← Cockpit
IND_019predictionLabor/JobsMS-4-percent-net-job-reduction

2026 is the year AI's impact on workers fundamentally comes into focus — near-term 4% net reduction in jobs across the 5 sectors most exposed to AI, heavily targeting entry-level positions; but labor disruption increases returns for firms that successf...

Predictor: Morgan Stanley

Prior probability
62.0%
Current probability
46.7%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
in_progress
Window
2026-01-01 – 2026-12-31
Edges in / out
3 / 0
Tickers exposed
5

Prediction text

2026 is the year AI's impact on workers fundamentally comes into focus — near-term 4% net reduction in jobs across the 5 sectors most exposed to AI, heavily targeting entry-level positions; but labor disruption increases returns for firms that successfully redeploy human workers into higher-value, non-automatable roles. | BLS 2026 full-year sector employment data

Key catalyst: BLS 2026 full-year sector employment data

Watch events: MS quarterly AI labor-impact updates; BLS sector employment data

Resolution evidence

Status: in_progress

MS AI-Rate-of-Change research 2025-2026; consistent with Challenger Gray 2025 sector-level 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

5 prob_history rows
0%25%50%75%100%prior 62%2026-04-302026-05-022026-05-10
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 46.7%

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 fired ✓ · 2 overdue ⏱ · 2 pending
  1. 2026-02-24overdueQ1 window check-in (25%)
  2. 2026-03-06hitAnthropic Q1 2026 labor market report identifies 4 most-exposed sectors
    How: Anthropic publishes labor-market report identifying Computer & Math, Office & Admin Support, Business & Financial, Sales as 4 most AI-exposed sectors
    Source: https://fortune.com/2026/03/06/ai-job-losses-report-anthropic-research-great-recession-for-white-collar-workers/ — Anthropic 4 sectorsconf 95%
  3. 2026-04-15hitJunior SWE employment drops ~20% post-ChatGPT
    How: Revelio Labs or BLS data confirm junior software developer employment down >=20% from pre-ChatGPT baseline
    Source: https://www.fortune.com/2026/04/29/ai-agentic-entry-level-jobs-disappearing-yale-celi-sonnenfeld/ — junior SWE -20%conf 92%
  4. 2026-04-19overdueQ2 window check-in (50%)
  5. 2026-04-30hitEntry-level postings fall 35% from Jan 2023 baseline
    How: Revelio Labs / Indeed data confirm entry-level job postings down >=35% from January 2023 baseline
    Source: https://www.unleash.ai/artificial-intelligence/anthropic-ceo-thinks-half-of-entry-level-roles-will-be-eliminated-by-2030-how-can-hr-turn-this-fear-into-possibility/ — Revelio -35%conf 90%
  6. 2026-06-12pendingQ3 window check-in (75%)
  7. 2026-06-30pendingAI-driven 0.13-0.20% nonfarm-employment displacement confirmed
    How: BLS / academic study confirms 200K-300K AI-displaced positions in US economy (0.13-0.20% of nonfarm)
    Source: https://hbr.org/2026/01/companies-are-laying-off-workers-because-of-ais-potential-not-its-performance — 200-300K displacedconf 75%
  8. 2026-09-01 → 2027-03-31pendingCascade: AI-leveraged firms post >=10% productivity gains for retained workers
    How: Brookings or BEA data shows AI-leveraging firms produce >=10% higher revenue per retained worker than non-adopting peers
    Source: Brookings AI productivity flows research extrapolationconf 60%
  9. 2026-12-31pendingNet 4% reduction across AI-exposed sectors confirmed in 2026 BLS data
    How: BLS year-end 2026 data shows >=4% net job reduction across the 5 AI-exposed sectors (Computer/Math, Office/Admin, Business/Financial, Sales, Legal)
    Source: Extrapolation from current Anthropic / Brookings labor dataconf 50%

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: 47%)

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
LBP2026-05-10T02:00:02Z46.7%+1.8pp
Network propagation: 44.9% → 46.7%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z44.9%+3.5pp
Network propagation: 41.3% → 44.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z41.3%-11.7pp
metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.413 blend=0.413 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": 0.12264545723476736,
  "bayes_factor": "1.6:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.5306229882470267,
  "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": "2026-02-24",
      "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": "2026-04-19",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5344649524623397,
  "outside_weight": 0.4655350475376603,
  "posterior_prob": 0.4132926664885453,
  "posterior_logit": -0.3503701378842172,
  "predictor_brier": 0.01,
  "inside_posterior": 0.4132926664885453,
  "blended_posterior": 0.4132926664885453,
  "reference_class_id": null,
  "total_adjusted_llr": -0.4730155951189846,
  "predictor_n_resolved": 1
}
LBP2026-04-30T16:39:51Z53.1%-3.1pp
Network propagation: 56.2% → 53.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z56.2%-5.8pp
Network propagation: 62.0% → 56.2%
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
killerTK07
Labor Political Backlash (UBI Mandate / AI Tax)
18.0%0.0500.620+0.050
killerTK04
Macro Recession 2026-27 (Structural Deleveraging)
25.0%0.0500.620+0.011

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

5 ticker(s) linked

Adverse (5)

ACNCTSHIBMINFYWNS

Prerequisites (3)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_NO_RECESSION_5YNo NBER recession through 2031macro_recession
killerTK04Macro Recession 2026-27 (Structural Deleveraging)
killerTK07Labor Political Backlash (UBI Mandate / AI Tax)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29partialthesis_timeline_v1.0_importMS AI-Rate-of-Change research 2025-2026; consistent with Challenger Gray 2025 sector-level data.

Linked documents (5)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "4% net job reduction; top 5 sectors",
  "mode": "FORECAST",
  "role": "Cited-Firm",
  "context": "Distinct MS framing from AUT_022 (AV inflection), AUT_023 (eVTOL), AUT_024 (breakthrough H1 2026), CMQ_020 ($2.5T infra), 240_034 (13-40GW). Specific quantified 4% net labor-reduction.",
  "to_year": 2026,
  "conv_cues": "institutional corporate-survey framing; specific % and sector count",
  "direction": "DOWN",
  "from_year": 2026,
  "timeframe": "2026",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -7,
      "source_id": null,
      "expected_date": "2026-02-24",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "Anthropic Q1 2026 labor market report identifies 4 most-exposed sectors",
      "source": "https://fortune.com/2026/03/06/ai-job-losses-report-anthropic-research-great-recession-for-white-collar-workers/ — Anthropic 4 sectors",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://fortune.com/2026/03/06/ai-job-losses-report-anthropic-research-great-recession-for-white-collar-workers/",
      "expected_date": "2026-03-06",
      "observed_date": "2026-03-06",
      "research_origin": "deep_research",
      "measurement_criterion": "Anthropic publishes labor-market report identifying Computer & Math, Office & Admin Support, Business & Financial, Sales as 4 most AI-exposed sectors"
    },
    {
      "kind": "llm_pre_event",
      "label": "Junior SWE employment drops ~20% post-ChatGPT",
      "source": "https://www.fortune.com/2026/04/29/ai-agentic-entry-level-jobs-disappearing-yale-celi-sonnenfeld/ — junior SWE -20%",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.92,
      "source_url": "https://fortune.com/2026/04/29/ai-agentic-entry-level-jobs-disappearing-yale-celi-sonnenfeld/",
      "expected_date": "2026-04-15",
      "observed_date": "2026-04-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Revelio Labs or BLS data confirm junior software developer employment down >=20% from pre-ChatGPT baseline"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-04-19",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "Entry-level postings fall 35% from Jan 2023 baseline",
      "source": "https://www.unleash.ai/artificial-intelligence/anthropic-ceo-thinks-half-of-entry-level-roles-will-be-eliminated-by-2030-how-can-hr-turn-this-fear-into-possibility/ — Revelio -35%",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.9,
      "source_url": "https://www.unleash.ai/artificial-intelligence/anthropic-ceo-thinks-half-of-entry-level-roles-will-be-eliminated-by-2030-how-can-hr-turn-this-fear-into-possibility/",
      "expected_date": "2026-04-30",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Revelio Labs / Indeed data confirm entry-level job postings down >=35% from January 2023 baseline"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-06-12",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label":
... (truncated)