← Cockpit
CMQ_020predictionMacro/EconomyAI-capex

Nearly $2.5 trillion of AI-related infrastructure investment will flow through the global economy by 2028; >80% of that spending is still ahead of the market as of early 2026.

Predictor: Morgan Stanley

Prior probability
72.0%
Current probability
67.2%
evolves via intake + LBP
Conviction
5/5
Signal quality
A
Resolution
pending
Window
2026-01-01 – 2026-12-31
Edges in / out
8 / 0
Tickers exposed
11

Prediction text

Nearly $2.5 trillion of AI-related infrastructure investment will flow through the global economy by 2028; >80% of that spending is still ahead of the market as of early 2026. | Quarterly hyperscaler capex disclosures

Key catalyst: Quarterly hyperscaler capex disclosures

Watch events: Hyperscaler capex guidance; sovereign AI infrastructure commitments; CHIPS Act / EU Chips Act disbursements.

Resolution evidence

Status: pending

2025 hyperscaler capex totals $300B+ across MSFT/GOOG/META/AMZN; Stargate $500B + xAI $200B + sovereign GCC deals validate trajectory.

Predictor: Morgan Stanley

κ + Brier as of 2026-05-22
κ (discount)
0.633
Brier
0.0442
excellent
Hits / Misses
1 / 0
of 2 resolved
Hit rate
50.0%
Calibration plot (stated vs observed)

Evidence about this node from Morgan Stanley 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: ai_capability_milestone_2y

Linked

AI reaches specific named capability (intern-level / world-class programmer / etc) within 2y of stated target

Base rate
5/15 historical
Inside weight
Outside weight
no pull
inside 67.2% → blend 67.2% 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

7 prob_history rows
0%25%50%75%100%prior 72%2026-04-302026-05-032026-05-24
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 67.2%

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: 2 overdue ⏱ · 1 pending
  1. 2026-02-26overdueQ1 window check-in (25%)
  2. 2026-04-23overdueQ2 window check-in (50%)
  3. 2026-06-18pendingQ3 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: 67%)

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
LBP2026-05-24T02:00:02Z67.2%-8.4pp
Network propagation: 75.5% → 67.2%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
intake_event_update2026-05-21T23:15:16Z75.5%+20.8pp
intake:7afeeb9a-f217-4dd2-b910-24ff14bdfc39 bayesian_v2 inside=0.755 blend=0.755 LLR=0.939 κ=0.58 no_blend
Raw metadata
{
  "trf": 0.6127227050582519,
  "kappa": 0.5833,
  "base_rate": null,
  "predictor": "Morgan Stanley",
  "total_llr": 1.6094379124341,
  "bayesian_v2": true,
  "prior_logit": 0.1881087531101633,
  "bayes_factor": "2.6:1 favoring",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.5468890061717089,
  "kappa_source": "predictor_table",
  "blend_applied": false,
  "contributions": [
    {
      "llr": 1.6094379124341,
      "kappa": 0.5833,
      "label": "Multiple sources converge on $700B+ for 2026; Manifold cross-check market resolved at 83% for >$300B (vs prior 55%) - pr",
      "adjusted_llr": 0.9387851343228107
    }
  ],
  "evidence_kind": "intake_event_update",
  "inside_source": "history_v2",
  "inside_weight": 1,
  "outside_weight": 0,
  "posterior_prob": 0.7552652216034469,
  "evidence_origin": "daily_intake",
  "llm_suggestions": [
    {
      "polarity": "corroborates",
      "status_change": "accelerated",
      "evidence_strength": "strong",
      "delta_prob_suggestion": 0.07
    }
  ],
  "posterior_logit": 1.126893887432974,
  "predictor_brier": 0.01,
  "evidence_doc_ids": [],
  "inside_posterior": 0.7552652216034469,
  "blended_posterior": 0.7552652216034469,
  "reference_class_id": null,
  "total_adjusted_llr": 0.9387851343228107,
  "predictor_n_resolved": 1
}
LBP2026-05-10T02:00:02Z54.7%+1.5pp
Network propagation: 53.2% → 54.7%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z53.2%+2.9pp
Network propagation: 50.4% → 53.2%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z50.4%-11.6pp
metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.504 blend=0.504 LLR=-0.473 κ=0.58 no_blend
Raw metadata
{
  "trf": 0.6650500679109432,
  "kappa": 0.5833,
  "base_rate": null,
  "predictor": "Morgan Stanley",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.48761894841185965,
  "bayes_factor": "1.6:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.6195453572459543,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.5833,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2365077975594923,
      "expected_date": "2026-02-26",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.5833,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2365077975594923,
      "expected_date": "2026-04-23",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5344649524623397,
  "outside_weight": 0.4655350475376603,
  "posterior_prob": 0.5036507734437512,
  "posterior_logit": 0.014603353292875043,
  "predictor_brier": 0.01,
  "inside_posterior": 0.5036507734437512,
  "blended_posterior": 0.5036507734437512,
  "reference_class_id": null,
  "total_adjusted_llr": -0.4730155951189846,
  "predictor_n_resolved": 1
}
LBP2026-04-30T16:39:51Z62.0%-3.6pp
Network propagation: 65.5% → 62.0%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z65.5%-6.5pp
Network propagation: 72.0% → 65.5%
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
killerTK05
Rate Regime Persistence (10y > 5% through 2028)
30.0%0.0500.720-0.153
killerTK04
Macro Recession 2026-27 (Structural Deleveraging)
25.0%0.0500.720-0.119
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.720-0.086
killerTK07
Labor Political Backlash (UBI Mandate / AI Tax)
18.0%0.0500.720-0.072
killerTK10
$100T Sovereign Debt Crisis
12.0%0.0500.720-0.032

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

11 ticker(s) linked

Beneficiaries (9)

APLDCRWVNVDAAMZNMETAMSFTGOOGLSFTBYORCL

Adverse (2)

INTCAMD

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_HUMANOID_ENTERPRISE_2028Humanoid R2: 100K+ enterprise by Nov 2028humanoid_deployment
correlateS_GRID_50GW_202750GW dedicated AI/data center grid by Dec 2027energy_grid_expansion
correlateS_NO_RECESSION_5YNo NBER recession through 2031macro_recession
killerTK05Rate Regime Persistence (10y > 5% through 2028)
killerTK04Macro Recession 2026-27 (Structural Deleveraging)
killerTK14Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
killerTK07Labor Political Backlash (UBI Mandate / AI Tax)
killerTK10$100T Sovereign Debt Crisis

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Expected milestones (1)

From Sheet 17 Monitoring Triggers
Expected byDescriptionStatus
2026-12-31[Capital Markets 2026-12] 2-month price performance vs semi index [CMQ_020] Hyperscaler capex guidance; sovereign AI infrastructure commitments; CHIPS Act / EU Chips Act disburpending

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": "~$2.5T",
  "mode": "FORECAST",
  "role": "Cited-Firm",
  "context": "Morgan Stanley macro thesis: AI is no longer thematic tech disruption but a primary macro variable influencing global GDP, credit markets, and industrial expansion.",
  "to_year": 2028,
  "cited_by": "Synthesis report",
  "conv_cues": "specific quantitative target; major bank",
  "direction": "NUMERIC_TARGET",
  "from_year": 2026,
  "timeframe": "by 2028",
  "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": "2026-02-26",
      "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": "2026-04-23",
      "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": "pending",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2026-06-18",
      "observed_date": null
    },
    {
      "kind": "event",
      "label": "Nearly $2.5 trillion of AI-related infrastructure investment will flow through the global economy by 2028; >80% of that spending is still ah",
      "status": "pending",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CMQ_020",
      "expected_date": "2026-08-14",
      "observed_date": null
    }
  ],
  "repeat_eps": 1,
  "sub_domain": "Economy",
  "affiliation": "Morgan Stanley Research",
  "attribution": "THIRD_PARTY_CITATION",
  "granularity": "YEAR",
  "source_refs": "12, 13",
  "target_date": "2028-06-15T00:00:00",
  "display_date": "2026-08-14",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Quarterly hyperscaler capex disclosures",
  "parse_method": "Report midpoint",
  "domain_bucket": "Markets",
  "episode_title": "The Global Architecture of Machine Intelligence: Exhaustive Synthesis of AI Compute, Memory & Quantum Predictions (2023-2026)",
  "fault_line_id": "F002, F003, F007",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI_Chip__Compute__Memory__Quantum_Predictions.md (2026-04-21)",
  "appears_in_eps": "CMQ-RPT",
  "futurist_phase": "Phase 2 (2027-2028)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 5,
  "ps_cluster_tags": [
    "C5"
  ],
  "report_evidence": "Canonical macro-capex anchor for AI infrastructure thesis — cited across sell-side community.",
  "active_end_month": "2026-12",
  "recent_statement": "MS April 2026 AI Market Trends report reaffirms $2.5T thesis.",
  "watch_events_raw": "Hyperscaler capex guidance; sovereign AI infrastructure commitments; CHIPS Act / EU Chips Act disbursements.",
  "months_from_today": 26,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2026-01",
  "flag_nia_bracketed": false,
  "track_record_grade": "A-",
  "track_record_notes": "Morgan Stanley AI research (led by Joseph Moore team) has been most-accurate among major-bank sell-side on compute capex.",
  "contradicting_notes": "Macroeconomic capacity constraints (power, permitting, skilled labor) may delay realization; $2.5T assumes current pace holds.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_phase2_at": "2026-05-01T18:14:03.523234+00:00",
  "milestones_derived_at": "2026-05-02T03:08:50.598727+00:00",
  "reference_class_match": {
    "decision": "keyword_filtered",
    "computed_at": "2026-04-30T01:49:13.796883+00:00",
    "best_id_unfiltered": "regulatory_freeze_window",
    "best_similar