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233_010predictionEducationAI-timing

Alpha aims to build 10,000 schools and reach a billion kids over 20 years.

Predictor: Joe Liemandt · ep#233 "This $40M AI Company Is Using AI Tutors to Teach 2 Hours/Day | #233" · source

Prior probability
45.0%
Current probability
39.2%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
pending
Window
2026-01-01 – 2046-09-30
Edges in / out
7 / 0
Tickers exposed
33

Prediction text

Alpha aims to build 10,000 schools and reach a billion kids over 20 years. | the biggest one that I think to hit scale, building schools and rebuilding from scratch is, you know, I got 20 years, so we're going to build 10,000 schools, you know, and that's going to be great and I love it. Um but how do I get to a billion kids, right? How do we drive this out to a billion kids?

Verbatim quote

From episode "This $40M AI Company Is Using AI Tutors to Teach 2 Hours/Day | #233"
the biggest one that I think to hit scale, building schools and rebuilding from scratch is, you know, I got 20 years, so we're going to build 10,000 schools, you know, and that's going to be great and I love it. Um but how do I get to a billion kids, right? How do we drive this out to a billion kids?

Predictor: Joe Liemandt

κ + Brier as of 2026-05-22
κ (discount)
0.583
Brier
0.0064
excellent
Hits / Misses
1 / 0
of 1 resolved
Hit rate
100.0%
Calibration plot (stated vs observed)

Evidence about this node from Joe Liemandt 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

3 prob_history rows
0%25%50%75%100%prior 45%2026-04-302026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 39.2%

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 ✓

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: 39%)

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
LBP2026-05-03T02:00:01Z39.2%-1.2pp
Network propagation: 40.4% → 39.2%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z40.4%-1.8pp
Network propagation: 42.2% → 40.4%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z42.2%-2.8pp
Network propagation: 45.0% → 42.2%
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%0.4500.050-0.051
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.4500.050-0.045
prereqSEM_008
Training runs costing $10 billion for a single model will coDario Amodei
76.9%0.4500.050-0.038
prereq238_009
Recursive self-improvement is already happening now (no longAlex Wissner-Gross
78.1%0.4500.050-0.033
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.450-0.022

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (7)

Predictions that must hit first
TypePredTitleDomainLag
prereq238_009Recursive self-improvement is already happening now (no longer three years out)AI
prereqSEM_008Training runs costing $10 billion for a single model will commence sometime in 2025.AI
prereqSEM_0422025 will be the definitive year that agentic systems finally hit the mainstream.AI/Agents
prereqSEM_012Nvidia quadrupled chip production output while only doubling human headcount — achieved by deploying AI coding tools (Cursor, Claude Code) across engineering.AI/Manufacturing
killerTK14Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "10,000 schools / 1 billion kids",
  "url": "https://www.youtube.com/watch?v=X94eBT-VZnc",
  "mode": "ASPIRATION",
  "role": "Guest-CEO",
  "context": "the biggest one that I think to hit scale, building schools and rebuilding from scratch is, you know, I got 20 years, so we're going to build 10,000 schools, you know, and that's going to be great and I love it. Um but how do I get to a billion kids, right? How do we drive this out to a billion kids? And if motivation is your number one issue, right? This engine part's easy in the scheme of things.",
  "to_year": 2046,
  "verbatim": "the biggest one that I think to hit scale, building schools and rebuilding from scratch is, you know, I got 20 years, so we're going to build 10,000 schools, you know, and that's going to be great and I love it. Um but how do I get to a billion kids, right? How do we drive this out to a billion kids?",
  "conv_cues": "we're going to build 10,000 schools",
  "direction": "NUMERIC_TARGET",
  "from_year": 2026,
  "timeframe": "20 years (~2046)",
  "conv_level": "HIGH",
  "milestones": [
    {
      "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": -4,
      "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": -3,
      "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": -2,
      "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": -1,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "event",
      "label": "Alpha aims to build 10,000 schools and reach a billion kids over 20 years.",
      "status": "pending",
      "weight": 1,
      "ordinal": 0,
      "source_id": "233_010",
      "expected_date": "2040-05-30",
      "observed_date": null
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Alpha Schools",
  "attribution": "FIRST_PERSON",
  "episode_num": 233,
  "granularity": "RELATIVE_DURATION",
  "target_date": "2046-06-15",
  "display_date": "2040-05-30",
  "episode_date": "2026-02-25",
  "parse_method": "RELATIVE +20y",
  "domain_bucket": "Labor",
  "episode_title": "This $40M AI Company Is Using AI Tutors to Teach 2 Hours/Day | #233",
  "fault_line_id": "F001, F002, F003",
  "flag_repeated": false,
  "in_5yr_window": false,
  "appears_in_eps": "233",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 3,
  "ps_cluster_tags": [
    "C2",
    "C3",
    "C5"
  ],
  "active_end_month": "2046-12",
  "months_from_today": 242,
  "active_start_month": "2026-01",
  "december_dispersal": {
    "reason": "december_dispersal: domain=Education → 09/2046",
    "new_date": "2046-09-30",
    "old_date": "2046-12-31",
    "applied_at": "2026-04-30T16:28:34.304992+00:00"
  },
  "flag_nia_bracketed": false,
  "track_record_grade": "C+",
  "track_record_notes": "Alpha Schools; scaling claims largely internal.",
  "flag_near_term_2027": true,
  "flag_high_conviction": true,
  "milestones_phase2_at": "2026-05-01T21:36:15.945180+00:00",
  "milestones_derived_at": "2026-05-02T03:08:48.963597+00:00",
  "reference_class_match": {
    "decision": "key