AI has transitioned from a thematic technology disruption to a primary macroeconomic variable influencing global GDP, credit markets, and industrial expansion.
Predictor: Morgan Stanley
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
AI-infrastructure capex already rivals fixed investment categories of large economies; already visible in Fed Beige Book and BEA GDP data.
Predictor: Morgan Stanley
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
This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.
Probability over time
Milestone chain
- 2026-01-15hitMorgan Stanley research formally classifies AI as macro variable in 2026 outlookHow: Morgan Stanley publishes research or institute note explicitly framing AI as a macro factor influencing GDP/credit/earnings, not just a sector themeSource: 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.'
- 2026-03-02overdueQ1 window check-in (25%)
- 2026-05-01overdueQ2 window check-in (50%)
- 2026-06-30pendingQ3 window check-in (75%)
- 2026-04-30hitAI capex contributes >=20% of total US GDP growthHow: Morgan Stanley or BEA analysis confirms AI-related hardware/software/datacenter spending contributes >=0.4ppt to 2026 US GDP growthSource: 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).
- 2026-01-01 → 2026-12-31pendingGlobal data-center buildout commitments cross $2.9T cumulative through 2028How: Aggregate disclosed/announced global data center construction cost (2024-2028 cumulative) reaches Morgan Stanley's $2.9T projectionSource: Morgan Stanley AI Market Trends Institute 2026conf 80%
- 2026-04-01 → 2026-12-31pendingInvestment-grade credit spreads widen on AI-related issuance volumeHow: Morgan Stanley credit research confirms IG corporate spreads widen >=20bps citing AI-capex-related issuance pressureSource: Morgan Stanley AI Market Trends Institute 2026 — credit market sectionconf 65%
- 2026-06-01 → 2027-12-31pendingMajor central bank or IMF publication treats AI as standalone macro factorHow: FOMC minutes, ECB monetary policy report, IMF WEO, or BIS quarterly explicitly models AI as distinct macro factor in growth/inflation projectionsSource: Composite — institutional adoption cascadeconf 55%
What if this resolves?
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
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
}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
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
Adverse (6)
Prerequisites (1)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| correlate | S_NO_RECESSION_5Y | No NBER recession through 2031 | macro_recession | — |
Dependents (0)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No dependents | ||||
Validations (1)
| Observed at | Status | By | Notes |
|---|---|---|---|
| 2026-04-29 | partial | thesis_timeline_v1.0_import | AI-infrastructure capex already rivals fixed investment categories of large economies; already visible in Fed Beige Book and BEA GDP data. |
Linked documents (2)
| Sim | Source | Title | Market prob | Polarity | Reviewed | Published |
|---|---|---|---|---|---|---|
| 0.697 | arxiv | When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge | — | mentions | pending | 2026-05-07 |
| 0.608 | arxiv | Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families | — | mentions | pending | 2026-06-01 |
Raw metadata
{
"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)