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240_021predictionAIAI-timing

Post-transformer architecture will be even more specialized than GPUs

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

Prior probability
45.0%
Current probability
35.1%
evolves via intake + LBP
Conviction
3/5
Signal quality
C
Resolution
pending
Window
2026-04-30 – 2029-03-31
Edges in / out
4 / 0
Tickers exposed
36

Prediction text

Post-transformer architecture will be even more specialized than GPUs | I would expect an architecture that's even more specialized than GPUs and yet even more useful.

Verbatim quote

From episode "NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse"
I would expect an architecture that's even more specialized than GPUs and yet even more useful.

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 45%2026-04-302026-05-032026-05-10
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 35.1%

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: 1 fired ✓ · 6 pending
  1. 2026-03-17hitNVIDIA integrates third-party Groq accelerator into Vera Rubin platform — first major architecture concession to specialized inference silicon
    How: GTC 2026 announcement that Vera Rubin platform incorporates Groq inference chips alongside NVIDIA GPUs
    Source: deep_research_enrichedconf 85%
  2. 2026-10-22pendingQ1 window check-in (25%)
  3. 2026-09-01 → 2027-09-30pendingGroq / Cerebras / Etched / SambaNova reach >5% combined inference market share
    How: Industry analyst (Dell'Oro / SemiAnalysis / IDC) estimate that non-NVIDIA specialized inference silicon collectively captures >=5% of measured AI inference compute hours
    Source: deep_research_enrichedconf 60%
  4. 2027-04-15pendingQ2 window check-in (50%)
  5. 2026-10-01 → 2028-03-31pendingFirst production frontier model trained on a non-transformer architecture (Mamba/SSM/Diffusion-LM/Mixture-of-Recursions class) ships at scale
    How: Major lab (top-10 by compute) ships a production model where >=50% of FLOPs use non-transformer block primitives, with public technical report
    Source: deep_research_enrichedconf 45%
  6. 2027-10-07pendingQ3 window check-in (75%)
  7. 2027-01-01 → 2028-12-31pendingASIC purpose-built for non-transformer post-attention architecture announced by major fab partner (TSMC/Samsung/Intel Foundry)
    How: Press release or technical paper describing taping out a chip whose primitives are not matmul-attention-dominant — intended for post-transformer workload class
    Source: deep_research_enrichedconf 30%
  8. 2027-06-01 → 2029-03-31pendingNVIDIA loses >=5 percentage points of AI training GPU share in a quarter
    How: Earnings or third-party analyst share data shows NVIDIA quarter-over-quarter share decline of >=5 pp in data-center training silicon
    Source: deep_research_enrichedconf 30%

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
LBP2026-05-10T02:00:02Z35.1%-1.8pp
Network propagation: 36.9% → 35.1%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z36.9%-3.7pp
Network propagation: 40.6% → 36.9%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z40.6%-1.5pp
Network propagation: 42.1% → 40.6%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z42.1%-2.9pp
Network propagation: 45.0% → 42.1%
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_MID_2029
AGI mid: Kurzweil 2029 path
35.0%0.4500.050-0.161
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.450+0.059
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.450+0.039
killerTK14
Superbubble Pop (S&P 500 -40%, Moonshot Capital Evaporates)
20.0%0.0500.450+0.019

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

36 ticker(s) linked

Beneficiaries (23)

APLDNVDAARMBBAITSMCEVAAISOUNCRWVSITMGTLBGOOGLMETAMRVLMSFTORCLIBMAMZNAVGOBABAAMDSFTBYQCOM

Adverse (6)

ACNCHGGCTSHIBMINFYWNS

Prerequisites (4)

Predictions that must hit first
TypePredTitleDomainLag
prereqS_AGI_MID_2029AGI mid: Kurzweil 2029 pathagi_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
SimSourceTitleMarket probPolarityReviewedPublished
0.669github_releasefacebookresearch/xformers v0.0.22.post4mentionspending2023-10-13
0.668github_releasepytorch/pytorch v1.13.0mentionspending2022-10-28
0.648github_releasepytorch/pytorch v2.5.0mentionspending2024-10-17
0.646github_releasepytorch/pytorch v1.10.0mentionspending2021-10-21
0.646github_releasepytorch/pytorch v2.0.0mentionspending2023-03-15
0.645github_releasepytorch/pytorch v2.3.0mentionspending2024-04-24
0.643github_releasepytorch/pytorch v1.12.0mentionspending2022-06-28
0.642github_releasefacebookresearch/xformers v0.0.24mentionspending2024-01-31
0.641github_releasefacebookresearch/xformers v0.0.29mentionspending2024-12-27
0.640github_releasefacebookresearch/xformers v0.0.25mentionspending2024-03-15

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=uOGHXAfvK8w",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "if there is is going to be an architectural disruptor for Nvidia, it's going to be the same sort of disruptive innovation that that Nvidia pulled on Intel, which is it's going to have to be I I would expect an architecture that's even more specialized than GPUs and yet even more useful.",
  "verbatim": "I would expect an architecture that's even more specialized than GPUs and yet even more useful.",
  "conv_cues": "I would expect",
  "direction": "HAPPEN",
  "timeframe": "Future",
  "conv_level": "MEDIUM",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "NVIDIA integrates third-party Groq accelerator into Vera Rubin platform — first major architecture concession to specialized inference silicon",
      "source": "deep_research_enriched",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://developer.nvidia.com/blog/inside-the-nvidia-rubin-platform-six-new-chips-one-ai-supercomputer/",
      "expected_date": "2026-03-17",
      "observed_date": "2026-03-17",
      "research_origin": "deep_research",
      "measurement_criterion": "GTC 2026 announcement that Vera Rubin platform incorporates Groq inference chips alongside NVIDIA GPUs"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2026-10-22",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "Groq / Cerebras / Etched / SambaNova reach >5% combined inference market share",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.6,
      "source_url": "https://www.delloro.com/gtc-2026-moving-from-chip-launches-to-infrastructure-architecture/",
      "expected_date": "2027-03-17",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2027-09-30",
        "from": "2026-09-01"
      },
      "measurement_criterion": "Industry analyst (Dell'Oro / SemiAnalysis / IDC) estimate that non-NVIDIA specialized inference silicon collectively captures >=5% of measured AI inference compute hours"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2027-04-15",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "First production frontier model trained on a non-transformer architecture (Mamba/SSM/Diffusion-LM/Mixture-of-Recursions class) ships at scale",
      "source": "deep_research_enriched",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.45,
      "source_url": "https://hakia.com/tech-insights/ai-chip-wars/",
      "expected_date": "2027-07-01",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2028-03-31",
        "from": "2026-10-01"
      },
      "measurement_criterion": "Major lab (top-10 by compute) ships a production model where >=50% of FLOPs use non-transformer block primitives, with public technical report"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2027-10-07",
      "observed_date": null
    },
    {
      "kind": "llm_post_event",
      "label": "ASIC purpose-built for non-transformer post-attention architecture announced by major fab partner (TSMC/Samsung/Intel Foundry)",
      "source": "deep_research_enriched",
      "status": "pending",
      
... (truncated)