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248_049predictionAIAI-timing

Humans (weaker intelligences) can successfully align/contain super-intelligences via weak-to-strong supervision.

Predictor: Alex Wissner-Gross · ep#248 "Sam Altman's Attack, Amazon vs. Starlink, and What Opus 4.7 Actually Means | #248" · source

Prior probability
50.0%
Current probability
42.3%
evolves via intake + LBP
Conviction
4/5
Signal quality
B
Resolution
pending
Window
2026-06-01 – 2026-06-30
Edges in / out
8 / 5
Tickers exposed
33

Prediction text

Humans (weaker intelligences) can successfully align/contain super-intelligences via weak-to-strong supervision. | this this entire exercise is a proxy for humans which are either already or about to be effectively weaker weaker intelligence is supervising the stronger int intelligence that that works... I I think this bodess very well for sort of a a tower of alignment where the weaker uh meat bodies, if you will, that that are humans unaded biologically are able to contain and align super intelligences

Verbatim quote

From episode "Sam Altman's Attack, Amazon vs. Starlink, and What Opus 4.7 Actually Means | #248"
this this entire exercise is a proxy for humans which are either already or about to be effectively weaker weaker intelligence is supervising the stronger int intelligence that that works... I I think this bodess very well for sort of a a tower of alignment where the weaker uh meat bodies, if you will, that that are humans unaded biologically are able to contain and align super intelligences

Predictor: Alex Wissner-Gross

κ + Brier as of 2026-05-22
κ (discount)
0.844
Brier
0.0341
excellent
Hits / Misses
6 / 1
of 11 resolved
Hit rate
54.5%
Calibration plot (stated vs observed)

Evidence about this node from Alex Wissner-Gross 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%2026-04-302026-05-032026-05-10
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: 7 fired ✓
  1. 2025-06-30hitMid-2025 benchmark of weak-to-strong methods improves narrow-domain oversight
    How: Independent benchmarks (DeepMind, Anthropic, academic labs) show weak-to-strong methods improving oversight fidelity in narrow domains by mid-2025
    Source: https://www.hushvault.ie/2026/01/27/superalignment-everything-you-need-to-know-for-ai-safety/conf 85%
    Notes: HIT — narrow-domain oversight gains documented; partially supports the prediction.
  2. 2024-12-14hitOpenAI Superalignment 'weak-to-strong generalization' research paper baseline
    How: OpenAI's foundational weak-to-strong generalization paper / replication / extension confirms GPT-2-class models can elicit GPT-3.5-level performance from GPT-4 via supervised fine-tuning
    Source: https://openai.com/index/weak-to-strong-generalization/conf 99%
    Notes: HIT — OpenAI's published research is the canonical evidence base. Wissner-Gross's claim is grounded in this research.
  3. 2026-04-01 → 2026-12-31pendingAnthropic / DeepMind / OpenAI publish post-2025 results on weak-to-strong supervising frontier models
    How: At least one frontier lab publishes results applying weak-to-strong techniques to GPT-5/Claude Mythos/Gemini-3 class models with measurable safety improvement
    Source: Anthropic, DeepMind, OpenAI alignment research blogsconf 55%
    Notes: Critical for moving from 2024 GPT-2/GPT-4 demo to actually-superhuman setup.
  4. 2026-06-01 → 2027-06-30pendingAdversarial demonstration: weak-to-strong fails when the strong model is intentionally deceptive
    How: Academic / industry research demonstrates weak-to-strong supervision can be defeated by adversarially-trained strong models that exhibit alignment-faking
    Source: Anthropic alignment-faking research, MATS / Apollo Research papersconf 65%
    Notes: Cascade — would partially refute Wissner-Gross's optimism. Anthropic's 2024-25 alignment-faking results already lean this direction.
  5. 2027-01-01 → 2029-12-31pendingFirst superintelligence-class system contained / aligned by weaker supervisor in deployment
    How: Frontier lab demonstrates production-scale supervision of a system characterized as superhuman in a domain by humans/weaker models, with measurable safety properties holding under adversarial testing
    Source: Frontier lab research and deployment reportsconf 20%
    Notes: Cascade — direct test of the entire prediction. Most analysts model this as 2027-2030+ open.

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
LBP2026-05-10T02:00:02Z42.3%-1.0pp
Network propagation: 43.3% → 42.3%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z43.3%-1.5pp
Network propagation: 44.8% → 43.3%
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
prereq234_012
Anthropic revenue will cross OpenAI revenue in middle of 202Peter Diamandis
67.1%0.5000.050-0.074
prereqSEM_042
2025 will be the definitive year that agentic systems finallKevin Weil
73.8%0.5000.050-0.045
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%0.5000.050-0.030

Top outgoing (children)

Predictions THIS node influences

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
prereq231_013
Math is cooked (will be solved), physics cooked, biology chaAlex Wissner-Gross
35.4%0.6200.050-0.067
prereq241_043
ASI will arrive within 2 years to 5 years to this next decadPeter Diamandis
35.9%0.6500.050-0.059
prereqCMQ_002
By 2028, AI systems will reach 'independent researcher' leveSam Altman
31.4%0.5500.050-0.056
prereq235_030
Ray Kurzweil predicts Longevity Escape Velocity (LEV) by 203Ray Kurzweil
39.2%0.7500.050-0.051
prereq232_055
We're exiting the industrial age permanently as recursive sePeter Diamandis
35.5%0.7000.050-0.034

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (8)

Predictions that must hit first
TypePredTitleDomainLag
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
prereq234_012Anthropic revenue will cross OpenAI revenue in middle of 2026Markets/Stocks
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
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

Linked documents (7)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=LVvleNtllPk",
  "mode": "THESIS",
  "role": "Host",
  "context": "humans unaded biologically are able to contain and align super intelligences that are stronger capability wise.",
  "to_year": 2026,
  "verbatim": "this this entire exercise is a proxy for humans which are either already or about to be effectively weaker weaker intelligence is supervising the stronger int intelligence that that works... I I think this bodess very well for sort of a a tower of alignment where the weaker uh meat bodies, if you will, that that are humans unaded biologically are able to contain and align super intelligences",
  "conv_cues": "bodes very well",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Mid-2025 benchmark of weak-to-strong methods improves narrow-domain oversight",
      "notes": "HIT — narrow-domain oversight gains documented; partially supports the prediction.",
      "source": "https://www.hushvault.ie/2026/01/27/superalignment-everything-you-need-to-know-for-ai-safety/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://www.hushvault.ie/2026/01/27/superalignment-everything-you-need-to-know-for-ai-safety/",
      "expected_date": "2025-06-30",
      "observed_date": "2025-06-30",
      "research_origin": "deep_research",
      "measurement_criterion": "Independent benchmarks (DeepMind, Anthropic, academic labs) show weak-to-strong methods improving oversight fidelity in narrow domains by mid-2025"
    },
    {
      "kind": "llm_pre_event",
      "label": "OpenAI Superalignment 'weak-to-strong generalization' research paper baseline",
      "notes": "HIT — OpenAI's published research is the canonical evidence base. Wissner-Gross's claim is grounded in this research.",
      "source": "https://openai.com/index/weak-to-strong-generalization/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://openai.com/index/weak-to-strong-generalization/",
      "expected_date": "2025-12-31",
      "observed_date": "2024-12-14",
      "research_origin": "deep_research",
      "measurement_criterion": "OpenAI's foundational weak-to-strong generalization paper / replication / extension confirms GPT-2-class models can elicit GPT-3.5-level performance from GPT-4 via supervised fine-tuning"
    },
    {
      "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": -5,
      "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": -4,
      "source_id": "SEM_008",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    },
    {
      "kind": "prereq",
      "label": "Anthropic revenue will cross OpenAI revenue in middle of 2026",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -3,
      "source_id": "234_012",
      "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",
    
... (truncated)