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CYB_009predictionAITPU-to-GPU-architectural-rewrite

Hardware-software alignment will force widespread foundational-codebase rewrites — exemplified by Midjourney's strategic migration from Google TPU architecture to native GPU framework, enabling hyper-fast personalization, HD generation, and massive par...

Predictor: David Holz

Prior probability
90.0%
Current probability
76.9%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
hit
Window
2026-01-01 – 2028-09-30
Edges in / out
3 / 0
Tickers exposed
28

Prediction text

Hardware-software alignment will force widespread foundational-codebase rewrites — exemplified by Midjourney's strategic migration from Google TPU architecture to native GPU framework, enabling hyper-fast personalization, HD generation, and massive parameter scaling previously impossible within legacy TPU constraints. | Next foundation-model company announcing TPU-to-GPU migration

Key catalyst: Next foundation-model company announcing TPU-to-GPU migration

Watch events: Hardware-migration announcements from foundation-model providers

Resolution evidence

Status: hit

Midjourney v6/v7 architectural transition documented in April 2026 Medium analysis; paralleled by other GenAI companies abandoning TPU/early-ASIC stacks.

Predictor: David Holz

κ + Brier as of 2026-05-22
κ (discount)
0.643
Brier
0.0163
excellent
Hits / Misses
2 / 0
of 2 resolved
Hit rate
100.0%
Calibration plot (stated vs observed)

Evidence about this node from David Holz 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 90%2026-04-292026-04-302026-05-10
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 76.9%

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: 3 overdue ⏱
  1. 2026-01-30overdueQ1 window check-in (25%)
  2. 2026-03-01overdueQ2 window check-in (50%)
  3. 2026-03-30overdueQ3 window check-in (75%)

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: 77%)

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
LBP2026-05-10T02:00:02Z76.9%-1.2pp
Network propagation: 78.1% → 76.9%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z78.1%-2.3pp
Network propagation: 80.4% → 78.1%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z80.4%-3.9pp
Network propagation: 84.3% → 80.4%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z84.3%-5.7pp
Network propagation: 90.0% → 84.3%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
resolution_terminal2026-04-29T22:23:18Z100.0%+19.6pp
resolution_terminal hit outcome=1.0 pre_resolution=0.804
Raw metadata
{
  "source": "backfill_resolution_history.py",
  "status": "hit",
  "bayesian_v2": false,
  "outcome_prob": 1,
  "evidence_kind": "resolution_terminal",
  "posterior_prob": 1,
  "delta_to_outcome": 0.19565999999999995,
  "inside_posterior": 0.80434,
  "validation_notes": "Midjourney v6/v7 architectural transition documented in April 2026 Medium analysis; paralleled by other GenAI companies abandoning TPU/early-ASIC stacks.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.80434,
  "resolution_evidence": "Midjourney v6/v7 architectural transition documented in April 2026 Medium analysis; paralleled by other GenAI companies abandoning TPU/early-ASIC stacks.",
  "does_not_update_current_prob": true
}

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
killerTK09
Energy Grid Cap (Data Center Power Wall)
35.0%0.0500.900-0.167
killerTK06
China-Taiwan Military Conflict
8.0%0.0500.900+0.063
killerTK02
AI Compute Supply Shock (TSMC/Taiwan Disruption)
12.0%0.0500.900+0.029

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Ticker exposure

28 ticker(s) linked

Beneficiaries (24)

TSMALABAMBAARMCEVACRWVDOCNIRENNBISNVDASITMAICSCODELLSIEGYGOOGLINTCNXPILNVGYAMDANETMRVLAVGOQCOM

Prerequisites (3)

Predictions that must hit first
TypePredTitleDomainLag
killerTK09Energy Grid Cap (Data Center Power Wall)
killerTK02AI Compute Supply Shock (TSMC/Taiwan Disruption)
killerTK06China-Taiwan Military Conflict

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importMidjourney v6/v7 architectural transition documented in April 2026 Medium analysis; paralleled by other GenAI companies abandoning TPU/early-ASIC stacks.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.703arxivHTAM: Hierarchical Transition-Attended Memory for Operator Optimizationmentionspending2026-05-28
0.677github_releasetensorflow/tensorflow v2.18.0-rc0mentionspending2024-09-30
0.676github_releasetensorflow/tensorflow v2.18.0-rc2mentionspending2024-10-16
0.676github_releasetensorflow/tensorflow v2.18.0-rc1mentionspending2024-10-07
0.673github_releasefacebookresearch/faiss v1.5.2mentionspending2019-05-30
0.672arxivEnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimizationmentionspending2026-05-14
0.672arxivCoral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUsmentionspending2026-05-05
0.670arxivOn the Scaling of PEFT: Towards Million Personal Models of Trillion Parametersmentionspending2026-06-01
0.668arxivA Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variabilitymentionspending2026-05-18
0.666arxivSearch Your Block Floating Point Scales!mentionspending2026-05-12

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "OBSERVATION+FORECAST",
  "role": "Cited-CEO",
  "context": "Extends 234_033 (Holz 5M humanoids Manhattan). Positions hardware-sw rewrites as mandatory competitive step for agentic-era apps.",
  "to_year": 2028,
  "conv_cues": "operational disclosure; CEO FIRST_PERSON",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026-2028",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2026-01-30",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-03-01",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2026-03-30",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "Hardware-software alignment will force widespread foundational-codebase rewrites — exemplified by Midjourney's strategic migration from Goog",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CYB_009",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "Midjourney",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR_RANGE",
  "resolved_at": "2026-04-29T22:23:18.231879+00:00",
  "source_refs": "21",
  "target_date": "2027-06-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Next foundation-model company announcing TPU-to-GPU migration",
  "parse_method": "YEAR_RANGE midpoint",
  "domain_bucket": "AI",
  "episode_title": "Convergence of Synthetic Cognition: Agents, Memory, Commerce & Cybersecurity (2023-2026)",
  "fault_line_id": "F003, F007",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI Cyber and Memory Predictions Request.md (2026-04-21)",
  "appears_in_eps": "CYB-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 3,
  "ps_cluster_tags": [
    "C2",
    "C3",
    "C5"
  ],
  "report_evidence": "Anchor section: Structural Architectural Rewrites.",
  "active_end_month": "2028-12",
  "recent_statement": "Midjourney architecture analyses 2026.",
  "watch_events_raw": "Hardware-migration announcements from foundation-model providers",
  "months_from_today": 14,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2026-01",
  "december_dispersal": {
    "reason": "december_dispersal: domain=AI → 09/2028",
    "new_date": "2028-09-30",
    "old_date": "2028-12-31",
    "applied_at": "2026-04-30T16:28:34.304992+00:00"
  },
  "flag_nia_bracketed": false,
  "resolved_at_source": "validations_observed_at",
  "track_record_grade": "B",
  "track_record_notes": "Holz product-strategy execution solid.",
  "contradicting_notes": "Google TPU improvements (Trillium, Ironwood) may reverse this trend; hardware portability improving.",
  "flag_near_term_2027": false,
  "flag_high_conviction": false,
  "milestones_derived_at": "2026-05-02T03:08:50.751691+00:00",
  "reference_class_match": {
    "decision": "keyword_filtered",
    "computed_at": "2026-04-30T01:49:13.796883+00:00",
    "best_id_unfiltered": "agi_breakthrough_5y",
    "best_similarity_u