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AI_016predictionAIAI-experience-curve-vs-Moore

The 'cost of intelligence' collapse has officially superseded traditional Moore's Law dynamics — an AI experience curve enables bulk problem-solving across engineering, medicine, and mathematics at a fraction of historical compute costs.

Predictor: Alex Wissner-Gross

Prior probability
90.0%
Current probability
81.5%
evolves via intake + LBP
Conviction
5/5
Signal quality
B
Resolution
hit
Window
2026-01-01 – 2026-09-30
Edges in / out
1 / 0
Tickers exposed
0

Prediction text

The 'cost of intelligence' collapse has officially superseded traditional Moore's Law dynamics — an AI experience curve enables bulk problem-solving across engineering, medicine, and mathematics at a fraction of historical compute costs. | Next API-pricing drop from frontier lab

Key catalyst: Next API-pricing drop from frontier lab

Watch events: Epoch AI cost-tracking; frontier-model API pricing

Resolution evidence

Status: hit

Epoch AI cost-per-FLOP tracking shows >40x/yr decline in frontier-model inference cost 2023-2026; OpenAI API pricing confirms directly.

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

4 prob_history rows
0%25%50%75%100%prior 90%2026-04-292026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 81.5%

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 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: 82%)

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
LBP2026-05-03T02:00:01Z81.5%-1.6pp
Network propagation: 83.1% → 81.5%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z83.1%-2.7pp
Network propagation: 85.8% → 83.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z85.8%-4.2pp
Network propagation: 90.0% → 85.8%
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%+16.9pp
resolution_terminal hit outcome=1.0 pre_resolution=0.831
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": 0.16900000000000004,
  "inside_posterior": 0.831,
  "validation_notes": "Epoch AI cost-per-FLOP tracking shows >40x/yr decline in frontier-model inference cost 2023-2026; OpenAI API pricing confirms directly.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.831,
  "resolution_evidence": "Epoch AI cost-per-FLOP tracking shows >40x/yr decline in frontier-model inference cost 2023-2026; OpenAI API pricing confirms directly.",
  "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
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.900-0.017

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Prerequisites (1)

Predictions that must hit first
TypePredTitleDomainLag
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importEpoch AI cost-per-FLOP tracking shows >40x/yr decline in frontier-model inference cost 2023-2026; OpenAI API pricing confirms directly.

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": "exponential collapse",
  "mode": "OBSERVATION+FORECAST",
  "role": "Cited-Other",
  "context": "Wissner-Gross experience-curve framing. Couples with INF_043 (Andreessen intelligence-deflation faster than Moore's Law) and 235_014 (Altman 40x hyperdeflation). The 'AI experience curve' explicitly positions intelligence as a cumulative learning-curve asset.",
  "to_year": 2026,
  "conv_cues": "framework-coining; explicit tipping-point claim",
  "direction": "DOWN",
  "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": "The 'cost of intelligence' collapse has officially superseded traditional Moore's Law dynamics — an AI experience curve enables bulk problem",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "AI_016",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Gemedy / MIT",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR",
  "resolved_at": "2026-04-29T22:23:18.207495+00:00",
  "source_refs": "29",
  "target_date": "2026-12-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Next API-pricing drop from frontier lab",
  "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": 4,
  "report_evidence": "Anchor section: Economics of Compute / Collapse of Intelligence Costs.",
  "active_end_month": "2026-12",
  "recent_statement": "Wissner-Gross 2026 podcast commentary.",
  "watch_events_raw": "Epoch AI cost-tracking; frontier-model API pricing",
  "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": "Wissner-Gross framework coining generally accurate; provocative framings sometimes aspirational.",
  "contradicting_notes": "Training capex still scaling super-linearly; cost-of-intelligence cost floor bounded by power and cooling, not silicon.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:50.491684+00:00",
  "reference_class_match": {
    "decision": "keyword_