← Cockpit
CMQ_021predictionMacro/EconomyAI-as-macro-variable

AI has transitioned from a thematic technology disruption to a primary macroeconomic variable influencing global GDP, credit markets, and industrial expansion.

Predictor: Morgan Stanley

Prior probability
80.0%
Current probability
71.4%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
in_progress
Window
2026-01-01 – 2026-12-31
Edges in / out
1 / 0
Tickers exposed
6

Prediction text

AI has transitioned from a thematic technology disruption to a primary macroeconomic variable influencing global GDP, credit markets, and industrial expansion. | Academic macro-model treatment of AI capex

Key catalyst: Academic macro-model treatment of AI capex

Watch events: AI contribution to GDP growth; sell-side macro-model inclusion of AI capex; BEA/BLS AI-sector breakouts.

Resolution evidence

Status: in_progress

AI-infrastructure capex already rivals fixed investment categories of large economies; already visible in Fed Beige Book and BEA GDP data.

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

Not linked

This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.

Probability over time

2 prob_history rows
0%25%50%75%100%prior 80%2026-05-022026-05-09
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 71.4%

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 fired ✓ · 2 overdue ⏱ · 3 pending
  1. 2026-01-15hitMorgan Stanley research formally classifies AI as macro variable in 2026 outlook
    How: Morgan Stanley publishes research or institute note explicitly framing AI as a macro factor influencing GDP/credit/earnings, not just a sector theme
    Source: Morgan Stanley: AI Market Trends Institute 2026conf 99%
    Notes: HIT — direct quote: 'AI is no longer a tech story — it is a macro variable influencing GDP, earnings, credit markets and geopolitics at industrial scale.'
  2. 2026-03-02overdueQ1 window check-in (25%)
  3. 2026-05-01overdueQ2 window check-in (50%)
  4. 2026-06-30pendingQ3 window check-in (75%)
  5. 2026-04-30hitAI capex contributes >=20% of total US GDP growth
    How: Morgan Stanley or BEA analysis confirms AI-related hardware/software/datacenter spending contributes >=0.4ppt to 2026 US GDP growth
    Source: Morgan Stanley AI Market Trends Institute 2026conf 90%
    Notes: HIT — Morgan Stanley confirms ~25% of US GDP growth from AI-related spend in 2025, ~0.4ppt in 2026/2027 (~20% of total).
  6. 2026-01-01 → 2026-12-31pendingGlobal data-center buildout commitments cross $2.9T cumulative through 2028
    How: Aggregate disclosed/announced global data center construction cost (2024-2028 cumulative) reaches Morgan Stanley's $2.9T projection
    Source: Morgan Stanley AI Market Trends Institute 2026conf 80%
  7. 2026-04-01 → 2026-12-31pendingInvestment-grade credit spreads widen on AI-related issuance volume
    How: Morgan Stanley credit research confirms IG corporate spreads widen >=20bps citing AI-capex-related issuance pressure
    Source: Morgan Stanley AI Market Trends Institute 2026 — credit market sectionconf 65%
  8. 2026-06-01 → 2027-12-31pendingMajor central bank or IMF publication treats AI as standalone macro factor
    How: FOMC minutes, ECB monetary policy report, IMF WEO, or BIS quarterly explicitly models AI as distinct macro factor in growth/inflation projections
    Source: Composite — institutional adoption cascadeconf 55%

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: 71%)

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
metadata_milestone_miss_sweep2026-05-09T22:14:10Z71.4%-4.6pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.714 blend=0.714 LLR=-0.237 κ=0.58 no_blend
Raw metadata
{
  "trf": 0.6458062869896278,
  "kappa": 0.5833,
  "base_rate": null,
  "predictor": "Morgan Stanley",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 1.149786563560399,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.7594719297524297,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "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": "2026-05-01",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5479355991072605,
  "outside_weight": 0.45206440089273947,
  "posterior_prob": 0.713670630849881,
  "posterior_logit": 0.9132787660009067,
  "predictor_brier": 0.01,
  "inside_posterior": 0.713670630849881,
  "blended_posterior": 0.713670630849881,
  "reference_class_id": null,
  "total_adjusted_llr": -0.2365077975594923,
  "predictor_n_resolved": 1
}
metadata_milestone_miss_sweep2026-05-02T22:07:21Z75.9%-4.1pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.759 blend=0.759 LLR=-0.237 κ=0.58 no_blend
Raw metadata
{
  "trf": 0.6650500679109432,
  "kappa": 0.5833,
  "base_rate": null,
  "predictor": "Morgan Stanley",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 1.3862943611198908,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.8,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "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": "2026-03-02",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "prior_prob",
  "inside_weight": 0.5344649524623397,
  "outside_weight": 0.4655350475376603,
  "posterior_prob": 0.7594719297524297,
  "posterior_logit": 1.1497865635603985,
  "predictor_brier": 0.01,
  "inside_posterior": 0.7594719297524297,
  "blended_posterior": 0.7594719297524297,
  "reference_class_id": null,
  "total_adjusted_llr": -0.2365077975594923,
  "predictor_n_resolved": 1
}

Network propagation neighbors

Top edges sorted by latest LBP cross-impact
All propagation →

No propagation data yet. Run inference/.venv/bin/python scripts/ops/run_loopy_belief_propagation.py on the droplet, or wait for the Sunday 02:00 UTC weekly cron.

Ticker exposure

6 ticker(s) linked

Adverse (6)

ACNCHGGIBMINFYMANRHI

Prerequisites (1)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_NO_RECESSION_5YNo NBER recession through 2031macro_recession

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29partialthesis_timeline_v1.0_importAI-infrastructure capex already rivals fixed investment categories of large economies; already visible in Fed Beige Book and BEA GDP data.

Linked documents (2)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "THESIS",
  "role": "Cited-Firm",
  "context": "Classification shift is itself the prediction — institutional investors must model AI as a macro factor alongside rates, commodities, FX.",
  "to_year": 2030,
  "cited_by": "Synthesis report",
  "conv_cues": "framework shift; major bank thesis",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026+",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Morgan Stanley research formally classifies AI as macro variable in 2026 outlook",
      "notes": "HIT — direct quote: 'AI is no longer a tech story — it is a macro variable influencing GDP, earnings, credit markets and geopolitics at industrial scale.'",
      "source": "Morgan Stanley: AI Market Trends Institute 2026",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://www.morganstanley.com/insights/articles/ai-market-trends-institute-2026",
      "expected_date": "2026-01-15",
      "observed_date": "2026-01-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Morgan Stanley publishes research or institute note explicitly framing AI as a macro factor influencing GDP/credit/earnings, not just a sector theme"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2026-03-02",
      "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": -5,
      "source_id": null,
      "expected_date": "2026-05-01",
      "observed_date": null,
      "miss_emitted_at": "2026-05-09T22:14:10.596691+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-06-30",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "AI capex contributes >=20% of total US GDP growth",
      "notes": "HIT — Morgan Stanley confirms ~25% of US GDP growth from AI-related spend in 2025, ~0.4ppt in 2026/2027 (~20% of total).",
      "source": "Morgan Stanley AI Market Trends Institute 2026",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.9,
      "source_url": "https://www.morganstanley.com/insights/articles/ai-market-trends-institute-2026",
      "expected_date": "2026-06-30",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Morgan Stanley or BEA analysis confirms AI-related hardware/software/datacenter spending contributes >=0.4ppt to 2026 US GDP growth"
    },
    {
      "kind": "llm_pre_event",
      "label": "Global data-center buildout commitments cross $2.9T cumulative through 2028",
      "source": "Morgan Stanley AI Market Trends Institute 2026",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.8,
      "source_url": "https://www.morganstanley.com/insights/articles/ai-market-trends-institute-2026",
      "expected_date": "2026-07-02",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-01-01"
      },
      "measurement_criterion": "Aggregate disclosed/announced global data center construction cost (2024-2028 cumulative) reaches Morgan Stanley's $2.9T projection"
    },
    {
      "kind": "llm_pre_event",
      "label": "Investment-grade credit spreads widen on 
... (truncated)