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232_056predictionAIAI-timing

Every single discipline (math, physics, chemistry, medicine) will be flattened by well-targeted generalist AIs.

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

Prior probability
50.0%
Current probability
39.6%
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

Every single discipline (math, physics, chemistry, medicine) will be flattened by well-targeted generalist AIs. | Peter and I just wrote uh call it a book, call it an extended essay called Solve Everything, solve everything.org. ... Where we argue that every single discipline, math, physics, chemistry, medicine, bunch of other disciplines are just going to get flattened, steamrolled by welltargeted generalist AIs. And in my mind, materials research, biology in in the previous slide, these are just case studies. Everything is going to start to look like Alpha Fold 3 where structural biology got solved overnight

Verbatim quote

From episode "Ben Horowitz: xAI Executive Exodus, Apple's AI Crisis, The Pace of AI | EP #232"
Peter and I just wrote uh call it a book, call it an extended essay called Solve Everything, solve everything.org. ... Where we argue that every single discipline, math, physics, chemistry, medicine, bunch of other disciplines are just going to get flattened, steamrolled by welltargeted generalist AIs. And in my mind, materials research, biology in in the previous slide, these are just case studies. Everything is going to start to look like Alpha Fold 3 where structural biology got solved overnight

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%2026-04-302026-05-022026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 39.6%

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 ✓ · 1 overdue ⏱ · 3 pending
  1. 2026-01-31overdueAlphaFold 3 reaches general availability for academic and commercial structural biology
    How: DeepMind opens AlphaFold 3 for non-commercial server access and commercial licensing supporting protein-ligand and DNA/RNA complex prediction
    Source: https://www.humanoidsdaily.com/news/the-shape-of-scale-new-figure-production-data-hints-at-exponential-ramp-up — directional precedent that AI is collapsing scientific timelines (AlphaFold 3 cited explicitly in prediction context as the canonical 'flattened overnight' example)conf 85%
    Notes: Diamandis/Wissner-Gross cite AlphaFold 3 as the prototype for the broader 'flatten every discipline' thesis.
  2. 2026-04-15hitAnthropic ships multi-agent harness for long-horizon scientific work
    How: Anthropic publicly announces a three-agent (planner/generator/evaluator) harness or 'Managed Agents' product enabling multi-hour autonomous research/coding sessions
    Source: https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents — Anthropic three-agent harness; InfoQ reporting April 2026conf 95%
    Notes: HIT — multi-agent harness shipped, supporting the 'generalist AI flattens disciplines' thesis on the tooling side.
  3. 2026-06-01 → 2027-06-30pendingMajor peer-reviewed paper claims AI-driven 'overnight' breakthrough in materials or chemistry comparable to AlphaFold
    How: Nature/Science publishes a paper where an AI system demonstrably solves a previously open problem in materials science, catalysis, or quantum chemistry at speed comparable to AlphaFold for structural biology
    Source: Nature, Science journal monitoringconf 55%
  4. 2026-09-01 → 2027-12-31pendingMultiple Nobel/Turing laureates publicly endorse 'AI flattens disciplines' framing
    How: At least 3 Nobel laureates (chemistry, physics, medicine) or Turing awardees publicly state that AI has 'collapsed' or 'flattened' research timelines in their field, in mainstream press
    Source: Nature News, Science magazine, Nobel media coverageconf 50%
  5. 2027-06-01 → 2028-06-30pendingAI-discovered drug clears Phase III for a major disease
    How: First AI-designed drug candidate (Insilico, Recursion, Isomorphic Labs lineage) reaches successful Phase III readout, validating the discipline-flattening thesis in medicine
    Source: Insilico Medicine, Isomorphic Labs, Recursion clinical updatesconf 40%
    Notes: Cascade — directly tests Diamandis's medicine claim within target window.

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: 40%)

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
LBP2026-05-03T02:00:01Z39.6%+1.9pp
Network propagation: 37.7% → 39.6%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z37.7%-7.0pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.377 blend=0.377 LLR=-0.291 κ=0.84 no_blend
Raw metadata
{
  "trf": 1,
  "kappa": 0.8438,
  "base_rate": null,
  "predictor": "Alex Wissner-Gross",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.21232187956239004,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.4471180434475905,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.7172299999999999,
      "label": "AlphaFold 3 reaches general availability for academic and commercial structural biology",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.85,
      "source_url": "https://deepmind.google/technologies/alphafold/alphafold-server/",
      "adjusted_llr": -0.2908117394884187,
      "expected_date": "2026-01-31",
      "measurement_criterion": "DeepMind opens AlphaFold 3 for non-commercial server access and commercial licensing supporting protein-ligand and DNA/RNA complex prediction"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.3,
  "outside_weight": 0.7,
  "posterior_prob": 0.37680453977376144,
  "posterior_logit": -0.5031336190508088,
  "predictor_brier": 0.03413,
  "inside_posterior": 0.37680453977376144,
  "blended_posterior": 0.37680453977376144,
  "reference_class_id": null,
  "total_adjusted_llr": -0.2908117394884187,
  "predictor_n_resolved": 11
}
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
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.500+0.059
prereq234_012
Anthropic revenue will cross OpenAI revenue in middle of 202Peter Diamandis
67.1%0.5000.050-0.047
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.500+0.037
prereqSEM_042
2025 will be the definitive year that agentic systems finallKevin Weil
73.8%0.5000.050-0.018
prereq235_002
Anthropic will exceed OpenAI in revenue this year (2026).Dave Blundin
74.6%0.5000.050-0.014

Top outgoing (children)

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prereq246_017
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37.7%0.6500.050-0.087
prereq247_035
Dario Amodei will solve most/all neurological diseases by enDario Amodei
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prereq246_016
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35.6%0.6500.050-0.065
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.061
prereqSEM_034
True artificial general intelligence will be achieved betweeDemis Hassabis
28.7%0.5500.050-0.036

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 (10)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=C1GLT9_tag0",
  "mode": "THESIS",
  "role": "Host",
  "context": "Where we argue that every single discipline, math, physics, chemistry, medicine, bunch of other disciplines are just going to get flattened, steamrolled by welltargeted generalist AIs. And in my mind, materials research, biology in in the previous slide, these are just case studies. Everything is going to start to look like Alpha Fold 3 where structural biology got solved overnight in including medicine.",
  "to_year": 2030,
  "verbatim": "Peter and I just wrote uh call it a book, call it an extended essay called Solve Everything, solve everything.org. ... Where we argue that every single discipline, math, physics, chemistry, medicine, bunch of other disciplines are just going to get flattened, steamrolled by welltargeted generalist AIs. And in my mind, materials research, biology in in the previous slide, these are just case studies. Everything is going to start to look like Alpha Fold 3 where structural biology got solved overnight",
  "conv_cues": "going to get flattened, steamrolled",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "unspecified future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "AlphaFold 3 reaches general availability for academic and commercial structural biology",
      "notes": "Diamandis/Wissner-Gross cite AlphaFold 3 as the prototype for the broader 'flatten every discipline' thesis.",
      "source": "https://www.humanoidsdaily.com/news/the-shape-of-scale-new-figure-production-data-hints-at-exponential-ramp-up — directional precedent that AI is collapsing scientific timelines (AlphaFold 3 cited explicitly in prediction context as the canonical 'flattened overnight' example)",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -10,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://deepmind.google/technologies/alphafold/alphafold-server/",
      "expected_date": "2026-01-31",
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "deep_research",
      "measurement_criterion": "DeepMind opens AlphaFold 3 for non-commercial server access and commercial licensing supporting protein-ligand and DNA/RNA complex prediction"
    },
    {
      "kind": "llm_pre_event",
      "label": "Anthropic ships multi-agent harness for long-horizon scientific work",
      "notes": "HIT — multi-agent harness shipped, supporting the 'generalist AI flattens disciplines' thesis on the tooling side.",
      "source": "https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents — Anthropic three-agent harness; InfoQ reporting April 2026",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -9,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.infoq.com/news/2026/04/anthropic-three-agent-harness-ai/",
      "expected_date": "2026-04-16",
      "observed_date": "2026-04-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Anthropic publicly announces a three-agent (planner/generator/evaluator) harness or 'Managed Agents' product enabling multi-hour autonomous research/coding sessions"
    },
    {
      "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": -8,
      "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": -7,
      "source_id": "SEM_008",
      "expected_date": "2026-04-29",
      "observed_da
... (truncated)