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231_007predictionAIAI-timing

AI will analyze scientific literature and shock humanity by revealing wrong turns made over the past century.

Predictor: Alex Wissner-Gross · ep#231 "Top AI News: Sonnet 4.6, Grok 4.2, Gemini 3 Deep Think, and OpenClaw | EP #231" · source

Prior probability
50.0%
Current probability
42.1%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
pending
Window
2028-06-01 – 2028-06-30
Edges in / out
8 / 5
Tickers exposed
33

Prediction text

AI will analyze scientific literature and shock humanity by revealing wrong turns made over the past century. | I think AI will will shock humanity to its core in terms of the mistakes that it discovers that we've made over the past century.

Verbatim quote

From episode "Top AI News: Sonnet 4.6, Grok 4.2, Gemini 3 Deep Think, and OpenClaw | EP #231"
I think AI will will shock humanity to its core in terms of the mistakes that it discovers that we've made over the past century.

Predictor: Alex Wissner-Gross

κ + Brier as of 2026-05-22
κ (discount)
0.844
Brier
0.0341
excellent
Hits / Misses
6 / 1
of 11 resolved
Hit rate
54.5%
Calibration plot (stated vs observed)

Evidence about this node from Alex Wissner-Gross 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

4 prob_history rows
0%25%50%75%100%prior 50%2026-04-302026-05-032026-05-10
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 42.1%

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: 6 fired ✓ · 4 pending
  1. 2026-02-01hitOpen-source AI literature-review tool beats major LLMs on citation accuracy
    How: Peer-reviewed Nature paper documents AI tool exceeding major LLMs at scientific literature review and matching human citation accuracy
    Source: Nature: Open-source AI tool beats giant LLMs in literature reviews and gets citations rightconf 85%
  2. 2026-06-01 → 2027-12-31pendingAutonomous AI agent publishes peer-reviewed scientific discovery
    How: AI agent (e.g., Sakana AI Scientist, FutureHouse, Microsoft AI Co-Scientist) credited as primary discoverer in peer-reviewed Nature/Science/Cell paper
    Source: OpenReview: Scientific Discoveries by LLM Agents; Google Research blogconf 60%
  3. 2026-09-01 → 2028-06-30pendingAI literature meta-analysis identifies replication crisis in major sub-field
    How: Published systematic review using AI methods identifies >=30% replication failure rate across a major scientific sub-discipline (analog to 2015 Reproducibility Project)
    Source: Educational Researcher / Royal Society replication studiesconf 55%
  4. 2027-01-01 → 2028-12-31pendingMajor scientific society reissues guidelines after AI-driven re-analysis
    How: AAAS, Royal Society, NIH, or analog body issues revised methodological guidance or treatment standard explicitly citing AI-driven literature re-analysis
    Source: Society guideline announcementsconf 35%
  5. 2027-06-01 → 2028-12-31pendingAI-discovered 'wrong turn' triggers retraction wave in named field
    How: AI-driven analysis directly cited as cause of >=10 paper retractions or formal correction notices in a single research domain
    Source: Retraction Watch databaseconf 40%
    Notes: Operationalizes 'shock humanity' claim as concrete retraction count.

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: 42%)

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
LBP2026-05-10T02:00:02Z42.1%-1.0pp
Network propagation: 43.2% → 42.1%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z43.2%-1.5pp
Network propagation: 44.7% → 43.2%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z44.7%-2.2pp
Network propagation: 46.9% → 44.7%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z46.9%-3.1pp
Network propagation: 50.0% → 46.9%
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
prereq234_012
Anthropic revenue will cross OpenAI revenue in middle of 202Peter Diamandis
67.1%0.5000.050-0.072
prereqSEM_042
2025 will be the definitive year that agentic systems finallKevin Weil
73.8%0.5000.050-0.044
prereq235_002
Anthropic will exceed OpenAI in revenue this year (2026).Dave Blundin
74.6%0.5000.050-0.040
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.5000.050-0.037
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.500+0.034

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq246_017
Europa Clipper will arrive at Jupiter in 2030, conducting 50Peter Diamandis
37.7%0.6500.050-0.079
prereq247_035
Dario Amodei will solve most/all neurological diseases by enDario Amodei
38.8%0.7000.050-0.068
prereq246_016
Dragonfly nuclear-powered octicopter arrives at Titan in 203Peter Diamandis
35.6%0.6500.050-0.057
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.052
prereqSEM_034
True artificial general intelligence will be achieved betweeDemis Hassabis
28.7%0.5500.050-0.030

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
prereq235_002Anthropic will exceed OpenAI in revenue this year (2026).AI
prereqSEM_008Training runs costing $10 billion for a single model will commence sometime in 2025.AI
prereq234_012Anthropic revenue will cross OpenAI revenue in middle of 2026Markets/Stocks
prereqSEM_012Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) across engineering.AI/Manufacturing
prereqSEM_0422025 will be the definitive year that agentic systems finally hit the mainstream.AI/Agents
killerTK14Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (5)

Predictions enabled by this
TypePredTitleDomainLag
prereq235_030Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 2033.Biotech/Longevity
prereq247_035Dario Amodei will solve most/all neurological diseases by end of decadeBiotech/Longevity
prereq246_017Europa Clipper will arrive at Jupiter in 2030, conducting 50 passes near Europa.Space
prereq246_016Dragonfly nuclear-powered octicopter arrives at Titan in 2034.Space
prereqSEM_034True artificial general intelligence will be achieved between 2032 and 2042 — 'first we solve AI, then use AI to solve everything else'.AI/AGI

Linked documents (3)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.693manifoldWill an AI system discover information theory from first principles before 2031?35%mentionspending2026-05-01
0.585arxivATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopediasmentionspending2026-05-04
0.576arxivConstruction of Historical Knowledge Graphs Based on BERT and Graph Neural Networksmentionspending2026-06-01

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=HklyjXKYFng",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "I can only imagine the left turns that human civilization has taken in the past, call it 80 years, when it should have taken a right turn instead. And we're going to discover that after the fact...I think AI will will shock humanity to its core in terms of the mistakes that it discovers that we've made over the past century.",
  "to_year": 2030,
  "verbatim": "I think AI will will shock humanity to its core in terms of the mistakes that it discovers that we've made over the past century.",
  "conv_cues": "I think; will shock",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Open-source AI literature-review tool beats major LLMs on citation accuracy",
      "source": "Nature: Open-source AI tool beats giant LLMs in literature reviews and gets citations right",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -10,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://www.nature.com/articles/d41586-026-00347-9",
      "expected_date": "2026-02-01",
      "observed_date": "2026-02-01",
      "research_origin": "deep_research",
      "measurement_criterion": "Peer-reviewed Nature paper documents AI tool exceeding major LLMs at scientific literature review and matching human citation accuracy"
    },
    {
      "kind": "prereq",
      "label": "Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) a",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -9,
      "source_id": "SEM_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Training runs costing $10 billion for a single model will commence sometime in 2025.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -8,
      "source_id": "SEM_008",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Anthropic revenue will cross OpenAI revenue in middle of 2026",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -7,
      "source_id": "234_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Anthropic will exceed OpenAI in revenue this year (2026).",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -6,
      "source_id": "235_002",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "2025 will be the definitive year that agentic systems finally hit the mainstream.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_042",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "llm_pre_event",
      "label": "Autonomous AI agent publishes peer-reviewed scientific discovery",
      "source": "OpenReview: Scientific Discoveries by LLM Agents; Google Research blog",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.6,
      "source_url": "https://openreview.net/forum?id=fxL6eFPsd1",
      "expected_date": "2027-03-17",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "AI agent (e.g., Sakana AI Scientist, FutureHouse, Microsoft AI Co-Scientist) credited as primary discoverer in peer-reviewed Nature/Science/Cell paper"
    },
    {
      "kind": "llm_pre_event",
      "label": "AI literature meta-analysis identifies replication crisis in major sub-field",
      "source": "Educational Researcher / Royal Society replica
... (truncated)