← Cockpit
CMQ_056predictionAI/Computeedge-SLM

Small Language Model (SLM) optimizations and model-distillation techniques will enable localized humanoid reasoning with extreme power efficiency — embedded AI without cloud dependency.

Predictor: Dario Amodei

Prior probability
82.0%
Current probability
18.9%
evolves via intake + LBP
Conviction
4/5
Signal quality
A
Resolution
in_progress
Window
2026-01-01 – 2026-12-31
Edges in / out
1 / 0
Tickers exposed
25

Prediction text

Small Language Model (SLM) optimizations and model-distillation techniques will enable localized humanoid reasoning with extreme power efficiency — embedded AI without cloud dependency. | Edge SLM capability benchmarks + robotics silicon shipments

Key catalyst: Edge SLM capability benchmarks + robotics silicon shipments

Watch events: SLM capability benchmarks vs frontier; robotics edge-silicon TAM; Qualcomm / NVIDIA Jetson revenue.

Resolution evidence

Status: in_progress

Phi-4, Gemma 3, Llama 3.2 / 4.0 mini classes; NVIDIA Jetson Thor, Qualcomm Ride robotics platforms 2025-2026.

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: humanoid_commercial_volume

Linked via embedding similarity 0.662

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

Base rate
10.0%
0/3 historical
Inside weight
0.588
TRF=0.59
Outside weight
0.412
pulling toward base rate
inside 28.1% → blend 18.9% -9.2pp)

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 82%2026-04-302026-05-032026-05-30
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 18.9%

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 ✓ · 2 overdue ⏱ · 2 pending
  1. 2026-01-31hitNVIDIA Jetson T4000 ships with Blackwell architecture for robotics
    How: NVIDIA Jetson T4000 module commercially available with 1,200 FP4 TFLOPS, 64GB memory, and Blackwell architecture for autonomous robotics at $1,999/unit (1K volume)
    Source: NVIDIA / Edge AI and Vision Alliance — Jetson T4000 launch with JetPack 7.1 in Jan 2026conf 95%
    Notes: HIT — Jetson T4000 (Blackwell) shipped Jan 2026 enabling on-robot LLM/VLA inference, validating SLM edge deployment thesis.
  2. 2026-03-11overdueQ1 window check-in (25%)
  3. 2026-05-19overdueQ2 window check-in (50%)
  4. 2026-07-27pendingQ3 window check-in (75%)
  5. 2026-06-01 → 2026-12-31pendingMajor humanoid platform demonstrates on-device SLM inference without cloud
    How: At least one humanoid OEM (Figure, 1X, Tesla Optimus, Apptronik, or NVIDIA partner) publicly demonstrates fully on-device language reasoning without cloud round-trip
    Source: NVIDIA Physical AI / National Robotics Week 2026 partner demosconf 75%
  6. 2026-07-01 → 2027-06-30pendingEdge SLM benchmark hits parity with frontier model on robotics task class
    How: Published benchmark where a sub-10B parameter SLM matches GPT-4-class performance on a defined manipulation/planning task suite
    Source: Anthropic / NVIDIA / DeepMind Gemini Robotics research papersconf 65%
  7. 2026-09-01 → 2027-12-31pendingPower consumption of localized humanoid reasoning drops below 50W sustained
    How: OEM publicly discloses humanoid robot performing real-time reasoning + manipulation with on-board compute drawing <50W average
    Source: Deloitte Tech Trends Physical AI / OEM disclosuresconf 50%
  8. 2027-01-01 → 2027-12-31pendingRobotics silicon shipments cross 1M-unit annual threshold for SLM-class NPUs
    How: NVIDIA, Qualcomm, or peer reports >1M units of edge AI accelerators specifically for humanoid/mobile robotics platforms in a calendar year
    Source: NVIDIA earnings, IDC robotics silicon trackerconf 55%

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: 19%)

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_sweep2026-05-30T22:15:00Z18.9%-15.2pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.281 blend=0.189 LLR=-0.279 κ=0.69 w_in=0.59 humanoid_commercial_volume
Raw metadata
{
  "trf": 0.5881123843731557,
  "kappa": 0.6875,
  "base_rate": 0.1,
  "predictor": "Dario Amodei",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.6607919366149184,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "blend 58% inside / 41% outside (TRF=0.588, base_rate=0.100 from humanoid_commercial_volume)",
  "inside_prior": 0.34056173641693016,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": true,
  "contributions": [
    {
      "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": "2026-05-19",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5883213309387909,
  "outside_weight": 0.4116786690612091,
  "posterior_prob": 0.1888795084186773,
  "posterior_logit": -0.9395491984392814,
  "predictor_brier": 0.0363,
  "inside_posterior": 0.2809914113915666,
  "blended_posterior": 0.1888795084186773,
  "reference_class_id": "humanoid_commercial_volume",
  "total_adjusted_llr": -0.278757261824363,
  "predictor_n_resolved": 3
}
LBP2026-05-17T02:00:01Z34.1%-1.0pp
Network propagation: 35.1% → 34.1%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z35.1%-2.1pp
Network propagation: 37.1% → 35.1%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z37.1%-4.2pp
Network propagation: 41.3% → 37.1%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z41.3%-3.0pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.778 blend=0.413 LLR=-0.261 κ=0.64 w_in=0.53 humanoid_commercial_volume
Raw metadata
{
  "trf": 0.6650500679109432,
  "kappa": 0.6429,
  "base_rate": 0.1,
  "predictor": "Dario Amodei",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 1.5163474893680882,
  "bayes_factor": "1.3:1 against",
  "blend_reason": "blend 53% inside / 46% outside (TRF=0.665, base_rate=0.100 from humanoid_commercial_volume)",
  "inside_prior": 0.82,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": true,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.6429,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2606735180027389,
      "expected_date": "2026-03-11",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5344649524623397,
  "outside_weight": 0.4655350475376603,
  "posterior_prob": 0.4129529469303463,
  "posterior_logit": 1.2556739713653493,
  "predictor_brier": 0.03445,
  "inside_posterior": 0.7782805072082809,
  "blended_posterior": 0.4129529469303463,
  "reference_class_id": "humanoid_commercial_volume",
  "total_adjusted_llr": -0.2606735180027389,
  "predictor_n_resolved": 2
}
legacy v12026-04-30T16:13:50Z44.3%+0.1pp
reference_class_assigned bayesian_v2 inside=0.820 blend=0.443 w_in=0.53 humanoid_commercial_volume
legacy v12026-04-30T01:56:50Z44.2%-37.8pp
reference_class_assigned bayesian_v2 inside=0.820 blend=0.442 w_in=0.53 humanoid_commercial_volume

Network propagation neighbors

Top edges sorted by latest LBP cross-impact
All propagation →

No propagation data yet. Run inference/.venv/bin/python scripts/ops/run_loopy_belief_propagation.py on the droplet, or wait for the Sunday 02:00 UTC weekly cron.

Ticker exposure

25 ticker(s) linked

Beneficiaries (22)

LYSCFSYMHSEHYMPALNTSERVRNSHFFANUYIRBTUSARMIELYAMZNBYDDYHYMTFIFNNYABBNYPHTERTSLATXNSTMTEL

Adverse (3)

RHIKFYMAN

Prerequisites (1)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_HUMANOID_CONSUMER_2030Humanoid R3: 1M+ consumer by Nov 2030humanoid_deployment

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29partialthesis_timeline_v1.0_importPhi-4, Gemma 3, Llama 3.2 / 4.0 mini classes; NVIDIA Jetson Thor, Qualcomm Ride robotics platforms 2025-2026.

Linked documents (10)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "edge SLM inference",
  "mode": "FORECAST",
  "role": "Cited-CEO",
  "context": "Physical AI requires low-latency local inference; SLM + distillation is the enabling compute stack.",
  "to_year": 2028,
  "cited_by": "Synthesis report",
  "conv_cues": "enables; industry direction",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026+",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "NVIDIA Jetson T4000 ships with Blackwell architecture for robotics",
      "notes": "HIT — Jetson T4000 (Blackwell) shipped Jan 2026 enabling on-robot LLM/VLA inference, validating SLM edge deployment thesis.",
      "source": "NVIDIA / Edge AI and Vision Alliance — Jetson T4000 launch with JetPack 7.1 in Jan 2026",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.edge-ai-vision.com/2026/01/accelerate-ai-inference-for-edge-and-robotics-with-nvidia-jetson-t4000-and-nvidia-jetpack-7-1/",
      "expected_date": "2026-01-31",
      "observed_date": "2026-01-31",
      "research_origin": "deep_research",
      "measurement_criterion": "NVIDIA Jetson T4000 module commercially available with 1,200 FP4 TFLOPS, 64GB memory, and Blackwell architecture for autonomous robotics at $1,999/unit (1K volume)"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-03-11",
      "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": -3,
      "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": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-07-27",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Major humanoid platform demonstrates on-device SLM inference without cloud",
      "source": "NVIDIA Physical AI / National Robotics Week 2026 partner demos",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://blogs.nvidia.com/blog/national-robotics-week-2026/",
      "expected_date": "2026-09-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "At least one humanoid OEM (Figure, 1X, Tesla Optimus, Apptronik, or NVIDIA partner) publicly demonstrates fully on-device language reasoning without cloud round-trip"
    },
    {
      "kind": "event",
      "label": "Small Language Model (SLM) optimizations and model-distillation techniques will enable localized humanoid reasoning with extreme power effic",
      "status": "pending",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CMQ_056",
      "expected_date": "2026-10-05",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Edge SLM benchmark hits parity with frontier model on robotics task class",
      "source": "Anthropic / NVIDIA / DeepMind Gemini Robotics research papers",
      "status": "pending",
      "weight": 0.4,
      "ordinal": 1,
      "source_id": null,
      "confidence": 0.65,
      "source_url": "https://deepmind.google/models/gemini-robotics/",
      "expected_date": "2026-12-30",
      "research_origin": "deep_research
... (truncated)