← Cockpit
233_019predictionEducationAI-timing

Alpha plans to conduct a million-student pharmaceutical-grade randomized control trial on Timeback.

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

Prior probability
40.0%
Current probability
34.8%
evolves via intake + LBP
Conviction
3/5
Signal quality
D
Resolution
pending
Window
2030-06-01 – 2030-06-30
Edges in / out
8 / 5
Tickers exposed
33

Prediction text

Alpha plans to conduct a million-student pharmaceutical-grade randomized control trial on Timeback. | one of the things that we'd love to do is be able to, you know, as we get all this data out there in this, you know, time back at scale is do a million student randomized control trial so that every pharmaceutical grade trial so everybody can be like, "This isn't a hoax, right? This this actually works."

Verbatim quote

From episode "This $40M AI Company Is Using AI Tutors to Teach 2 Hours/Day | #233"
one of the things that we'd love to do is be able to, you know, as we get all this data out there in this, you know, time back at scale is do a million student randomized control trial so that every pharmaceutical grade trial so everybody can be like, "This isn't a hoax, right? This this actually works."

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 40%2026-04-302026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 34.8%

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: 5 fired ✓ · 4 pending
  1. 2026-06-01 → 2027-12-31pendingAlpha School publicly announces formal RCT protocol with IRB approval
    How: Alpha School / Timeback / Trilogy publishes preregistered RCT protocol on ClinicalTrials.gov, OSF Registries, AEA RCT Registry, or equivalent — with IRB approval letter
    Source: deep_research_enrichedconf 45%
  2. 2026-09-01 → 2028-06-30pendingTimeback enrollment reaches 100K students
    How: Alpha School / Timeback discloses or third-party verifies 100,000+ enrolled students using the Timeback platform
    Source: llm_enrichedconf 50%
  3. 2027-06-01 → 2029-12-31pendingFirst-stage RCT (10K-50K students) results published
    How: Peer-reviewed or NBER working paper publishes outcomes of an Alpha-Timeback RCT with sample size >=10K students
    Source: llm_enrichedconf 40%
  4. 2028-01-01 → 2030-06-30pendingMillion-student RCT enrollment begins
    How: Alpha School / Timeback announces enrollment opening for an RCT with declared target sample size >=1,000,000 students — preregistered protocol
    Source: llm_enrichedconf 25%
  5. 2029-06-01 → 2031-12-31pendingMillion-student RCT primary endpoint readout
    How: Primary endpoint results of the million-student Timeback RCT published in peer-reviewed journal or top economics working paper venue
    Source: llm_enrichedconf 15%
  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: 35%)

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
LBP2026-05-03T02:00:01Z34.8%-1.1pp
Network propagation: 35.9% → 34.8%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z35.9%-1.6pp
Network propagation: 37.5% → 35.9%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z37.5%-2.5pp
Network propagation: 40.0% → 37.5%
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%0.4000.050-0.043
prereq235_038
David Sinclair begins partial epigenetic reprogramming trialPeter Diamandis
74.0%0.4000.050-0.042
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.4000.050-0.038
prereqSEM_008
Training runs costing $10 billion for a single model will coDario Amodei
76.9%0.4000.050-0.032
prereq238_009
Recursive self-improvement is already happening now (no longAlex Wissner-Gross
78.1%0.4000.050-0.028

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.104
prereq246_016
Dragonfly nuclear-powered octicopter arrives at Titan in 203Peter Diamandis
35.6%0.6500.050-0.102
prereq240_036
TEPCO's restarted reactor will support 20% of Japan's electrPeter Diamandis
34.3%0.6500.050-0.089
prereq239_008
Moon base will exist in 10 yearsElon Musk
28.8%0.5500.050-0.068
prereqSEM_034
True artificial general intelligence will be achieved betweeDemis Hassabis
28.7%0.5500.050-0.067

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
prereq238_009Recursive self-improvement is already happening now (no longer three years out)AI
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
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
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 (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.609fdaFDA BLA761055: DUPIXENT (DUPILUMAB) — REGENERON PHARMACEUTICALSmentionspending2026-04-22
0.606arxivAdaptive Experimentation for Censored Survival Outcomesmentionspending2026-05-18
0.603fdaFDA NDA218197: TRUQAP (CAPIVASERTIB) — ASTRAZENECAmentionspending2026-05-27
0.602arxivDARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experimentsmentionspending2026-05-07
0.600fdaFDA NDA213756: KOSELUGO (SELUMETINIB SULFATE) — ASTRAZENECAmentionspending2026-05-26
0.598fdaFDA ANDA078257: ONDANSETRON HYDROCHLORIDE (ONDANSETRON HYDROCHLORIDE) — ONESOURCE SPECIALTYmentionspending2026-04-15
0.597fdaFDA ANDA201446: FALLBACK SOLO (LEVONORGESTREL) — LUPINmentionspending2026-05-01
0.597fdaFDA NDA219943: KOSELUGO (SELUMETINIB) — ASTRAZENECAmentionspending2026-05-26
0.593fdaFDA ANDA079099: LAMOTRIGINE (LAMOTRIGINE) — GLENMARK PHARMS LTDmentionspending2026-04-17
0.592fdaFDA NDA217180: JAKAFI XR (RUXOLITINIB PHOSPHATE) — INCYTE CORPORATIONmentionspending2026-05-01

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "1 million students",
  "url": "https://www.youtube.com/watch?v=X94eBT-VZnc",
  "mode": "ASPIRATION",
  "role": "Guest-CEO",
  "caveats": "Aspirational rather than committed",
  "context": "I would love, you know, one of the things that we'd love to do is be able to, you know, as we get all this data out there in this, you know, time back at scale is do a million student randomized control trial so that every pharmaceutical grade trial so everybody can be like, \"This isn't a hoax, right? This this actually works.\"",
  "to_year": 2035,
  "verbatim": "one of the things that we'd love to do is be able to, you know, as we get all this data out there in this, you know, time back at scale is do a million student randomized control trial so that every pharmaceutical grade trial so everybody can be like, \"This isn't a hoax, right? This this actually works.\"",
  "conv_cues": "we'd love to do",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "future (unspecified)",
  "conv_level": "MEDIUM",
  "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": -9,
      "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": -8,
      "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": -7,
      "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": -6,
      "source_id": "238_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "David Sinclair begins partial epigenetic reprogramming trials with Life Biosciences in March 2026.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "235_038",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "llm_pre_event",
      "label": "Alpha School publicly announces formal RCT protocol with IRB approval",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.45,
      "source_url": "https://alpha.school/blog/introducing-timeback-the-next-evolution-of-alphas-model/",
      "expected_date": "2027-03-17",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Alpha School / Timeback / Trilogy publishes preregistered RCT protocol on ClinicalTrials.gov, OSF Registries, AEA RCT Registry, or equivalent — with IRB approval letter"
    },
    {
      "kind": "llm_pre_event",
      "label": "Timeback enrollment reaches 100K students",
      "source": "llm_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.5,
      "expected_date": "2027-08-01",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2028-06-30",
        "from": "2026-09-01"
      },
      "measurement_criterion": "Alpha School / Timeback discloses or third-party verifies 100,000+ enrolled students using the Timeback platform"
  
... (truncated)