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243_008predictionAuto/Transportautonomous

Streets will be safer as autonomous cars don't get distracted or tired

Predictor: Dara Khosrowshahi · ep#243 "Uber vs. Tesla, Robotaxi Timelines, and the End of Human Driving | Uber CEO Dara Khosrowshahi | #243" · source

Prior probability
60.0%
Current probability
44.4%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
pending
Window
2026-04-30 – 2030-11-30
Edges in / out
3 / 0
Tickers exposed
31

Prediction text

Streets will be safer as autonomous cars don't get distracted or tired | the streets are going to be safer, right? These These cars they don't get these drivers don't get distracted. They don't get tired.

Watch events: Waymo 1M rides/wk (end-2026); Tesla Robotaxi scaling; NHTSA AV rules

Verbatim quote

From episode "Uber vs. Tesla, Robotaxi Timelines, and the End of Human Driving | Uber CEO Dara Khosrowshahi | #243"
the streets are going to be safer, right? These These cars they don't get these drivers don't get distracted. They don't get tired.

Predictor: Dara Khosrowshahi

κ + Brier as of 2026-05-22
κ (discount)
0.688
Brier
0.0105
excellent
Hits / Misses
2 / 0
of 3 resolved
Hit rate
66.7%
Calibration plot (stated vs observed)

Evidence about this node from Dara Khosrowshahi 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

5 prob_history rows
0%25%50%75%100%prior 60%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 44.4%

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 ✓ · 6 pending
  1. 2025-09-15hitWaymo crash-rate study shows 80%+ injury reduction vs human drivers
    How: Peer-reviewed study confirms Waymo Driver injury rate >=80% lower than human-driver baseline (Waymo Safety Impact)
    Source: https://waymo.com/safety/impact/conf 99%
    Notes: HIT — peer-reviewed: 0.6 injury incidents/M miles vs 2.80 humans (79% airbag-crash reduction, 91% serious-crash reduction). Direct validation of safer streets claim.
  2. 2026-12-31pendingWaymo crosses 1 million weekly autonomous rides milestone
    How: Waymo publicly reports >=1,000,000 paid autonomous rides per week
    Source: https://www.automotiveworld.com/news/waymos-metric-for-2026-success-one-million-weekly-rides/conf 85%
    Notes: Waymo at 400K/week as of Q1 2026, on trajectory for 1M/week by EOY. Critical scale signal — sample size large enough that crash-rate edge becomes statistically dominant.
  3. 2027-02-02pendingQ1 window check-in (25%)
  4. 2026-06-01 → 2027-12-31pendingWaymo or Tesla operate driverless service in 20+ US metros
    How: Waymo + Tesla Robotaxi cumulatively offer rider-only service in >=20 distinct US metropolitan areas
    Source: https://waymo.com/blog/2026/02/dallas-houston-san-antonio-orlando/conf 75%
    Notes: Waymo at 10 cities + 5 announced 2026 (15) + Tesla 7 new = 22 within range.
  5. 2027-11-07pendingQ2 window check-in (50%)
  6. 2027-01-01 → 2028-12-31pendingNHTSA publishes official AV crash-rate report showing safety advantage
    How: NHTSA or IIHS publishes federal-level annual report on AV vs human-driven crash rates with statistically significant safety advantage
    Source: NHTSA AV Transparency, IIHS reportsconf 55%
  7. 2028-08-11pendingQ3 window check-in (75%)
  8. 2028-01-01 → 2030-12-31pendingUS traffic fatality rate falls 10%+ from 2024 baseline
    How: NHTSA reports US road fatalities below 36,000/year (down from ~40,000 in 2024) attributable to AV adoption
    Source: NHTSA Fatality Analysis Reporting System (FARS)conf 40%

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: 44%)

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
LBP2026-05-17T02:00:01Z44.4%-1.7pp
Network propagation: 46.2% → 44.4%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z46.2%-3.4pp
Network propagation: 49.6% → 46.2%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z49.6%-6.8pp
Network propagation: 56.4% → 49.6%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z56.4%-1.2pp
Network propagation: 57.6% → 56.4%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z57.6%-2.4pp
Network propagation: 60.0% → 57.6%
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
prereqS_ROBOTAXI_MASS_2030
Robotaxi >10% urban miles by Nov 2030
30.0%0.6000.050-0.229
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.600+0.112
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.600+0.101

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

31 ticker(s) linked

Beneficiaries (24)

INVZWRDLIDRAEVAMBLYPONYOUSTVRRMAMBAAURAIOTHSAIMBGAFBIDUBMWYYGMGOOGLHMCIOTQCOMTMTSLAUBERVWAGY

Adverse (5)

MCYALLCINFPGRTRV

Prerequisites (3)

Predictions that must hit first
TypePredTitleDomainLag
prereqS_ROBOTAXI_MASS_2030Robotaxi >10% urban miles by Nov 2030robotaxi_deployment
killerTK11Autonomous Regulatory Block (Level 4 Halt)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Linked documents (2)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.641arxivWhat Can Eye Gaze Teach Us About Real-World Cycling? Insights From the Oxford RobotCycle Projectmentionspending2026-06-03
0.590manifoldDriver allowed to sleep while car drives itself by?mentionspending2026-05-17

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=fzKVYNBg50E",
  "mode": "PREDICTION",
  "role": "Guest-CEO",
  "context": "the streets are going to be safer, right? These These cars they don't get these drivers don't get distracted. They don't get tired.",
  "verbatim": "the streets are going to be safer, right? These These cars they don't get these drivers don't get distracted. They don't get tired.",
  "conv_cues": "are going to be",
  "direction": "UP",
  "timeframe": "As autonomous deploys",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Waymo crash-rate study shows 80%+ injury reduction vs human drivers",
      "notes": "HIT — peer-reviewed: 0.6 injury incidents/M miles vs 2.80 humans (79% airbag-crash reduction, 91% serious-crash reduction). Direct validation of safer streets claim.",
      "source": "https://waymo.com/safety/impact/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://pubmed.ncbi.nlm.nih.gov/39485678/",
      "expected_date": "2025-09-30",
      "observed_date": "2025-09-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Peer-reviewed study confirms Waymo Driver injury rate >=80% lower than human-driver baseline (Waymo Safety Impact)"
    },
    {
      "kind": "llm_pre_event",
      "label": "Waymo crosses 1 million weekly autonomous rides milestone",
      "notes": "Waymo at 400K/week as of Q1 2026, on trajectory for 1M/week by EOY. Critical scale signal — sample size large enough that crash-rate edge becomes statistically dominant.",
      "source": "https://www.automotiveworld.com/news/waymos-metric-for-2026-success-one-million-weekly-rides/",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://techcrunch.com/2026/03/27/waymo-skyrocketing-ridership-in-one-chart/",
      "expected_date": "2026-12-31",
      "research_origin": "deep_research",
      "measurement_criterion": "Waymo publicly reports >=1,000,000 paid autonomous rides per week"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -5,
      "source_id": null,
      "expected_date": "2027-02-02",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Waymo or Tesla operate driverless service in 20+ US metros",
      "notes": "Waymo at 10 cities + 5 announced 2026 (15) + Tesla 7 new = 22 within range.",
      "source": "https://waymo.com/blog/2026/02/dallas-houston-san-antonio-orlando/",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://waymo.com/blog/2026/02/dallas-houston-san-antonio-orlando/",
      "expected_date": "2027-03-17",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Waymo + Tesla Robotaxi cumulatively offer rider-only service in >=20 distinct US metropolitan areas"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2027-11-07",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "NHTSA publishes official AV crash-rate report showing safety advantage",
      "source": "NHTSA AV Transparency, IIHS reports",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.55,
      "expected_date": "2028-01-01",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2028-12-31",
        "from": "2027-01-01"
      },
      "measuremen
... (truncated)