← Cockpit
247_041predictionBiotech/LongevityAI-timing

AI-powered drugs have 85% phase 1 success vs 52% traditional

Predictor: Peter Diamandis · ep#247 "Elon Musk vs. Sam Altman, AI Job Loss, and OpenAI's $852B Valuation EP #247" · source

Prior probability
65.0%
Current probability
55.2%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
pending
Window
2026-01-01 – 2026-06-30
Edges in / out
7 / 7
Tickers exposed
33

Prediction text

AI-powered drugs have 85% phase 1 success vs 52% traditional | we're seeing is a, you know, phase one success rate of these AI developed drugs at 85% compared to 52%. And phase 2 success rates of AI developed drugs at 70% compared to 38%.

Verbatim quote

From episode "Elon Musk vs. Sam Altman, AI Job Loss, and OpenAI's $852B Valuation EP #247"
we're seeing is a, you know, phase one success rate of these AI developed drugs at 85% compared to 52%. And phase 2 success rates of AI developed drugs at 70% compared to 38%.

Predictor: Peter Diamandis

κ + Brier as of 2026-05-22
κ (discount)
0.875
Brier
0.0367
excellent
Hits / Misses
10 / 0
of 15 resolved
Hit rate
66.7%
Calibration plot (stated vs observed)

Evidence about this node from Peter Diamandis 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

7 prob_history rows
0%25%50%75%100%prior 65%2026-04-302026-05-032026-05-24
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 55.2%

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: 7 fired ✓
  1. 2025-05-31hitRecursion / Exscientia setback contrasts with Insilico success
    How: Recursion discontinues lead candidate REC-994 for cerebral cavernous malformation citing efficacy non-confirmation
    Source: Recursion (RXRX) press release / 8-K from May 2025conf 95%
    Notes: HIT — Recursion failure provides counter-evidence; AI-drug discovery has both successes (Insilico) and failures (Recursion).
  2. 2025-06-30hitInsilico Medicine Phase IIa positive results (TNIK inhibitor for IPF)
    How: Insilico Medicine publishes positive Phase IIa data for ISM001-055 (rentosertib) in idiopathic pulmonary fibrosis with measurable efficacy
    Source: https://www.morningglorysciences.com/en/from-beginner-to-expert-ai-in-drug-discovery-a-definitive-guide-from-lab-to-market-extra-chapter/ — Insilico TNIK inhibitor showed 98.4 mL FVC improvement vs 20.3 mL decline in placeboconf 99%
    Notes: HIT — first AI-designed molecule with both safety and efficacy in controlled trial. Validates 80-90% Phase 1 success rate framing.
  3. 2026-02-01hitIndustry analyst report confirms 80-90% AI-drug Phase 1 success rate
    How: Published research shows AI-discovered drugs have Phase 1 success rate 80-90% vs 52% historical baseline
    Source: https://www.humai.blog/ai-drugs-reach-the-clinic-2026-is-the-year-of-the-first-large-scale-test/conf 95%
    Notes: HIT — multiple sources confirm 80-90% Phase 1 success vs 52% baseline. Diamandis's 85% claim within range.
  4. 2026-06-01 → 2027-12-31pendingFirst AI-designed drug enters Phase III pivotal trial
    How: AI-discovered/designed drug from Insilico, Recursion, Isomorphic, or peer enters Phase III pivotal trial
    Source: ClinicalTrials.gov, BiopharmaDive, FierceBiotechconf 70%
  5. 2026-09-01 → 2027-12-31pendingIsomorphic Labs first IsoDDE molecule enters clinical trials
    How: Isomorphic Labs (Alphabet/GOOGL subsidiary) advances first internally-discovered or J&J-partnered molecule into Phase 1
    Source: Isomorphic Labs press, J&J pipeline disclosuresconf 55%

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: 55%)

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
LBP2026-05-24T02:00:02Z55.2%-1.3pp
Network propagation: 56.5% → 55.2%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
LBP2026-05-17T02:00:01Z56.5%-2.5pp
Network propagation: 59.0% → 56.4%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z59.0%-4.7pp
Network propagation: 63.6% → 59.0%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z63.6%-8.0pp
Network propagation: 71.7% → 63.6%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
legacy v12026-04-30T19:17:54Z71.7%+13.5pp
intake:99aa73db-75b1-4b1e-8470-a11f87b23937 bayesian_v2 inside=0.717 blend=0.717 LLR=0.597 κ=0.86 no_blend
LBP2026-04-30T16:39:51Z58.2%-2.7pp
Network propagation: 60.9% → 58.2%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z60.9%-4.1pp
Network propagation: 65.0% → 60.9%
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%0.6500.050-0.065
prereqSEM_012
Nvidia quadrupled chip production output while only doublingJensen Huang
75.0%0.6500.050-0.056
prereqSEM_008
Training runs costing $10 billion for a single model will coDario Amodei
76.9%0.6500.050-0.045
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.650+0.038
prereq238_009
Recursive self-improvement is already happening now (no longAlex Wissner-Gross
78.1%0.6500.050-0.038

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq232_055
We're exiting the industrial age permanently as recursive sePeter Diamandis
35.5%0.7000.050+0.049
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050+0.039
prereq241_043
ASI will arrive within 2 years to 5 years to this next decadPeter Diamandis
35.9%0.6500.050+0.018
prereqCMQ_002
By 2028, AI systems will reach 'independent researcher' leveSam Altman
31.4%0.5500.050+0.008
prereq231_013
Math is cooked (will be solved), physics cooked, biology chaAlex Wissner-Gross
35.4%0.6200.050+0.006

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 (7)

Predictions enabled by this
TypePredTitleDomainLag
prereq235_030Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 2033.Biotech/Longevity
prereq232_055We're exiting the industrial age permanently as recursive self-improvement unfolds.AI
prereq241_043ASI will arrive within 2 years to 5 years to this next decadeAI
prereq231_013Math is cooked (will be solved), physics cooked, biology char broiled.AI
prereqCMQ_002By 2028, AI systems will reach 'independent researcher' level — driving autonomous scientific discoveries without human intervention.AI
correlateCOD_BIO_001FDA finalizes or materially advances AI-for-drug-submission guidance by end 2026Biotech/Longevity
correlateCOD_BIO_002An AI-designed Isomorphic or peer drug enters or completes Phase 1 by end 2027Biotech/Longevity

Expected milestones (1)

From Sheet 17 Monitoring Triggers
Expected byDescriptionStatus
2026-12-31[Biology 2026-12] t readout (likely late 2026/early 2027) [247_041] AI-powered drugs have 85% phase 1 success vs 52% traditional [248_031] Enhanced Games world records will accelerate on a scaling-law-style trajectory ypending

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.750codex_research_packIsomorphic Labs - $600M funding to advance therapeutic programs into the cliniccorroboratespending2025-03-31
0.589fdaFDA ANDA078582: LEVETIRACETAM (LEVETIRACETAM) — STRIDES PHARMAmentionspending2026-04-21
0.584fdaFDA ANDA204345: CAPECITABINE (CAPECITABINE) — DR REDDYSmentionspending2026-05-07
0.579fdaFDA ANDA220274: SODIUM ACETATE (SODIUM ACETATE) — EXTROVISmentionspending2026-04-17
0.579fdaFDA ANDA204741: CAPECITABINE (CAPECITABINE) — AMNEAL PHARMSmentionspending2026-05-07
0.578fdaFDA ANDA077420: LAMOTRIGINE (LAMOTRIGINE) — RISINGmentionspending2026-05-15
0.571fdaFDA ANDA214216: EMTRICITABINE AND TENOFOVIR ALAFENAMIDE FUMARATE (EMTRICITABINE) — MACLEODS PHARMS LTDmentionspending2026-04-07
0.567fdaFDA ANDA219869: LOSARTAN POTASSIUM AND HYDROCHLOROTHIAZIDE (HYDROCHLOROTHIAZIDE) — HETERO LABS LTD Vmentionspending2026-05-27
0.563fdaFDA ANDA208002: LURASIDONE HYDROCHLORIDE (LURASIDONE HYDROCHLORIDE) — AMNEAL PHARMS COmentionspending2026-05-19
0.562fdaFDA ANDA208028: LURASIDONE HYDROCHLORIDE (LURASIDONE HYDROCHLORIDE) — INVAGEN PHARMSmentionspending2026-05-15

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "85% vs 52% phase 1; 70% vs 38% phase 2",
  "url": "https://www.youtube.com/watch?v=5ak26W2YNRY",
  "mode": "THESIS",
  "role": "Host",
  "context": "phase one success rate of these AI developed drugs at 85% compared to 52%",
  "to_year": 2026,
  "verbatim": "we're seeing is a, you know, phase one success rate of these AI developed drugs at 85% compared to 52%. And phase 2 success rates of AI developed drugs at 70% compared to 38%.",
  "direction": "NUMERIC_TARGET",
  "from_year": 2026,
  "timeframe": "Current observation",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_post_event",
      "label": "Recursion / Exscientia setback contrasts with Insilico success",
      "notes": "HIT — Recursion failure provides counter-evidence; AI-drug discovery has both successes (Insilico) and failures (Recursion).",
      "source": "Recursion (RXRX) press release / 8-K from May 2025",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.95,
      "expected_date": "2025-05-31",
      "observed_date": "2025-05-31",
      "research_origin": "deep_research",
      "measurement_criterion": "Recursion discontinues lead candidate REC-994 for cerebral cavernous malformation citing efficacy non-confirmation"
    },
    {
      "kind": "llm_pre_event",
      "label": "Insilico Medicine Phase IIa positive results (TNIK inhibitor for IPF)",
      "notes": "HIT — first AI-designed molecule with both safety and efficacy in controlled trial. Validates 80-90% Phase 1 success rate framing.",
      "source": "https://www.morningglorysciences.com/en/from-beginner-to-expert-ai-in-drug-discovery-a-definitive-guide-from-lab-to-market-extra-chapter/ — Insilico TNIK inhibitor showed 98.4 mL FVC improvement vs 20.3 mL decline in placebo",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://www.morningglorysciences.com/en/from-beginner-to-expert-ai-in-drug-discovery-a-definitive-guide-from-lab-to-market-extra-chapter/",
      "expected_date": "2025-06-30",
      "observed_date": "2025-06-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Insilico Medicine publishes positive Phase IIa data for ISM001-055 (rentosertib) in idiopathic pulmonary fibrosis with measurable efficacy"
    },
    {
      "kind": "llm_pre_event",
      "label": "Industry analyst report confirms 80-90% AI-drug Phase 1 success rate",
      "notes": "HIT — multiple sources confirm 80-90% Phase 1 success vs 52% baseline. Diamandis's 85% claim within range.",
      "source": "https://www.humai.blog/ai-drugs-reach-the-clinic-2026-is-the-year-of-the-first-large-scale-test/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.humai.blog/ai-drugs-reach-the-clinic-2026-is-the-year-of-the-first-large-scale-test/",
      "expected_date": "2026-04-01",
      "observed_date": "2026-02-01",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-06-30",
        "from": "2026-01-01"
      },
      "measurement_criterion": "Published research shows AI-discovered drugs have Phase 1 success rate 80-90% vs 52% historical baseline"
    },
    {
      "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",
    
... (truncated)