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Sam Altman predicts another architecture breakthrough as big as transformers over LSTMs

Predictor: Sam Altman · ep#240 "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse" · source

Prior probability
55.0%
Current probability
40.9%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2026-04-30 – 2027-09-30
Edges in / out
4 / 0
Tickers exposed
33

Prediction text

Sam Altman predicts another architecture breakthrough as big as transformers over LSTMs | I bet there is another new architecture defined that is going to be like as big of a gain as transformers were over LSTMs.

Verbatim quote

From episode "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse"
I bet there is another new architecture defined that is going to be like as big of a gain as transformers were over LSTMs.

Predictor: Sam Altman

κ + Brier as of 2026-05-22
κ (discount)
0.583
Brier
0.0625
excellent
Hits / Misses
0 / 0
of 1 resolved
Hit rate
0.0%
Calibration plot (stated vs observed)

Evidence about this node from Sam Altman 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

5 prob_history rows
0%25%50%75%100%prior 55%2026-04-302026-05-032026-05-17
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 40.9%

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: 2 fired ✓ · 5 pending
  1. 2026-02-15hitSam Altman publicly states transformer is not the end; new architecture as revolutionary as transformers/LSTMs needed
    How: Public Altman statement (interview / blog / podcast) explicitly framing post-transformer architecture as needed for AGI
    Source: deep_research_enrichedconf 90%
  2. 2026-03-15hitMamba-3 publication validates active progress on post-transformer SSM line
    How: Mamba-3 paper / preprint confirms continued architectural progress beyond pure transformer attention
    Source: deep_research_enrichedconf 80%
  3. 2026-07-24pendingQ1 window check-in (25%)
  4. 2026-10-18pendingQ2 window check-in (50%)
  5. 2026-09-01 → 2027-03-31pendingOpenAI Q1 next-year model releases reference 5.2-architecture gains (Altman roadmap commitment)
    How: OpenAI ships frontier model whose technical post or Altman commentary credits a substantively novel architecture component for the gain
    Source: deep_research_enrichedconf 50%
  6. 2027-01-12pendingQ3 window check-in (75%)
  7. 2026-09-01 → 2027-09-30pendingIndependent benchmark verification: novel architecture matches or beats transformer SOTA at matched compute
    How: Public leaderboard / paper result: a non-pure-transformer architecture matches/beats best transformer at equal training FLOPs on a recognized eval
    Source: deep_research_enrichedconf 40%
  8. 2027-01-01 → 2027-09-30pendingTransformer-LSTM-magnitude leap consensus emerges (≥ 3 frontier labs adopt or replicate the new architecture)
    How: Three or more of OpenAI/Anthropic/Google DeepMind/Meta/xAI publicly adopt or commit to the new architecture for flagship models, paralleling 2017-2020 transformer adoption arc
    Source: deep_research_enrichedconf 25%

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: 41%)

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
LBP2026-05-17T02:00:01Z40.9%-1.2pp
Network propagation: 42.1% → 40.9%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z42.1%-2.5pp
Network propagation: 44.6% → 42.1%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z44.6%-4.9pp
Network propagation: 49.5% → 44.6%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z49.5%-1.9pp
Network propagation: 51.4% → 49.5%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z51.4%-3.6pp
Network propagation: 55.0% → 51.4%
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
prereqS_AGI_FAST_2027
AGI fast: drop-in remote worker by 2027-09
30.0%0.5500.050-0.209
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.550+0.091
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.550+0.066
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.550+0.041

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
prereqS_AGI_FAST_2027AGI fast: drop-in remote worker by 2027-09agi_general_capability
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 (10)

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

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
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  "nia": false,
  "url": "https://www.youtube.com/watch?v=uOGHXAfvK8w",
  "mode": "CITED_PREDICTION",
  "role": "Cited-Executive",
  "context": "I bet there is another new architecture defined that is going to be like as big of a gain as transformers were over LSTMs. And I think you finally have models that are smart enough to help do that kind of research.",
  "cited_by": "Peter Diamandis",
  "verbatim": "I bet there is another new architecture defined that is going to be like as big of a gain as transformers were over LSTMs.",
  "conv_cues": "I bet",
  "direction": "HAPPEN",
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  "milestones": [
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      "kind": "llm_pre_event",
      "label": "Sam Altman publicly states transformer is not the end; new architecture as revolutionary as transformers/LSTMs needed",
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      "expected_date": "2026-02-15",
      "observed_date": "2026-02-15",
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      "expected_date": "2026-04-01",
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      "weight": 0.05,
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      "source_id": null,
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      "observed_date": null
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      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
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      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2026-10-18",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
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      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.5,
      "source_url": "https://www.theneuron.ai/explainer-articles/openais-vision-for-2026-sam-altman-lays-out-the-roadmap/",
      "expected_date": "2026-12-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-03-31",
        "from": "2026-09-01"
      },
      "measurement_criterion": "OpenAI ships frontier model whose technical post or Altman commentary credits a substantively novel architecture component for the gain"
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    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
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    {
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      "ordinal": -1,
      "source_id": null,
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      "sourc
... (truncated)