INF_049predictionEnergygigawatt-factory-bottleneck

Modern AI data centers are no longer server farms but "gigawatt factories"; energy — not silicon supply — has become the absolute primary bottleneck for AI scalability, and these concentrated power needs cannot be reliably connected to existing public ...

Predictor: Jensen Huang

Prior probability

92.0%

Current probability

50.2%

evolves via intake + LBP

Conviction

5/5

Signal quality

Resolution

hit

Window

2026-01-01 – 2026-11-30

Edges in / out

3 / 0

Tickers exposed

Prediction text

Modern AI data centers are no longer server farms but "gigawatt factories"; energy — not silicon supply — has become the absolute primary bottleneck for AI scalability, and these concentrated power needs cannot be reliably connected to existing public grids. | Next hyperscaler gigawatt-scale PPA

Key catalyst: Next hyperscaler gigawatt-scale PPA

Watch events: NVIDIA quarterly earnings commentary; hyperscaler PPA announcements; BYOP permits

Resolution evidence

Status: hit

xAI Colossus (35+ on-site gas turbines), Stargate Abilene (1.2 GW), AWS-Talen nuclear PPA, Meta Hyperion (5 GW) all validate gigawatt-factory reality. Grid interconnection queues 7-12 years confirm bypass thesis.

Predictor: Jensen Huang

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.808

Brier

0.0128

excellent

Hits / Misses

6 / 0

of 8 resolved

Hit rate

75.0%

Calibration plot (stated vs observed)

Evidence about this node from Jensen Huang 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: energy_grid_rebuild_5y

Linked via embedding similarity 0.586

All classes →

Major grid expansion (>50GW) within 5y of announce

Base rate

50.0%

3/6 historical

Inside weight

—

Outside weight

—

no pull

inside 50.3% → blend 50.3% (Δ 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

intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 50.2%

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: 3 overdue ⏱

2026-01-30overdueQ1 window check-in (25%)
2026-03-01overdueQ2 window check-in (50%)
2026-03-30overdueQ3 window check-in (75%)
2026-04-29hitModern AI data centers are no longer server farms but "gigawatt factories"; energy — not silicon supply — has become the absolute primary bo

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: 50%)

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

LBP2026-05-24T02:00:02Z50.2%-1.5pp

Network propagation: 51.7% → 50.3%

4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8

LBP2026-05-17T02:00:01Z51.7%-3.0pp

Network propagation: 54.7% → 51.7%

5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96

LBP2026-05-10T02:00:02Z54.7%-5.8pp

Network propagation: 60.5% → 54.7%

6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29

LBP2026-05-03T02:00:01Z60.5%-10.5pp

Network propagation: 71.0% → 60.5%

6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9

LBP2026-04-30T16:39:51Z71.0%-7.5pp

Network propagation: 78.5% → 71.0%

5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3

legacy v12026-04-30T16:13:50Z78.5%+7.5pp

reference_class_assigned bayesian_v2 inside=0.920 blend=0.785 w_in=0.53 energy_grid_rebuild_5y

LBP2026-04-30T02:18:57Z71.0%-7.5pp

Network propagation: 78.4% → 71.0%

5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef

legacy v12026-04-30T01:56:50Z78.4%-13.6pp

reference_class_assigned bayesian_v2 inside=0.920 blend=0.784 w_in=0.53 energy_grid_rebuild_5y

resolution_terminal2026-04-29T22:23:18Z100.0%+29.0pp

resolution_terminal hit outcome=1.0 pre_resolution=0.710

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": 0.28996999999999995,
  "inside_posterior": 0.71003,
  "validation_notes": "xAI Colossus (35+ on-site gas turbines), Stargate Abilene (1.2 GW), AWS-Talen nuclear PPA, Meta Hyperion (5 GW) all validate gigawatt-factory reality. Grid interconnection queues 7-12 years confirm bypass thesis.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.71003,
  "resolution_evidence": "xAI Colossus (35+ on-site gas turbines), Stargate Abilene (1.2 GW), AWS-Talen nuclear PPA, Meta Hyperion (5 GW) all validate gigawatt-factory reality. Grid interconnection queues 7-12 years confirm bypass thesis.",
  "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

Kind	Node	Their prob	P(c\|s=T)	P(c\|s=F)	Δ implied
killer	TK09 Energy Grid Cap (Data Center Power Wall)	35.0%	0.050	0.920	+0.113

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

27 ticker(s) linked

Beneficiaries (21)

FSLR ARGAN WULF APLD IREN EQIX CRWV FLNC NBIS IRM META MSFT ETN ORCL SFTBY STX AAPL AMZN AMT GOOGL HUBB

Prerequisites (3)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
correlate	S_GRID_50GW_2027	50GW dedicated AI/data center grid by Dec 2027	energy_grid_expansion	—
correlate	S_COMPUTE_100GW_2030	Compute: 100GW national-scale by Dec 2030	compute_scale	—
killer	TK09	Energy Grid Cap (Data Center Power Wall)	—	—

Dependents (0)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
No dependents

Expected milestones (1)

From Sheet 17 Monitoring Triggers

Expected by	Description	Status
2026-12-31	[Energy/Regulation 2026-12] [INF_049] NVIDIA quarterly earnings commentary; hyperscaler PPA announcements; BYOP permits [CMQ_017] US electricity production YoY; SMR deployment; behind-the-meter gas turbine deployments at AI campus [235_0	pending

Validations (1)

Resolution events

Observed at	Status	By	Notes
2026-04-29	hit	thesis_timeline_v1.0_import	xAI Colossus (35+ on-site gas turbines), Stargate Abilene (1.2 GW), AWS-Talen nuclear PPA, Meta Hyperion (5 GW) all validate gigawatt-factory reality. Grid interconnection queues 7-12 years confirm bypass thesis.

Linked documents (1)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Sim	Source	Title	Market prob	Polarity	Reviewed	Published
0.746	arxiv	From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design	—	mentions	pending	2026-05-04

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "qty": "GW-scale",
  "mode": "FORECAST",
  "role": "Cited-CEO",
  "context": "Gigawatt-factory language is Huang's primary framing device since CES 2026. Pairs with INF_016 (Musk innermost-loop framing) and INF_019 (Musk 5M gal/day water). Huang simultaneously notes Vera Rubin chip efficiency gains are insufficient to offset aggregate cluster deployment.",
  "to_year": 2026,
  "conv_cues": "\"absolute primary\" framing; CEO FIRST_PERSON",
  "direction": "HAPPEN",
  "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": "Modern AI data centers are no longer server farms but \"gigawatt factories\"; energy — not silicon supply — has become the absolute primary bo",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "INF_049",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "NVIDIA",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR",
  "resolved_at": "2026-04-29T22:23:18.163553+00:00",
  "source_refs": "9, 12",
  "target_date": "2026-12-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Next hyperscaler gigawatt-scale PPA",
  "parse_method": "Current-state midpoint",
  "domain_bucket": "Energy",
  "episode_title": "The Paradigm Shift in Global Power: Nuclear, Solar, Battery & EV-Charging Predictions (2023-2026)",
  "fault_line_id": "F002, F004",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "Energy Predictions Research Plan.md (2026-04-21)",
  "appears_in_eps": "INF-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 5,
  "ps_cluster_tags": [
    "C3",
    "C4"
  ],
  "report_evidence": "Anchor section: Gigawatt Constraint and Grid Failure.",
  "active_end_month": "2026-12",
  "recent_statement": "NVIDIA CES 2026 keynote and subsequent investor communications reiterate gigawatt-factory framing.",
  "watch_events_raw": "NVIDIA quarterly earnings commentary; hyperscaler PPA announcements; BYOP permits",
  "months_from_today": 8,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2026-01",
  "december_dispersal": {
    "reason": "december_dispersal: domain=Energy → 11/2026",
    "new_date": "2026-11-30",
    "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": "Huang directional calls on AI compute demand have been remarkably accurate 2020-2026.",
  "contradicting_notes": "Inference-efficiency gains (Vera Rubin, MI400) may eventually ease the per-token energy math; some cluster workloads consolidating.",
  "flag_near_term_2027"
... (truncated)