← Cockpit
238_031predictionOtherAI-timing

Future is unpredictable beyond three weeks in the AI era

Predictor: Salim Ismail · ep#238 "Meta Buys Moltbook, GPT 5.4, and Fruitfly Brain Upload | Moonshots Live at The Abundance Summit 238" · source

Prior probability
50.0%
Current probability
45.0%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
— – —
Edges in / out
4 / 0
Tickers exposed
33

Prediction text

Future is unpredictable beyond three weeks in the AI era | I keep on asking the experts I run into how far out can you predict the future? Yeah. And it used to be like 20 years and then it was like 10 years and now it's like 3 weeks

Verbatim quote

From episode "Meta Buys Moltbook, GPT 5.4, and Fruitfly Brain Upload | Moonshots Live at The Abundance Summit 238"
I keep on asking the experts I run into how far out can you predict the future? Yeah. And it used to be like 20 years and then it was like 10 years and now it's like 3 weeks

Predictor: Salim Ismail

κ + Brier as of 2026-05-22
κ (discount)
0.643
Brier
0.0144
excellent
Hits / Misses
1 / 0
of 2 resolved
Hit rate
50.0%
Calibration plot (stated vs observed)

Evidence about this node from Salim Ismail 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

2 prob_history rows
0%25%50%75%100%prior 50%2026-04-302026-04-30
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 45.0%

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 pending
  1. 2026-05-01 → 2026-12-31pendingFrontier model releases continue at <90 day cadence (GPT-5.5, Claude 4.7, Gemini 3.1) showing capability shift index updating weekly
    How: Stanford AI Index 2026 confirms <90-day frontier release cadence and Artificial Analysis / LiveBench leaderboards continue weekly score updates with capability deltas >2 points/quarter
    Source: deep_research_enrichedconf 85%
  2. 2026-06-01 → 2027-03-31pendingGPQA Diamond saturation crossing 95% expert ceiling (forecast horizons collapse below 1 month)
    How: At least one frontier model scores >=95% on GPQA Diamond per Stanford HAI / LMM leaderboard, and analyst commentary cites 'unforecastable' AI progress
    Source: deep_research_enrichedconf 78%
  3. 2026-09-01 → 2027-12-31pendingPublic commentary from McKinsey / WEF / Anthropic explicitly references prediction horizon shrinking from years to weeks/months due to AI velocity
    How: Two or more Tier-1 institutions (McKinsey, WEF, Anthropic, OpenAI, Stanford HAI) publish reports/posts explicitly citing collapse in forward forecasting reliability attributable to AI velocity
    Source: deep_research_enrichedconf 62%
  4. 2027-01-01 → 2028-06-30pendingAI capability shift index (week-over-week) becomes a tracked metric on Epoch AI / Artificial Analysis dashboards
    How: Epoch AI or Artificial Analysis publishes a named 'capability velocity' or 'WoW capability shift' index dashboard tracked publicly
    Source: deep_research_enrichedconf 45%

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: 45%)

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
LBP2026-04-30T16:39:51Z45.0%-1.7pp
Network propagation: 46.7% → 45.0%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z46.7%-3.3pp
Network propagation: 50.0% → 46.7%
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
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.500-0.040
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.500-0.018
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.500+0.005

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

33 ticker(s) linked

Beneficiaries (23)

SOUNCRWVSITMNVDAARMGTLBBBAITSMAPLDCEVAAIMSFTMRVLSFTBYORCLQCOMAVGOBABAAMDGOOGLIBMAMZNMETA

Adverse (6)

WNSCHGGCTSHIBMINFYACN

Prerequisites (4)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_ASI_MID_2034ASI mid: Schmidt 'ASI in 6 years'asi_recursive_self_improvement
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

Linked documents (1)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.666arxivCan AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rolloutsmentionspending2026-05-28

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "3 weeks",
  "url": "https://www.youtube.com/watch?v=d__HRChE2ZE",
  "mode": "THESIS",
  "role": "Host",
  "context": "I keep I it's it's I'm just something I keep on asking the experts I run into how far out can you predict the future? Yeah. And it used to be like 20 years and then it was like 10 years and now it's like 3 weeks [laughter] if that.",
  "verbatim": "I keep on asking the experts I run into how far out can you predict the future? Yeah. And it used to be like 20 years and then it was like 10 years and now it's like 3 weeks",
  "conv_cues": "now it's like",
  "direction": "DOWN",
  "timeframe": "Now",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Frontier model releases continue at <90 day cadence (GPT-5.5, Claude 4.7, Gemini 3.1) showing capability shift index updating weekly",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performance",
      "expected_date": "2026-08-31",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-12-31",
        "from": "2026-05-01"
      },
      "measurement_criterion": "Stanford AI Index 2026 confirms <90-day frontier release cadence and Artificial Analysis / LiveBench leaderboards continue weekly score updates with capability deltas >2 points/quarter"
    },
    {
      "kind": "llm_pre_event",
      "label": "GPQA Diamond saturation crossing 95% expert ceiling (forecast horizons collapse below 1 month)",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.78,
      "source_url": "https://smartchunks.com/gpqa-diamond-score-explained-ai-benchmark-2026/",
      "expected_date": "2026-10-30",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-03-31",
        "from": "2026-06-01"
      },
      "measurement_criterion": "At least one frontier model scores >=95% on GPQA Diamond per Stanford HAI / LMM leaderboard, and analyst commentary cites 'unforecastable' AI progress"
    },
    {
      "kind": "llm_post_event",
      "label": "Public commentary from McKinsey / WEF / Anthropic explicitly references prediction horizon shrinking from years to weeks/months due to AI velocity",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -2,
      "source_id": null,
      "confidence": 0.62,
      "source_url": "https://www.weforum.org/stories/2025/12/hiroshima-ai-process-governance/",
      "expected_date": "2027-05-02",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-12-31",
        "from": "2026-09-01"
      },
      "measurement_criterion": "Two or more Tier-1 institutions (McKinsey, WEF, Anthropic, OpenAI, Stanford HAI) publish reports/posts explicitly citing collapse in forward forecasting reliability attributable to AI velocity"
    },
    {
      "kind": "llm_post_event",
      "label": "AI capability shift index (week-over-week) becomes a tracked metric on Epoch AI / Artificial Analysis dashboards",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -1,
      "source_id": null,
      "confidence": 0.45,
      "source_url": "https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performance",
      "expected_date": "2027-10-01",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2028-06-30",
        "from": "2027-01-01"
      },
      "measurement_criterion": "Epoch AI or Artificial Analysis publishes a named 'capability velocity' or 'WoW capability shift' index dashboard tracked publicly"
    }
  ],
  "repeat_eps": 1,
  "untimeabl
... (truncated)