← Cockpit
237_029predictionAIAI-scaling

AI agents can do productive tasks indefinitely, for days on end, fundamentally changing the AI experience.

Predictor: Dave Blundin · ep#237 "OpenClaw Explained: Baby AGI, Security Threats, Mac Mini Became Everyone's Supercomputer" · source

Prior probability
60.0%
Current probability
46.6%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
pending
Window
2026-06-01 – 2026-06-30
Edges in / out
10 / 5
Tickers exposed
37

Prediction text

AI agents can do productive tasks indefinitely, for days on end, fundamentally changing the AI experience. | I you I'm so with you on that too. I you know one thing that's really new in the world is >> it can do productive things indefinitely like days and days and days.

Verbatim quote

From episode "OpenClaw Explained: Baby AGI, Security Threats, Mac Mini Became Everyone's Supercomputer"
I you I'm so with you on that too. I you know one thing that's really new in the world is >> it can do productive things indefinitely like days and days and days.

Predictor: Dave Blundin

κ + Brier as of 2026-05-22
κ (discount)
0.821
Brier
0.0491
excellent
Hits / Misses
3 / 2
of 9 resolved
Hit rate
33.3%
Calibration plot (stated vs observed)

Evidence about this node from Dave Blundin 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 60%2026-04-302026-05-102026-05-24
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 46.6%

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: 6 fired ✓ · 1 overdue ⏱
  1. 2026-01-31hitClaude Code 99.9th percentile turn duration crosses 45 minutes
    How: Anthropic reports >=45 minute autonomous turn duration at 99.9th percentile in Claude Code sessions
    Source: https://resources.anthropic.com/hubfs/2026%20Agentic%20Coding%20Trends%20Report.pdf — 2026 Agentic Coding Trends Reportconf 95%
    Notes: HIT — Anthropic reports 25min->45min P99.9 turn duration Oct 2025 to Jan 2026, signaling sustained autonomy uplift.
  2. 2026-04-30hitLong-running Claude scientific computing case studies published
    How: Anthropic publishes case studies of Claude running scientific computing workloads autonomously for >12 hours continuously
    Source: https://www.anthropic.com/research/long-running-Claude — Long-running Claude for scientific computingconf 95%
  3. 2026-01-01 → 2026-08-31overduePublic benchmark for multi-day agent task completion launches
    How: METR, Anthropic, or third-party publishes a benchmark measuring agent capability on tasks expected to take humans >=24 hours of work, with frontier models scoring >=20%
    Source: https://www.anthropic.com/research/measuring-agent-autonomy — Measuring AI agent autonomyconf 85%
  4. 2026-06-01 → 2027-06-30pendingAgent runs autonomously for >=72 hours on production task without human intervention
    How: Public case study from Anthropic, OpenAI, or enterprise customer documents agent running autonomously for >=72 hours on production engineering or research task with verified deliverable
    Source: Anthropic blog, OpenAI research, enterprise case studiesconf 60%
  5. 2026-09-01 → 2027-12-31pendingMulti-agent orchestration runs >=1 week continuously
    How: Documented multi-agent system completes self-directed task spanning >=7 days with periodic checkpoints but no continuous human supervision
    Source: Anthropic CoWork, OpenAI multi-agent research, Manus.imconf 40%
    Notes: Cascade — Blundin's 'days and days' framing requires multi-day, not multi-hour.

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: 47%)

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
LBP2026-05-24T02:00:02Z46.6%+1.3pp
Network propagation: 45.2% → 46.6%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
LBP2026-05-17T02:00:01Z45.2%+2.7pp
Network propagation: 42.5% → 45.2%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
metadata_milestone_miss_sweep2026-05-10T22:10:52Z42.5%-7.0pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.425 blend=0.425 LLR=-0.283 κ=0.82 no_blend
Raw metadata
{
  "trf": 1,
  "kappa": 0.8214,
  "base_rate": null,
  "predictor": "Dave Blundin",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.01913257734403183,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.4952170015666818,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "llm_pre_event",
      "kappa": 0.69819,
      "label": "Public benchmark for multi-day agent task completion launches",
      "weight": 0.4,
      "strength": "weak",
      "confidence": 0.85,
      "source_url": "https://www.anthropic.com/research/measuring-agent-autonomy",
      "adjusted_llr": -0.2830916838300393,
      "expected_date": "2026-05-02",
      "measurement_criterion": "METR, Anthropic, or third-party publishes a benchmark measuring agent capability on tasks expected to take humans >=24 hours of work, with frontier models scoring >=20%"
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.3,
  "outside_weight": 0.7,
  "posterior_prob": 0.4250138342982685,
  "posterior_logit": -0.30222426117407114,
  "predictor_brier": 0.0491,
  "inside_posterior": 0.4250138342982685,
  "blended_posterior": 0.4250138342982685,
  "reference_class_id": null,
  "total_adjusted_llr": -0.2830916838300393,
  "predictor_n_resolved": 9
}
LBP2026-05-10T02:00:02Z49.5%-1.2pp
Network propagation: 50.7% → 49.5%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z50.7%-2.2pp
Network propagation: 52.9% → 50.7%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z52.9%-2.9pp
Network propagation: 55.8% → 52.9%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z55.8%-4.2pp
Network propagation: 60.0% → 55.8%
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%0.0500.600+0.079
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.600+0.068
killerTK09
Energy Grid Cap (Data Center Power Wall)
35.0%0.0500.600-0.058
prereqSEM_014
Nvidia's Arizona-based TSMC factory successfully fabricated Jensen Huang
86.1%0.6000.050+0.053
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.600+0.052

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq248_040
Pausing AI will fail and only accelerate race dynamics.Alex Wissner-Gross
53.0%0.9200.050-0.069
prereq247_023
AI will be able to do everything a white collar worker does Dave Blundin
40.8%0.7200.050-0.041
prereq242_031
Most large companies' business models will be disrupted in 2Peter Diamandis
36.1%0.6500.050-0.027
prereq244_019
Peter's son won't need a driver's license in 2 yearsPeter Diamandis
48.4%0.9200.050-0.023
prereq232_055
We're exiting the industrial age permanently as recursive sePeter Diamandis
35.5%0.7000.050+0.003

Ticker exposure

37 ticker(s) linked

Beneficiaries (24)

MUWULFIRENEQIXALABAPLDASMIYASMLPLABNVDANBISCRWVAAPLAMTAMZNDELLGOOGLIRMLNVGYMETAMSFTORCLSFTBYSTX

Adverse (6)

ACNGENCHGGIBMWNSLRN

Prerequisites (10)

Predictions that must hit first
TypePredTitleDomainLag
prereqSEM_011Nvidia became the world's first $5 trillion company (late 2025), operating a near-monopoly on advanced AI chips.Capital Markets
prereqSEM_027Nvidia Data Center revenue +66% YoY, contributing ~90% of $57B fiscal Q3 revenue; >$4.5T market cap entirely underpinned by AI silicon.Capital Markets
prereqSEM_014Nvidia's Arizona-based TSMC factory successfully fabricated cutting-edge semiconductors on US soil for first time in decades (October 2025).Manufacturing
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_015Nvidia agreed to remit 15% of China chip-sale revenue directly to US government in exchange for reversing specific AI chip export bans.Policy/Semis
killerTK09Energy Grid Cap (Data Center Power Wall)
killerTK05Rate Regime Persistence (10y > 5% through 2028)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (5)

Predictions enabled by this
TypePredTitleDomainLag
prereq244_019Peter's son won't need a driver's license in 2 yearsAuto/Transport
prereq248_040Pausing AI will fail and only accelerate race dynamics.AI
prereq247_023AI will be able to do everything a white collar worker does imminentlyAI
prereq232_055We're exiting the industrial age permanently as recursive self-improvement unfolds.AI
prereq242_031Most large companies' business models will be disrupted in 2-5 yearsMarkets/Stocks

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.706arxivAIs and Humans with Agencymentionspending2026-05-04
0.686arxivMulti-Agent Computer Usementionspending2026-06-01
0.670arxivcotomi Act: Learning to Automate Work by Watching Youmentionspending2026-05-04
0.661arxivAGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agentsmentionspending2026-06-01
0.596manifoldWill I use Hermes Agent for more than a week?36%mentionspending2026-05-09
0.577manifoldOn what days will I be productive this week?mentionspending2026-05-10
0.577manifoldOn what days will I be productive this week?mentionspending2026-05-03
0.577manifoldOn what days will I be productive this week?mentionspending2026-04-27
0.577manifoldOn what days will I be productive this week?mentionspending2026-06-01
0.573manifoldOn what days will I be productive [rest of the month]mentionspending2026-05-16

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "days",
  "url": "https://www.youtube.com/watch?v=qP73cGLQmCU",
  "mode": "THESIS",
  "role": "Host",
  "caveats": "Mostly present-tense; near-future implication",
  "context": "I you know one thing that's really new in the world is >> it can do productive things indefinitely like days and days and days. >> And when I use my APIs that I loved a month ago, >> I have no idea what the bill is going to be. Like I literally have no idea if I turn it loose.",
  "to_year": 2026,
  "verbatim": "I you I'm so with you on that too. I you know one thing that's really new in the world is >> it can do productive things indefinitely like days and days and days.",
  "conv_cues": "can do productive things indefinitely",
  "direction": "UP",
  "from_year": 2026,
  "timeframe": "present/near-future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Claude Code 99.9th percentile turn duration crosses 45 minutes",
      "notes": "HIT — Anthropic reports 25min->45min P99.9 turn duration Oct 2025 to Jan 2026, signaling sustained autonomy uplift.",
      "source": "https://resources.anthropic.com/hubfs/2026%20Agentic%20Coding%20Trends%20Report.pdf — 2026 Agentic Coding Trends Report",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://resources.anthropic.com/hubfs/2026%20Agentic%20Coding%20Trends%20Report.pdf",
      "expected_date": "2026-01-31",
      "observed_date": "2026-01-31",
      "research_origin": "deep_research",
      "measurement_criterion": "Anthropic reports >=45 minute autonomous turn duration at 99.9th percentile in Claude Code sessions"
    },
    {
      "kind": "prereq",
      "label": "Nvidia became the world's first $5 trillion company (late 2025), operating a near-monopoly on advanced AI chips.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -6,
      "source_id": "SEM_011",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Nvidia Data Center revenue +66% YoY, contributing ~90% of $57B fiscal Q3 revenue; >$4.5T market cap entirely underpinned by AI silicon.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_027",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Nvidia's Arizona-based TSMC factory successfully fabricated cutting-edge semiconductors on US soil for first time in decades (October 2025).",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -4,
      "source_id": "SEM_014",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "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": -3,
      "source_id": "SEM_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "llm_pre_event",
      "label": "Long-running Claude scientific computing case studies published",
      "source": "https://www.anthropic.com/research/long-running-Claude — Long-running Claude for scientific computing",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.anthropic.com/research/long-running-Claude",
      "expected_date": "2026-04-30",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Anthropic publishes case studies of Claude running scientific computing workloads autonomously for >12 hours continuously"
    },
    {
      "kind": "llm_pre_event",
      "label": "Public benchmark for multi-day agent task completion launches",
      "source": 
... (truncated)