← Cockpit
240_049predictionLabor/JobsAI-scaling

Last jobs to be automated will be government jobs, university jobs

Predictor: Dave Blundin · ep#240 "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse" · source

Prior probability
60.0%
Current probability
49.2%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
pending
Window
2026-04-30 – 2040-12-31
Edges in / out
11 / 5
Tickers exposed
37

Prediction text

Last jobs to be automated will be government jobs, university jobs | I think the last job to be automated will be government jobs. Also, other similar things like university jobs and so forth.

Watch events: BLS employment reports; tech layoff trackers; Underemployment rate; Yang quarterly updates

Verbatim quote

From episode "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse"
I think the last job to be automated will be government jobs. Also, other similar things like university jobs and so forth.

Predictor: Dave Blundin

κ + Brier as of 2026-05-22
κ (discount)
0.821
Brier
0.0491
excellent
Hits / Misses
3 / 2
of 9 resolved
Hit rate
33.3%
Calibration plot (stated vs observed)

Evidence about this node from Dave Blundin 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 60%2026-04-302026-05-032026-05-10
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 49.2%

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: 4 fired ✓ · 5 pending
  1. 2026-06-01 → 2030-12-31pendingMajor automation cuts in private-sector white-collar roles announced (legal, finance, consulting, support) precede any equivalent government RIFs
    How: At least 5 Fortune 500 firms announce >=10% white-collar headcount reduction explicitly tied to AI/agentic automation, while federal/state RIFs from comparable AI automation remain absent or limited
    Source: Company press releases; Challenger Job Cuts reportsconf 65%
  2. 2026-01-01 → 2032-12-31pendingPublic-sector union activity (AFGE, NEA, AFT, state employee unions) successfully blocks or substantially limits AI displacement of routine government/university roles
    How: At least 3 federal or state collective bargaining agreements include explicit limits on AI-driven workforce reduction, OR union-backed legislation passes restricting government AI workforce displacement
    Source: AFGE/NEA contract disclosures; state legislative trackingconf 55%
  3. 2027-01-01 → 2032-12-31pendingFederal/state government workforce shows persistent agentic AI adoption gap vs private sector (>=24 months lag in any benchmarked workflow category)
    How: GAO, OPM, or comparable analyst report documents that public-sector AI agent deployment for routine workflows lags equivalent private-sector adoption by at least 24 months in 3+ workflow categories
    Source: GAO reports; OPM Federal Workforce Dataconf 70%
  4. 2028-01-01 → 2035-12-31pendingUniversity faculty headcount remains stable or grows even as enterprise white-collar roles in equivalent skill bands contract >=20%
    How: IPEDS/NCES faculty employment data shows tenure-track and full-time faculty headcount stable (within +/-5% of 2025 baseline) while BLS data shows >=20% decline in equivalent-band private-sector roles (analysts, consultants, researchers)
    Source: NCES IPEDS; BLS Occupational Employment Statisticsconf 50%
  5. 2035-01-01 → 2040-12-31pendingAggregate timeline: by terminal date, government and university employment shows the smallest cumulative AI-driven decline among all major sectors
    How: BLS or OECD sector employment data shows public administration + higher education with smallest aggregate headcount decline (vs 2025 baseline) among 10 major NAICS sectors over the period
    Source: BLS CES; OECD Employment Outlookconf 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: 49%)

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
LBP2026-05-10T02:00:02Z49.2%-1.5pp
Network propagation: 50.7% → 49.2%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z50.7%-2.7pp
Network propagation: 53.4% → 50.7%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z53.4%-2.7pp
Network propagation: 56.1% → 53.4%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z56.1%-3.9pp
Network propagation: 60.0% → 56.1%
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_ASI_SLOW_2040PLUS
ASI slow: post-2040 / soft takeoff
60.0%0.6000.050-0.112
prereqSEM_015
Nvidia agreed to remit 15% of China chip-sale revenue directJensen Huang
66.3%0.6000.050-0.073
prereqSEM_027
Nvidia Data Center revenue +66% YoY, contributing ~90% of $5Joseph Moore
68.3%0.6000.050-0.072
killerTK05
Rate Regime Persistence (10y > 5% through 2028)
30.0%0.0500.600-0.057
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.600+0.053

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq232_057
First person to arrive on moon/Mars will find bed made by OpDave Blundin
34.8%0.6000.050-0.032
prereq242_023
World will have 10x more wealth around 2034-2036Dave Blundin
34.2%0.6000.050-0.025
prereq246_016
Dragonfly nuclear-powered octicopter arrives at Titan in 203Peter Diamandis
35.6%0.6500.050-0.016
prereq230_020
Peter's 14-year-old son Milan will never get a driver's licePeter Diamandis
34.7%0.6500.050-0.006
prereq240_036
TEPCO's restarted reactor will support 20% of Japan's electrPeter Diamandis
34.3%0.6500.050-0.002

Ticker exposure

37 ticker(s) linked

Beneficiaries (24)

MUWULFIRENEQIXALABAPLDASMIYASMLPLABNVDANBISCRWVAAPLAMTAMZNDELLGOOGLIRMLNVGYMETAMSFTORCLSFTBYSTX

Adverse (6)

ACNGENCHGGIBMWNSLRN

Prerequisites (11)

Predictions that must hit first
TypePredTitleDomainLag
prereqSEM_011Nvidia became the world's first $5 trillion company (late 2025), operating a near-monopoly on advanced AI chips.Capital Markets
prereqSEM_027Nvidia Data Center revenue +66% YoY, contributing ~90% of $57B fiscal Q3 revenue; >$4.5T market cap entirely underpinned by AI silicon.Capital Markets
prereqSEM_014Nvidia's Arizona-based TSMC factory successfully fabricated cutting-edge semiconductors on US soil for first time in decades (October 2025).Manufacturing
prereqSEM_012Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) across engineering.AI/Manufacturing
prereqSEM_015Nvidia agreed to remit 15% of China chip-sale revenue directly to US government in exchange for reversing specific AI chip export bans.Policy/Semis
prereqS_ASI_SLOW_2040PLUSASI slow: post-2040 / soft takeoffasi_recursive_self_improvement
killerTK05Rate Regime Persistence (10y > 5% through 2028)
killerTK04Macro Recession 2026-27 (Structural Deleveraging)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (5)

Predictions enabled by this
TypePredTitleDomainLag
prereq230_020Peter's 14-year-old son Milan will never get a driver's license.Auto/Transport
prereq246_016Dragonfly nuclear-powered octicopter arrives at Titan in 2034.Space
prereq240_036TEPCO's restarted reactor will support 20% of Japan's electric needs by 2040Energy
prereq232_057First person to arrive on moon/Mars will find bed made by Optimus robots; robots not astronauts lead exploration.Robotics
prereq242_023World will have 10x more wealth around 2034-2036Macro/Economy

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=uOGHXAfvK8w",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "I think the last job to be automated will be government jobs. Also, other similar things like university jobs and so forth.",
  "verbatim": "I think the last job to be automated will be government jobs. Also, other similar things like university jobs and so forth.",
  "conv_cues": "I think",
  "direction": "HAPPEN",
  "timeframe": "Future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "prereq",
      "label": "Nvidia became the world's first $5 trillion company (late 2025), operating a near-monopoly on advanced AI chips.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -9,
      "source_id": "SEM_011",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Nvidia Data Center revenue +66% YoY, contributing ~90% of $57B fiscal Q3 revenue; >$4.5T market cap entirely underpinned by AI silicon.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -8,
      "source_id": "SEM_027",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Nvidia's Arizona-based TSMC factory successfully fabricated cutting-edge semiconductors on US soil for first time in decades (October 2025).",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -7,
      "source_id": "SEM_014",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) a",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -6,
      "source_id": "SEM_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Nvidia agreed to remit 15% of China chip-sale revenue directly to US government in exchange for reversing specific AI chip export bans.",
      "status": "pending",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_015",
      "expected_date": "2026-06-25",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Major automation cuts in private-sector white-collar roles announced (legal, finance, consulting, support) precede any equivalent government RIFs",
      "source": "Company press releases; Challenger Job Cuts reports",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.65,
      "expected_date": "2028-09-15",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2030-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "At least 5 Fortune 500 firms announce >=10% white-collar headcount reduction explicitly tied to AI/agentic automation, while federal/state RIFs from comparable AI automation remain absent or limited"
    },
    {
      "kind": "llm_post_event",
      "label": "Public-sector union activity (AFGE, NEA, AFT, state employee unions) successfully blocks or substantially limits AI displacement of routine government/university roles",
      "source": "AFGE/NEA contract disclosures; state legislative tracking",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.55,
      "expected_date": "2029-07-02",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2032-12-31",
        "from": "2026-01-01"
      },
      "measurement_criterion": "At least 3 federal or state collective bargaining agreements include explicit limits on AI-driven workforce reduction, OR union-backed legislation passes restricting government AI workforce displacement"
    },
    {
      "kind": "llm_pre_event",
      "label": "Federal/state gov
... (truncated)