← Cockpit
229_028predictionRoboticsAI-timing

Figure will NOT license out its neural net or hardware IP to third-party form-factor builders.

Predictor: Brett Adcock · ep#229 "The Humanoid Takeover: $50T Market, Figure's Full Body Autonomy, and Robots in Dorms #229" · source

Prior probability
92.0%
Current probability
69.2%
evolves via intake + LBP
Conviction
5/5
Signal quality
C
Resolution
hit
Window
2026-04-30 – 2030-11-30
Edges in / out
6 / 0
Tickers exposed
33

Prediction text

Figure will NOT license out its neural net or hardware IP to third-party form-factor builders. | do you think you might franchise out the neural net and the circuitry around it... no just I think it's super unsafe... just giving this AI system or even hardware to anybody that would want like this is like uh not something we will entertain.

Watch events: Figure 03 shipment volume; BMW, 2nd customer deployment count

Verbatim quote

From episode "The Humanoid Takeover: $50T Market, Figure's Full Body Autonomy, and Robots in Dorms #229"
do you think you might franchise out the neural net and the circuitry around it... no just I think it's super unsafe... just giving this AI system or even hardware to anybody that would want like this is like uh not something we will entertain.

Resolution evidence

Status: hit

Figure won't license IP to 3rd parties — consistent with their vertical integration stance.

Predictor: Brett Adcock

κ + Brier as of 2026-05-22
κ (discount)
0.773
Brier
0.0040
excellent
Hits / Misses
5 / 0
of 6 resolved
Hit rate
83.3%
Calibration plot (stated vs observed)

Evidence about this node from Brett Adcock 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: humanoid_commercial_volume

Linked via embedding similarity 0.562

>10,000 unit cumulative deployment of humanoid robot SKU within 3 years of debut

Base rate
10.0%
0/3 historical
Inside weight
Outside weight
no pull
inside 69.2% → blend 69.2% 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

8 prob_history rows
0%25%50%75%100%prior 92%2026-04-292026-04-302026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 69.2%

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.
No leading signals identified yet.

No upstream prereqs identified — milestones are derived from window quartiles only. 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: 69%)

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
LBP2026-05-17T02:00:01Z69.2%+1.8pp
Network propagation: 67.4% → 69.2%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z67.4%+3.8pp
Network propagation: 63.6% → 67.4%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z63.6%+7.9pp
Network propagation: 55.7% → 63.6%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z55.7%+24.7pp
Network propagation: 30.9% → 55.7%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
legacy v12026-04-30T16:13:50Z30.9%-24.7pp
reference_class_assigned bayesian_v2 inside=0.920 blend=0.309 w_in=0.30 humanoid_commercial_volume
LBP2026-04-30T02:18:57Z55.6%+24.7pp
Network propagation: 30.9% → 55.6%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
legacy v12026-04-30T01:56:50Z30.9%-61.1pp
reference_class_assigned bayesian_v2 inside=0.920 blend=0.309 w_in=0.30 humanoid_commercial_volume
resolution_terminal2026-04-29T22:23:17Z100.0%+44.3pp
resolution_terminal hit outcome=1.0 pre_resolution=0.557
Raw metadata
{
  "source": "backfill_resolution_history.py",
  "status": "hit",
  "bayesian_v2": false,
  "outcome_prob": 1,
  "evidence_kind": "resolution_terminal",
  "posterior_prob": 1,
  "delta_to_outcome": 0.44345999999999997,
  "inside_posterior": 0.55654,
  "validation_notes": "Figure won't license IP to 3rd parties — consistent with their vertical integration stance.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.55654,
  "resolution_evidence": "Figure won't license IP to 3rd parties — consistent with their vertical integration stance.",
  "does_not_update_current_prob": true
}

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
prereqS_HUMANOID_CONSUMER_2030
Humanoid R3: 1M+ consumer by Nov 2030
20.0%0.9200.050-0.468
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.920+0.159
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.920+0.141
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.920+0.124
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.920+0.098

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (6)

Predictions that must hit first
TypePredTitleDomainLag
prereqS_HUMANOID_CONSUMER_2030Humanoid R3: 1M+ consumer by Nov 2030humanoid_deployment
killerTK09Energy Grid Cap (Data Center Power Wall)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importFigure won't license IP to 3rd parties — consistent with their vertical integration stance.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.681arxivThe Complexity of Verifying Feedforward Neural Networks in Quantised Settingsmentionspending2026-05-28
0.661github_releasefacebookresearch/xformers v0.0.34mentionspending2026-01-23
0.656arxivCriticality and Saturation in Orthogonal Neural Networksmentionspending2026-05-07
0.653github_releasefacebookresearch/neuroai v0.2.2mentionspending2026-05-26
0.646github_releasefacebookresearch/xformers v0.0.24mentionspending2024-01-31
0.641arxivMost ReLU Networks Admit Identifiable Parametersmentionspending2026-05-05
0.639arxivUnified generalization analysis for physics informed neural networksmentionspending2026-05-13
0.638arxivA No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?mentionspending2026-05-18
0.636arxivAffineLens: Capturing the Continuous Piecewise Affine Functions of Neural Networksmentionspending2026-05-07
0.635arxivParametrizing Convex Sets Using Sublinear Neural Networksmentionspending2026-05-05

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=S_fXhVT67Uw",
  "mode": "THESIS",
  "role": "Guest-CEO",
  "context": "I think it's super unsafe I think we see these robots out there like this I think like uh they're around humans we don't have like we don't own the hardware we don't know what they're doing... just giving this AI system or even hardware to anybody that would want like this is like uh not something we will entertain.",
  "verbatim": "do you think you might franchise out the neural net and the circuitry around it... no just I think it's super unsafe... just giving this AI system or even hardware to anybody that would want like this is like uh not something we will entertain.",
  "conv_cues": "will not entertain",
  "direction": "NOT_HAPPEN",
  "timeframe": "future (unspecified)",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "event",
      "label": "Figure will NOT license out its neural net or hardware IP to third-party form-factor builders.",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "229_028",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Figure",
  "attribution": "FIRST_PERSON",
  "episode_num": 229,
  "granularity": "VAGUE",
  "resolved_at": "2026-04-29T22:23:17.076515+00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-02-11",
  "parse_method": "UNMAPPABLE",
  "domain_bucket": "Robotics",
  "episode_title": "The Humanoid Takeover: $50T Market, Figure's Full Body Autonomy, and Robots in Dorms #229",
  "fault_line_id": "F001, F002, F003",
  "flag_repeated": false,
  "in_5yr_window": false,
  "appears_in_eps": "229",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 4,
  "ps_cluster_tags": [
    "C2",
    "C3",
    "C5",
    "C8"
  ],
  "active_end_month": 0,
  "watch_events_raw": "Figure 03 shipment volume; BMW, 2nd customer deployment count",
  "active_start_month": 0,
  "flag_nia_bracketed": false,
  "resolved_at_source": "validations_observed_at",
  "track_record_grade": "B",
  "track_record_notes": "Figure has shipped 3 generations of robots in 3.5 years (faster than Tesla's Optimus); BMW deployment validated. Track record <5y; billion-humanoid claim is untestable in near term.",
  "flag_near_term_2027": false,
  "primary_scenario_id": "S_HUMANOID_CONSUMER_2030",
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:48.702276+00:00",
  "reference_class_match": {
    "top_n": [
      {
        "id": "humanoid_commercial_volume",
        "cosine": 0.5624
      }
    ],
    "margin": 0.5624,
    "best_id": "humanoid_commercial_volume",
    "decision": "assigned",
    "second_id": null,
    "threshold": 0.55,
    "computed_at": "2026-04-30T01:49:13.796883+00:00",
    "best_similarity": 0.5624,
    "margin_required": 0.05,
    "second_similarity": null
  },
  "validation_status_raw": "CONFIRMED",
  "composite_signal_score": 36.8,
  "scenario_assignment_at": "2026-04-30T16:04:16.912851+00:00",
  "flag_priority_watchlist": false,
  "flag_timeline_near_term": false,
  "ps_displacement_mechanism": "AI agents displace mid-skill knowledge workflows; BPO/IT-services margin compression accelerates 2026-2028 as enterprise automation matures.",
  "scenario_assignment_reasoning": "predictor='Brett Adcock' tilt=mid (year~2029) → S_HUMANOID_CONSUMER_2030",
  "scenario_assignment_confidence": "MEDIUM",
  "scenario_assignment_similarity": 0.621
}