario matters because compromise can accelerate rival programs, enable scalable abuse or undermine confidence in critical AI deployments. Near-term exposure remains high because legacy systems, concentrated cloud services and accelerated AI adoption are outpacing defensive modernization. The annual probability is an approximate analytical estimate of 20.2% (Moderate), with consequence magnitude 5/5. This row is a plausible, overlapping scenario—not a prediction.

Stable scenario ID
2028-S070
Track ID
CE070
Illustrative window
Q3 2028
Category
Cyber / espionage
Impact scope
Global
Outcome direction
Adverse
Broad geography
San Francisco Bay Area and major AI-compute hubs · United States / China / European Union / Global
Coordinate precision
Technology-cluster centroid; not a data-center coordinate
Confidence label
Moderate
Operational use
Not allowed by this educational transformation

SOURCE-AUTHORED ANALYTICAL ESTIMATE — NOT CALIBRATED OR ADDITIVE

Annual estimate
20.2%
Band
Moderate
Magnitude
5/5
Weighted score
20.2/100
Planning priority
5/5

Workbook formula: probability ÷ 100 × magnitude × 20. It is a comparison aid only. Scenario probabilities overlap and must not be summed.

Scenario premise

Against a background of rapid capability gains, concentrated expertise and strategic competition over general-purpose AI, a government publicly attributes a major intrusion or espionage campaign and responds with sanctions, expulsions or disruptive countermeasures.

Named actor classes

state intelligence services; AI laboratories; cloud providers; insiders; criminal groups; regulators

Warning indicators

coordinated public statements; emergency diplomatic consultations; legal indictments; sanctions preparation; elevated network scanning and defensive alerts

Indicators are planning prompts, not proof that the scenario is occurring and not a monitoring or targeting instruction.

Key uncertainties

the quality of attribution, hidden damage, the adversary's red lines and whether allies remain unified

ILLUSTRATIVE CONSEQUENCE PATH — NOT A PREDICTION

Conditional consequence sequence

  1. 0–30 days

    If this scenario occurs in 2028: Diplomatic channels narrow, cyber defenses move to crisis footing and private companies are warned to expect follow-on activity. Sector-level effects include model access is suspended, credentials are rotated and regulators demand evidence about the scope of exposure.

  2. 1–3 years

    During 2029–2031: Reciprocal legal, economic and cyber measures expand; allies are pressured to endorse attribution and coordinate consequences. Over one to three years, labs adopt classified-style compartmentation, stronger compute monitoring and international controls on high-risk capabilities.

  3. 4–10 years

    Across 2032–2038: Cyber conflict becomes more explicitly integrated into deterrence doctrine, but divergent attribution standards keep escalation risks high. Over four to ten years, frontier AI development consolidates among a smaller number of highly regulated firms and states, with persistent espionage pressure.

Baseline source — not endorsement of this scenario or estimate

OECD AI Policy Observatory; used for AI incidents, governance and frontier-model risk framing.

Open the workbook’s baseline source

BASELINE START POINT ONLY

Track evidence review remains open

Review ID
SER-CE070
Review state
READY FOR CLAIM-SPECIFIC REVIEW
Premise verification
NOT REVIEWED
Probability calibration
NOT PERFORMED
Independent origins
0
Assignment state
OPEN_UNASSIGNED

A source relationship records where review can begin. It does not verify this fictional premise, calibrate the annual estimates, validate the consequence path, or grant operational authority.

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ASSUMPTION IS NOT FACT

Track assumptions and retirement remain open

Assumption ID
SAR-CE070
Premise review
NOT REVIEWED
Indicator review
NOT REVIEWED
Retirement state
NOT EVALUATED

The sidecar exposes dependencies, disconfirming questions, alternative explanations, false-alarm risks, consequence interruptions, and retirement triggers. It does not predict, monitor, or retire this track.

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Safety and use boundary

High-level defensive cyber/counterintelligence scenario; no exploit, persistence, evasion or targeting instructions are included. This site adds no tactical target data, exploit steps, evasion methods, weapon construction, casualty optimization, or authorization for real-world action.

SOURCE-AUTHORED ANALYTICAL ESTIMATE — NOT CALIBRATED OR ADDITIVE

Ten-year track: CE070

The workbook annualizes the same scenario family once per year so readers can compare how its assumptions and numerical estimates change. This is not a time-series forecast.

Source-authored annualized rows for track CE070
YearScenario IDProbabilityBandMagnitudeWeighted scorePriorityConfidence
2027 2027-S069 19.6% Moderate 5/5 19.6 5/5 Moderate
2028 2028-S070 20.2% Moderate 5/5 20.2 5/5 Moderate
2029 2029-S050 23.7% Moderate 5/5 23.7 5/5 Moderate
2030 2030-S047 24.7% Moderate 5/5 24.7 5/5 Moderate
2031 2031-S045 26.6% Moderate 5/5 26.6 5/5 Moderate
2032 2032-S042 28.1% Moderate 5/5 28.1 5/5 Moderate
2033 2033-S037 30.4% Elevated 5/5 30.4 5/5 Moderate
2034 2034-S038 30.6% Elevated 5/5 30.6 5/5 Moderate
2035 2035-S042 31.1% Elevated 5/5 31.1 5/5 Moderate
2036 2036-S032 34.9% Elevated 5/5 34.9 5/5 Moderate

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Scenario-register literacy

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