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239_002predictionAIAGI

AI recursive self-improvement will be fully automated by end of 2026 or 2027 at latest

Predictor: Elon Musk · ep#239 "Elon Musk: The Economy Will Be 10x the Size in 10 Years | #239" · source

Prior probability
50.0%
Current probability
43.6%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2026-01-01 – 2027-10-31
Edges in / out
7 / 5
Tickers exposed
21

Prediction text

AI recursive self-improvement will be fully automated by end of 2026 or 2027 at latest | every successive model uh is is built by the one before it. So that that that is happening to a large degree, but it's it's not yet fully automated. Um it may be there end of this year, but not later than next year.

Verbatim quote

From episode "Elon Musk: The Economy Will Be 10x the Size in 10 Years | #239"
every successive model uh is is built by the one before it. So that that that is happening to a large degree, but it's it's not yet fully automated. Um it may be there end of this year, but not later than next year.

Predictor: Elon Musk

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0142
excellent
Hits / Misses
1 / 0
of 3 resolved
Hit rate
33.3%
Calibration plot (stated vs observed)

Evidence about this node from Elon Musk 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: agi_breakthrough_5y

Linked via embedding similarity 0.559

Major capability discontinuity (e.g. AGI by named target year, 5-year horizon)

Base rate
20.0%
1/5 historical
Inside weight
Outside weight
no pull
inside 43.6% → blend 43.6% 0.0pp)

Tetlock-style outside view: at TRF=1 (just predicted), outside view dominates (w_in=0.3). At TRF=0 (deadline), inside view dominates (w_in=1.0). The blend regularizes overconfident inside views toward the historical base rate.

Probability over time

9 prob_history rows
0%25%50%75%100%prior 50%2026-04-302026-05-022026-05-24
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 43.6%

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: 2 fired ✓ · 1 overdue ⏱ · 4 pending
  1. 2026-04-20overdueQ1 window check-in (25%)
  2. 2026-04-27hitICLR 2026 Workshop on AI with Recursive Self-Improvement convened in Rio (community legitimization)
    How: ICLR 2026 workshop proceedings confirm RSI workshop took place; first dedicated academic venue for RSI
    Source: deep_research_enrichedconf 95%
  3. 2026-08-07pendingQ2 window check-in (50%)
  4. 2026-04-01 → 2027-04-30pendingAlphaEvolve-class system autonomously discovers novel SOTA algorithm in published paper
    How: Peer-reviewed publication or DeepMind blog confirming an AI system autonomously generated and validated an algorithm that exceeds prior human SOTA on a recognized benchmark
    Source: deep_research_enrichedconf 55%
  5. 2026-08-01 → 2026-12-31pendingOpenAI ships intern-level AI research agent (publicly demonstrated or deployed)
    How: OpenAI announces or demos intern-level AI research agent capable of running independent ML experiments end-to-end — explicit company target by Sep 2026
    Source: deep_research_enrichedconf 55%
  6. 2026-11-24pendingQ3 window check-in (75%)
  7. 2026-09-01 → 2027-09-30pendingFrontier lab "effective workforce" disclosure shows >10x ratio of AI agents to human researchers
    How: OpenAI / Anthropic / DeepMind public statement or 10-K equivalent disclosing AI-agent research workforce >10x human staff
    Source: deep_research_enrichedconf 35%
  8. 2027-01-01 → 2027-10-31pendingAnthropic / OpenAI public claim of "fully automated AI research" pipeline
    How: Anthropic or OpenAI publishes claim that end-to-end AI research (idea → experiment → paper → deployed model) runs without human-in-the-loop, matching prediction's "fully automated" threshold
    Source: deep_research_enrichedconf 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: 44%)

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
LBP2026-05-24T02:00:02Z43.6%+1.4pp
Network propagation: 42.2% → 43.6%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
LBP2026-05-17T02:00:01Z42.2%+2.8pp
Network propagation: 39.4% → 42.2%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z39.4%+5.4pp
Network propagation: 34.0% → 39.4%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z34.0%+9.6pp
Network propagation: 24.4% → 34.0%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z24.4%-12.7pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.313 blend=0.244 LLR=-0.261 κ=0.64 w_in=0.43 agi_breakthrough_5y
Raw metadata
{
  "trf": 0.8174823723347057,
  "kappa": 0.6429,
  "base_rate": 0.2,
  "predictor": "Elon Musk",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.527677440995565,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "blend 42% inside / 57% outside (TRF=0.817, base_rate=0.200 from agi_breakthrough_5y)",
  "inside_prior": 0.3710587507587537,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": true,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6429,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2606735180027389,
      "expected_date": "2026-04-20",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.427762339365706,
  "outside_weight": 0.572237660634294,
  "posterior_prob": 0.24406575971772326,
  "posterior_logit": -0.788350958998304,
  "predictor_brier": 0.01,
  "inside_posterior": 0.312522860124513,
  "blended_posterior": 0.24406575971772326,
  "reference_class_id": "agi_breakthrough_5y",
  "total_adjusted_llr": -0.2606735180027389,
  "predictor_n_resolved": 2
}
LBP2026-04-30T16:39:51Z37.1%+6.3pp
Network propagation: 30.8% → 37.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
legacy v12026-04-30T16:13:50Z30.8%-6.4pp
reference_class_assigned bayesian_v2 inside=0.500 blend=0.308 w_in=0.41 agi_breakthrough_5y
LBP2026-04-30T02:18:57Z37.1%+6.4pp
Network propagation: 30.7% → 37.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
legacy v12026-04-30T01:56:50Z30.7%-19.3pp
reference_class_assigned bayesian_v2 inside=0.500 blend=0.307 w_in=0.41 agi_breakthrough_5y

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
prereq238_009
Recursive self-improvement is already happening now (no longAlex Wissner-Gross
78.1%0.5000.050-0.039
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.500+0.019
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.500-0.004

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq239_001
Global economy will be 10x its current size in 10 yearsElon Musk
37.7%0.6000.050-0.083
prereqCMQ_003
By 2030, AI models will surpass peak human expert levels acrSam Altman
22.8%0.3500.050-0.045
prereq241_043
ASI will arrive within 2 years to 5 years to this next decadPeter Diamandis
35.9%0.6500.050-0.042
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.032
prereqSEM_034
True artificial general intelligence will be achieved betweeDemis Hassabis
28.7%0.5500.050-0.015

Ticker exposure

21 ticker(s) linked

Beneficiaries (14)

SOUNNVDAGTLBAIBBAITCEHYAMZNBABAGOOGLIBMMETAMSFTORCLSHOP

Adverse (7)

ACNCTSHFRSHCHGGIBMINFYPEGA

Prerequisites (7)

Predictions that must hit first
TypePredTitleDomainLag
prereq238_009Recursive self-improvement is already happening now (no longer three years out)AI
correlateS_ASI_SLOW_2040PLUSASI slow: post-2040 / soft takeoffasi_recursive_self_improvement
correlateS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_general_capability
correlateS_AI_PAUSE_2027AI pause beginning 2027ai_regulatory_pause
correlateS_AGI_WINTER_2036PLUSAGI delayed: capability plateau or AI winteragi_general_capability
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
prereq241_043ASI will arrive within 2 years to 5 years to this next decadeAI
prereq239_001Global economy will be 10x its current size in 10 yearsMacro/Economy
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
prereqCMQ_003By 2030, AI models will surpass peak human expert levels across virtually all cognitive domains — onset of true superintelligence.AI

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.765manifoldWill AI continue to improve?84%mentionspending2026-06-01
0.760manifoldIs ai going to be self repairing?10%mentionspending2026-05-25
0.724manifoldMay 2026 AI model releasesmentionspending2026-04-30
0.704manifoldWill ASI be achieved less than a year after continual learning?31%mentionspending2026-05-28
0.697manifoldWill 2+ bots beat master league in 2026?32%mentionspending2026-05-27
0.691manifoldWill any frontier lab be near-fully automated before 2029?30%mentionspending2026-05-10
0.666manifoldWill I use pattern/structure/structural/stance/substance/intent/level by 2026-06-15? [Convince the Machine #13]66%mentionspending2026-05-15
0.647manifoldWill "How Go Players Disempower Themselves to AI" make the top fifty posts in LessWrong's 2026 Annual Review?34%mentionspending2026-05-02
0.639manifoldWhen will consumer AI be able to read me a bookmentionspending2026-04-25
0.638arxivSelf-Improvement for Fast, High-Quality Plan Generationmentionspending2026-05-05

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=N5KCm_55xeQ",
  "mode": "PREDICTION",
  "role": "Guest-CEO",
  "context": "humans are gradually getting less and less in the loop on the recursive self-improvement... It may be there end of this year, but not later than next year.",
  "to_year": 2027,
  "verbatim": "every successive model uh is is built by the one before it. So that that that is happening to a large degree, but it's it's not yet fully automated. Um it may be there end of this year, but not later than next year.",
  "conv_cues": "may be; not later than",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "end of 2026 to 2027",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -7,
      "source_id": null,
      "expected_date": "2026-04-20",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "ICLR 2026 Workshop on AI with Recursive Self-Improvement convened in Rio (community legitimization)",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://iclr.cc/virtual/2026/workshop/10000796",
      "expected_date": "2026-04-27",
      "observed_date": "2026-04-27",
      "research_origin": "deep_research",
      "measurement_criterion": "ICLR 2026 workshop proceedings confirm RSI workshop took place; first dedicated academic venue for RSI"
    },
    {
      "kind": "prereq",
      "label": "Recursive self-improvement is already happening now (no longer three years out)",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-08-07",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "AlphaEvolve-class system autonomously discovers novel SOTA algorithm in published paper",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.55,
      "source_url": "https://itcanthink.substack.com/p/how-close-are-we-to-self-improving",
      "expected_date": "2026-10-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-04-30",
        "from": "2026-04-01"
      },
      "measurement_criterion": "Peer-reviewed publication or DeepMind blog confirming an AI system autonomously generated and validated an algorithm that exceeds prior human SOTA on a recognized benchmark"
    },
    {
      "kind": "llm_pre_event",
      "label": "OpenAI ships intern-level AI research agent (publicly demonstrated or deployed)",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.55,
      "source_url": "https://www.hyperdimensional.co/p/on-recursive-self-improvement-part",
      "expected_date": "2026-10-16",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-08-01"
      },
      "measurement_criterion": "OpenAI announces or demos intern-level AI research agent capable of running independent ML experiments end-to-end — explicit company target by Sep 2026"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": nul
... (truncated)