← Cockpit
CMQ_047predictionAIautonomous-agents

Autonomous code agents and AutoResearch systems will close the loop on complex scientific experimentation without human-in-the-loop.

Predictor: Andrej Karpathy

Prior probability
65.0%
Current probability
51.1%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
pending
Window
2026-01-01 – 2026-11-30
Edges in / out
2 / 0
Tickers exposed
4

Prediction text

Autonomous code agents and AutoResearch systems will close the loop on complex scientific experimentation without human-in-the-loop. | Agentic research-product maturity + publication attribution

Key catalyst: Agentic research-product maturity + publication attribution

Watch events: Agentic scientific-paper authorship; AutoResearch-class product releases.

Resolution evidence

Status: pending

AutoGPT / OpenClaw / Manus-style agent products in rapid iteration 2025-2026; Karpathy personal workflow documented publicly.

Predictor: Andrej Karpathy

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0067
excellent
Hits / Misses
3 / 0
of 3 resolved
Hit rate
100.0%
Calibration plot (stated vs observed)

Evidence about this node from Andrej Karpathy 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

7 prob_history rows
0%25%50%75%100%prior 65%2026-04-302026-05-032026-05-30
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 51.1%

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: 3 overdue ⏱
  1. 2026-02-16overdueQ1 window check-in (25%)
  2. 2026-04-03overdueQ2 window check-in (50%)
  3. 2026-05-19overdueQ3 window check-in (75%)

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: 51%)

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_sweep2026-05-30T22:15:00Z51.1%-6.9pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.511 blend=0.511 LLR=-0.279 κ=0.69 no_blend
Raw metadata
{
  "trf": 0.5497684922277138,
  "kappa": 0.6875,
  "base_rate": null,
  "predictor": "Andrej Karpathy",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.32289813724319016,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.5800303875738667,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q3 window check-in (75%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2026-05-19",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.6151620554406003,
  "outside_weight": 0.3848379445593997,
  "posterior_prob": 0.5110334274365221,
  "posterior_logit": 0.044140875418827163,
  "predictor_brier": 0.00667,
  "inside_posterior": 0.5110334274365221,
  "blended_posterior": 0.5110334274365221,
  "reference_class_id": null,
  "total_adjusted_llr": -0.278757261824363,
  "predictor_n_resolved": 3
}
LBP2026-05-17T02:00:01Z58.0%+1.5pp
Network propagation: 56.5% → 58.0%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z56.5%+3.1pp
Network propagation: 53.4% → 56.5%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z53.4%+6.1pp
Network propagation: 47.3% → 53.4%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z47.3%-13.8pp
metadata_milestone_miss_sweep bayesian_v2 n=2 inside=0.473 blend=0.473 LLR=-0.558 κ=0.69 no_blend
Raw metadata
{
  "trf": 0.6338685427014515,
  "kappa": 0.6875,
  "base_rate": null,
  "predictor": "Andrej Karpathy",
  "total_llr": -0.8109302162163288,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 0.45006752591317634,
  "bayes_factor": "1.7:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.6106552887201543,
  "kappa_source": "predictor_table",
  "n_milestones": 2,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2026-02-16",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6875,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.278757261824363,
      "expected_date": "2026-04-03",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5562920201089838,
  "outside_weight": 0.4437079798910162,
  "posterior_prob": 0.4731640636876952,
  "posterior_logit": -0.10744699773554967,
  "predictor_brier": 0.00667,
  "inside_posterior": 0.4731640636876952,
  "blended_posterior": 0.4731640636876952,
  "reference_class_id": null,
  "total_adjusted_llr": -0.557514523648726,
  "predictor_n_resolved": 3
}
LBP2026-04-30T16:39:51Z61.1%-1.4pp
Network propagation: 62.4% → 61.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z62.4%-2.6pp
Network propagation: 65.0% → 62.4%
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
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.650+0.091
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.650+0.079

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

4 ticker(s) linked

Adverse (4)

ALLPGRTRVUBER

Prerequisites (2)

Predictions that must hit first
TypePredTitleDomainLag
killerTK11Autonomous Regulatory Block (Level 4 Halt)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.698arxivLearning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selectionmentionspending2026-05-28
0.687arxivDiscovering Cooperative Pipelines: Autoresearch for Sequential Social Dilemmasmentionspending2026-05-28
0.682arxivARIS: Autonomous Research via Adversarial Multi-Agent Collaborationmentionspending2026-05-04
0.673arxivAutomating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiencymentionspending2026-05-28
0.652arxivHumanwashing -- It Should Leave You Feeling Dirtymentionspending2026-05-13
0.624github_releasefacebookresearch/habitat-lab v0.3.1mentionspending2024-03-15
0.620github_releasefacebookresearch/spdl v0.4.0mentionspending2026-05-11
0.602arxivAgentic Molecular Recovery via Molecule-Aware Explorationmentionspending2026-06-04
0.588github_releasefacebookresearch/hydra v1.0.0rc4mentionspending2020-08-18
0.576github_release1x-technologies/halodi-unity-package-registry-manager 0.1.2mentionspending2020-06-12

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "autonomous science",
  "mode": "FORECAST",
  "role": "Cited-Executive",
  "context": "Karpathy's personal workflow evidence: shifted from boilerplate-code assistance to agentic 'second brain' on raw markdown — no DB pipelines.",
  "to_year": 2028,
  "conv_cues": "rapid rise; operator evidence",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026+",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2026-02-16",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-04-03",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2026-05-19",
      "observed_date": null,
      "miss_emitted_at": "2026-05-30T22:15:00.756418+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "Autonomous code agents and AutoResearch systems will close the loop on complex scientific experimentation without human-in-the-loop.",
      "status": "pending",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CMQ_047",
      "expected_date": "2026-07-04",
      "observed_date": null
    }
  ],
  "repeat_eps": 1,
  "affiliation": "ex-Tesla / ex-OpenAI",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR",
  "source_refs": "30",
  "target_date": "2027-06-15T00:00:00",
  "display_date": "2026-07-04",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Agentic research-product maturity + publication attribution",
  "parse_method": "Report midpoint",
  "domain_bucket": "AI",
  "episode_title": "The Global Architecture of Machine Intelligence: Exhaustive Synthesis of AI Compute, Memory & Quantum Predictions (2023-2026)",
  "fault_line_id": "F006",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI_Chip__Compute__Memory__Quantum_Predictions.md (2026-04-21)",
  "appears_in_eps": "CMQ-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 4,
  "report_evidence": "Operator-level signal on where software engineering + research is heading — high-credibility leading indicator.",
  "active_end_month": "2026-12",
  "recent_statement": "Karpathy X posts + No Priors appearances 2025-2026 continue to describe agentic 'second brain' workflow.",
  "watch_events_raw": "Agentic scientific-paper authorship; AutoResearch-class product releases.",
  "months_from_today": 14,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2026-01",
  "december_dispersal": {
    "reason": "december_dispersal: domain=AI → 11/2026",
    "new_date": "2026-11-30",
    "old_date": "2026-12-31",
    "applied_at": "2026-04-30T16:28:34.304992+00:00"
  },
  "flag_nia_bracketed": false,
  "track_record_grade": "A-",
  "track_record_notes": "Karpathy track record: consistently ahead of curve on LLM + agent architecture trends since 2017.",
  "contradicting_notes": "Many agentic products still fragile at production scale; long-horizon reliability gap remains.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_phase2_at": "2026-05-01T18:11:32.961490+00:00",
  "milestones_derived_at": "2026-05-02T03:08:50.663274+00:00",
  "reference_class_match": {
    "decision": "keyword_fil