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247_012predictionAIAI-timing

The bar for AI startups will rise to require being recursively self-improving

Predictor: Alex Wissner-Gross · ep#247 "Elon Musk vs. Sam Altman, AI Job Loss, and OpenAI's $852B Valuation EP #247" · source

Prior probability
45.0%
Current probability
32.4%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2026-06-01 – 2026-06-30
Edges in / out
8 / 5
Tickers exposed
33

Prediction text

The bar for AI startups will rise to require being recursively self-improving | I would forecast in the near term the bar is going up in fact from just being an AI startup to being now a recursively self-improving AI startup

Verbatim quote

From episode "Elon Musk vs. Sam Altman, AI Job Loss, and OpenAI's $852B Valuation EP #247"
I would forecast in the near term the bar is going up in fact from just being an AI startup to being now a recursively self-improving AI startup

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%2026-04-302026-05-032026-06-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 32.4%

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: 5 fired ✓ · 2 overdue ⏱
  1. 2026-05-01 → 2026-06-21overdueAI startup pitch decks publicly require recursive self-improvement narrative
    How: At least 3 top-quartile VCs (a16z, Sequoia, Founders Fund, Khosla) publish blog or memo declaring RSI mandatory for Series A AI investment
    Source: Wissner-Gross thesis benchmarkconf 30%
  2. 2026-05-01 → 2026-06-21overdueFrontier lab discloses live recursive self-improvement loop in production training
    How: Anthropic, OpenAI, DeepMind, or xAI public statement that current training run uses model-generated training data or model-as-judge in closed loop with measurable capability gain
    Source: futurist:Wissner-Gross + 238_009 RSI prereqconf 55%
  3. 2026-05-15 → 2026-08-31pendingFunding round for AI startup explicitly cites RSI architecture in announcement
    How: Series A/B round of >=$50M for AI startup whose press release uses 'recursive self-improvement' or 'self-improving' as core differentiator
    Source: training-window inferenceconf 50%
  4. 2026-07-01 → 2026-12-31pendingDown-round or shutdown of non-RSI AI startup signals bar-raise
    How: At least 2 prior unicorn AI startups raise flat-to-down rounds OR fold, with VC commentary citing 'no RSI moat'
    Source: training-window inferenceconf 35%
  5. 2026-09-01 → 2027-03-31pendingAI capex shifts towards training-time-compute over inference
    How: Hyperscaler 10-Q discloses materially higher training-cluster capex share, or NVIDIA earnings call cites RSI workloads as driver
    Source: training-window inferenceconf 30%

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: 32%)

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
metadata_milestone_miss_sweep2026-06-03T22:11:45Z32.4%-6.7pp
metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.324 blend=0.324 LLR=-0.291 κ=0.84 no_blend
Raw metadata
{
  "trf": 0.8991436897318007,
  "kappa": 0.8438,
  "base_rate": null,
  "predictor": "Alex Wissner-Gross",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.4467167529816215,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.39014167041364994,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.25314,
      "label": "AI startup pitch decks publicly require recursive self-improvement narrative",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.3,
      "source_url": "https://www.youtube.com/watch?v=5ak26W2YNRY",
      "adjusted_llr": -0.10263943746650073,
      "expected_date": "2026-05-26",
      "measurement_criterion": "At least 3 top-quartile VCs (a16z, Sequoia, Founders Fund, Khosla) publish blog or memo declaring RSI mandatory for Series A AI investment"
    },
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.46409000000000006,
      "label": "Frontier lab discloses live recursive self-improvement loop in production training",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.55,
      "source_url": null,
      "adjusted_llr": -0.18817230202191804,
      "expected_date": "2026-05-26",
      "measurement_criterion": "Anthropic, OpenAI, DeepMind, or xAI public statement that current training run uses model-generated training data or model-as-judge in closed loop with measurable capability gain"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.3705994171877395,
  "outside_weight": 0.6294005828122605,
  "posterior_prob": 0.3235448306563658,
  "posterior_logit": -0.7375284924700403,
  "predictor_brier": 0.03413,
  "inside_posterior": 0.3235448306563658,
  "blended_posterior": 0.3235448306563658,
  "reference_class_id": null,
  "total_adjusted_llr": -0.29081173948841876,
  "predictor_n_resolved": 11
}
LBP2026-05-03T02:00:01Z39.0%-1.3pp
Network propagation: 40.4% → 39.0%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z40.4%-1.8pp
Network propagation: 42.2% → 40.4%
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
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.450+0.086
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.450+0.066
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.450+0.046
prereq238_009
Recursive self-improvement is already happening now (no longAlex Wissner-Gross
78.1%0.4500.050+0.035
prereqSEM_008
Training runs costing $10 billion for a single model will coDario Amodei
76.9%0.4500.050+0.031

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq231_013
Math is cooked (will be solved), physics cooked, biology chaAlex Wissner-Gross
35.4%0.6200.050-0.107
prereq241_043
ASI will arrive within 2 years to 5 years to this next decadPeter Diamandis
35.9%0.6500.050-0.101
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.101
prereqCMQ_002
By 2028, AI systems will reach 'independent researcher' leveSam Altman
31.4%0.5500.050-0.091
prereq232_055
We're exiting the industrial age permanently as recursive sePeter Diamandis
35.5%0.7000.050-0.080

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
prereqSEM_008Training runs costing $10 billion for a single model will commence sometime in 2025.AI
prereq238_009Recursive self-improvement is already happening now (no longer three years out)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
prereq232_055We're exiting the industrial age permanently as recursive self-improvement unfolds.AI
prereq241_043ASI will arrive within 2 years to 5 years to this next decadeAI
prereq231_013Math is cooked (will be solved), physics cooked, biology char broiled.AI
prereqCMQ_002By 2028, AI systems will reach 'independent researcher' level — driving autonomous scientific discoveries without human intervention.AI

Linked documents (7)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.634arxivThree-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecastingmentionspending2026-05-13
0.619arxivNovel Dynamic Batch-Sensitive Adam Optimiser for Vehicular Accident Injury Severity Predictionmentionspending2026-05-14
0.615github_releasefacebookresearch/neuroai v0.1.1mentionspending2026-05-05
0.614manifoldWill "Maybe I was too harsh on deep learning theory..." make the top fifty posts in LessWrong's 2026 Annual Review?11%mentionspending2026-05-11
0.595manifoldWhich of these will I achieve?mentionspending2026-04-24
0.592manifoldCan anyone get talkie-1930 to describe Chomskian recursive syntax?19%mentionspending2026-05-13
0.570github_releasefacebookresearch/exca 0.5.0mentionspending2025-10-22

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=5ak26W2YNRY",
  "mode": "FORECAST",
  "role": "Host",
  "context": "I would forecast in the near term the bar is going up in fact from just being an AI startup to being now a recursively self-improving AI startup",
  "to_year": 2027,
  "verbatim": "I would forecast in the near term the bar is going up in fact from just being an AI startup to being now a recursively self-improving AI startup",
  "conv_cues": "I would forecast",
  "direction": "UP",
  "from_year": 2026,
  "timeframe": "Near term",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "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": "2025 will be the definitive year that agentic systems finally hit the mainstream.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -4,
      "source_id": "SEM_042",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Recursive self-improvement is already happening now (no longer three years out)",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -3,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "llm_pre_event",
      "label": "AI startup pitch decks publicly require recursive self-improvement narrative",
      "source": "Wissner-Gross thesis benchmark",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.3,
      "source_url": "https://www.youtube.com/watch?v=5ak26W2YNRY",
      "expected_date": "2026-05-26",
      "miss_emitted_at": "2026-06-03T22:11:45.571008+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-06-21",
        "from": "2026-05-01"
      },
      "measurement_criterion": "At least 3 top-quartile VCs (a16z, Sequoia, Founders Fund, Khosla) publish blog or memo declaring RSI mandatory for Series A AI investment"
    },
    {
      "kind": "llm_pre_event",
      "label": "Frontier lab discloses live recursive self-improvement loop in production training",
      "source": "futurist:Wissner-Gross + 238_009 RSI prereq",
      "status": "overdue",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.55,
      "expected_date": "2026-05-26",
      "miss_emitted_at": "2026-06-03T22:11:45.571008+00:00",
      "miss_emitted_by": "metadata_milestone_sweep",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2026-06-21",
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      "measurement_criterion": "Anthropic, OpenAI, DeepMind, or xAI public statement that current training run uses model-generated training data or model-as-judge in closed loop with measurable capability gain"
    },
    {
      "kind": "event",
      "label": "The bar for AI startups will rise to require being recursively self-improving",
      "status": "pending",
      "weight": 1,
  
... (truncated)