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232_058predictionAIAI-timing

Solving physics may reveal 'doors behind doors' of new opportunities.

Predictor: Alex Wissner-Gross · ep#232 "Ben Horowitz: xAI Executive Exodus, Apple's AI Crisis, The Pace of AI | EP #232" · source

Prior probability
45.0%
Current probability
37.9%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2028-06-01 – 2028-06-30
Edges in / out
8 / 5
Tickers exposed
33

Prediction text

Solving physics may reveal 'doors behind doors' of new opportunities. | I I think there probably will be doors behind the doors, but there are so many doors that are right in front of us that we haven't yet unlocked that would be, I think, completely economically transformative if if we could use AI to solve them.

Verbatim quote

From episode "Ben Horowitz: xAI Executive Exodus, Apple's AI Crisis, The Pace of AI | EP #232"
I I think there probably will be doors behind the doors, but there are so many doors that are right in front of us that we haven't yet unlocked that would be, I think, completely economically transformative if if we could use AI to solve them.

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 45%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 37.9%

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: 8 fired ✓ · 2 pending
  1. 2025-10-01hitMaterials science AI cuts quantum-phase identification time from months to minutes
    How: Published demonstration of AI achieving >100x speedup in quantum materials characterization
    Source: deep_research_enrichedconf 80%
  2. 2026-01-15hitBEE-NET ML model reduces 1.3M superconductor candidates to 741 thermodynamically stable structures with Tc>5K
    How: Peer-reviewed paper showing AI-driven discovery of superconductor candidates at >1000x screening throughput
    Source: deep_research_enrichedconf 85%
  3. 2026-03-10hitAQuaRef AI+quantum-calc protein-mapping tool produces higher-quality structures at lower cost (71/71 validated)
    How: Berkeley Lab publishes AI tool that solves previously-intractable protein structures
    Source: deep_research_enrichedconf 85%
  4. 2027-01-01 → 2028-06-30pendingFirst room-temperature superconductor or near-room-temperature material independently replicated via AI-discovery pipeline
    How: Peer-reviewed publication with >2 independent labs confirming AI-discovered material with Tc near 273K at ambient pressure
    Source: deep_research_enrichedconf 20%
  5. 2027-06-01 → 2028-06-30pendingAI-discovered drug or material reaches FDA approval / commercial production milestone
    How: Material or molecule wholly identified by generative AI receives regulatory approval or enters mass production
    Source: deep_research_enrichedconf 45%

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: 38%)

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
LBP2026-05-17T02:00:01Z37.9%-1.0pp
Network propagation: 38.9% → 37.9%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-03T02:00:01Z38.9%-1.4pp
Network propagation: 40.3% → 38.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z40.3%-1.9pp
Network propagation: 42.2% → 40.3%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z42.2%-2.8pp
Network propagation: 45.0% → 42.2%
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
prereq234_012
Anthropic revenue will cross OpenAI revenue in middle of 202Peter Diamandis
67.1%0.4500.050-0.063
prereqSEM_042
2025 will be the definitive year that agentic systems finallKevin Weil
73.8%0.4500.050-0.038
prereq235_002
Anthropic will exceed OpenAI in revenue this year (2026).Dave Blundin
74.6%0.4500.050-0.034
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.4500.050-0.032
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.450+0.031

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%0.6500.050-0.103
prereq247_035
Dario Amodei will solve most/all neurological diseases by enDario Amodei
38.8%0.7000.050-0.095
prereq246_016
Dragonfly nuclear-powered octicopter arrives at Titan in 203Peter Diamandis
35.6%0.6500.050-0.082
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.081
prereqSEM_034
True artificial general intelligence will be achieved betweeDemis Hassabis
28.7%0.5500.050-0.050

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 (1)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.539arxivRevisiting semiclassical scalar QED in 1+1 dimensionsmentionspending2026-05-04

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=C1GLT9_tag0",
  "mode": "SPECULATION",
  "role": "Host",
  "context": "I'll I'll answer your your question back to my because I I think about this all day long like what what does great with AI even look like uh and I I I think there probably will be doors behind the doors, but there are so many doors that are right in front of us that we haven't yet unlocked that would be, I think, completely economically transformative if if we could use AI to solve them.",
  "to_year": 2031,
  "verbatim": "I I think there probably will be doors behind the doors, but there are so many doors that are right in front of us that we haven't yet unlocked that would be, I think, completely economically transformative if if we could use AI to solve them.",
  "conv_cues": "probably",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "unspecified",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Materials science AI cuts quantum-phase identification time from months to minutes",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -10,
      "source_id": null,
      "confidence": 0.8,
      "source_url": "https://phys.org/news/2025-10-physics-ai-excels-large-scale.html",
      "expected_date": "2025-10-01",
      "observed_date": "2025-10-01",
      "research_origin": "deep_research",
      "measurement_criterion": "Published demonstration of AI achieving >100x speedup in quantum materials characterization"
    },
    {
      "kind": "llm_pre_event",
      "label": "BEE-NET ML model reduces 1.3M superconductor candidates to 741 thermodynamically stable structures with Tc>5K",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -9,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://www.nature.com/articles/s41524-026-01964-8",
      "expected_date": "2026-01-15",
      "observed_date": "2026-01-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Peer-reviewed paper showing AI-driven discovery of superconductor candidates at >1000x screening throughput"
    },
    {
      "kind": "llm_pre_event",
      "label": "AQuaRef AI+quantum-calc protein-mapping tool produces higher-quality structures at lower cost (71/71 validated)",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://newscenter.lbl.gov/2026/03/10/cracking-the-code-using-ai-to-solve-difficult-to-map-proteins/",
      "expected_date": "2026-03-10",
      "observed_date": "2026-03-10",
      "research_origin": "deep_research",
      "measurement_criterion": "Berkeley Lab publishes AI tool that solves previously-intractable protein structures"
    },
    {
      "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": -7,
      "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": -6,
      "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": -5,
      "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).",
   
... (truncated)