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
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
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
AI doing millions of human-hours of work in hours — already observable in research synthesis, code generation.
Predictor: Andrew Yang
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
This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.
Probability over time
Milestone chain
- 2026-06-01 → 2027-06-30pendingU.S. labor productivity growth turns positive ≥2% YoYHow: BLS multifactor productivity reports YoY productivity growth ≥2% (vs ~1% historical baseline)Source: BLS Multifactor Productivity statistics, NBER working papersconf 55%
- 2026-04-01 → 2027-12-31pendingMajor economic forecaster (IMF, OECD, World Bank) raises GDP forecast citing AIHow: IMF World Economic Outlook, OECD, or World Bank explicitly cites AI productivity as reason for upgrading global GDP forecastSource: IMF.org WEO, OECD Economic Outlook, World Bank Global Economic Prospectsconf 65%
- 2026-06-01 → 2027-12-31pendingS&P 500 earnings growth exceeds 15% YoYHow: S&P 500 EPS growth exceeds 15% YoY for at least one quarter, attributable to AI-driven margin expansionSource: FactSet Earnings Insight, Bloomberg, S&P Globalconf 50%
- 2026-09-01 → 2028-06-30pendingAI-attributable contribution to GDP growth quantified by economistsHow: Goldman Sachs, NBER paper, or peer published estimate quantifying AI-driven GDP contribution at ≥1% of annual growthSource: NBER Working Papers, Goldman Sachs research, Brookingsconf 60%
No upstream prereqs identified — milestones are derived from window quartiles only.
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
{
"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
}Network propagation neighbors
Top incoming (parents)
Edges that influence THIS node's belief
| Kind | Node | Their prob | P(c|s=T) | P(c|s=F) | Δ implied |
|---|---|---|---|---|---|
| prereq | S_AGI_MID_2029 AGI mid: Kurzweil 2029 path | 35.0% | 0.720 | 0.050 | -0.265 |
| killer | TK03 AI Regulatory Moratorium (EU/US Capability Freeze) | 10.0% | 0.050 | 0.720 | +0.103 |
| killer | TK01 AGI Capability Plateau (2026-27 Training Stall) | 15.0% | 0.050 | 0.720 | +0.070 |
| killer | TK14 Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates) | 20.0% | 0.050 | 0.720 | +0.036 |
Top outgoing (children)
Predictions THIS node influences
No outgoing edges.
Ticker exposure
Beneficiaries (23)
Adverse (6)
Prerequisites (4)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| prereq | S_AGI_MID_2029 | AGI mid: Kurzweil 2029 path | agi_general_capability | — |
| killer | TK14 | Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates) | — | — |
| killer | TK01 | AGI Capability Plateau (2026-27 Training Stall) | — | — |
| killer | TK03 | AI Regulatory Moratorium (EU/US Capability Freeze) | — | — |
Dependents (0)
| Type | Pred | Title | Domain | Lag |
|---|---|---|---|---|
| No dependents | ||||
Validations (1)
| Observed at | Status | By | Notes |
|---|---|---|---|
| 2026-04-29 | hit | thesis_timeline_v1.0_import | AI doing millions of human-hours of work in hours — already observable in research synthesis, code generation. |
Raw metadata
{
"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)