The 'cost of intelligence' collapse has officially superseded traditional Moore's Law dynamics — an AI experience curve enables bulk problem-solving across engineering, medicine, and mathematics at a fraction of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
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The 'cost of intelligence' collapse has officially superseded traditional Moore's Law dynamics — an AI experience curve enables bulk problem-solving across engineering, medicine, and mathematics at a fraction of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
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
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
This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.
Probability over time
Milestone chain
- 2026-01-30overdueQ1 window check-in (25%)
- 2026-03-01overdueQ2 window check-in (50%)
- 2026-03-30overdueQ3 window check-in (75%)
No downstream cascades — this prediction is a leaf in the dependency graph.
What if this resolves?
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Evidence chain
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 incoming (parents)
Edges that influence THIS node's belief
| Kind | Node | Their prob | P(c|s=T) | P(c|s=F) | Δ implied |
|---|---|---|---|---|---|
| killer | TK02 AI Compute Supply Shock (TSMC/Taiwan Disruption) | 12.0% | 0.050 | 0.900 | -0.017 |
Top outgoing (children)
Predictions THIS node influences
No outgoing edges.
Prerequisites (1)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| killer | TK02 | AI Compute Supply Shock (TSMC/Taiwan Disruption) | — | — |
Dependents (0)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No dependents | ||||
Validations (1)
| Observed at | Status | By | Notes |
|---|---|---|---|
| 2026-04-29 | hit | thesis_timeline_v1.0_import | Epoch AI cost-per-FLOP tracking shows >40x/yr decline in frontier-model inference cost 2023-2026; OpenAI API pricing confirms directly. |
Linked documents (10)
Raw metadata
{
"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_