← Cockpit
INF_008predictionAIIQ-growth-rate

Leading AI model intelligence has been improving by roughly 2.5 IQ points per month since May 2024 — a sustained compounding rate that rapidly surpasses human-level baselines and mandates continuous hardware refresh cycles.

Predictor: Alex Wissner-Gross

Prior probability
45.0%
Current probability
39.5%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
partial
Window
2024-01-01 – 2026-11-30
Edges in / out
5 / 0
Tickers exposed
0

Prediction text

Leading AI model intelligence has been improving by roughly 2.5 IQ points per month since May 2024 — a sustained compounding rate that rapidly surpasses human-level baselines and mandates continuous hardware refresh cycles. | Quarterly model releases; benchmark publications

Key catalyst: Quarterly model releases; benchmark publications

Watch events: New benchmark results (ARC-AGI, GDPval, HLE) rather than IQ tests; capability-frontier tracking

Resolution evidence

Status: partial

Norwegian Mensa test scores for frontier models rose from ~90 (GPT-4) to ~130+ (GPT-5/Claude Opus 4.7/Gemini 2.5) over ~24 months — consistent with claim in aggregate, though IQ tests are contested as AI benchmarks.

Predictor: Alex Wissner-Gross

κ + Brier as of 2026-05-22
κ (discount)
0.844
Brier
0.0341
excellent
Hits / Misses
6 / 1
of 11 resolved
Hit rate
54.5%
Calibration plot (stated vs observed)

Evidence about this node from Alex Wissner-Gross 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

3 prob_history rows
0%25%50%75%100%prior 45%2026-04-302026-04-302026-05-01
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 39.5%

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 fired ✓ · 3 overdue ⏱
  1. 2024-07-31overdueQ1 window check-in (25%)
  2. 2025-03-01overdueQ2 window check-in (50%)
  3. 2025-09-30overdueQ3 window check-in (75%)
  4. 2026-04-15hit2026 Stanford AI Index publishes year-over-year benchmark gains
    How: Stanford HAI releases 2026 AI Index Report containing technical-performance section with YoY benchmark deltas
    Source: https://hai.stanford.edu/ai-index/2026-ai-index-reportconf 95%
  5. 2026-04-30hitMensa Norway IQ benchmark top score climbs from 135 to 145
    How: TrackingAI Mensa Norway leaderboard documents 10+ point year-over-year improvement in top frontier model IQ
    Source: https://binaryverseai.com/ai-iq-test-2025/conf 90%
    Notes: HIT — Grok-4.20 / GPT-5.4 Pro tied at 145 in April 2026, up 10 from 135 prior year. Implies ~0.8 IQ/month, below 2.5/month claim but trend confirmed.
  6. 2026-04-30hitHumanity's Last Exam jumps from 8.8% to 50%+ inside 12 months
    How: Stanford AI Index / Epoch AI report documents top-model HLE accuracy rising from 8.8% (early 2025) to >=50% (April 2026)
    Source: https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performanceconf 95%
    Notes: HIT — HLE accuracy climbed >40 points YoY, far steeper than 2.5 IQ/month equivalent.
  7. 2026-01-01 → 2026-12-31pendingFrontier model releases sustain quarterly cadence through 2026
    How: At least 4 quarterly frontier-model releases (Anthropic/OpenAI/Google/xAI) in 2026 with measurable benchmark step-ups
    Source: https://llm-stats.com/ai-trendsconf 85%
  8. 2026-06-01 → 2027-06-30pendingHardware refresh cycle: GPU shipments accelerate to support compounding intelligence
    How: NVIDIA datacenter revenue grows >=2x YoY in any consecutive 4-quarter window during Wissner-Gross window
    Source: NVIDIA quarterly earnings, https://investor.nvidia.comconf 70%

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: 39%)

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
resolution_terminal2026-05-01T00:00:00Z50.0%+10.5pp
resolution_terminal partial outcome=0.5 pre_resolution=0.395
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": 0.10524,
  "inside_posterior": 0.39476,
  "validation_notes": "Norwegian Mensa test scores for frontier models rose from ~90 (GPT-4) to ~130+ (GPT-5/Claude Opus 4.7/Gemini 2.5) over ~24 months — consistent with claim in aggregate, though IQ tests are contested as AI benchmarks.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.39476,
  "resolution_evidence": "Norwegian Mensa test scores for frontier models rose from ~90 (GPT-4) to ~130+ (GPT-5/Claude Opus 4.7/Gemini 2.5) over ~24 months — consistent with claim in aggregate, though IQ tests are contested as AI benchmarks.",
  "does_not_update_current_prob": true
}
LBP2026-04-30T16:39:51Z39.5%-1.9pp
Network propagation: 41.3% → 39.5%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z41.3%-3.7pp
Network propagation: 45.0% → 41.3%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef

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
killerTK09
Energy Grid Cap (Data Center Power Wall)
35.0%0.0500.450-0.085
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.450+0.023
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.450+0.007

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

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
killerTK09Energy Grid Cap (Data Center Power Wall)
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)
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_importNorwegian Mensa test scores for frontier models rose from ~90 (GPT-4) to ~130+ (GPT-5/Claude Opus 4.7/Gemini 2.5) over ~24 months — consistent with claim in aggregate, though IQ tests are contested as AI benchmarks.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.677arxivIntelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impactmentionspending2026-05-14
0.665gdeltintelligence trust the equation that will decide australias ai winners 625399mentionspending2026-04-30
0.655arxivZAYA1-8B Technical Reportmentionspending2026-05-06
0.648arxivImplicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scalementionspending2026-05-13
0.635manifoldWill HackerNews #1 story score go up in 24h?52%mentionspending2026-05-03
0.633github_releasegoogle-deepmind/alphafold v2.2.0mentionspending2022-03-10
0.622github_releasegoogle-deepmind/alphafold v2.3.1mentionspending2023-01-12
0.614arxivBoosting Self-Consistency with Rankingmentionspending2026-06-03
0.608arxivEQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triagementionspending2026-05-05
0.590manifoldWhat will my custom Zetamac score (average of 5) be in a week?mentionspending2026-05-16

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "+2.5 IQ pts/mo",
  "mode": "OBSERVATION+FORECAST",
  "role": "Host",
  "context": "Wissner-Gross argues model IQ velocity is the key leading indicator of infrastructure obsolescence: even newly-deployed hardware becomes a legacy platform within 12-18 months.",
  "to_year": 2026,
  "conv_cues": "measurable velocity; quantified monthly rate",
  "direction": "NUMERIC_TARGET",
  "from_year": 2024,
  "timeframe": "May 2024 - ongoing",
  "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": "2024-07-31",
      "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": -5,
      "source_id": null,
      "expected_date": "2025-03-01",
      "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": -4,
      "source_id": null,
      "expected_date": "2025-09-30",
      "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": "2026 Stanford AI Index publishes year-over-year benchmark gains",
      "source": "https://hai.stanford.edu/ai-index/2026-ai-index-report",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://hai.stanford.edu/ai-index/2026-ai-index-report",
      "expected_date": "2026-04-15",
      "observed_date": "2026-04-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Stanford HAI releases 2026 AI Index Report containing technical-performance section with YoY benchmark deltas"
    },
    {
      "kind": "llm_pre_event",
      "label": "Mensa Norway IQ benchmark top score climbs from 135 to 145",
      "notes": "HIT — Grok-4.20 / GPT-5.4 Pro tied at 145 in April 2026, up 10 from 135 prior year. Implies ~0.8 IQ/month, below 2.5/month claim but trend confirmed.",
      "source": "https://binaryverseai.com/ai-iq-test-2025/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.9,
      "source_url": "https://binaryverseai.com/ai-iq-test-2025/",
      "expected_date": "2026-04-30",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "measurement_criterion": "TrackingAI Mensa Norway leaderboard documents 10+ point year-over-year improvement in top frontier model IQ"
    },
    {
      "kind": "llm_pre_event",
      "label": "Humanity's Last Exam jumps from 8.8% to 50%+ inside 12 months",
      "notes": "HIT — HLE accuracy climbed >40 points YoY, far steeper than 2.5 IQ/month equivalent.",
      "source": "https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performance",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performance",
      "expected_date": "2026-04-30",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Stanford AI Index / Epoch AI report documents top-model HLE accuracy rising from 8.8% (early 2025) to >=50% (April 2026)"
    },
    {
      "kind": "event",
      "label": "Leading AI model intelligence has been improving by roughly 2.5 IQ points per month since May 2024 
... (truncated)