← Cockpit
CYB_015predictionLabor/Jobsbot-on-bot-hiring

The human hiring process becomes progressively distorted as autonomous bot agents actively screen resumes generated by other bot agents, completely removing the human connection required to accurately assess contextual experience — bot-on-bot recursive...

Predictor: Emad Mostaque

Prior probability
92.0%
Current probability
85.5%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
hit
Window
2025-01-01 – 2027-12-31
Edges in / out
2 / 0
Tickers exposed
5

Prediction text

The human hiring process becomes progressively distorted as autonomous bot agents actively screen resumes generated by other bot agents, completely removing the human connection required to accurately assess contextual experience — bot-on-bot recursive selection loop. | Fortune 500 policy mandating in-person final-round interviews

Key catalyst: Fortune 500 policy mandating in-person final-round interviews

Watch events: LinkedIn AI-detection features; "human-verified hiring" emergence

Resolution evidence

Status: hit

LinkedIn, Workday, ATS systems all deploy AI resume-screening; GPT-generated resumes proliferate. 2025 surveys show >70% of screening automated.

Predictor: Emad Mostaque

κ + Brier as of 2026-05-22
κ (discount)
0.722
Brier
0.0073
excellent
Hits / Misses
3 / 0
of 4 resolved
Hit rate
75.0%
Calibration plot (stated vs observed)

Evidence about this node from Emad Mostaque 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

4 prob_history rows
0%25%50%75%100%prior 92%2026-04-292026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 85.5%

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: 3 overdue ⏱
  1. 2025-05-01overdueQ1 window check-in (25%)
  2. 2025-08-30overdueQ2 window check-in (50%)
  3. 2025-12-29overdueQ3 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: 86%)

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
LBP2026-05-03T02:00:01Z85.5%-1.2pp
Network propagation: 86.7% → 85.5%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z86.7%-2.1pp
Network propagation: 88.8% → 86.7%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z88.8%-3.2pp
Network propagation: 92.0% → 88.8%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
resolution_terminal2026-04-29T22:23:18Z100.0%+13.3pp
resolution_terminal hit outcome=1.0 pre_resolution=0.867
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": 0.13271999999999995,
  "inside_posterior": 0.86728,
  "validation_notes": "LinkedIn, Workday, ATS systems all deploy AI resume-screening; GPT-generated resumes proliferate. 2025 surveys show >70% of screening automated.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.86728,
  "resolution_evidence": "LinkedIn, Workday, ATS systems all deploy AI resume-screening; GPT-generated resumes proliferate. 2025 surveys show >70% of screening automated.",
  "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
killerTK11
Autonomous Regulatory Block (Level 4 Halt)
10.0%0.0500.920-0.022
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.920-0.005

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

5 ticker(s) linked

Beneficiaries (1)

GOOGL

Adverse (4)

ALLPGRTRVUBER

Prerequisites (2)

Predictions that must hit first
TypePredTitleDomainLag
killerTK11Autonomous Regulatory Block (Level 4 Halt)
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_importLinkedIn, Workday, ATS systems all deploy AI resume-screening; GPT-generated resumes proliferate. 2025 surveys show >70% of screening automated.

Linked documents (10)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "OBSERVATION+FORECAST",
  "role": "Cited-CEO",
  "context": "Distinct from 238_006 (coders go away), AI_015 (Last Economy). Specific hiring-market distortion claim.",
  "to_year": 2027,
  "conv_cues": "specific recursive-loop observation",
  "direction": "HAPPEN",
  "from_year": 2025,
  "timeframe": "2025-2027",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2025-05-01",
      "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": -2,
      "source_id": null,
      "expected_date": "2025-08-30",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2025-12-29",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "The human hiring process becomes progressively distorted as autonomous bot agents actively screen resumes generated by other bot agents, com",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CYB_015",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "sub_domain": "Jobs",
  "affiliation": "Intelligent Internet (ex-Stability AI)",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR_RANGE",
  "resolved_at": "2026-04-29T22:23:18.235890+00:00",
  "source_refs": "28",
  "target_date": "2026-06-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Fortune 500 policy mandating in-person final-round interviews",
  "parse_method": "YEAR_RANGE midpoint",
  "domain_bucket": "Labor",
  "episode_title": "Convergence of Synthetic Cognition: Agents, Memory, Commerce & Cybersecurity (2023-2026)",
  "fault_line_id": "F006",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI Cyber and Memory Predictions Request.md (2026-04-21)",
  "appears_in_eps": "CYB-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 3,
  "ps_cluster_tags": [
    "C5"
  ],
  "report_evidence": "Anchor section: Elimination of Traditional Service Sectors.",
  "active_end_month": "2027-12",
  "recent_statement": "Mostaque Moonshots 238.",
  "watch_events_raw": "LinkedIn AI-detection features; \"human-verified hiring\" emergence",
  "months_from_today": 2,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2025-01",
  "flag_nia_bracketed": false,
  "resolved_at_source": "validations_observed_at",
  "track_record_grade": "B",
  "track_record_notes": "Mostaque directionally accurate on displacement claims.",
  "contradicting_notes": "Employer pushback, in-person interview requirements may counter-balance; \"human verified\" hiring emerging.",
  "flag_near_term_2027": false,
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:50.765877+00:00",
  "reference_class_match": {
    "decision": "keyword_filtered",
    "computed_at": "2026-04-30T01:49:13.796883+00:00",
    "best_id_unfiltered": "regulatory_freeze_window",
    "best_similarity_unfiltered": 0.5678
  },
  "validation_status_raw": "CONFIRMED",
  "composite_signal_score": 22.08,
  "flag_priority_watchlist": false,
  "flag_timeline_near_term": false,
  "rece