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SEM_002predictionAIAI-scaling

By 2025-2026, AI model outputs will outpace the cognitive capabilities of college graduates (driven by hundreds of millions of GPUs).

Predictor: Leopold Aschenbrenner

Prior probability
75.0%
Current probability
55.8%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
partial
Window
2025-01-01 – 2026-09-30
Edges in / out
9 / 5
Tickers exposed
37

Prediction text

By 2025-2026, AI model outputs will outpace the cognitive capabilities of college graduates (driven by hundreds of millions of GPUs). | hundreds of millions of Graphic Processing Units (GPUs) humming across vast solar farms in Nevada and shale fields in Pennsylvania | Frontier model benchmark releases

Key catalyst: Frontier model benchmark releases

Watch events: Next-gen model benchmark scores (GPQA, HLE, SWE-Bench); agentic reliability metrics

Verbatim quote

From episode "Forecasts and Strategic Vectors in the Global Semiconductor and Compute Manufacturing Ecosystem (2023-2026)"
hundreds of millions of Graphic Processing Units (GPUs) humming across vast solar farms in Nevada and shale fields in Pennsylvania

Resolution evidence

Status: partial

GPT-5/Claude Opus 4.x/Gemini 3 already outperform median college grads on MMLU/GPQA/HLE by late 2025. Aschenbrenner thesis largely vindicated on knowledge-work benchmarks.

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 0.635

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

Base rate
5.0%
0/4 historical
Inside weight
0.856
TRF=0.21
Outside weight
0.144
pulling toward base rate
inside 68.3% → blend 55.8% -12.5pp)

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 75%2026-04-302026-04-302026-05-21
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 55.8%

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. 2025-05-02overdueQ1 window check-in (25%)
  2. 2025-08-31overdueQ2 window check-in (50%)
  3. 2025-12-30overdueQ3 window check-in (75%)
  4. 2026-01-01 → 2026-09-30pendingGPT-5 / Claude Opus 5 release with claimed PhD-level reasoning
    How: OpenAI or Anthropic releases successor model claiming PhD-level performance on at least 3 expert benchmarks (GPQA, MMLU, HumanEval, SWE-Bench, FrontierMath)
    Source: Anthropic blog, OpenAI blog, conference keynotesconf 85%
    Notes: By April 2026, OpenAI's GPT-5.2 already demonstrating physics breakthroughs (per SEM_033 research).
  5. 2026-01-01 → 2026-09-30pendingMMLU saturated (≥95%) by all frontier models
    How: Top 5 frontier models (Anthropic, OpenAI, DeepMind, Meta, DeepSeek) all score ≥95% on MMLU — saturation marks 'better than college-grad' threshold
    Source: Papers With Code MMLU leaderboardconf 85%
    Notes: GPT-4 Turbo at ~88%, Claude 3.5 Opus ~88-91%; saturation by 2026 likely already happened.
  6. 2026-04-01 → 2026-12-31pendingGPQA-Diamond benchmark crosses 90% by frontier model
    How: Frontier model achieves ≥90% on GPQA-Diamond (Graduate-level Physics Q&A, designed to be unsolvable by non-experts)
    Source: Papers With Code GPQA leaderboard, Anthropic/OpenAI evals pagesconf 70%
  7. 2026-06-01 → 2027-06-30pendingAschenbrenner (or peer) publishes 'Situational Awareness II' or similar treatise marking AGI threshold
    How: Influential AI researcher (Aschenbrenner, Sutskever, Amodei, peer) publishes essay or book arguing AGI threshold has been crossed
    Source: situational-awareness.ai, research lab blogsconf 50%

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: 56%)

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
intake_event_update2026-05-21T23:15:16Z55.8%-1.4pp
intake:7afeeb9a-f217-4dd2-b910-24ff14bdfc39 bayesian_v2 inside=0.683 blend=0.558 LLR=0.477 κ=0.69 w_in=0.86 regulatory_freeze_window
Raw metadata
{
  "trf": 0.2057002584634281,
  "kappa": 0.6875,
  "base_rate": 0.05,
  "predictor": "Leopold Aschenbrenner",
  "total_llr": 0.6931471805599453,
  "bayesian_v2": true,
  "prior_logit": 0.2925896353230114,
  "bayes_factor": "1.6:1 favoring",
  "blend_reason": "blend 86% inside / 14% outside (TRF=0.206, base_rate=0.050 from regulatory_freeze_window)",
  "inside_prior": 0.57263,
  "kappa_source": "predictor_table",
  "blend_applied": true,
  "contributions": [
    {
      "llr": 0.6931471805599453,
      "kappa": 0.6875,
      "label": "Frontier model benchmarks (GPT-5.5, Claude Mythos, Gemini 3.1) clearly past college-grad threshold in technical domains.",
      "adjusted_llr": 0.4765386866349624
    }
  ],
  "evidence_kind": "intake_event_update",
  "inside_source": "history_v2",
  "inside_weight": 0.8560098190756003,
  "outside_weight": 0.1439901809243997,
  "posterior_prob": 0.5583358951206584,
  "evidence_origin": "daily_intake",
  "llm_suggestions": [
    {
      "polarity": "corroborates",
      "status_change": "unchanged",
      "evidence_strength": "moderate",
      "delta_prob_suggestion": 0.05
    }
  ],
  "posterior_logit": 0.7691283219579738,
  "predictor_brier": 0.04167,
  "evidence_doc_ids": [],
  "inside_posterior": 0.6833323021138943,
  "blended_posterior": 0.5583358951206584,
  "reference_class_id": "regulatory_freeze_window",
  "total_adjusted_llr": 0.4765386866349624,
  "predictor_n_resolved": 3
}
resolution_terminal2026-05-01T00:00:00Z50.0%-7.3pp
resolution_terminal partial outcome=0.5 pre_resolution=0.573
Raw metadata
{
  "source": "backfill_resolution_history.py",
  "status": "partial",
  "bayesian_v2": false,
  "outcome_prob": 0.5,
  "evidence_kind": "resolution_terminal",
  "posterior_prob": 0.5,
  "delta_to_outcome": -0.07262999999999997,
  "inside_posterior": 0.57263,
  "validation_notes": "GPT-5/Claude Opus 4.x/Gemini 3 already outperform median college grads on MMLU/GPQA/HLE by late 2025. Aschenbrenner thesis largely vindicated on knowledge-work benchmarks.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.57263,
  "resolution_evidence": "GPT-5/Claude Opus 4.x/Gemini 3 already outperform median college grads on MMLU/GPQA/HLE by late 2025. Aschenbrenner thesis largely vindicated on knowledge-work benchmarks.",
  "does_not_update_current_prob": true
}
LBP2026-04-30T16:39:51Z57.3%+3.5pp
Network propagation: 53.7% → 57.3%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
legacy v12026-04-30T16:13:50Z53.7%-3.5pp
reference_class_assigned bayesian_v2 inside=0.750 blend=0.537 w_in=0.77 regulatory_freeze_window
LBP2026-04-30T02:18:57Z57.2%+3.5pp
Network propagation: 53.7% → 57.2%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
legacy v12026-04-30T01:56:50Z53.7%-21.3pp
reference_class_assigned bayesian_v2 inside=0.750 blend=0.537 w_in=0.76 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
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.750+0.122
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.750+0.108
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.750+0.087
killerTK09
Energy Grid Cap (Data Center Power Wall)
35.0%0.0500.750-0.053
killerTK05
Rate Regime Persistence (10y > 5% through 2028)
30.0%0.0500.750-0.018

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq232_055
We're exiting the industrial age permanently as recursive sePeter Diamandis
35.5%0.7000.050+0.058
prereq244_019
Peter's son won't need a driver's license in 2 yearsPeter Diamandis
48.4%0.9200.050+0.051
prereq230_020
Peter's 14-year-old son Milan will never get a driver's licePeter Diamandis
34.7%0.6500.050+0.038
prereq242_031
Most large companies' business models will be disrupted in 2Peter Diamandis
36.1%0.6500.050+0.024
prereq247_023
AI will be able to do everything a white collar worker does Dave Blundin
40.8%0.7200.050+0.016

Ticker exposure

37 ticker(s) linked

Beneficiaries (24)

MUWULFIRENEQIXALABAPLDASMIYASMLPLABNVDANBISCRWVAAPLAMTAMZNDELLGOOGLIRMLNVGYMETAMSFTORCLSFTBYSTX

Adverse (6)

ACNGENCHGGIBMWNSLRN

Prerequisites (9)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_general_capability
correlateS_COMPUTE_100GW_2030Compute: 100GW national-scale by Dec 2030compute_scale
correlateS_AGI_WINTER_2036PLUSAGI delayed: capability plateau or AI winteragi_general_capability
correlateS_AI_PAUSE_2026Major-country AI pause beginning 2026ai_regulatory_pause
killerTK09Energy Grid Cap (Data Center Power Wall)
killerTK05Rate Regime Persistence (10y > 5% through 2028)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (5)

Predictions enabled by this
TypePredTitleDomainLag
prereq244_019Peter's son won't need a driver's license in 2 yearsAuto/Transport
prereq247_023AI will be able to do everything a white collar worker does imminentlyAI
prereq232_055We're exiting the industrial age permanently as recursive self-improvement unfolds.AI
prereq242_031Most large companies' business models will be disrupted in 2-5 yearsMarkets/Stocks
prereq230_020Peter's 14-year-old son Milan will never get a driver's license.Auto/Transport

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importGPT-5/Claude Opus 4.x/Gemini 3 already outperform median college grads on MMLU/GPQA/HLE by late 2025. Aschenbrenner thesis largely vindicated on knowledge-work benchmarks.

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": "PREDICTION",
  "role": "Guest-VC/Researcher",
  "context": "Aschenbrenner forecasts machine-model output outpacing college-grad cognition, powered by GPU swarms on Nevada solar farms and Pennsylvania shale fields.",
  "to_year": 2026,
  "verbatim": "hundreds of millions of Graphic Processing Units (GPUs) humming across vast solar farms in Nevada and shale fields in Pennsylvania",
  "conv_cues": "drives; mathematically reflects",
  "direction": "HAPPEN",
  "from_year": 2025,
  "timeframe": "2025-2026",
  "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": "2025-05-02",
      "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": "2025-08-31",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2025-12-30",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "By 2025-2026, AI model outputs will outpace the cognitive capabilities of college graduates (driven by hundreds of millions of GPUs).",
      "status": "partial",
      "weight": 1,
      "ordinal": 0,
      "source_id": "SEM_002",
      "expected_date": "2026-05-01",
      "observed_date": "2026-05-01"
    },
    {
      "kind": "llm_pre_event",
      "label": "GPT-5 / Claude Opus 5 release with claimed PhD-level reasoning",
      "notes": "By April 2026, OpenAI's GPT-5.2 already demonstrating physics breakthroughs (per SEM_033 research).",
      "source": "Anthropic blog, OpenAI blog, conference keynotes",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 1,
      "source_id": null,
      "confidence": 0.85,
      "expected_date": "2026-05-17",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-09-30",
        "from": "2026-01-01"
      },
      "measurement_criterion": "OpenAI or Anthropic releases successor model claiming PhD-level performance on at least 3 expert benchmarks (GPQA, MMLU, HumanEval, SWE-Bench, FrontierMath)"
    },
    {
      "kind": "llm_pre_event",
      "label": "MMLU saturated (≥95%) by all frontier models",
      "notes": "GPT-4 Turbo at ~88%, Claude 3.5 Opus ~88-91%; saturation by 2026 likely already happened.",
      "source": "Papers With Code MMLU leaderboard",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 2,
      "source_id": null,
      "confidence": 0.85,
      "expected_date": "2026-05-17",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-09-30",
        "from": "2026-01-01"
      },
      "measurement_criterion": "Top 5 frontier models (Anthropic, OpenAI, DeepMind, Meta, DeepSeek) all score ≥95% on MMLU — saturation marks 'better than college-grad' threshold"
    },
    {
      "kind": "llm_pre_event",
      "label": "GPQA-Diamond benchmark crosses 90% by frontier model",
      "source": "Papers With Code GPQA leaderboard, Anthropic/OpenAI evals pages",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 3,
      "source_id": null,
      "confidence": 0.7,
      "expected_date": "2026-08-16",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-1
... (truncated)