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229_044predictionAIhumanoids

Positive transfer learning will continue to emerge, meaning more diverse data makes Figure robots broadly better at many tasks.

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

Prior probability
50.0%
Current probability
35.5%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2026-04-30 – 2028-11-30
Edges in / out
6 / 0
Tickers exposed
29

Prediction text

Positive transfer learning will continue to emerge, meaning more diverse data makes Figure robots broadly better at many tasks. | we've already shown positive transfer now with all this data. Like coming in the robot can journalize better with more information even like more knowledge is better... I hope this will lead to really um uh I think you'll see a lot of positive transfer emerge from a robot that's able to like generalized to a lot of things.

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"
we've already shown positive transfer now with all this data. Like coming in the robot can journalize better with more information even like more knowledge is better... I hope this will lead to really um uh I think you'll see a lot of positive transfer emerge from a robot that's able to like generalized to a lot of things.

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 0.572

>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 35.5% → blend 35.5% 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

7 prob_history rows
0%25%50%75%100%prior 50%2026-04-302026-04-302026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 35.5%

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: 1 fired ✓ · 7 pending
  1. 2026-09-18pendingQ1 window check-in (25%)
  2. 2026-04-30partialFigure publishes positive-transfer benchmark showing diverse-data → broader task generalization
    How: Figure / Helix research note quantifies that adding non-target-task data improves performance on held-out tasks (positive transfer coefficient > 1.0 across domains)
    Source: Figure already demonstrated zero-shot human-to-robot transfer with Project Go-Big using egocentric human videoconf 85%
    Notes: PARTIAL — Project Go-Big and Helix-02 already demonstrate cross-embodiment positive transfer (human video → robot navigation). Quantified benchmark across many tasks pending.
  3. 2027-02-06pendingQ2 window check-in (50%)
  4. 2026-06-01 → 2027-12-31pendingHelix 02 (or successor) demonstrates room-scale autonomy on novel tasks not in training distribution
    How: Figure releases technical report or video of Helix model executing 30+ unseen home/warehouse tasks zero-shot from natural language
    Source: Helix 02 Full-Body Autonomy announcement; Adcock's 'end of C++' framingconf 65%
  5. 2026-06-01 → 2028-06-30pendingIndustry-wide acceptance of VLA (Vision-Language-Action) models as standard humanoid stack
    How: Tesla, 1X, Unitree, Agility all ship VLA-based stacks; transformer-based end-to-end policies become default robotics curriculum
    Source: State of Robotics 2026 already calls VLA adoption the dominant 2026 trendconf 80%
  6. 2027-06-27pendingQ3 window check-in (75%)
  7. 2026-09-01 → 2028-06-30pendingCross-domain transfer: warehouse Helix model generalizes to home tasks (or vice versa)
    How: Same Helix checkpoint demonstrably executes tasks across logistics + household + manufacturing without per-domain finetuning
    Source: Helix Logistics deployment + home Brookfield pilots running parallel — natural test of cross-domain transferconf 50%
  8. 2026-12-01 → 2028-11-30pendingRobot foundation model hits 100M+ data hours — scaling-law inflection
    How: Figure or peer publishes that humanoid foundation model trained on >100M hours of robot or egocentric video — analog to GPT-3 inflection
    Source: Project Go-Big internet-scale pretraining visionconf 45%

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: 35%)

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
LBP2026-05-17T02:00:01Z35.5%+1.2pp
Network propagation: 34.3% → 35.5%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z34.3%+2.3pp
Network propagation: 32.0% → 34.3%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z32.0%+4.3pp
Network propagation: 27.7% → 32.0%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z27.7%+10.0pp
Network propagation: 17.7% → 27.7%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
legacy v12026-04-30T16:13:50Z17.7%-9.4pp
reference_class_assigned bayesian_v2 inside=0.500 blend=0.177 w_in=0.30 humanoid_commercial_volume
LBP2026-04-30T02:18:57Z27.1%+9.5pp
Network propagation: 17.7% → 27.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
legacy v12026-04-30T01:56:50Z17.7%-32.3pp
reference_class_assigned bayesian_v2 inside=0.500 blend=0.177 w_in=0.30 humanoid_commercial_volume

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_ENTERPRISE_2028
Humanoid R2: 100K+ enterprise by Nov 2028
50.0%0.5000.050-0.080
killerTK08
Humanoid Capital Collapse (Figure/Apptronik Flop)
22.0%0.0500.500+0.046

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

29 ticker(s) linked

Beneficiaries (24)

LYSCFSERVFANUYMPNVDAHSEHYRNSHFALNTCGNXIRBTUSARSYMMIELYAMZNBYDDYHYMTFIFNNYABBNYPHSTMTELTERTSLATXN

Adverse (5)

RHIMANKELYAKFYTNET

Prerequisites (6)

Predictions that must hit first
TypePredTitleDomainLag
prereqS_HUMANOID_ENTERPRISE_2028Humanoid R2: 100K+ enterprise by Nov 2028humanoid_deployment
correlateS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_general_capability
correlateS_AGI_FAST_2027AGI fast: drop-in remote worker by 2027-09agi_general_capability
correlateS_AGI_SLOW_2031AGI slow: Schmidt/Hassabis 5-10 year pathagi_general_capability
correlateS_AGI_WINTER_2036PLUSAGI delayed: capability plateau or AI winteragi_general_capability
killerTK08Humanoid Capital Collapse (Figure/Apptronik Flop)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=S_fXhVT67Uw",
  "mode": "PREDICTION",
  "role": "Guest-CEO",
  "context": "I hope this will lead to really um uh I think you'll see a lot of positive transfer emerge from a robot that's able to like generalized to a lot of things.",
  "verbatim": "we've already shown positive transfer now with all this data. Like coming in the robot can journalize better with more information even like more knowledge is better... I hope this will lead to really um uh I think you'll see a lot of positive transfer emerge from a robot that's able to like generalized to a lot of things.",
  "conv_cues": "I hope; I think",
  "direction": "HAPPEN",
  "timeframe": "future (unspecified)",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -8,
      "source_id": null,
      "expected_date": "2026-09-18",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Figure publishes positive-transfer benchmark showing diverse-data → broader task generalization",
      "notes": "PARTIAL — Project Go-Big and Helix-02 already demonstrate cross-embodiment positive transfer (human video → robot navigation). Quantified benchmark across many tasks pending.",
      "source": "Figure already demonstrated zero-shot human-to-robot transfer with Project Go-Big using egocentric human video",
      "status": "partial",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://www.figure.ai/news/project-go-big",
      "expected_date": "2026-11-14",
      "observed_date": "2026-04-30",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-06-30",
        "from": "2026-04-01"
      },
      "measurement_criterion": "Figure / Helix research note quantifies that adding non-target-task data improves performance on held-out tasks (positive transfer coefficient > 1.0 across domains)"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2027-02-06",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Helix 02 (or successor) demonstrates room-scale autonomy on novel tasks not in training distribution",
      "source": "Helix 02 Full-Body Autonomy announcement; Adcock's 'end of C++' framing",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.65,
      "source_url": "https://www.humanoidsdaily.com/news/the-end-of-c-brett-adcock-on-helix-02-and-figure-s-path-to-room-scale-autonomy",
      "expected_date": "2027-03-17",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Figure releases technical report or video of Helix model executing 30+ unseen home/warehouse tasks zero-shot from natural language"
    },
    {
      "kind": "llm_post_event",
      "label": "Industry-wide acceptance of VLA (Vision-Language-Action) models as standard humanoid stack",
      "source": "State of Robotics 2026 already calls VLA adoption the dominant 2026 trend",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.8,
      "source_url": "https://www.roboticscenter.ai/state-of-robotics-2026",
      "expected_date": "2027-06-16",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2028-06-30",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Tesla, 1X, Unitree, Agility all ship VLA-based stacks; transformer-based end-to-end policies become default robotics curriculum"
    },
    {
   
... (truncated)