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232_005predictionLabor/JobsAI-timing

Big enterprises will not reach total AI efficiency very fast.

Predictor: Ben Horowitz · ep#232 "Ben Horowitz: xAI Executive Exodus, Apple's AI Crisis, The Pace of AI | EP #232" · source

Prior probability
50.0%
Current probability
42.3%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2028-06-01 – 2028-06-30
Edges in / out
9 / 5
Tickers exposed
33

Prediction text

Big enterprises will not reach total AI efficiency very fast. | I also think they're not going to go to total efficiency very fast. Like I mean I could be wrong but like I've dealt with these guys. They've had plenty of opportunities to be more efficient and uh we'll see.

Verbatim quote

From episode "Ben Horowitz: xAI Executive Exodus, Apple's AI Crisis, The Pace of AI | EP #232"
I also think they're not going to go to total efficiency very fast. Like I mean I could be wrong but like I've dealt with these guys. They've had plenty of opportunities to be more efficient and uh we'll see.

Predictor: Ben Horowitz

κ + Brier as of 2026-05-22
κ (discount)
0.500
Brier
Hits / Misses
0 / 0
Hit rate

Evidence about this node from Ben Horowitz 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 50%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 42.3%

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: 8 fired ✓ · 2 pending
  1. 2026-03-01hitWriter 2026 enterprise AI report: 79% of orgs face challenges adopting AI (double-digit increase from 2025)
    How: Major enterprise survey shows >70% of orgs cite blockers slowing AI deployment
    Source: deep_research_enrichedconf 92%
  2. 2026-04-01hitGoldman Sachs: 80% of US companies have NOT yet deployed AI productively (2026 Q1)
    How: Bank-research survey shows majority of US firms still pre-deployment despite 2-3 yr ChatGPT availability
    Source: deep_research_enrichedconf 85%
  3. 2026-04-01hitROI gap persists: 97% executives say benefiting from AI but only 29% see significant org ROI
    How: Two-tier finding: positive perception >80% but measurable ROI <40% across multiple Fortune-class surveys
    Source: deep_research_enrichedconf 85%
  4. 2027-09-01 → 2028-06-30pendingAverage net productivity increase from AI remains <15% in 2027 enterprise surveys (vs hyped 30-50%)
    How: BCG/McKinsey/Deloitte annual survey shows median enterprise productivity gain from AI <15%
    Source: deep_research_enrichedconf 65%
  5. 2028-01-01 → 2028-06-30pendingFortune 500 mass-layoff wave attributable to AI displacement does NOT materialize at >5% headcount level by 2028
    How: Aggregate Fortune-500 headcount reduction attributed to AI <5% on TTM basis (Challenger Gray report or BLS)
    Source: deep_research_enrichedconf 55%
  6. 2036-09-06pendingMoon base will exist in 10 years

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: 42%)

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
LBP2026-05-17T02:00:01Z42.3%-1.0pp
Network propagation: 43.4% → 42.3%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-03T02:00:01Z43.4%-1.4pp
Network propagation: 44.8% → 43.4%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z44.8%-2.1pp
Network propagation: 46.9% → 44.8%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z46.9%-3.1pp
Network propagation: 50.0% → 46.9%
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
prereqSEM_042
2025 will be the definitive year that agentic systems finallKevin Weil
73.8%0.5000.050-0.046
prereq235_038
David Sinclair begins partial epigenetic reprogramming trialPeter Diamandis
74.0%0.5000.050-0.043
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.5000.050-0.039
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.500+0.032
prereqSEM_008
Training runs costing $10 billion for a single model will coDario Amodei
76.9%0.5000.050-0.031

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq246_016
Dragonfly nuclear-powered octicopter arrives at Titan in 203Peter Diamandis
35.6%0.6500.050-0.056
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.050
prereq240_036
TEPCO's restarted reactor will support 20% of Japan's electrPeter Diamandis
34.3%0.6500.050-0.042
prereq239_008
Moon base will exist in 10 yearsElon Musk
28.8%0.5500.050-0.029
prereqSEM_034
True artificial general intelligence will be achieved betweeDemis Hassabis
28.7%0.5500.050-0.028

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (9)

Predictions that must hit first
TypePredTitleDomainLag
prereq235_038David Sinclair begins partial epigenetic reprogramming trials with Life Biosciences in March 2026.Biotech/Longevity
prereqSEM_008Training runs costing $10 billion for a single model will commence sometime in 2025.AI
prereq238_009Recursive self-improvement is already happening now (no longer three years out)AI
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_0422025 will be the definitive year that agentic systems finally hit the mainstream.AI/Agents
correlateS_HUMANOID_ENTERPRISE_2028Humanoid R2: 100K+ enterprise by Nov 2028humanoid_deployment
killerTK14Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (5)

Predictions enabled by this
TypePredTitleDomainLag
prereq235_030Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 2033.Biotech/Longevity
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
prereqSEM_034True artificial general intelligence will be achieved between 2032 and 2042 — 'first we solve AI, then use AI to solve everything else'.AI/AGI
prereq239_008Moon base will exist in 10 yearsSpace

Linked documents (2)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.650arxivIt`s All About Speed: AI`s Impact on Workflow in Music Productionmentionspending2026-05-28
0.601manifoldIf we survive the singularity, will the average guy be able to get a catgirl harem with almost zero effort?20%mentionspending2026-05-08

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=C1GLT9_tag0",
  "mode": "PREDICTION",
  "role": "Guest-VC",
  "context": "I also think they're not going to go to total efficiency very fast. Like I mean I could be wrong but like I've dealt with these guys. They've had plenty of opportunities to be more efficient and uh we'll see. But we'll see. We'll see. We'll see.",
  "to_year": 2030,
  "verbatim": "I also think they're not going to go to total efficiency very fast. Like I mean I could be wrong but like I've dealt with these guys. They've had plenty of opportunities to be more efficient and uh we'll see.",
  "conv_cues": "I could be wrong; we'll see",
  "direction": "NOT_HAPPEN",
  "from_year": 2026,
  "timeframe": "near-term",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Writer 2026 enterprise AI report: 79% of orgs face challenges adopting AI (double-digit increase from 2025)",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -10,
      "source_id": null,
      "confidence": 0.92,
      "source_url": "https://writer.com/blog/enterprise-ai-adoption-2026/",
      "expected_date": "2026-03-01",
      "observed_date": "2026-03-01",
      "research_origin": "deep_research",
      "measurement_criterion": "Major enterprise survey shows >70% of orgs cite blockers slowing AI deployment"
    },
    {
      "kind": "llm_pre_event",
      "label": "Goldman Sachs: 80% of US companies have NOT yet deployed AI productively (2026 Q1)",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -9,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://fortune.com/2026/04/01/ai-worker-productivity-adoption-goldman-sachs-saves-60-minutes-per-day/",
      "expected_date": "2026-04-01",
      "observed_date": "2026-04-01",
      "research_origin": "deep_research",
      "measurement_criterion": "Bank-research survey shows majority of US firms still pre-deployment despite 2-3 yr ChatGPT availability"
    },
    {
      "kind": "llm_pre_event",
      "label": "ROI gap persists: 97% executives say benefiting from AI but only 29% see significant org ROI",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://writer.com/blog/enterprise-ai-adoption-2026/",
      "expected_date": "2026-04-01",
      "observed_date": "2026-04-01",
      "research_origin": "deep_research",
      "measurement_criterion": "Two-tier finding: positive perception >80% but measurable ROI <40% across multiple Fortune-class surveys"
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    {
      "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": -7,
      "source_id": "SEM_012",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Training runs costing $10 billion for a single model will commence sometime in 2025.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -6,
      "source_id": "SEM_008",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "2025 will be the definitive year that agentic systems finally hit the mainstream.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_042",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Recursive self-improvement is already happening now (no longer three years out)",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -4,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "ob
... (truncated)