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236_033predictionAIAI-timing

Pie will grow very quickly as AI does work of millions of humans in hours

Predictor: Andrew Yang · ep#236 "Andrew Yang: UBI Before UHI, Solving Job Loss, and the Future of Work | #236" · source

Prior probability
72.0%
Current probability
55.0%
evolves via intake + LBP
Conviction
5/5
Signal quality
B
Resolution
partial
Window
2026-04-30 – 2029-03-31
Edges in / out
4 / 0
Tickers exposed
33

Prediction text

Pie will grow very quickly as AI does work of millions of humans in hours | like everyone can see the pie is going to grow uh like very very quickly because a AI is going to do the work of millions of humans in like hours instead of years

Verbatim quote

From episode "Andrew Yang: UBI Before UHI, Solving Job Loss, and the Future of Work | #236"
like everyone can see the pie is going to grow uh like very very quickly because a AI is going to do the work of millions of humans in like hours instead of years

Resolution evidence

Status: partial

AI doing millions of human-hours of work in hours — already observable in research synthesis, code generation.

Predictor: Andrew Yang

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0178
excellent
Hits / Misses
0 / 0
of 3 resolved
Hit rate
0.0%
Calibration plot (stated vs observed)

Evidence about this node from Andrew Yang 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
0%25%50%75%100%prior 72%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 55.0%

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.
No leading signals identified yet.
  1. 2026-06-01 → 2027-06-30pendingU.S. labor productivity growth turns positive ≥2% YoY
    How: BLS multifactor productivity reports YoY productivity growth ≥2% (vs ~1% historical baseline)
    Source: BLS Multifactor Productivity statistics, NBER working papersconf 55%
  2. 2026-04-01 → 2027-12-31pendingMajor economic forecaster (IMF, OECD, World Bank) raises GDP forecast citing AI
    How: IMF World Economic Outlook, OECD, or World Bank explicitly cites AI productivity as reason for upgrading global GDP forecast
    Source: IMF.org WEO, OECD Economic Outlook, World Bank Global Economic Prospectsconf 65%
  3. 2026-06-01 → 2027-12-31pendingS&P 500 earnings growth exceeds 15% YoY
    How: S&P 500 EPS growth exceeds 15% YoY for at least one quarter, attributable to AI-driven margin expansion
    Source: FactSet Earnings Insight, Bloomberg, S&P Globalconf 50%
  4. 2026-09-01 → 2028-06-30pendingAI-attributable contribution to GDP growth quantified by economists
    How: Goldman Sachs, NBER paper, or peer published estimate quantifying AI-driven GDP contribution at ≥1% of annual growth
    Source: NBER Working Papers, Goldman Sachs research, Brookingsconf 60%

No upstream prereqs identified — milestones are derived from window quartiles only.

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: 55%)

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
LBP2026-05-17T02:00:01Z55.0%-1.5pp
Network propagation: 56.4% → 55.0%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z56.4%-2.9pp
Network propagation: 59.3% → 56.4%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z59.3%-5.5pp
Network propagation: 64.8% → 59.3%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
resolution_terminal2026-05-01T00:00:00Z50.0%-14.8pp
resolution_terminal partial outcome=0.5 pre_resolution=0.648
Raw metadata
{
  "source": "backfill_resolution_history.py",
  "status": "partial",
  "bayesian_v2": false,
  "outcome_prob": 0.5,
  "evidence_kind": "resolution_terminal",
  "posterior_prob": 0.5,
  "delta_to_outcome": -0.14797000000000005,
  "inside_posterior": 0.64797,
  "validation_notes": "AI doing millions of human-hours of work in hours — already observable in research synthesis, code generation.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.64797,
  "resolution_evidence": "AI doing millions of human-hours of work in hours — already observable in research synthesis, code generation.",
  "does_not_update_current_prob": true
}
LBP2026-04-30T16:39:51Z64.8%-2.5pp
Network propagation: 67.3% → 64.8%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z67.3%-4.7pp
Network propagation: 72.0% → 67.3%
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
prereqS_AGI_MID_2029
AGI mid: Kurzweil 2029 path
35.0%0.7200.050-0.265
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.720+0.103
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.720+0.070
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.720+0.036

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (4)

Predictions that must hit first
TypePredTitleDomainLag
prereqS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_general_capability
killerTK14Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importAI doing millions of human-hours of work in hours — already observable in research synthesis, code generation.

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=toE56X2h0wk",
  "mode": "PREDICTION",
  "role": "Guest-Politician",
  "context": "like everyone can see the pie is going to grow uh like very very quickly because a AI is going to do the work of millions of humans in like hours instead of years and then do the work better like run down all these loose balls that we never would have identified like uh enhance the discovery of life-saving drugs uh material sciences... the the pie is going to grow I mean it can't not in in that scenario.",
  "verbatim": "like everyone can see the pie is going to grow uh like very very quickly because a AI is going to do the work of millions of humans in like hours instead of years",
  "conv_cues": "everyone can see; can't not",
  "direction": "UP",
  "timeframe": "Unspecified future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "event",
      "label": "Pie will grow very quickly as AI does work of millions of humans in hours",
      "status": "partial",
      "weight": 1,
      "ordinal": 0,
      "source_id": "236_033",
      "expected_date": "2026-05-01",
      "observed_date": "2026-05-01"
    },
    {
      "kind": "llm_pre_event",
      "label": "U.S. labor productivity growth turns positive ≥2% YoY",
      "source": "BLS Multifactor Productivity statistics, NBER working papers",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 1,
      "source_id": null,
      "confidence": 0.55,
      "expected_date": "2026-12-15",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-06-01"
      },
      "measurement_criterion": "BLS multifactor productivity reports YoY productivity growth ≥2% (vs ~1% historical baseline)"
    },
    {
      "kind": "llm_pre_event",
      "label": "Major economic forecaster (IMF, OECD, World Bank) raises GDP forecast citing AI",
      "source": "IMF.org WEO, OECD Economic Outlook, World Bank Global Economic Prospects",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 2,
      "source_id": null,
      "confidence": 0.65,
      "expected_date": "2027-02-14",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-04-01"
      },
      "measurement_criterion": "IMF World Economic Outlook, OECD, or World Bank explicitly cites AI productivity as reason for upgrading global GDP forecast"
    },
    {
      "kind": "llm_pre_event",
      "label": "S&P 500 earnings growth exceeds 15% YoY",
      "source": "FactSet Earnings Insight, Bloomberg, S&P Global",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 3,
      "source_id": null,
      "confidence": 0.5,
      "expected_date": "2027-03-17",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "S&P 500 EPS growth exceeds 15% YoY for at least one quarter, attributable to AI-driven margin expansion"
    },
    {
      "kind": "llm_post_event",
      "label": "AI-attributable contribution to GDP growth quantified by economists",
      "source": "NBER Working Papers, Goldman Sachs research, Brookings",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 4,
      "source_id": null,
      "confidence": 0.6,
      "expected_date": "2027-08-01",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2028-06-30",
        "from": "2026-09-01"
      },
      "measurement_criterion": "Goldman Sachs, NBER paper, or peer published estimate quantifying AI-driven GDP contribution at ≥1% of annual growth"
    }
  ],
  "repeat_eps": 3,
  "affiliation": "Forward Party",
  "attribution": "FIRST_PERSON",
  "episode_num": 236,
  "granularity": "VAGUE",
  "resolved_at": "2026-05-01T00:00:00+00:00",
  "display_date": "2026-05-01",
  "episode_date": "2026-03-07",
  "parse_method": "UNMAPPABLE",
  "domain_b
... (truncated)