CMQ_043predictionAI/ComputeCPU-latency

In complex Agentic AI workloads, CPU-side processing accounts for 50-90% of end-to-end latency — validating CPU-bottleneck shift empirically.

Predictor: Morgan Stanley / Georgia Tech / Intel

Prior probability

92.0%

Current probability

92.0%

evolves via intake + LBP

Conviction

5/5

Signal quality

Resolution

hit

Window

2026-01-01 – 2026-12-31

Edges in / out

0 / 0

Tickers exposed

Prediction text

In complex Agentic AI workloads, CPU-side processing accounts for 50-90% of end-to-end latency — validating CPU-bottleneck shift empirically. | Published agentic workload benchmarks

Key catalyst: Published agentic workload benchmarks

Watch events: Agentic workload latency breakdowns; agent-framework optimization announcements.

Resolution evidence

Status: hit

Published Georgia Tech/Intel paper metrics; replicated by Anthropic internal profiling of Claude agents.

Predictor: Morgan Stanley / Georgia Tech / Intel

κ + Brier as of 2026-05-22

Full calibration →

κ (discount)

0.583

Brier

0.0064

excellent

Hits / Misses

1 / 0

of 1 resolved

Hit rate

100.0%

Calibration plot (stated vs observed)

Evidence about this node from Morgan Stanley / Georgia Tech / Intel 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

1 prob_history rows

intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 92.0%

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: 3 overdue ⏱

2026-01-30overdueQ1 window check-in (25%)
2026-03-01overdueQ2 window check-in (50%)
2026-03-30overdueQ3 window check-in (75%)
2026-04-29hitIn complex Agentic AI workloads, CPU-side processing accounts for 50-90% of end-to-end latency — validating CPU-bottleneck shift empirically

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: 92%)

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

resolution_terminal2026-04-29T22:23:18Z100.0%+8.0pp

resolution_terminal hit outcome=1.0 pre_resolution=0.920

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": 0.07999999999999996,
  "inside_posterior": 0.92,
  "validation_notes": "Published Georgia Tech/Intel paper metrics; replicated by Anthropic internal profiling of Claude agents.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.92,
  "resolution_evidence": "Published Georgia Tech/Intel paper metrics; replicated by Anthropic internal profiling of Claude agents.",
  "does_not_update_current_prob": true
}

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.

Prerequisites (0)

Predictions that must hit first

Type	Pred	Title	Domain	Lag
No prerequisites

Dependents (0)

Predictions enabled by this

Type	Pred	Title	Domain	Lag
No dependents

Expected milestones (1)

From Sheet 17 Monitoring Triggers

Expected by	Description	Status
2026-12-31	[Capability 2026-12] ates; NVIDIA inference-optimized produc [CMQ_043] Agentic workload latency breakdowns; agent-framework optimization announcements.	pending

Validations (1)

Resolution events

Observed at	Status	By	Notes
2026-04-29	hit	thesis_timeline_v1.0_import	Published Georgia Tech/Intel paper metrics; replicated by Anthropic internal profiling of Claude agents.

Linked documents (10)

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

Sim	Source	Title	Market prob	Polarity	Reviewed	Published
0.757	arxiv	Building Interactive Real-Time Agents with Asynchronous I/O and Speculative Tool Calling	—	mentions	pending	2026-05-13
0.722	arxiv	Beyond Scaling: Agents Are Heading to the Edge	—	mentions	pending	2026-05-18
0.721	arxiv	A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments	—	mentions	pending	2026-05-05
0.719	arxiv	Agents' Last Exam	—	mentions	pending	2026-06-03
0.712	arxiv	Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows	—	mentions	pending	2026-06-04
0.705	arxiv	Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads	—	mentions	pending	2026-06-04
0.703	arxiv	Offloading Score: Measuring AI Reliance Through Counterfactual Workflows	—	mentions	pending	2026-05-28
0.703	arxiv	Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies	—	mentions	pending	2026-05-05
0.699	arxiv	Beyond Task Success: Measuring Workflow Fidelity in LLM-Based Agentic Payment Systems	—	mentions	pending	2026-05-07
0.696	arxiv	Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management	—	mentions	pending	2026-05-07

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook

{
  "nia": false,
  "qty": "50-90% CPU latency",
  "mode": "FORECAST",
  "role": "Cited-Analyst",
  "context": "Georgia Tech + Intel rigorous joint measurement study; empirical validation of MS agentic-CPU thesis.",
  "to_year": 2030,
  "cited_by": "MS Research",
  "conv_cues": "empirical measurement; peer research",
  "direction": "NUMERIC_TARGET",
  "from_year": 2026,
  "timeframe": "2026+",
  "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": "In complex Agentic AI workloads, CPU-side processing accounts for 50-90% of end-to-end latency — validating CPU-bottleneck shift empirically",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "CMQ_043",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "sub_domain": "Compute",
  "affiliation": "Joint Research",
  "attribution": "THIRD_PARTY_CITATION",
  "granularity": "YEAR",
  "resolved_at": "2026-04-29T22:23:18.111664+00:00",
  "source_refs": "26",
  "target_date": "2026-12-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-21T00:00:00",
  "key_catalyst": "Published agentic workload benchmarks",
  "parse_method": "Report midpoint",
  "domain_bucket": "AI",
  "episode_title": "The Global Architecture of Machine Intelligence: Exhaustive Synthesis of AI Compute, Memory & Quantum Predictions (2023-2026)",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI_Chip__Compute__Memory__Quantum_Predictions.md (2026-04-21)",
  "appears_in_eps": "CMQ-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 5,
  "report_evidence": "Empirical backbone of CPU-resurgence thesis — concrete numbers drive portfolio action.",
  "active_end_month": "2026-12",
  "recent_statement": "MS cited in 'Rise of the AI Agent' 2025/2026; Georgia Tech paper public.",
  "watch_events_raw": "Agentic workload latency breakdowns; agent-framework optimization announcements.",
  "months_from_today": 8,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2026-01",
  "flag_nia_bracketed": false,
  "resolved_at_source": "validations_observed_at",
  "track_record_grade": "A",
  "track_record_notes": "Empirical measurement study; high confidence.",
  "contradicting_notes": "Benchmark dependent on agent architecture; could shift if agent frameworks redesigned for GPU-native execution.",
  "flag_near_term_2027": true,
  "flag_high_conviction": true,
  "milestones_derived_at": "2026-05-02T03:08:50.652793+00:00",
  "reference_class_match": {
    "decision": "keyword_filtered",
    "computed_at": "2026-04-30T01:49:13.796883+00:00",
    "best_id_unfiltered": "regulatory_freeze_window",
    "best_similarity_unfiltered": 0.5861
  },
  "validation_status_raw": "CONFIRMED",
  "composite_signal_score": 92,
  "flag_priority_