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243_022predictionAuto/Transportautonomous

Cost per trip will come down and safety per trip will come up as autonomous proliferates

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

Prior probability
65.0%
Current probability
48.4%
evolves via intake + LBP
Conviction
5/5
Signal quality
C
Resolution
pending
Window
2026-04-30 – 2030-11-30
Edges in / out
3 / 0
Tickers exposed
31

Prediction text

Cost per trip will come down and safety per trip will come up as autonomous proliferates | As these cars proliferate, the cost of autonomous is going to the cost per trip is going to come down. Safety per trip is absolutely going to come up.

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"
As these cars proliferate, the cost of autonomous is going to the cost per trip is going to come down. Safety per trip is absolutely going to come up.

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 65%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 48.4%

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: 3 pending
  1. 2027-02-26pendingQ1 window check-in (25%)
  2. 2027-12-25pendingQ2 window check-in (50%)
  3. 2028-10-22pendingQ3 window check-in (75%)

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: 48%)

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
LBP2026-05-17T02:00:01Z48.4%-1.9pp
Network propagation: 50.2% → 48.4%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z50.2%-3.7pp
Network propagation: 53.9% → 50.2%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z53.9%-7.1pp
Network propagation: 61.1% → 53.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z61.1%-1.4pp
Network propagation: 62.4% → 61.1%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z62.4%-2.6pp
Network propagation: 65.0% → 62.4%
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%0.6500.050-0.254
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.650+0.118
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.650+0.106

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

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": "As these cars proliferate, the cost of autonomous is going to the cost per trip is going to come down. Safety per trip is absolutely going to come up.",
  "verbatim": "As these cars proliferate, the cost of autonomous is going to the cost per trip is going to come down. Safety per trip is absolutely going to come up.",
  "conv_cues": "absolutely going to",
  "direction": "MIXED",
  "timeframe": "As cars proliferate",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2027-02-26",
      "observed_date": null
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2027-12-25",
      "observed_date": null
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2028-10-22",
      "observed_date": null
    },
    {
      "kind": "event",
      "label": "Cost per trip will come down and safety per trip will come up as autonomous proliferates",
      "status": "pending",
      "weight": 1,
      "ordinal": 0,
      "source_id": "243_022",
      "expected_date": "2029-08-21",
      "observed_date": null
    }
  ],
  "repeat_eps": 1,
  "sub_domain": "Transport",
  "affiliation": "Uber",
  "attribution": "FIRST_PERSON",
  "episode_num": 243,
  "granularity": "VAGUE",
  "display_date": "2029-08-21",
  "episode_date": "2026-03-31",
  "parse_method": "UNMAPPABLE",
  "domain_bucket": "Auto",
  "episode_title": "Uber vs. Tesla, Robotaxi Timelines, and the End of Human Driving | Uber CEO Dara Khosrowshahi | #243",
  "fault_line_id": "F005, F006",
  "flag_repeated": false,
  "in_5yr_window": false,
  "appears_in_eps": "243",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 4,
  "ps_cluster_tags": [
    "C9"
  ],
  "active_end_month": 0,
  "recent_statement": "April 2 2026 Moonshots: Uber cash flow $10B in 2026. 15 cities with autonomous partners by end-2026. 'By 2030 we'll have significantly more drivers than today, including US.' Machines more predictable + higher acceptance rates than humans.",
  "watch_events_raw": "Waymo 1M rides/wk (end-2026); Tesla Robotaxi scaling; NHTSA AV rules",
  "active_start_month": 0,
  "flag_nia_bracketed": false,
  "track_record_grade": "B+",
  "track_record_notes": "Uber CEO; 'drivers increase not decrease' 2030 call is counterintuitive but has been accurate on Uber's trajectory 2017-2026.",
  "flag_near_term_2027": false,
  "primary_scenario_id": "S_ROBOTAXI_MASS_2030",
  "flag_high_conviction": true,
  "milestones_phase2_at": "2026-05-01T21:12:50.943364+00:00",
  "milestones_derived_at": "2026-05-02T03:08:49.965943+00:00",
  "reference_class_match": {
    "decision": "keyword_filtered",
    "computed_at": "2026-04-30T01:49:13.796883+00:00",
    "best_id_unfiltered": "energy_grid_rebuild_5y",
    "best_similarity_unfiltered": 0.4987
  },
  "validation_status_raw": "UNRESEARCHED",
  "composite_signal_score": 26,
  "scenario_assignment_at": "2026-04-30T16:04:16.912851+00:00",
  "flag_priority_watchlist": false,
  "flag_timeline_near_term": false,
  "recent_statement_source": "diamandis.com/podcast/ep-244",
  "ps_displacement_mechanism": "AV fleet scale 2027-2030 structurally reduces auto insurance loss-pool; gross premiums compress as accident frequency falls.",
  "scenario_assignment_reasoning": "predictor='Dara Khosrowshahi' tilt=mid (year~2029) → S_ROBOTAXI_MASS_2030",
  "scenario_assignment_confidence": "MEDIUM",
  "scenario_assignment_similarity": 0.6222