← Cockpit
CMQ_030predictionAI/ComputeCPU-resurgence

In the modern AI pipeline, the CPU no longer merely supports the model — it drives the model (agentic workloads invert historical CPU:GPU ratio).

Predictor: Jensen Huang

Prior probability
78.0%
Current probability
33.7%
evolves via intake + LBP
Conviction
5/5
Signal quality
A
Resolution
in_progress
Window
2026-01-01 – 2026-12-31
Edges in / out
1 / 0
Tickers exposed
26

Prediction text

In the modern AI pipeline, the CPU no longer merely supports the model — it drives the model (agentic workloads invert historical CPU:GPU ratio). | the CPU no longer merely supports the model; it drives the model | Server CPU TAM growth rate 2026-2030

Key catalyst: Server CPU TAM growth rate 2026-2030

Watch events: Server CPU market growth vs GPU; NVIDIA Grace adoption; Intel/AMD agentic-optimized CPU launches.

Verbatim quote

From episode "The Global Architecture of Machine Intelligence: Exhaustive Synthesis of AI Compute, Memory & Quantum Predictions (2023-2026)"
the CPU no longer merely supports the model; it drives the model

Resolution evidence

Status: in_progress

Georgia Tech/Intel research: CPU = 50-90% of end-to-end latency in Agentic AI workloads; NVIDIA Grace CPU development validates.

Predictor: Jensen Huang

κ + Brier as of 2026-05-22
κ (discount)
0.808
Brier
0.0128
excellent
Hits / Misses
6 / 0
of 8 resolved
Hit rate
75.0%
Calibration plot (stated vs observed)

Evidence about this node from Jensen Huang 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

6 prob_history rows
0%25%50%75%100%prior 78%2026-05-022026-05-172026-05-30
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 33.7%

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 fired ✓ · 2 overdue ⏱ · 1 pending
  1. 2026-02-15hitNVIDIA standalone CPU deployed in Meta data centers signals new infrastructure category
    How: NVIDIA announces standalone (non-paired-with-GPU) CPU deployment in Meta data centers for agentic workloads
    Source: CNBC — Nvidia GTC AI pivot CPU center stageconf 90%
  2. 2026-03-07overdueQ1 window check-in (25%)
  3. 2026-03-25hitArm publishes AGI CPU thesis confirming CPU renaissance for agentic AI
    How: Arm or major industry analyst publishes formal AGI CPU thesis treating CPU as essential agentic AI infrastructure
    Source: Shashi.co — Arm Bets on Silicon: The AGI CPU and the CPU Renaissanceconf 85%
  4. 2026-03-18hitJensen Huang at GTC publicly frames CPU as central to agentic AI
    How: Jensen Huang at GTC 2026 keynote publicly frames CPU as core to agentic AI infrastructure (not merely supporting GPU)
    Source: CNBC — Nvidia GTC AI pivot CPU center stage (March 2026)conf 95%
  5. 2026-04-20hitMorgan Stanley publishes Rise of AI Agents thesis: GPU bottleneck shifts to CPU
    How: Morgan Stanley publishes major research report on agentic AI shifting value from GPUs to CPUs/memory with $32.5-60B incremental CPU TAM by 2030
    Source: Morgan Stanley research / Bitget News — Rise of AI Agents Global Impact (April 2026)conf 99%
    Notes: HIT — Morgan Stanley published April 2026 report explicitly stating CPU bottleneck for agentic, $32.5-60B incremental TAM. Direct cross-reference.
  6. 2026-05-12overdueQ2 window check-in (50%)
  7. 2026-07-17pendingQ3 window check-in (75%)
  8. 2026-12-01 → 2027-12-31pendingCPU:GPU ratio in agentic workloads exceeds 1:1 cluster-level by 2027
    How: Hyperscaler procurement disclosures or analyst reports confirm CPU:GPU ratio ≥1:1 at cluster level for agentic AI deployments
    Source: AI2.work — Why Agentic AI Is Making CPUs the New Bottleneckconf 60%
    Notes: Cascade — CPU-side orchestration is 50-90% of agentic workload latency per Morgan Stanley.
  9. 2027-01-01 → 2030-12-31pendingServer CPU TAM grows ≥15% annually 2026-2030 per Morgan Stanley estimate
    How: Annual server CPU TAM growth rate averages ≥15% across 2026-2030 per Morgan Stanley/IDC tracking
    Source: Morgan Stanley April 2026 report — $32.5-60B incremental CPU TAM by 2030conf 70%
    Notes: Direct measurement of source's quoted criterion.

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: 34%)

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
metadata_milestone_miss_sweep2026-05-30T22:15:00Z33.7%-7.7pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.337 blend=0.337 LLR=-0.327 κ=0.81 no_blend
Raw metadata
{
  "trf": 0.5881123843731557,
  "kappa": 0.8077,
  "base_rate": null,
  "predictor": "Jensen Huang",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": -0.3494317766421371,
  "bayes_factor": "1.4:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.413520220545267,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.8077,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.32749416781896434,
      "expected_date": "2026-05-12",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "history_v2",
  "inside_weight": 0.5883213309387909,
  "outside_weight": 0.4116786690612091,
  "posterior_prob": 0.3369477449161395,
  "posterior_logit": -0.6769259444611014,
  "predictor_brier": 0.01276,
  "inside_posterior": 0.3369477449161395,
  "blended_posterior": 0.3369477449161395,
  "reference_class_id": null,
  "total_adjusted_llr": -0.32749416781896434,
  "predictor_n_resolved": 8
}
LBP2026-05-24T02:00:02Z41.4%-2.1pp
Network propagation: 43.5% → 41.4%
4-iter LBP, residual 0.01000 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 806b02f8
LBP2026-05-17T02:00:01Z43.5%-4.3pp
Network propagation: 47.8% → 43.5%
5-iter LBP, residual 0.00689 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e607fa96
LBP2026-05-10T02:00:02Z47.8%-8.6pp
Network propagation: 56.4% → 47.8%
6-iter LBP, residual 0.00584 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run e5c18d29
LBP2026-05-03T02:00:01Z56.4%-15.5pp
Network propagation: 71.9% → 56.4%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
metadata_milestone_miss_sweep2026-05-02T22:07:21Z71.9%-6.1pp
metadata_milestone_miss_sweep bayesian_v2 n=1 inside=0.719 blend=0.719 LLR=-0.327 κ=0.81 no_blend
Raw metadata
{
  "trf": 0.6650500679109432,
  "kappa": 0.8077,
  "base_rate": null,
  "predictor": "Jensen Huang",
  "total_llr": -0.4054651081081644,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 1.265666373331276,
  "bayes_factor": "1.4:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.78,
  "kappa_source": "predictor_table",
  "n_milestones": 1,
  "blend_applied": false,
  "contributions": [
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.8077,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.32749416781896434,
      "expected_date": "2026-03-07",
      "measurement_criterion": null
    }
  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "prior_prob",
  "inside_weight": 0.5344649524623397,
  "outside_weight": 0.4655350475376603,
  "posterior_prob": 0.7187303036352096,
  "posterior_logit": 0.9381722055123117,
  "predictor_brier": 0.01276,
  "inside_posterior": 0.7187303036352096,
  "blended_posterior": 0.7187303036352096,
  "reference_class_id": null,
  "total_adjusted_llr": -0.32749416781896434,
  "predictor_n_resolved": 8
}

Network propagation neighbors

Top edges sorted by latest LBP cross-impact
All propagation →

No propagation data yet. Run inference/.venv/bin/python scripts/ops/run_loopy_belief_propagation.py on the droplet, or wait for the Sunday 02:00 UTC weekly cron.

Ticker exposure

26 ticker(s) linked

Beneficiaries (22)

TSMAMBAARMCEVACRWVDOCNIRENNBISNVDASITMALABCSCODELLSIEGYINTCNXPILNVGYAMDANETMRVLAVGOQCOM

Prerequisites (1)

Predictions that must hit first
TypePredTitleDomainLag
correlateS_COMPUTE_100GW_2030Compute: 100GW national-scale by Dec 2030compute_scale

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29partialthesis_timeline_v1.0_importGeorgia Tech/Intel research: CPU = 50-90% of end-to-end latency in Agentic AI workloads; NVIDIA Grace CPU development validates.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT
SimSourceTitleMarket probPolarityReviewedPublished
0.667github_releasetensorflow/tensorflow v2.17.0mentionspending2024-07-11
0.662github_releasetensorflow/tensorflow v2.17.0-rc0mentionspending2024-06-18
0.660github_releasetensorflow/tensorflow v2.17.0-rc1mentionspending2024-07-02
0.649arxivAMDP: Asynchronous Multi-Directional Pipeline Parallelism for Large-Scale Models Trainingmentionspending2026-05-28
0.623github_releasetensorflow/tensorflow v2.16.1mentionspending2024-03-07
0.615github_releasetensorflow/tensorflow v2.15.0mentionspending2023-11-14
0.612github_releasetensorflow/tensorflow v2.21.0-rc0mentionspending2026-02-09
0.612github_releasetensorflow/tensorflow v2.13.1mentionspending2023-09-26
0.610github_releasetensorflow/tensorflow v2.21.0-rc1mentionspending2026-03-02
0.610github_releasetensorflow/tensorflow v2.15.0-rc0mentionspending2023-10-25

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "THESIS",
  "role": "Cited-Executive",
  "context": "Huang 2026 observation validates Morgan Stanley 'Rise of the AI Agent' thesis; agentic workflows demand sequential CPU logic for scheduling/orchestration.",
  "to_year": 2030,
  "verbatim": "the CPU no longer merely supports the model; it drives the model",
  "conv_cues": "declarative; CEO",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026+",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "NVIDIA standalone CPU deployed in Meta data centers signals new infrastructure category",
      "source": "CNBC — Nvidia GTC AI pivot CPU center stage",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.9,
      "source_url": "https://www.cnbc.com/2026/03/13/nvidia-gtc-ai-jensen-huang-cpu-gpu.html",
      "expected_date": "2026-02-15",
      "observed_date": "2026-02-15",
      "research_origin": "deep_research",
      "measurement_criterion": "NVIDIA announces standalone (non-paired-with-GPU) CPU deployment in Meta data centers for agentic workloads"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -6,
      "source_id": null,
      "expected_date": "2026-03-07",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "Arm publishes AGI CPU thesis confirming CPU renaissance for agentic AI",
      "source": "Shashi.co — Arm Bets on Silicon: The AGI CPU and the CPU Renaissance",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.85,
      "source_url": "https://www.shashi.co/2026/03/arm-bets-on-silarm-bets-on-silicon-agi.html",
      "expected_date": "2026-03-25",
      "observed_date": "2026-03-25",
      "research_origin": "deep_research",
      "measurement_criterion": "Arm or major industry analyst publishes formal AGI CPU thesis treating CPU as essential agentic AI infrastructure"
    },
    {
      "kind": "llm_pre_event",
      "label": "Jensen Huang at GTC publicly frames CPU as central to agentic AI",
      "source": "CNBC — Nvidia GTC AI pivot CPU center stage (March 2026)",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.cnbc.com/2026/03/13/nvidia-gtc-ai-jensen-huang-cpu-gpu.html",
      "expected_date": "2026-03-30",
      "observed_date": "2026-03-18",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2026-04-15",
        "from": "2026-03-15"
      },
      "measurement_criterion": "Jensen Huang at GTC 2026 keynote publicly frames CPU as core to agentic AI infrastructure (not merely supporting GPU)"
    },
    {
      "kind": "llm_pre_event",
      "label": "Morgan Stanley publishes Rise of AI Agents thesis: GPU bottleneck shifts to CPU",
      "notes": "HIT — Morgan Stanley published April 2026 report explicitly stating CPU bottleneck for agentic, $32.5-60B incremental TAM. Direct cross-reference.",
      "source": "Morgan Stanley research / Bitget News — Rise of AI Agents Global Impact (April 2026)",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://www.bitget.com/amp/news/detail/12560605375963",
      "expected_date": "2026-04-20",
      "observed_date": "2026-04-20",
      "research_origin": "deep_research",
      "measurement_criterion": "Morgan Stanley publishes major research report on agentic AI shifting value from GPUs to CPUs/memory with $32.5-60B incremental CPU TAM by 2030"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "st
... (truncated)