INF_019predictionEnergyDC-water-consumption

A modern hyperscale AI data center can consume up to 5 million gallons of freshwater per day for evaporative cooling — roughly equivalent to municipal supply for a town of 50,000 people.

Predictor: Elon Musk

Prior probability

90.0%

Current probability

61.7%

evolves via intake + LBP

Conviction

4/5

Signal quality

Resolution

hit

Window

2026-01-01 – 2026-11-30

Edges in / out

1 / 0

Tickers exposed

Prediction text

A modern hyperscale AI data center can consume up to 5 million gallons of freshwater per day for evaporative cooling — roughly equivalent to municipal supply for a town of 50,000 people. | Closed-loop cooling standardization

Key catalyst: Closed-loop cooling standardization

Watch events: AZ/TX/NV DC water-disclosure requirements; closed-loop cooling adoption

Resolution evidence

Status: hit

Google/Microsoft sustainability disclosures show hyperscale facility water use in the 2-6M gal/day range. EPA/USGS confirm.

Predictor: Elon Musk

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.688

Brier

0.0142

excellent

Hits / Misses

1 / 0

of 3 resolved

Hit rate

33.3%

Calibration plot (stated vs observed)

Evidence about this node from Elon Musk 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

6 prob_history rows

intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 61.7%

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%)
2026-04-29hitA modern hyperscale AI data center can consume up to 5 million gallons of freshwater per day for evaporative cooling — roughly equivalent to

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: 62%)

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

LBP2026-05-17T02:00:01Z61.7%-1.4pp

Network propagation: 63.1% → 61.7%

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

LBP2026-05-10T02:00:02Z63.1%-2.7pp

Network propagation: 65.8% → 63.1%

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

LBP2026-05-03T02:00:01Z65.8%-5.0pp

Network propagation: 70.9% → 65.8%

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

LBP2026-04-30T16:39:51Z70.9%-8.3pp

Network propagation: 79.2% → 70.9%

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

LBP2026-04-30T02:18:57Z79.2%-10.8pp

Network propagation: 90.0% → 79.2%

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%+29.1pp

resolution_terminal hit outcome=1.0 pre_resolution=0.709

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": 0.29120999999999997,
  "inside_posterior": 0.70879,
  "validation_notes": "Google/Microsoft sustainability disclosures show hyperscale facility water use in the 2-6M gal/day range. EPA/USGS confirm.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.70879,
  "resolution_evidence": "Google/Microsoft sustainability disclosures show hyperscale facility water use in the 2-6M gal/day range. EPA/USGS confirm.",
  "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	0.900	-0.015

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

30 ticker(s) linked

Beneficiaries (24)

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

Prerequisites (1)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
killer	TK09	Energy Grid Cap (Data Center Power Wall)	—	—

Dependents (0)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
No dependents

Validations (1)

Resolution events

Observed at	Status	By	Notes
2026-04-29	hit	thesis_timeline_v1.0_import	Google/Microsoft sustainability disclosures show hyperscale facility water use in the 2-6M gal/day range. EPA/USGS confirm.

Linked documents (5)

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

Sim	Source	Title	Market prob	Polarity	Reviewed	Published
0.606	manifold	What amounts of mana will manifolders managram me?	—	mentions	pending	2026-05-24
0.558	manifold	What is Tetra's total iron-binding capacity?	—	mentions	pending	2026-05-05
0.556	manifold	On what days will I go to the bathroom more than 5 times a day	—	mentions	pending	2026-04-28
0.556	manifold	On what days will I go to the bathroom more than 5 times a day	—	mentions	pending	2026-05-08
0.556	manifold	On what days will I go to the bathroom more than 5 times a day	—	mentions	pending	2026-05-18

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "qty": "up to 5M gal/day",
  "mode": "OBSERVATION+FORECAST",
  "role": "Cited-CEO",
  "context": "Creates direct competition between DC operators and municipalities, particularly acute in water-stressed regions (American Southwest, Imperial County California where proposed $10B campuses have stalled on environmental capacity).",
  "to_year": 2026,
  "conv_cues": "specific gallons; municipal comparison",
  "direction": "NUMERIC_TARGET",
  "from_year": 2026,
  "timeframe": "current / 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": "A modern hyperscale AI data center can consume up to 5 million gallons of freshwater per day for evaporative cooling — roughly equivalent to",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "INF_019",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "xAI",
  "attribution": "CITED",
  "granularity": "YEAR",
  "resolved_at": "2026-04-29T22:23:18.142668+00:00",
  "source_refs": "31",
  "target_date": "2026-06-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Closed-loop cooling standardization",
  "parse_method": "Current-state midpoint",
  "domain_bucket": "Energy",
  "episode_title": "The Architecture of Intelligence: AI Infrastructure, Energy & Networking Predictions (2023-2026)",
  "fault_line_id": "F002, F004",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI_Leaders_Infrastructure_Predictions.md (2026-04-21)",
  "appears_in_eps": "INF-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 4,
  "ps_cluster_tags": [
    "C3",
    "C4"
  ],
  "report_evidence": "Water competition is the report's secondary environmental flashpoint.",
  "active_end_month": "2026-12",
  "recent_statement": "Various 2026 public filings confirm; Imperial County $10B campus paused on water capacity.",
  "watch_events_raw": "AZ/TX/NV DC water-disclosure requirements; closed-loop cooling adoption",
  "months_from_today": 2,
  "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": "B+",
  "track_record_notes": "Water-use numbers are well-documented and stable.",
  "contradicting_notes": "Shift to liquid-to-chip closed-loop cooling reduces WUE; newest facilities trending to 0.5-1 L/kWh.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:50.957722+00