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CYB_026predictionGeopoliticsUS-moat-open-source-diffusion

The initial assumption that the United States can maintain a durable long-term 'moat' based purely on proprietary algorithms and compute concentration has been heavily challenged — rapid uncontrollable diffusion of highly capable open-source models (De...

Predictor: Leopold Aschenbrenner

Prior probability
85.0%
Current probability
76.9%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
hit
Window
2026-01-01 – 2030-11-30
Edges in / out
1 / 0
Tickers exposed
14

Prediction text

The initial assumption that the United States can maintain a durable long-term 'moat' based purely on proprietary algorithms and compute concentration has been heavily challenged — rapid uncontrollable diffusion of highly capable open-source models (DeepSeek, Qwen, Llama derivatives) continually democratizes access worldwide, ensuring geopolitical balance remains highly volatile. | Next Chinese open-source frontier-model release

Key catalyst: Next Chinese open-source frontier-model release

Watch events: Open-source capability-closing metrics; BIS export-control actions

Resolution evidence

Status: hit

DeepSeek-V3 / R2, Qwen 3.5, Llama 4 closed frontier-model gap 2024-2026; capability diffusion measured by Epoch AI, Stanford AI Index.

Predictor: Leopold Aschenbrenner

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0417
excellent
Hits / Misses
2 / 0
of 3 resolved
Hit rate
66.7%
Calibration plot (stated vs observed)

Evidence about this node from Leopold Aschenbrenner 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

4 prob_history rows
0%25%50%75%100%prior 85%2026-04-292026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 76.9%

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 ⏱
  1. 2026-01-30overdueQ1 window check-in (25%)
  2. 2026-03-01overdueQ2 window check-in (50%)
  3. 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: 77%)

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
LBP2026-05-03T02:00:01Z76.9%-1.4pp
Network propagation: 78.3% → 76.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z78.3%-2.5pp
Network propagation: 80.8% → 78.3%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z80.8%-4.2pp
Network propagation: 85.0% → 80.8%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
resolution_terminal2026-04-29T22:23:18Z100.0%+21.7pp
resolution_terminal hit outcome=1.0 pre_resolution=0.783
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": 0.21696000000000004,
  "inside_posterior": 0.78304,
  "validation_notes": "DeepSeek-V3 / R2, Qwen 3.5, Llama 4 closed frontier-model gap 2024-2026; capability diffusion measured by Epoch AI, Stanford AI Index.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.78304,
  "resolution_evidence": "DeepSeek-V3 / R2, Qwen 3.5, Llama 4 closed frontier-model gap 2024-2026; capability diffusion measured by Epoch AI, Stanford AI Index.",
  "does_not_update_current_prob": true
}

Network propagation neighbors

Top edges sorted by latest LBP cross-impact
All propagation →

Top incoming (parents)

Edges that influence THIS node's belief

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.850-0.015

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

14 ticker(s) linked

Beneficiaries (14)

BBAINVDAGTLBSOUNAIIBMMETAMSFTORCLTCEHYAMZNBABABIDUGOOGL

Prerequisites (1)

Predictions that must hit first
TypePredTitleDomainLag
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Expected milestones (1)

From Sheet 17 Monitoring Triggers
Expected byDescriptionStatus
2030-12-31[Geopolitics 2030-12] announcements; military AI deployments [CYB_026] Open-source capability-closing metrics; BIS export-control actions [246_020] Starcloud Blackwell satellite (Oct 2026); Google Project Suncatcher demo satellite; FCC DC filingspending

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importDeepSeek-V3 / R2, Qwen 3.5, Llama 4 closed frontier-model gap 2024-2026; capability diffusion measured by Epoch AI, Stanford AI Index.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "FORECAST",
  "role": "Cited-VC/Researcher",
  "context": "Retrospective revision of Situational Awareness 2024 thesis. Distinct from INF_002 (The Project) — this is a counter-pressure on the moat-thesis. Couples with SEM_022 (quantization decoupling).",
  "to_year": 2030,
  "conv_cues": "self-revision of prior framework; specific geopolitical framing",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026-2030",
  "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": "The initial assumption that the United States can maintain a durable long-term 'moat' based purely on proprietary algorithms and compute con",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CYB_026",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Situational Awareness LP",
  "attribution": "CITED",
  "granularity": "YEAR_RANGE",
  "resolved_at": "2026-04-29T22:23:18.245968+00:00",
  "source_refs": "6",
  "target_date": "2028-06-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Next Chinese open-source frontier-model release",
  "parse_method": "YEAR_RANGE midpoint",
  "domain_bucket": "Geopolitics",
  "episode_title": "Convergence of Synthetic Cognition: Agents, Memory, Commerce & Cybersecurity (2023-2026)",
  "fault_line_id": "F001, F002",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI Cyber and Memory Predictions Request.md (2026-04-21)",
  "appears_in_eps": "CYB-RPT",
  "futurist_phase": "Phase 2 (2027-2028)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 5,
  "ps_cluster_tags": [
    "C5"
  ],
  "report_evidence": "Anchor section: Trillion-Dollar Compute Clusters / US-China Dynamic.",
  "active_end_month": "2030-12",
  "recent_statement": "EA Forum retrospective 2026.",
  "watch_events_raw": "Open-source capability-closing metrics; BIS export-control actions",
  "months_from_today": 26,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2026-01",
  "december_dispersal": {
    "reason": "december_dispersal: domain=Geopolitics → 11/2030",
    "new_date": "2030-11-30",
    "old_date": "2030-12-31",
    "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": "Aschenbrenner revising earlier moat-thesis is itself high-signal.",
  "contradicting_notes": "US export controls (H20, Blackwell, HBM restrictions) may partially re-establish compute moat; capability-deployment gap persists.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:50.795079+00:00",
  "reference_class_match": {
    "decision":