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CMQ_013predictionAIOOM-scaling

A 5-OOM (100,000x) effective-compute leap will occur between 2024-2027 — bridging GPT-4 high-schooler to fully automated AI researcher.

Predictor: Leopold Aschenbrenner

Prior probability
55.0%
Current probability
40.2%
evolves via intake + LBP
Conviction
5/5
Signal quality
A
Resolution
pending
Window
2024-01-01 – 2027-10-31
Edges in / out
5 / 0
Tickers exposed
11

Prediction text

A 5-OOM (100,000x) effective-compute leap will occur between 2024-2027 — bridging GPT-4 high-schooler to fully automated AI researcher. | Epoch AI FLOP tracking; frontier model releases

Key catalyst: Epoch AI FLOP tracking; frontier model releases

Watch events: Frontier training FLOPs; published algorithmic-efficiency papers; emergent capability benchmarks.

Resolution evidence

Status: pending

Epoch AI tracking shows frontier training compute doubled ~every 6 months 2022-2025; algorithmic efficiency gains (MoE, distillation) material.

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: regulatory_freeze_window

Linked via embedding similarity 0.649

Major-country regulatory pause/moratorium on AI capability research lasting >6 months

Base rate
5.0%
0/4 historical
Inside weight
Outside weight
no pull
inside 40.2% → blend 40.2% 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

9 prob_history rows
0%25%50%75%100%prior 55%2026-04-302026-05-022026-05-24
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 40.2%

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: 2 fired ✓ · 3 overdue ⏱ · 1 pending
  1. 2024-09-11overdueQ1 window check-in (25%)
  2. 2025-05-24overdueQ2 window check-in (50%)
  3. 2026-01-31hitTest-time compute / reasoning OOM unlocked via o1, o3, R1
    How: Three+ frontier reasoning models shipped (o1, o3, DeepSeek R1) demonstrating Aschenbrenner's predicted unhobbling OOM
    Source: Stockalarm Pro / Dwarkesh Patel — Aschenbrenner test-time compute call validatedconf 95%
    Notes: HIT — o1 (Sep 2024), o3 (late 2024/early 2025), R1 (Jan 2026) shipped. Unhobbling axis validated.
  4. 2026-02-02overdueQ3 window check-in (75%)
  5. 2026-04-30hitAschenbrenner 1GW per cluster prediction validated by 2026
    How: Public confirmation of 1GW-class AI training cluster operational, validating Aschenbrenner's 'compute scaling 0.5 OOM/year' axis
    Source: Stockalarm Pro 'Situational Awareness Two Years Later' — '1 GW per cluster by 2026: hit'conf 95%
    Notes: HIT — 1GW cluster milestone confirmed; 10GW under construction. Compute axis on track for 5-OOM stack.
  6. 2026-06-01 → 2026-12-31pendingFrontier model demonstrates 1 full OOM effective compute over GPT-4
    How: Public release of model with ≥10x effective compute vs GPT-4 (per Epoch AI FLOP estimation) — would mark cumulative ~3 OOM gain since 2024
    Source: Epoch AI tracking / OpenAI, Anthropic, Google releasesconf 75%
  7. 2026-09-01 → 2027-03-31pendingAlgorithmic efficiency gains tracked at 0.5+ OOM/year through 2026
    How: Epoch AI or peer publication confirms algorithmic efficiency improved by ≥0.5 OOM YoY over 2025-2026 window
    Source: Epoch AI compute efficiency researchconf 65%
  8. 2027-01-01 → 2027-12-31pendingFull automated AI researcher milestone — autonomous research agent
    How: Frontier lab publicly demonstrates AI system performing end-to-end ML research (hypothesis, experiment, paper) at level matching mid-tier human researcher
    Source: Anthropic, OpenAI, DeepMind research demonstrationsconf 40%
    Notes: Cascade endpoint of Aschenbrenner thesis — high uncertainty but on his original 2027 timeline.

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: 40%)

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
LBP2026-05-24T02:00:02Z40.2%+2.1pp
Network propagation: 38.2% → 40.2%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
LBP2026-05-17T02:00:01Z38.2%+4.0pp
Network propagation: 34.1% → 38.2%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z34.1%+7.4pp
Network propagation: 26.8% → 34.1%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z26.8%+11.2pp
Network propagation: 15.5% → 26.8%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z15.5%-24.9pp
metadata_milestone_miss_sweep bayesian_v2 n=3 inside=0.227 blend=0.155 LLR=-0.836 κ=0.69 w_in=0.73 regulatory_freeze_window
Raw metadata
{
  "trf": 0.3903346852891947,
  "kappa": 0.6875,
  "base_rate": 0.05,
  "predictor": "Leopold Aschenbrenner",
  "total_llr": -1.2163953243244932,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.3896748373344811,
  "bayes_factor": "2.3:1 against",
  "blend_reason": "blend 72% inside / 27% outside (TRF=0.390, base_rate=0.050 from regulatory_freeze_window)",
  "inside_prior": 0.4037955794452396,
  "kappa_source": "predictor_table",
  "n_milestones": 3,
  "blend_applied": true,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2024-09-11",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2025-05-24",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q3 window check-in (75%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2026-02-02",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.7267657202975637,
  "outside_weight": 0.2732342797024363,
  "posterior_prob": 0.15505421390054963,
  "posterior_logit": -1.2259466228075702,
  "predictor_brier": 0.04167,
  "inside_posterior": 0.2268916488382616,
  "blended_posterior": 0.15505421390054963,
  "reference_class_id": "regulatory_freeze_window",
  "total_adjusted_llr": -0.836271785473089,
  "predictor_n_resolved": 3
}
LBP2026-04-30T16:39:51Z40.4%+7.6pp
Network propagation: 32.8% → 40.4%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
legacy v12026-04-30T16:13:50Z32.8%-7.6pp
reference_class_assigned bayesian_v2 inside=0.550 blend=0.328 w_in=0.71 regulatory_freeze_window
LBP2026-04-30T02:18:57Z40.4%+7.6pp
Network propagation: 32.8% → 40.4%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
legacy v12026-04-30T01:56:50Z32.8%-22.2pp
reference_class_assigned bayesian_v2 inside=0.550 blend=0.328 w_in=0.71 regulatory_freeze_window

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%0.0500.550+0.088

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

11 ticker(s) linked

Beneficiaries (11)

AIBBAIGTLBNVDASOUNIBMMETAAMZNMSFTGOOGLORCL

Prerequisites (5)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_general_capability
correlateS_AGI_FAST_2027AGI fast: drop-in remote worker by 2027-09agi_general_capability
correlateS_AGI_WINTER_2036PLUSAGI delayed: capability plateau or AI winteragi_general_capability
correlateS_AI_PAUSE_2026Major-country AI pause beginning 2026ai_regulatory_pause
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

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": "5 OOMs (100,000x)",
  "mode": "FORECAST",
  "role": "Cited-Researcher",
  "caveats": "Unhobbling gains hardest to quantify; algorithmic efficiency may saturate.",
  "context": "Same size jump as GPT-2 → GPT-4 (5 OOM). Derived from three vectors: physical compute (~0.5 OOM/yr), algorithmic efficiency (~0.5 OOM/yr), and unhobbling (RLHF, CoT, tools, memory).",
  "to_year": 2027,
  "conv_cues": "specific quantitative target; derivation provided",
  "direction": "NUMERIC_TARGET",
  "from_year": 2024,
  "timeframe": "2024-2027",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2024-09-11",
      "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": -5,
      "source_id": null,
      "expected_date": "2025-05-24",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "Test-time compute / reasoning OOM unlocked via o1, o3, R1",
      "notes": "HIT — o1 (Sep 2024), o3 (late 2024/early 2025), R1 (Jan 2026) shipped. Unhobbling axis validated.",
      "source": "Stockalarm Pro / Dwarkesh Patel — Aschenbrenner test-time compute call validated",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.dwarkesh.com/p/leopold-aschenbrenner",
      "expected_date": "2026-01-31",
      "observed_date": "2026-01-31",
      "research_origin": "deep_research",
      "measurement_criterion": "Three+ frontier reasoning models shipped (o1, o3, DeepSeek R1) demonstrating Aschenbrenner's predicted unhobbling OOM"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2026-02-02",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "Aschenbrenner 1GW per cluster prediction validated by 2026",
      "notes": "HIT — 1GW cluster milestone confirmed; 10GW under construction. Compute axis on track for 5-OOM stack.",
      "source": "Stockalarm Pro 'Situational Awareness Two Years Later' — '1 GW per cluster by 2026: hit'",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://pro.stockalarm.io/blog/situational-awareness-two-years-later",
      "expected_date": "2026-04-30",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Public confirmation of 1GW-class AI training cluster operational, validating Aschenbrenner's 'compute scaling 0.5 OOM/year' axis"
    },
    {
      "kind": "llm_pre_event",
      "label": "Frontier model demonstrates 1 full OOM effective compute over GPT-4",
      "source": "Epoch AI tracking / OpenAI, Anthropic, Google releases",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://epochai.org/data/notable-ai-models",
      "expected_date": "2026-09-15",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Public release of model with ≥10x effective compute vs GPT-4 (per Epoch AI FLOP estima
... (truncated)