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AUT_009predictionAIPowerful-AI-nation-of-geniuses

'Powerful AI' (functional AGI) arrives 2026-2027 — data centers house a 'nation of geniuses' consisting of millions of highly specialized autonomous agents operating orders of magnitude faster than human counterparts; entire software development lifecy...

Predictor: Dario Amodei

Prior probability
35.0%
Current probability
22.3%
evolves via intake + LBP
Conviction
5/5
Signal quality
A
Resolution
pending
Window
2026-01-01 – 2027-11-30
Edges in / out
8 / 0
Tickers exposed
5

Prediction text

'Powerful AI' (functional AGI) arrives 2026-2027 — data centers house a 'nation of geniuses' consisting of millions of highly specialized autonomous agents operating orders of magnitude faster than human counterparts; entire software development lifecycle handled end-to-end within 6-12 months, eliminating up to 50% of white-collar employment. | First public Anthropic ACI (Artificial Capable Intelligence) claim

Key catalyst: First public Anthropic ACI (Artificial Capable Intelligence) claim

Watch events: SWE-Bench saturation; Claude 5+ / GPT-6 capability releases

Resolution evidence

Status: pending

Claude 4 / Claude Code SWE-Bench >70%; SDLC automation emerging. "Nation of geniuses" aspirational; 6-12mo full SDLC aggressive.

Predictor: Dario Amodei

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0363
excellent
Hits / Misses
1 / 0
of 3 resolved
Hit rate
33.3%
Calibration plot (stated vs observed)

Evidence about this node from Dario Amodei 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 0.590

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

Base rate
20.0%
1/5 historical
Inside weight
0.450
TRF=0.79
Outside weight
0.550
pulling toward base rate
inside 25.3% → blend 22.3% -3.0pp)

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

7 prob_history rows
0%25%50%75%100%prior 35%2026-04-302026-04-302026-05-30
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 22.3%

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 ⏱ · 5 pending
  1. 2024-10-11hitAnthropic CEO Amodei warns AI matches 'country of geniuses' by 2026
    How: Dario Amodei publishes essay/interview stating powerful AI ('country of geniuses in a datacenter') arrives late 2026 or early 2027
    Source: https://www.darioamodei.com/essay/machines-of-loving-grace — Amodei 'Machines of Loving Grace' essayconf 99%
  2. 2025-03-15hitAnthropic 2026 OSTP submission states AGI by early 2027
    How: Anthropic OSTP recommendations explicitly forecast AGI by early 2027, only AI lab making this institutional commitment
    Source: https://blog.redwoodresearch.org/p/whats-up-with-anthropic-predicting — Redwood Research analysis of Anthropic's AGI early-2027 predictionconf 95%
  3. 2026-05-13overdueQ1 window check-in (25%)
  4. 2026-09-23pendingQ2 window check-in (50%)
  5. 2026-06-01 → 2027-06-30pendingSingle training run cost exceeds $10B
    How: Major AI lab (OpenAI/Anthropic/Google DeepMind/xAI) publicly confirms training compute spend exceeding $10B for a single model
    Source: https://www.darioamodei.com/essay/machines-of-loving-grace — Amodei prediction of $10B training runs in 2026conf 65%
  6. 2027-02-02pendingQ3 window check-in (75%)
  7. 2026-06-01 → 2027-11-30pendingAI completes end-to-end software development tasks lasting hours/days
    How: Frontier AI agent completes ≥40-hour software development task end-to-end at ≥50% success rate per METR or equivalent benchmark
    Source: https://spectrum.ieee.org/state-of-ai-index-2026 — Stanford AI Index METR doubling researchconf 55%
  8. 2026-06-01 → 2027-11-30pendingMajor employer announces ≥30% white-collar headcount reduction citing AI
    How: Fortune 500 employer announces ≥30% white-collar (knowledge worker) headcount reduction explicitly attributed to AI/agents, signaling step toward 50% white-collar elimination thesis
    Source: SEC 10-K filings, WARN notices, corporate press releasesconf 40%
    Notes: Cascade — Amodei's 50% white-collar elimination is aggressive; ≥30% from a single major employer is more plausible early signal.

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: 22%)

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_sweep2026-05-30T22:15:00Z22.3%-8.7pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.253 blend=0.223 LLR=-0.279 κ=0.69 w_in=0.45 agi_breakthrough_5y
Raw metadata
{
  "trf": 0.7852047391286945,
  "kappa": 0.6875,
  "base_rate": 0.2,
  "predictor": "Dario Amodei",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.8017829396315219,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "blend 45% inside / 54% outside (TRF=0.785, base_rate=0.200 from agi_breakthrough_5y)",
  "inside_prior": 0.309644260035229,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": true,
  "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": "2026-05-13",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.45035668260991385,
  "outside_weight": 0.5496433173900861,
  "posterior_prob": 0.22294328433185537,
  "posterior_logit": -1.080540201455885,
  "predictor_brier": 0.0363,
  "inside_posterior": 0.2534038021649466,
  "blended_posterior": 0.22294328433185537,
  "reference_class_id": "agi_breakthrough_5y",
  "total_adjusted_llr": -0.278757261824363,
  "predictor_n_resolved": 3
}
LBP2026-05-10T02:00:02Z31.0%+1.1pp
Network propagation: 29.9% → 31.0%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z29.9%+2.1pp
Network propagation: 27.8% → 29.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z27.8%+2.2pp
Network propagation: 25.6% → 27.8%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
legacy v12026-04-30T16:13:50Z25.6%-2.2pp
reference_class_assigned bayesian_v2 inside=0.350 blend=0.256 w_in=0.41 agi_breakthrough_5y
LBP2026-04-30T02:18:57Z27.8%+2.2pp
Network propagation: 25.6% → 27.8%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
legacy v12026-04-30T01:56:50Z25.6%-9.4pp
reference_class_assigned bayesian_v2 inside=0.350 blend=0.256 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
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.350+0.103
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.350+0.097
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.350+0.097
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.350+0.082
killerTK09
Energy Grid Cap (Data Center Power Wall)
35.0%0.0500.350+0.022

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

5 ticker(s) linked

Adverse (5)

ACNCTSHIBMINFYWNS

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_GRID_50GW_202750GW dedicated AI/data center grid by Dec 2027energy_grid_expansion
correlateS_AGI_FAST_2027AGI fast: drop-in remote worker by 2027-09agi_general_capability
correlateS_COMPUTE_100GW_2030Compute: 100GW national-scale by Dec 2030compute_scale
killerTK09Energy Grid Cap (Data Center Power Wall)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK11Autonomous Regulatory Block (Level 4 Halt)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Linked documents (3)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.672arxivThe New Pro Se: Generative AI and the Surge in Federal Civil Self-Representationmentionspending2026-05-28
0.550codex_research_packMETR - Measuring AI Ability to Complete Long Taskscorroboratespending2025-03-19
0.550codex_research_packOECD - Exploring Possible AI Trajectories Through 2030corroboratespending2026-04-26

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "6-12mo full SDLC; 50% WC elim",
  "mode": "FORECAST",
  "role": "Cited-CEO",
  "context": "Integrates Amodei specific framings distinct from 236_005 (50% entry-level), CMQ_008 (rapid agentic automation), CMQ_006 (cancer/Alzheimer's 5-10yr), INF_074 (AGI high GDP growth). The 'nation of geniuses' coinage + 6-12mo full SDLC timeline.",
  "to_year": 2027,
  "conv_cues": "CEO FIRST_PERSON; coined phrase; specific metrics",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026-2027",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Anthropic CEO Amodei warns AI matches 'country of geniuses' by 2026",
      "source": "https://www.darioamodei.com/essay/machines-of-loving-grace — Amodei 'Machines of Loving Grace' essay",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://www.darioamodei.com/essay/machines-of-loving-grace",
      "expected_date": "2025-01-31",
      "observed_date": "2024-10-11",
      "research_origin": "deep_research",
      "measurement_criterion": "Dario Amodei publishes essay/interview stating powerful AI ('country of geniuses in a datacenter') arrives late 2026 or early 2027"
    },
    {
      "kind": "llm_pre_event",
      "label": "Anthropic 2026 OSTP submission states AGI by early 2027",
      "source": "https://blog.redwoodresearch.org/p/whats-up-with-anthropic-predicting — Redwood Research analysis of Anthropic's AGI early-2027 prediction",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://blog.redwoodresearch.org/p/whats-up-with-anthropic-predicting",
      "expected_date": "2025-03-31",
      "observed_date": "2025-03-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Anthropic OSTP recommendations explicitly forecast AGI by early 2027, only AI lab making this institutional commitment"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2026-05-13",
      "observed_date": null,
      "miss_emitted_at": "2026-05-30T22:15:00.756418+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -5,
      "source_id": null,
      "expected_date": "2026-09-23",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Single training run cost exceeds $10B",
      "source": "https://www.darioamodei.com/essay/machines-of-loving-grace — Amodei prediction of $10B training runs in 2026",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.65,
      "source_url": "https://www.darioamodei.com/essay/machines-of-loving-grace",
      "expected_date": "2026-12-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Major AI lab (OpenAI/Anthropic/Google DeepMind/xAI) publicly confirms training compute spend exceeding $10B for a single model"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2027-02-02",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "AI completes end-to-end software development tasks lasting hours/days",
      "source": "https://spectrum.ieee.org/state-of-ai-index-2026 — Stanford AI Index METR doubling research",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
      "s
... (truncated)