FUT_003predictionAIextinction-risk-0.38-vs-3-percent
Superforecaster consensus assigns 0.38% probability to AI-driven human extinction by 2100 vs domain-expert consensus of 3% — ~8x discrepancy per XPT 2022 adversarial collaboration tournament (89 superforecasters + 80 domain experts). Superforecasters m...
Predictor: Superforecaster Community
Prior probability
100.0%
Current probability
100.0%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
hit
Window
2026-01-01 – 2026-09-30
Edges in / out
0 / 0
Tickers exposed
0
Prediction text
Superforecaster consensus assigns 0.38% probability to AI-driven human extinction by 2100 vs domain-expert consensus of 3% — ~8x discrepancy per XPT 2022 adversarial collaboration tournament (89 superforecasters + 80 domain experts). Superforecasters model physical-friction/supply-chain-governor on AI scaling; domain experts model frictionless scaling. | Next adversarial-collaboration xrisk tournament
Key catalyst: Next adversarial-collaboration xrisk tournament
Watch events: Next XPT follow-up tournament; aggregate AI-xrisk tracking
Resolution evidence
Status: hit
XPT 2022 tournament results published; 0.38% SF median vs 3% expert median on AI-extinction-by-2100 empirically documented.
Predictor: Superforecaster Community
κ + Brier as of 2026-05-22
κ (discount)
0.643
Brier
0.0000
excellent
Hits / Misses
2 / 0
of 2 resolved
Hit rate
100.0%
Calibration plot (stated vs observed)
Evidence about this node from Superforecaster Community is multiplied by κ in /api/intake. Lower κ = less weight; floors at 0.10 (effectively silenced) and caps at 1.00 (full weight).
Reference class
Not linked
This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.
Probability over time
1 prob_history rows
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 100.0%
Milestone chain
Pre-event signals (upstream prereqs + window checkpoints) → resolution event → downstream cascades. Status/dates update from linked nodes; re-derive nightly via scripts/ops/derive_milestones.py.
Leading chain: 3 overdue ⏱
- 2026-01-30overdueQ1 window check-in (25%)
- 2026-03-01overdueQ2 window check-in (50%)
- 2026-03-30overdueQ3 window check-in (75%)
No downstream cascades — this prediction is a leaf in the dependency graph.
What if this resolves?
Clamp this prediction TRUE or FALSE and run a counterfactual Gibbs sample. Surfaces the predictions whose marginals shift most under that assumption.
(live posterior: 100%)
Click a button to clamp this prediction and run a Gibbs sample. Returns the predictions whose marginals shift most. ~30s per run; ideal for stress-testing "if X resolves, what else moves?"
Evidence chain
Every probability update with full Bayesian provenance — chronological, latest first
resolution_terminal2026-04-29T22:23:18Z100.0%+0.0pp
resolution_terminal hit outcome=1.0 pre_resolution=1.000
Raw metadata
{
"source": "backfill_resolution_history.py",
"status": "hit",
"bayesian_v2": false,
"outcome_prob": 1,
"evidence_kind": "resolution_terminal",
"posterior_prob": 1,
"delta_to_outcome": 0,
"inside_posterior": 1,
"validation_notes": "XPT 2022 tournament results published; 0.38% SF median vs 3% expert median on AI-extinction-by-2100 empirically documented.",
"validation_status": "hit",
"pre_resolution_prob": 1,
"resolution_evidence": "XPT 2022 tournament results published; 0.38% SF median vs 3% expert median on AI-extinction-by-2100 empirically documented.",
"does_not_update_current_prob": true
}Network propagation neighbors
Top edges sorted by latest LBP cross-impact
No propagation data yet. Run inference/.venv/bin/python scripts/ops/run_loopy_belief_propagation.py on the droplet, or wait for the Sunday 02:00 UTC weekly cron.
Prerequisites (0)
Predictions that must hit first
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No prerequisites | ||||
Dependents (0)
Predictions enabled by this
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No dependents | ||||
Validations (1)
Resolution events
| Observed at | Status | By | Notes |
|---|---|---|---|
| 2026-04-29 | hit | thesis_timeline_v1.0_import | XPT 2022 tournament results published; 0.38% SF median vs 3% expert median on AI-extinction-by-2100 empirically documented. |
Linked documents (10)
Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
Raw metadata
From Thesis_Timeline_v1.0_FINAL workbook
{
"nia": false,
"qty": "0.38% SF vs 3% domain",
"mode": "FORECAST",
"role": "Cited-Other",
"context": "Key epistemological anchor on AI-xrisk. Distinct from ROB_027 (Bostrom paperclip embodied), AUT_025 (Bostrom Deep Utopia), AI_036 (RLHF fails for ASI). Provides the empirical forecaster-vs-expert xrisk delta.",
"to_year": 2100,
"conv_cues": "empirical tournament aggregate; specific probability delta",
"direction": "NUMERIC_TARGET",
"from_year": 2026,
"timeframe": "by 2100",
"conv_level": "HIGH",
"milestones": [
{
"kind": "quartile_checkpoint",
"label": "Q1 window check-in (25%)",
"status": "overdue",
"weight": 0.05,
"ordinal": -3,
"source_id": null,
"expected_date": "2026-01-30",
"observed_date": null,
"miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
"miss_emitted_by": "metadata_milestone_sweep"
},
{
"kind": "quartile_checkpoint",
"label": "Q2 window check-in (50%)",
"status": "overdue",
"weight": 0.05,
"ordinal": -2,
"source_id": null,
"expected_date": "2026-03-01",
"observed_date": null,
"miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
"miss_emitted_by": "metadata_milestone_sweep"
},
{
"kind": "quartile_checkpoint",
"label": "Q3 window check-in (75%)",
"status": "overdue",
"weight": 0.05,
"ordinal": -1,
"source_id": null,
"expected_date": "2026-03-30",
"observed_date": null,
"miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
"miss_emitted_by": "metadata_milestone_sweep"
},
{
"kind": "event",
"label": "Superforecaster consensus assigns 0.38% probability to AI-driven human extinction by 2100 vs domain-expert consensus of 3% — ~8x discrepancy",
"status": "hit",
"weight": 1,
"ordinal": 0,
"source_id": "FUT_003",
"expected_date": "2026-04-29",
"observed_date": "2026-04-29"
}
],
"repeat_eps": 1,
"affiliation": "XPT 2022 tournament / Good Judgment",
"attribution": "CITED",
"granularity": "YEAR",
"resolved_at": "2026-04-29T22:23:18.356241+00:00",
"source_refs": "3",
"target_date": "2100-12-15T00:00:00",
"display_date": "2026-04-29",
"episode_date": "2026-04-22T00:00:00",
"key_catalyst": "Next adversarial-collaboration xrisk tournament",
"parse_method": "YEAR endpoint",
"domain_bucket": "AI",
"episode_title": "The Convergence Architecture: High-Fidelity Macro-Forecasting for the 2026-2031 Global System",
"flag_repeated": false,
"in_5yr_window": false,
"source_report": "Futurist Predictions for 2026-2031.md (2026-04-22)",
"appears_in_eps": "FUT-RPT",
"futurist_phase": "Phase 3 (2029+)",
"is_macro_claim": false,
"total_mentions": 1,
"priority_weight": 4,
"report_evidence": "Section: Collapse of AGI Horizon / extinction-risk delta.",
"active_end_month": "2026-12",
"recent_statement": "XPT 2022 tournament published results.",
"watch_events_raw": "Next XPT follow-up tournament; aggregate AI-xrisk tracking",
"months_from_today": 896,
"probability_layer": "Base-case",
"active_start_month": "2026-01",
"december_dispersal": {
"reason": "december_dispersal: domain=AI → 09/2026",
"new_date": "2026-09-30",
"old_date": "2026-12-31",
"applied_at": "2026-04-30T16:28:34.304992+00:00"
},
"flag_nia_bracketed": false,
"resolved_at_source": "validations_observed_at",
"track_record_grade": "A",
"track_record_notes": "XPT tournament methodology rigorous; 8x delta persistent across updates.",
"contradicting_notes": "Forecast itself is the probability distribution; base rates uncertain over 74-year horizon. Both numbers have wide error bars.",
"flag_near_term_2027": false,
"flag_high_conviction": false,
"milestones_derived_at": "2026-05-02T03:08:50.805061+00:00",
"reference_class_match": {
"decision": "keyword_filtered",
"computed_at": "2026-04-30T01:49:13.796