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240_033predictionAIAI-timing

AI will compress decades of research into years, months, weeks

Predictor: Salim Ismail · ep#240 "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse" · source

Prior probability
55.0%
Current probability
41.9%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
pending
Window
2026-04-30 – 2029-03-31
Edges in / out
5 / 0
Tickers exposed
33

Prediction text

AI will compress decades of research into years, months, weeks | this is going to compress like decades of research into years, months, weeks.

Verbatim quote

From episode "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse"
this is going to compress like decades of research into years, months, weeks.

Predictor: Salim Ismail

κ + Brier as of 2026-05-22
κ (discount)
0.643
Brier
0.0144
excellent
Hits / Misses
1 / 0
of 2 resolved
Hit rate
50.0%
Calibration plot (stated vs observed)

Evidence about this node from Salim Ismail 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 55%2026-04-302026-05-022026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 41.9%

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: 1 overdue ⏱ · 7 pending
  1. 2024-04-01overdueAlphaFold-class breakthrough in second scientific domain (materials/chem)
    How: DeepMind GNoME or equivalent compresses materials discovery — 380K+ stable materials predicted, ~8x historical human catalog
    Source: https://deepmind.google/blog/alphafold-using-ai-for-scientific-discovery-2020/conf 95%
    Notes: GNoME shipped Nov 2023; AlphaFold 3 (May 2024) extended to ligand/DNA. Already validated as compression precedent.
  2. 2026-01-01 → 2027-06-30pendingFirst clinical trial for AlphaFold-derived drug enters Phase 1
    How: FDA IND filing for AI-designed drug citing AlphaFold structure prediction as load-bearing in design
    Source: FDA IND filings, Isomorphic Labs pipelineconf 65%
  3. 2026-10-23pendingQ1 window check-in (25%)
  4. 2026-04-01 → 2027-12-31pendingDOE Genesis or equivalent national-AI-for-science program operational
    How: US DOE Genesis mission with DeepMind partnership reaches operational milestones — exascale simulation runs, materials/energy outputs published
    Source: https://www.bnl.gov/newsroom/news.php?a=222774conf 70%
  5. 2027-04-17pendingQ2 window check-in (50%)
  6. 2027-10-10pendingQ3 window check-in (75%)
  7. 2026-10-01 → 2028-12-31pendingNobel Prize awarded to AI-led research workflow
    How: Nobel Prize (Chemistry/Physics/Medicine) awarded for discovery where AI tool was load-bearing — beyond Hassabis/Jumper 2024
    Source: Nobel Committee announcementsconf 40%
  8. 2027-01-01 → 2029-03-31pendingTime-to-discovery in major journal cohort drops measurably
    How: Bibliometric study shows median hypothesis-to-publication time in computational biology / materials drops by >=30% vs 2024 baseline
    Source: Nature / Science bibliometric analysesconf 50%

No downstream cascades — this prediction is a leaf in the dependency graph.

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
LBP2026-05-03T02:00:01Z41.9%-1.4pp
Network propagation: 43.3% → 41.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z43.3%-6.2pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.433 blend=0.433 LLR=-0.248 κ=0.64 no_blend
Raw metadata
{
  "trf": 0.9972591226262507,
  "kappa": 0.6429,
  "base_rate": null,
  "predictor": "Salim Ismail",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.020254659005855134,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.49493650835598435,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.610755,
      "label": "AlphaFold-class breakthrough in second scientific domain (materials/chem)",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.95,
      "source_url": "https://deepmind.google/blog/alphafold-using-ai-for-scientific-discovery-2020/",
      "adjusted_llr": -0.24763984210260195,
      "expected_date": "2024-04-01",
      "measurement_criterion": "DeepMind GNoME or equivalent compresses materials discovery — 380K+ stable materials predicted, ~8x historical human catalog"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.30191861416162447,
  "outside_weight": 0.6980813858383755,
  "posterior_prob": 0.4334240647810704,
  "posterior_logit": -0.2678945011084571,
  "predictor_brier": 0.01445,
  "inside_posterior": 0.4334240647810704,
  "blended_posterior": 0.4334240647810704,
  "reference_class_id": null,
  "total_adjusted_llr": -0.24763984210260195,
  "predictor_n_resolved": 2
}
LBP2026-04-30T16:39:51Z49.5%-1.9pp
Network propagation: 51.4% → 49.5%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z51.4%-3.6pp
Network propagation: 55.0% → 51.4%
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%0.5500.050-0.194
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.550+0.081
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.550+0.056
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.550+0.031

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (5)

Predictions that must hit first
TypePredTitleDomainLag
prereqS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_general_capability
correlateS_AGI_SLOW_2031AGI slow: Schmidt/Hassabis 5-10 year 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

Linked documents (10)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "decades into weeks",
  "url": "https://www.youtube.com/watch?v=uOGHXAfvK8w",
  "mode": "THESIS",
  "role": "Host",
  "context": "this is going to compress like decades of research into years, months, weeks.",
  "verbatim": "this is going to compress like decades of research into years, months, weeks.",
  "conv_cues": "going to",
  "direction": "HAPPEN",
  "timeframe": "Future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "AlphaFold-class breakthrough in second scientific domain (materials/chem)",
      "notes": "GNoME shipped Nov 2023; AlphaFold 3 (May 2024) extended to ligand/DNA. Already validated as compression precedent.",
      "source": "https://deepmind.google/blog/alphafold-using-ai-for-scientific-discovery-2020/",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://deepmind.google/blog/alphafold-using-ai-for-scientific-discovery-2020/",
      "expected_date": "2024-04-01",
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "deep_research",
      "measurement_criterion": "DeepMind GNoME or equivalent compresses materials discovery — 380K+ stable materials predicted, ~8x historical human catalog"
    },
    {
      "kind": "llm_pre_event",
      "label": "First clinical trial for AlphaFold-derived drug enters Phase 1",
      "source": "FDA IND filings, Isomorphic Labs pipeline",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.65,
      "expected_date": "2026-09-30",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-01-01"
      },
      "measurement_criterion": "FDA IND filing for AI-designed drug citing AlphaFold structure prediction as load-bearing in design"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2026-10-23",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "DOE Genesis or equivalent national-AI-for-science program operational",
      "source": "https://www.bnl.gov/newsroom/news.php?a=222774",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.7,
      "source_url": "https://www.bnl.gov/newsroom/news.php?a=222774",
      "expected_date": "2027-02-14",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-04-01"
      },
      "measurement_criterion": "US DOE Genesis mission with DeepMind partnership reaches operational milestones — exascale simulation runs, materials/energy outputs published"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2027-04-17",
      "observed_date": null
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2027-10-10",
      "observed_date": null
    },
    {
      "kind": "llm_post_event",
      "label": "Nobel Prize awarded to AI-led research workflow",
      "source": "Nobel Committee announcements",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.4,
      "expected_date": "2027-11-16",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2028-12-31",
        "from": "2026-10-01"
      },
      "measurement_criterion": "Nobel Prize (Chemistry/Phy
... (truncated)