← Cockpit
SPC_025predictionAI300-to-1-safety-ratio

Critical misalignment in the AI industry: approximately 300 AI capabilities researchers for every one AGI safety researcher — extreme systemic risk as these autonomous systems are deployed into physical domains like space and embodied robotics.

Predictor: Leopold Aschenbrenner

Prior probability
80.0%
Current probability
64.1%
evolves via intake + LBP
Conviction
5/5
Signal quality
A
Resolution
hit
Window
2024-01-01 – 2026-09-30
Edges in / out
8 / 0
Tickers exposed
4

Prediction text

Critical misalignment in the AI industry: approximately 300 AI capabilities researchers for every one AGI safety researcher — extreme systemic risk as these autonomous systems are deployed into physical domains like space and embodied robotics. | Next major frontier-lab safety-team restructuring

Key catalyst: Next major frontier-lab safety-team restructuring

Watch events: AISI capacity; frontier-lab alignment-team sizing

Resolution evidence

Status: hit

Situational Awareness 2024 paper documents alignment-researcher shortfall; AISI / Anthropic / RAND research validates ratio broadly.

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: agi_breakthrough_5y

Linked via embedding similarity 0.650

Major capability discontinuity (e.g. AGI by named target year, 5-year horizon)

Base rate
20.0%
1/5 historical
Inside weight
Outside weight
no pull
inside 64.1% → blend 64.1% 0.0pp)

Tetlock-style outside view: at TRF=1 (just predicted), outside view dominates (w_in=0.3). At TRF=0 (deadline), inside view dominates (w_in=1.0). The blend regularizes overconfident inside views toward the historical base rate.

Probability over time

6 prob_history rows
0%25%50%75%100%prior 80%2026-04-292026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 64.1%

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. 2024-07-31overdueQ1 window check-in (25%)
  2. 2025-02-28overdueQ2 window check-in (50%)
  3. 2025-09-28overdueQ3 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: 64%)

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
LBP2026-05-17T02:00:01Z64.1%-1.2pp
Network propagation: 65.4% → 64.1%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z65.4%-2.4pp
Network propagation: 67.7% → 65.4%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z67.7%-4.4pp
Network propagation: 72.2% → 67.7%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
legacy v12026-04-30T16:13:50Z72.2%+0.0pp
reference_class_assigned bayesian_v2 inside=0.800 blend=0.722 w_in=0.84 agi_breakthrough_5y
legacy v12026-04-30T01:56:50Z72.2%-7.8pp
reference_class_assigned bayesian_v2 inside=0.800 blend=0.722 w_in=0.84 agi_breakthrough_5y
resolution_terminal2026-04-29T22:23:18Z100.0%+27.8pp
resolution_terminal hit outcome=1.0 pre_resolution=0.722
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": 0.27823,
  "inside_posterior": 0.72177,
  "validation_notes": "Situational Awareness 2024 paper documents alignment-researcher shortfall; AISI / Anthropic / RAND research validates ratio broadly.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.72177,
  "resolution_evidence": "Situational Awareness 2024 paper documents alignment-researcher shortfall; AISI / Anthropic / RAND research validates ratio broadly.",
  "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
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.800+0.099
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.800+0.084
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.800+0.084
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.800+0.046
killerTK08
Humanoid Capital Collapse (Figure/Apptronik Flop)
22.0%0.0500.800-0.006

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

4 ticker(s) linked

Adverse (4)

ALLPGRTRVUBER

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_AGI_FAST_2027AGI fast: drop-in remote worker by 2027-09agi_general_capability
correlateS_ROBOTAXI_MASS_2030Robotaxi >10% urban miles by Nov 2030robotaxi_deployment
correlateS_AGI_WINTER_2036PLUSAGI delayed: capability plateau or AI winteragi_general_capability
killerTK08Humanoid Capital Collapse (Figure/Apptronik Flop)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK11Autonomous Regulatory Block (Level 4 Halt)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importSituational Awareness 2024 paper documents alignment-researcher shortfall; AISI / Anthropic / RAND research validates ratio broadly.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.774arxivAutomated alignment is harder than you thinkmentionspending2026-05-07
0.738arxivSafe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulationmentionspending2026-06-04
0.724arxivPathways to AGImentionspending2026-05-07
0.721arxivBrainrot: Deskilling and Addiction are Overlooked AI Risksmentionspending2026-05-05
0.713arxivPosition: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demandsmentionspending2026-05-14
0.712arxivGram: Assessing sabotage propensities via automated alignment auditingmentionspending2026-05-28
0.709arxivAgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Securitymentionspending2026-05-28
0.705gdelton the risks of ai in the workplace.htmmentionspending2026-04-30
0.704arxivJiao: Bridging Isolation and Customization in Mixed Criticality Roboticsmentionspending2026-05-05
0.704arxivHuman-in-the-Loop Uncertainty Analysis in Self-Adaptive Robots Using LLMsmentionspending2026-05-04

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "300:1 ratio",
  "mode": "OBSERVATION+WARNING",
  "role": "Cited-VC/Researcher",
  "context": "Specific quantitative imbalance framing. Couples with AI_036 (RLHF fails for ASI), CYB_026 (US moat undermined), INF_002 (The Project).",
  "to_year": 2026,
  "conv_cues": "specific quantitative ratio; safety-risk framing",
  "direction": "HAPPEN",
  "from_year": 2024,
  "timeframe": "2024-2026",
  "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": "2024-07-31",
      "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": "2025-02-28",
      "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": "2025-09-28",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "Critical misalignment in the AI industry: approximately 300 AI capabilities researchers for every one AGI safety researcher — extreme system",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "SPC_025",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Situational Awareness LP",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR_RANGE",
  "resolved_at": "2026-04-29T22:23:18.270287+00:00",
  "source_refs": "39, 41",
  "target_date": "2026-06-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Next major frontier-lab safety-team restructuring",
  "parse_method": "Current-state observation",
  "domain_bucket": "AI",
  "episode_title": "Strategic Forecasts in Space, Satellites, and Propulsion: 2023-2026 Retrospective and Future Outlook",
  "fault_line_id": "F006",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "Space Prediction Research Plan.md (2026-04-21)",
  "appears_in_eps": "SPC-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 5,
  "report_evidence": "Anchor section: Singularity / AGI / Infrastructure Bottleneck.",
  "active_end_month": "2026-12",
  "recent_statement": "Aschenbrenner Situational Awareness 2024 / 2026 updates.",
  "watch_events_raw": "AISI capacity; frontier-lab alignment-team sizing",
  "months_from_today": 2,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2024-01",
  "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": "Aschenbrenner quantitative framings reliable.",
  "contradicting_notes": "Alignment-researcher hiring accelerated 2024-2026 (Anthropic alignment team, OpenAI Superalignment before dissolution); ratio may be narrowing.",
  "flag_near_term_2027": false,
  "flag_high_conviction": false,
  "milestones_derived_at": "2026-05-02T03:08:51.329598+00:00",
  "reference_class_match": {
    "top_n": [
      {
        "id": "agi_breakthrough_5y",
        "cosine": 0.6504
      }
    ],