← Cockpit
AI_005predictionAIjagged-intelligence

Current frontier models suffer from 'jagged intelligence' — excelling at international mathematics olympiads while simultaneously failing rudimentary arithmetic and spatial logic; true AGI requires smoothing these jagged edges via continual learning, d...

Predictor: Demis Hassabis

Prior probability
92.0%
Current probability
83.2%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
hit
Window
2026-01-01 – 2026-09-30
Edges in / out
2 / 0
Tickers exposed
0

Prediction text

Current frontier models suffer from 'jagged intelligence' — excelling at international mathematics olympiads while simultaneously failing rudimentary arithmetic and spatial logic; true AGI requires smoothing these jagged edges via continual learning, dynamic memory allocation, and robust world models. | Novel world-model architecture from DeepMind

Key catalyst: Novel world-model architecture from DeepMind

Watch events: ARC-AGI-2 saturation; GDPval subscore analysis; world-model benchmarks

Resolution evidence

Status: hit

Multiple empirical demonstrations 2024-2026: IMO Gold medals via o-series / DeepMind; simultaneous failures on Tower of Hanoi, counting, spatial reasoning across same frontier models.

Predictor: Demis Hassabis

κ + Brier as of 2026-05-22
κ (discount)
0.583
Brier
0.0064
excellent
Hits / Misses
1 / 0
of 1 resolved
Hit rate
100.0%
Calibration plot (stated vs observed)

Evidence about this node from Demis Hassabis 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 92%2026-04-292026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 83.2%

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: 83%)

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
LBP2026-05-03T02:00:01Z83.2%-1.7pp
Network propagation: 84.9% → 83.2%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z84.9%-2.9pp
Network propagation: 87.7% → 84.9%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z87.7%-4.3pp
Network propagation: 92.0% → 87.7%
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%+15.1pp
resolution_terminal hit outcome=1.0 pre_resolution=0.849
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": 0.1513,
  "inside_posterior": 0.8487,
  "validation_notes": "Multiple empirical demonstrations 2024-2026: IMO Gold medals via o-series / DeepMind; simultaneous failures on Tower of Hanoi, counting, spatial reasoning across same frontier models.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.8487,
  "resolution_evidence": "Multiple empirical demonstrations 2024-2026: IMO Gold medals via o-series / DeepMind; simultaneous failures on Tower of Hanoi, counting, spatial reasoning across same frontier models.",
  "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
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.920-0.042
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.920+0.001

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Prerequisites (2)

Predictions that must hit first
TypePredTitleDomainLag
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importMultiple empirical demonstrations 2024-2026: IMO Gold medals via o-series / DeepMind; simultaneous failures on Tower of Hanoi, counting, spatial reasoning across same frontier models.

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-CEO",
  "context": "Hassabis structural-skepticism framing constrains bull-case AGI timelines. Couples with CMQ_010 (requires AlphaFold-class scientific breakthroughs) and INF_072 (50/50 scaling alone reaches AGI).",
  "to_year": 2026,
  "conv_cues": "CEO FIRST_PERSON; specific diagnostic framing",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026 ongoing",
  "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": "Current frontier models suffer from 'jagged intelligence' — excelling at international mathematics olympiads while simultaneously failing ru",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "AI_005",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Google DeepMind",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR",
  "resolved_at": "2026-04-29T22:23:18.198758+00:00",
  "source_refs": "2, 9",
  "target_date": "2026-12-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Novel world-model architecture from DeepMind",
  "parse_method": "Current-state observation",
  "domain_bucket": "AI",
  "episode_title": "Forecasting the Inference Epoch: Expert AI Predictions & Macroeconomic Trajectories (2023-2026)",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI Predictions Search Plan.md (2026-04-21)",
  "appears_in_eps": "AI-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 5,
  "report_evidence": "Anchor section: Pragmatic Forecasters / Jagged Intelligence.",
  "active_end_month": "2026-12",
  "recent_statement": "Hassabis 2025-2026 interviews and DeepMind blog.",
  "watch_events_raw": "ARC-AGI-2 saturation; GDPval subscore analysis; world-model benchmarks",
  "months_from_today": 8,
  "probability_layer": "Higher (in-flight)",
  "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": "Hassabis research-and-capability-assessment accurate; founded AlphaFold Nobel lineage.",
  "contradicting_notes": "Gemini 3 Deep Think, GPT-5 continuous-learning variants narrowing gap in 2026; some researchers argue jaggedness is transient.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:50.484120+00:00",
  "reference_class_match": {
    "top_n": [
      {
        "id": "agi_breakthrough_5y",
        "cosine": 0.595
      },
      {
        "i